STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES Timothy James Yuji Birkle Christ’s College Department of Biochemistry University of Cambridge Supervised by Professor Guy C Brown This dissertation is submitted for the degree of Doctor of Philosophy November 20 i DECLARATION This thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. I further state that no substantial part of my thesis has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit for the relevant Degree Committee. Some of the contents of this dissertation have been published in the following publications: Birkle, T., & Brown, G. C. (2021). I'm Infected, Eat Me! Innate Immunity Mediated by Live, Infected Cells Signaling To Be Phagocytosed. Infection and immunity, 89(5), e00476-20. https://doi.org/10.1128/IAI.00476-20. Birkle, T. J. Y., & Brown, G. C. (2023). Syk inhibitors protect against microglia- mediated neuronal loss in culture. Frontiers in aging neuroscience, 15, 1120952. https://doi.org/10.3389/fnagi.2023.1120952. Birkle, T. J. Y., Willems, H., Skidmore, J., & Brown, G. C. (2023). Disease phenotypic screening in neuron-glia co-cultures identifies blockers of inflammatory neurodegeneration. iScience, 27(4), 109454. https://doi.org/10.1016/j.isci.2024.109454. Timothy James Yuji Birkle November 2023 ii ABSTRACT Microglia are resident innate immune cells of the central nervous system with potent phagocytic and inflammatory capabilities. These cells are crucial in both health and disease, including neurodegenerative diseases. Genetics studies have linked Alzheimer’s disease and other diseases to genes that affect microglial functions, and evidence indicates that microglial phagocytosis and inflammation control neuronal function and survival. Some microglial activity may be beneficial during neurodegeneration, but excessive release of pro-inflammatory cytokines and reactive oxygen/nitrogen species is a hallmark of neurodegenerative disease and can promote neurodegeneration. Additionally, microglial phagocytosis of the protein aggregates driving proteinopathic diseases may be beneficial, but excessive microglial phagocytosis of synapses or neurons may contribute to neurodegeneration. Overall, study of microglia-mediated neurodegeneration is essential when working towards microglia-targeted therapies for dementias, such as Alzheimer’s disease, and other conditions. In order to study these interactions between microglia and neurons, in vitro model systems are required that include both cell types. Use of these models has often been limited to low-throughput experiments due to practical challenges and the need for careful manual analysis of individual cell types. In this work, I used a neuron-glia coculture model in which inflammatory activation of microglia with lipopolysaccharide (LPS) or other stimuli results in neuronal loss, addressing the above limitations by building an imaging and analysis workflow using recent methods and machine learning tools. This enabled accurate, automated analysis of images from primary cocultures. Early tests of these higher-throughput assays identified potential roles for urokinase (uPA) and spleen tyrosine kinase (SYK) in microglia-mediated neurodegeneration. uPA is an extracellular protease that may also regulate migration, inflammation and proliferation in association with its receptor uPAR. SYK signals downstream of other microglial cell surface receptors that have been linked to brain diseases, including Alzheimer’s, such as TREM2, CR3 and CSF1R. In this work, uPA and SYK were investigated further using assays for microglial survival, inflammation, and phagocytosis. Here, I found that uPA may influence inflammatory neurodegeneration, as well as microglial proliferation and phagocytosis, but it remains unclear which of uPA’s many signalling mechanisms drive this. A broad uPA inhibitor affecting both STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES Timothy James Yuji Birkle – November 2023 iii proteolysis and receptor binding prevented LPS-induced, microglia-dependent neuronal loss in cocultures, potentially by depleting microglia and affecting their morphological and phagocytic response to LPS. However, more specific inhibitors of either proteolysis or receptor binding produced only weak effects, if any. Interestingly, exogenous uPA caused proliferation of microglia, suggesting a further role for uPA signalling in these cells. Meanwhile, inhibitor studies found that SYK also regulates neurodegeneration while affecting microglial survival, inflammation, and phagocytosis, which fits with existing knowledge on SYK and its upstream receptors. Finally, a high-content screen for drugs and targets that control microglia-mediated neurodegeneration was developed, using the primary neuron-glia cocultures and new image analysis methods. This novel proof-of-concept validated the use of neuron-glia cocultures in high-content assays when combined with the image analysis developed here. The data identified contributions from steroid hormones, adrenergic receptors, and MAPK signalling (amongst other pathways). Overall, this work has used updated image analysis methods to investigate the roles of uPA and SYK in microglial biology and microglia-mediated neurodegeneration, as well as showing proof-of-concept for using neuron-glia cocultures in screens for drugs and targets influencing neurodegenerative disease. This adds to the increasing literature on targeting microglia for therapies against neurodegeneration, while validating new assays to study neuron-glia interactions for both target discovery and investigation of the complex mechanisms controlling microglial function. iv ACKNOWLEDGEMENTS I would like to thank all those who have made this work possible, both inside and outside of the lab. Firstly, Professor Guy Brown has consistently supported me from the initial PhD applications through to the end, and I am grateful for his consistent and valuable advice. This has not only enabled some fruitful research at the bench but has also supported my increasing appreciation of what it means to be a scientist and how to navigate the academic world. I would also like to thank the whole Brown Lab for their scientific input in the lab and during our many, many lab meetings. I am particularly grateful to AstraZeneca for funding this PhD work. The friendly supervision provided by both Damian Crowther and Mike Perkinton has been invaluable, including the advice provided through Postgraduate Thesis Panel meetings. I would like to extend this thanks to other members of AstraZeneca who gave technical advice on immunocytochemistry protocols, and to Kushal Rugjee who kindly performed an AlphaLISA assay for me (which does not feature in this dissertation). I would also like to thank the other members of my PTP, Aviva Tolkovsky and Marc de la Roche, as well as Svetlana Khoronenkova for their kind support. The final year of work on the high-content screening project would not have been possible without the support and collaboration of the ALBORADA Drug Discovery Institute, and I would particularly like to thank John Skidmore, Jon Clarke, Henriette Willems, David Winpenny, David Allendorf, Esperanza Agullo Pascual, Cathryn Ugalde, and Nikhita Annaiyappa. The ADDI team could not have provided a more welcoming and supportive environment for this work, and my time there has been a highlight of my PhD. This work has used animal tissue throughout. I would like to thank all the fantastic staff of the University Biomedical Services who supported this work, as well as all the animals that were used. On a personal level, I could not have reached this point without the many wonderful people around me who have never failed to prop me up through difficult days in the lab (or weeks, months, years…). I am especially grateful for the friendship of David, Emily, and Alma, who I would always look forward to seeing in the office (or at STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES Timothy James Yuji Birkle – November 2023 v coffee/the pub, though obviously not during work hours). Clearly this time in the Department wasn’t enough for David, who was also a wonderful housemate for the last three years while always still making time to help me through challenges at work. I also cannot thank Tyler enough for being a fantastic friend. In particular, for instigating, aiding, and abetting all the gardening, cooking, chatting, and other miscellaneous activities that have kept us dubiously occupied almost every week throughout our PhDs. I look forward to our many adventures to come. Another near- weekly feature that I have always looked forward to is pub quizzes with the whole Flim Flam team, who are all amazing people, and I am especially grateful for Rachel with whom I have enjoyed many cycles, a few runs, and 7 minutes of tennis. Further afield from Cambridge, I have also had the unfailing support of Ned, who may be the best godbrother ever. Perhaps most importantly, thank you Indy for being there for me at my best and my worst, and for being a wonderful human for the last three years. I would not have made it through without you being there at the end of every day. Finally, I would like to thank my whole family for their constant support and interest in my work, and for getting me to this point. I wouldn’t be here without them, and for that I will always be grateful. vi CONTENTS 1 INTRODUCTION ........................................................................................................ 1 1.1 SCOPE ....................................................................................................................... 1 1.2 MICROGLIA .............................................................................................................. 2 1.3 PHAGOCYTOSIS ........................................................................................................ 3 1.3.1 Find-me signalling from the target .................................................................. 4 1.3.2 Eat-me signalling at the target-phagocyte interface ....................................... 4 1.3.3 Intracellular signalling in the phagocyte ......................................................... 6 1.4 MICROGLIAL PHAGOCYTOSIS ................................................................................... 9 1.4.1 Microglial phagocytosis of pathogens ............................................................. 9 1.4.2 Microglial phagocytosis of synapses ............................................................. 10 1.4.3 Microglial phagocytosis of myelin ................................................................. 11 1.4.4 Microglial phagocytosis of dead cells ........................................................... 11 1.4.5 Microglial phagocytosis of live cells ............................................................. 12 1.4.6 Microglial phagocytosis of protein aggregates ............................................. 13 1.5 MICROGLIAL INFLAMMATION ................................................................................. 15 1.5.1 Response to PAMPs ....................................................................................... 17 1.5.2 Response to DAMPs ....................................................................................... 18 1.5.3 Inflammatory response to other factors ......................................................... 19 1.6 OTHER MICROGLIAL FEATURES AND FUNCTIONS .................................................... 20 1.6.1 Regulation of neuronal activity ...................................................................... 20 1.6.2 Microglial proliferation ................................................................................. 20 1.6.3 Microglial morphology .................................................................................. 21 1.7 MICROGLIA AND NEURODEGENERATIVE DISEASE ................................................... 22 1.7.1 Microglial inflammation is a shared feature of neurodegenerative diseases 23 1.7.2 Genetic studies implicate microglia in neurodegenerative disease risk ........ 24 1.7.3 The contribution of microglia to Alzheimer’s disease ................................... 25 1.7.4 The contribution of microglia to other neurodegenerative diseases and acute injuries .................................................................................................................... 28 1.8 TARGETING MICROGLIA TO TREAT NEURODEGENERATIVE DISEASES ...................... 31 1.9 UROKINASE AND SPLEEN TYROSINE KINASE ........................................................... 32 1.10 METHODS FOR STUDYING MICROGLIA-MEDIATED NEURODEGENERATION ............ 35 1.10.1 Modelling inflammation with LPS ............................................................... 35 1.10.2 Cell models ................................................................................................... 38 vii 2 AIMS ........................................................................................................................... 41 3 METHODS ................................................................................................................. 43 3.1 ETHICAL STATEMENT ............................................................................................. 43 3.2 COMMON CULTURE MATERIALS ............................................................................. 43 3.3 CELL CULTURES ..................................................................................................... 44 3.3.1 BV2 cells ........................................................................................................ 44 3.3.2 Primary glial cultures .................................................................................... 44 3.3.3 Primary neuron-glia cocultures ..................................................................... 44 3.4 GENERAL EXPERIMENTAL PROTOCOLS ................................................................... 45 3.4.1 Cerebellar mixed culture neuronal loss assay setup...................................... 45 3.4.2 Mixed culture inflammatory neuronal loss assay imaging and image analysis ................................................................................................................................. 46 3.4.3 Pre- vs post-fix and NeuO vs NeuN comparison assay .................................. 48 3.4.4 Segmentation accuracy comparative validation assay .................................. 49 3.4.5 Automated classification validation ............................................................... 50 3.4.6 Immunocytochemistry .................................................................................... 50 3.4.7 LME-mediated microglial depletion .............................................................. 51 3.4.8 Primary microglia cell death assay ............................................................... 51 3.4.9 Primary combined glia cell death assay ........................................................ 51 3.4.10 Primary microglia phagocytosis assay ........................................................ 52 3.4.11 Synaptosome preparation ............................................................................ 52 3.4.12 Western blotting ........................................................................................... 53 3.4.13 Urokinase activity assays ............................................................................. 53 3.4.14 Supernatant cytokine ELISAs ....................................................................... 54 3.4.15 pH (phenol red absorbance) assays ............................................................. 54 3.4.16 Lactate assays .............................................................................................. 55 3.5 HIGH-CONTENT SCREEN COMPOUND LIBRARY PREPARATION ................................. 55 3.5.1 Compound selection ....................................................................................... 55 3.5.2 Plate design .................................................................................................... 56 3.5.3 Assay-ready treatment plate preparation ...................................................... 57 3.6 DATA PROCESSING AND STATISTICS ....................................................................... 57 3.6.1 Low-throughput .............................................................................................. 57 3.6.2 High-throughput ............................................................................................. 57 3.7 SOFTWARE ............................................................................................................. 58 3.8 TABLE OF TREATMENTS .......................................................................................... 59 viii 4 AUTOMATED IMAGE ANALYSIS OF NEURON-GLIA COCULTURES ...... 61 4.1 INTRODUCTION ....................................................................................................... 61 4.2 RESULTS ................................................................................................................. 66 4.2.1 α-NeuN immunocytochemistry accurately labels neurons in neuron-glia cocultures ................................................................................................................ 66 4.2.2 Immunocytochemistry displaces microglia in the cocultures ........................ 68 4.2.3 NeuO specifically stains live neurons and gives a good estimate of neuron density ..................................................................................................................... 69 4.2.4 QuPath and Cellpose segmentation algorithms accurately identify cell nuclei in coculture images ................................................................................................. 71 4.2.5 Machine learning-based classification of cell types enables automated analysis of coculture images ................................................................................... 73 4.2.6 Automated coculture analysis identifies targets of interest for inflammatory neurodegeneration .................................................................................................. 75 4.2.7 NeuO staining can be affected by treatments ................................................ 77 4.3 DISCUSSION ............................................................................................................ 79 5 THE ROLE OF UROKINASE IN MICROGLIA-MEDIATED NEURODEGENERATION ......................................................................................... 82 5.1 INTRODUCTION ....................................................................................................... 82 5.2 RESULTS ................................................................................................................. 86 5.2.1 BC11 hydrobromide protects against LPS-induced neuronal loss in neuron- glia coculture .......................................................................................................... 86 5.2.2 LPS-induced neuronal loss in neuron-glia cocultures is microglia-dependent ................................................................................................................................. 88 5.2.3 BC11 depletes microglia from cocultures ...................................................... 89 5.2.4 BC11 prevents LPS-induced changes to microglial morphology, but does not affect TNFα release ................................................................................................. 90 5.2.5 Alternative uPA inhibitors do not prevent LPS-induced neuronal loss, and exogenous uPA does not cause significant neuronal loss ....................................... 92 5.2.6 Inhibition of the uPA-uPAR interaction may affect glia, unlike catalytic inhibition of uPA ..................................................................................................... 93 5.2.7 uPA inhibition does not cause microglial death in monoculture over 24 hours, but exogenous uPA may induce proliferation .............................................. 95 5.2.8 Prolonged exposure to uPA/uPAR inhibitors in glial cultures may reduce microglial proliferation or survival ........................................................................ 97 ix 5.2.9 Exogenous uPA stimulates microglial proliferation in glial cultures .......... 100 5.2.10 Only BC11 affects microglial phagocytosis ............................................... 101 5.3 DISCUSSION .......................................................................................................... 102 6 THE ROLE OF SPLEEN TYROSINE KINASE IN MICROGLIA-MEDIATED NEURODEGENERATION ....................................................................................... 109 6.1 INTRODUCTION ..................................................................................................... 109 6.2 RESULTS ............................................................................................................... 111 6.2.1 BAY61 and P505 inhibit SYK activation in microglia ................................. 111 6.2.2 SYK inhibition protects against LPS-induced neuronal loss ....................... 113 6.2.3 Validation and dose-response of SYK inhibitor-mediated neuroprotection 113 6.2.4 SYK inhibition reduces spontaneous neuronal loss ..................................... 115 6.2.5 Microglia express Syk, which may be relevant to the neuronal loss ........... 116 6.2.6 SYK inhibitors partially deplete microglia in neuron-glia cocultures ......... 117 6.2.7 SYK inhibition may slightly reduce astrocyte numbers in coculture ........... 118 6.2.8 SYK inhibitors induce apoptosis and necrosis in primary microglial monocultures ......................................................................................................... 119 6.2.9 BAY61 does not change microglial morphology but may alter proinflammatory cytokine release ......................................................................... 121 6.2.10 SYK inhibition reduces microglial phagocytosis of synaptic material ...... 122 6.2.11 SYK inhibition may reduce microglial phagocytosis of other cells in coculture ................................................................................................................ 124 6.2.12 SYK inhibitors prevent LPS-induced media acidification and reduce lactic acid production ..................................................................................................... 126 6.2.13 Media acidification alone may cause neuronal loss .................................. 127 6.2.14 Reducing the LPS-induced pH change is insufficient to prevent neuronal loss ........................................................................................................................ 128 6.2.15 BAY61 also prevents pTau-induced neuronal loss in a dose-dependent manner .................................................................................................................. 130 6.3 DISCUSSION .......................................................................................................... 131 7 HIGH-CONTENT SCREENING FOR BLOCKERS OF MICROGLIA- MEDIATED NEURODEGENERATION ................................................................ 136 7.1 INTRODUCTION ..................................................................................................... 136 7.2 RESULTS ............................................................................................................... 138 7.2.1 Design and analysis of a high-content screen for neuroprotective compounds ............................................................................................................................... 138 x 7.2.2 UMAP validation of assigned cell classes ................................................... 141 7.2.3 Cell count changes with treatment were reproducible and as expected ...... 143 7.2.4 Data quality control and normalisation ...................................................... 143 7.2.5 Identification of neuroprotective compounds and pathways ....................... 148 7.2.6 Analysis of LPS-untreated cultures identifies neurotoxic compounds ......... 149 7.2.7 Parallel analysis of microglia suggests mechanisms of neuroprotection .... 153 7.2.8 Multidimensional phenotypic data prioritises hit compounds based on overall disease phenotype ................................................................................................. 156 7.3 DISCUSSION .......................................................................................................... 160 7.3.1 Summary of findings .................................................................................... 160 7.3.2 Screen hits .................................................................................................... 161 7.3.3 Screen methods ............................................................................................ 165 8 DISCUSSION ........................................................................................................... 169 8.1 SUMMARY ............................................................................................................ 169 8.2 LIMITATIONS ........................................................................................................ 171 8.2.1 Cerebellar neuron-glia cocultures ............................................................... 171 8.2.2 Image analysis ............................................................................................. 174 8.2.3 Orthogonal assays and treatments ............................................................... 175 8.3 FUTURE DIRECTIONS ............................................................................................ 176 8.3.1 Automated image analysis ........................................................................... 176 8.3.2 uPA ............................................................................................................... 177 8.3.3 SYK ............................................................................................................... 177 8.3.4 High-content screening ................................................................................ 178 8.4 IMPLICATIONS AND CONCLUSIONS ........................................................................ 178 9 BIBLIOGRAPHY .................................................................................................... 181 10 APPENDICES ........................................................................................................ 246 xi LIST OF TABLES TABLE 1. TABLE OF ALL TREATMENTS USED IN ANY CHAPTER ......................................... 59 TABLE 2. NEUROPROTECTIVE HITS AND TARGETS .......................................................... 150 TABLE 3. NEUROTOXIC COMPOUNDS AND TARGETS ....................................................... 151 TABLE 4. ALL CELL MEASUREMENTS PRODUCED BY THE CELLPROFILER PIPELINE ........ 260 TABLE 5. TOP 20 CELL MEASUREMENTS USED FOR CLASSIFICATION DURING ASSAY DEVELOPMENT ....................................................................................................... 262 TABLE 6. NAMES OF ALL COMPOUNDS IN THE SCREEN ................................................... 263 TABLE 7. TOP 20 CELL MEASUREMENTS USED FOR CLASSIFICATION IN THE SCREEN ...... 267 TABLE 8. COMPOUNDS AND TARGETS AFFECTING MICROGLIAL COUNTS IN THE ABSENCE OF LPS ................................................................................................................... 268 TABLE 9. COMPOUNDS AND TARGETS AFFECTING MICROGLIAL COUNTS IN THE PRESENCE OF LPS ................................................................................................................... 269 xii LIST OF FIGURES Page numbers refer to Figure Legends; Figures may appear on the previous page. FIGURE 1. STAGES OF PHAGOCYTOSIS ................................................................................ 8 FIGURE 2. MICROGLIAL PHAGOCYTIC RECEPTORS MEDIATING ENGULFMENT OF DIFFERENT TARGETS .................................................................................................................. 15 FIGURE 3. STIMULI AND RESPONSES CONTRIBUTING TO MICROGLIA-MEDIATED NEURODEGENERATION ............................................................................................. 30 FIGURE 4. OVERVIEW OF THE UPA/UPAR SYSTEM .......................................................... 33 FIGURE 5. OVERVIEW OF SYK SIGNALLING ..................................................................... 35 FIGURE 6. PREVIOUS NEURON-GLIA COCULTURE IMAGE ANALYSIS WAS PERFORMED MANUALLY .............................................................................................................. 64 FIGURE 7. Α-NEUN IMMUNOCYTOCHEMISTRY ACCURATELY LABELS NEURONS IN NEURON- GLIA COCULTURES ................................................................................................... 67 FIGURE 8. IMMUNOCYTOCHEMISTRY DISPLACES MICROGLIA IN THE COCULTURES .......... 69 FIGURE 9. NEUO SPECIFICALLY STAINS LIVE NEURONS AND MATCHES Α-NEUN IMMUNOCYTOCHEMISTRY ........................................................................................ 71 FIGURE 10. QUPATH AND CELLPOSE SEGMENTATION ALGORITHMS ACCURATELY IDENTIFY CELL NUCLEI IN COCULTURE IMAGES ....................................................................... 72 FIGURE 11. MACHINE LEARNING-BASED CLASSIFICATION OF CELL TYPES IN COCULTURE IMAGES COMPLETES AN ACCURATE AUTOMATED ANALYSIS APPROACH ................... 75 FIGURE 12. AUTOMATED COCULTURE ANALYSIS IDENTIFIES TARGETS OF INTEREST FOR INFLAMMATORY NEURODEGENERATION .................................................................. 76 FIGURE 13. NEUO STAINING CAN BE AFFECTED BY TREATMENTS .................................... 78 FIGURE 14. BC11 HYDROBROMIDE PROTECTS AGAINST LPS-INDUCED NEURONAL LOSS IN NEURON-GLIA COCULTURE ....................................................................................... 87 FIGURE 15. LPS-INDUCED NEURONAL LOSS IN NEURON-GLIA COCULTURES IS MICROGLIA- DEPENDENT .............................................................................................................. 88 FIGURE 16. BC11 DEPLETES MICROGLIA FROM COCULTURES .......................................... 89 xiii FIGURE 17. BC11 PREVENTS LPS-INDUCED CHANGES TO MICROGLIAL MORPHOLOGY BUT DOES NOT AFFECT TNFΑ RELEASE ........................................................................... 91 FIGURE 18. ALTERNATIVE UPA INHIBITION FAILS TO RESCUE NEURONS FROM LPS- INDUCED LOSS, AND EXOGENOUS UPA FAILS TO CAUSE NEURONAL LOSS ................ 92 FIGURE 19. INHIBITION OF THE UPA-UPAR INTERACTION MAY AFFECT GLIA, UNLIKE CATALYTIC INHIBITION OF UPA ............................................................................... 94 FIGURE 20. UPA INHIBITION DOES NOT CAUSE MICROGLIAL DEATH IN MONOCULTURE OVER 24 HOURS, BUT EXOGENOUS UPA MAY INDUCE PROLIFERATION ..................... 97 FIGURE 21. PROLONGED EXPOSURE TO INHIBITORS ACHIEVES MICROGLIAL TOXICITY, WHICH MAY ONLY ARISE FROM INHIBITION OF UPA-UPAR INTERACTIONS .............. 99 FIGURE 22. EXOGENOUS UPA MAY STIMULATE MICROGLIAL PROLIFERATION ............... 101 FIGURE 23. ONLY BC11 AFFECTS MICROGLIAL PHAGOCYTOSIS ..................................... 102 FIGURE 24. BAY61 AND P505 INHIBIT SYK ACTIVITY IN MOUSE MICROGLIA ............... 111 FIGURE 25. SYK INHIBITION PROTECTS AGAINST LPS-INDUCED NEURONAL LOSS ......... 113 FIGURE 26. VALIDATION AND DOSE-RESPONSE OF SYK INHIBITOR-MEDIATED NEUROPROTECTION ................................................................................................ 114 FIGURE 27. SYK INHIBITION REDUCES SPONTANEOUS NEURONAL LOSS ........................ 115 FIGURE 28. MICROGLIA EXPRESS SYK, WHICH MAY BE RELEVANT TO THE NEURONAL LOSS ............................................................................................................................... 116 FIGURE 29. SYK INHIBITORS PARTIALLY DEPLETE MICROGLIA IN NEURON-GLIA COCULTURES .......................................................................................................... 118 FIGURE 30. SYK INHIBITION MAY SLIGHTLY REDUCE ASTROCYTE NUMBERS IN COCULTURE ............................................................................................................ 118 FIGURE 31. SYK INHIBITORS INDUCE APOPTOSIS AND NECROSIS IN PRIMARY MICROGLIAL MONOCULTURES ..................................................................................................... 120 FIGURE 32. BAY61 DOES NOT CHANGE MICROGLIAL MORPHOLOGY BUT MAY ALTER PROINFLAMMATORY CYTOKINE RELEASE ............................................................... 122 FIGURE 33. SYK INHIBITION REDUCES MICROGLIAL PHAGOCYTOSIS OF SYNAPTIC MATERIAL .............................................................................................................. 123 xiv FIGURE 34. SYK INHIBITION MAY REDUCE MICROGLIAL PHAGOCYTOSIS OF OTHER CELLS IN COCULTURE ....................................................................................................... 125 FIGURE 35. SYK INHIBITORS PREVENT LPS-INDUCED MEDIA ACIDIFICATION AND REDUCE LACTIC ACID PRODUCTION ..................................................................................... 126 FIGURE 36. MEDIA ACIDIFICATION ALONE MAY CAUSE NEURONAL LOSS ....................... 128 FIGURE 37. REDUCING LPS-INDUCED PH CHANGE IS INSUFFICIENT TO PREVENT NEURONAL LOSS ..................................................................................................... 129 FIGURE 38. BAY61 ALSO PREVENTS PTAU-INDUCED NEURONAL LOSS IN A DOSE- DEPENDENT MANNER ............................................................................................. 130 FIGURE 39. DESIGN AND ANALYSIS OF A HIGH-CONTENT SCREEN FOR NEUROPROTECTIVE COMPOUNDS ........................................................................................................... 140 FIGURE 40. UMAP VALIDATION OF ASSIGNED CELL CLASSES ........................................ 142 FIGURE 41. CELL COUNT CHANGES WITH TREATMENT WERE REPRODUCIBLE AND AS EXPECTED .............................................................................................................. 144 FIGURE 42. SCREEN DATA QUALITY CONTROL AND NORMALISATION............................. 147 FIGURE 43. PROTECTIVE COMPOUNDS ARE IDENTIFIED AND FREQUENTLY AFFECT STEROID SIGNALLING, ADRENORECEPTORS, AND MAP KINASES .......................................... 148 FIGURE 44. IMAGES OF CULTURES TREATED WITH EACH NEUROPROTECTIVE HIT COMPOUND IN THE PRESENCE OF LPS .................................................................... 152 FIGURE 45. PARALLEL ANALYSIS OF MICROGLIA SUGGESTS MECHANISMS IN PRIMARY SCREENING ............................................................................................................. 155 FIGURE 46. FULL CLUSTERED HEATMAP ANALYSIS ........................................................ 157 FIGURE 47. MULTIDIMENSIONAL PHENOTYPIC DATA PRIORITISES HIT COMPOUNDS BASED ON OVERALL DISEASE PHENOTYPE ......................................................................... 159 xv LIST OF ABBREVIATIONS Standard protein/gene names, symbols, or common abbreviations are used throughput this work, in addition to the following: AD Alzheimer’s disease ADP Adenosine diphosphate ADR Adrenergic receptor ALS Amyotrophic lateral sclerosis ALSP Adult-onset leukoencephalopathy with axonal spheroids and pigmented glia ANOVA Analysis of variance ATF Amino-terminal fragment ATP Adenosine triphosphate BAM Border-associated macrophage BAY61 BAY61-3606 CNS Central nervous system CRISPR Clustered regularly interspaced short palindromic repeats CTE Chronic traumatic encephalopathy CytD Cytochalasin D DAMP Damage-associated molecular pattern DIV Days in vitro DMEM Dulbecco’s modified Eagle medium DMSO Dimethyl sulfoxide DNA Deoxyribonucleic acid dsDNA Double-stranded DNA dsRNA Double-stranded RNA ELISA Enzyme-linked immunosorbent assay FACS Fluorescence-activated cell sorting FBS Foetal bovine serum FcR Fc receptor FDA Food and Drug Administration FTD Frontotemporal dementia Gal-3 Galectin-3 gDNA Genomic DNA GFD Growth factor domain xvi GWAS Genome-wide association study HCS High-content screening HD Huntington’s disease ICC Immunocytochemistry iPSC Induced pluripotent stem cell ITAM Immunoreceptor tyrosine-based activation motif KD Kringle domain LDL Low-density lipoprotein LME L-leucine methyl ester LPS Lipopolysaccharide MS Multiple sclerosis MSD Mesoscale discovery NPC Neuronal precursor cell NSAID Non-steroidal anti-inflammatory drug P505 P505-15 (PRT062607) PAMP Pathogen-associated molecular pattern PCA Principal component analysis PD Parkinson’s disease PET Positron emission tomography PI Propidium iodide PRR Pattern recognition receptor PVM Perivascular macrophage QTL Quantitative trait loci RM Repeated measures RNA Ribonucleic acid RNS Reactive nitrogen species ROS Reactive oxygen species RT-qPCR Reverse transcription quantitative polymerase chain reaction S1P Sphingosine-1-phosphate SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 SCI Spinal cord injury SGZ Subgranular zone SH2 Src homology 2 siRNA Silencing RNA SLI Selectivity index SNP Single-nucleotide polymorphism xvii STS Staurosporine TAM Tyro3/Axl/Mer TBI Traumatic brain injury UMAP Uniform manifold approximation projection uPA Urokinase, urokinase plasminogen activator uPAR Urokinase plasminogen activator receptor xviii LIST OF APPENDICES APPENDIX 1 – I’M INFECTED, EAT ME! ............................................................................ 247 APPENDIX 2 – SUPPORTING INFORMATION FOR CHAPTER 4............................................ 260 APPENDIX 3 – SUPPORTING INFORMATION FOR CHAPTER 7............................................ 263 Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 1 1 INTRODUCTION 1.1 Scope Microglia are resident innate immune cells of the central nervous system (CNS) that have important functions in both health and disease. These include innate immunity, synaptic pruning, clearance of dead cells, and response to pathological protein aggregation and neurodegeneration. Increasing evidence supports that they also contribute to the pathogenesis of neurodegenerative diseases such as Alzheimer’s disease (AD) through both protective and detrimental functions, and therefore that they may be valuable targets for therapeutic interventions (Colonna and Butovsky, 2017; Greenhalgh et al., 2020; Hickman et al., 2018). However, for almost all neurodegenerative diseases, microglia-targeted therapies have not yet progressed to the clinic despite theoretically being suitable for a wide variety of patients. This includes those at later stages of diseases like AD who may not see benefit from therapies targeting the initial pathogenic mechanisms such as proteinopathy. Microglial research has risen exponentially in recent years (Tremblay et al., 2015), but much more work is required to understand the influence of microglia on disease and how this is controlled. One ongoing challenge is modelling neurodegeneration in the presence of microglia in vitro, for mechanistic study. In this work, I aimed to improve these methods and use them to investigate druggable targets that modify microglia-mediated neurodegeneration. For much of this work, primary neuron-glia cocultures were used with the help of improved image analysis workflows, thereby directly assaying neurodegenerative phenotypes in physiologically-relevant cultures. I first validated the improved methods, then used STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 2 Timothy James Yuji Birkle – November 2023 them to identify and investigate urokinase (uPA) and spleen tyrosine kinase (SYK) as microglial targets of therapeutic interest. Microglial inflammation and phagocytosis are a common focus of this work. Finally, a rich dataset from a high-content screen using the cocultures is presented, which has not previously been possible. This identifies cellular pathways that modify microglia-dependent neurodegeneration which would be of interest for further study. Given this focus, this literature review will examine microglial phagocytosis, inflammation, and the microglial contribution to neurodegeneration in depth, before introducing uPA, SYK, and relevant methodological considerations for studying these diseases. It does not aim to comprehensively review the broader pathogenesis and biology of specific neurodegenerative diseases, nor to cover the literature on the entirety of microglial biology. 1.2 Microglia Microglia are resident macrophages of the mammalian central nervous system (CNS) that were first described by Pío del Río-Hortega in a series of papers in 1919 (del Río- Hortega Bereciartu, 2020; del Río-Hortega, 1919a, 1919b, 1919c, 1919d; Ginhoux and Prinz, 2015; Sierra et al., 2016). At the time, it had been proposed by Ramón y Cajal that CNS tissue had three constituents: nerve cells, neuroglia (largely astrocytes, in modern terms), and an elusive ‘third element’ (Cajal, 1913). Río-Hortega improved staining approaches to visualise this third element and found that it in fact consisted of two distinct cell populations, which he named microglia and oligodendrocytes. He observed that microglia were unique in being mesodermally-derived during development, while oligodendrocytes were ectodermally-derived and retrospectively grouped with the second, neuroglial element of the CNS (del Río-Hortega, 1939). Only recently has the developmental origin of microglia from the mesoderm been confirmed with modern fate-mapping techniques, showing that they derive from embryonic yolk sac progenitors (Ginhoux et al., 2010; Gomez Perdiguero et al., 2015; Kierdorf et al., 2013). This is unlike most macrophages, but similar to some other tissue resident macrophages including Kupffer and Langerhans cells. Microglial development is independent from master transcription factors controlling the haematopoiesis of blood macrophages, such as MYB, and instead depends on factors including CSF1R, PU.1 (SPI1) and IRF8 (Kierdorf et al., 2013; Schulz et al., 2012). In the adult brain, microglia constitute around 10% of all cells in both human and mouse, though there is Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 3 considerable regional heterogeneity (Lawson et al., 1990; Mittelbronn et al., 2001; Ochocka and Kaminska, 2021). Another CNS-resident macrophage population has recently been identified in the form of border-associated macrophages (BAMs; also named perivascular macrophages, PVMs) (Dermitzakis et al., 2023; Mildenberger et al., 2022), which are also yolk sac-derived via a parallel differentiation pathway to microglia (Goldmann et al., 2016; Utz et al., 2020). Even at first characterisation, microglia were recognised as strongly reactive cells exhibiting high motility, morphological changes, and phagocytic activity in response to CNS pathology in the form of meningitis or physical tissue damage (del Río-Hortega, 1920). Subsequent studies built on this to clearly establish the importance of microglia in CNS homeostasis, and in the 1990s microglia were already understood to be immunocompetent cells contributing to axonal regeneration, synaptic pruning, growth factor support of neurons, CNS development, as well as diseases including ischemia, infection, and neurodegeneration (Barron, 1995). Microglial research is now accelerating alongside increasing efforts to tackle the developmental and age-related conditions that they may influence (Tremblay et al., 2015), and their phagocytic and inflammatory functions are the primary focus of this work. 1.3 Phagocytosis Phagocytosis is a fundamental function of microglia that has been recognised since the first characterisation of these cells (del Río-Hortega, 1919a, 1919b). This process is defined as the engulfment of extracellular particles over 0.5µm in size into a phagosome, which matures inside the cell and ultimately fuses with a lysosome for degradation of the ingested material (Uribe-Querol and Rosales, 2020). Phagocytosis is an ancient evolutionary process that supports cellular nutrition in single-celled organisms, but which has become co-opted for various essential clearance functions in multicellular life. Indeed, the vast majority of cells can phagocytose certain targets such as adjacent dead cells; however, only specific ‘professional phagocytes’ can phagocytose a wider variety of targets for purposes such as innate immunity and subsequent antigen presentation for an adaptive immune response (Rabinovitch, 1995). Overall, phagocytosis consists of sequential steps: target identification, target engulfment, and then phagosome maturation and phagolysosome formation (Figure 1) (Underhill and Goodridge, 2012), all of which are necessary for efficient and continued phagocytosis. To achieve this with the necessary specificity to avoid phagocytosis of STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 4 Timothy James Yuji Birkle – November 2023 unintended targets, such as healthy host cells, many signalling pathways have evolved. Find-me signals are chemotactic signals that guide phagocytes towards a certain target. Eat-me signals displayed at the target provide an engulfment signal for phagocytes, in conjunction with opsonins and in opposition to don’t-eat-me signals, negative opsonins and phagocyte suppressants. Cell surface phagocytic receptors on a phagocyte can then receive these signals and ultimately trigger phagocytosis and downstream events, which requires cytoskeletal rearrangements and membrane trafficking to successfully complete target engulfment and degradation. These factors will be explored further below. 1.3.1 Find-me signalling from the target To phagocytose a particular target, a phagocyte must first be guided to that target in a specific manner. Find-me signals achieve this by providing a chemotactic gradient for phagocyte migration and have been reviewed extensively elsewhere (Cockram et al., 2021; Ravichandran, 2011). Briefly, necrotic cells release formyl peptides and generate soluble complement components C3a/C5a, which are detected by phagocyte formyl peptide receptor 1 (FPR1) and C3a/C5a receptors respectively (Westman et al., 2020). Meanwhile, the more stringently controlled apoptotic cell death produces specific signals to stimulate clearance of the still-intact cell, including RPS19 (detected by C5a receptor) (Nishimura et al., 2001; Shibuya et al., 2001), EMAPII (detected by CXCR3) (Hou et al., 2006; Knies et al., 1998), nucleotides such as ATP (detected by P2 receptors) (Elliott et al., 2009), fractalkine (detected by CX3CR1) (Truman et al., 2008), lysophosphatidylcholine (detected by G2A) (Lauber et al., 2003; Peter et al., 2008), and sphingosine-1-phosphate (detected by S1P receptors) (Hait et al., 2006). These collectively guide phagocytes to their targets in a specific manner depending on the receptors expressed by different phagocytic cells. 1.3.2 Eat-me signalling at the target-phagocyte interface Next, ‘eat-me’ signals on the target act as a local and (usually) contact-dependent signal to induce phagocytosis once the phagocyte and target are adjacent (Cockram et al., 2021; Flannagan et al., 2012; Ravichandran, 2011). These are engaged by phagocytic receptors on the phagocyte to initiate phagocytosis, potentially with the aid of an intervening opsonin acting as an adaptor; phagocytic receptors can therefore be classified as either opsonic or non-opsonic (Uribe-Querol and Rosales, 2020). The prototypical eat-me signal is externalised phosphatidylserine, which is generated by scramblase activity during apoptosis or cellular stress (Naeini et al., 2020). Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 5 Phosphatidylserine can be directly bound by non-opsonic receptors including TREM2, TIM-1/4, Stabilin-2, and BAI1 (Flannagan et al., 2012; Kobayashi et al., 2007; Park et al., 2007, 2008). Alternatively, phosphatidylserine may be bound by opsonins including MFGE8 and GAS6/PROS1, which can then bind the opsonic αVβ3 integrin (vitronectin receptor) or TAM receptors (TYRO3, AXL, and MERTK) respectively (Hanayama et al., 2002; Lemke, 2013). Other membrane lipids can also act as eat-me signals once externalised, such as phosphatidylinositides recently found to stimulate phagocytosis through CD14 (O.-H. Kim et al., 2022). Other eat-me signals include desialylated surface glycans, calreticulin, oxidised phospholipids, as well as surface-exposed DNA and histones (Cockram et al., 2021). Accordingly, the range of phagocytic receptors is equally broad. Non-opsonic receptors include scavenger receptors and C-type lectin receptors (CLRs, which include Dectin-1/2), and other opsonic receptors include complement receptors (such as CR3) and Fc receptors (FcRs). Phagocytosis through different receptors can result in forms of target engulfment that are visually distinct (for example, ‘reaching’ phagocytosis versus ‘sinking’ phagocytosis), likely due to evolutionary variation in the cytoskeletal dynamics that come downstream (Underhill and Goodridge, 2012). Phagocytic receptors have frequently been found to act cooperatively, and it remains an open question whether activation of a single phagocytic signalling pathway is sufficient to provoke phagocytosis (Cockram et al., 2021; Freeman and Grinstein, 2014). Antibody-dependent phagocytosis through FcRs is reduced in macrophages lacking CR3, for example, and there may also be crosstalk between scavenger receptors and the toll-like receptor TLR4 (Onyishi et al., 2023; van Spriel et al., 2001). Some of this cooperation has been understood with the help of recent membrane biophysics studies where other components of the plasma membrane were found to influence the intermolecular interactions of phagocytic receptors (Jaumouillé and Grinstein, 2011; Ostrowski et al., 2016). In one example, FcR activation caused inside-out activation of integrins to form an actin-tethered diffusion barrier within the plasma membrane (Freeman et al., 2016). This ultimately restricted access of the integral membrane phosphatase CD45 to the phagocytic site, promoting phosphorylation-dependent phagocytic signalling. Receptor crosstalk can also occur directly through common downstream pathway components such as SYK, which transduces signals from TREM2, CR3, Dectin-1, and FcRs (Mócsai et al., 2010). Thus, the efficiency of eat-me signalling through phagocytic receptors can be modified by other receptors’ activation. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 6 Timothy James Yuji Birkle – November 2023 Some regulation of phagocytic efficiency is not molecularly determined. Target size, shape and orientation have all been found to affect the efficiency of phagocytosis and subsequent inflammatory signalling (Champion and Mitragotri, 2009, 2006; Doshi and Mitragotri, 2010; Rettig et al., 2010; Underhill and Goodridge, 2012). Other studies have identified an effect of target rigidity, with stiffer targets being more efficiently phagocytosed by both FcR- and CR3-dependent pathways (Beningo and Wang, 2002; Jaumouillé et al., 2019). Finally, some phagocytosis may be achieved without specific eat-me signalling through the inadvertent uptake of extracellular material by macropinocytosis (the ingestion of fluid through formation of large membrane compartments at the cell surface) (Alpuche-Aranda et al., 1994; Underhill and Goodridge, 2012). Eat-me signalling is opposed by don’t-eat-me signalling, where other signals at the target surface are bound by receptors on the phagocyte that actively inhibit phagocytic signalling (Cockram et al., 2021). These signals include adenosine, CD24, CD47, MHC-I and sialylated glycans, many of which are expressed ubiquitously by host cells to prevent aberrant phagocytosis by passing phagocytes. Eat-me signalling can also be inhibited in the extracellular space through masking of eat-me signals by negative opsonins such as sRAGE, HMGB1 and sMERTK, or masking of phagocytic receptors by phagocyte suppressants including vitronectin and oxidised LDL. These signals are essential to prevent phagocytosis of unintended targets, but can also be also exploited by cancer and pathogens to prevent the clearance of malignant or infected cells (Z.-H. Wang et al., 2020; W. Zhang et al., 2020). 1.3.3 Intracellular signalling in the phagocyte Eat-me signalling through phagocytic receptors must first physically coordinate engulfment of the target, which is achieved through cytoskeletal rearrangements and secondary messenger signalling including adaptor proteins and lipid phosphorylation cascades (Flannagan et al., 2012; Yu et al., 2006). Phagocytic receptor signalling frequently converges on small G-proteins, including RAC1, CDC42 and RHOA. These proteins activate actin nucleation pathways including WASp and ARP2/3 to drive membrane extension around the target (Lorenzi et al., 2000), while also stimulating actin depolymerisation elsewhere to enable the dynamic protrusions and shape changes necessary for target engulfment. Contraction of this dynamic actin network by various myosins is important at sequential stages of the engulfment process (Diakonova et al., Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 7 2002; Swanson et al., 1999). The extension of membrane surface around the target also uses more membrane itself, which is delivered by Rab11- and VAMP-mediated focal exocytosis of endosomal compartments (Bajno et al., 2000; Braun et al., 2004; Cox et al., 2000). After successfully enveloping the target, the phagosome must then undergo a maturation process and lysosomal fusion (Canton, 2014; Levin et al., 2016). New phagosomes are bound by Rab5 proteins and Rab5-binding effectors including EEA1, VPS34 and MON1, which then drive a transition from Rab5 to Rab7 binding (Kinchen and Ravichandran, 2010; Mishra et al., 2010; Poteryaev et al., 2010; Vieira et al., 2003, 2001). LAMP proteins are also essential for Rab7 recruitment and subsequent microtubule-mediated trafficking of the phagosome towards lysosomal compartments (Binker et al., 2007; Huynh et al., 2007). The phagosome acidifies during this maturation process through V-ATPase activity (Lukacs et al., 1991), and fusion with a lysosome enables the degradation of the engulfed material. Throughout this process, antigen presentation machinery may be recruited to enable future adaptive immune activation, if a pathogen is the phagocytic target (Brewer et al., 2004). Overall, these intracellular pathways are as important for ongoing phagocytosis as the initial cell surface signalling events. All stages of phagocytosis are summarised in Figure 1. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 8 Timothy James Yuji Birkle – November 2023 Figure 1. Stages of phagocytosis Schematic summarising the 4 key stages of phagocytosis: 1) chemotaxis using find-me signals released from the target site; 2) engulfment of the target via eat-me signals on the target surface and phagocytic receptors on the phagocyte, with intracellular signalling for the necessary endosomal exocytic supply of membrane and cytoskeletal changes; 3) maturation of the phagosome, including trafficking towards a lysosome and, potentially, retrieval of antigens for presentation; 4) degradation of the target by fusion of the phagosome with the lysosome. Created with BioRender.com. Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 9 1.4 Microglial phagocytosis With modern molecular insight, we now appreciate that microglia express a range of phagocytic receptors including CR3, TREM2 and MERTK, while also producing a number of opsonins including Galectin-3 (Gal-3), MFGE8 and C1q (Butler et al., 2021). Similarly, high expression of chemotactic receptors such as P2Y12 and CX3CR1 gives microglia the capacity to move rapidly towards sites where phagocytosis is required, including infected or dying cells (Blume et al., 2020; Casano et al., 2016; Fekete et al., 2018). For all these molecules, expression depends on the activation state of the cell and phagocytosis is therefore heavily influenced by inflammatory state. Moreover, these distinct phagocytic pathways coordinate the phagocytosis of different targets, including pathogens, synapses, myelin, dead cells, live cells, and protein aggregates (Figure 2). 1.4.1 Microglial phagocytosis of pathogens Similar to other macrophages, one function of microglial phagocytosis is in anti- microbial innate immunity. CNS infections are less frequent than in the periphery but may be more dangerous (McMahon and Conrick-Martin, 2023), and pathogens may gain entry across the blood-brain barrier, blood-cerebrospinal fluid barrier, or using the olfactory or trigeminal nerves (Kristensson, 2011). These pathogens include bacteria, viruses, fungi, and parasites (Le Govic et al., 2022), and they or their products may be recognised by microglial receptors, leading to a phagocytic response (Mariani and Kielian, 2009; Nau et al., 2014). Amongst bacteria, Mycobacterium tuberculosis is engulfed by microglia in a manner dependent on CD14, though this may be detrimental by leading to intracellular infection by the bacteria (Peterson et al., 1995). Microglia also phagocytose both Escherichia coli and Staphylococcus aureus, and both phagocytosis and subsequent intracellular killing is enhanced by toll-like receptor (TLR) stimulation (Kielian, 2004; Ribes et al., 2009). Interestingly, expression of scavenger receptors and release of the opsonins calreticulin and Gal-3 are induced after activation of microglial TLRs, which may underlie the elevated phagocytosis in these conditions (Cockram et al., 2019; Husemann et al., 2002). Similar signalling may support microglial phagocytosis of the fungus Cryptococcus neoformans, which is also stimulated by TLR agonists (Redlich et al., 2013). In contrast, ATP signalling via P2Y12 may guide microglia to virally-infected cells and then induce phagocytosis to promote viral clearance (Fekete et al., 2018). Finally, microglia may phagocytose parasites such as Trypanosoma brucei directly (Figarella et al., 2018) or through STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 10 Timothy James Yuji Birkle – November 2023 engulfment of T. brucei-infected cells (Figarella et al., 2020; Shrivastava et al., 2017). In these ways, microglial phagocytosis mediated by TLRs and scavenger receptor signalling contributes to innate immunity in the CNS. 1.4.2 Microglial phagocytosis of synapses Perhaps the best-studied phagocytic function of microglia is pruning of the synaptic connections between neurons. The removal of synapses occurs across the healthy lifespan but peaks during development as neuronal circuits form and are optimised (Andoh and Koyama, 2021; Geloso and D’Ambrosi, 2021; Kettenmann et al., 2013). Microglia (or the similar glia in Drosophila) contact synapses in an activity-dependent manner in Drosophila, zebrafish, and rodents (Fuentes-Medel et al., 2009; Li et al., 2012; Sipe et al., 2016; Tremblay et al., 2010; Wake et al., 2009), and subsequent activity-dependent phagocytosis of synaptic components has been observed (Fuentes- Medel et al., 2009; Schafer et al., 2012; Sipe et al., 2016; Tremblay et al., 2010). Microglia-mediated pruning may also occur by trogocytosis, a related process to phagocytosis in which small regions of membrane from the target cell may be rapidly captured by a phagocyte in a less disruptive manner (Weinhard et al., 2018). Synaptic pruning must usually target specific synapses to correctly fine-tune network activity, and the molecular mechanisms guiding microglia to synapses with too little or too much activity have been studied in detail (Miyamoto et al., 2013; Schafer et al., 2013; Wilton et al., 2019). With regards to find-me signals, ATP is released by synapse activity and its detection by microglial P2Y12 receptors may be one guidance mechanism (Sipe et al., 2016). Next, complement components are common eat-me signals for synapses, with C1q being expressed by postnatal rodent neurons and tagging individual synapses for removal (Stevens et al., 2007). Signalling by C3 and microglial CR3 is important for appropriate activity-dependent pruning during retinogeniculate system development (Schafer et al., 2012), and C1q-deficient mice maintain higher synaptic densities that can result in seizures due to excessive excitatory network activity across the brain (Chu et al., 2010; Fonseca et al., 2004; Ma et al., 2013). TREM2 may also be an important phagocytic receptor for synapses given that both TREM2- and DAP12-deficient mice exhibit impaired brain development with reduced synapse elimination and increased excitatory neurotransmission (Filipello et al., 2018; Roumier et al., 2004), though this may also be explained by broader alterations to microglial activation state. MHC-I has also been found to play a role during development of the Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 11 visual system (Huh et al., 2000). Later in development, expression of complement components is reduced and the contribution of other eat-me mechanisms may increase (Schafer et al., 2016), and in adults MHC-I may be important for stripping synapses after axotomy (Oliveira et al., 2004). Finally, recent studies have uncovered a role for activity-dependent local phosphatidylserine exposure at synapses intended for pruning, which acts as an eat-me signal for microglial phagocytosis in multiple brain regions potentially via TREM2 (Kurematsu et al., 2022; Scott-Hewitt et al., 2020). 1.4.3 Microglial phagocytosis of myelin Myelin phagocytosis is essential for myelination across the lifespan based on the recent discovery that new myelin is often inaccurately produced and requires pruning and maintenance by microglia to achieve efficient ensheathment of axons (Djannatian et al., 2023; Hughes and Appel, 2020; McNamara et al., 2023). Indeed, individuals with heterozygous CSF1R mutations resulting in diminished microglial activity develop adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP), the neuropathology of which includes increased myelin outfolding and unravelling, excessively thick myelin, and demyelination (McNamara et al., 2023). Clearance of myelin debris arising from demyelinating injury, age, or disease is similarly important, as this debris inhibits neurite outgrowth, oligodendrocyte differentiation and remyelination (Pinto and Fernandes, 2020; Safaiyan et al., 2016; Sen et al., 2022). With demyelinating disease, CXCL10 is one signal that may guide microglia towards the lesion site (Pinto and Fernandes, 2020). Then, complement-mediated phagocytosis via microglial CR3 has long been implicated in myelin uptake (Brück and Friede, 1990; Reichert and Rotshenker, 2003). TREM2 is also highly expressed by microglia clearing myelin from lesions and deficiency impairs clearance while agonism enhances it (Cignarella et al., 2020; Poliani et al., 2015). Other studies have also identified contributions by microglial MERTK and phosphatidylserine exposure on myelin (Djannatian et al., 2023; Healy et al., 2017, 2016), as well as fractalkine signalling and scavenger receptors (Grajchen et al., 2020; Lampron et al., 2015; Reichert and Rotshenker, 2003). Thus, various classical pathways are implicated in microglial phagocytosis of this physiological target. 1.4.4 Microglial phagocytosis of dead cells Dead cells are another critical target for microglial phagocytosis during the development and maintenance of neuronal circuits. As the brain grows, neuronal STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 12 Timothy James Yuji Birkle – November 2023 precursor cells (NPCs) are produced in excess, and many of them fail to integrate with existing circuits and undergo apoptosis (Neumann et al., 2009; Pinto and Fernandes, 2020). Similarly, neurogenic niches in the adult brain such as the hippocampal subgranular zone (SGZ) require the constant remove of apoptotic cells (Márquez- Ropero et al., 2020; Sierra et al., 2013). Inefficient phagocytosis of ‘immunologically- silent’ apoptotic cells (also known as efferocytosis) results in inflammatory cell death instead (Blume et al., 2020; Pinto and Fernandes, 2020). Microglia appear to be central to this, as depletion of microglia causes accumulation of apoptotic neurons/NPCs, microglia can be seen in close contact with NPCs in the SGZ, and the remains of apoptotic neurons/NPCs have been frequently observed within microglia (Blume et al., 2020; Marı́n-Teva et al., 2004; Márquez-Ropero et al., 2020; Perez-Pouchoulen et al., 2015; Sierra et al., 2010). Microglia are also very efficient at this clearance, with efferocytosis in the SGZ taking as little as 90 minutes (Sierra et al., 2010). To attract microglia to apoptotic cells, ATP release may again be an important find-me signal via microglial P2Y12 and, at least in zebrafish, this signalling may even drive the long-term colonisation of brain regions by microglia (Blume et al., 2020; Casano et al., 2016). As might be expected, much of the reported eat-me signalling for efferocytosis depends on phosphatidylserine exposure; microglial AXL/MERTK and their opsonins GAS6/PROS1 contribute to this (Fourgeaud et al., 2016; Lemke, 2013), and phosphatidylserine exposure itself is necessary for microglial efferocytosis of apoptotic glioma cells (Chang et al., 2000). Finally, microglial TREM2 may coordinate engulfment of apoptotic neurons in a manner that may be particularly anti-inflammatory and ‘immunologically-silent’ (Takahashi et al., 2005). 1.4.5 Microglial phagocytosis of live cells Microglial phagocytosis of live cells is less well-studied, but is increasingly appreciated alongside an increasing understanding of ‘cell death by phagocytosis’ throughout the body as a whole (Brown, 2023). During neurogenesis in the cortex, amygdala, cerebellum and hippocampus at least, microglia target newborn cells lacking any signs of cell death, and microglial depletion is sufficient to cause an increase in total live cells numbers rather than just an increase in dead cells (Cunningham et al., 2013; Luo et al., 2016; Marı́n-Teva et al., 2004; VanRyzin et al., 2019). This can have immediate functional consequences, with androgen-driven NPC removal in the juvenile male rat amygdala promoting the masculinisation of play behaviour (VanRyzin et al., 2019). Interestingly, this process was complement-dependent in this study, suggesting another Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 13 important role for microglial complement-mediated phagocytosis. Meanwhile, MERTK may be important in live cell phagocytosis as well as efferocytosis, as deficiency can result in excessive neurons developing in the dentate gyrus SGZ leading to fatal seizures (Huang and Lemke, 2022). Microglia do not target just neurons during development either, given recent evidence that they phagocytose live oligodendrocyte precursor cells prior to developmental myelination (Nemes-Baran et al., 2020). This was dependent on fractalkine signalling and apparently independent of phosphatidylserine. As the importance of MERTK to phagocytosis of live NPCs suggests that phosphatidylserine is contrastingly relevant there, this may suggest that engulfment of different cell types by microglia is controlled by distinct phagocytic signalling pathways. In the diseased brain, live cell phagocytosis by microglia has also been shown to remove virally-infected neurons and astrocytes, which may help limit infection given that infected cells are usually most virally-productive while still alive (Fekete et al., 2018; Tufail et al., 2017). ATP signalling through P2Y12 may be an important find-me signal for infected cell phagocytosis, while infection can also stimulate phosphatidylserine exposure for eat-me signalling. In this way, microglial may remove live infected cells similar to other macrophages, a process that I have reviewed in greater detail previously (Appendix 1) (Birkle and Brown, 2021). Finally, microglia phagocytose live neurons under pro-inflammatory disease conditions including the presence of LPS, amyloid-β, or tau, and microglial P2Y6, TREM2, MERTK and CR3 may all contribute (Brelstaff et al., 2018; Brown, 2023; Neher et al., 2014; Neniskyte and Brown, 2013; Pampuscenko et al., 2020; Popescu et al., 2022; Puigdellívol et al., 2021). In a mouse model of retinitis pigmentosa, microglia were also found to engulf live rod cells via phosphatidylserine signalling through the vitronectin receptor (L. Zhao et al., 2015). Thus, cell death by microglial phagocytosis is important to brain homeostasis during both development and disease. 1.4.6 Microglial phagocytosis of protein aggregates Last to be reviewed here, protein aggregates may also be an important target for microglial phagocytosis. Such aggregates are believed to be the starting point for many proteinopathic diseases, which include Alzheimer's disease, Parkinson’s disease, prion diseases and others. Mechanisms for clearing these aggregates as they begin to form are therefore essential, and phagocytic degradation by microglia is one such mechanism. For amyloid-β, microglial uptake may be dependent on inflammatory state STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 14 Timothy James Yuji Birkle – November 2023 (Koenigsknecht-Talboo and Landreth, 2005; Pan et al., 2011), and many microglial receptors may contribute to this phagocytosis (Lee and Landreth, 2010; Ries and Sastre, 2016). The scavenger receptors SR-A and CD36 may both mediate amyloid-β uptake (Fu et al., 2012; Paresce et al., 1996; Yamanaka et al., 2012; Yang et al., 2011), with CD36 potentially acting through an unconventional mechanism in complex with α6β1 integrin and CD47 (Bamberger et al., 2003; Koenigsknecht and Landreth, 2004). Unusually, TLRs and their co-receptor CD14 may also directly initiate phagocytosis of aggregates, and deficiency can exacerbate amyloid pathology in mouse models of AD (Liu et al., 2005; Reed-Geaghan et al., 2009; Richard et al., 2008; Tahara et al., 2006). This may not be an indirect result of general activation of microglia, as CD14 mediates amyloid-β phagocytosis at lower concentrations of amyloid-β than elicit any inflammatory response (Liu et al., 2005). Next, some studies report that microglial CR3 mediates some uptake of amyloid-β (Czirr et al., 2017; Fu et al., 2012), likely driven by amyloid-β directly activating the classical and alternative complement pathways to result in opsonisation by C3b (Bradt et al., 1998). Lastly, both FPRL1 and LRP1 are also implicated in microglial phagocytosis of amyloid-β (L.-O. Brandenburg et al., 2008; N’Songo et al., 2013). Therefore, many receptors contribute to this uptake. Interestingly, this may be partly due to the heterogenous nature of amyloid-β plaques, at least in vivo. Plaques are not simply fibrillised amyloid-β, but also contain large quantities of other proteins, lipids, and nucleic acids, all of which may activate specific phagocytic pathways from the microglial cell surface (Stewart and Radford, 2017). Meanwhile, microglia also internalise aggregates of other proteinopathic proteins including tau and α-synuclein, the mechanisms of which have been less well-studied (Das et al., 2020; Majerova et al., 2014; Scheiblich et al., 2021). In summary, microglia phagocytose diverse targets within the CNS that are associated with normal development and homeostasis, as well as with disease. The engulfment of these different targets is coordinated by different phagocytic receptors, though there is some overlap. Receptors that frequently contribute to microglial uptake of targets relevant to neurodegenerative diseases include CR3 and TREM2. This literature is summarised in Figure 2. Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 15 1.5 Microglial inflammation Inflammation is a broad term covering the overall response of the immune system to disease or damage of tissues, which aims to correct the detrimental stimulus and initiate any necessary healing and recovery (L. Chen et al., 2017; Medzhitov, 2010). Stimuli may be infectious pathogens or any number of non-infectious stimuli including physical damage, chemical toxicity, cell death from ongoing disease, or psychological stressors (L. Chen et al., 2017). On a cellular level, inflammation involves the release of immune signalling molecules such as chemokines, which guide other immune cells towards the site, and cytokines, which activate other immune cells to promote phagocytosis and Figure 2. Microglial phagocytic receptors mediating engulfment of different targets Schematic summarising the targets phagocytosed by microglia and the most relevant phagocytic receptors for each. Created with BioRender.com. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 16 Timothy James Yuji Birkle – November 2023 further inflammatory signalling. Inflammation also encompasses the release of reactive oxygen/nitrogen species (ROS/RNS) for signalling or anti-microbial purposes and recruitment of adaptive immune cells to the site of damage. Pro-inflammatory signalling acts to increase immune cell responses to the stimulus, while anti-inflammatory signalling acts to decrease the response and return the tissue to its usual homeostatic condition, in principle once the stimulus has been addressed. However, this return to homeostasis after acute inflammation may be unsuccessful, resulting in chronic inflammation that may have longer-term effects on the tissue and drive chronic diseases (Bennett et al., 2018; Nathan and Ding, 2010). This may occur with persistence of the original stimulus or failure of resolving anti-inflammatory mechanisms due to an abnormal or excessive initial inflammatory response. Chronic inflammation may also arise gradually with age (so-called ‘inflammageing’) due to factors including genetics, gut microbiome dysregulation, cellular senescence, persistent inflammatory stimulus, and immune cell dysfunction (Ferrucci and Fabbri, 2018). Inflammatory signalling by innate immune cells like microglia is initiated by pattern recognition receptors (PRRs) for PAMPs and DAMPs. These PRRs include toll- like receptors (TLRs), AIM2-like receptors, RIG-I-like receptors, NOD-like receptors, nucleic acid receptors, and C-type lectin receptors (CLRs) (Colonna and Butovsky, 2017), as well as non-canonical PRRs such as TREM2 (Magno et al., 2021; Zhu et al., 2020). These PRRs detect stimuli that indicate an immune response is needed, which may be either pathogen-associated molecular patterns (PAMPs), such as pathogen membranes, or damage-associated molecular patterns (DAMPs). For example, lipopolysaccharide (LPS) is a component of gram negative bacterial cell walls detected by TLR4, and is therefore a PAMP, while ATP released from necrotic cells is a DAMP detected by P2 purinergic receptors (Di Virgilio et al., 2020). Over recent years, microglial inflammation has been increasingly appreciated as a central hallmark (or indeed pathogenic mechanism) in many CNS diseases (Muzio et al., 2021). While some have previously argued that microglia may not generate true inflammation given the supposed lack of adaptive immune cell recruitment (Graeber et al., 2011), a wealth of recent studies have identified that microglia do in fact recruit adaptive immune cells during conditions ranging from viral infection to ageing and neurodegenerative disease (Chen et al., 2023; Fekete et al., 2018; Greenhalgh et al., 2020; Klein et al., 2019; Tsai et al., 2016; Wheeler et al., 2018; X. Zhang et al., 2022). As they are also the major source of inflammatory cytokines and ROS/RNS in the CNS, Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 17 microglia therefore mediate ‘true’ inflammation. Importantly for long-term CNS homeostasis, these cells can also suppress and resolve inflammation through diverse pathways including many sub-types of nuclear receptors (Picard et al., 2021; Ros- Bernal et al., 2011; Saijo et al., 2013; Zhang-Gandhi and Drew, 2007) and cell surface receptors including the non-canonical NLRC3-like receptor, NLRX1, and TREM2 (Eitas et al., 2014; Liu et al., 2020; Shiau et al., 2013). 1.5.1 Response to PAMPs Microglia express PRRs for many common PAMPs, and in general their activation results in pro-inflammatory signalling (Colonna and Butovsky, 2017; Saijo et al., 2013). mRNA for all TLRs is expressed by microglia, and diverse TLR ligands all provoke inflammatory responses from microglia including cytokine and chemokine release, as well as MHC-II and costimulatory receptor expression to support antigen presentation to adaptive immune cells (Olson and Miller, 2004). These receptors largely respond to PAMPs such as bacterial cell wall lipoproteins or intracellular viral DNA, but can also be activated by endogenous DAMPs including heat shock proteins and the protein high mobility group box 1, released from necrotic cells (Takeuchi and Akira, 2010). For example, TLR2 is the primary TLR responsible for detecting pneumococcal cell wall lipoproteins (Tomlinson et al., 2014), and stimulation of microglia with these cell walls evokes release of pro-inflammatory cytokines TNFα, IL-6 and IL-12, chemokines MCP-1, MIP-1α, MIP-2 and RANTES, and reactive nitrogen species (Hanisch et al., 2001; Kim and Täuber, 1996; Prinz et al., 1999). Some bacterial infections can also stimulate the release of specific anti-microbial molecules from microglia, such as cathelicidin LL-37 (Lars-Ove Brandenburg et al., 2008). Meanwhile, microglial TLR3 senses dsRNA (indicative of viral infection) to stimulate pro-inflammatory cytokine release via MAPK pathways (Town et al., 2006), and this may underlie the strong response of microglia to viral infections including West Nile virus and SARS-CoV-2 (Jeong et al., 2022; Klein et al., 2019; Quick et al., 2014). Of particular importance to this work, TLR4 is the canonical receptor for LPS, a component of the outer membrane of gram-negative bacteria including E. coli and S. enterica. LPS is a classic pro-inflammatory stimulus for microglia, provoking strong cytokine release and ROS/RNS production (Skrzypczak-Wiercioch and Sałat, 2022). Some microglial response may also be mediated by NLRC5, as knockout murine microglia have a diminished reaction to LPS (Liu et al., 2023). Overall, microglia STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 18 Timothy James Yuji Birkle – November 2023 express a wide range of canonical PRRs detecting PAMPs, and these PRRs consistently stimulate a pro-inflammatory response. Microglia are not only sensitive to the presence of PAMPs within the brain; they also become activated by peripheral challenge with PAMPs including LPS (Shemer et al., 2020; Sousa et al., 2018). A 2015 meta-analysis overwhelmingly supports this, based on 51 studies employing peripheral injection of either LPS or bacteria (Hoogland et al., 2015). This is most likely indirect through sensing of pro- inflammatory cytokines produced in the periphery, and the resolution of microglial activation back to baseline appears to depend on detection of the anti-inflammatory cytokine IL-10 via microglia IL-10 receptors (Shemer et al., 2020). Interestingly, prolonging microglial activation after peripheral LPS challenge with IL-10R knockout resulted in prolonged sickness behaviour and neuronal dysfunction in mice, suggesting that microglial inflammatory response to peripheral stimulus directly influences adaptive sickness behaviour such as lethargy during illness. 1.5.2 Response to DAMPs Specific microglial pathways responding to DAMPs include the cGAS-STING pathway, which responds to intracellular dsDNA, as well as purinergic P2 receptors and CLRs. cGAS-STING mediates protection against genomic instability that may lead to cancer, and its activation leads to cellular senescence including the development of the proinflammatory ‘senescence-associated secretory phenotype’ (Coppé et al., 2010; Glück et al., 2017; Yang et al., 2017). Cellular ageing or deficient DNA-damage repair can cause the release of dsDNA into the cytosol from either the nucleus or mitochondria, activating microglial cGAS-STING and resulting in pro-inflammatory release of cytokines including IL-1β (Dou et al., 2017; Gulen et al., 2023; Song et al., 2019). This microglial activation may then contribute to cognitive decline with age (Gulen et al., 2023). P2 receptors, on the other hand, sense extracellular ATP/ADP, which are canonical DAMPs (ATP is released from necrotic cells and is rapidly hydrolysed to ADP). P2X7 is the best-studied receptor for this damage sensing, and stimulation of P2X7 on microglia drives inflammatory activation including upregulation of TNFα and NOS2, NLRP3 activation, and IL-1β release (Campagno and Mitchell, 2021; Monif et al., 2009). Additionally, alongside its main role in chemotaxis, P2Y12 stimulation with ADP can also activate microglia to produce IL-1β and IL-6 through a similar mechanism involving NLRP3 (Suzuki et al., 2020). Finally, CLRs on microglia Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 19 include Dectin-1 (CLEC7A), which can also mediate cellular responses to endogenous proteins and DAMPs including Galectin-9, annexins, and thioredoxin (Mata-Martínez et al., 2022; Roesner et al., 2019). Though these responses are typically anti-inflammatory in the periphery, recent studies suggest that Dectin-1 may be a pro-inflammatory receptor on microglia, at least in the context of stroke (Fu et al., 2021; Ye et al., 2020). The related CLEC2D also promotes microglial inflammation, albeit in response to extracellular nucleosomes released from dead cells instead (Marsman et al., 2016; Wu et al., 2022). Taken together, microglia express PAMP receptors including cGAS (dsDNA), P2Rs (ATP/ADP) and CLRs (various, including thioredoxin and nucleosomes), which are all capable of stimulating pro-inflammatory responses. 1.5.3 Inflammatory response to other factors Microglial inflammatory responses can be stimulated by alternative stimuli to PAMPs and DAMPs including cancer and proteinopathy. While these diseases can cause cell death and therefore generation of DAMPs, they also directly provoke microglial responses. Gliomas attract microglia and infiltrating macrophages using chemokines including CCL2 and fractalkine, prior to ‘reprogramming’ these cells into glioma- associated microglia/macrophages (Gutmann and Kettenmann, 2019). Much of this reprogramming consists of pro-inflammatory activation of microglia using endogenous ligands agonising TLR4, TLR7, and EGFR (Midwood et al., 2009). This activation results in microglial release of matrix metalloproteinases, facilitating tumour growth and invasion, and cytokines including IL-6, which stimulates tumour proliferation (Liu et al., 2021). However, the microglial response is not solely in the tumour’s favour, as the same cytokines can promote T-cell infiltration and activation of an adaptive immune response (Lanza et al., 2021). Proteopathic aggregation of proteins can also directly activate microglia, including aggregates of amyloid-β, tau, and prion proteins. Amyloid-β directly stimulates microglial inducible nitric oxide synthase (iNOS) and cytokine expression, apparently through TLR2 and downstream MYD88 (Jana et al., 2008; Pan et al., 2011). Meanwhile, tau can evoke similar responses, with one study finding that for intracellular tau this depends on activation of the cGAS-STING pathway (Jin et al., 2021; Morales et al., 2013; Perea et al., 2018). Finally, prion proteins can cause IL-6 and TNFα release from human microglia (Veerhuis et al., 2005). Therefore, microglia STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 20 Timothy James Yuji Birkle – November 2023 are not only reactive to standard immune stimuli but also those generated by specific, complex disease states. 1.6 Other microglial features and functions 1.6.1 Regulation of neuronal activity As made possible by their close contact with and surveillance of neurons, microglia can directly modify neuronal activity, and this is independent of their role in pruning synapses (Kettenmann et al., 2013; Robinson et al., 2019; Schafer et al., 2013). Neurons with excessive excitability can rapidly become damaged or die due to excitotoxicity (Kato et al., 2016), so the ability for microglia to efficiently correct this is beneficial. Over-excited neurons release ATP, potentially by volume-activated anion channels opened by the axonal swelling resulting from over-activity (Kato et al., 2016). This agrees with another study in which neuronal ATP release was independent of the usual pannexin 1 hemichannel-mediated mechanism (Dissing-Olesen et al., 2014), but in zebrafish pannexin 1 may still contribute to this process (Li et al., 2012). In any case, this ATP then guides microglial process extension and contact with the neuron, which temporally correlates with reduction in neuronal excitability (Kato et al., 2016; Li et al., 2012). This may be result of catabolism of extracellular ATP to adenosine by microglia, with adenosine then suppressing neuronal activity via adenosine receptors (Badimon et al., 2020). This contact-dependent (or at least, close contact-dependent) mechanism may work in parallel to a contact-independent one in which ATP stimulation of microglial P2X7 stimulates production of TNFα that is then protective against excitatory stimulus (Masuch et al., 2016). This may be via synaptic scaling (in which the strength of all synaptic inputs to a neuron are modulated collectively rather than individually), as this was previously shown to depend on glial TNFα production (Stellwagen and Malenka, 2006). Interestingly, microglial activation can result in ATP release from microglia themselves, which may then increase neuronal excitability by an indirect route through nearby astrocytes (Pascual et al., 2012). This contrast between microglia-derived ATP increasing neuronal activity, and neuron-derived ATP causing microglia to instead decrease neuronal activity, will require further study to fully understand. 1.6.2 Microglial proliferation Given the importance of microglial functions, the regulation of their proliferation is also a crucial aspect of their biology. Like other macrophages, this is in large part controlled Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 21 by signalling through the cell surface colony-stimulating factor-1 receptor (CSF1R), without which microglia are unable to proliferate and survive (Bohlen et al., 2017; Ulland et al., 2015). CSF1 and IL-34 are ligands for CSF1R that are expressed in different brain regions and therefore responsible for maintaining distinct microglial populations, with IL-34 being produced by neurons themselves (Easley-Neal et al., 2019; Greter et al., 2012; Nandi et al., 2012; Wang et al., 2012). IL-34 is particularly important for microglial development (Wang et al., 2012), but loss-of-function of IL- 34, CSF1 or CSF1R results in reduced microglial numbers (Greter et al., 2012; Obst et al., 2020; Węgiel et al., 1998). CSF1R signals via the adaptor protein DAP12 similar to TREM2 (Otero et al., 2009; Stanley and Chitu, 2014), and this similarity explains why TREM2 signalling regulates microglial survival in a manner that can be complemented by CSF1R signalling (Cheng et al., 2021). PU.1 and C/EBPα also regulate microglial proliferation, likely by directly regulating the transcription of CSF1R (Celada et al., 1996; Gómez-Nicola et al., 2013; Zhang et al., 1994). Finally, TGFβ and cholesterol are more recently-identified factors influencing microglial number as well (Bohlen et al., 2017; Butovsky et al., 2014). 1.6.3 Microglial morphology Given the roots of microglial research in microscopy and imaging methods, microglial morphology has unsurprisingly been a longstanding topic of interest, especially given the dramatic shape changes that they can display. Classically, their resting state consists of a small cell body with long, fine processes that survey large areas of the brain parenchyma, and they respond to activation and inflammatory stimulus through enlargement of the soma, process retraction and process thickening to achieve an ‘amoeboid’ morphology (Kloss et al., 2001; Vidal-Itriago et al., 2022). As with many binary paradigms, this model is slowly becoming replaced with a more detailed understanding of the links between microglial morphologies and particular functions. Nonetheless, microglia do dramatically change shape with the inflammatory activation that is particularly relevant to this work, and these changes have frequently been used as a proxy measure for activation. Some of these changes are preserved in vitro, despite the very different microenvironment in which the microglia sit. For example, LPS causes increased expression of adhesion proteins such as integrins, resulting in flattening and enlargement of microglia on the culture surface and changes to their ramification (process extension) (Fan et al., 2018; He et al., 2021; Kloss et al., 2001; Lively and Schlichter, 2018). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 22 Timothy James Yuji Birkle – November 2023 However, the interpretation of microglial morphology is complex, and microglia display clear differences in shape depending on the brain region being assessed (Balion et al., 2022; Colombo et al., 2022; Pinho et al., 2023). This is notable given the unusually ramified morphology of cortical microglia on which many general morphological observations have been made (Pinho et al., 2023). Age and sex also influence microglial morphology, further complicating matters (Vidal-Itriago et al., 2022). Finally, the precise culture surface on which cell cultures are prepared can have a large impact on microglial morphology, so care must be taken when comparing morphological data from different model systems and cell preparations (Balion et al., 2022). 1.7 Microglia and neurodegenerative disease Neurodegenerative diseases are those in which there is a degradation of the structure and function of neuronal networks that ultimately leads to impaired cognitive functions, which may include memory, behaviour, and motor capabilities. These diseases are frequently age-related, and ageing populations worldwide contribute to the ever- increasing prevalence of these challenging conditions (Deuschl et al., 2020). These diseases may be classified as either proteinopathic, where the condition is caused by the misfolding and/or aggregation of a protein, or non-proteinopathic (Wilson et al., 2023). Proteinopathic neurodegenerative diseases include Alzheimer’s disease (AD; driven by amyloid-β and tau), Parkinson’s disease (PD; α-synuclein), frontotemporal dementia/amyotrophic lateral sclerosis (FTD/ALS; SOD1, TDP43, FUS), primary tauopathies (tau), synucleinopathies (α-synuclein), Huntington’s disease (HD; huntingtin), and prion disease (prion protein). Onset of these conditions can be due to both gain-of-toxic-function of the misfolding/aggregating protein, or loss-of-function of the protein as is it mis-localised away from where it is required in the cell. There is also increasing appreciation that different proteinopathies frequently coincide in the same individual, complicating studies of individual pathologies (Karanth et al., 2020). Meanwhile, non-proteinopathic neurodegenerative diseases include multiple sclerosis (MS) and chronic traumatic encephalopathy (CTE), and acute conditions can also cause non-proteinopathic neuronal loss including stroke, traumatic brain injury (TBI), and spinal cord injury (SCI). Notably though, even these diseases can include a secondary proteinopathic component. For example, tau and amyloid-β may aggregate at levels comparable to AD secondary to both CTE and MS (Anderson et al., 2008; Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 23 Blennow et al., 2012), and pathological tau can be elevated and spread after traumatic CNS injury (Nakhjiri et al., 2020). Each neurodegenerative disease is the subject of a dedicated field of study in its own right and has been reviewed extensively elsewhere (Abramzon et al., 2020; Dobson and Giovannoni, 2019; Gardner et al., 2014; Knopman et al., 2021; Scheckel and Aguzzi, 2018; Tabrizi et al., 2020; Vázquez-Vélez and Zoghbi, 2021; Y. Zhang et al., 2022). 1.7.1 Microglial inflammation is a shared feature of neurodegenerative diseases Common hallmarks across the spectrum of neurodegenerative diseases include neuroinflammation (Wilson et al., 2023), as well as synaptic dysfunction and altered proteostasis. Such hallmarks are defined based on their association with both familial and sporadic forms of disease, their direct contribution to neurodegeneration, and their being strongly affected by disease state. Neuroinflammation is observed in all neurodegenerative diseases in the form of microgliosis and increased pro-inflammatory factors including cytokines. In general, this may be a result of DAMPs or protein aggregates from ongoing neurodegenerative disease activating microglia (sections 1.5.2 and 1.5.3). In AD, microglia accumulate substantially around pathological plaques of amyloid-β and exhibit an activated phenotype (Glass et al., 2010; Hickman et al., 2018; Martin et al., 2017; Philips and Robberecht, 2011; Tooyama et al., 1990). This includes high expression of MHC-I and MHC-II (McGeer et al., 1988; Tooyama et al., 1990), pro-inflammatory cytokines, ROS/RNS, and chemokines (Martin et al., 2017), and is provoked by multiple receptors including TLR2 (Jana et al., 2008; Pan et al., 2011). Tauopathy also causes microglial activation (Lee et al., 2010), potentially directly (Jin et al., 2021; Morales et al., 2013; Perea et al., 2018). In brains with Parkinson’s pathology, activated microglia are abundant (McGeer and McGeer, 2008), most prominently in brains from individuals who are conclusively diagnosed with PD prior to death (McGeer et al., 1988). For HD, activated microglia can be detected by PET scan measurements in live patients or presymptomatic gene carriers, and the extent of this correlates with increasing neuronal dysfunction and faster disease onset or severity (Pavese et al., 2006; Tai et al., 2007). Interestingly, this may be a result of mutant huntingtin within microglia themselves, as this can promote STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 24 Timothy James Yuji Birkle – November 2023 inflammation and microgliosis including via upregulation of PU.1 and C/EBP proteins (Crotti et al., 2014). Other PET studies have shown microglial activation in ALS and prion disease, amongst other literature (Hall et al., 1998; Hickman et al., 2018; Iaccarino et al., 2018; McGeer and McGeer, 2002; Philips and Robberecht, 2011; Turner et al., 2004; Veerhuis et al., 2005). Finally, active lesions in MS exhibit high densities of activated microglia as well (Fischer et al., 2013; Hickman et al., 2018; Kuhlmann et al., 2017; Mahad et al., 2015; Prineas et al., 2001; Zrzavy et al., 2017). In sum, microglial inflammation is a common occurrence across different neurodegenerative diseases and is therefore worthy of further study from a translational perspective. 1.7.2 Genetic studies implicate microglia in neurodegenerative disease risk Crucially, these activated microglial phenotypes are not a causally-irrelevant reaction to ongoing pathology in neurodegenerative diseases, but instead may contribute to or protect against disease onset or progression. This conclusion has been reached partly due to the substantial genetic evidence for a causal role of microglia in disease. Genome-wide association studies (GWAS) search for statistical association between variations in genetic loci and disease risk using single nucleotide polymorphism microarrays or whole genome sequencing data. Strikingly, GWAS have frequently identified links between neurodegenerative diseases and the loci of genes that are highly expressed by microglia, and/or loci that have substantial influence over potentially disease-relevant microglial functions (Wilson et al., 2023). This evidence is particularly strong for sporadic AD, where heritable genetic factors may account for up to 80% of overall risk of developing disease (Gatz et al., 2006). Much of this genetic risk is tied to microglia-relevant genes including APOE, TREM2, CD33, PICALM, CR1, MS4A, ABCA7, ABI3, PLCG2 and NLRP3 (Bellenguez et al., 2022; Liang et al., 2021). The products of these genes frequently contribute to microglial inflammation and phagocytosis, including TREM2 (Cignarella et al., 2020; Deczkowska et al., 2020; Filipello et al., 2018; Liu et al., 2020; Poliani et al., 2015; Popescu et al., 2022) and one of its ligands APOE (which also has a variety of independent functions) (Atagi et al., 2015; Bailey et al., 2015; Yeh et al., 2016). TREM2 is notable given its additional links to other neurodegenerative diseases as well, including FTD and Nasu-Hakola disease (Yeh et al., 2017). In Nasu-Hakola disease, homozygous TREM2 mutations, or mutations in the gene for its coreceptor DAP12, result in an unusual early-onset dementia (Bianchin et al., 2004; Deczkowska et al., Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 25 2020). The second strongest risk locus for AD is BIN1, where the lead SNP has been fine-mapped to a microglia specific enhancer (Young et al., 2021); notably, microglial BIN1 has recently been found to regulate microglial pro-inflammatory responses to neurodegeneration (Sudwarts et al., 2022). A recent GWAS for AD, the largest to-date, used quantitative trait loci (QTL) analysis to prioritise proteins affected by disease- linked genetic variation before applying pathway analysis to conclude that regulation of inflammatory TNFα signalling may mechanistically link a various associated genetic variants to disease (Bellenguez et al., 2022). Human genetic variation affecting microglia and inflammation therefore strongly affects risk of developing AD. Microglia-related genetic risk is not unique to AD. Genetic studies of PD have also linked disease to genes that implicate microglia. Recent data identifies contributing genetic variation at the FCGR2A locus (which encodes the FcγRIIA Fc receptor, highly expressed by microglia), with two of the SNPs being situated within a microglia- specific enhancer (Schilder and Raj, 2022), and a role for MHC-II (expressed by activated microglia) has also been suggested (Pierce and Coetzee, 2017). Mutations affecting LRRK2 strongly associate with risk for both familial and sporadic PD (Kluss et al., 2019; Pan et al., 2023; Schilder and Raj, 2022; Zimprich et al., 2004), and the LRRK2 protein regulates microglial pro-inflammatory activation and iron homeostasis under inflammatory conditions (Mamais et al., 2021; Moehle et al., 2012). More broadly, genetic risk for PD is associated with genomic regions that are active in microglia specifically (Andersen et al., 2021), and other microglia-related risk loci include GPNMB, which regulates microglial inflammation (Saade et al., 2021), and the homeostatic microglia marker gene P2RY12 (Andersen et al., 2021; Lopes et al., 2022; Nott and Holtman, 2023). As a final example, a recent collaborative genetics studies on multiple sclerosis identified many risk loci associated with disease risk, and this set of loci was enriched for genes highly expressed by human microglia (and not those of either neurons or astrocytes) (INTERNATIONAL MULTIPLE SCLEROSIS GENETICS CONSORTIUM, 2019). These loci included the MHC locus, and one of the identified SNPs affects microglial expression of the CLR CLECL1, which is notable given the role of CLRs in microglial inflammation and phagocytosis (section 1.4). 1.7.3 The contribution of microglia to Alzheimer’s disease Alzheimer’s disease is the most common neurodegenerative disease and dementia. Clinically, AD manifests through cognitive impairments in memory, executive function, STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 26 Timothy James Yuji Birkle – November 2023 language, and visuospatial functions (Knopman et al., 2021). Familial, early-onset AD is caused by rare, fully penetrant mutations in genes controlling production and processing of amyloid-β and accounts for 5-10% of all AD (Hoogmartens et al., 2021). Sporadic AD has various genetic risk factors (section 1.7.2), is pathologically similar to familial AD, and presents increasingly with age in individuals over 65 years old. Pathologically, AD is generally believed to originate from overproduction and aggregation of pathological forms of amyloid-β, and pathology spreads from the entorhinal cortex to the hippocampus and cortex as the disease progresses (Braak and Braak, 1991). This amyloid-β pathology typically begins many years in advance of any obvious cognitive changes. However, a secondary proteinopathy of accumulating and fibrillising tau protein arises later, and this tauopathy correlates better with cognitive decline than does amyloid-β pathology (Bejanin et al., 2017; Brier et al., 2016; Hanseeuw et al., 2019; Ma et al., 2021; Tanner and Rabinovici, 2021). The amyloid hypothesis of AD pathogenesis proposes that AD does indeed originate from amyloid-β deposition and that the consequent cellular dysfunction, including that of microglia, then accelerates the severity and spread of tau pathology that ultimately causes the bulk of the progressive cognitive decline (Karran and De Strooper, 2022). Motivated by the relevance of microglia to the genetic risk for AD, there is now a body of literature on microglial functions that affect disease, and many of these restrict pathology. Early in the disease progression, pro-inflammatory signalling and highly phagocytic microglial states may be beneficial by mediating rapid clearance of amyloid-β (McFarland and Chakrabarty, 2022). Overexpression of pro-inflammatory cytokines including TNFα, IL-6 and IFN-γ in the brains of AD model mice can cause microgliosis and reduce amyloid-β pathology (Chakrabarty et al., 2011, 2010a, 2010b; Shaftel et al., 2007). Similarly, anti-inflammatory signalling by IL-4 and IL-10 appears to promote early AD pathology (Chakrabarty et al., 2015, 2012; Guillot-Sestier et al., 2015), and impairing microglial chemotaxis towards pathology also ultimately worsens disease progression (El Khoury et al., 2007). Pro-inflammatory TLR signalling may also be protective at early stages of disease given that TLR2 deficiency worsens pathology in AD mouse models (Richard et al., 2008; Tahara et al., 2006). Overall, pro- inflammatory microglial states can be protective, particularly through elevated phagocytosis of amyloid-β early in AD progression. Interestingly, microglial expression of phagocytic receptors for amyloid-β decreases with age and chronic inflammation alongside worsening pathology in AD model mice (Hickman et al., 2008). Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 27 Along the same lines, the microglial response through the AD risk-associated TREM2 pathway is beneficial for controlling early AD pathology. TREM2 can bind to oligomeric amyloid-β, an interaction that is weakened by the disease-promoting R47H mutation, and TREM2 deficiency impairs amyloid-β degradation and microglial responses including cytokine release, chemotaxis, and proliferation (Zhao et al., 2018). Accordingly, TREM2 has a vital role in coordinating the microglial response to amyloid-β plaques. TREM2-positive microglial processes intimately contact plaques, compacting them and shielding other cells from their neurotoxic effects, and TREM2 deficiency or R47H mutation impairs this protective function (Parhizkar et al., 2019; Song et al., 2018; Wang et al., 2016; Yuan et al., 2016). TREM2 overexpression is also protective in the 5xFAD mouse model of AD (Lee et al., 2018). Therefore, a strong microglial response appears to be protective for early amyloid-β pathology, particularly through TREM2. There is also some evidence that microglial TREM2-mediated responses can limit the secondary tau pathology of AD around amyloid-β plaques (Bemiller et al., 2017; Leyns et al., 2019). However, other studies have in contrast shown that TREM2 pathway activity may worsen the progression of tau pathology, and the relationship between TREM2 and tau pathology overall remains unclear (Gratuze et al., 2020; Schweig et al., 2019). As may be the case for TREM2 and tau, some microglial activity in AD- afflicted brains can do more harm than good (Figure 3). Indeed, the amyloid hypothesis of AD pathogenesis proposes that microglial inflammation is provoked by amyloid-β pathology and then contributes to tauopathy and cognitive decline (Karran and De Strooper, 2022). Microglia around amyloid-β plaques can be strongly pro-inflammatory, releasing cytokines such as TNFα, IL-6, and IL-1 (Glass et al., 2010). Though potentially protective in early AD as discussed above, these molecules can be directly neurotoxic (Conroy et al., 2004; McCoy and Tansey, 2008; Simi et al., 2007), or provoke neurotoxicity by nearby astrocytes (Saijo et al., 2009). This balance between protective and harmful effects of pro-inflammatory signalling may shift as inflammation is drawn out and becomes chronic. Aside from excessive inflammation, amyloid-β or tau can induce neurotoxicity by direct microglial phagocytosis of neurons (Brelstaff et al., 2018; Fricker et al., 2012b; Pampuscenko et al., 2020; Puigdellívol et al., 2021), and complement-dependent phagocytosis through CR3 may mediate amyloid-β-induced synapse loss (Fonseca et al., 2004; Hong et al., 2016; Rajendran and Paolicelli, 2018; Roy et al., 2020). Microglial inflammation can also directly exacerbate the underlying STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 28 Timothy James Yuji Birkle – November 2023 proteinopathy of AD, as activation of NLRP3 by amyloid-β can stimulate oligomerisation and secretion of ASC protein that then encourages further aggregation and spread of amyloid-β (Venegas et al., 2017). Tau also directly activates microglia (Das et al., 2020), and microglial activation can contribute to tau phosphorylation and tau spread via microglial exosomes, as well as shortening of lifespan in mice (Asai et al., 2015; Hickman et al., 2018; Lee et al., 2010; Yoshiyama et al., 2007). In this way, there are many microglial activities apparent in AD that it may be beneficial to prevent, and these activities may contribute to some of the genetic AD risk that resides in microglia. 1.7.4 The contribution of microglia to other neurodegenerative diseases and acute injuries The progression of other proteinopathies also partly depends on microglia. PD is driven by misfolded and aggregated α-synuclein, which can activate microglia to produce pro- inflammatory cytokines and ROS/RNS (Glass et al., 2010; Zhang et al., 2005). These factors can be directly neurotoxic as previously discussed for AD (McCoy and Tansey, 2008; Simi et al., 2007), resulting in microglia exacerbating α-synuclein-induced death of neurons in vitro (Zhang et al., 2005). On the other hand, microglia may also rapidly internalise α-synuclein and remove it from the parenchyma, and its subsequent degradation can be assisted by sharing it with nearby microglia through tunnelling nanotubes (Scheiblich et al., 2021). For ALS, microglial inflammation has generally been found to exacerbate disease, particularly where causative SOD1 mutations are involved. Activated microglia accumulate in ALS and produce ROS, which correlates with oxidative damage to neurons (Glass et al., 2010; McGeer and McGeer, 2002). This may be explained by both extracellular and intracellular mutant SOD1 being able to activate microglia and promote neurotoxic ROS production (Harraz et al., 2008; Hickman et al., 2018; Zhao et al., 2010). Crucially, anti-inflammatory drugs including lenalidomide, celecoxib and minocycline all ameliorate disease in SOD1-mutant mouse models of ALS, and this is associated with reduced pro-inflammatory cytokine levels and microglial activation (Boillée et al., 2006; Drachman et al., 2002; Kriz et al., 2002; Neymotin et al., 2009; Van Den Bosch et al., 2002; Zhu et al., 2002). The progressive worsening of pathology in these models appears to depend on microglia, as knockout of microglial mutant SOD1 slows disease progression (but does not delay onset) (Boillée et al., 2006). Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 29 In HD, mutant huntingtin can also activate microglia to a neurotoxic inflammatory state that may contribute to neurodegeneration, which is supported by the fact that microglial activation in patients correlates with disease progression and cognitive decline (Crotti et al., 2014; Pavese et al., 2006; Tai et al., 2007). Finally, activated microglia in prion-mediated Creutzfeld-Jakob disease produce ROS through elevated NOX2 expression, and mice deficient for NOX2 survive longer after prion inoculation (Sorce et al., 2014). Overall, microglia therefore actively contribute to the progression of many proteinopathic neurodegenerative diseases, and this is a result of their pro-inflammatory activation (Figure 3). Microglia modify ongoing non-proteinopathic neurodegenerative diseases too, such as MS, as well as the secondary degeneration occurring after initial injuries of stroke or trauma. In MS, clearance of damaged myelin by microglia is essential and occurs through phagocytosis mediated by complement receptors, scavenger receptors, and potentially TREM2 (Brück and Friede, 1990; Pinto and Fernandes, 2020; Poliani et al., 2015; Reichert and Rotshenker, 2003). This activity of activated microglia is therefore beneficial, though the differences between these pathways may be important for proper resolution of inflammation in MS given that complement-mediated phagocytosis may drive pro-inflammatory signalling while TREM2-mediated phagocytosis may by anti-inflammatory (Pinto and Fernandes, 2020). However, the ROS produced by these same activated microglia at MS lesions may be detrimental as discussed elsewhere; notably, mice deficient in NLRX1, which is an anti-inflammatory receptor in microglia, have heightened microglial activation and worse pathology in the experimental autoimmune encephalitis model of MS (Eitas et al., 2014). Microglia can also directly contribute to the neurodegeneration that occurs secondary to the initial damage in trauma or stroke. In these conditions, microglial complement-mediated phagocytosis appears to be actively detrimental given that complement components are elevated around these initial sites of damage in mice, and that inhibition or knockout of complement proteins improves pathology after both TBI and stroke (Hammad et al., 2018). This may be by inhibiting the phagocytosis and killing of live neurons in the periphery of the injury (Neher et al., 2013). Inhibition of the pro-inflammatory IL-1 signalling can also limit neurodegeneration after ischemic, excitotoxic and traumatic stimuli (Simi et al., 2007). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 30 Timothy James Yuji Birkle – November 2023 Overall, microglia can be beneficial or detrimental during neurodegenerative diseases upon activation by a variety of disease-associated stimuli including protein aggregates, DAMPs, and pro-inflammatory cytokines. Microglia may be detrimental due to: 1) exacerbation of proteinopathy; 2) further release of potentially neurotoxic pro-inflammatory cytokines; 3) production of ROS/RNS that cause oxidative stress of neurons; 4) excessive phagocytosis of synapses; and 5) excessive phagocytosis of neurons themselves. The stimuli and responses contributing to microglia-mediated neurodegeneration are summarised in Figure 3. Figure 3. Stimuli and responses contributing to microglia-mediated neurodegeneration Schematic summarising the role of microglia in promoting neurodegeneration under certain disease conditions, particularly in chronic disease. Factors both internal and external to microglia may provoke detrimental activity, which can include exacerbation of ongoing proteinopathy and inflammation, as well as active phagocytic damage to synapses and neurons. Created with BioRender.com. Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 31 1.8 Targeting microglia to treat neurodegenerative diseases Based on the above evidence, understanding microglial biology is now considered to be of central importance in tackling neurodegenerative disease. Alongside their many beneficial actions in response to neurodegenerative disease, microglia may also initiate or exacerbate disease through various molecular and cellular mechanisms. It is an ongoing challenge to identify these mechanisms and develop therapeutically-beneficial interventions that selectively target them without compromising the positive roles for microglia. Microglia-mediated neurodegenerative mechanisms are outlined in Figure 3 above. This possibility of limiting the detrimental activity of microglia in the context of neurodegenerative diseases is being increasingly explored. A common initial strategy of interest has been the use of general anti-inflammatory drugs such as non-steroidal anti-inflammatory drugs (NSAIDs), not least due to epidemiological data associating increased use of NSAIDs with decreased prevalence of neurodegenerative diseases including AD (Rivers-Auty et al., 2020). In addition, anti-inflammatory drugs have frequently been shown to be protective in mouse models of AD, PD, and ALS (Kurkowska-Jastrzębska et al., 2004; Petrov et al., 2017; Rivers-Auty et al., 2020; W. Zhang et al., 2023). However, the association of NSAIDs with reduced AD prevalence is now believed to be largely artefactual (Rivers-Auty et al., 2020), and such general anti-inflammatory drugs have consistently failed in clinical trials for AD, PD, and ALS (Howard et al., 2020; Petrov et al., 2017; W. Zhang et al., 2023). Other general anti-inflammatory treatments are of interest for neurodegenerative disease therapy, but so far little clinical trial data is available. Targeted inhibition of the NLRP3 inflammasome, which is activated across many neurodegenerative diseases in microglia, is showing more promise (Lewcock et al., 2020). Inhibition protects against mouse models of MS, AD, and PD, while reducing microglial activation (Coll et al., 2015; Dempsey et al., 2017; Gordon et al., 2018; Huang et al., 2021; Thawkar and Kaur, 2019; W. Zhang et al., 2023; Zhou et al., 2016), and multiple inhibitors of NLRP3 are now in clinical trials (W. Zhang et al., 2023). Inhibitors of RIPK1, another protein involved in the inflammasome response and regulation of cytokine and ROS release, are also of therapeutic interest for diseases including AD, PD, ALS, TBI and stroke, having been found to be protective in various pre-clinical models (Degterev et al., 2019; Yuan et al., 2019). TLR2 inhibitors have had similar efficacy in mouse models of PD (Kim et al., 2018; Sun et al., 2021). Finally, STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 32 Timothy James Yuji Birkle – November 2023 edaravone is a clinically-approved drug for ALS that acts by scavenging free radicals including microglia-derived ROS/RNS (Cho and Shukla, 2021). Another general microglia-targeted strategy is to simply deplete microglia from the brain and therefore prevent most of their deleterious effects. This can be achieved through inhibition of CSF1R, signalling through which is necessary for microglial survival (Bohlen et al., 2017; Elmore et al., 2014). CSF1R inhibition successfully depletes microglia (or simply prevents proliferation, depending on the inhibitor and dose used) and is beneficial in multiple mouse models of AD (Dagher et al., 2015; Olmos-Alonso et al., 2016; Spangenberg et al., 2016). This treatment has surprisingly little effect on the general brain health and behaviour of mice given that homozygous CSF1R mutation in humans causes ALSP, which includes various neurological deficits. The efficacy of a CSF1R inhibitor against AD is currently being tested in clinical trials. Finally, the AD field is awaiting the results of the first phase II clinical trial of an anti-TREM2 agonist antibody, AL002, with first data expected towards the end of 2024 (Paul et al., 2021). TREM2 agonism has been a prominent proposal for AD therapy based on the R47H variant increasing AD risk and this variant generally being considered to reduce TREM2 function (W. Zhang et al., 2023). Corroborating that TREM2 function is beneficial against AD, genetic knockout of TREM2 in a mouse model of AD worsens amyloid pathology and neuronal loss (Wang et al., 2015). The AL002 agonist antibody was previously found to protect in AD model mice, while reducing microglial inflammation (S. Wang et al., 2020). This is therefore another microglia-targeted therapy that shows promise through reducing inflammation. However, activating TREM2 also has more complex effects on amyloid-β including compaction of plaques by microglia and this will likely also contribute to any effects of TREM2 agonists on disease progression (Meilandt et al., 2020). 1.9 Urokinase and spleen tyrosine kinase Two chapters of this thesis focus on urokinase (uPA) and spleen tyrosine kinase (SYK). The reasons for this are discussed comprehensively in the Introductions to their respective chapters but will be briefly outlined here. Study of both proteins aims to fill a gap in our understanding of their microglial function. Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 33 As outlined above, excessive microglial phagocytosis and/or inflammation may contribute to neurodegeneration. Thus, finding new ways to block this microglial phagocytosis or inflammation via specific pathways may be beneficial. On analysis of a recent screen for proteins regulating microglial phagocytosis (Pluvinage et al., 2019), uPA stood out as an interesting target. uPA is an extracellular protease encoded by the human PLAU gene that binds to the cell surface receptor uPAR, an interaction that canonically serves to localise uPA to the leading edge of migrating cells (Figure 4) (Estreicher et al., 1990; Mahmood et al., 2018). However, this interaction also controls intracellular and extracellular signalling events including proliferation, inflammation, and integrin-ligand binding (Kwak et al., 2005; Magnussen et al., 2017; Mehra et al., 2016; Schmidt and Grünsfelder, 2001). uPA and uPAR expression increase across both proteinopathic and non-proteinopathic neurodegenerative diseases or brain injuries, including AD, stroke, MS, and ALS (Barker et al., 2012; Chang et al., 2003; Cho et al., 2012; Davis et al., 2003; Diaz et al., 2017; Glas et al., 2007; Gur-Wahnon et al., 2013; Gveric et al., 2001; Hosomi et al., 2001; Lahtinen et al., 2006; Mehra et al., 2016; Tucker et al., 2000; Walker et al., 2002), and variation at the PLAU locus associates with AD risk (Ertekin-Taner et al., 2005; Riemenschneider et al., 2006). Despite this, Figure 4. Overview of the uPA/uPAR system Schematic illustrating the diverse functions of uPA, from canonical proteolytic functions (including the plasminogen activation cascade) to intracellular signalling events effected through uPAR. In microglia, the existence and mechanisms of these functions remains tentative. Created with BioRender.com. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 34 Timothy James Yuji Birkle – November 2023 research into microglial uPA is limited. Microglia express uPA and uPAR (Cross and Woodroofe, 1999; Cunningham et al., 2009; Nakajima et al., 2005, 1992; Shin et al., 2010), which increases in AD (Davis et al., 2003; Walker et al., 2002), and some studies have identified a role in microglial chemotaxis and phagocytosis (Jeon et al., 2012; Pluvinage et al., 2019; Shin et al., 2010). However, the role of uPA in microglia- mediated neurodegeneration has not been studied, nor its role in regulating microglial proliferation, inflammation, and activation. Meanwhile, SYK was originally of interest to this work based on the literature linking it to neurodegenerative diseases, particularly through its role in microglia. Microglial SYK is a kinase signalling downstream of important receptors including TREM2, CR3, and CSF1R, which are involved in regulation of microglial phagocytosis, inflammation, and proliferation (Figure 5) (Mócsai et al., 2010). Both in microglia and elsewhere, inhibitors of SYK block phagocytosis induced by multiple phagocytic receptors, including Dectin-1 and FcRs (Crowley et al., 1997; Gevrey et al., 2005; McQuade et al., 2020; Murakami et al., 2014; Scheib et al., 2012; Song et al., 2004; Walbaum et al., 2021; H. Yao et al., 2019). Crucially for neurodegenerative research, SYK is upregulated in animals models of AD and genetic variants are associated with AD risk at a sub-genome-wide level (Sierksma et al., 2020). As a result, microglial SYK has been the subject of considerable research in pre-clinical models of disease. It has been found to promote deleterious microglial activity in various mouse models, including of tauopathy, stroke, trauma, and inflammation (He et al., 2022; M. W. Kim et al., 2022; Schweig et al., 2019; Ye et al., 2020), but it also protects against amyloid-β pathology, likely by transducing protective signalling from TREM2 (Ennerfelt et al., 2022). This double-edged relationship between SYK and neurodegeneration remains unresolved, and it is increasingly important to understand this given the ongoing interest in therapies against AD that target TREM2 and CSF1R upstream of SYK. Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 35 1.10 Methods for studying microglia-mediated neurodegeneration 1.10.1 Modelling inflammation with LPS Microglial inflammation and phagocytosis are frequently activated during neurodegenerative diseases and may contribute to disease progression. For this reason, the use of lipopolysaccharide (LPS; also known as endotoxin) has long been considered useful to model the inflammatory aspects of these diseases, as it is a classical inflammatory stimulus (Skrzypczak-Wiercioch and Sałat, 2022). LPS is a key component of the outer membrane surrounding Gram-negative bacteria and consists of Figure 5. Overview of SYK signalling Schematic illustrating signalling events at the plasma membrane involving SYK. Activation of receptors stimulates the phosphorylation of their (or their adaptors’) ITAM domains by SRC family kinases. SYK can then bind via SH2 domains and autophosphorylate to become active, binding downstream signalling molecules including PI3K and PLCG2 to ultimately influence diverse cellular functions. Created with BioRender.com. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 36 Timothy James Yuji Birkle – November 2023 three components: lipid A, a core oligosaccharide, and O-antigen. Lipid-A is the most inflammatory and immunogenic of these, and its potency in stimulating an immune response varies depending on its level of phosphorylation and acylation (Bertani and Ruiz, 2018). Hexa-acylated lipid A is considered the most potent, and is the version of lipid A found in E. coli and S. enterica typhimurium (the latter being the bacteria from which the LPS used in this work was derived) (Maldonado et al., 2016; Raetz et al., 2007; Steimle et al., 2016). Lipid A and LPS activate TLR4 via the accessory protein MD-2, inducing dimerization of the receptor and subsequent activation of the intracellular Toll/IL-1 receptor (TIR) domain (Gern et al., 2021). This then initiates intracellular signalling through a cascade involving MYD88, Toll/interleukin-1-receptor domain-containing adaptor-inducing interferon β (TRIF), and other molecules to ultimately result in NF-κB activation and other core inflammatory pathways (Owen et al., 2021; Skrzypczak-Wiercioch and Sałat, 2022). Importantly, this cascade of TLR4 signalling is directly relevant to neurodegenerative disease and LPS is therefore a relevant inflammatory stimulus. Microglia are the major cell type in the CNS that expresses TLR4, with little to no expression in neurons, astrocytes, and oligodendrocytes (Lehnardt et al., 2003). In disease, microglial TLR4 can be activated by pathological proteins including amyloid-β (J. Yang et al., 2020), tau (Pampuscenko et al., 2023), and α-synuclein (Heidari et al., 2022), and LPS can therefore mimic some of the inflammatory effects of these proteinopathies. The relevance of LPS for modelling this inflammation is supported by studies finding that differentially-expressed genes in LPS-stimulated microglia overlap with those differentially-regulated in human AD patients (Monzón-Sandoval et al., 2022) and AD mouse models (Shippy et al., 2022), including genes for important inflammatory proteins such as chemokines, cytokines (including TNFα and IL-1β), and the complement pathway protein C3. The gene expression changes induced by treatment of microglia with LPS are relevant beyond AD, too, and also include genes implicated in PD and HD (Pulido-Salgado et al., 2018). Therefore, LPS can model some aspects of the microglial inflammatory response in neurodegenerative disease. As a direct result of this, LPS has been used extensively to model neuroinflammation, and in doing so it has been found that it can successfully recapitulate many disease-relevant phenotypes including neurodegeneration itself. Injection of LPS directly into the rodent brain is a longstanding model of PD, causing microglial activation and selective loss of nigrostriatal dopaminergic neurons (Castaño Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 37 et al., 1998). This accurate modelling of PD-like neurodegeneration extends even to mimicking behavioural symptoms of PD, including the depressive symptoms often associated with this disease (Zhang et al., 2022). With relevance to AD, LPS can provoke amyloidogenesis and amyloid accumulation in the brain when administered peripherally (Gu et al., 2018; Ma et al., 2016), while also increasing levels of phosphorylated tau, AD-like axonal dystrophy, and loss of dendrites and synapses (Deng et al., 2014; Wang et al., 2018). Peripheral LPS can also promote aggregation of TDP43 in mouse and in vitro models of ALS (Batista et al., 2019; Correia et al., 2015), and cerebellar injection of LPS can model cerebellar ataxia (Hong et al., 2020). More generally, LPS reliably produces cognitive deficits at a behavioural level (Zhao et al., 2019). Therefore, LPS can not only model disease-relevant microglial inflammation at the level of signalling pathways and changes in gene expression, but also the neurodegeneration and cognitive decline that is driven by this inflammation. LPS and TLR4 signalling can not only provoke neurodegeneration on their own, but they can also exacerbate neuronal loss in specific mouse models of disease (Fiebich et al., 2018). In mouse models of both ALS and HD, TLR signalling can synergise with the disease-causing mutations’ effects and exacerbate microglia- mediated neurodegeneration (Crotti et al., 2014; Zhao et al., 2010). For AD, some studies using APP/PS1 and P301S mouse models have suggested a protective effect of TLR4 signalling (Y. Qin et al., 2016; Song et al., 2011), but other studies using acute amyloid injection paradigms in both mice and non-human primates have found that it can instead exacerbate pathology (Balducci et al., 2017; Philippens et al., 2017). Overall, the ability of TLR4 stimulation or loss-of-function to affect disease progression in specific mouse models of neurodegenerative diseases is further evidence that LPS affects disease-relevant microglial biology. Moreover, this has been used to argue that targeting TLR4 specifically may be useful in developing therapies for neurodegenerative diseases (Leitner et al., 2019). Overall, LPS can therefore model: activation of TLR4 by proteinopathies; some aspects of microglial activation and inflammation that occur during neurodegenerative diseases; and consequent neurodegeneration and behavioural deficits that resemble those found in diverse neurodegenerative diseases. This ability of LPS to model neurodegenerative diseases has contributed to the hypothesis that LPS itself (from gut microbiota and infections) may play a causal role in onset and progression of neurodegenerative diseases (Brown, 2019). Interestingly, LPS levels in the blood and STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 38 Timothy James Yuji Birkle – November 2023 brain are often elevated in patients with neurodegenerative diseases including AD, PD and ALS (Brown et al., 2023; Zhan et al., 2016; Zhang et al., 2009; Zhao et al., 2017), and the possible contributions of LPS to AD and PD in particular have been reviewed recently (Brown et al., 2023; Zhan et al., 2018). Whether or not these hypotheses are true, LPS is a useful and accessible stimulus used widely to model neuroinflammation and neurodegeneration. However, as with any model, using LPS to mimic neurodegeneration comes with various caveats, and these will be discussed at various relevant points throughout this work. Generally, LPS is considered a good tool for studying inflammation in neurodegenerative diseases, but its effects may be too far removed from in vivo disease biology for most results using LPS-induced models to be considered for direct translation (Skrzypczak-Wiercioch and Sałat, 2022). For example, despite some similarities as noted above, the transcriptomic state of LPS-treated microglia nonetheless differs in many important aspects from that of disease-associated microglia in AD or microglia in other chronic diseases (Holtman et al., 2015; Sousa et al., 2018). Therefore, LPS models generally need to be complemented by other models that model other aspects of a particular disease, such as amyloidopathy or tauopathy. 1.10.2 Cell models Detailed study of microglia-mediated neurodegeneration, and other neuron-glia interactions, requires appropriate representatives of these cells to use for in vitro culture. These cells may be: immortalised cell lines, primary cells prepared from live tissue or, more recently, cells re-differentiated from somatic cells either directly or via induced pluripotent stem cells (iPSCs) (Slanzi et al., 2020). Common neuronal cell lines used in neurodegenerative research include human-derived SH-SY5Y or LUHMES cells, while microglia are often represented by murine BV2 or N9 cells, or human HMC3s. Such cell lines are readily available, easy to maintain and use to generate high cell yields, and can be of human origin (Cetin et al., 2022). They are also relatively easy to genetically modify. However, their immortalised and highly proliferative nature introduces unavoidable differences between their biology and the biology of the human adult cells in vivo that they are used to model. Reflecting this, the transcriptional profiles of cell line microglia have frequently been found to differ substantially from ex vivo microglia, both at baseline and after inflammatory activation (Butovsky et al., 2014; Das et al., 2016; Melief et al., 2016; Timmerman et al., 2018). Chapter 1: Introduction Timothy James Yuji Birkle – November 2023 39 In these same studies, primary microglia were instead found to offer a better representation of microglia and to engage transcriptional programmes in response to LPS that are absent in cell lines. Thus, primary cultures have often been considered the most translationally-valuable model systems (Cetin et al., 2022). However, severe ethical and practical limitations heavily restrict the use of primary human cells, and the more feasible use of rodent primary cultures instead introduces the possibility of species-specific effects that may limit translational relevance. Now, stem cell-derived microglia have become increasingly available thanks to a better understanding of the developmental pathways leading to microglial differentiation (Abud et al., 2017; Douvaras et al., 2017; Haenseler et al., 2017; Muffat et al., 2016; Pandya et al., 2017). These promise to be a powerful resource given that they can be generated from human somatic tissue, including from patients with neurodegenerative diseases, such that they have genetic variation and mutations that are directly relevant to human disease. For age-related diseases including these diseases, protocols that transdifferentiate cells and bypass the epigenetic reprogramming of the iPSC state are likely to become particularly valuable (Mertens et al., 2015; Prasad et al., 2016). The value of an in vitro model of neurodegenerative diseases also depends on how well it replicates the complex mix of cells found in vivo. This is particularly true for studying neuron-glia interactions including microglia-mediated neurodegeneration, as these processes inherently require the modelling of multiple cell types side by side in ‘cocultures’. However, cocultures generally promote more physiologically accurate biology regardless of the experimental aims. Factors secreted by neurons, including CD22, influence microglial inflammation and maturation in cultures (Bassil et al., 2021; Biber et al., 2007; Haenseler et al., 2017; Mott et al., 2004; Szepesi et al., 2018; Warden et al., 2023), and microglial factors such as TNFα and IL-1β reciprocally affect neuronal differentiation (Schmidt et al., 2021; Warden et al., 2023). Cocultures of neurons, microglia and astrocytes (referred to as neuron-glia cocultures for the remainder of this work) are the most complex examples of such cocultures used currently, and the use of all three major brain cell types together results in culture models that are particularly relevant to in vivo disease (Goshi et al., 2022; Guttikonda et al., 2021; Jin et al., 2007). For example, reciprocal interactions between microglia and astrocytes elevates the secretion of certain complement pathway components that contribute to neurodegeneration, enabling neuron-glia cocultures to better model complement- STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 40 Timothy James Yuji Birkle – November 2023 dependent neurodegenerative pathology (Guttikonda et al., 2021). Cocultures may be prepared from cell lines, primary cells, or iPSC-derived cells, and may also be extended to three-dimensional culture systems such as organoids or spheroids to further recapitulate in vivo tissues. This additional complexity has a significant impact on cellular functions and disease modelling, but use of these three-dimensional cultures systems remains limited to more specialised studies due to practical constraints (Cetin et al., 2022). Overall, cocultures of neurons and glia are highly valuable model systems with which to investigate neurodegenerative processes, and indeed are essential for the study of direct neuron-glia interactions such as phagocytosis of neurons by microglia (Brelstaff et al., 2018; Brown, 2023; Neher et al., 2014; Neniskyte and Brown, 2013; Pampuscenko et al., 2020; Popescu et al., 2022; Puigdellívol et al., 2021). These models are particularly powerful when using cells derived from primary cell preparations or iPSCs. However, experimental use and analysis of neuron-glia cocultures can be challenging, as will be further discussed in Chapters 4 and 7. Methodological improvements that facilitate their use would therefore significantly benefit neurodegenerative research. Chapter 2: Aims Timothy James Yuji Birkle – November 2023 41 2 AIMS The overall aim of this work was to identify and investigate novel regulators of microglia-mediated neurodegeneration that may be of therapeutic interest. Early work, not presented here, tested the microglial function of specific cellular proteins (including BIN1 and the P2Y12 receptor) first in microglial cell lines before progressing to isolated primary microglia and primary neuron-glia cocultures. However, findings in microglial cell lines were often not reproducible in primary cell cultures. Moreover, these primary cultures were too time consuming to use for exploratory experiments due to the manual analysis of imaging data, despite their translational value. Therefore, for the work presented here, the first aim was to develop an automated image analysis approach to quantify the diverse cell types present in neuron- glia cocultures in order to enable efficient exploratory work using these models. This required optimisation of new staining protocols and construction of a robust analysis pipeline to identify and classify individual cells from captured images. I specifically aimed to take advantage of both modern molecular tools and recent advances in open- source machine learning methods, and these results are presented in Chapter 4. The above methods identified urokinase (uPA) and spleen tyrosine kinase (SYK) as cellular targets that may be important for microglia-mediated neurodegeneration, as inhibitors of either target were strongly protective against inflammatory neurodegeneration in neuron-glia cocultures. uPA has been little studied in microglia, and my work on this target aimed to better understand what microglial functions were affected by uPA and by what mechanisms. Specifically, I aimed to assess the role of uPA in microglial inflammation, turnover, and phagocytosis, as well STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 42 Timothy James Yuji Birkle – November 2023 as investigate whether any effects were due to the proteolytic or non-proteolytic functions of this protease. These results are presented in Chapter 5. SYK is better studied in microglia owing to its association with the TREM2 pathway and thereby its potential relevance to Alzheimer’s disease. However, as with the TREM2 pathway in general, this protein can have both beneficial and detrimental roles depending on disease state. Here, I aimed to add to the literature describing the microglial functions of SYK by investigating the role of SYK in microglial inflammation, turnover, phagocytosis, and metabolism, as well as neuronal loss. These results are presented in Chapter 6. Finally, I aimed to use the automated analysis methods to identify novel regulators of microglia-mediated neurodegeneration in a phenotypic high-content screen, while also demonstrating the specific advantages of the approach and the rich dataset that it produces. These results are presented in Chapter 7. Chapter 3: Methods Timothy James Yuji Birkle – November 2023 43 3 METHODS 3.1 Ethical statement All experiments using animal tissue complied with the UK Animals (Scientific Procedures) Act 1986 and were approved by the local ethical committee at Cambridge University. 3.2 Common culture materials The following media and solutions were obtained from ThermoFisher: high- glucose DMEM (41965062), PBS (70011051), HBSS (14175095), trypsin- EDTA (15400054), Versene (15040066), formaldehyde (28906), and certified FBS for primary cells (10082147). Penicillin/Streptomycin (P/S) was from Merck (P4333), as was poly-L-lysine (PLL; P4707), cytochalasin D (C8273), and LPS (L6143). Gentamicin (G38000) was from Melford. Multi-well F- bottom cell culture plates and T75 flasks were from Greiner Bio-One. Cell strainers were from Falcon (352340/352360). All cultures were maintained at 37⁰C in humidified 5% CO2 incubators and all media were supplemented with either penicillin (100U/mL) and streptomycin (100µg/mL), or gentamicin (50µg/mL) unless explicitly specified. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 44 Timothy James Yuji Birkle – November 2023 3.3 Cell cultures 3.3.1 BV2 cells BV2 cells were a generous gift from Professor Jennifer Pocock (Department of Neuroinflammation, University College London, UK). These cells were cultured in high-glucose DMEM with 10% FBS (Merck, F9665) in T75 flasks, passaging at 80-90% confluency using 0.05% trypsin-EDTA. Cultures were restarted after reaching 25 total lifetime passages. 3.3.2 Primary glial cultures Microglia-astrocyte glial cultures, from which pure microglial cultures were obtained, were prepared as previously described (Bal-Price and Brown, 2001). Briefly, P3-6 rat cerebral cortices were dissected, diced, and incubated at 37⁰C in 0.033% trypsin (diluted in HBSS) for 15 minutes. Cells were then mechanically dissociated by trituration, 100µm- and 40µm-strained, and finally seeded at a ratio of 1 brain (2 cortices) worth of cells per T75 flask coated with 0.005% PLL. After 24h, debris was shaken off and media replaced. From DIV7 and 18- 24h prior to experiments, microglia were shaken off, resuspended in 1-part conditioned media:2-parts fresh, then seeded as needed in 0.005% PLL-coated plates. Glial media was high-glucose DMEM supplemented with 10% certified FBS. 3.3.3 Primary neuron-glia cocultures 3.3.3.1 Low-throughput 3.3.3.1.1 Cerebellar cultures Neuron-glia cocultures were prepared as previously described (Kinsner et al., 2005). Briefly, P3-6 rat cerebella were dissected in ice-cold HBSS, finely diced, and incubated in Versene for 5-10 minutes at 37⁰C. Cells were then mechanically dissociated by trituration, transferred to warm media, pelleted, resuspended, and 40µm-strained. Live cells were counted using trypan blue and cultures were seeded at 295,000 live cells/cm2 in multi-well plates coated with 0.01% PLL. After 24h, debris was shaken off manually and the media replaced. Mixed cultures were treated after at least 7 days in vitro (DIV). Coculture media consisted of high-glucose DMEM supplemented with 5% certified foetal bovine Chapter 3: Methods Timothy James Yuji Birkle – November 2023 45 serum (FBS; Gibco; 10082147), 5% horse serum (Gibco; 26050088), 2mM L- glutamine, 13mM glucose, 5mM HEPES, and 20mM KCl. The only exception to this is the low-throughput assay testing for BAY61- mediated protection against pTau-induced neuronal loss (Fig. 38), for which cells were prepared using high-throughput protocols (3.3.3.2) 3.3.3.1.2 Hippocampal cultures Hippocampal neuron-glia cocultures were generated using a published protocol (Moutin et al., 2020) with minor alterations. Specifically, cells were counted prior to plating to ensure consistent plating at an optimised 50,000 cells per well in 96-well plates, and AraC treatment was omitted to ensure undisturbed glial populations in the cultures. 3.3.3.2 High-throughput cerebellar cultures P4 rat pups were used to generate cultures by the same protocol as for low- throughput assays (see 3.3.1.1 above). However, for high-content screening cultures were seeded at 30,000 live cells/well in 384-well high-content imaging plates (PerkinElmer, 6057302) coated with 0.01% PLL, in 30µL media. Additionally, no 24hr media swap was performed, which was validated not to affect culture viability in this format. 3.4 General experimental protocols 3.4.1 Cerebellar mixed culture neuronal loss assay setup 3.4.1.1 Low-throughput To assay for treatments’ effects on inflammatory neuronal loss, treatments were added to cultures at DIV7 at the stated concentrations (see also Table 1) for 30 minutes prior to the addition of LPS at 100ng/mL (L6143; Salmonella enterica serotype typhimurium) or pTau at 50nM (T08-50FN-SGC; recombinant, phosphorylated by human GSK3β). Treatments were diluted in mixed culture media, and vehicle controls consisted of DMSO diluted to the equivalent concentration as present in the treatment wells (or highest concentration in any treatment well, for experiments with multiple treatments). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 46 Timothy James Yuji Birkle – November 2023 The only exception to this is the low-throughput assay testing for BAY61- mediated protection against pTau-induced neuronal loss (Fig. 38), which was conducted using high-throughput protocols (3.4.1.2). 3.4.1.2 High-throughput On DIV7, assay-ready compound plates were warmed to room temperature and 6.25µL culture media was added per well. Plates were centrifuged to settle media and mix with the previously dispensed compound, resulting in 12µM final concentration for each compound/control. Using a custom program on a Viaflo384 (Integra) instrument, 3µL was transferred from each compound plate well to matching wells in a 384-well plate of DIV7 neuron-glia cocultures. After 30 minutes incubation, 3µL was then transferred to the same cultures from a ±LPS stock plate, with PBS or 1.2µg/mL LPS (in PBS, after 30 minutes water- bath sonication) arranged in the desired chequerboard pattern. This resulted in a final culture volume of 36µL, with 1µM of each treatment and ±100ng/mL LPS per well. 3.4.2 Mixed culture inflammatory neuronal loss assay imaging and image analysis 3.4.2.1 Low-throughput 3.4.2.1.1 Classification analysis 52µL culture media was removed at the end of treatment (and saved for other analyses where necessary), prior to staining of cultures through topical addition of staining stock (2µL) for a final volume of 50µL. The neuron-glia cocultures were stained for 1 hour with (final concentrations): NeuroFluor NeuO (200nM; 01801) (Er et al., 2015), Isolectin IB4-AF594 (2µg/mL; I21413) and Hoechst 33342 (1µg/mL; 62249). Without removal of staining media, cultures were then imaged using the 10x objective on an EVOS M5000 epifluorescence microscope, with 4 images taken in consistent positions around each well. Image sets were analysed for the number of each cell type present using a custom CellProfiler (Stirling et al., 2021b)/CellProfiler Analyst (Stirling et al., 2021a) pipeline, validated in Chapter 4. The only exception to this is the low-throughput assay testing for BAY61- mediated protection against pTau-induced neuronal loss (Fig. 38), which was Chapter 3: Methods Timothy James Yuji Birkle – November 2023 47 conducted using high-throughput protocols and the analysis pipeline and classifier established during the screen (3.4.2.2). 3.4.2.1.2 Morphology analysis Images pre-processed with ImageJ were imported into QuPath for nuclei segmentation and microglial classification. Through a cross-platform script between QuPath and ImageJ, marker-based watershed from the MorphoLibJ plugin library (Legland et al., 2016) was used to flood-fill automatically thresholded microglial cell masks from microglial nuclei, and each resulting microglial object (typically around 2,000 per technical replicate per condition) was analysed using ImageJ shape measurements. 3.4.2.1.3 Analysis of IB4-stained objects lacking nuclear DNA Coculture images were pre-processed with rolling ball background subtraction on all channels using a custom ImageJ macro. IB4-positive objects were then identified using cell detection on QuPath, and objects lacking DNA were then identified by thresholding based on the total Hoechst fluorescence signal within each object. 3.4.2.1.4 Dead cells inside microglia analysis Images pre-processed with ImageJ were imported into QuPath for nuclei segmentation and classification of condensed nuclei based on having small size and high Hoechst staining intensity. Nuclei within microglia were further determined based on IB4-AF594 staining intensity in and around the nucleus area. 3.4.2.2 High-throughput 3.4.2.2.1 Classification analysis On DIV10, after 3 days of treatment, culture plates were sequentially taken for staining and imaging. For each plate, a fresh stain mix was prepared from identical stocks, distributed to one column of a 384-well intermediate plate, and then 4µL was added into each well via multichannel pipetting. This gave a final volume of 40µL per well. Final stain concentrations were 1µg/mL Hoechst 33342 (1mg/mL stocks prepared fresh on each repeat; 62249), 1µg/mL Isolectin IB4-AF594 (I21413), 100nM NeuroFluor NeuO (01801), and 0.75µM DRAQ7 (DR71000). Plates were incubated at 37⁰C for 1 hour and then, without removal STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 48 Timothy James Yuji Birkle – November 2023 of media, imaged at 37⁰C on an IN Cell Analyzer 6000 instrument (GE Healthcare), with 4 fields of view captured per well (covering approximately 80% of the well) and taking around 30 minutes per plate. The final CellProfiler pipeline used to process images and generate cell object measurements from the screen can be found on GitHub at https://github.com/timjyb/Birkle-et-al-2023-HTS (Pipe1). Background subtraction was omitted owing to the uniformity of image illumination achieved on the high-content imager, which helped reduce computation time. Object measurements were stored in an SQLite database, which was handled using the freely available DB Browser for SQLite software (https://sqlitebrowser.org/). A randomly selected subset of the final object set from each repeat was then loaded into CPA (this reduced the tile fetch times while fetching objects for classification in CPA), and randomly selected objects from this set were classified manually into 7 classes, aiming for 400 objects each: Neuron, Microglia, Astrocyte, Other, Condensed, Necrotic, Debris. Once all four repeats were completed, object sets and training sets were combined, resulting in 1,600 total objects annotated per class and spread evenly across repeats such that the final classifier should be robust to any variation in cells, staining, or image quality over the entire screen. According to internal accuracy validation, the Random Trees Classifier within CPA performed the best, and this was used for final analysis. Full details of the classifier validation can be found in the Results. 3.4.2.2.2 Morphology analysis The final CellProfiler pipeline used to generate microglial shape measurements from the screen can be found on GitHub at https://github.com/timjyb/Birkle-et- al-2023-HTS (Pipe2). Objects from Pipe1 were filtered by classification to select only microglial nuclei. These were then used to seed masks based on the microglial IB4-AF594 staining. The shape of these microglial masks was measured, and the median value for each shape statistic across all microglia per image was used for downstream analysis. 3.4.3 Pre- vs post-fix and NeuO vs NeuN comparison assay 96-well plate cerebellar neuron-glia cocultures were treated at DIV7 with the stated compounds for 30 minutes, prior to the addition of LPS at 100ng/mL. At DIV10, cultures were stained for 1 hour with topical addition of 200nM Chapter 3: Methods Timothy James Yuji Birkle – November 2023 49 NeuroFluor NeuO (01801) and 1µg/mL Hoechst 33342 (62249). Without removal of staining media, cultures were imaged using the 10x objective on an EVOS M5000 epifluorescence microscope, with 4 images taken in consistent positions around each well. Cultures were then fixed with 4% paraformaldehyde for 15 minutes at room temperature, then washed once with 50mM NH4Cl- containing PBS and twice with standard PBS. Cells were permeabilised and blocked for 1 hour with 0.1% TX-100 and 2% BSA in PBS, washed three times, and stained overnight at 4⁰C with 1:1000 mouse α-NeuN (MAB377) primary antibody in 1% BSA PBS. Cells were then washed three times, followed by 1 hour room temperature incubation with 1:400 goat α-mouse AF594 (A21125) secondary antibody. After three more washes, cells were left in PBS and imaged using the EVOS M5000 microscope again. Fields of view were precisely aligned manually with the previously captured images of the NeuO staining, and illumination settings were used that provided comparable signal from α-NeuN immunostaining as was achieved with NeuO. Analysis was performed using identical cell segmentation parameters on QuPath, then thresholding for positivity (either NeuO or NeuN) using thresholds set at the trough between negative and positive populations in the mean intensity histogram, to ensure that the detection threshold was comparable. To check for loss of microglia during fixation, microglia were manually counted from NeuO (pre-fixation) and NeuN (post-fixation) images from untreated control wells with the help of the transmitted light channel, in which microglia have a distinct morphology. 3.4.4 Segmentation accuracy comparative validation assay The accuracy of the different cell segmentation methods (as provided by ImageJ, QuPath, CellProfiler, and the CellProfiler plugin CellPose) was performed manually. 3 cerebellar neuron-glia coculture images were selected at random from 3 unrelated experiments, for 9 images total. The Hoechst channel images were subjected to background subtraction and then cropped, ready for analysis by each method. ImageJ (IJ) segmentation consisted of a Gaussian filter (1px radius) followed by Otsu auto-thresholding and a binary watershed operation; this was intended to be a basic segmentation approach. QuPath (QP) segmentation consisted of QuPath’s cell detection function using optimised parameters after exhaustive testing. CellProfiler (CP) segmentation consisted of STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 50 Timothy James Yuji Birkle – November 2023 CellProfiler’s IdentifyPrimaryObjects module using optimised parameters after exhaustive testing. Cellpose segmentation consisted of a RunCellPose module within CellProfiler using optimised parameters. Segmentation errors of the indicated types were counted manually with reference to the original Hoechst image. Code for each approach can be found on GitHub at https://github.com/timjyb/Birkle-et-al-2023-HTS. 3.4.5 Automated classification validation All machine learning-based classifiers were tested using the in-built k-fold evaluation available in CellProfiler Analyst (k=5), including the classifier for the high-content screen. For the cerebellar coculture classifiers of both Chapter 4 and Chapter 7, external validation was performed by comparing classification to manual annotation of random cells separate from the training data. 3.4.6 Immunocytochemistry 3.4.6.1 NeuN testing Neuron-glia cocultures in 96-well plates were fixed in 4% formaldehyde for 10 minutes at room temperature, then washed once with 50mM NH4Cl-containing PBS and twice with standard PBS. Cells were permeabilised and blocked for 1 hour with 0.1% TX-100 and 2% BSA in PBS, washed three times, and stained overnight at 4⁰C with 1:1000 mouse α-NeuN (MAB377) primary antibody in 1% BSA PBS. Cells were then washed three times, followed by 1 hour room temperature incubation with 1:400 goat α-mouse AF594 (A21125) secondary antibody, 1µg/mL Hoechst 33342 (62249), and 4µg/mL IB4-AF488 (I21411). After three more washes, cells were imaged using the 20x objective on an EVOS M5000 microscope. 3.4.6.2 SYK Neuron-glia cocultures in 96-well plates were fixed in 4% formaldehyde for 10 minutes at room temperature, then washed once with 50mM NH4Cl-containing PBS and twice with standard PBS. Cells were then permeabilised for 10 minutes with 0.1% TX-100 in PBS, washed three times, blocked with 5% goat serum in PBS for 1.5 hours, and stained overnight at 4⁰C with 1:200 mouse α-SYK (626202) and 1:200 rabbit α-Iba1 (019-19741) primary antibodies. Cells were then washed three times, followed by 1 hour room temperature incubation with Chapter 3: Methods Timothy James Yuji Birkle – November 2023 51 1µg/mL Hoechst 33342, and 1:1000 goat α-mouse AF568 (A11004) and 1:1000 goat α-rabbit AF488 (A11008) secondary antibodies. After three more washes, cells were imaged using the 20x objective on an EVOS M5000 microscope. 3.4.7 LME-mediated microglial depletion DIV6 neuron-glia cocultures were treated with 25mM L-leucine methyl ester (LME; L1002) for 1 hour as previously published (Jebelli et al., 2015), omitting washes before the final media replacement. Further treatments were carried out as usual from DIV7-10. 3.4.8 Primary microglia cell death assay Primary microglia were seeded at 50,000 cells/well in 0.005% PLL-coated 96- well plates and allowed to settle over 4-6 hours. Cells were then treated overnight for 18-24 hours (10µM staurosporine positive control treatment was added 2 hours prior to imaging), prior to 1 hour staining with Hoechst 33342 (1µg/mL; 62249), Isolectin IB4-AF488 (4µg/mL; I21411), and propidium iodide (PI; 2µg/mL; P4170). After microscopy (EVOS M5000), images were pre- processed (background-subtracted and smoothed) using a custom ImageJ macro and nuclei were segmented and classified for cell death-relevant staining using QuPath. 3.4.9 Primary combined glia cell death assay Primary glial cultures were prepared by the primary glial culture protocol and seeded in poly-L-lysine-coated 96-well plates at an equivalent density to the usual T-75 flasks. A media swap was carried out at 24 hours as usual, and cultures were then maintained until DIV7, at which point they were treated with the stated compounds and maintained for a further 3 days. 10µM staurosporine positive control treatment was added 2 hours prior to imaging. 1 hour before imaging, Hoechst 33342 (1µg/mL; 62249), Isolectin IB4-AF488 (4µg/mL; I21411), and propidium iodide (PI; 2µg/mL; P4170) were added for staining at 37⁰C. After microscopy (EVOS M5000), images were pre-processed (background-subtracted and smoothed) using a custom ImageJ macro and nuclei were segmented and classified for cell death-relevant staining using QuPath. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 52 Timothy James Yuji Birkle – November 2023 3.4.10 Primary microglia phagocytosis assay Primary microglia were seeded at 25,000 cells/well in PLL-coated 48-well plates and allowed to settle of 4-6 hours. Cells were subjected overnight to treatments and LPS at the specified concentrations. Cytochalasin D treatments involved addition to 5µM, 2 hours prior to target addition. Targets used were 5µm fluorescent beads (Spherotech; CFP-5070-2; 10uL of 0.025% w/v beads per well) and pHrodo-labelled synaptosomes (16µg/well). In all cases, phagocytic targets were dispersed throughout wells via both pipetting and gentle plate agitation. After target incubation for 2 hours, media was aspirated, microglia resuspended in 100µL ice-cold PBS, and cells were transferred to microfuge tubes and kept on ice for subsequent flow cytometry analysis. All flow cytometry was carried out on a BD CytoFlex S instrument. DAPI was spiked into each sample at 1µg/mL final concentration 1 minute prior to analysis, and standard gating was used: cells gated by FSC-A/SSC-A; singlets gated by FSC-A/FSC-H; live cells gated by lack of DAPI staining. In synaptosome experiments, phagocytic cells were gated using a gate with a 1% positive rate for the cytochalasin D negative controls. In 5µm bead experiments, any bead uptake caused a large shift in fluorescence and gate boundaries were therefore set halfway between the clearly distinct populations. At least 5,000 events were recorded for each sample. 3.4.11 Synaptosome preparation Synaptosomes were prepared from juvenile rat cortices: tissue was homogenised by Dounce homogenisation and synaptosomes purified using Percoll gradient centrifugation according to a published protocol (Dunkley et al., 2008), then frozen in liquid nitrogen. Protein concentrations were measured via Nanodrop. For experiments, synaptosomes were thawed and transferred to warm H-KRP buffer, pelleted at 20,000g for 5 minutes, and resuspended for staining with pHrodo (10µM in H-KRP buffer; Invitrogen; P35372) for 15 minutes at 37⁰C. Synaptosomes were then washed and resuspended in buffer. H-KRP buffer was an aqueous solution of: 143mM NaCl, 4.7mM KCl, 1.3mM MgSO4, 1.2mM CaCl2, 20mM HEPES (stock at pH7.4 with saturated Tris), 0.1mM Na2HPO4, and 10mM D-glucose. This was prepared fresh, 0.22µm-filtered, and infused with CO2 in a 5% CO2 incubator for 60 minutes prior to use. Chapter 3: Methods Timothy James Yuji Birkle – November 2023 53 3.4.12 Western blotting For validation of SYK inhibition, BV2 cells were resuspended in un- supplemented high-glucose DMEM and seeded at 500,000 cell/well in 6-well plates. 24 hours later, wells were pre-treated with SYK inhibitors at the stated concentrations for 2 hours prior to 10min treatment with 50µg/mL Concanavalin A (ConA; C5275). For measurement of Homer1 protein levels in neuron-glia cocultures, cells were plated in 6-well plates and treatments performed as described. In either case, media was then quickly aspirated and 100uL of lysis buffer added to each well. Lysis buffer consisted of 1% TX-100, 150mM NaCl, 50mM Tris (pH8), supplemented with protease and phosphatase inhibitors (11697498001; 4906845001) at recommended concentrations. Lysates were collected with scraping, lysed on ice for 20 minutes with intermittent vortexing, and then cleared at 15,000g for 15 minutes at 4⁰C prior to storage at -20⁰C. 19.5uL of lysates were mixed with 3uL 10x reducing agent (NP0004) and 7.5uL NuPAGE loading buffer (NP0007), denatured at 90⁰C for 10min, and then loaded onto a 4-12% Bis-Tris NuPAGE precast gel (NP0321) alongside a fluorescent protein ladder (928-40000). After iBlot transfer onto PVDF, blots were blocked in 5% milk TBST for 1 hour before overnight staining at 4⁰C with primary antibodies in 5% milk TBST. After 3x washes with TBST (15min, 5min, 5min), blots were stained with secondary antibodies, then washed again before imaging and analysis using a LI-COR Odyssey CLx instrument. For pSYK BV2 blots, primary antibodies were 1:000 diluted rabbit α- pSYK (Tyr525/526; MA5-14918) and 1:5000 diluted mouse α-β-actin (66009-1) antibodies, and secondary antibodies were 1:5000 diluted α-rabbit IRDye 800CW (925-32213) and 1:20000 diluted α-mouse AF680 (A10038). For Homer1 neuron-glia blots, rabbit α-Homer1 (160003) and mouse α-NeuN (MAB377) primary antibodies were diluted 1:1000, and α-rabbit IRDye 800CW and α-mouse AF680 secondary antibodies were diluted 1:10000. 3.4.13 Urokinase activity assays BV2 cells were harvested from maintenance flasks, washed with PBS, and seeded at 25,000 cells/well in 96-well plates in phenol-red-free DMEM F-12 media supplemented with 0.5% FBS. After allowing cells to settle, treatments STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 54 Timothy James Yuji Birkle – November 2023 were added 1 hours prior to the start of the assay. For the assay, Z-GGR-AMC substrate (Merck; 672159) was added to a final concentration of 10µM and fluorescence (350nm excitation, 450nm emission) was measured every immediately and every 5 minutes thereafter for 1 hour using a FlexStation 3 instrument (Molecular Devices). 3.4.14 Supernatant cytokine ELISAs Supernatants were collected from each well of 96-well plate cultures (treated in technical triplicates) and each replicate supernatant was tested once. Standards were each assayed in triplicate. All standard curves were fitted and interpolated using 4PL non-linear regression on GraphPad Prism and achieved R2>0.99. ELISA assays provided without pre-coated plates were carried out in Maxisorp plates (ThermoFisher; 442404), and all absorbance measurements were made using a FlexStation 3 instrument (Molecular Devices). IL-6 measurement: a BioLegend LEGEND MAX™ Rat IL-6 ELISA Kit (437107) was used according to the provided protocol, with 5uL of supernatant tested per well. TNFα measurement: a BioLegend ELISA MAX™ Deluxe Set Rat TNF-α Kit (438204) was used according to the provided protocol, with 10uL of supernatant tested per well. IL-1β measurement: an Invitrogen IL-1β Rat Uncoated ELISA Kit (88-6010-22) was used according to the provided protocol, with 40uL of supernatant tested per well. 3.4.15 pH (phenol red absorbance) assays pH measurement was achieved using phenol red absorbance. The absorbance of neuron-glia mixed culture wells at 415nm and 560nm was measured using a FlexStation 3 instrument (Molecular Devices). Standards consisted of culture wells with media replaced with media adjusted to a range of known pH levels at increments of 0.5 from pH6-8. Samples and standards were prepared, kept sealed, and tested as rapidly as possible to minimise any changes in pH as a result of equilibration of dissolved CO2 with atmospheric levels, and there was no change in visible phenol red absorbance between sample/standard collection/preparation and subsequent measurement. The ratio Abs(560nm)/Abs(415nm) was calculated for all samples, and pH values for unknown samples were interpolated from the standard curve. Chapter 3: Methods Timothy James Yuji Birkle – November 2023 55 3.4.16 Lactate assays Lactate concentration in supernatants was measured with an L-lactic dehydrogenase (LDH) based enzymatic assay. Briefly, 10uL of supernatants were included in a 200uL reaction with 2mM NAD (N1511), 10U of LDH (L- 2500), and a buffer consisting of 200mM glycine and 170mM hydrazine sulfate (Merck, 216046) adjusted to pH 9.2 with NaOH. Standards consisted of 2-fold serial dilutions of sodium L-lactate solution (Thermofisher, 71718) in water from 50mM to 0.78mM. After mixing, the assay plate was immediately loaded into a FlexStation 3 instrument (Molecular Devices) pre-warmed to 37C, and endpoint 340nm absorbance (deriving from the NADH byproduct of lactate to pyruvate conversion) measured after 1 hour. Data were normalised by t0 absorbance and blank absorbance measurements, and lactate concentrations for the supernatant samples were then derived via standard curve. Coculture media alone showed minimal lactate levels, and supernatants themselves were shown to not harbour any NAD or LDH that might affect reaction rate (data not shown). 3.5 High-content screen compound library preparation 3.5.1 Compound selection Compounds were selected from the available annotated library (as described in the Results): 1) Compounds were excluded that had any of the following features: a. 0 known targets. b. >9 known targets. c. Topological polar surface area >120 (compounds unlikely to be cell permeant). d. Best on-target potency >100nM by biochemical assays, or >1µM by cell-based assays. 2) 300 compounds were then randomly selected from the remaining list, and SYK inhibitors and a TLR4 inhibitor were included as positive controls for neuroprotection. 3) Finally, an iterative strategy was used to randomly remove compounds for which all their targets were already hit 3 or more times, as 2-fold STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 56 Timothy James Yuji Birkle – November 2023 overlap of compounds on a given target was considered optimal. This enabled random addition of some compounds adding new targets to the target set, or which added 2-fold overlap on existing targets. In this way, the compound set was reduced at random, while maintaining 2-fold overlap on as many targets as possible and without reducing the total number of unique targets hit by the compound set. The final compound set consisted of 228 compounds targeting 439 unique targets. 328 of these were hit once by the compound set, and 83, 17, 6, 3, and 2 targets were hit 2, 3, 4, 5, and 6 times respectively. One compound failed to dispense when preparing assay-ready plates (see 5.3 below), resulting in 227 compounds being tested in the final screen. 3.5.2 Plate design Controls were DMSO (negative) and BAY61-3606 (positive). For each, there were 6 LPS- replicate wells per plate, and 6 LPS+ replicate wells per plate. This resulted in 252 wells total being used per plate, in a 18x14 layout. Each compound was to be tested in the presence and absence of LPS, each in triplicate. As a result, six 384-well plates were necessary per repeat of the screen. The compound set was divided randomly in half and each half-set was tested in three 384-well plates, with one LPS- and one LPS+ replicate for each compound present on each plate. This pairing of LPS- and LPS+ replicates was chosen to stop plate effects, the most likely source of experimental variation, from confounding the important LPS- vs LPS+ comparison for each compound. Maintaining independent halves of the screen would also have allowed for screening of only one half-set, if made necessary by a low yield from a particular preparation of primary cells. Within each plate, wells were divided evenly into LPS- and LPS+ wells in a chequerboard pattern, with LPS to be treated every other well. Within the LPS- area, one replicate for each compound was randomly assigned a location, and similarly for the LPS+ area. The 6 LPS- replicates and 6 LPS+ replicates for each control were also randomly assigned locations. Chapter 3: Methods Timothy James Yuji Birkle – November 2023 57 3.5.3 Assay-ready treatment plate preparation Having randomly distributed the LPS- and LPS+ replicates of each compound within each plate, assay-ready plates were generated using the Cherry Pick functionality of an Echo 520 instrument (Labcyte). 7.5nL of each compound or control was dispensed from 10mM DMSO stock plates into compound plates according to the randomised layout. Plates were centrifuged and stored at -20⁰C until use. 3.6 Data processing and statistics 3.6.1 Low-throughput Statistical analyses were performed using GraphPad Prism 9 for Windows (GraphPad Software, San Diego, California, USA). For each experimental condition, at least 3 technical replicates were performed, using the mean for analysis. At least 3 biological repeats were performed per experiment: for primary cell experiments, these were distinct preparations of cells from different rat litter; for BV2 cell line experiments, these represent distinct passages and/or flasks of cells. Most data were analysed as repeat-wise matched data by one- or two-way ANOVA (or equivalent mixed-effects models), while assuming circularity/sphericity. Matching was advisable given the repeat-wise variability that is to be expected in primary cell preparations; this preserves power where there is such variation and retains power in the absence of it as well (Lew, 2007). Where only two conditions were compared, paired t-tests were used instead. Post-hoc tests were as stated in figure legends. 3.6.2 High-throughput 3.6.2.1 Uniform Manifold Approximation Projection UMAP (McInnes et al., 2020) analysis was conducted on the CellProfiler- generated feature data for all objects across four randomly selected DMSO LPS- images, one image being taken from each biological repeat of the screen. Data was standardised prior to analysis and irrelevant features were excluded in the same way as for cell classification (Appendix 2, Table 4). Analysis was performed in R using the umap package with default hyperparameters (most importantly, n_neighbours = 15, min_dist = 0.1). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 58 Timothy James Yuji Birkle – November 2023 3.6.2.2 Normalisation Neuron and microglial count data from the high-content screen were normalised prior to analysis given the clear (and expected) differences between LPS- and LPS+ counts for these cell types. For neuron counts, data from each repeat was rescaled between 0 and 1 using the median of LPS+ counts as the minimum (0) and the median of the LPS- counts as the maximum, in an adaptation of typical Min-Max normalisation. For microglia counts, the LPS- median was used to set the minimum and the LPS+ median used for the maximum instead, to reflect the directionality in the raw data (neurons decrease with LPS, microglia increase). 3.6.2.3 Statistical analysis Statistical analyses were performed using GraphPad Prism 9 for Windows (GraphPad Software, San Diego, CA, USA). 4 biological repeats were performed (screen repeats on cells prepared from different rat litters at different times), each with 3 technical replicates per condition. Data were analysed as repeat-wise matched data by two-way ANOVA (or equivalent mixed-effects models), while assuming circularity/sphericity. For quality control, principal component analysis (PCA) was used on the cell counts data from each plate/row/column/row (as stated) after standardisation. Where indicated, PC1 and PC2 were assessed for outliers by ROUT (Q = 1%). 3.6.2.4 Hierarchical clustering and heatmapping For heatmapping, data for each parameter (neurons, microglia, etc.) was first min-max normalised across both LPS- and LPS+ data together. Data was then clustered and displaying using the heatmap() function in R, using Euclidean distance for the distance function and complete linkage clustering via hclust() (code available on GitHub at https://github.com/timjyb/Birkle-et-al-2023-HTS). For heatmapping neuroprotective hits only (and DMSO, for comparison), the normalised data was filtered to only include data for these compounds, and clustering/heatmapping was conducted in the same way as before. 3.7 Software All custom macros for ImageJ and QuPath, pipelines for CellProfiler, and CellProfiler Analyst classifiers can be found on Github under the user timjyb. Chapter 3: Methods Timothy James Yuji Birkle – November 2023 59 CellProfiler 4.2.4 (Stirling et al., 2021b) was built from source and the neuron- glia coculture analysis pipeline included a Cellpose 2.1.0 module (Stringer et al., 2021). CellProfiler Analyst 3.0 (Stirling et al., 2021) was used for cell classification. ImageJ 1.52-1.53 (Schindelin et al., 2015; Schneider et al., 2012) via FIJI (Schindelin et al., 2012) and QuPath 0.3.0 (Bankhead et al., 2017) were also used. Statistical analyses and general data plotting were performed using GraphPad Prism 9 for Windows (GraphPad Software, San Diego, CA, USA). The heatmaps of Figures 46 and 47 used the viridis colour palette package for R (Garnier et al., 2023), and R version 3.6.3 was used. UMAP analysis used the umap package for R, version 0.2.7.0. 3.8 Table of treatments Treatment Final concentration (unless otherwise stated) Catalogue number BC11 hydrobromide 50µM ab141194 IPR803 10µM HY-111192 UK122 50µM J64874 Active recombinant uPA 1-10µg/mL ab92604 BAY61-3606 1µM 11423 P505-15 (PRT06207) 10µM HY-15323 Annotated screening library 1µM Supplied by the ALBORADA Drug Discovery Institute Table 1. Table of all treatments used in any Chapter STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 60 Timothy James Yuji Birkle – November 2023 Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 61 4 AUTOMATED IMAGE ANALYSIS OF NEURON-GLIA COCULTURES All data presented in this chapter are my own work and some of the contents of this chapter have been published in: Birkle, T. J. Y., Willems, H., Skidmore, J., & Brown, G. C. (2023). Disease phenotypic screening in neuron-glia co-cultures identifies blockers of inflammatory neurodegeneration. iScience, 27(4), 109454. https://doi.org/10.1016/j.isci.2024.109454. 4.1 Introduction Cocultures of neurons and glia are increasingly important in translational neuroscience. Monocultures of single cell types can model simple biological processes but become inadequate for the often-complex processes that are most relevant to human disease. In vivo, diseases result from interactions between many cell types, tissues, or indeed organs of the body. This is particularly apparent for neurodegenerative diseases, and glial cells and their interactions with neurons are now understood to be central to diverse neuropathologies, including neurodegeneration (Kwon and Koh, 2020). Inflammation from microglia is now a particularly important topic, as has been extensively studied and reviewed here and elsewhere (Guzman-Martinez et al., 2019; Kwon and Koh, 2020). In sporadic Alzheimer’s disease, for example, a large proportion of genetic risk derives from genes that are mostly or exclusively expressed by microglia STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 62 Timothy James Yuji Birkle – November 2023 in the brain (Bellenguez et al., 2022). Microglia are also considered to contribute to stroke, brain trauma, autism, schizophrenia, and Parkinson’s disease (Blaylock and Faria, 2021; Colonna and Butovsky, 2017; Greenhalgh et al., 2020; Qin et al., 2019), and in culture these cells mediate neuronal loss in response to lipopolysaccharide (LPS), amyloid-β, and tau (Birkle and Brown, 2023; Fricker et al., 2012a; Neniskyte et al., 2016; Pampuscenko et al., 2020). Astrocytes (the other major glial cell type) also regulate neuronal health and disease, and furthermore are necessary for microglial survival ex vivo in the absence of specially-modified culture media (Beretta et al., 2020; Bohlen et al., 2017; Chen et al., 2021; Habib et al., 2020; Katsouri et al., 2019; Wan et al., 2022). These cells also strongly modulate microglial inflammatory responses (Baxter et al., 2021; von Bernhardi and Eugenín, 2004). As such, mixed cultures of neurons, microglia and astrocytes together are most relevant for modelling neuroinflammation and neurodegenerative disease. A typical neuron-glia coculture approach is to use primary cells from regions of the rodent brain. This can produce cocultures of neurons, microglia, and astrocytes in appropriate proportions, and using primary cells rather than cell lines allows for more physiologically-relevant experimental results. Our lab and others’ have extensively characterised neuron-glia cocultures derived from rodent cerebellum (a historically popular coculture model in the field), using this model to investigate the role of microglia in neuroinflammation and neurodegeneration (Bal-Price and Brown, 2001; Birkle and Brown, 2023; Brown, 2019; Brown and Neher, 2012; Brown and Vilalta, 2015; Conroy et al., 2004; Fricker et al., 2012a, 2012b; Hornik et al., 2016; Mander and Brown, 2005; Neniskyte et al., 2016; Pampuscenko et al., 2021, 2020). Microglia mediate inflammation including release of cytokines and reactive oxygen and nitrogen species (ROS/RNS) in response to stimuli including LPS, lipoteichoic acid (LTA), phorbol myristate acetate (PMA) and amyloid-β (Lively and Schlichter, 2018; Neher et al., 2011; Smith et al., 1998; von Bernhardi and Eugenín, 2004). In certain activated states, microglia can kill neurons by releasing cytokines or ROS/RNS, or by phagocytosis (Conroy et al., 2004; Fricker et al., 2012b; Neher et al., 2014, 2011; Pampuscenko et al., 2020). Overall, the coculture model is relevant to disease and may recapitulate important disease mechanisms. For example, some inflammatory stimuli induce removal of synapses by microglia in these cultures (Birkle and Brown, 2023; Dundee et al., 2023), analogous to aberrant synaptic pruning by activated microglia in neurodegenerative disease (Hong et al., 2016; Paolicelli et al., 2017). Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 63 With their ability to model in vivo cell dynamics and interactions, cocultures are well-suited to experiments on functional phenotypes (such as cell motility, phagocytosis, adhesion). For neurodegenerative research, this often means experiments on neuronal degeneration or survival. However, bulk measures of cellular survival fail to capture the nuance within a coculture, as any change to one cell type could be masked be concurrent changes in others. Therefore, experiments with cocultures often require identification of individual cells, which necessitates techniques with single-cell resolution such as fluorescent microscopy, flow cytometry, or single-cell sequencing. Microscopy is a particular workhorse technique for this work, as it captures a wealth of functional information in situ and generates spatial, subcellular resolution data. Analysis of images from coculture experiments has generally been a significant challenge. Previously in the lab, analysis of images from neuron-glia coculture experiments was performed manually, as the standard live cell stains used were insufficient for automated cell classification. These stains consisted of Hoechst 33342, IB4-AF488 and propidium iodide (PI), which stain DNA, microglial membranes, and necrotic cell nuclei respectively (Fig. 6A). By eye, therefore, microglia were readily quantifiable by IB4 positivity around any given nucleus, but neurons required more careful counting. Neuronal identity of a given nucleus was manually determined based on nuclear morphology, which tends to be round and medium-sized, as well as the brightfield channel in which neurons have a prominent appearance with long processes above the base-layer of astrocytes and small cell body size relative to microglia (Fig. 6A). Finally, dead cells were distinguished through their condensed nuclear morphology and determined to be either necrotic or apoptotic depending on positivity for PI (which only stains necrotic cell nuclei). Astrocytes were quantified based on their nuclear morphology (dimly stained with Hoechst, oval and large), but were rarely counted due to the focus on neurons and microglia in typical experiments. Crucially, this quantification of different cell types was performed manually as identification of neurons was challenging and relied on careful consideration of nuclear morphology and brightfield channel information. The latter is particularly difficult for incorporate into automated approaches, as objects in brightfield images are not identified by increased signal, but instead by morphology and gradients of intensity. Overall, a given experiment with this manual approach would quantify up to 4,000 neurons per treatment condition, which would take at least an hour of laborious analysis to complete, and experiments would typically require at least 4 treatment conditions. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 64 Timothy James Yuji Birkle – November 2023 Figure 6. Previous neuron-glia coculture image analysis was performed manually A: Representative 20x images (cropped) of neuron-glia cocultures treated ±LPS (100ng/mL) for 3 days (DIV10) and stained with Hoechst 33342 (nuclei), IB4-AF488 (microglia) and propidium iodide (PI; necrotic nuclei), with brightfield channel images alongside. Arrowheads indicate examples of each visible cell type: 1 – microglia; 2 – neurons; 3 – necrotic; 4 – apoptotic; 5 – astrocytes. Scale bars = 50µm. B-E: Average counts per frame (4 frames captured per well, 3 wells per repeat) in cultures treated ±LPS (100ng/mL) for 3 days (DIV10), quantified by previous manual approach, for neurons, microglia, necrotic, and apoptotic cells respectively. Paired t-tests. Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. Error bars = S.D. * p<0.05, ** p<0.01. Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 65 Moreover, neuron counting by this method is relatively subjective and potentially open to bias. Nonetheless, the above approach has been used by various studies on neuron- glia cocultures. Fig. 6B-E present typical data from manual analysis of the cultures after treatment with and without the inflammatory stimulus LPS: neuronal counts significantly decrease, microglial counts significantly increase, and data is also obtained on necrotic and apoptotic cell numbers. Automating the extraction of single-cell data from images instead requires identification of single-cell objects (segmentation), followed by classification of objects into one of a range of possible cell types based on their intensity and shape features (Mattiazzi Usaj et al., 2016). Images of neuron-glia cocultures are particularly challenging for segmentation due to the varied cell/nucleus morphologies present and the tendency for neurons to aggregate in proximity with one another. Then, classification is hindered by the complexity of the cultures and the number of different cell types needing to be classified (at least neurons, microglia, and astrocytes, but potentially also dead cells and other cell types). With limited microscopy filter channels to use for positive labelling of cell types, most classification power must derive from combining low signal-to-noise object features instead, such as shape or stain intensity distributions, and this can be near-impossible to build into a conventional computer- based analysis. Therefore, analysis of neuron-glia cocultures for single-cell discrimination has often been performed manually (Baxter et al., 2021; Luchena et al., 2022; Neher et al., 2014; Polazzi et al., 2015). Some studies have automated quantification of one or two cell types, and frequently use proprietary software accompanying modern imaging platforms (Anderl et al., 2009; Batenburg et al., 2022). All such studies have used immunocytochemistry (ICC) to stain marker proteins for specific cell types, which can be an issue with sensitive primary cultures as I will present here. One recent study presents automatic quantification of neurons, microglia and astrocytes in a neuron-glia coculture setting, but again using ICC and proprietary software (Phadke et al., 2022). Thankfully, powerful open-source software for both segmentation and classification steps has become widely available in recent years, much of which uses machine learning to deliver impressive accuracy. Such platforms include QuPath (Bankhead et al., 2017), CellProfiler (Stirling et al., 2021b), CellProfiler Analyst (Stirling et al., 2021a), and a host of plugins available to run either standalone or via the ubiquitous ImageJ/FIJI (Schindelin et al., 2015), including Ilastik (Berg et al., 2019), STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 66 Timothy James Yuji Birkle – November 2023 DeepCell (Valen et al., 2016), and Cellpose (Stringer et al., 2021). These approaches have already been proven in challenging segmentation and classification tasks elsewhere, including analysis of: protein subcellular localisation; dividing nuclear morphology; simple cocultures of two cell lines; and unsupervised, label-free cell classification (Chong et al., 2015; Logan et al., 2016; Neumann et al., 2010; Reynolds et al., 2013; Shan et al., 2013; Valen et al., 2016; K. Yao et al., 2019). In this work, I therefore aimed to develop a staining and analysis pipeline for cocultures of neurons, microglia, and astrocytes that would enable more efficient use of this important type of model system. This would use open-source tools to be accessible to all researchers. Ultimately, I intended to enable both more effective low-throughput work investigating inflammatory neurodegenerative disease mechanisms and higher- throughput assays at a level not previously achieved. 4.2 Results 4.2.1 α-NeuN immunocytochemistry accurately labels neurons in neuron- glia cocultures Antibodies against neuronal markers can be used for positive staining of neurons through standard immunocytochemistry (ICC) techniques. In general, nuclear markers are most tractable for automated quantification, as cells are often best separated by distinguishing their nuclei, and it therefore helps to have the signal for positive cell identification localised to the nucleus. For neurons, the standard nuclear marker to stain for neuronal identification has long been NeuN (Gusel’nikova and Korzhevskiy, 2015; Mullen et al., 1992), and I therefore tested whether ICC for this marker was applicable in the neuron-glia cocultures. NeuN staining correlated well with neuronal identity as determined by the previous approach (assessing nuclear shape and brightfield morphology; Fig. 7A, B). I used the open-source QuPath software to segment nuclei using the nuclear Hoechst staining, and then positively identify neuronal nuclei using a threshold for NeuN positivity (Fig. 7C). Finally, I compared this automated approach to manual quantification of the same images, either using the original approach (using Hoechst, IB4 and brightfield staining; Fig. 7A) or using just Hoechst and NeuN channel information (Fig. 7B). The two manual approaches showed no difference in neuron counts, while the automated approach produced slightly higher counts (Fig. 7D). Notably, this approach is Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 67 potentially sensitive to the imaging threshold for NeuN, and it is possible that small alterations to this threshold could affect neuronal detection. However, the increase when Figure 7. α-NeuN immunocytochemistry accurately labels neurons in neuron-glia cocultures A: Representative 20x image (cropped) of a neuron-glia coculture stained with Hoechst 33342 (nuclei) and IB4-AF488 (microglia), with brightfield channel overlaid. B: Same field-of-view as (A) with Hoechst 33342 and NeuN images displayed. C: Same image as (B), with classification of neurons overlaid after segmentation and thresholding on NeuN signal. D: Comparison of neurons counted per frame, as quantified using: original manual approach using Hoechst, IB4 and brightfield (Previous); manual counting using Hoechst and NeuN (NeuN Manual); and automated counting using segmentation on the Hoechst channel and thresholding on NeuN signal (NeuN Auto). RM 1- way ANOVA with Tukey’s post-hoc test. N = 24. * p<0.05, ** p<0.01. All panels: Scale bars = 50µm. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 68 Timothy James Yuji Birkle – November 2023 using automatic counting was only by 3-4%, which is negligible in the context of these experiments. Indeed, these NeuN counts may be a better estimate of the ‘true’ neuronal count, being based on an established neuronal marker rather than more subjective assessment using cell morphology. Therefore, NeuN appears to be an effective tool to power automated neuronal identification in neuron-glia cocultures. 4.2.2 Immunocytochemistry displaces microglia in the cocultures The most useful output data from these cocultures, after neuron counts, is microglial counts. This data is often crucial to determine how a given treatment might be acting to achieve its affect, as neuronal loss in this system is often dependent on microglia. While conducting tests with NeuN, microglia appeared to be adversely affected by the staining Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 69 protocol. Intracellular antibody stains require fixation, permeabilization, blocking, and staining steps, all with multiple wash steps in between. By contrast, the original staining with live cell stains only required topical addition of stains to the cultures, with no wash steps involved before imaging cells live. To assess the effect of ICC, cultures were imaged while live (prior to ICC), then processed and imaged again after staining for NeuN. The same fields of view were captured before and after processing. As was immediately apparent in brightfield images, microglia appeared to be selectively removed from cultures by the ICC processing (Fig. 8A, B). Total cells slightly, but significantly, decreased after processing (Fig. 8C), and microglia specifically were near completely removed from the surface of the cultures (Fig. 8D). Therefore, though NeuN was effective for automated neuronal quantification, the necessary ICC processing disturbed microglia in the cocultures to such a significant extent that an estimate of microglial density would not be possible. 4.2.3 NeuO specifically stains live neurons and gives a good estimate of neuron density To produce cultures in which live neurons were positively identifiable with a fluorescent marker, it could have been possible to use transgenic animals in which neurons are endogenously labelled; however, this solution would have severely hindered the application of any final approach to new model systems, or to scenarios where other genetic alterations would be desired for experimental reasons. Therefore, I aimed to find a live cell stain that identifies neurons specifically. This had been lacking in the field until recently, when Er et al. (2015) published a paper on the dye NeuO. NeuO was identified in a screen of fluorescent molecules based on having both good signal and high specificity for neurons. As reported by Er et al., NeuO has a selectivity index (SLI) for neurons of 26, where SLI is equal to the intensity difference between neurons and non-neuron populations, divided by 2 times the standard deviation of the Figure 8. Immunocytochemistry displaces microglia in the cocultures A, B: Representative 10x images (cropped) of neuron-glia cocultures in the transmitted light channel; matching fields-of-view before and after fixation and ICC. Arrows indicate microglia in (A), with equivalent positioned marked in (B) to show loss of microglia. C: Average total cell counts per well in untreated neuron-glia cocultures, before and after fixation and ICC. Paired t-test. D: Average microglia counts per well in untreated neuron-glia cocultures, before and after fixation and ICC. Paired t-test. All panels: Scale bars = 100µm. N = 3. * p<0.05. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 70 Timothy James Yuji Birkle – November 2023 negative population’s staining intensity. NeuO also showed perinuclear cytoplasmic staining, which is tractable for automated analysis given the localisation of positive signal around nuclei (which are typically the objects that are initially segmented). I first aimed to confirm that NeuO labels neurons similarly to NeuN, which is arguably the gold standard nuclear marker for neuronal identification. Having stained and imaged live neuron-glia cocultures with Hoechst and NeuO, cells were then processed for ICC against NeuN and the same fields-of-view imaged again (Fig. 9A, B). NeuO staining showed good correspondence to NeuN staining, but with less background signal. Note, there are reports of off-target staining by common α-NeuN antibody clones, including of the synaptic protein Synapsin1 (Kim et al., 2009). This also confirmed that the perinuclear distribution of NeuO staining results in high signal specifically around nuclei, which is tractable for automation. Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 71 To both confirm specificity of NeuO for neurons in these cultures, and to validate its use in alternative neuron-glia coculture systems, I stained primary neuron- glia cocultures from cerebellum (our usual cultures; Fig. 9C) and hippocampus (Fig. 9D) with Hoechst, NeuO, and IB4-AF594 and imaged them live. Both types of culture consist mostly of neurons, microglia and astrocytes. Microglia are identifiable by IB4 staining, and astrocytes by their distinctive large, dim, and oval nuclear morphology in the Hoechst channel. This method of identifying astrocytes in cocultures is supported by images from other studies (Goshi et al., 2020; Jebelli et al., 2015) and was validated here in separate cultures using anti-GFAP staining (Fig. 9E). In both cultures, NeuO cleanly labelled neurons with no visible colocalization with either microglia or astrocytes. The hippocampal cocultures also contained a large population of cells of unknown identity, but these cells were also not stained with NeuO. Therefore, NeuO successfully and specifically stains neurons in primary neuron-glia cocultures, and thus is potentially sufficient as the basis for an automated analysis pipeline. 4.2.4 QuPath and Cellpose segmentation algorithms accurately identify cell nuclei in coculture images For image analysis, the first step is to apply a segmentation algorithm to identify individual objects within images of the cultures. As was used here, a DNA stain such as Hoechst 33342 provides a useful image channel for this process, as nuclei of adjacent cells are generally more easily distinguishable than the cell bodies. Nonetheless, segmentation can be challenging, and particularly so for neuron-glia cocultures. While the often-distinctive nuclear morphology of different cell types can be useful for later cell classification, it hinders segmentation, as algorithms can typically be fine-tuned to only cope accurately with a small range of nuclear sizes, shapes, and intensities. Nuclei Figure 9. NeuO specifically stains live neurons and matches α-NeuN immunocytochemistry A, B: Representative 10x images (cropped) of matching fields-of-view in neuron-glia cocultures stained with Hoechst 33342 (nuclei) and α-NeuN ICC (fixed neurons; A) or NeuO (live neurons; B). C: Representative 10x image (cropped) of a cerebellar neuron-glia coculture stained with Hoechst 33342 (nuclei), NeuO (live neurons), and IB4-AF594 (microglia). D: Representative 10x image (cropped) of a hippocampal neuron-glia coculture stained with Hoechst 33342 (nuclei), NeuO (live neurons), and IB4-AF594 (microglia). E: Representative 20x image (separate channels and merged; cropped) of neuron-glia cocultures stained with Hoechst 33342 (nuclei) and anti-GFAP immunocytochemistry (astrocytes). Arrowheads indicate astrocyte nuclei. All panels: Scale bars = 50µm. Arrows indicate microglia nuclei, arrowheads indicate astrocyte nuclei. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 72 Timothy James Yuji Birkle – November 2023 outside of this optimised range will frequently be poorly handled. Additionally, neurons tend to settle and grow in close contact with one another, resulting in clumps of cell bodies/nuclei that are particularly hard to tease apart. As a result of these factors, automated segmentation on cocultures is particularly error-prone, with the usual issues being over-segmentation of some nuclei (too many boundaries drawn amongst objects; Fig. 10A) and under-segmentation of others (missing boundaries between distinct objects; Fig. 10B). To select the best segmentation algorithm for the challenging coculture images, I compared available tools within three open-source image analysis programmes: ImageJ (Schneider et al., 2012), QuPath (Bankhead et al., 2017), and CellProfiler (Stirling et al., 2021b). In general, these tools can define edges of nuclei using the shape and intensity features of the objects via standard image processing techniques. I also considered Cellpose (Stringer et al., 2021), which instead uses published deep learning- based models to complete the same task. The parameters for each method were extensively optimised manually to achieve the best possible segmentation result, except ImageJ which was limited to use of only basic in-built functions for segmentation (namely, smoothing, auto-thresholding, and binary watershed), to function as a baseline comparison. When presented with the same random images from previous neuron- glia experiments, QuPath and Cellpose outperformed ImageJ and CellProfiler by a large margin (Fig. 10C). These two approaches were both used for further work, as QuPath was more accessible for very low-throughput work and image exploration, while Cellpose (as a plugin within CellProfiler) was more suitable for high-throughput implementation. Figure 10. QuPath and Cellpose segmentation algorithms accurately identify cell nuclei in coculture images A: Example segmented Hoechst 33342 (nuclei) image depicting over-segmentation error. B: Example segmented Hoechst 33342 (nuclei) image depicting under-segmentation error. C: Percentage of total cells affected segmentation errors when using optimised algorithms in ImageJ (IJ), QuPath (QP) and CellProfiler (CP), or using Cellpose. N = 3. Error bars = S.D. Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 73 4.2.5 Machine learning-based classification of cell types enables automated analysis of coculture images Next, segmented cells are measured for their shape and intensity features across all channels (a typical set of such features obtained through CellProfiler can be found in Appendix 2, Table 4); finally, this data is used for classification (Fig. 11A-C). Classical classification approaches can be curated, consisting of various thresholding steps for different features; however, machine-learning methods take all object parameters into consideration (here, over 150 in total) and can more accurately classify cells than a manual thresholding approach, particularly for classification of many different cell types (Valen et al., 2016). Through exploration with CellProfiler Analyst (Stirling et al., 2021a), the companion software to CellProfiler, I found that the most effective type of models for the data were Random Trees and Support Vector Machines. These were trained to distinguish neuron, microglia, astrocyte, and condensed nuclei, as well as ‘other’ nuclei and debris. ‘Other’ represents a class of rare cells that appear to be dying neurons based on neuronal brightfield morphology, very weak NeuO staining, and Hoechst staining intensity between that of neurons and condensed cells. Manual annotation of a few hundred randomly selected cells of each class was sufficient to achieve highly accurate classification of cells in neuron-glia cocultures, as validated on images from experiments separate from those used for model training (Fig. 11D). This model was also validated by cross-validation, which iteratively trains the model on a subset of training images and tests it on those that are withheld (Fig. 11E). Importantly, when using Random Trees classification I was able to show that the classifier prioritised the expected object features to discriminate cell types, such as Hoechst or NeuO intensity measures (Appendix 2, Table 5). Furthermore, I successfully used the same method for hippocampal neuron-glia cocultures, showing that this approach generalises to neuron-glia cocultures from different brain regions (Fig. 11F). This machine learning-based classification can be performed similarly using either QuPath or CellProfiler/CellProfiler Analyst. Overall, machine learning-based classification can produce accurate identification of at least 6 different cell classes in neuron-glia cocultures. In combination with the validated segmentation approaches of Figure 10, this produces a fully automated analysis pipeline for classifying all cell classes in these images. In comparison to the previous manual approach, which would typically assess ~4,000 neurons per hour of laborious analysis, this automated method accurately identifies STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 74 Timothy James Yuji Birkle – November 2023 ~200,000 neurons per hour, unsupervised. Additionally, the analysis produces accurate count data for microglia, astrocytes, and other cell classes in parallel, and can be sped up with improved hardware. Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 75 4.2.6 Automated coculture analysis identifies targets of interest for inflammatory neurodegeneration While starting to use the new analysis pipeline, I tested whether two cellular targets might be involved in inflammatory neurodegeneration: spleen tyrosine kinase (SYK) and urokinase plasminogen activator (uPA). SYK was of interest due to its role in the microglial TREM2 pathway, which is heavily implicated in Alzheimer’s disease. Meanwhile, uPA was selected as a target of interest after analysis of data from a recent screen for genes involved in microglial phagocytosis (Pluvinage et al., 2019), a process that is known to be important for inflammatory neuronal loss in these cultures (Brown and Neher, 2012; Fricker et al., 2012a; Neher et al., 2014, 2013, 2011). For both targets, a suitable inhibitor compound was identified and tested for its ability to prevent inflammatory neurodegeneration induced by LPS, while using an automated analysis pipeline within QuPath. LPS treatment reduced neuronal numbers in the cultures as described previously (Fricker et al., 2012b) (Fig. 12A). Strikingly, both the SYK inhibitor BAY61-3606 (BAY61) (Yamamoto et al., 2003) and the uPA inhibitor BC11 hydrobromide (BC11) (Longo et al., 2015) prevented LPS-induced neuronal loss. BAY61 was used at 1µM and BC11 was used at 50µM, which was based on their respective potencies reported in the literature. In addition, both inhibitors reduced the spontaneous neuronal loss in the absence of LPS. Total cell counts (regardless of classification) were also reduced by LPS and rescued by the inhibitors (Fig. 12B), indicating that the neuronal loss and rescue could not be as a result of misclassification of neurons in the presence of LPS. Figure 11. Machine learning-based classification of cell types in coculture images completes an accurate automated analysis approach A: Representative 10x image (cropped) of neuron-glia cocultures after staining with Hoechst 33342 (nuclei), NeuO (live neurons), and IB4-AF594 (microglia). B: Schematic of the necessary image processing steps required to achieve cell-by-cell classification. C: Image from (A) with segmentations and classifications overlaid. D: Heatmap confusion matrix plotting proportion of manually annotated cells of each class (Actual) being predicted to be any given class (Predicted) by a classifier model for cerebellar neuron-glia cocultures, during classifier validation on images from experiments separate from the training set. E: Heatmap confusion matrix for a cerebellar neuron-glia coculture classifier model during k-fold cross-validation (k=5). F: Heatmap confusion matrix for a hippocampal neuron-glia coculture classifier model during k-fold cross-validation (k=5). All panels: Scale bars = 50µm. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 76 Timothy James Yuji Birkle – November 2023 To further test the accuracy of the analysis for counting neurons specifically, I treated new cultures in the same way before imaging with Hoechst and NeuO, then staining for NeuN and imaging the same fields-of-view again. Simple threshold-based classification of neurons using either NeuO or NeuN replicated the findings of the automated analysis, showing that both simple quantification with NeuO and the complete automated analysis agree with results obtained using the gold-standard NeuN approach (Fig. 12C). Therefore, the automated analysis was capable of accurately identifying treatments Figure 12. Automated coculture analysis identifies targets of interest for inflammatory neurodegeneration A: Average neuronal counts per well in cocultures treated ±LPS (100ng/mL) and ±BAY61 (1µM) or ±BC11 (50µM) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. B: Average total cell counts per well in cocultures treated ±LPS (100ng/mL) and ±BAY61 (1µM) or ±BC11 (50µM) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. C: Average neuronal counts per well in cocultures treated ±LPS (100ng/mL) and ±BAY61 (1µM) or ±BC11 (50µM) for 3 days (DIV10), as quantified either by NeuO positivity (stained and imaged before fixation) or NeuN positivity (fixation and ICC). All panels: Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. Error bars = S.D. * p<0.05, ** p<0.01, *** p<0.001. Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 77 protecting against inflammatory neurodegeneration, and further study of both SYK and uPA are presented in subsequent chapters. 4.2.7 NeuO staining can be affected by treatments I noticed that NeuO staining intensity varied systematically depending on the treatments applied to the cultures. In the case of the two compounds tested in Figure 12, BAY61 generally increased NeuO staining intensity while BC11 generally decreased it (Fig. 13A-C). NeuO does not have a defined intracellular target that it binds to, as it was identified in a screen for compounds simply producing the desired specific neuronal staining. The treatments may be affecting something that alters uptake of NeuO into neurons, such as membrane potential, or altering the abundance of a binding target. I therefore constructed the automated image analysis to be robust against this staining variation. First, the pixel values in the NeuO channel of each image were normalised to make intensities more consistent between images. Second, images used to train the classifier model were evenly selected from all treatment conditions, such that the model would have equal training exposure to images of all original NeuO staining intensities (as normalisation does not perfectly correct for all differences). I previously validated the accuracy of this approach across all images (Fig. 11D, E), but it was also essential to confirm that the above measures mitigated against any bias in neuronal classification between treatment conditions. Therefore, I compared the accuracy of the pipeline between images of untreated, BAY61-treated, and BC11-treated cultures, using the F1 score accuracy metric (F1 score is the harmonic mean of precision and recall, where precision is the proportion of predicted objects of each class that are correct, and recall is the proportion of actual objects of each class that are correctly identified). Accuracy was consistent between treatment conditions for all major cell types, with only minor distortion for the ‘Dead Cells’ and ‘Debris’ classes (Fig. 13D). The analysis pipeline was therefore suitably robust against any NeuO staining variation. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 78 Timothy James Yuji Birkle – November 2023 Figure 13. NeuO staining can be affected by treatments A: Representative 10x images (cropped) of neuron-glia cocultures treated with vehicle, BAY61 (1µM) or BC11 (50µM) for 3 days (DIV10) and stained with NeuO (live neurons), to show variation in NeuO staining intensity after BAY61 (higher) and BC11 (lower) treatments. B: F1 accuracy score of QuPath automated image analysis pipeline, separated by cell type and by application to images of vehicle-, BAY61- or BC11-treated cocultures. Final classification model tested on pooled sets of cells from 4 separate experiments. Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 79 4.3 Discussion In this work, I established an automated image analysis approach to extract quantitative data on multiple cell types within neuron-glia cocultures. I validated the use of NeuO (Er et al., 2015) as a positive neuronal marker of equal or greater utility than α-NeuN ICC, and demonstrated the accuracy of machine learning methods for both segmentation and classification analysis steps. This pipeline can be set up within open-source analysis software including QuPath (Bankhead et al., 2017) and CellProfiler/CellProfiler Analyst (Stirling et al., 2021b, 2021a). In all tests, including with an alternative coculture model, this pipeline accurately quantified all cell classes with over 90% accuracy, increasing to around 95% for the major cell types (neurons, microglia and astrocytes). Crucially, this work increases the throughput of neuron-glia coculture image analysis by at least 50- fold in terms of overall time taken. This enables higher-throughput work, more robust experimental results by generating more data for a given experimental condition, and diminished influence of experimenter bias on results. I then used this method to investigate two potentially interesting targets identified from the literature, which produced promising initial results for subsequent study. Early on, I found that performing ICC for NeuN disturbed microglia within the neuron-glia cocultures. As information on microglia is central to the purpose of these model systems, this was an unacceptable side effect of this approach. Even if enough microglia were left for analysis, the fraction remaining may depend on the treatment conditions applied previously. For example, inflammatory activation induces higher adhesion and migration of microglia into the astrocyte base layer (Kloss et al., 2001; Wollmer et al., 2001) (own observations), and these cells may be less susceptible to being washed off. Extrapolating, any treatment affecting microglial activation state could therefore artefactually change the proportion of microglia remaining after ICC, and this would render microglial data uninformative. Notably, ICC is commonly used in research on cell cultures, but the possibility of such bias is rarely noted. One significant advantage of the final staining approach used here is the avoidance of any such ICC issues by using live cell stains exclusively. These concerns may be most significant with sensitive primary cultures, such as the ones used here, but should be a general concern for any ICC-based experiment in cell culture. The use of NeuO as a live neuronal stain has additional benefits. Firstly, it is considerably more straightforward than any ICC protocol. Secondly, it provides reduced background signal compared to NeuN in my hands. Common NeuN antibody clones STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 80 Timothy James Yuji Birkle – November 2023 including the one used here have known off-target specificities, including for synaptic proteins that are abundant in neuronal cultures (Kim et al., 2009), and cytoplasmic or dendritic staining is expected despite NeuN nominally being a nuclear antigen (Gusel’nikova and Korzhevskiy, 2015). On the other hand, having been identified in a screen in 2015, the labelling mechanism of NeuO is unknown and there have been few publications using it. I did note some faint staining of microglia by NeuO, which interestingly is most prominent with LPS. However, this microglial signal remains far below that of neurons, and did not affect classification accuracy. I also noticed some variation in staining intensity using NeuO on cultures under different treatment regimes, but this could be corrected for with appropriate image processing. This was only noted for BAY61 and BC11, but should be taken into consideration more generally when using NeuO. Finally, NeuO only works with live neurons so is unsuitable for any assay requiring fixation, and its fixed fluorescence in the FITC channel renders it inflexible when designing panels of fluorescent stains. Having established an effective staining protocol, this work validates the use of both QuPath (Bankhead et al., 2017) and Cellpose (Stringer et al., 2021) for segmentation of images from neuron-glia cocultures. The latter method is particularly useful given its availability as a plugin in common analysis software including CellProfiler, which is a mainstay of high-content microscopy analysis. Cellpose is an example of deep learning-based approaches that are increasingly used for image analysis and has been shown to surpass other methods for segmentation (Chen and Murphy, 2022; Valen et al., 2016). The established utility of machine learning extends to classification tasks, too, having been used to classify: different subcellular protein localisation patterns; dividing nuclear morphologies; cocultures of two cell lines; and unlabelled cells in culture flasks in an unsupervised manner (Chong et al., 2015; Logan et al., 2016; Neumann et al., 2010; Reynolds et al., 2013; Shan et al., 2013; Valen et al., 2016; K. Yao et al., 2019). In general, the use of machine learning for classification is accepted to provide greater accuracy and greater sensitivity to subtle phenotypic changes than human-curated procedures (Mattiazzi Usaj et al., 2016). My method does, however, have certain limitations. Once a pipeline is established and a classifier model trained, it is inflexible to most changes in culture preparation or cell staining (as might be introduced during assay optimisation or by accident). This is because machine learning-based classifier parameters cannot easily be adjusted to suit new objects with (for example) a different staining intensity than before, Chapter 4: Automated image analysis of neuron-glia cocultures Timothy James Yuji Birkle – November 2023 81 unlike a manually-curated threshold-based method. Instead, a new model must be trained from scratch if the previous one performs poorly, or at a minimum the old model may be updated with additional training data. The method also limits flexibility in that it uses 3 fluorescence filter channels, and most fluorescence microscopes only have 3 or, at most, 4 such channels. Therefore, there is a harsh limitation on the use of additional stains and markers for more detailed information on top of what the pipeline provides. In the future, it may prove possible to replace some of the fluorescence channels with information from brightfield images, particularly for neuronal identification given that neurons always have distinctive morphology (unlike microglia, which sometimes infiltrate the astrocyte layer to be barely visible by brightfield). Indeed, recent machine learning-based methods have been successful in segmenting and classifying cells in culture flasks based on brightfield images alone (K. Yao et al., 2019). Finally, though I validated that the larger, dim nuclei in these cocultures belong to astrocytes according to anti-GFAP staining, and that these nuclei are accurately counted by the pipeline, it would be preferable to include a positive astrocyte stain in the staining panel. However, this was not done here given the limited availability of fluorescence channels and the focus on neurons and microglia in the intended assays. Overall, the image analysis approach here enables accurate quantification of all cell types in neuron-glia cocultures beyond what was previously possible. Studies in the literature tend to either quantify specific proteins, such as synaptic proteins of neurons (Bassil et al., 2021), or perhaps extract information on two cell types at a time (Anderl et al., 2009; Batenburg et al., 2022). All such studies rely on laborious ICC that also risks disturbing cultures. By contrast, the approach here extracts information on all visible cell types at the expense of any detailed information on levels of specific disease-relevant proteins, as the focus here is on functional cellular phenotypes such as cell survival. The accurate classification of nuclei also allows further analysis of the images to extract optimal morphological data on specific cell types, as will be used in future chapters. Finally, the approach is shown to be applicable to hippocampal primary neuron-glia cocultures, and in principle should be valid for other culture systems. This novel, validated image analysis strategy supported further neuron-glia coculture experiments that form the basis of the following chapters. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 82 Timothy James Yuji Birkle – November 2023 5 THE ROLE OF UROKINASE IN MICROGLIA-MEDIATED NEURODEGENERATION All data presented in this chapter is my own work, and some has been published in: Birkle, T. J. Y., & Brown, G. C. (2023). Syk inhibitors protect against microglia- mediated neuronal loss in culture. Frontiers in aging neuroscience, 15, 1120952. https://doi.org/10.3389/fnagi.2023.1120952. 5.1 Introduction In the cerebellar neuron-glia cocultures that my lab and others’ use to model neuron-glia interactions, neuronal loss can be induced by various stimuli, including lipopolysaccharide (LPS), lipoteichoic acid, amyloid-β and tau (Fricker et al., 2012a, 2012b; Kinsner et al., 2005; Pampuscenko et al., 2020). These stimuli are believed to cause glial cytokine release and reactive oxygen species production, which lead to phosphatidylserine exposure by neurons and consequent susceptibility to microglial phagocytosis (Brown and Neher, 2012; Hornik et al., 2016; Neher et al., 2011; Neniskyte et al., 2011). However, with depletion of microglia or inhibition of phagocytosis, these neurons instead survive the inflammatory insult (Birkle and Brown, 2023; Fricker et al., 2012a; Kinsner et al., 2005; Neher et al., 2011). Therefore, microglial inflammation and phagocytosis can cause neuronal death under inflammatory conditions produced by disease-relevant stimuli. Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 83 With this in mind, I analysed the data of a recent pooled CRISPR screen looking for modifiers of phagocytosis in BV2 mouse microglial cells (Pluvinage et al., 2019). In this study, BV2 cells were subjected to random gene knockout using CRISPR before being incubated with fluorescent phagocytic targets. Phagocytic and non- phagocytic cells were separated by flow-activated cell sorting (FACS) prior to gDNA sequencing, and the resulting data was assessed for non-random changes in abundance associated with each knockout. From this data I extracted genes for which knockout reduced phagocytosis. I then used the Drug-Gene Interaction Database (Freshour et al., 2021) and literature search to identify genes for which the encoded proteins have both validated inhibitor compounds (i.e. they are ‘druggable’) and literature supporting their relevance to microglia or neurodegeneration. One of the top genes was Plau, which encodes urokinase plasminogen activator (uPA) in mice, and in preliminary testing an inhibitor of this protein prevented LPS-induced neuronal loss in neuron-glia cocultures (Figure 12). Thus, here I sought to confirm this neuroprotection and elucidate the mechanisms involved. uPA is a 411 amino acid serine protease forming a key part of the plasminogen activation cascade by converting plasminogen into plasmin, which then mediates thrombolysis and tissue remodelling via protease activity on a variety of extracellular matrix (ECM) proteins and activation of matrix metalloproteases (Crippa, 2007; Lin et al., 2020). The biology around this protein has been of high interest to the cancer field for some time (Mahmood et al., 2018; Zhai et al., 2022). uPA is secreted as a single- chain pro-uPA zymogen, which is then activated by Lys158-Ile159 cleavage to generate two-chain active uPA held together by a disulfide bond. The C-terminal protease domain (159-411) has the proteolytic functions of uPA, while the N-terminus has a growth factor-like domain (GFD; 1-49) and a kringle domain (KD; 50-131) that mediate protein-protein interactions (Alfano et al., 2022; Masucci et al., 2022). These domains are linked to the protease domain through a connecting peptide domain, and this ‘high- molecular weight uPA’ can undergo additional cleavage to release an amino-terminal fragment (ATF) consisting almost entirely of the GFD and KD, as well as ‘low molecular-weight uPA’ consisting of the protease domain and most of the connecting peptide. The N-terminal GFD and KD enable uPA to bind to its glycosylphosphatidyl- inositol (GPI)-anchored cell-surface receptor uPAR (Barinka et al., 2006). uPAR was originally thought only to polarise uPA to the leading edge of migrating cells STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 84 Timothy James Yuji Birkle – November 2023 (Estreicher et al., 1990). However, uPAR is now understood to initiate diverse intracellular signalling pathways and thereby influence many cellular functions (Alfano et al., 2022). uPA can modulate uPAR activity either by binding to uPAR or by proteolytic cleavage of uPAR (Magnussen et al., 2017), so uPA can act either by binding or catalysis. One exemplary effect of uPA binding is on uPAR’s binding to vitronectin, an abundant extracellular glycoprotein that binds to uPAR’s binding domain using a somatomedin B domain (Huai et al., 2008). uPA binding to uPAR substantially enhances the affinity of the uPAR for vitronectin (Gårdsvoll and Ploug, 2007; Sidenius et al., 2002; Sidenius and Blasi, 2000; Wei et al., 1994), and the opposite may also be true (B. Zhao et al., 2015). Finally, the uPA-uPAR complex has further interactions with various integrins including the vitronectin receptors αVβ3 and αVβ5, which may bind via the connecting peptide of uPA (Franco et al., 2006). The cellular functions of the uPA-uPAR system are very broad. One of the canonical roles of uPA is extracellular proteolysis to promote migration, and high uPAR expression and uPA activity correlates with increased ECM degradation, migration, and cell invasion (MacDonald et al., 1998). However, even this function is not solely influenced by proteolysis, as the addition of the non-catalytic ATF of uPA can stimulate migration through binding to uPAR. The uPA-uPAR interaction can also promote proliferation in cancer cell lines, dependent on both binding to and cleavage of uPAR by uPA (Magnussen et al., 2017; Schmidt and Grünsfelder, 2001). Interestingly, inflammation has also emerged as a central target of the uPA-uPAR system. For example, uPA can stimulate cytokine production via αVβ3 integrin, and enhance adhesion and migration via complement receptor-3 (CR3) in neutrophils (Kwak et al., 2005; Mehra et al., 2016). This may occur through uPAR clustering, as crosslinking antibodies can cause calcium rise, upregulation of CD11b (a component of CR3), degranulation, and reactive oxygen species production in neutrophils similar to exogenous uPA (Sitrin et al., 2000). uPAR expression also increases during colitis in mice, and knockout exacerbates macrophage-mediated inflammation (Genua et al., 2015). Interactions with vitronectin can also directly affect inflammation, as vitronectin exposure in glioma cells induces proinflammatory cytokine production dependent on uPA activity and binding to uPAR (Keasey et al., 2018). Lastly, phagocytosis by peripheral innate immune or cancer cells has frequently been shown to either increase with higher uPA/uPAR expression, or decrease with lower expression (D’mello et al., Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 85 2009; Genua et al., 2015; Gyetko et al., 2004; Kawao et al., 2012; Wiersinga et al., 2010; Yang et al., 2014). Importantly for this work, the uPA system is expressed in the brain and is reactive to disease states. uPA and uPAR expression increases after ischemic injury, including in non-human primates (Chang et al., 2003; Diaz et al., 2017; Hosomi et al., 2001), and uPAR knockout reduces resulting neurodegeneration in mice (Nagai et al., 2008). Levels of uPA also increase in both humans and mouse models with epilepsy (Cho et al., 2012; Lahtinen et al., 2006) or multiple sclerosis (Gur-Wahnon et al., 2013; Gveric et al., 2001), and modifying uPA/uPAR activity affects progression of these diseases. Finally, there are links between uPA and various neurodegenerative proteinopathic diseases. uPAR expression has been considered a marker of microglial activation in Alzheimer’s disease (AD) and is increased in post-mortem AD brain and in vitro upon exposure to amyloid-β (Barker et al., 2012; Davis et al., 2003; Mehra et al., 2016; Tucker et al., 2000; Walker et al., 2002). Strikingly, single-nucleotide polymorphisms at the PLAU locus associate with AD risk and amyloid-β levels (Ertekin-Taner et al., 2005; Riemenschneider et al., 2006), and increasing uPA activity can diminish amyloid-β levels and cognitive deficits in APP mice (Jacobsen et al., 2008), potentially by enhancing plasmin-mediated degradation of amyloid-β (Davis et al., 2003; Tucker et al., 2000). uPA may also play a role in Parkinson’s disease (Reuland and Church, 2020). In amyotrophic lateral sclerosis, too, uPA and uPA levels are elevated, and uPA inhibition improves motor performance and survival in mouse models (Glas et al., 2007). However, microglial uPA and uPAR have been studied relatively little. Microglia do express both uPA and uPAR at low baseline levels in the mouse brain, and uPAR expression increases specifically in microglia in response to LPS or kainate- induced neurodegeneration, although cell specific expression of uPA was not tested (Cunningham et al., 2009). In vitro rodent microglia also release uPA at baseline, and this can be stimulated by either ATP treatment, mimicking proximity to injured neurons, or conditioned media from neurons themselves (Nakajima et al., 2005, 1992; Shin et al., 2010). Interestingly, LPS stimulation of microglia in vitro results in reduced uPA activity in conditioned media, though this may be a result of increased uPAR expression and therefore increased sequestration of uPA at the cell surface (Cross and Woodroofe, 1999; Nakajima et al., 1992). Meanwhile, microglial uPA expression definitely increases under the neuroinflammatory conditions of AD (Davis et al., 2003; STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 86 Timothy James Yuji Birkle – November 2023 Walker et al., 2002). Finally, addition of the endogenous uPA inhibitor plasminogen activator inhibitor-1 (PAI-1) to BV2 mouse microglia promotes motility and reduces phagocytosis (Jeon et al., 2012). Given the link between uPA and microglial phagocytosis identified in the BV2 CRISPR screen and the general literature connecting uPA biology to neuroinflammation and neurodegeneration, I aimed to characterise the roles of uPA in microglia-mediated inflammatory neurodegeneration using the neuron-glia cocultures. I first present data using the commonly-used uPA inhibitor BC11 hydrobromide (BC11), which has been shown to both inhibit uPA catalytic activity and bind to the ATF such that binding to uPAR is diminished (Álvarez et al., 2018; Keasey et al., 2018; Longo et al., 2015; Magnussen et al., 2014, 2017). I then present experiments using more specific inhibitors of either uPA-mediated proteolysis or uPA-uPAR binding. 5.2 Results 5.2.1 BC11 hydrobromide protects against LPS-induced neuronal loss in neuron-glia coculture Previously, I identified a potential protective effect of 50µM BC11 hydrobromide (BC11) against LPS-induced neuronal loss using a QuPath-based analysis pipeline (Fig. 12), and this effect can be clearly observed by eye in the cultures (Fig. 14A). BC11 is an established inhibitor of uPA’s proteolytic activity and may also act by binding to the growth factor-like domain of uPA that binds uPAR (Longo et al., 2015). As further work would instead be supported by the CellProfiler-based analysis, here I reanalysed this data using CellProfiler with entirely new training data for classification and achieved near-identical results, with BC11 preventing neuronal loss (Fig. 14B). The close similarity further supports the robustness of the analysis approach independent of analysis platform, or the exact image processing, segmentation method, or training data used. While not significant here (p = 0.1034), BC11 treatment also tended to increase neuronal numbers in the absence of LPS, suggesting a protective effect against spontaneous neuronal loss over the 3-day treatment period. When normalising for this effect and assessing neuronal loss as a percentage of LPS-untreated culture neuronal counts, BC11 treatment was again found to completely prevent loss in the presence of LPS (Fig. 14C). This suggests that LPS-induced neuronal loss depends on uPA activity. Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 87 Figure 14. BC11 hydrobromide protects against LPS-induced neuronal loss in neuron-glia coculture A: Representative 10x images (cropped) of neuron-glia cocultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10) and stained with Hoechst 33342 (nuclei), NeuO (live neurons) and IB4-AF594 (microglia). Scale bars = 100µm. B: Average neuron counts per image in cultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. C: LPS-induced neuronal loss ±BC11 calculated from the neuronal counts of (B) as percentage of neuronal counts in absence of LPS. Paired t-test. All panels: Each datapoint represents the mean of 3 technical replicates, N = 4. Error bars = S.D. * p<0.05. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 88 Timothy James Yuji Birkle – November 2023 5.2.2 LPS-induced neuronal loss in neuron-glia cocultures is microglia- dependent Prior to investigating the mechanism of BC11-mediated neuroprotection, I tested whether the LPS-induced neuronal loss was microglia dependent. Previous work in these neuron-glia cocultures has found that microglia are essential for LPS-induced neuronal loss (Birkle and Brown, 2023; Fricker et al., 2012a; Kinsner et al., 2005). However, as the LPS-induced neuronal loss that I found was greater than in these previous studies, I wanted to test whether all neuronal loss was still microglia dependent. I found that LPS caused loss of almost all neurons over 3 days, but that pretreatment with L-leucine methyl ester (LME), which selectively depletes microglia from cocultures (Jebelli et al., 2015), almost completely prevented the LPS-induced neuronal loss (Fig. 15A). Meanwhile, the treatment significantly depleted cultures of microglia as expected (Fig. 15B) and had only minor effects on astrocyte numbers (Fig. 15C). The increase in astrocyte numbers in the presence of LPS and LME Figure 15. LPS-induced neuronal loss in neuron- glia cocultures is microglia-dependent A: Average neuron counts per image in cultures treated ±LME at DIV6 to deplete microglia, then ±LPS (100ng/mL) for 3 days (DIV10). B: Average microglia counts per image in cultures treated ±LME at DIV6 to deplete microglia, then ±LPS (100ng/mL) for 3 days (DIV10). C: Average astrocyte counts per image in cultures treated ±LME at DIV6 to deplete microglia, then ±LPS (100ng/mL) for 3 days (DIV10). All panels: RM 2-way ANOVA with Dunnett’s post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = 3. Error bars = S.D. * p<0.05, ** p<0.01. Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 89 together is likely a result of astrocyte proliferation to recover from reduced cell density after microglial depletion. Overall, the protection by LME against LPS-induced neuronal loss seen here indicates that microglia are vital to this neuronal loss, and therefore suggests that effects of BC11 on neuronal loss may be mediated by effects on microglia. 5.2.3 BC11 depletes microglia from cocultures As a first test of whether BC11 specifically affects microglia in the neuron-glia cocultures, I analysed coculture experiments with respect to numbers of microglia and Figure 16. BC11 depletes microglia from cocultures A, B: Representative 10x images (cropped) of neuron-glia cocultures treated ±BC11 (50µM) for 3 days (DIV10) and stained with Hoechst 33342 (nuclei) and IB4-AF594 (microglia). Arrows indicate example IB4-stained objects lacking nuclear DNA signal. Scale bars = 100µm. C: Average microglia counts per image in cultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. D: Average percentage of IB4-stained objects which lack nuclear DNA staining in images from LPS-untreated neuron-glia cocultures treated ±BC11 (50µM) for 3 days (DIV10). Paired t-test. E: Average astrocyte counts per image in cultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. All panels: Each datapoint represents the mean of 3 technical replicates, N = 4. Error bars = S.D. * p<0.05, ** p<0.01 STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 90 Timothy James Yuji Birkle – November 2023 astrocytes. Strikingly, BC11 significantly reduced the number of microglia in the cultures in the presence of LPS after 3 days of treatment (Fig. 16A-C). LPS itself induced an increase in microglial numbers, which was largely prevented by BC11. BC11 also appeared to reduce microglial numbers in the absence of LPS, but this difference was not significant (Fig. 16C). In the absence of LPS, BC11-treated cocultures had a greatly increased number of IB4-positive objects lacking nuclear DNA (Fig. 16B, D). IB4 stains microglial membranes (Boscia et al., 2013), and these objects may be fragments of microglial membrane attached to the culture plate after death of the cell. The focal plane’s depth of field was about 10 µm, around the width of a microglia, and adjustment of the focal plane did not reveal out-of-plane nuclei (not shown). Finally, BC11 had a small but significant effect on astrocytes, reducing their numbers marginally (Fig. 16E). 5.2.4 BC11 prevents LPS-induced changes to microglial morphology, but does not affect TNFα release Microglial morphology is an important marker of microglial activation state, which in turn represents broad cellular functions including phagocytosis, inflammation and motility (Vidal-Itriago et al., 2022; Woodburn et al., 2021). To examine the effect of BC11 on microglia beyond simple cell counts, I therefore tested whether microglial morphology was affected by the compound. Upon addition of LPS, microglial adhesion increases, which in two-dimensional culture can result in flattening and ramification of the cell against the substrate (Fan et al., 2018; Kloss et al., 2001). In these cocultures, I similarly observed an increase in cell area and a decrease in circularity, which reflects increasing ramification (Fig. 17A-C). Importantly, addition of BC11 prevented the LPS- induced morphological change, and microglial shape remained as in LPS-untreated cultures (Fig. 17A-E). This may suggest that BC11 treatment, and the resulting uPA inhibition, prevent the LPS-induced microglial activation, which could explain this data and some of the previously-observed decrease in microglial numbers in the presence of LPS. However, BC11 had no effect on the LPS-induced release of the proinflammatory cytokine TNFα from cocultures (Fig. 17F). Thus, BC11 appears to have no effect on the cellular pathways mediating TNFα release in response to LPS, and the neuroprotection by BC11 appears not to be mediated by preventing pro-inflammatory cytokine release. Instead, BC11 may selectively inhibit LPS-induced changes in microglial viability, motility, phagocytosis and/or morphology. Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 91 Figure 17. BC11 prevents LPS-induced changes to microglial morphology but does not affect TNFα release A: Representative 10x images (cropped) of neuron-glia cocultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10) and stained with Hoechst 33342 (nuclei) and IB4-AF594 (microglia). Scale bars = 50µm. B: Violin plot of microglial area (px2) in cultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10). C: Violin plot of microglial circularity in cultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10). Panels B, C: Data for all microglia across 4 biological repeats used. From left to right, N = 6007, 11427, 2222, 5928. D: Average microglial area (px2) in cultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. E: Average microglial circularity in cultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. F: Average supernatant TNFα concentration (pg/mL) in cultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. N.D. = not detectable. Panels D-F: Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. Error bars = S.D. ** p<0.01. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 92 Timothy James Yuji Birkle – November 2023 5.2.5 Alternative uPA inhibitors do not prevent LPS-induced neuronal loss, and exogenous uPA does not cause significant neuronal loss BC11 blocks uPA both by inhibiting its proteolytic activity and by blocking its binding to uPAR. To try to distinguish which of these effects is important for neuroprotection, I tested neuroprotection with UK122 (an active site inhibitor of uPA) and IPR803 (an Figure 18. Alternative uPA inhibition fails to rescue neurons from LPS-induced loss, and exogenous uPA fails to cause neuronal loss A: Representative 10x images (cropped) of neuron-glia cocultures treated ±LPS (100ng/mL) and ±UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 3 days (DIV10) and stained with Hoechst 33342 (nuclei), NeuO (live neurons) and IB4-AF594 (microglia). Scale bars = 50µm. B: Average neuron counts per image in cultures treated ±LPS (100ng/mL) and ±UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. Error bars = S.D. C: Normalised average fluorescence (generated by cleavage of substrate by uPA) over time for live BV2 microglia treated with vehicle or UK122 at 10-50µM. N = 1. Error bars = S.D. of technical replicates. Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 93 inhibitor of the uPA-uPAR interaction that binds to uPAR). However, when applied to neuron-glia cocultures at the maximum concentrations that were not toxic to neurons, neither 50µM UK122 nor 10µM IPR803 affected neuronal numbers either in the presence or absence of LPS (Fig. 18A, B). I confirmed that, at these concentrations, UK122 significantly inhibited uPA activity in the media of BV2 microglia, measured by a substrate cleavage assay producing a fluorescent product (Fig. 18C). The failure of UK122 to protect, despite inhibiting uPA activity, suggests that inhibition of uPA’s catalytic activity alone is not sufficient to protect against LPS-induced neuronal loss. Moreover, the lack of any neuroprotective effect of IPR803 suggests that uPA’s interaction with uPAR is also not essential for neuronal loss. However, I did not confirm that IPR803 blocks uPAR at the concentrations used. If blocking uPA is neuroprotective, then adding uPA may be neurotoxic. To test this, I added 10µg/mL recombinant active uPA to the cultures ± LPS. uPA had no significant effect of neuronal numbers in the presence or absence of LPS, although there was a tendency to reduce neuronal numbers in the absence of LPS (Fig. 18A, B). Thus, uPA appears insufficient to induce neuronal loss on its own. Notably, this data was limited by particularly high variability between primary culture preparations, which may have prevented the identification of neuroprotective effects. Therefore, the stated lack of effects by the above treatments is only tentative. 5.2.6 Inhibition of the uPA-uPAR interaction may affect glia, unlike catalytic inhibition of uPA BC11 had various effects on microglia, including reducing their number, increasing the number of dead microglial objects, and preventing any LPS-induced morphology change. Though effects of UK122, IPR803 and exogenous uPA on neurons could not be demonstrated here, this does not rule out the possibility of effects on glia. Therefore, I tested glial numbers and microglial morphology in the cocultures for each of the new treatments. Reflecting the lack of effect on neurons, no treatment affected the number of microglia in coculture whether in the presence or absence of LPS (Fig. 19A). However, IPR803 treatment caused a small but significant increase in the number of IB4 objects lacking nuclear DNA, indicative of a small increase in microglial death (Fig. 19B). With such a limited increase, it is possible that microglial proliferation was sufficient to recover total microglial numbers. Microglial morphology was largely unchanged by any treatment except for IPR803, which slightly reduced the enlarged microglial area STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 94 Timothy James Yuji Birkle – November 2023 induced by LPS (Fig. 19C, D). Finally, none of the treatments affected astrocyte numbers except for IPR803 again, which reduced astrocyte numbers in the absence of LPS (Fig. 19E). In general, the effects of IPR803 are similar to some of Figure 19. Inhibition of the uPA-uPAR interaction may affect glia, unlike catalytic inhibition of uPA A: Average microglia counts per image in cultures treated ±LPS (100ng/mL) and ±UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. B: Average percentage of IB4-stained objects which lack nuclear DNA staining in images from LPS-untreated neuron-glia cocultures treated ±UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 3 days (DIV10). RM 1-way ANOVA with Dunnett’s post-hoc test. C: Average microglial area (px2) in cultures treated ±LPS (100ng/mL) and ±UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. D: Average microglial circularity in cultures treated ±LPS (100ng/mL) and ±BC11 (50µM) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. E: Average astrocyte counts per image in cultures treated ±LPS (100ng/mL) and ±UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 3 days (DIV10). RM 2-way ANOVA with Dunnett’s post-hoc test. All panels: Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. Error bars = S.D. Error bars = S.D. * p<0.05, ** p<0.01. Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 95 those observed for BC11 albeit considerably less potent, which is interesting given that these inhibitors may have a common effect of preventing uPA-uPAR interaction. 5.2.7 uPA inhibition does not cause microglial death in monoculture over 24 hours, but exogenous uPA may induce proliferation BC11 appeared to induce microglial death in neuronal-glial cultures, as indicated by reduced microglial numbers and an increase in microglia lacking nuclei (Fig. 16B, D), but it was unclear whether this was a direct effect on microglia. So, I specifically tested for the effect of uPA or uPA inhibitors on primary microglial monocultures with the aim of identifying direct toxicity of the treatments to microglia in the absence of other cell types. Treatments were applied to cultures for 24 hours and compared to vehicle- treated control cultures and positive control cultures treated with the cytotoxic stimulus staurosporine (STS; 2-hour treatment at 10µM; Fig. 20A). Staurosporine increased the percentage of necrotic cells (propidium iodide positive) and apoptotic cells (bright, condensed nuclei) as expected, but none of BC11, UK122, IPR803 or exogenous uPA treatments increased either of these cell death measures (Fig. 20B, C). The uPA inhibitors also did not reduce the total number of cells remaining after 24 hours (Fig. 20D). uPA inhibition is therefore not acutely toxic to microglia over this timeframe, even for BC11, which strongly reduced microglial numbers in neuron-glia cocultures. However, exogenous addition of uPA mildly increased the number of microglia (Fig. 20D) and altered morphology (Fig. 20A). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 96 Timothy James Yuji Birkle – November 2023 Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 97 5.2.8 Prolonged exposure to uPA/uPAR inhibitors in glial cultures may reduce microglial proliferation or survival Primary microglial monocultures do not last longer than 24 hours using the media used here, as astrocyte growth factors are necessary for microglial survival (Bohlen et al., 2017). Given the discrepancy between 3-day neuron-glia coculture experiments and the 1-day microglia monoculture results, I wanted to investigate the effects of uPA/uPAR inhibitors on microglial survival and proliferation in 3-day cultures without neurons. For this, I used primary glial cultures, containing microglia and astrocytes, and treated with uPA/uPAR inhibitors for 3 days. With this longer treatment, both BC11 and IPR803 significantly reduced microglial numbers (but not astrocyte numbers; Fig. 21A- C). Neither treatment significantly increased the proportion of necrotic or apoptotic cells, though there was a trend towards an increase in necrotic cells for IPR803 (Fig. 21D), and a trend towards an increase in apoptotic cells for BC11 (Fig. 21E). Overall, BC11 and IPR803 reduce microglial numbers, with a no apparent increase in microglial death. It should be noted that it remains possible for these inhibitors to be reducing numbers by inducing cell death, as debris in glial cultures is rapidly cleared through phagocytosis and may therefore be difficult to capture in an endpoint assay. Figure 20. uPA inhibition does not cause microglial death in monoculture over 24 hours, but exogenous uPA may induce proliferation A: Representative 10x images (cropped) of microglia monocultures treated with vehicle, BC11 (50µM), UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 24 hours or treated with 10µM staurosporine for 2 hours (STS). Cells were stained with Hoechst 33342 (nuclei), IB4- AF488 (microglia) and propidium iodide (PI; necrotic nuclei). Scale bars = 50µm. B: Average proportion of necrotic (PI-positive) cells out of total cells in cultures treated with vehicle, BC11 (50µM), UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 24 hours or treated with 10µM staurosporine for 2 hours (STS). C: Average proportion of cells with condensed, apoptotic nuclear appearance out of total cells in cultures treated with vehicle, BC11 (50µM), UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 24 hours or treated with 10µM staurosporine for 2 hours (STS). D: Average total cells per image in cultures treated with vehicle, BC11 (50µM), UK122 (50µM), IPR803 (10µM), or exogenous uPA (10µg/mL) for 24 hours or treated with 10µM staurosporine for 2 hours (STS). All panels: RM 1-way ANOVA with Dunnett’s post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. Error bars = S.D. * p<0.05, **** p<0.0001. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 98 Timothy James Yuji Birkle – November 2023 Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 99 Figure 21. Prolonged exposure to inhibitors achieves microglial toxicity, which may only arise from inhibition of uPA-uPAR interactions A: Representative 10x images (cropped) of glial cultures treated with vehicle, BC11 (50µM), UK122 (50µM) or IPR803 (10µM) for 72 hours or treated with 10µM staurosporine for 2 hours (STS). Cells were stained with Hoechst 33342 (nuclei), IB4-AF488 (microglia) and propidium iodide (PI; necrotic nuclei). Scale bars = 50µm. B: Average microglia counts per image in cultures treated with vehicle, BC11 (50µM), UK122 (50µM) or IPR803 (10µM) for 72 hours or treated with 10µM staurosporine for 2 hours (STS). C: Average astrocyte counts per image in cultures treated with vehicle, BC11 (50µM), UK122 (50µM) or IPR803 (10µM) for 72 hours or treated with 10µM staurosporine for 2 hours (STS). D: Average proportion of necrotic (PI-positive) cells out of total cells in cultures treated with vehicle, BC11 (50µM), UK122 (50µM) or IPR803 (10µM) for 72 hours or treated with 10µM staurosporine for 2 hours (STS). E: Average proportion of cells with condensed, apoptotic nuclear appearance out of total cells in cultures treated with vehicle, BC11 (50µM), UK122 (50µM) or IPR803 (10µM) for 72 hours or treated with 10µM staurosporine for 2 hours (STS). All panels: RM 1-way ANOVA with Dunnett’s post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = 3. Error bars = S.D. * p<0.05, ** p<0.01, *** p<0.001. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 100 Timothy James Yuji Birkle – November 2023 5.2.9 Exogenous uPA stimulates microglial proliferation in glial cultures To follow up on the potential proliferative effect of exogenous uPA observed in microglia monoculture over 24 hours (Fig. 20D), I also used the 3-day mixed glial cultures to test this finding over a longer timeframe. 1µg/mL and 10µg/mL uPA caused Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 101 a dose-dependent increase in microglial number, with the higher concentration nearly doubling microglial counts (Fig. 22A, B). uPA also changed microglial morphology substantially (Fig. 22A). Meanwhile, astrocyte numbers were largely unaltered in the cultures (Fig. 22C). Finally, exogenous uPA tended to reduce the proportion of necrotic and apoptotic cells, but this was not significant (Fig. 22D, E). Overall, uPA seems to cause microglial proliferation and a morphological change similar to LPS, though without inducing significant neuronal loss. 5.2.10 Only BC11 affects microglial phagocytosis As a final comparison of the microglial effects of different uPA inhibitors, I tested whether any of the inhibitors affected microglial phagocytosis. This was motivated by the previous screening data identifying an apparent role of uPA in phagocytosis (Pluvinage et al., 2019), which in turn led to my interest in this protein. Microglial monocultures were treated with inhibitors in the presence and absence of LPS for 24 hours, before addition of 5µm diameter carboxylated fluorescent beads for a further 2 hours. Cells were then analysed by flow cytometry for their uptake of the beads, after gating for live cells using a live-dead stain (Fig. 23). This uptake was near-completely abolished by treatment with cytochalasin D (an inhibitor of actin polymerisation and phagocytosis), confirming that it can be attributed to phagocytosis. BC11 significantly reduced bead uptake in both the presence and absence of LPS, while UK122 and IPR803 treatments had no effect. Interestingly, this is reminiscent of how only BC11 was effective at preventing LPS-induced neuronal loss. Figure 22. Exogenous uPA may stimulate microglial proliferation A: Representative 10x images (cropped) of glial cultures treated with vehicle or 1-10µg/mL exogenous uPA for 72 hours. Cells were stained with Hoechst 33342 (nuclei), IB4-AF488 (microglia) and propidium iodide (PI; necrotic nuclei). Scale bars = 50µm. B: Average microglia counts per well in cultures treated with vehicle or 1-10µg/mL exogenous uPA for 72 hours. C: Average astrocyte counts per well in cultures treated with vehicle or 1-10µg/mL exogenous uPA for 72 hours. D: Average proportion of necrotic (PI-positive) cells out of total cells in cultures treated with vehicle or 1-10µg/mL exogenous uPA for 72 hours. E: Average proportion of cells with condensed, apoptotic nuclear appearance out of total cells in cultures treated with vehicle or 1- 10µg/mL exogenous uPA for 72 hours. All panels: RM 1-way ANOVA with Dunnett’s post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = 3. Error bars = S.D. ** p<0.01. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 102 Timothy James Yuji Birkle – November 2023 5.3 Discussion This study aimed to identify contributions of uPA towards the neurodegenerative, inflammatory, or survival functions of microglia. This was motivated by the potential role for uPA in microglial phagocytosis identified in a recent screen (Pluvinage et al., 2019) and the literature linking uPA biology to innate immune inflammation. The initial hypothesis was that inhibition of uPA might limit microglia-mediated neuronal loss, potentially by decreasing microglial phagocytosis. First, I observed a neuroprotective effect against LPS-induced neuronal loss using the inhibitor BC11 in neuron-glia cocultures. This inhibitor has been shown to inhibit both proteolysis and uPAR binding by uPA at the concentration used (Álvarez et al., 2018; Longo et al., 2015; Magnussen et al., 2014, 2017). Depleting these cultures of microglia with LME prevented LPS-induced neuronal loss, and BC11 similarly depleted the cultures of microglia and prevented neuronal loss. Thus, it is possible that BC11 prevented LPS-induced neuronal loss by depleting microglia in the cultures. BC11 also prevented LPS-induced microglial proliferation and morphology change but did not affect LPS-induced TNFα release. To differentiate the role of uPA-mediated proteolysis versus uPAR binding (and downstream signal transduction), UK122 and IPR803 were used to inhibit each factor respectively, but neither were able to recapitulate the clear neuroprotective effect of BC11. However, IPR803 and BC11 reduced microglial numbers in glial cultures and may induce some microglial death. Exogenous active uPA stimulated microglial proliferation in microglia monoculture and microglia-astrocyte mixed culture, but not in neuron-glia cocultures. Finally, only BC11 reduced microglial Figure 23. Only BC11 affects microglial phagocytosis Average percentage of non-necrotic primary microglia in monoculture that took up fluorescent 5µm beads over 2 hours as measured by flow cytometry, after 24-hour treatment ±LPS (100ng/mL) and with vehicle, BC11 (50µM), UK122 (50µM) or IPR803 (10µM). RM mixed- effects analysis with Dunnett’s post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. Error bars = S.D. ** p<0.01, **** p<0.0001. Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 103 phagocytosis in a direct phagocytic assay, which mirrored how only BC11 was neuroprotective in cocultures. The lack of any effect of UK122, despite having confirmed catalytic inhibition at the concentration used, suggests that uPA proteolysis is not sufficient for the LPS- induced neuronal loss or microglial functions tested here. This is not wholly surprising, as most of the potential inflammatory functions of uPA are linked to protein-protein interactions with and through uPAR rather than proteolysis itself (Mehra et al., 2016). Moreover, the canonical proteolytic target of uPA, plasminogen, is not expected to be present at high levels in vitro, though there may be some expression in certain brain tissues including the cerebellum (Basham and Seeds, 2001; Sappino et al., 1993; Tsirka et al., 1997). It should be noted that inhibition of uPA activity by UK122 was not complete in the uPA activity assay in the media above BV2 microglia, raising the possibility that residual uPA catalysis may have been sufficient to maintain microglial functions. However, given that 10µM UK122 strongly inhibited activity, 50µM did not further inhibit, and the IC50 of UK122 is 0.2µM, it is likely that uPA inhibition was maximal and that this residual cleavage activity was instead an artefact of non-specific cleavage of the substrate by other proteases secreted from the BV2 cells. Notably, I also observed that sample preparation using trypsin was not compatible with the assay due to rapid cleavage of the substrate by the trace amounts of trypsin remaining afterwards (data not shown), showing that non-specific cleavage of the substrate by proteases other than uPA can be a confounding factor. The effects of BC11, IPR803 and exogenous uPA hint at potentially interesting non-proteolytic functions of uPA for microglia. BC11 binds to the N-terminal GFD of uPA that mediates uPAR binding (Longo et al., 2015), while IPR803 acts similarly but by instead binding to uPAR to block the interaction with uPA (Khanna et al., 2011). Both inhibitors similarly inhibit proliferation and survival of MDA-MB-231 breast cancer cells, supporting a similar mechanism of action. My data converge on a role for the uPA-uPAR interaction in microglial proliferation, as both inhibitors reduced microglial numbers in neuron-glia coculture and/or glial cultures, while exogenous uPA strongly stimulated microglial proliferation in glial cultures. Meanwhile, despite leading to increased levels of IB4-positive debris in neuron-glia cocultures that may be indicative of microglial death and reducing microglia numbers as mentioned, neither BC11 nor IPR803 significantly increased the number of visible dead cells in glial cultures. Therefore, it cannot be concluded that these inhibitors are inducing microglial STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 104 Timothy James Yuji Birkle – November 2023 death; however, it should be noted that in glial cultures the dense population of highly phagocytic cells can rapidly clear cellular debris, rendering it difficult to capture increased cell death by endpoint imaging. This work also provides evidence that BC11 modulates microglial morphology and phagocytosis. These effects were not a result of preventing microglial activation by LPS in a general manner, as BC11 did not affect LPS-induced TNFα release. Instead, inhibition of the uPA-uPAR interaction or other uPA interactions may more specifically alter microglial morphology and phagocytosis. In turn, these phenotypes may affect microglia-mediated inflammatory neurodegeneration, as is supported by the data on BC11. However, as neither IPR803 nor exogenous uPA impacted this process, for now this remains tentative, and it would be particularly important to rule out off-target effects of BC11 (see below) to verify that BC11 acts via uPA. Nonetheless, it is interesting that both phagocytosis and neuronal loss were reduced by BC11 alone, which supports that this neuronal loss is dependent on phagocytosis. The only existing literature on microglial proliferation and uPA is a study showing that proliferation of BV2 cell line microglia is not altered by addition of the endogenous uPA inhibitor PAI-1 (Jeon et al., 2012). However, extrapolation of data from immortalised cell lines can be problematic, particularly when studying proliferation and survival. Here, I have used primary microglia that may be more physiologically relevant. Beyond microglia, there are extensive records of proliferative effects of the uPA-uPAR system arising from the relevance to cancer biology (Alfano et al., 2005). In breast cancer, knockdown of uPA or inhibition of uPA with BC11 reduces proliferation and may activate an S-phase cell cycle checkpoint (Arens et al., 2005; Longo et al., 2015), and an anti-uPAR antibody blocking uPA binding decreases breast cancer cell proliferation in vitro and in vivo (Mahmood et al., 2020). Proteolysis- independent, uPAR-dependent mitogenicity of uPA has also been observed for melanoma, ovarian cancer and osteosarcoma cells (Fischer et al., 1998; Kirchheimer et al., 1989; Rabbani et al., 1992), and uPA knockout mice are resistant to melanoma (Shapiro et al., 1996). A potential mechanism for this has been examined in human epidermoid carcinoma cells, for which uPAR deficiency pushes them into dormancy (Yu et al., 1997); uPAR can activate α5β1 integrin that then activates focal adhesion kinase (FAK), SRC and, ultimately, ERK downstream, which promotes proliferation (Aguirre-Ghiso, 2002; Aguirre-Ghiso et al., 2001). Proliferation of these cells also depends on EGFR independent of EGF (Liu et al., 2002), and crosstalk between this Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 105 receptor and uPAR promotes proliferation in other cells, too (Hu et al., 2011; Jo et al., 2005). Overall, the possible induction of proliferation by uPA in my data is consistent with data from the wider literature, though most of these previous studies have focused on cancer cells that cannot be directly compared to microglia. Interestingly, some studies have found a proliferative role for uPA that is both proteolysis- and uPAR-independent, suggesting alternative transduction pathways for uPA besides uPAR (Kanse et al., 1997; Kim et al., 2003). One such study identified an alternative, low-affinity cell-surface binding site for uPA on melanoma cells, but the precise identity of this binding partner remains unclear (Koopman et al., 1998). Computational and structural studies since have suggested the possibility of a direct, uPAR-independent interaction of uPA with integrins including αVβ3 (Degryse et al., 2008; Tarui et al., 2006). These additional interactions may be relevant to the interpretation of the data presented here, where IPR803 affected microglia more weakly than BC11. As BC11 may interfere with uPA binding to unknown binding partners while IPR803 specifically inhibits the uPA-uPAR interaction, one possible explanation is that some relevant uPA interactions are maintained in the presence of IPR803 (which binds uPAR) but not in the presence of BC11 (which binds uPA). Though this work could not conclude whether uPA increases microglial cell death or not, there is consistent literature on the pro-survival functions of uPA and uPAR signalling. This is unsurprising given that proliferation and cell death tend to be opposite ends of the same functional spectrum. A variety of studies show induction of apoptosis upon uPA or uPAR knockdown, including loss of mitochondrial membrane potential, cytochrome C release and caspase activation (Arens et al., 2005; Besch et al., 2007; Gondi et al., 2007; Krishnamoorthy et al., 2001; Matheis et al., 2016; Yanamandra et al., 2000). Directly supporting the importance of the uPA-uPAR interaction, antibody blocking of this binding also results in increased apoptosis (Ma et al., 2001). Interestingly, the uPA-uPAR interaction was observed to be in a positive feedback loop with ERK activation, with ERK activity promoting uPA and uPAR expression, and intervention anywhere in the loop promoting apoptosis. There is considerable literature on the anti-apoptotic function of ERK (Hildenbrand et al., 2007), and this likely intersects with its role in promoting proliferation (see above), thereby connecting the proliferative and survival effects of uPA at the molecular level. However, in one study, uPA knockdown-induced apoptosis was independent of ERK (and FAK) activity (Besch et al., 2007), and increased apoptosis might partially be a STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 106 Timothy James Yuji Birkle – November 2023 result of a separate increase in sensitivity to TRAIL-induced apoptosis (Krishnamoorthy et al., 2001). Finally, isolated sections of both the kringle and connecting peptide domains of uPA are capable of inducing cell death, including in mice (Guo et al., 2000; Kim et al., 2007), and necrotic death induced by the anticancer molecule UCD38B was found to be partially caused by excessive endocytosis and mis-trafficking of uPA system components (Pasupuleti et al., 2015). Therefore, the data here suggesting an increase in microglial cell death with inhibition of the uPA-uPAR interaction would be concordant with the broader literature on uPA and cell death, but again the results here were not definitive. The possibility of an effect of uPA proteolysis or uPAR interaction in microglial phagocytosis would also coincide with most literature on the topic, though again almost all of this comes from other cell types. Phagocytosis has been shown to be positively influenced by uPA and uPAR across a variety of cell types including peripheral innate immune cells (D’mello et al., 2009; Genua et al., 2015; Gyetko et al., 2004; Kawao et al., 2012; Wiersinga et al., 2010; Yang et al., 2014). However, one study observed no change in latex bead phagocytosis by the bone marrow-derived macrophages of uPA-deficient mice (Bryer et al., 2008), and in another study exogenous uPA inhibited efferocytosis of apoptotic neutrophils, contrary to the other data (Yang et al., 2010). Nonetheless, in general the uPA system promotes phagocytosis and one study has in fact reported this in microglia, as the endogenous uPA inhibitor PAI-1 was found to inhibit microglial phagocytosis (Jeon et al., 2012). My data would support this conclusion, as BC11-mediated uPA inhibition reduced microglial phagocytosis as well. Lastly, a role for uPA in microglial morphology would not be surprising given the clear links between uPA and integrins, extracellular matrix proteins, and general cell adhesion and migration. Based on the data here, uPA may affect morphology not by proteolysis and restructuring of extracellular proteins, but instead through uPAR- dependent signalling such as the known interactions with vitronectin and integrins (Aguirre-Ghiso, 2002; Aguirre-Ghiso et al., 2001; Alfano et al., 2022; Keasey et al., 2018). In HEK293 cells, uPAR-dependent cytoskeletal rearrangement and morphology change can be observed, and the interaction between uPAR and vitronectin is necessary and sufficient for this process (Hillig et al., 2008; Madsen et al., 2007). Interestingly, this interaction is strongly enhanced by concomitant uPA binding (Gårdsvoll and Ploug, 2007; Sidenius et al., 2002; Sidenius and Blasi, 2000; Wei et al., 1994), and in my data Chapter 5: The role of urokinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 107 BC11 or IPR803 may be preventing this and thereby causing morphological changes. Similarly, I observed starkly altered microglial morphology with the addition of exogenous urokinase, which might instead enhance uPAR-vitronectin binding. A major caveat to my data is the risk of influences from off-target effects given the high concentrations of treatments used. Both BC11 and IPR803 were used at high concentrations, and concentrations beyond these were broadly cytotoxic. Moreover, in this work I have not confirmed the on-target activity of either compound in a direct assay, instead relying on the known effective concentrations in the literature. It would benefit this study to have assessed uPA-uPAR binding in the model systems used here, in the presence and absence of each inhibitor. For example, the extent of binding could be assessed semi-quantitatively using Western blot and coimmunoprecipitation, or quantitatively using surface plasmon resonance. Doing so would both strengthen the results and help confirm the possible discrepancy between BC11 and IPR803 treatment, as the results could be explained by IPR803 inhibiting the uPA-uPAR interaction less effectively than BC11 at the concentrations used. The concentration of exogenous uPA (1-10µg/mL) used was also considerably higher than might be expected in the brain parenchyma; uPA levels in human serum may be around 1000-fold lower than this, and measurements of brain uPA levels could not be found in the literature (Tsai et al., 2019). However, tight localisation by uPAR could in theory result in high local concentrations similar to those used here. This study also lacks confirmation of the effect of the uPA-uPAR interaction on microglia using catalytically-inactive uPA or the ATF (containing only the GFD and KD domains mediating uPAR interaction) as other studies have used. Along these lines, glial culture experiments on proliferation induced by uPA would benefit from addition of heat-killed uPA and/or cotreatment with a catalytic inhibitor such as UK122, as well as cotreatment with a uPA-uPAR interaction inhibitor to confirm the specific role of uPA-uPAR binding. It would also be interesting to compare knockdown/knockout of uPA, uPAR, or functionally-related proteins to confirm which protein-protein interactions are relevant to microglial functions. This work was limited by the difficulty of such approaches in primary cultures and cocultures, though similar genetic manipulation has previously been achieved (Carrillo-Jimenez et al., 2018). Overall, my work adds to the literature by suggesting that some of the known functions of uPA might be relevant to microglial biology. These functions include promoting proliferation, morphology change and phagocytosis, and are well-established STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 108 Timothy James Yuji Birkle – November 2023 to be influenced by uPA within oncology. uPA and uPAR expression increases in a wide range of neuropathologies, and this data suggests that this may be relevant to the microglial proliferation and inflammation that accompanies almost all of these diseases. It would be interesting to build on this work by first confirming any proliferative and pro-survival effect of proteolysis-independent uPA functions with appropriate assays. Next, mechanisms could be explored starting from the existing literature in the cancer field. For example, the involvement of integrins and intracellular ERK signalling could be tested, as well as any functional interactions between uPA/uPAR and known microglial proteins affecting proliferation and survival such as CSF1R. It would be especially interesting to test the role of uPA in microglial adhesion, as this could underlie all of the putative microglial functions suggested in this work (proliferation, morphology change, and phagocytosis) (Jones et al., 2019; Moreno-Layseca and Streuli, 2014) and is a known function in other cell types (De Lorenzi et al., 2016; Ferraris et al., 2014; Hillig et al., 2008; Madsen et al., 2007). If uPA/uPAR are involved in inflammatory neurodegeneration, it may be worth investigating testing whether uPA/uPAR inhibitors are neuroprotective in vivo and can be translated as treatments for relevant brain pathologies. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 109 6 THE ROLE OF SPLEEN TYROSINE KINASE IN MICROGLIA-MEDIATED NEURODEGENERATION All data presented in this chapter is my own work, and much of this has been published in: Birkle, T. J. Y., & Brown, G. C. (2023). Syk inhibitors protect against microglia- mediated neuronal loss in culture. Frontiers in aging neuroscience, 15, 1120952. https://doi.org/10.3389/fnagi.2023.1120952. 6.1 Introduction Spleen tyrosine kinase (SYK), a cytosolic tyrosine kinase first cloned over 30 years ago (Taniguchi et al., 1991), is expressed by microglia (Ennerfelt et al., 2022; Satoh et al., 2012) and mediates inflammatory and/or phagocytic responses induced by cell surface receptors (Mócsai et al., 2010). SYK is activated by binding to phosphorylated ITAM (immunoreceptor tyrosine-based activation motif) domains of ligated cell surface receptors (Dectin-1, FcγRIIA) or adaptors (DAP12, FcR γ chain) of cell surface receptors (Fcγ receptors; TREM2; complement receptor 3, CR3; colony stimulating factor 1 receptor, CSF1R) (Mócsai et al., 2010; Walbaum et al., 2021). Some of these receptors are particularly relevant to disease: TREM2 is a phagocytic receptor mediating microglial phagocytosis of amyloid plaques (Yuan et al., 2016), synapses STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 110 Timothy James Yuji Birkle – November 2023 (Filipello et al., 2018), and neurons (Linnartz-Gerlach et al., 2019; Popescu et al., 2022), and TREM2 variants can confer risk for AD and other neurodegenerative diseases (Zhou et al., 2019). Similarly, CR3 mediates microglial phagocytosis of synapses during both development and neurodegeneration (Hong et al., 2016; Schafer et al., 2012; Stevens et al., 2007; Vasek et al., 2016). In contrast, CSF1R maintains microglial proliferation, and variants causing developmental CNS pathology (Cheng et al., 2021; Gerber et al., 2018; Hume et al., 2020; Mancuso et al., 2019; Sosna et al., 2018). Activated SYK then signals via PI3K/AKT, JNK, and phospholipase-C γ2 (PLCG2, variants of which associate with AD risk) to promote cell survival, proliferation and phagocytosis (Arndt et al., 2004; Ennerfelt et al., 2022; Sims et al., 2017; Tsai et al., 2020). Overall, SYK may be a hub of disease-relevant signalling in microglia. SYK has been shown to coordinate detrimental microglial activity in mouse models of tauopathy, brain trauma, stroke, and inflammation (He et al., 2022; M. W. Kim et al., 2022; Schweig et al., 2019; Ye et al., 2020). However, targeted deletion of the Syk gene in microglia exacerbated pathology in an amyloid model of AD and in a demyelinating model of multiple sclerosis, apparently by reducing microglial phagocytosis of amyloid and myelin debris (Ennerfelt et al., 2022). Syk expression is upregulated in amyloid models of AD (Sierksma et al., 2020), and SYK partly mediates induction of the disease-associated microglia (DAM) expression profile in such models (Ennerfelt et al., 2022). SYK gene variants are associated with AD risk, though not at genome-wide statistical significance (Sierksma et al., 2020). Thus, SYK is implicated in a variety of brain pathologies, but its mechanism of action and its overall beneficial/detrimental role in different brain pathologies is unclear. Brain inflammation is associated with most brain pathologies, and there is evidence that chronic brain inflammation can be detrimental (DiSabato et al., 2016). Microglia are key mediators of this brain inflammation, and microglia activated by inflammation can damage neurons (Kwon and Koh, 2020). Inflammation greatly increases microglial phagocytosis, and one mechanism by which activated microglia are damaging is via excessive microglial phagocytosis of live synapses and neurons (Fricker et al., 2012b; Golia et al., 2019; Hong et al., 2016). Thus, it is important to find treatments that block these processes. SYK mediates phagocytosis induced by a variety of microglial receptors, and SYK inhibitors block such phagocytosis (Crowley et al., 1997; Gevrey et al., 2005; McQuade et al., 2020; Murakami et al., 2014; Scheib et al., 2012; Song et al., 2004; Walbaum et al., 2021; H. Yao et al., 2019). Thus, it is of Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 111 interest to know whether SYK inhibitors are neuroprotective via inhibition of microglial phagocytosis. There are safe and effective drugs to inhibit SYK in humans and mice, so if SYK inhibitors were protective, they could potentially be used as treatments. Recently, it was reported that LPS-induced neuronal loss in mouse brain can be prevented by a SYK inhibitor, and this was attributed to blockade of the inflammatory activation of microglia (M. W. Kim et al., 2022). However, the role of phagocytosis was not tested as this is challenging to do in vivo. Here, I investigated whether LPS-induced neuronal loss in culture could be prevented by SYK inhibitors, and if so by what mechanism. I tested this in primary neuron-glia cocultures in which LPS causes neuronal loss mediated by microglial inflammation and phagocytosis (Fricker et al., 2012b). I observed complete protection of neurons from neurodegeneration by two structurally unrelated SYK inhibitors, BAY61-3606 (at 1µM) and P505-15 (at 10µM), and present data on microglial effects of these compounds including survival, inflammation, phagocytosis, and metabolism to suggest neuroprotective mechanisms. 6.2 Results 6.2.1 BAY61 and P505 inhibit SYK activation in microglia I previously observed a protective effect of the SYK inhibitor BAY61-3606 (hereafter, BAY61) (Yamamoto et al., 2003) against LPS-induced neuronal loss in primary neuron- glia cocultures at 1µM (Figure 12). To validate this result, here I completed additional Figure 24. BAY61 and P505 inhibit SYK activity in mouse microglia A: Representative Western blot of lysates from BV2 mouse microglia pre-treated ±BAY61 (1µM) or ±P505 (10µM) for 1 hour, prior to 10 minutes of stimulation ±Concanavalin A (ConA; 50µg/mL). Red channel (700) = β-actin, green channel (800) = phospho-SYK (Tyr525/526). Actin and pSYK bands are as expected at 42kDa and 72kDa respectively. B: pSYK signal normalised against β-actin signal from Western blots as in (A). RM mixed-effects analysis with Šídák’s post- hoc test. N.D. = not detectable. N = 4. *** p<0.001, **** p<0.0001. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 112 Timothy James Yuji Birkle – November 2023 biological repeats, reanalysed the data using the CellProfiler-based automated analysis, and also tested the alternative SYK inhibitor, P505-15 (hereafter, P505), at 10µM (Coffey et al., 2012). To first confirm that SYK was indeed inhibited by BAY61 and P505 at these respective concentrations, I measured SYK autophosphorylation on its Tyr519/520 residues (equivalent to human SYK Tyr525/526 residues) using Western blot after receptor stimulation of BV2 microglia with Concanavalin A. This confirmed that both inhibitors reduced SYK autophosphorylation at the concentrations used (Fig. 24A, B). Therefore, these inhibitors were suitable for further study of SYK. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 113 6.2.2 SYK inhibition protects against LPS-induced neuronal loss With further testing of whether BAY61-mediated SYK inhibition could prevent LPS- induced neuronal loss, I confirmed the previous result that this compound is indeed neuroprotective at the 1µM concentration used here (Fig. 25A-C). Interestingly, BAY61 also increased neuronal density in the absence of LPS (Fig. 25B). To limit the possibility that this protection by BAY61 was due to off-target effects I also tested P505, an alternative and more specific SYK inhibitor. P505 also completely prevented neuronal loss, albeit at a higher concentration of 10µM, with some protection against spontaneous loss (Fig. 25D, E). Both compounds protected against LPS-induced loss independent of any effects on spontaneous neuronal loss (Fig. 25C, E). 6.2.3 Validation and dose-response of SYK inhibitor-mediated neuroprotection Though the analysis approach used for these neuron-glia coculture experiments was validated previously (Chapter 4), it was important to confirm that the strong effects of the SYK inhibitors were not a result of either misclassification of neurons as other cell types, or misclassification of other cell types as neurons. This may theoretically occur when treatments substantially change the appearance of different cell types, though the training of the classifier was designed to mitigate against any such effect. Here, the same effects as before were observed when quantifying total cell count, confirming that the changes in neuronal number are genuine rather than reflecting changes in neuronal differentiation or NeuO staining efficacy because of LPS and/or SYK inhibitor Figure 25. SYK inhibition protects against LPS-induced neuronal loss A: Representative 10x images (cropped) of primary neuron-glia cocultures treated ±BAY61 (1µM) ±LPS (100ng/mL) for 3 days and stained with Hoechst 33342 (nuclei), IB4-AF594 (microglia), and NeuO (live neurons). Scale bars = 100µm. B: Average neuronal counts per image in cultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10). RM 2-way ANOVA with Šídák's post- hoc test. C: LPS-induced neuronal loss ±BAY61 calculated from the neuronal counts of (B) as percentage of neuronal counts in absence of LPS. Paired t-test. D: Average neuronal counts per image in cultures treated ±P505 (10µM) and ±LPS (100ng/mL) for 3 days (DIV10). RM 2-way ANOVA with Šídák's post-hoc test. E: LPS-induced neuronal loss ±P505 calculated from the neuronal counts of (D) as percentage of neuronal counts in absence of LPS. Paired t-test. All panels: Each datapoint represents the mean of 3 technical replicates, N= number of datapoints. Error bars = S.D. * p<0.05, ** p<0.01, *** p<0.001. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 114 Timothy James Yuji Birkle – November 2023 treatments (Fig. 26A, B). I also tested both inhibitors at 10-fold lower concentrations and observed no protection by BAY61 but still some protection by P505, which indicates a dose-dependent protective response by SYK inhibition (Fig. 26C, D). For BAY61, intermediate concentrations between 0.1µM and 1µM may be necessary to see dose-dependency. Figure 26. Validation and dose-response of SYK inhibitor-mediated neuroprotection A: Average total cell counts per image in cultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10). B: Average total cell counts per image in cultures treated ±P505 (10µM) and ±LPS (100ng/mL) for 3 days (DIV10). C: Average neuron counts per image in cultures treated ±LPS (100ng/mL) and with vehicle, 0.1µM or 1µM BAY61 for 3 days (DIV10). D: Average neuron counts per image in cultures treated ±LPS (100ng/mL) and with vehicle, 1µM or 10µM P505 for 3 days (DIV10). All panels: RM 2-way ANOVA with Šídák's post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. Error bars = S.D. * p<0.05, ** p<0.01, *** p<0.001 **** p<0.0001. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 115 6.2.4 SYK inhibition reduces spontaneous neuronal loss The data from LPS-untreated conditions suggested that there is some spontaneous neuronal loss in these cultures over the three-day treatment period, as both BAY61 and P505 were able to significantly increase neuron numbers in the absence of LPS in addition to in the presence of LPS (Fig. 25B, D). To test for spontaneous neuronal loss, live neurons were counted in neuron-glia cocultures 7, 9/10, and 12 days after isolation. This confirmed that there was a spontaneous loss of neurons, which was more marked in the older cultures (Fig. 27A-D). Treatment of these older cultures with BAY61 from day 9 to day 12 after isolation reduced the spontaneous neuronal loss (Fig 27E). Thus, BAY61 protects against both spontaneous and LPS-induced neuronal loss. Figure 27. SYK inhibition reduces spontaneous neuronal loss A-C: Representative 10x images (cropped) of neuron-glia cocultures stained with NeuO (live neurons) and imaged at DIV7, DIV10, and DIV12. Scale bars = 100µm. D: Average DIV7, DIV9-10, and DIV12 neuronal counts per image in untreated neuron-glia cocultures. RM 1-way ANOVA with post-hoc test for linear trend. E: Average DIV12 neuronal counts per image in neuron-glia cocultures treated ±BAY61 (1µM) for 3 days. Paired t-test. All panels: Each datapoint represents the mean of 3 technical replicates, N = number of datapoints per column. ** p<0.01, **** p<0.0001. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 116 Timothy James Yuji Birkle – November 2023 6.2.5 Microglia express Syk, which may be relevant to the neuronal loss To test which cells in the neuron-glia cocultures expressed Syk, I stained the cultures using immunocytochemistry. SYK was present in both microglia and neurons in these cultures, with stronger expression in microglia (Fig. 28). I previously found that LPS- induced neuronal loss is dependent on the presence of microglia (Fig. 15), which agrees with previous observations on the importance of microglia for neuronal loss induced by LPS, amyloid-β, and other stimuli (Birkle and Brown, 2023; Fricker et al., 2012a, 2012b; Neher et al., 2011; Pampuscenko et al., 2020). Therefore, LPS-induced neuronal loss depends on both SYK and microglia, and microglia have the highest levels of SYK in these cultures. This suggests that SYK inhibition could be neuroprotective through effects on microglia. Figure 28. Microglia express Syk, which may be relevant to the neuronal loss Representative 20x immunofluorescence images of neuron-glia cocultures fixed and stained with α- SYK (red) and α-Iba1 (green) antibodies, and Hoechst 33342 (nuclei; blue). All samples were incubated with both secondary antibodies, but secondary only and single-colour controls lacked incubation with the indicated primary antibody/antibodies. Scale bars = 100µm. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 117 6.2.6 SYK inhibitors partially deplete microglia in neuron-glia cocultures To investigate whether the effect of SYK inhibition on microglia, I first analysed microglial density in the original experiment of Fig. 25. In the absence of LPS, both BAY61 and P505 significantly reduced microglial numbers in the cocultures (Fig. 29A- D). However, P505 failed to deplete microglia from LPS-treated neuron-glia cocultures, and for BAY61 the depletion with LPS present was modest, though significant (Fig. 29A-D). Further examination of the cocultures revealed IB4-stained objects lacking nuclei (Fig. 29B, inset), similar to previous observations while studying urokinase (Chapter 5, Fig. 16). In neuron-glia cocultures not treated with LPS, the prevalence of these IB4-positive objects lacking nuclei more than doubled after either BAY61 or P505 treatment, suggesting that these treatments induce microglial death (Fig. 29A, B, E, F). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 118 Timothy James Yuji Birkle – November 2023 6.2.7 SYK inhibition may slightly reduce astrocyte numbers in coculture Astrocytes are the third major cell type in these cocultures alongside neurons and microglia, and it was therefore of interest to check for any effect of SYK inhibition on these cells. According to α-SYK immunocytochemistry, astrocytes have little SYK protein (Fig. 28). Nonetheless, BAY61 did cause a small but significant decrease in Figure 30. SYK inhibition may slightly reduce astrocyte numbers in coculture A: Average DIV10 astrocyte counts per image in neuron-glia cocultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days. RM 2-way ANOVA with Šídák's post-hoc test. B: Average DIV10 astrocyte counts per image in neuron-glia cocultures treated ±P505 (10µM) and ±LPS (100ng/mL) for 3 days. RM 2-way ANOVA with Šídák's post-hoc test. All panels: Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. * p<0.05. Figure 29. Syk inhibitors partially deplete microglia in neuron-glia cocultures A, B: Representative 10x images (cropped) of LPS-untreated neuron-glia cocultures treated ±BAY61 (1µM) for 3 days and stained with Hoechst 33342 (nuclei) and IB4-AF594 (microglia). Scale bar = 100µm. Arrows indicate IB4-stained objects lacking any nuclear staining. B, inset: Magnified view of an IB4-stained object lacking nuclear staining (arrow) adjacent to intact microglia (indicated by arrowheads). Scale bar = 10µm. C: Average microglial counts per image in neuron-glia cocultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10). RM 2-way ANOVA with Šídák's post-hoc test. D: Average microglial counts per image in neuron-glia cocultures treated ±P505 (10µM) and ±LPS (100ng/mL) for 3 days (DIV10). RM mixed-effects analysis with Šídák's post-hoc test. E: Percentage of IB4-stained objects which lack nuclear DNA staining in images from LPS- untreated neuron-glia cocultures treated ±BAY61 (1µM) for 3 days (DIV10). Paired t-test. F: Percentage of IB4-stained objects which lack nuclear DNA staining in images from LPS-untreated neuron-glia cocultures treated ±P505 (10µM) for 3 days (DIV10). Paired t-test. All panels: Each datapoint represents the mean of 3 technical replicates, N = number of datapoints per column. * p<0.05, ** p<0.01, *** p<0.001. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 119 number in both the absence and presence of LPS, and P505 did so only in the presence of LPS (Fig. 30A, B). Given the negligible expression of SYK in astrocytes, this may be a secondary effect of other changes to the cultures, such as effects on microglia and microglia-secreted factors that then influence these cells. 6.2.8 SYK inhibitors induce apoptosis and necrosis in primary microglial monocultures To test whether SYK inhibition causes microglial cell death independent of other cell types, I next used cultures of isolated cortical microglia treated over 24 hours and imaged after staining with Hoechst 33342 (to stain all nuclei) and propidium iodide (PI, for nuclei of necrotic cells). 1µM BAY61 and 10µM P505 caused significant increases in the percentage of PI-positive cells, comparable to or greater than the increase in cultures treated for 2 hours with 10µM of the apoptosis-inducing agent staurosporine (STS; Fig. 31A-D). I also observed increases in the number of cells negative for PI staining, but with a condensed and apoptotic nuclear morphology (Fig. 31E), indicating that SYK inhibitors induce some microglial apoptosis. There was also a decrease in the total number of microglial cells (Fig. 31F). Overall, SYK inhibition clearly reduces microglial survival in the absence of LPS. However, microglial depletion could not explain the full neuroprotection by the SYK inhibitors, as in the presence of LPS microglia numbers were only modestly reduced by the treatments (Fig. 29C, D). Thus, I looked for other effects of the SYK inhibitors on microglia in neuron-glia cocultures that might explain the neuroprotection, focusing on microglial activation and the associated inflammatory functions that are already known to be important in this model. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 120 Timothy James Yuji Birkle – November 2023 Figure 31. SYK inhibitors induce apoptosis and necrosis in primary microglial monocultures A-C: Representative 10x images (cropped) of cortical microglia treated with ±BAY61 (1µM) or ±P505 (10µM) for 24 hours and stained with Hoechst 33342 (nuclei), IB4-AF488 (microglia), and propidium iodide (PI; necrotic nuclei). Scale bars = 100µm. D: Average percentage of PI-positive nuclei in images of primary rat cortical microglia treated with BAY61 (1µM) or P505 (10µM) for 24 hours, or staurosporine (10µM) for 2 hours (STS). E: Average percentage of nuclei that are condensed and apoptotic in images of cortical microglia treated with BAY61 (1µM) or P505 (10µM) for 24 hours, or staurosporine (10µM) for 2 hours (STS). F: Average total cells in images of primary rat cortical microglia treated with BAY61 (1µM) or P505 (10µM) for 24 hours. All panels: RM mixed-effects analysis with Dunnett’s post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 121 6.2.9 BAY61 does not change microglial morphology but may alter proinflammatory cytokine release To test the effect of SYK inhibition on general inflammatory phenotypes after LPS treatment of neuron-glia cocultures, I first quantified microglial morphology. Cultured microglia treated with LPS attach to the cell culture well, then flatten down and ramify across the surface (Fig. 32A, B), as previously described (Fan et al., 2018; Kloss et al., 2001; Lively and Schlichter, 2018; Wollmer et al., 2001). This morphological transition was quantified by outlining all microglia in an automated manner using the IB4 stain and setting thresholds to define: i) an increase in cell area, and ii) a decrease in cell circularity (due to extension of processes). LPS strongly increased microglial ramification by either measure, as expected, but microglial morphology was unchanged by SYK inhibition with BAY61 either with or without LPS (Fig. 32A-F). Thus, SYK inhibition has no effect on the morphological activation induced by LPS. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 122 Timothy James Yuji Birkle – November 2023 Cytokine release is another hallmark of inflammation, and I confirmed that LPS greatly increased release of the proinflammatory cytokines IL-6 and TNFα as expected (Fig. 32G, H). Cytokine levels in the absence of LPS were undetectable ± BAY61 treatment. However, in the presence of LPS, BAY61 significantly reduced IL-6 levels while significantly increasing TNFα release (Fig. 32G, H). Notably, BAY61 reduced microglial density in these cultures by about 30% (Fig. 29C), so the decrease in IL-6 (by about 45%) may partially reflect fewer microglia being present, rather than a reduction in IL-6 release per cell. The IL-6 level per microglia (at the end of the culture) was reduced by about 20% by BAY61, and the TNFα level per microglia was increased by about 150%. Thus, BAY61 mildly reduced LPS-induced IL-6 release per microglia and increased the release of proinflammatory TNFα per microglia. Nonetheless, the reduction in IL-6 levels is significant and could contribute to the neuroprotection given that IL-6 has been implicated in neuronal loss previously (Conroy et al., 2004). 6.2.10 SYK inhibition reduces microglial phagocytosis of synaptic material Previous work has shown that microglial phagocytosis of neurons is a primary cause of LPS-induced neuronal loss in these neuron-glia cocultures (Fricker et al., 2012b; Neher et al., 2011). I therefore tested whether SYK inhibition blocks LPS-induced microglial phagocytosis by quantifying microglial phagocytosis of beads over 2 hours. Both BAY61 and P505 reduced this phagocytosis in the presence and absence of LPS, as well as potentially reducing the LPS-induced increase in phagocytosis (Fig. 33A). Figure 32. BAY61 does not change microglial morphology but may alter proinflammatory cytokine release A-D: Representative 10x images (cropped) of neuron-glia cocultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10) and stained with Hoechst 33342 (nuclei) and IB4-AF594 (microglia). Scale bars = 50µm. E: Average percentage ramified microglia in images of neuron-glia cocultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10), as defined by cell area. F: Average percentage ramified microglia in images of neuron-glia cocultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10), as defined by cell circularity. G: Average concentration of IL-6 in supernatants from neuron-glia cocultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10). H: Average concentration of TNFα in supernatants from neuron- glia cocultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10). All panels: RM 2-way ANOVA with Šídák's post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. ** p<0.01, **** p<0.0001. N.D. = not detectable. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 123 Next, I tested microglial phagocytosis of a more physiological target: synaptosomes prepared from mouse brains, which are essentially isolated synapses (Dunkley et al., 2008). There was large variability in synaptosome uptake between experimental repeats, which may be due to variation in synaptosome preparation and staining that was performed independently for each experiment. Regardless, BAY61 Figure 33. SYK inhibition reduces microglial phagocytosis of synaptic material A: Average percentage of non-necrotic microglia that took up fluorescent 5µm beads over 2 hours, after 24-hour treatment ±BAY61 (1µM) or ±P505 (10µM), and ±LPS (100ng/mL), as measured by flow cytometry. RM mixed-effects analysis with Šídák's post-hoc test. Comparisons without brackets are against the appropriate vehicle control (±LPS). B: Average percentage of non-necrotic microglia that took up pHrodo-labelled synaptosomes over 2 hours, after 24-hour treatment ±BAY61 (1µM), as measured by flow cytometry. Paired t-test. C: Representative Western blot on lysates from neuron- glia cocultures treated ±BAY61 (1µM) and ±LPS (5-10ng/mL) for 3 days (DIV10). Red = NeuN, green = Homer1. D: Average neuronal counts per image in cultures ±LPS (5-10ng/mL) for 3 days (DIV10). RM 1-way ANOVA with Dunnett’s post-hoc test. E: Homer1 signal normalised against NeuN signal from Western Blots of lysates from neuron-glia cocultures treated ±BAY61 (1µM) and ±LPS (5-10ng/mL) for 3 days (DIV10). RM 2-way ANOVA with Šídák's post-hoc test. All panels: Datapoints represent the mean of 3 technical replicates. N = number of datapoints. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 124 Timothy James Yuji Birkle – November 2023 reduced synaptosome uptake by isolated microglia within each experiment, and this reduction was significant (Fig. 33B). Thus, SYK inhibitors reduce phagocytosis by isolated microglia of beads and isolated synapses. Given the reduction in synaptosome phagocytosis, I tested whether SYK inhibition could reduce loss of neuronal synapses in the neuronal-glial cultures, which can be induced by low doses of LPS (Dundee et al., 2023). This is reflective of microglia-mediated synaptic pruning during both development and disease (Hong et al., 2016; Wu et al., 2015). Synaptic density in the cultures was estimated 3 days after LPS treatment ± BAY61 by measuring Homer1 levels (a post-synaptic marker) by Western blot and normalising to NeuN levels (as a measure of neuronal density specifically, rather than total protein; Fig. 33C). Bands for Homer 1 and NeuN could be identified in the blots at the expected molecular weights. Normalisation to NeuN aimed to correct for any neuronal loss caused by the low dose LPS treatment, which I found to be limited but significant (Fig. 33D). Both 5ng/mL and 10ng/mL LPS induced significant loss of normalised Homer1 levels, and this was reduced by BAY61 such that the loss was no longer significant (Fig. 33E). 6.2.11 SYK inhibition may reduce microglial phagocytosis of other cells in coculture As SYK inhibition reduced microglial phagocytosis, and the LPS-induced neuronal loss in the neuronal-glial cultures is known to be mediated by microglial phagocytosis (Fricker et al., 2012a), it was possible that SYK inhibition prevented the LPS-induced neuronal loss by inhibiting microglial phagocytosis of stressed neurons. To test this, I reanalysed the images of LPS-induced neuronal loss ± BAY61 in the neuronal-glial cultures. Condensed nuclei within microglial objects were quantified as a proxy for neuronal soma phagocytosed by microglia (Fig. 34A); however, at this resolution it is not certain that all these events are due to phagocytosis. Most of these condensed nuclei should be stressed neurons based on previous data showing that the nuclei of live-but- stressed neurons condense (Hornik et al., 2016) and that the nuclei of phagocytosed neurons condense (Neher et al., 2014), as well as most cells in these coculture being neurons. With these caveats, I found that LPS treatment caused a significant increase in the number of condensed nuclei within microglia, and this was prevented by BAY61 treatment (Fig. 34B). Importantly, this effect did not coincide with changes in the total number of condensed nuclei in the images, which could have otherwise explained Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 125 Figure 34. SYK inhibition may reduce microglial phagocytosis of other cells in coculture A: Representative fields of view depicting condensed nuclei (Hoechst 33342) within microglial membrane staining (IB4-AF594) from DIV10 images of neuron-glia cocultures treated with 100ng/mL LPS for 3 days. Arrowheads indicate condensed nuclei within microglia. Scale bars = 10µm. B: Average counts of condensed nuclei within microglia per image in neuron-glia cocultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10). RM 2-way ANOVA with Šídák's post-hoc test. C: Average counts of total condensed nuclei per image in neuron-glia cocultures treated ±BAY61 (1µM) and ±LPS (100ng/mL) for 3 days (DIV10). All panels: Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. * p<0.05, ** p<0.01. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 126 Timothy James Yuji Birkle – November 2023 changes in the number of condensed nuclei within microglia (Fig. 34C). Overall, this suggests that SYK inhibition prevents LPS-induced phagocytosis of neurons, and this may contribute to SYK inhibitors protecting against LPS-induced neuronal loss. 6.2.12 SYK inhibitors prevent LPS-induced media acidification and reduce lactic acid production During experiments using LPS and SYK inhibitors in the cocultures, I noticed that 3- day LPS treatment caused media acidification and that this was visibly prevented by SYK inhibition according to colour changes in the phenol red-containing culture media. LPS-induced acidification reflects the glycolytic shift that astrocytes and microglia undergo upon inflammatory activation, which generates high levels of lactic acid (Ghosh et al., 2018), and this phenomenon is therefore informative as to the inflammatory state of the cultures. I therefore quantified this pH change using ratiometric absorbance measurements at relevant wavelengths for the Figure 35. SYK inhibitors prevent LPS-induced media acidification and reduce lactic acid production A: Average pH measurements of neuron-glia coculture conditioned media after treatment ±BAY61 (1µM) or ±P505 (10µM), and ±LPS (100ng/mL) for 3 days (DIV10). B: Average lactate concentrations in neuron-glia coculture conditioned media after treatment ±BAY61 (1µM) or ±P505 (10µM), and ±LPS (100ng/mL) for 3 days (DIV10). All panels: RM 2-way ANOVA with Šídák's post-hoc test. Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. * p<0.05. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 127 phenol red in the media, which is a standard method for culture media pH measurement (Michl et al., 2019). This confirmed the visible drop in media pH with LPS, and BAY61 significantly prevented this while P505 did so non-significantly (Fig. 35A). To test whether this was a result of altered lactic acid production and therefore glycolytic metabolism, I measured lactate levels in culture supernatants using a lactate dehydrogenase-based assay. As expected, lactate levels increased with LPS, and this was reduced by BAY61 treatment (though lactate still increased with LPS in the presence of the compound, albeit to a lesser extent; Fig. 35B). Therefore, SYK inhibition may limit the glycolytic shift in metabolism induced by LPS. 6.2.13 Media acidification alone may cause neuronal loss Given the strong drop in media pH caused by LPS treatment, and its reversal by SYK inhibitors, I considered the possibility that this drop itself may directly cause neuronal loss independent of other changes to glia. To artificially reduce media pH, I cultured some cocultures in the presence of 10% CO2 instead of the usual 5%, which should acidify media given the standard bicarbonate buffering system used here (Michl et al., 2019). With phenol red absorbance-based pH measurement, I first confirmed that culture media was indeed acidified in under 10% CO2 conditions, though this relative acidification was strongest earlier in the treatment period and relatively STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 128 Timothy James Yuji Birkle – November 2023 weak after 3 days (Fig. 36A). In 10% CO2, neuronal density in the absence of any LPS stimulus was significantly reduced after 3 days of treatment (Fig. 36B, C). Therefore, media acidification may directly cause some limited neuronal loss. On the other hand, a major issue with this experiment is that 10% CO2 will also induce hypercapnia in the cultures due to increased levels of dissolved CO2 in culture media, and this may have independent deleterious effects on the cells. In 10% CO2, LPS induced an even greater acidification of the medium (Fig. 36A), but this did not increase the LPS-induced neuronal loss, which was if anything reduced although the variance was high (Fig. 36C). This is not easily compatible with the LPS-induced medium acidification driving the neuronal loss. 6.2.14 Reducing the LPS-induced pH change is insufficient to prevent neuronal loss To more definitively test whether media acidification is a neurotoxic mechanism induced by LPS treatment, I aimed to prevent this acidification and test if the intervention protected neurons. Primary cultures are extremely sensitive to media changes, so any intervention had to be achieved through topical addition of a pH- modulating treatment at the DIV7 treatment point. Here, I first tested whether addition of millimolar levels of NaOH or NaHCO3 might be able to act against LPS-induced acidification, with comparison against equal treatments of NaCl to control for osmolarity changes. NaOH is a base, while NaHCO3 is basic while also adding bicarbonate buffering capacity to the media to further resist acidification. As expected, these treatments raised the pH of the media on cocultures, and this was maintained over 3 days (though media still acidified over time to some extent; Fig. 37A). Of the Figure 36. Media acidification alone may cause neuronal loss A: Average pH measurements of neuron-glia coculture conditioned media after treatment ±LPS (100ng/mL) and culture in either a 5% or 10% CO2 environment for 1-3 days after treatment on DIV7. Pre-treat = pH reading immediately prior to treatment. B: Representative 10x images (cropped) of neuron-glia cocultures treated ±LPS (100ng/mL) and cultured in either a 5% or 10% CO2 environment for 3 days (DIV10) and stained with Hoechst 33342 (nuclei), IB4-AF594 (microglia), and NeuO (live neurons). Scale bars = 50µm. C: Average neuron counts per image in cocultures treated ±LPS (100ng/mL) and cultured in either a 5% or 10% CO2 environment for 3 days (DIV10). RM 2-way ANOVA with Dunnett's post-hoc test, * p<0.05. All panels: Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 129 treatments tested, the most effective was 16mM NaHCO3, which resulted in significantly higher media pH in the presence of LPS at the end of the 3-day treatment period (Fig. 37B). This increase is comparable to that caused by P505 treatment previously (Fig. 35A), which completely protected against neuronal loss (Fig. 25D, E). Figure 37. Reducing LPS-induced pH change is insufficient to prevent neuronal loss A: Average pH measurements of neuron-glia coculture conditioned media after treatment ±LPS (100ng/mL) and with 4mM NaOH, 8mM NaHCO3 or 16mM NaHCO3, or with corresponding NaCl controls for osmolarity at the specific times after treatment on DIV7. Pre-treat = pH reading immediately prior to treatment. B: Average pH measurements of neuron-glia coculture conditioned media after 3 days of treatment (DIV10) ±LPS (100ng/mL) and with 16mM NaHCO3 or 16mM NaCl control. RM 2-way ANOVA with Dunnett's post-hoc test. C: Representative 10x images (cropped) of neuron-glia cocultures treated ±LPS (100ng/mL) and with 16mM NaHCO3 or 16mM NaCl control for 3 days (DIV10) and stained with Hoechst 33342 (nuclei), IB4-AF594 (microglia), and NeuO (live neurons). Scale bars = 50µm. D: Average neuron counts per image in cocultures treated ±LPS (100ng/mL) and with 16mM NaHCO3 or 16mM NaCl control for 3 days (DIV10). RM 2-way ANOVA with Dunnett's post-hoc test. All panels: Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. * p<0.05. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 130 Timothy James Yuji Birkle – November 2023 Next, I tested whether this NaHCO3 treatment might accordingly protect against LPS-induced neuronal loss. However, despite the observed elevation of pH counteracting most of the LPS-induced acidification, NaHCO3 did not protect neurons (Fig. 37C, D). Therefore, despite being substantial, media acidification does not appear to measurably contribute to LPS-induced neuronal loss. 6.2.15 BAY61 also prevents pTau-induced neuronal loss in a dose- dependent manner While LPS has its uses as a neurodegenerative model, I also wanted to test whether SYK inhibition was protective against neuronal loss induced instead by the AD-relevant stimulus phospho-tau (pTau). This would support that the effects of SYK inhibition explored here are relevant not only to general microglial inflammation but also to more specific insults. This assay became feasible later in this work, and was based on previous studies showing that low nanomolar pTau induces microglia-mediated neuronal loss in cerebellar neuron-glia cocultures (Pampuscenko et al., 2023, 2021). Here, 50nM pTau (recombinant and phosphorylated by human GSK3β) induced strong neuronal loss, and this was significantly prevented to an increasing extent by increasing concentrations of BAY61 (Fig. 38). Therefore, the effects of SYK inhibition may not only prevent LPS-induced neuronal loss but also loss caused by low levels of pTau, supporting the relevance of this work to AD and other tauopathies. Figure 38. BAY61 also prevents pTau- induced neuronal loss in a dose- dependent manner Average neuron counts per image in cocultures treated ±pTau (50nM) and increasing concentrations of BAY61 (from 0.1µM to 3µM) for 3 days (DIV10). RM 2- way ANOVA with Dunnett's post-hoc test. Comparisons without brackets are against the appropriate vehicle control (±pTau). Each datapoint represents the mean of 3 technical replicates, N = number of datapoints. * p<0.05, *** p<0.001, **** p<0.0001. Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 131 6.3 Discussion In this project, SYK activity is shown to mediate inflammatory neuronal loss. Two structurally unrelated SYK inhibitors completely prevented LPS-induced neuronal loss in primary rat neuron-glia cocultures, which is a microglia-dependent process. I also found that SYK inhibition prevents pTau-induced neuronal loss, which has previously been shown to be microglia-dependent as well (Pampuscenko et al., 2023, 2021), supporting the relevance of broader data in this work to AD and tauopathies. SYK inhibition caused some selective death of microglia in the absence of LPS. Given that SYK mediates signalling downstream of CSF1R (Bohlen et al., 2017), and that CSF1R inhibition or loss-of-function selectively depletes microglia (Elmore et al., 2014; Erblich et al., 2011), it is not surprising that SYK inhibition also depletes microglia. However, in cocultures in the presence of LPS, SYK inhibition caused only mild depletion (~30%). Therefore, microglial depletion may contribute to neuroprotection by SYK inhibitors in the presence of LPS but cannot fully explain it. SYK inhibition also had no effect on the morphological transition induced by LPS, suggesting that SYK inhibition did not directly block inflammatory signalling. However, care should be taken when comparing this in vitro morphological data to in vivo data, where resting microglia are ramified and become more amoeboid with activation (Ennerfelt et al., 2022). In this in vitro model and as seen by others (Balion et al., 2022), resting microglia adopt a largely unramified morphology and become more adherent upon inflammatory stimulation, which in two-dimensional cultures may resemble ramification, but may simply reflect greater adhesion to the coated plate (Wollmer et al., 2001). Irrespective of this difference of morphology in vitro and in vivo, SYK inhibition did not inhibit the morphological transition induce by LPS, and thus SYK inhibition does not block this part of the inflammatory response of microglia. SYK inhibition did reduce LPS-induced IL-6 release (by about 45%) and increased TNFα release (by about 80%). It should be noted that previous studies have found that neither of these cytokines is produced in LPS-stimulated neuron-astrocyte cocultures lacking microglia, so microglia produce all (or nearly all) of these factors (Goshi et al., 2022). As the density of microglia was also reduced by SYK inhibition in these experiments (by about 30%), the amount of IL-6 released per microglia was only mildly decreased, and the amount of TNFα released per microglia was increased. Thus, SYK inhibition did not prevent inflammatory activation of microglia by LPS in STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 132 Timothy James Yuji Birkle – November 2023 conditions where it fully prevented LPS-induced neuronal loss. However, as IL-6 can be toxic to neurons (Conroy et al., 2004), the observed decrease in IL-6 might in principle contribute to the neuroprotection. In contrast, Kim et al. (2022) found that 100nM BAY61 inhibited LPS-induced expression of TNFα and IL-1β in cultured BV2 microglia, and in vivo in mice BAY61 reduced LPS-induced TNFα and IL-1β expression. Thus, SYK inhibitors may be neuroprotective in part by reducing neuroinflammation, but this effect was limited in this culture system. SYK inhibition did reduce microglial phagocytosis in the presence and absence of LPS. BAY61 reduced phagocytosis of isolated synapses by microglia and reduced the synaptic loss (as measured by Homer1 protein levels) induced by low doses of LPS in neuron-glia cocultures. This suggests that SYK inhibition reduces microglial phagocytosis of synapses. Synaptic loss/pruning is known to occur by microglial phagocytosis through complement receptor 3 (CR3; Hong et al., 2016) or TREM2 (Filipello et al., 2018; Linnartz-Gerlach et al., 2019), which induce phagocytosis through SYK, so the reduction in phagocytosis of synaptic material by SYK inhibition may be mediated by these or other SYK-dependent phagocytic receptors. The SYK inhibitor BAY61 has previously been shown to prevent synaptic loss in a tauopathy mouse model (Schweig et al., 2019), indicating that SYK inhibitors can prevent synaptic loss in vivo as well as in culture. Previous studies have shown that microglial phagocytosis can mediate LPS- induced loss of entire neurons, and that inhibiting this phagocytosis in a variety of ways can prevent the neuronal loss (Fricker et al., 2012a, 2012b; Neher et al., 2014). As SYK inhibition blocks phagocytosis induced by CR3, TREM2, Dectin-1, and Fcγ receptors (Crowley et al., 1997; Gevrey et al., 2005; McQuade et al., 2020; Mócsai et al., 2010; Murakami et al., 2014; Scheib et al., 2012; Song et al., 2004; Walbaum et al., 2021; H. Yao et al., 2019), inhibition of microglial phagocytosis by SYK inhibitors may help explain the prevention of LPS-induced neuronal loss, although the reduced microglial density and IL-6 release may also contribute to the neuroprotection by SYK inhibitors. Consistent with SYK inhibition preventing neuronal loss by blocking microglial phagocytosis of neurons, I found that LPS increased the number of condensed nuclei found within microglia, and SYK inhibition prevented this. The lab has previously shown that the neurons can undergo reversible nuclear condensation prior to phagocytosis (Hornik et al., 2016) and are condensed after phagocytosis by microglia (Neher et al., 2014). The imaging is not sufficient to be sure that all the condensed Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 133 nuclei belong to neurons and that all the condensed nuclei are phagocytosed by, rather than co-localised with, microglia. However, most such events are likely to be due to microglial phagocytosis of neurons. I investigated metabolic changes after LPS treatment and SYK inhibition, as there is increasing literature linking microglial metabolism and SYK to inflammatory functions across many neurodegenerative conditions (Aldana, 2019; Lynch, 2020). Amyloid-β can cause a metabolic shift away from oxidative phosphorylation and towards glycolysis in microglia, potentially via SYK (Gao et al., 2022; Jung et al., 2022; McIntosh et al., 2019), and mutations in TREM2 (upstream of SYK) impair microglial glycolysis, which can be rescued by an alternative SYK activation (Piers et al., 2020; Ulland et al., 2017). Conversely, TREM2 activating antibodies can alter microglial metabolism and increase metabolic activity both in vitro and in vivo (van Lengerich et al., 2023). These metabolic effects can then control many of the important microglial functions such as phagocytosis and cytokine release (Ghosh et al., 2018; Li et al., 2022), and so this may be one route by which SYK affects these activities. Here, LPS activation appeared to induce a glycolytic shift and increased lactic acid production, as previously reported (Ghosh et al., 2018). SYK inhibition reduced this shift according to media pH and lactate levels, though a thorough assessment of intracellular metabolism was not performed. Nonetheless, this fits with the literature on SYK and TREM2 controlling microglial metabolism. Media acidification caused by LPS treatment of cocultures did not appear to directly mediate LPS-induced neurodegeneration as reducing acidification did not prevent neuronal loss, and so the small neuronal loss seen with culture in 10% CO2 might be a result of hypercapnia, rather than acidification. Therefore, any contribution of microglial glycolytic shift (and its prevention by SYK inhibition) to neurodegeneration, if at all, was likely indirect via microglial functions such as phagocytosis (which was decreased by SYK inhibition). One notable limitation of this measurement of media acidification and lactate levels is that the observed effects were not specifically linked to microglia. This also applies to the earlier measurements of media cytokine levels. Media acidification and lactate levels were also not normalised to microglial number, which was also the case for cytokine data and considered in the above discussion of those assays. Therefore, one reason for the reduced acidification and lactate in SYK-inhibited cocultures may be the presence of fewer microglia. For these reasons, future work should more thoroughly STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 134 Timothy James Yuji Birkle – November 2023 assess the role of SYK in microglial metabolism specifically and differentiate broad effects on microglial survival from specific effects on glycolytic shift. Although I showed that LPS-induced neuronal loss requires microglia, SYK inhibitors may nonetheless prevent neuronal loss by acting on SYK in neurons rather in microglia. However, as the LPS-induced neuronal loss requires microglia, study of any such potential protection would require genetic manipulation of individual cell types, and this is challenging in primary cocultures. SYK inhibition also protected against spontaneous neuronal loss (in the absence of LPS) that occurred with age in the neuron- glia cocultures. The mechanism of this loss and protection is unclear, but it does indicate that SYK inhibition is neuroprotective in conditions other than LPS-induced inflammation. SYK inhibition by BAY61 has previously been shown to increase baseline survival of hippocampal neurons in culture (M. W. Kim et al., 2022). Although LPS has widely been used to model inflammation, its use here to model acute neuroinflammation has limitations, as the neuroinflammation of neurodegenerative diseases is chronic and of complex origin. Care must therefore be exercised when extrapolating these results to human disease, and future work will need to use other models and disease relevant stimuli. To this end, here I confirmed that the SYK inhibitor BAY61 is protective against pTau as well as LPS, but further work using this model will be important. In vivo, SYK activity has been reported to be either beneficial or detrimental for neurons, depending on the model and disease parameters being studied. In a 5xFAD amyloid mouse model of AD, SYK promoted microglial phagocytosis and compaction of amyloid plaques, which reduced cognitive decline (Ennerfelt et al., 2022). However, SYK inhibition reduced neuronal and/or synaptic loss in mouse models of traumatic brain injury (He et al., 2022), stroke (Ye et al., 2020), LPS-induced inflammation (M. W. Kim et al., 2022), and tauopathy (Schweig et al., 2019). Notably, tauopathy more closely correlates with neuronal loss and cognitive decline in AD than amyloid pathology (King-Robson et al., 2021). It may be that SYK-mediated microglial phagocytosis is beneficial early on in AD by phagocytosis of amyloid, but detrimental later on by excessive phagocytosis of synapses and neurons. More generally, phagocytosis has important physiological and protective functions, but this may change depending on the phagocytic target, the specific disease in question, and the stage of disease. It will therefore be an ongoing challenge for the field to determine when Chapter 6: The role of spleen tyrosine kinase in microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 135 phagocytosis might be targeted with therapeutic benefit for any specific neuropathology. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 136 Timothy James Yuji Birkle – November 2023 7 HIGH-CONTENT SCREENING FOR BLOCKERS OF MICROGLIA-MEDIATED NEURODEGENERATION All data presented in this chapter are my own work and much of this has been published in: Birkle, T. J. Y., Willems, H., Skidmore, J., & Brown, G. C. (2023). Disease phenotypic screening in neuron-glia co-cultures identifies blockers of inflammatory neurodegeneration. iScience, 27(4), 109454. https://doi.org/10.1016/j.isci.2024.109454. Chemical and target data for each compound was extracted from ChEMBL by H. Willems, which I alone then used for compound selection and analysis of hits. 7.1 Introduction Preclinical translational neuroscience often relies on relatively simple in vitro culture systems such as monocultures, and this is particularly true for mid- to high-throughput biology where the simplicity and robustness of experiments and analysis is paramount. However, monocultures of neurons, for example, are of limited utility for modelling neuropathology, as glial cells and their interactions with neurons are important for neuropathology (Kwon and Koh, 2020). This lack of realistic disease models at early preclinical stages may contribute to failures when translating results to later stages, and therefore may also contribute to pharmaceutical companies considering drug Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 137 development for neurodegenerative diseases to be high-risk. Two-dimensional cultures of primary cells or stem cell-derived cells are at the forefront of what is currently possible to use for screens (Fell and Nagy, 2021; Pampaloni et al., 2007). More physiological disease models are available, such as in vivo mouse models and three- dimensional cultures/organoids, but these are generally not tractable for screening currently, though there has been some recent progress (Gonzalez et al., 2018; H.-K. Lee et al., 2016; Li et al., 2023). Previously, I validated a live cell staining protocol and an automated analysis pipeline that together allow efficient use of two-dimensional primary neuron-glia cocultures for neurodegeneration assays (Chapter 4). This method was then used to investigate the microglial biology of urokinase plasminogen activator (Chapter 5) and spleen tyrosine kinase (Chapter 6) in relatively low-throughput assays. However, the methodological improvements presented the possibility of using these primary neuron- glia cocultures for high-content screening (HCS) to identify drugs and cellular targets that modify inflammatory neurodegeneration. A limited selection of HCS studies on synaptic loss or neurodegeneration have used primary or stem-cell derived monoculture models (Jiang et al., 2020; Nieland et al., 2014; Sharma et al., 2012; Tian et al., 2019), and some have proposed using coculture or organotypic slice culture models (Bassil et al., 2021; Cho et al., 2007; Goshi et al., 2022; Reinhart et al., 2011; Wang et al., 2006). Where model systems are of similar complexity to the neuron-glia cocultures used here, there is usually a lack of comprehensive classification of each cell type in culture, and more specific measures of the abundance of certain proteins may be chosen instead. This data is valuable but omits collection of general data on each cell type, which is important for insight into the complicated disease mechanisms that can be uniquely modelled with such complex models. Where any substantial cell type classification has been performed, this has been achieved with commercial assay platforms that are extremely expensive, limiting these important coculture screening assays to relatively few labs (Kaltenbach et al., 2010; Phadke et al., 2022). Moreover, even in these studies the achieved throughput remains limited. With this in mind, here I conducted HCS for treatments protecting against inflammatory neurodegeneration in the neuron-glia cocultures, using the aforementioned analysis to analyse individual cell types. 227 compounds were screened, and through this I found that modulators of steroid, adrenergic and MAPK signalling prevented inflammatory neurodegeneration, alongside modulators of other STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 138 Timothy James Yuji Birkle – November 2023 pathways. The multidimensional data extracted on all cell types in the cocultures helped to categorise and prioritise hits, and this has not previously been possible with other HCS approaches. While based on well-accepted methods, this approach is relatively novel in that it combines: i) a complex, cellular model of disease (LPS-induced neuronal loss in neuronal-glial cultures), ii) automated extraction of information on all cell types, enabling HCS, and iii) an annotated drug library to implicate both drugs and targets in neuroprotection (Wassermann et al., 2014). This combination enables efficient identification of potential drug therapies and discovery of novel biology. Moreover, all analysis methods are open-source and can be found on GitHub (https://github.com/timjyb/Birkle-et-al-2023-HTS), and the image data from this study will be available on BioImage Archive after publication (accession: S-BIAD890). 7.2 Results 7.2.1 Design and analysis of a high-content screen for neuroprotective compounds In this screen, I used an annotated library of compounds with known targets, aiming to identify both neuroprotective drugs and targets based on those drugs’ annotated activities. Compounds were selected from this library as described in detail in the Methods (Chapter 3) and were screened at 1µM. Details for all selected compounds can be found in Appendix 3 (Table 6). Three technical replicates for each compound were included per repeat, with and without LPS, and these were randomly distributed within each plate while ensuring one replicate per compound per plate, again with and without LPS (Fig. 39A). Controls were also randomly distributed within each plate and consisted of DMSO (negative control) and BAY61 (1µM, positive control for neuroprotection against LPS) represented six times per plate with and without LPS. Treatments were added topically to cultures in 384-well plates at 7 days in vitro (DIV7), then NeuO, Hoechst 33342, IB4-AF594 and DRAQ7 were topically added at DIV10 for staining (Fig. 39B). DRAQ7 is a cell-impermeant DNA stain that therefore only stains nuclei of necrotic cells and could be added to the previously described staining protocol (Chapter 4) thanks to the availability of a fourth, far-red filter channel on the microscope. 4 images were captured per well at 10x objective magnification on an IN Cell Analyzer 6000 instrument, with fields-of-view collectively covering ~80% of each well and capturing roughly 10,000 neurons. Though the cells were live, this was an endpoint assay and each field-of-view was only imaged once, avoiding complications Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 139 from phototoxicity with repeated imaging. Four biological repeats of the screen were conducted using distinct culture preparations. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 140 Timothy James Yuji Birkle – November 2023 Image analysis was performed as previously described (Chapter 4), with the inclusion of classifier training for additional identification of necrotic cells with the help of DRAQ7 staining. Cells used for training were picked equally from all four repeats and thereafter sampled randomly from all treatment conditions. The final training data consisted of 11,274 cells, balanced between LPS-untreated and LPS-treated wells (5,692 and 5,582 cells, respectively) and across the different cell types (approximately 1,600 cells per class). The resulting classifier achieved high accuracy for all cell types including >92% recall (the proportion of actual cells of each type being correctly classified) for the three major cell types (neurons, microglia, and astrocytes; Fig. 39C), and prioritised the expected cell features for cell type discrimination including percentile/mean intensity measurements of each stain and the size of the nuclei (Appendix 3, Table 7). Images of control culture wells were as expected, with strong neuronal loss induced by LPS and strong neuroprotection provided by BAY61 (Fig. 39D-G). The classifier maintained high accuracy for each of the four control conditions separately (DMSO ± LPS, BAY61 ± LPS), validating the analysis for cultures with phenotypes ranging from extreme neurodegeneration to complete protection (Fig. 39H-K). Figure 39. Design and analysis of a high-content screen for neuroprotective compounds A: Schematic of the treatment distribution for the screen. Treatments were randomly assigned to 2 wells per plate within their half of the screen, 1 LPS-untreated and 1 LPS-treated. Controls were also randomly distributed. 1 treatment compound failed to dispense as intended, resulting in 227 compounds being tested in the final screen. B: Representative 10x image (cropped) of neuron-glia cocultures stained with Hoechst 33342 (nuclei), NeuO (live neurons), IB4-AF594 (microglia), and DRAQ7 (necrotic cells). Scale bar = 100µm. C: Heatmap confusion matrix plotting proportion of manually annotated cells of each class (Actual) being predicted to be any given class (Predicted) by the final trained classifier model from the screen, from 5-fold cross-validation. D-F: Representative images (cropped) of cultures treated ± LPS (100ng/mL) and ± BAY61 (1µM) for 3 days (DIV10) and stained with Hoechst 33342 (nuclei), NeuO (live neurons), IB4-AF594 (microglia), and DRAQ7 (necrotic cells). Scale bars = 100µm. H-K: Heatmap confusion matrices plotting the proportion of manually annotated cells of each class (Actual) being predicted to be any given class (Predicted) by the final trained classifier model from the screen, upon application to random images of each control condition (DMSO LPS-, DMSO LPS+, BAY61 LPS-, BAY61 LPS+) from all 4 screen repeats. 200 cells per class per condition were manually annotated for validation. Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 141 7.2.2 UMAP validation of assigned cell classes As the data analysis of the screen intended to comprehensively assess all cell types, I sought to further validate the classification approach by confirming that the cell types identified subjectively correspond well with unsupervised analysis of the raw data. Uniform manifold approximation and projection (UMAP) analysis of the object feature data for 15,710 cells from random negative control wells across all four screen repeats STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 142 Timothy James Yuji Birkle – November 2023 successfully differentiated the cell types that had been identified by eye in the images (Fig. 40A). The boundaries of these clusters corresponded well with the classifications that had been assigned by the analysis pipeline, these in turn being an accurate reflection of the original manual training. Furthermore, these did not vary between biological repeats (Fig. 40H). The most important cell features identified by the trained classifier (Appendix 3, Table 7) also clearly distinguished cell types in the dimensionality reduction (Fig. 40B-F). Each cell classifier, such as astrocytes or microglia, corresponded to one cluster, except neurons, which appeared in three clusters (Fig. 40A). On analysis of the corresponding cells (e.g. Fig. 40Ai, ii & iii), it became clear that these three clusters corresponded to neurons that were: i) isolated from other cells, ii) adjacent to other cells/neurons (thus having additional Hoechst staining at their border, or iii) adjacent to a microglia (thus having some IB4 staining). Accordingly, these neuronal groups were clearly distinguished by both Hoechst intensity measurements taken specifically at the edge of cell objects and IB4 intensity measures (Fig. 40G). This ability to distinguish neurons that differ only in their immediate proximity to other cells reflects the richness of the object data generated by CellProfiler, but these groupings are unlikely to indicate neuronal subtypes. Overall, the labels applied during classification were found to correspond with genuinely distinct cell types in the original imaging data. Figure 40. UMAP validation of assigned cell classes A: UMAP plot of the object feature data for 15,710 cells from random negative control wells across all four screen repeats, with retrospective labelling according to the assigned cell type from the screen’s classifier. i, ii and iii indicate distinct clusters of neurons on the UMAP, which correspond to the inset image tiles (representative neurons from each cluster). B-G: UMAP plots labelled according to the stated feature. Features in B-F were chosen for visualisation based on being the highest priority features of each general type (shape, Hoechst, NeuO, IB4, DRAQ7) according to the screen’s Random Trees classification (Appendix 3, Table 7). The MeanIntensityEdge_Hoechst plot (G) was chosen as it clearly distinguishes the two left-hand neuron clusters. All features were log2 transformed prior to plotting. H: UMAP plot labelled by the screen repeat from which each cell was taken. All panels: Key UMAP hyperparameters: n_neighbours = 15, min_dist = 0.1. DR7 = DRAQ7. Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 143 7.2.3 Cell count changes with treatment were reproducible and as expected Raw count data from the screen was visualised for preliminary inspection of the data, which included the means and standard deviations of cell counts for each treatment for the four biological repeats (Fig. 41). This confirmed that the assay worked as intended, for example with LPS treatment causing loss of 90-95% of the live neurons (Fig. 41A), increasing the number of necrotic cells about 10-fold (Fig. 41D), and increasing the number of microglia 2- to 3-fold (Fig. 41B). Astrocyte counts were largely unaffected by LPS (Fig. 41C). The strong neurodegeneration induced by LPS in the screen, as compared to some of my previous data (for example, Fig. 12, 14, 25) may be explained by the optimised LPS preparation protocol developed for the screen, where the LPS was sonicated prior to use, which may disperse the LPS-containing micelles and increase the effective concentration of LPS available to activate microglia. Different gas exchange or the different plate bottom material in the screening plates might also have contributed to the increased neuronal loss induced by LPS in the screen. 7.2.4 Data quality control and normalisation For quality control, I first visualised plate-to-plate variation in LPS-untreated DMSO wells by using principal component analysis on the full data for all cell types (PCA; Fig. 42A). Outlier analysis of the cell type counts (ROUT, Q = 1%) highlighted one plate in the second repeat that had higher necrosis and 25% lower neuron counts than other plates in that repeat, so this plate was subsequently excluded. One plate of the fourth repeat was flagged due to a small increase of condensed cells (~30 cells per frame), but with no other change, the data from this plate was retained. The responses of all plates to LPS were similar overall (Fig. 42B). Cell counts did vary between repeats, likely due to variable plating densities (Fig. 42C), as did cell type proportions (though, the relative numbers of neurons and microglia, the two most important cell types for this assay, were similar; Fig. 42D). This variation accounted for most of each treatment’s variation across the four biological repeats (Fig. 42E). To minimise any effect of this on further analysis, count data for neurons and microglia were Min-Max normalised using the LPS-untreated and LPS- treated repeat-wise median counts (Fig. 42F). This was possible because of the large increase in microglial numbers and large decrease in neuronal numbers after LPS treatment (Fig. 42A, B) and Min-Max normalisation successfully reduced per-condition standard deviations by 32%. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 144 Timothy James Yuji Birkle – November 2023 Figure 41. Cell count changes with treatment were reproducible and as expected A-G: Raw cell count data for each class – Neurons, Microglia, Astrocytes, Necrotic, Condensed, Other, and Debris respectively – in neuron-glia cocultures treated ±LPS (100ng/mL) and ± each compound in the screen (1µM) for 3 days (DIV10). Ordered according to arbitrary compound ID. Data are represented as mean ± SD, N = 4. Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 145 After normalisation, I calculated relevant Z-factors for the primary neuroprotection assay, which captures how well separated a screen’s negative and positive populations are. This ranges from -∞ to 1, with 1 being a perfect assay. Typically, Z-factors between 0 and 0.5 are considered adequate, particularly for HCS phenotypic screening, while Z-factors > 0.5 are excellent (Bray et al., 2004). Here, the neuroprotection assay achieved an overall Z-factor of 0.927 between DMSO-treated wells in the presence and absence of LPS, and 0.724 between DMSO- and BAY61- treated wells in the presence of LPS. The robust Z-factor (non-parametric equivalent suitable for data with expected outliers – in this case, hits) for all neuron count data was 0.448, which in principle gives a highly conservative estimate of the statistical power of the assay based on the true variability observed in the complete screening data. Data from all conditions was used to assess per-row and per-column variation, and small trends were observed in both cases (Fig. 42G-N). However, any effects were minor and treatment well locations had been randomised. I confirmed that this randomisation was effective by analysing average row and column locations for each treatment and observed no difference between compounds subsequently found to be neuroprotective and non-hits (Fig. 42O, P). In a per-well analysis, no individual wells were identified as strong outliers across the screen (Fig. 42Q, R), and any small effects of well location would have been similarly mitigated by randomised treatment locations. Overall, given the small size of any plate location effects and the mitigation by randomisation of treatment layouts, no further corrections were made for row, column, or well effects. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 146 Timothy James Yuji Birkle – November 2023 Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 147 Figure 42. Screen data quality control and normalisation A: Plot of PC1 and PC2 (combined 74.55% of variance) after PCA on the average count data of each cell type taken from the DMSO LPS- wells of each plate. Each datapoint represents the average of all control wells of one plate, and datapoints are colour-labelled according to the biological repeat which they were part of. Highlighted plates were identified as outliers by ROUT (Q = 1%). B: Same as (A), but for DMSO LPS+ wells instead (combined 66.33% of variance). No outliers detected by ROUT (Q = 1%). C: Average total cells per frame in DMSO LPS- wells from each biological repeat. Data are represented as mean ± SD, and individual datapoints represent the average total cells per frame for a single control well. D: Average proportion of each cell type out of total cells for DMSO LPS- wells from each biological repeat. Data are represented as mean ± SD. E: Average non- normalised neuron count data from neuron-glia cocultures treated ±LPS (100ng/mL) and ± each compound in the screen (1µM) for 3 days (DIV10), with individual mean datapoints from each biological repeat (N = 4) plotted. Ordered according to arbitrary compound ID. Each datapoint represents the mean of 3 technical replicates. F: Average neuron count data from (E), min-max normalised for each repeat between the median LPS-untreated (1) and median LPS-treated (0) count values, with individual mean datapoints from each biological repeat (N = 4) plotted. Ordered according to arbitrary compound ID. Each datapoint represents the mean of 3 technical replicates. G: Average neuron count per frame in each row of each 384-well plate, across all LPS- wells (controls and treatments combined). Data are represented as mean ± SD. H: Plot of PC1 and PC2 (combined 84.27% of variance) after PCA on the average count data of each cell type taken from all LPS- wells in each row across all plates. I: Same as (G), but for LPS+ wells instead. J: Same as (H), but for LPS+ wells instead (combined 83.34% of variance). K: Average neuron count per frame in each column of each 384-well plate, across all LPS- wells (controls and treatments combined). Data are represented as mean ± SD. L: Plot of PC1 and PC2 (combined 82.33% of variance) after PCA on the average count data of each cell type taken from all LPS- wells in each column across all plates. M: Same as (K), but for LPS+ wells instead. N: Same as (L), but for LPS+ wells instead (combined 71.34% of variance). O: Average row location (row B = 1, row 0 = 14) for each treatment over the course of all screen repeats, comparing treatment which were identified as neuroprotective hits versus non-hits. Unpaired t-test. P: Average column location for each treatment over the course of all screen repeats, comparing treatment which were identified as neuroprotective hits versus non- hits. Unpaired t-test. Q: Plot of PC1 and PC2 (combined 54.08% of variance) after PCA on the average count data of each cell type taken from each well (LPS- only) across all plates. R: Plot of PC1 and PC2 (combined 66.93% of variance) after PCA on the average count data of each cell type taken from each well (LPS+ only) across all plates. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 148 Timothy James Yuji Birkle – November 2023 7.2.5 Identification of neuroprotective compounds and pathways Average normalised neuronal count data from all four repeats was analysed to identify compounds causing a significant decrease or increase of live neurons versus DMSO within LPS- untreated and LPS-treated wells, respectively (Fig. 43A, B). Significant compounds from this analysis are therefore neurotoxic or neuroprotective, respectively, and the latter were deemed ‘hits’. In total, 29 compounds were identified as hits, in Figure 43. Protective compounds are identified and frequently affect steroid signalling, adrenoreceptors, and MAP kinases A, B: Average normalised neuron counts from neuron-glia cocultures treated without (A) and with (B) LPS and each compound in the screen, and ordered by increasing neurotoxicity or neuroprotection respectively. RM 2-way ANOVA with Dunnett’s post-hoc test comparing all treatments to DMSO in the presence and absence of LPS. Darker regions of each graph indicate p<0.05 significance. Data are represented as mean ± S.D., N = 4. C-E: Random 10x images from DMSO-treated LPS-untreated, DMSO-treated LPS-treated, and hit compound-treated (compound 31) cultures at DIV10 after 3 days treatment with 100ng/mL LPS and staining with Hoechst 33342 (nuclei), NeuO (live neurons), IB4-AF594 (microglia), and DRAQ7 (necrotic cells). Scale bars = 100µm. Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 149 addition to the positive control (BAY61, a SYK inhibitor), which strongly protected cultures as expected (Table 2). One hit was another SYK inhibitor (compound 26), corroborating my previous finding that SYK inhibitors are neuroprotective in this model of inflammatory neuronal loss (Birkle and Brown, 2023). The TLR4 inhibitor TAK- 242/CLI-095/resatorvid (compound 214) also significantly protected, which is to be expected based on LPS activating microglia via this receptor. Images of cultures treated with all hit compounds were manually checked, which confirmed the neuroprotection reported by the analysis (Fig. 43C-E; see Figure 44 for randomly selected images from all hit compound cultures). Analysis of the hits revealed that multiple hit compounds targeted proteins of the same class or in the same pathways (Table 2). For example, 6 hits targeted receptors/enzymes involved in steroid hormone synthesis and signalling, with many of these compounds being among some of the most protective hits found. Also, 5 hit compounds targeted members of the adrenoreceptor family (ADRs) and 6 inhibited MAPK family members. 7.2.6 Analysis of LPS-untreated cultures identifies neurotoxic compounds While identification of neuroprotective compounds and targets was the primary goal of this approach, toxicology data is also essential for guiding therapeutic development, and may give insight into neuronal biology. Analysis of neuronal counts identified 20 compounds that significantly decreased the number of live neurons in the absence of LPS (Fig. 43A; Table 3). The most neurotoxic compound was rotenone (219), widely used to model Parkinson’s disease (Johnson and Bobrovskaya, 2015). Rotenone is an inhibitor of mitochondrial complex I, and its neurotoxicity confirms that neurons cannot survive without mitochondrial respiration (Radad et al., 2006). Calcium channel inhibitors were also neurotoxic, though previous studies indicate that inhibitors of some calcium channels can be protective (Nimmrich and Eckert, 2013). Neurotoxicity was also induced by inhibitors of the DNA damage response proteins TDP1 and ATM (167 and 50, respectively), indicating that DNA repair is essential for neuronal survival in culture. The TLR7 agonist vesatolimod (157) was also partially neurotoxic, which may be due to a strong microglial response as indicated by the 6-fold increase in microglial numbers in the absence of LPS (Fig. 41B; Appendix 3, Table 8). TLR7 recognises single stranded RNA, particularly of viruses, so this finding suggests that viruses known to affect cognitive function (such as SARS-CoV-2) could induce neuroinflammation STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 150 Timothy James Yuji Birkle – November 2023 Table 2. Neuroprotective hits and targets Ordered by decreasing effect size. Predicted (LS) mean diff. = mean normalised neuron count after DMSO LPS+ treatment minus after compound LPS+ treatment. -1 represents full protection back to LPS- neuron levels. Targets: Gene symbols of known targets for each compound extracted from ChEMBL, with addition mechanism of action (MOA) information. Default MOA is inhibition; suffixed a indicates agonism. Italicised MOAs are those which are repeated amongst all neuroprotective compounds (e.g. NR3C1 is agonised by compounds 31 and 114). Label: Terms indicate association of targets with common pathways of interest amongst the hit data. Steroid = steroid synthesis/signalling modulation; ADR = adrenoreceptor modulation; MAPK = mitogen-activated protein kinase inhibition. Cluster: Terms indicate the cluster of the overall hit phenotype as analysed in Figure 6. ID C H EM B L ID Pred icted (LS) m ean diff. 95.00% C I o f diff. Sum m ary A djusted P V alue N am e Target + M O A Label C luster catego ry B A Y61 C H EM B L1242100 -1.411 -1.659 to -1.164 **** <0 .0001 B A Y61-3606 SYK O ther 3 31 C H EM B L1451 -1.298 -1.546 to -1.050 **** <0 .0001 Triam cino lo ne N R 3C 1* N FKB 1 N FE2L2 Stero id signalling 3 105 C H EM B L603 -1.208 -1.456 to -0.9603 **** <0 .0001 Zafirlukast C YSLTR 1 C YSLTR 2 M A PK14 M A PK signalling 3 29 C H EM B L1399 -1.001 -1.249 to -0.7530 **** <0 .0001 A nastro zo le C YP19A 1 Stero id signalling 2 67 C H EM B L277535 -0.9812 -1.229 to -0.7335 **** <0 .0001 B ifo nazo le C YP17A 1 C YP51A 1 C YP3A 4 Stero id signalling 2 28 C H EM B L1358 -0.966 -1.214 to -0.7183 **** <0 .0001 Fulvestrant PG R ESR 1 ESR 2 ESR R A EPH X2 N R 1H 4 Stero id signalling 2 139 C H EM B L373250 -0.9072 -1.155 to -0.6594 **** <0 .0001 L-838417 G A B R A 3* G A B R B 3* G A B R G 2* G A B R A 2* G A B R A 1* G A B R A 5* O ther 2 77 C H EM B L36 -0.8951 -1.143 to -0.6473 **** <0 .0001 Pyrim etham ine D H FR SLC 47A 1 O ther 2 114 C H EM B L717 -0.8911 -1.139 to -0.6434 **** <0 .0001 M ed ro xypro gestero ne A cetate PG R * A R * N R 3C 1* A KR 1C 3 ESR 1* ESR 2* Stero id signalling 2 30 C H EM B L1437 -0.8901 -1.138 to -0.6423 **** <0 .0001 N o rep inep hrine A D R A 1A * A D R A 1D * A D R A 2A * A D R A 2B * A D R A 2C * A D R B 3* A D R B 1* A dren ergic signalling 1 109 C H EM B L679 -0.8781 -1.126 to -0.6304 **** <0 .0001 Epinep hrine A D R A 2A * A D R A 2B * A D R A 2C * A D R A 1A * A D R A 1B * A D R A 1D * A D R B 3* A D R B 2* C A 1* A dren ergic signalling 1 161 C H EM B L2204502 -0.871 -1.119 to -0.6232 **** <0 .0001 XL888 H SP90A A 1 H SP90A B 1 O ther 3 111 C H EM B L707 -0.8618 -1.110 to -0.6140 **** <0 .0001 D o xazo sin A D R A 1B A D R A 1D A D R A 1A SLC 6A 3 H TR 2B A D R A 2C H TR 2C A D R A 2A H TR 4 A dren ergic signalling 2 214 C H EM B L426184 -0.8595 -1.107 to -0.6117 **** <0 .0001 TA K-242/C LI-095/resato rvid TLR 4 O ther 2 152 C H EM B L3672369 -0.8444 -1.092 to -0.5966 **** <0 .0001 O TS964 PB K N EK1 O ther 3 26 C H EM B L1235110 -0.7291 -0.9769 to -0.4814 **** <0 .0001 N A SYK M A PK1 M A PK3 G P6 M A PK signalling 2 24 C H EM B L41 -0.6942 -0.9419 to -0.4464 **** <0 .0001 Fluo xetine SLC 6A 4 A D R A 2A H TR 1A * H TR 1B * H TR 1D * H TR 1F* C YP2C 19 H TR 2A * H TR 2C * SLC 6A 2 A C H E N ET SIG M A R 1 C YP2D 6 O ther 2 130 C H EM B L379225 -0.6927 -0.9404 to -0.4449 **** <0 .0001 N -(4-(1,1,1,3,3,3-hexafluo ro -2-hydro xypro pan-2-yl)phen yl)-N -m ethylben zam ide N R 1H 3* N R 1H 2* Stero id signalling 1 22 C H EM B L1093059 -0.6838 -0.9315 to -0.4360 **** <0 .0001 N A N O S2 KEA P1 N Q O 1* O ther 1 165 C H EM B L1088752 -0.662 -0.9097 to -0.4143 **** <0 .0001 Lo sm apim o d M A PK14 STK24 M A PK11 M A PKA PK2 M A PK signalling 1 14 C H EM B L1232461 -0.6431 -0.9466 to -0.3395 **** <0 .0001 N A B R D 4 B R D 3 B R D 2 B R D T O ther 3 191 C H EM B L577784 -0.5855 -0.8332 to -0.3377 **** <0 .0001 B X-795 TB K1 IKB KE C G A S PD K1 U LK1 U LK2 PD PK1 O ther 1 42 C H EM B L1914489 -0.559 -0.8068 to -0.3113 **** <0 .0001 A ZD 1981 PTG D R 2 O ther 1 119 C H EM B L119385 -0.4899 -0.7376 to -0.2421 **** <0 .0001 N EFLA M A PIM O D M A PK11 M A PK12 M A PK13 M A PK14 A B L2 M A PK signalling 1 112 C H EM B L709 -0.4362 -0.6840 to -0.1885 **** <0 .0001 A lfuzo sin A D R A 1B A D R A 1D A D R A 1A A C H E A dren ergic signalling 1 96 C H EM B L507361 -0.3815 -0.6292 to -0.1338 **** <0 .0001 PD -0325901 M A P2K1 B R A F M A P2K2 M A PK1 M A P2K5 M A PK signalling 3 6 C H EM B L1200323 -0.3681 -0.6158 to -0.1203 **** <0 .0001 Labetalo l H ydro chlo ride A D R B 1 A D R B 2 A D R B 3 A D R A 1A A D R A 1B A D R A 1D A D R A 2A A D R A 2B A D R A 2C A dren ergic signalling 1 210 C H EM B L2103875 -0.3457 -0.5934 to -0.09795 *** 0.0003 Tram etinib M A P2K1 M A P2K2 A B C B 1 M A PK signalling 1 20 C H EM B L3798846 -0.3284 -0.5761 to -0.08065 *** 0.0008 O IC R -9429 W D R 5 O ther 1 79 C H EM B L385517 -0.312 -0.5597 to -0.06426 ** 0.002 Saxagliptin D PP4 D PP9 D PP8 O ther 1 M ean (D M SO LPS+ no rm alised neu ro n co unt - Treatm en t LPS+ no rm alised neu ro n co unt) Inhibitio n is default m o de o f actio n; * indicates ago nism Therefo re 0 = no pro tectio n and -1 = appro xim ately co m plete pro tectio n H ighlighted = duplicate M O A am o ngst hits Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 151 Table 3. Neurotoxic compounds and targets Ordered by decreasing effect size. Predicted (LS) mean diff. = mean normalised neuron count after DMSO LPS- treatment minus after compound LPS- treatment. 1 represents loss of neurons equal to when treated with LPS (near 100%). #### in Name column represents (1H-Benzoimidazol-2-yl)- (3,4-dichlorobenzyl)amine. Targets: Gene symbols of known targets for each compound. Default on-target mechanism of action (MOA) is inhibition; suffixed * indicates agonism, # indicates binding, and ~ indicates unknown activity. Highlighted target-MOAs are those which are repeated amongst all target-MOAs of the neurotoxic compounds (e.g. SLC6A3 is inhibited by compounds 84, 73, and 125 in this list). Continuation of the table: ID C H EM B L ID Pred icted (LS) m ean diff. 95.00% C I o f diff. Sum m ary A dj. p value N am e Targets 219 C H EM B L429023 1.096 0.8480 to 1.343 **** <0 .0001 R O TEN O N E M T-N D 4 M TO R H TR 6# A R C YB A M T-N D 1 N D U FA B 1 N FE2L2 167 C H EM B L1870314 0.9401 0.6924 to 1.188 **** <0 .0001 SID 124896949 IN SR # TD P1 84 C H EM B L26320 0.8078 0.5601 to 1.055 **** <0 .0001 G br-12935 SLC 6A 3 SIG M A R 1 SLC 6A 2 SLC 6A 4 C YP2D 6 C B X1 KLH L6 127 C H EM B L119247 0.7942 0.5465 to 1.042 **** <0 .0001 N PY5R # 73 C H EM B L30008 0.7931 0.5454 to 1.041 **** <0 .0001 Flunarizine SIG M A R 1 H R H 1 A D R A 2C D R D 3 SC N 1A SC N 2A SC N 3A C A C N A 1B C A C N A 2D 1 C A C N B 1 C YP2J2 H TR 2A D R D 2 A D R A 2A SLC 6A 4 C H R M 3 H TR 2C A D R A 1A SLC 6A 3 KC N H 2 C A C N A 1G A D R A 1D A D R A 1B H TR 2B C H R M 5 C A C N A 1I 224 C H EM B L2093893 0.7546 0.5069 to 1.002 **** <0 .0001 C A C N A 1C C A C N A 1D C A C N A 1S 15 C H EM B L1484 0.7311 0.4834 to 0.9788 **** <0 .0001 N IC A R D IPIN E C A C N A 1C C A C N A 1D C A C N A 1S C YP2C 9 C YP3A 4 TR PA 1* 140 C H EM B L145 0.712 0.4643 to 0.9597 **** <0 .0001 C A FFEIC A C ID M M P9 M M P2 C O M T M M P1 227 C H EM B L1814749 0.5252 0.2775 to 0.7729 **** <0 .0001 D PP8 123 C H EM B L3040440 0.4979 0.2502 to 0.7456 **** <0 .0001 PN D -1186 PTK2 M A PK9 M A PK10 145 C H EM B L456444 0.4656 0.2179 to 0.7133 **** <0 .0001 #### KC N N 3 68 C H EM B L27759 0.4263 0.1786 to 0.6740 **** <0 .0001 Entino stat H D A C 1 H D A C 2 N C O R 2 C YP3A 4 125 C H EM B L781 0.3736 0.1259 to 0.6213 **** <0 .0001 M azindo l SLC 6A 2 SLC 6A 3 SLC 6A 4 SIG M A R 1~ H R H 2 126 C H EM B L2204995 0.3323 0.08461 to 0.5800 *** 0.0006 G SK343 EED EZH 2 R B B P4 R B B P7 SU Z12 A EB P2 EZH 1 143 C H EM B L101326 0.3269 0.07921 to 0.5746 *** 0.0008 fipro nil G A B R A 1 G A B R A 2 G A B R A 3 G A B R A 4 G A B R A 5 G A B R A 6 G A B R B 1 G A B R B 2 G A B R B 3 G A B R D G A B R E G A B R G 1 G A B R G 2 G A B R G 3 G A B R P G A B R Q 53 C H EM B L2347651 0.2868 0.03906 to 0.5345 ** 0.008 Ispinesib KIF11 50 C H EM B L222102 0.2797 0.03200 to 0.5274 * 0.0115 Ku-55933 A TM PIK3C B PR KD C PIK3C A 24 C H EM B L41 0.2752 0.02754 to 0.5229 * 0.0143 Fluo xetine SLC 6A 4 A D R A 2A H TR 1A * H TR 1B * H TR 1D * H TR 1F* C YP2C 19 H TR 2A * H TR 2C * SLC 6A 2 A C H E N ET SIG M A R 1 C YP2D 6 157 C H EM B L2424780 0.2566 0.008854 to 0.5042 * 0.0342 V esato lim o d TLR 7* 59 C H EM B L2396661 0.2488 0.001115 to 0.4965 * 0.0477 A lpelisib PIK3C A PIK3R 1 PIK3C D PIK3C G Inhibitio n is default m o de o f actio n; * indicates ago nism ; # indicates binding; ~ indicates unkno w n activity ID C H EM B L ID Pred icted (LS) m ean diff. 95.00% C I o f diff. Sum m ary A dj. p value N am e Targets 219 C H EM B L429023 1.096 0.8480 to 1.343 **** <0 .0001 R O TEN O N E M T-N D 4 M TO R H TR 6# A R C YB A M T-N D 1 N D U FA B 1 N FE2L2 167 C H EM B L1870314 0.9401 0.6924 to 1.188 **** <0 .0001 SID 124896949 IN SR # TD P1 84 C H EM B L26320 0.8078 0.5601 to 1.055 **** <0 .0001 G br-12935 SLC 6A 3 SIG M A R 1 SLC 6A 2 SLC 6A 4 C YP2D 6 C B X1 KLH L6 127 C H EM B L119247 0.7942 0.5465 to 1.042 **** <0 .0001 N PY5R # 73 C H EM B L30008 0.7931 0.5454 to 1.041 **** <0 .0001 Flunarizine SIG M A R 1 H R H 1 A D R A 2C D R D 3 SC N 1A SC N 2A SC N 3A C A C N A 1B C A C N A 2D 1 C A C N B 1 C YP2J2 H TR 2A D R D 2 A D R A 2A SLC 6A 4 C H R M 3 H TR 2C A D R A 1A SLC 6A 3 KC N H 2 C A C N A 1G A D R A 1D A D R A 1B H TR 2B C H R M 5 C A C N A 1I 224 C H EM B L2093893 0.7546 0.5069 to 1.002 **** <0 .0001 C A C N A 1C C A C N A 1D C A C N A 1S 15 C H EM B L1484 0.7311 0.4834 to 0.9788 **** <0 .0001 N IC A R D IPIN E C A C N A 1C C A C N A 1D C A C N A 1S C YP2C 9 C YP3A 4 TR PA 1* 140 C H EM B L145 0.712 0.4643 to 0.9597 **** <0 .0001 C A FFEIC A C ID M M P9 M M P2 C O M T M M P1 227 C H EM B L1814749 0.5252 0.2775 to 0.7729 **** <0 .0001 D PP8 123 C H EM B L3040440 0.4979 0.2502 to 0.7456 **** <0 .0001 PN D -1186 PTK2 M A PK9 M A PK10 145 C H EM B L456444 0.4656 0.2179 to 0.7133 **** <0 .0001 #### KC N N 3 68 C H EM B L27759 0.4263 0.1786 to 0.6740 **** <0 .0001 Entino stat H D A C 1 H D A C 2 N C O R 2 C YP3A 4 125 C H EM B L781 0.3736 0.1259 to 0.6213 **** <0 .0001 M azindo l SLC 6A 2 SLC 6A 3 SLC 6A 4 SIG M A R 1~ H R H 2 126 C H EM B L2204995 0.3323 0.08461 to 0.5800 *** 0.0006 G SK343 EED EZH 2 R B B P4 R B B P7 SU Z12 A EB P2 EZH 1 143 C H EM B L101326 0.3269 0.07921 to 0.5746 *** 0.0008 fipro nil G A B R A 1 G A B R A 2 G A B R A 3 G A B R A 4 G A B R A 5 G A B R A 6 G A B R B 1 G A B R B 2 G A B R B 3 G A B R D G A B R E G A B R G 1 G A B R G 2 G A B R G 3 G A B R P G A B R Q 53 C H EM B L2347651 0.2868 0.03906 to 0.5345 ** 0.008 Ispinesib KIF11 50 C H EM B L222102 0.2797 0.03200 to 0.5274 * 0.0115 Ku-55933 A TM PIK3C B PR KD C PIK3C A 24 C H EM B L41 0.2752 0.02754 to 0.5229 * 0.0143 Fluo xetine SLC 6A 4 A D R A 2A H TR 1A * H TR 1B * H TR 1D * H TR 1F* C YP2C 19 H TR 2A * H TR 2C * SLC 6A 2 A C H E N ET SIG M A R 1 C YP2D 6 157 C H EM B L2424780 0.2566 0.008854 to 0.5042 * 0.0342 V esato lim o d TLR 7* 59 C H EM B L2396661 0.2488 0.001115 to 0.4965 * 0.0477 A lpelisib PIK3C A PIK3R 1 PIK3C D PIK3C G Inhibitio n is default m o de o f actio n; * indicates ago nism ; # indicates binding; ~ indicates unkno w n activity STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 152 Timothy James Yuji Birkle – November 2023 Figure 44. Images of cultures treated with each neuroprotective hit compound in the presence of LPS Randomly selected images of cultures treated with all hit compounds in the presence of LPS, with DMSO LPS- and DMSO LPS+ images for comparison, after staining with Hoechst 33342 (nuclei), NeuO (live neurons), IB4-AF594 (microglia), and DRAQ7 (necrotic cells). Image side lengths = 525µm. Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 153 and neuronal loss by these means. Overall, this secondary data from the screen represents valuable toxicology data for translational research but may also suggest important neurodegenerative biology. Conversely, some compounds appeared to increase neuronal counts in the absence of LPS, similarly to the positive control, BAY61 (Fig. 43A). However, none of these baseline protective effects were significant here. 7.2.7 Parallel analysis of microglia suggests mechanisms of neuroprotection An advantage of this approach is the parallel analysis of all cell types in the cultures. Astrocyte numbers were largely unchanged with LPS (Fig. 41C). In contrast, microglia numbers increased with LPS, as expected (Fig. 41B), and many treatments altered microglial numbers (Fig. 45A, B; Appendix 3, Table 8, 9). Microglia are crucial for LPS-induced neuronal loss, as removal of microglia is sufficient to prevent the loss (Birkle and Brown, 2023; Fricker et al., 2012a). Interestingly, most treatments altering microglial numbers in LPS-treated wells were neuroprotective, regardless of whether microglia increased or decreased (Fig. 45C). As LPS induces microglial proliferation, a treatment effect on microglial number may indicate an effect on microglial response to LPS. However, compounds may affect microglial proliferation independent of LPS- induced signalling pathways. Hits modulating ADRs protected neurons without affecting microglia number (Fig. 45C), suggesting no effect on microglial proliferative state. Meanwhile, some MAPK inhibitors (and other compounds) dramatically reduced microglial numbers while protecting neurons, clearly blocking LPS-induced proliferation either directly or indirectly. Other MAPK inhibitors had little or no effect on microglial numbers, suggesting dependence on the specific kinases targeted by each compound. Finally, compounds targeting steroid signalling tended to increase microglial numbers in the presence of LPS (but not in its absence; Appendix 3, Table 8, 9) despite their strong neuroprotective effects, implying an effect on microglial polarisation (i.e. the type of microglial activation). Many other hit compounds outside of these categories acted similarly, and these differences are also reflected in representative images (Fig. 44). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 154 Timothy James Yuji Birkle – November 2023 In order to gain more insight into whether the compounds were affecting microglial state, I analysed microglial morphology ± LPS in the cultures. It is not possible to define microglial state based on microglial morphology alone, and particularly not in two-dimensional culture; however, microglial morphology in these cultures does change with LPS treatment and may therefore loosely indicate microglial state. Microglial shape was inferred from microglial cell masks obtained with the IB4 staining, and results were improved considerably by use of the accurately classified microglial nuclei for marker-based segmentation of those masks, particularly in microglia-dense images. The FormFactor measurement, equivalent to ‘circularity’ in Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 155 ImageJ and ranging between 1 (perfectly circular) and 0, gave the best signal-noise ratio for detecting the microglial morphology change induced by LPS. I identified a clear shift in microglial FormFactor with LPS as expected, with microglia becoming rounder and more amoeboid with LPS activation (Fig. 45D, E). Notably, those hit compounds that increased the number of microglia in the presence of LPS (such as compounds affecting steroid metabolism/signalling) tended to decrease the microglial FormFactor back towards (or beyond) that of LPS-untreated microglia (Fig. 45F). This suggests that these neuroprotective compounds inhibited the LPS-induced activation of microglia, despite increasing microglial numbers. It is noticeable that the LPS-induced increase in FormFactor here is opposite to data from my previous low-throughput studies, where LPS activation caused increased ramification and reduced microglial circularity (Fig. 17, 32). This difference may be explained by LPS-induced neuronal loss in this screen being more substantial than in the previous work, resulting in microglia being in a very different microenvironment at the end of LPS treatment here compared to previous assays. The high-throughput culture format in 384-well plates may also affect microglial morphology (for example, the culture surface in these plates is different from the plates used in previous assays), Figure 45. Parallel analysis of microglia suggests mechanisms in primary screening A-B: Average normalised microglia counts from neuron-glia cocultures treated without (A) and with (B) LPS and each compound in the screen, and ordered by increasing microglia count. RM 2-way ANOVA with Dunnett’s post-hoc test comparing all treatments to DMSO in the presence and absence of LPS. Darker regions of each graph indicate p<0.05 significance. Data are represented as mean ± S.D., N = 4. C: Average normalised neuron counts plotted against average normalised microglia counts for all compounds ± LPS, with LPS-treated data from hit compounds highlighted and coloured by target class. Data are represented as mean ± SD, N = 4. D: representative Hoechst-IB4 labelled images (cropped) and corresponding segmented microglial masks from cultures treated with DMSO for 3 days (DIV10) in the presence and absence of LPS, illustrating the microglial morphology analysis. Scale bars = 100µm. Mask colours are arbitrary. E: Average microglial FormFactor (circularity) from neuron-glia cocultures treated ±LPS and ± each compound in the screen. Data are represented as mean ± S.D., N = 4. F: Average microglial FormFactor plotted against average normalised microglia counts for all compounds in the presence of LPS, with data from hit compounds highlighted and coloured by target class. Data are represented as mean ± S.D., N = 4. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 156 Timothy James Yuji Birkle – November 2023 and finally the analysis here was performed using different software where different image processing steps were available. Differences with previous data aside, many compounds altered microglial morphology either in the absence or presence of LPS and, interestingly, most of these also protected neurons and/or altered microglial numbers. 7.2.8 Multidimensional phenotypic data prioritises hit compounds based on overall disease phenotype To meaningfully aggregate the multidimensional data output and more closely examine the potential mechanisms of different hit compounds, I hierarchically-clustered hits based on their range of output measures taken together. This analysis aimed to identify groups of compounds that induced similar overall phenotypes in the cultures (with respect to counts for all cell types and microglial morphology) in both the presence and absence of LPS, which was beyond the scope of the previous two-dimensional analyses (Fig. 45). As visualised via the heatmap in Fig. 46, this approach segregated nearly all the neuroprotective compounds (group ‘X’) from the unprotective compounds as expected. In addition, neurotoxic compounds largely clustered together and generally increased microglial and astrocyte numbers (group ‘Y’). I focused my analysis on the neuroprotective compounds, within which three broad phenotypes were apparent in the data (Fig. 47A). The relevance of these categories is supported by significant differences between them in various dimensions of the data (Fig. 47B-I). However, it should be noted that this analysis will lack the nuance to capture differences in phenotype that are not measured in the original analysis. As displayed at the top of Fig. 47A, one group of hit compounds (hereafter, category 1) clustered closer to the negative DMSO control than the rest and gave the weakest neuroprotection of around 57% on average (Fig. 47B). These compounds did little to alter glial numbers or microglial morphology (Fig. 47C, D, F, G) and may therefore be inhibiting specific neurotoxic pathways rather than, for example, preventing broadly neurotoxic states of glia. Consistent with this, compound 22 in this group is a NOS2 inhibitor – nitric oxide production is a specific neurotoxic mechanism previously identified in mixed culture (Bal-Price and Brown, 2001; Mander and Brown, 2005). Other neuroprotective hits in this category include the majority of the adrenergic receptor modulators, and some MAPK inhibitors. Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 157 Figure 46. Full clustered heatmap analysis Clustered heatmap of all compounds (top to bottom) and all data (counts by each cell type and microglial morphology in the absence and presence of LPS). E.g. “Neuron –“ and “Neuron +” columns display mean neuron counts in the absence and presence of LPS respectively. Each data type was min-max normalised between 0 and 1 based on min and max values across both LPS- treated and LPS-untreated conditions, to ensure comparability. ‘X’ indicates cluster containing the vast majority of neuroprotective hits. ‘Y’ indicates a branch containing most neurotoxic compounds (defined by reduced neuronal counts in the absence of LPS). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 158 Timothy James Yuji Birkle – November 2023 Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 159 By contrast, in the middle of the Fig. 47A there is a more strongly protective cluster of compounds (hereafter category 2; average of around 91% protection; Fig. 47B) that increased microglial numbers in the presence of LPS by 39% on average and altered microglial morphology (Fig. 47C, F, G). These hits may be directly affecting microglial states or polarisation and thereby protecting against LPS-induced neuronal loss. Supporting this hypothesis, compound 214 in this group is the TLR4 inhibitor TAK-242/CLI-095/resatorvid, which directly antagonises LPS-induced signalling and microglial activation via TLR4. TLR4 inhibition would be expected to prevent microglia activation by LPS, supporting the notion that other hits in this cluster may be neuroprotective by altering microglial state. Notably, this cluster also includes most of the steroid signalling-related hits and has little representation of adrenergic signalling- related compounds. Finally, at the bottom of the Fig. 47A there is a cluster of hits (hereafter, category 3) that include the most strongly neuroprotective compounds of the screen and the positive control BAY61 as well as some less effective hits (average of 98% protection; Fig. 47B). The most striking feature of this cluster is a decrease in microglial and/or astrocyte numbers in both the presence and absence of LPS (average of 50-60% microglial depletion either ±LPS, Fig. 47C, H; average of 35% astrocyte depletion either ±LPS, Fig. 47D, I), and an increase in necrotic cells (average increase of 400% in the absence of LPS; Fig. 47E), which are probably glial cells given that live neurons increased in number. Glial depletion, particularly of microglia, is known to protect Figure 47. Multidimensional phenotypic data prioritises hit compounds based on overall disease phenotype A: Clustered heatmap of data from neuroprotective hits and DMSO (counts of each cell type and microglial morphology in the absence and presence of LPS). E.g. “Neuron –“ and “Neuron +” columns display mean neuron counts in the absence and presence of LPS respectively. In the analysis of all compounds (Fig. 46), each data type was min-max normalised between 0 and 1 based on min and max values across both LPS-treated and LPS-untreated conditions to ensure comparability, and this data was then filtered for hits and DMSO here. Legend and left-hand side colour labels indicate the target pathway for each hit. Compound IDs and category numbers provided on the right-hand side. B-I: Average data (variable: see Y-axes) for hit compounds of each category. Each datapoint is the average value for one hit compound over all screen repeats. Categories 1, 2, and 3 included 13, 10, and 7 hit compounds respectively. One-way ANOVA with Tukey’s post hoc test. Blue and orange dashed lines indicate the average data value in LPS-untreated and LPS-treated DMSO wells respectively. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 160 Timothy James Yuji Birkle – November 2023 neurons against LPS in mixed cultures (Birkle and Brown, 2023; Fricker et al., 2012a), so these compounds probably protect neurons by depleting glia. The most strongly glia- depleting hit compounds 152 and 161 are OTS964 (a PBK/NEK1 inhibitor) and XL888 (a HSP90 inhibitor) respectively. 7.3 Discussion 7.3.1 Summary of findings Having previously validated an automated image analysis workflow for neuron-glia cocultures, here I present results from a phenotypic high-content screen aiming to identify compounds (and their cellular targets) that have neuroprotective activity against lipopolysaccharide (LPS)-induced neuronal loss in such cultures. I tested 227 compounds over four biological repeats in a screen that included approximately 24,000 image sets and classified 100 million cells. This achieved an overall Z-factor in the primary neuroprotective assay of 0.927 for DMSO LPS- against DMSO LPS+, 0.724 for DMSO LPS+ against positive control LPS+, and a robust Z-factor of 0.448 for all data taken together. All images will be available as a resource at BioImage Archive after publication (accession S-BIAD890) and associated analysis pipelines can be found on GitHub (https://github.com/timjyb/Birkle-et-al-2023-HTS). The screen found 29 significantly neuroprotective compounds, of which 14 (and the positive control SYK inhibitor, BAY61) rescued neurons by at least 75%, and a further 8 rescued by at least 50% (Table 2). Some hits reflect the known biology of LPS-induced neuronal loss in these mixed cultures, including: a second SYK inhibitor that protected by 73% (compound 26), the TLR4 inhibitor TAK-242/CLI-095/resatorvid that protected by 86% (compound 214), and an inhibitor of NOS2 that protected by 68% (compound 22). This confirms that the screening method can recapture previously published findings within this model system (Bal-Price and Brown, 2001; Birkle and Brown, 2023). Data from LPS-untreated wells was also valuable in identifying neurotoxic compounds, including expected compounds such as rotenone (Table 3). It would be interesting to study these neurotoxic compounds further in neuronal monocultures to determine whether they act directly on neurons or via glia, but this was not investigated here. The hit compounds were assessed for overlap with respect to targets and pathways, and there were found to be multiple hits affecting steroid signalling, Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 161 adrenergic signalling, and MAPK signalling. The multidimensional data output from the analysis allowed clustering of neuroprotective hits into 3 general categories: 1) compounds weakly protecting neurons without affecting glia, perhaps by inhibiting specific neurotoxic mechanisms (e.g. the NOS2 inhibitor, compound 22); 2) compounds strongly protecting neurons while modulating microglial number and morphology, suggesting that their protection was by affecting microglial state (e.g. the TLR4 inhibitor, compound 214); and 3) compounds protecting neurons while depleting microglia and/or astrocyte populations (though microglial state may also be affected; e.g. the positive control, BAY61). Category 2 may be particularly relevant for further investigation, as modifying microglial state may be a powerful means by which to influence neuroinflammatory disease progression (Bartels et al., 2020). Category 1 compounds/targets may be of lesser interest due to weaker effect sizes, and category 3 compounds/targets may be less promising targets for therapies given the possible deleterious effects of removing microglia and/or astrocytes – although, microglia- depleting CSF1R antagonists appear safe in clinical studies (Cannarile et al., 2017). It is important to note that there is no clear relationship between microglial morphology and their activation state, given that morphology depends on cellular adhesion and motility that differ between two-dimensional cultures and the in vivo environment. ‘Activation’ is also a broad and poorly-defined term, but LPS modifies microglial morphology in these cultures, and this modification is altered by category 2 hits in particular. Similarly, there is no simple link between proliferation and activation, though microgliosis is frequently considered to indicate some form of activation under disease conditions (Paolicelli et al., 2022). In this study, the classical inflammatory stimulus LPS induces proliferation as part of its broad effects on microglia. Many hits (particularly category 2) increased this proliferation, potentially by pushing microglia to alternative states that are both proliferative and neuroprotective, and which may have an altered morphology. 7.3.2 Screen hits The top hits from the screen were assessed for relationships and overlap between their targets to prioritise these pathways for discussion here. For all targets of interest, the screen data was also checked for non-neuroprotective compounds that also had relevant activity, as their existence would reduce confidence in that target. Where identified, these are noted below as well. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 162 Timothy James Yuji Birkle – November 2023 Within the hit compound cluster 2, an unexpected enrichment for steroid signalling modulators was identified. Cholesterol is metabolised to steroid hormones within cells, including testosterone, oestrogen, progesterone and cortisol, largely through cytochrome P450 (CYP) enzyme activities (Oskarsson et al., 2016). Strikingly, out of the top 8 most protective hit compounds (all with >89% protection), 5 interfered with either CYP enzymes or steroid hormone receptors. Anastrozole (29) and bifonazole (67) together inhibited CYP3A4/17A1/19A1/51A1, though as 3 other screen compounds targeting CYP3A4 were not neuroprotective, inhibition of CYP3A4 is unlikely to be neuroprotective. The remaining CYPs (CYP17A1/19A1/51A1) all have known links to neurodegeneration, with variants in each being associated with 2-fold or higher risk for AD or PD in certain populations (Bahado-Singh et al., 2023; Chace et al., 2012; Hartz et al., 2023; Huang and Poduslo, 2006; Zheng et al., 2016). Notably, microglia have little to no expression of these genes, and the principal steroidogenic cells in the brain are instead astrocytes (Gottfried-Blackmore et al., 2008; Lin et al., 2022; Zwain and Yen, 1999). Based on this data, steroids may regulate inflammatory neurodegeneration, potentially by limiting inflammation. One important steroid signalling pathway downstream of CYP activity may be glucocorticoid signalling, as the strongest neuroprotective hit, triamcinolone (31), and the category 2 hit compound medroxyprogesterone acetate (114) both agonise glucocorticoid receptors (NR3C1). In the literature, there is strong evidence that glucocorticoid receptors in particular regulate macrophage functions, including microglia (Diaz-Jimenez et al., 2021). Indeed, lack of glucocorticoid receptor in mice exacerbates inflammatory neurodegeneration in the context of stress, LPS, and Parkinson’s disease (Carrillo-de Sauvage et al., 2013; Maatouk et al., 2018; Picard et al., 2021; Ros-Bernal et al., 2011). The glucocorticoid receptor may also have neuron- intrinsic roles in neuronal health, as it can regulate gene expression and splicing in neurons, including that of the important neuronal homeostasis protein BDNF (H. Chen et al., 2017; L. Li et al., 2023). My data align with these established roles of the glucocorticoid receptor. This may explain some of the protective efficacy of CYP inhibitors, as these could in principle modify production of all steroids, including glucocorticoids. Similarly, oestrogen and progesterone signalling are particularly implicated in the LPS-induced neuronal loss. Anastrozole (29, category 2, see above) is an antagonist of CYP19A1 (also known as aromatase or oestrogen synthetase), fulvestrant (28, Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 163 category 2) is an oestrogen receptor antagonist that also protects, and bifonazole (67, category 2) is an inhibitor of CYP17A1, required for synthesis of oestrogen (and other steroids). Though not present in the ChEMBL data, bifonazole has also been reported to inhibit CYP19A1 with nanomolar potency (Egbuta et al., 2014). Interestingly, CYP19A1 antagonists have previously been found to reduce dementia risk in women with breast cancer (Branigan et al., 2020), but CYP19A1 has reported neuroprotective roles as well (Garcia-Segura et al., 2003). Collectively, the data support a role of oestrogen in LPS-induced neuronal loss. Meanwhile, medroxyprogesterone acetate (114, category 2) is a progesterone analogue that activates the progesterone receptor and is used clinically to prevent ovulation and reduce menopause symptoms in women, and to reduce sex drive in men. Progesterone has various neuroprotective effects, including reducing neuroinflammation (Guennoun, 2020). Overall, the data suggests that progesterone protects, and oestrogen promotes, LPS-induced neuronal loss, as this can be prevented by any of the above compounds. Finally in relation to steroid signalling, compound 130 was 69% protective and agonises the liver X receptors-α/β (LXRs), which are activated by cholesterol derivatives and directly activate LXR-responsive genes (Bilotta et al., 2020). LXRs have established roles in microglia/macrophage regulation, but also astrocytes and oligodendrocytes (Song et al., 2022). Notably, agonism inhibits NOS2 expression, NO production and proinflammatory cytokine release in LPS-treated microglia and astrocytes (Secor McVoy et al., 2015; Wu et al., 2016; Zelcer et al., 2007; Zhang- Gandhi and Drew, 2007). In vivo LXR knock-out exacerbates autoimmunity in wild- type mice and amyloid-β pathology in APP/PS1 mice, while agonism ameliorates pathology in experimental autoimmune encephalitis and intracerebral haemorrhage (A- Gonzalez et al., 2009; Secor McVoy et al., 2015; Wu et al., 2016; Zelcer et al., 2007). Overall, protection by this LXR agonist adds to the evidence within this screening data that steroid signalling and related pathways are important for inflammatory neurodegeneration. Adrenoreceptor (ADR) modulators were also amongst the hit compounds. There are three subgroups of adrenoreceptors: α1 (Gq-coupled), α2 (Gi-coupled), and β (Gs-coupled). Previous studies suggest that cerebellar neurons express high levels of α2- ADRs, while microglia highly express β2-ADRs and low levels of α1A, with induction of α2A expression after LPS treatment (Gyoneva and Traynelis, 2013; Hertz et al., 2010; Schambra et al., 2005; Schulz et al., 2022; Sugama et al., 2019). The endogenous STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 164 Timothy James Yuji Birkle – November 2023 agonists noradrenaline and adrenaline (30, 109) were both ~88% protective, which may reflect the known neuroprotective functions of β2-ADRs in microglia/macrophages (Freire et al., 2022). β2 agonism reduces Parkinson’s disease risk in human populations (Cepeda et al., 2019; Mittal et al., 2017) and can reduce microglial inflammation and phagocytosis both in vitro and in vivo (Colton and Chernyshev, 1996; Damo et al., 2023; Evans et al., 2020; Fujita et al., 1998; Prinz et al., 2001; Qian et al., 2011; Stowell et al., 2019; Torrente et al., 2023). Interestingly, there were 3 ADR antagonists that protected neurons in this screen. However, two of these (doxasozin and alfuzosin, 111 and 112) are relatively specific α1-ADR inhibitors, so would not block endogenous neuroprotective activity from β-ADRs while instead inhibiting potentially proinflammatory α1-ADRs (Konig et al., 2020; Sharma and Farrar, 2020; Staedtke et al., 2018; Yu et al., 2022). The remaining ADR antagonist, labetalol (6), inhibits both α1- ADR and β-ADRs, which alongside its low potency may explain its lower protective efficacy (37%). Finally, some ADR modulators were not protective including the α1- and β-ADR agonist isoproterenol (83) (Copik et al., 2015), which is compatible with β- ADRs protecting while α1-ADRs are detrimental. Amiodarone (108, α2-ADR inhibitor among other activities), dexmedetomidine (115, α2-ADR agonist) and flunarizine (73, weak α2-ADR inhibitor) also did not protect, indicating that α2-ADRs do not regulate LPS-induced neuronal loss. As noted above, noradrenaline and adrenaline (30, 109) were both strongly protective at 1 µM, which is interesting given that these are endogenous neuromodulators whose extracellular concentrations in the brain change in different physiological and pathological states. This suggests the possibility, for example, that physiological arousal/excitement/stress may supress neuroinflammation in the brain, and that the early loss of noradrenaline-releasing neurons in AD may contribute to subsequent neurodegeneration (Gutiérrez et al., 2022). A number of the hit compounds were anti-inflammatory, consistent with the inflammatory nature of the LPS-induced model, including resatorvid (214, TLR4 inhibitor), a SYK inhibitor (26), a NOS2 inhibitor (22), triamcinolone (31, glucocorticoid receptor agonist), a liver X receptor antagonist (130), OTS964 (152, TOPK/PBK and NEK1 inhibitor), and zafirlukast (105, cysteinyl leukotriene receptor 1/2 and MAPK14 inhibitor). Cysteinyl leukotriene receptor 1 antagonists have previously been found to be protective in rodent models of brain trauma, stroke, multiple sclerosis, Parkinson’s disease and Alzheimer’s disease (Ghosh et al., 2016). Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 165 This increases confidence that hits from this screen may be protective in relevant brain pathologies. Finally, MAPK pathway proteins were strongly represented among hit compound targets, including MAPK14 (p38α MAPK), MAPK11 (p38β MAPK), MAPK1, upstream kinases (MAP2K1/2/5) and the downstream kinase MAPKAPK2 (from compounds 105, 26, 165, 119, 96, and 210). MAPK signalling is diverse, but p38 MAPK signalling may be particularly relevant based on the higher protective effects observed for inhibitors of this MAPK class. Notably, p38 MAPK activity is increased in AD, and may contribute to hyperphosphorylation of tau (Feijoo et al., 2005; Goedert et al., 1997; Reynolds et al., 2000, 1997). More relevant to these cocultures, p38 MAPKs are also pivotal in mediating proinflammatory microglial responses to tau and LPS, potentially via MAPKAPK2 (Bachstetter et al., 2011; Culbert et al., 2006; Perea et al., 2022). Interestingly, β-ADR, steroid receptor and LXR agonism all exert some of their anti-inflammatory effects by diminishing microglial p38 MAPK activation (Qian et al., 2011; Wu et al., 2016; L. Yang et al., 2020). Notably, erlotinib (101) inhibits MAP2K5 but was not protective, and nor was the p38α/β inhibitor VX-72 (121). However, there is sufficient evidence amongst the neuroprotective compounds to suggests that these MAPK proteins are of interest to control inflammatory neurodegeneration. I have focused here on proteins/pathways that were strongly indicated to be of interest by multiple neuroprotective hits. Interestingly, many of these cellular targets have also been highlighted as novel druggable targets for Alzheimer’s disease treatment by the US National Institute on Aging-funded AMP AD and TREAT-AD consortiums, including CYP19A1, LXRs, p38 MAPKs, NR3C1, and SYK (Potjewyd et al., 2022). However, there are many other interesting treatment effects in the data that may be explored further. Other proteins of interest include γ-aminobutyric acid receptors, heat shock protein 90, PDZ binding kinase, NIMA related kinase 1, and bromodomain- containing proteins. 7.3.3 Screen methods Phenotypic screening with neurons has typically used monocultures and simple measurement of neuronal numbers or synaptic density to identify targets of interest within neuronal development/survival or neurodegeneration (Jiang et al., 2020; Nieland et al., 2014; Sharma et al., 2012; Tian et al., 2019). There are some exceptions to this, such as a recent study proposing screening with neuron-glia mixed cultures (Goshi et STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 166 Timothy James Yuji Birkle – November 2023 al., 2022). However, this study extended to testing 28 conditions using live/dead cell counts and manual analysis of glial morphology. If scaled up, this approach would still only capture a limited amount of data. Another study established an impressive mid- throughput approach for modelling AD with iPSC-derived mixed cultures, and used this to test 70 compounds (at multiple concentrations) for their ability to improve high- content measurements of dendrites, axons, synapses, or cell counts (Bassil et al., 2021). This detailed analysis of neuronal features has both advantages and disadvantages versus measuring coarser phenotypes across all cell types in culture. It would certainly be interesting to combine this advanced model with the type of analysis presented here. Other studies have used cell type classification in neuron-glia coculture high-content assays using commercial platforms (Kaltenbach et al., 2010; Phadke et al., 2022), but the lack of open-source methods means that similar assays are challenging or impossible to replicate for most labs. In a notable exception to the use of in vitro two- dimensional cultures, over 1,000 compounds were tested for their ability to protect neurons survival against ischemia in mouse brain slices (Wang et al., 2006). While impressive, this was undoubtedly an extremely laborious study given the tissue and resources required to culture thousands of brain slices and manually count and scor surviving neurons (though this might now be automated). The same researchers reviewed brain slice models as a platform for drug discovery and conducted a similar screen for Huntington’s disease (Cho et al., 2007; Reinhart et al., 2011); however, the approach has not become established. I present a slightly less physiological but substantially more practical approach, such that this screen was conducted over a few months by a single researcher. In addition, multidimensional data beyond just neuronal counts is extracted here, and in an automated manner. The methods presented here may be better applied to different complex models depending on the experimental aims. For example, cerebellar cultures are only directly relevant to neurodegenerative diseases affecting this brain region, such as ataxia- telangiectasia, and cerebellar neurons and microglia are somewhat distinct within the brain (Colombo et al., 2022; Stowell et al., 2018). Cerebellar cultures have been extensively used due the high yields of relatively homogenous cerebellar granule neurons that they produce (these neurons being the most abundant in the brain) (Bilimoria and Bonni, 2008). However, the methods used here could in principle be extended to brain cells from regions of the brain that are more frequently the focus of neurodegenerative diseases such as the cortex or hippocampus. Alternative Chapter 7: High-content screening for blockers of microglia-mediated neurodegeneration Timothy James Yuji Birkle – November 2023 167 neurodegenerative stimuli may also be preferred depending on the experimental aims. Here I used LPS, which models neuroinflammation and has been directly implicated in neurodegenerative diseases including AD (Zhan et al., 2016; Zhang et al., 2009; Zhao et al., 2017). Additionally, LPS has been widely used to recapitulate Parkinson’s-like pathology (Skrzypczak-Wiercioch and Sałat, 2022), and LPS treatment also alters microglial transcriptomes to resemble those from Alzheimer's disease (AD) (Monzón- Sandoval et al., 2022). However, to model a specific disease such as AD this approach would be improved through use instead of hippocampal neuron-glia cocultures (for which I validated the staining and analysis method in Chapter 4 as well) and/or a more physiological insult such as amyloid-β or homogenates from AD-afflicted brains (Bassil et al., 2021; Jiang et al., 2020). In this study I use primary cell cultures to model neurodegeneration. However, scalability is a pressing concern for primary cultures in screens, as terminally differentiated primary cells cannot be expanded like immortalised cells or stem cells. Here, I achieved sufficient cells through use of rats instead of mice and postnatal animals instead of prenatal animals. In addition, miniaturisation to 384-well microplates nearly quadruples available wells compared to 96-well assays, and here I reliably generated over 1,500 wells of cells per preparation. For future work, it may be possible to pool the litters of multiple time-mated animals, and whole-brain or cortical culture preparations could produce significantly higher yields than the cerebellar preparations here. Looking forwards, it will become increasingly important to use more disease- relevant models beyond primary rodent cultures. It is now feasible to conduct similar work using iPSC-derived cells with greater potential throughput, albeit greater expense. Individual cell types need to be separately differentiated and mixed carefully to generate self-sustaining cultures that reflect the in vivo milieu, and this approach has recently been used to produce a useful neuron-glia coculture model of AD (Bassil et al., 2021). iPSCs will be central to future translational biology, not least given the possibility of patient-specific cellular assays, and iPSC-derived neuron-glia cocultures could be analysed as reported here to maximise data extraction. Meanwhile, three-dimensional organoid cultures are progressing such that their use in future screens may be considered (Gonzalez et al., 2018; Ivascu and Kubbies, 2006; H.-K. Lee et al., 2016; Pampaloni et al., 2007). It is easy to imagine a high-content pipeline for organoids similar to that which has been achieved for whole zebrafish (Early et al., 2018). For the STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 168 Timothy James Yuji Birkle – November 2023 moment, however, these cultures largely lie beyond the realm of high-throughput neurobiology. Chapter 8: Discussion Timothy James Yuji Birkle – November 2023 169 8 DISCUSSION The specific targets and biology studied in this work have been discussed in detail in their respective chapters. This section summarises these projects, discusses limitations that apply to all of them, proposes future directions, and contextualises this work within a field that is working towards microglia-targeted therapies for neurodegenerative diseases. 8.1 Summary While microglia have diverse beneficial roles in neurodegenerative disease, their activity can also have adverse effects. Translational neuroscience is now focusing on these disease-modifying roles of microglia, both good and bad, in efforts to influence these cells for therapeutic benefit (Kwon, 2022). This may be fundamentally more tractable than previous approaches; for example, though targeting the underlying proteinopathy has recently shown promise for Alzheimer’s disease (AD), this approach faces limitations (Gauthier et al., 2022). Treating the underlying proteinopathy of some neurodegenerative diseases may be useful only early on in pathological development, prior to the onset of symptoms and prior to stages of disease where reasonable biomarkers may be available. Later on, feedback loops of inflammatory processes may drive the bulk of disease progression and symptomatic deterioration instead, so microglia and their inflammatory functions may be better targets for the majority of symptomatic individuals. Indeed, for non-proteinopathic neurodegenerative diseases targeting the inflammatory response and associated recovery may be the only viable course of action (Kim et al., 2015). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 170 Timothy James Yuji Birkle – November 2023 Particularly at the later stages of diseases like AD, the adverse effects of microglia can become pronounced, and these cells may cause neurodegeneration themselves, having become dysregulated due to the ongoing pathology and cellular damage (Colonna and Butovsky, 2017; Wilson et al., 2023). This microglia-mediated neurodegeneration can result from: microglial exacerbation of proteinopathy; release of neurotoxic soluble factors such as reactive oxygen species or proinflammatory cytokines, which may act on neurons either directly or by driving damaging phenotypes in other glia or the immune system; or direct phagocytic removal of neurons and synapses. In this work, I set out to identify and investigate novel regulators of microglia- mediated neurodegeneration, as detailed in Chapter 2. I first aimed to develop a staining and automated image analysis methods for neuron-glia cocultures that would allow for their more effective use (Chapter 4). With increasing focus on the interactions of neurons and glia in neurodegenerative disease, robust methods to use neuron-glia coculture models efficiently are increasingly valuable. This project validated the use of open-source machine learning-assisted image analysis tools for accurate identification of the various cell types in neuron-glia cocultures, and the specific analysis pipelines that were constructed are publicly available at https://github.com/timjyb/Birkle-et-al- 2023-HTS. Early exploratory work using these methods identified urokinase (uPA) and spleen tyrosine kinase (SYK) as targets that may contribute to microglia-mediated neurodegeneration. Further investigation of uPA found that broadly inhibiting this extracellular protease (both its proteolytic functions and its binding to its receptor, uPAR) is strongly protective against lipopolysaccharide (LPS)-induced neurodegeneration, potentially by reducing microglial numbers and microglial phagocytosis (Chapter 5). However, specific inhibitors of neither uPA’s proteolytic function nor its binding to uPAR conclusively replicated these effects, with the exception of the uPA-uPAR interaction inhibitor reducing microglial numbers in glial cultures. Addition of exogenous uPA also stimulated strong microglial proliferation in glial cultures. It should be noted that this project lacked validation of the on-target effects of the inhibitors used. However, this study suggested that uPA may promote microglial proliferation, most likely via its signalling functions through uPAR. uPA inhibition may be protective against LPS-induced neuronal loss by reducing microglial number. Chapter 8: Discussion Timothy James Yuji Birkle – November 2023 171 Next, inhibition of SYK was also found to protect against LPS- and pTau- induced neuronal loss. This project benefitted from confirmation of this effect by a second SYK inhibitor and both inhibitors having been validated to have the expected on-target effect. SYK inhibition reliably reduced microglial numbers in neuron-glia cocultures, at least in part by selectively inducing microglial death, while also altering proinflammatory cytokine release. Further, inhibitors reduced microglial phagocytosis of beads, synaptosomes, synapses, and other cells, which may be their primary neuroprotective mechanism. Finally, the protective effect of SYK inhibition was associated with a reduction in the glycolytic shift in response to LPS, but the acidification caused by this shift did not appear to directly cause neuronal loss. Finally, I adapted the neuron-glia coculture neurodegeneration assay and automated image analysis for a high-content screening project. This addressed one of the overarching aims of this work, the identification of novel regulators of microglia- mediated neurodegeneration, in a more thorough way than the previous candidate- driven approaches. 227 annotated compounds were screened for protective effects against LPS-induced neurodegeneration, and 29 were identified as neuroprotective alongside various neurotoxic compounds and others affecting microglial number. The rich data output was further used to categorise hit compounds into functionally meaningful groups and highlight the advantages of phenotypic screening in neuron-glia cocultures using these analysis methods. Particular biological pathways that were highlighted included steroid signalling pathways, adrenergic signalling, and MAPK signalling, all of which have previously been implicated in microglial inflammation and neurodegeneration. However, many of these targets had not previously been tested functionally in a neuron-glia coculture neurodegeneration model, and a number of novel targets and compounds were identified as neuroprotective or neurotoxic. All image data from this screen will be publicly available at the BioImage Archive after publication (accession: S-BIAD890) alongside treatment metadata as a useful resource for the field. 8.2 Limitations 8.2.1 Cerebellar neuron-glia cocultures While the neuron-glia coculture assay that is validated and used here across various projects is useful, it faces various limitations that therefore affect much of this work. The first of these concerns the cellular model system itself. The neuron-glia cocultures STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 172 Timothy James Yuji Birkle – November 2023 here are prepared from rat cerebellum, which is a well-established source due to its provision of a dense population of relatively homogenous neurons (cerebellar granule neurons being the most abundant in the brain) (Bilimoria and Bonni, 2008). However, species-specific biology means that some of the findings here may not be relevant to human cells. Even amongst rodent primary models, cerebellar cocultures are also less directly relevant to most neurodegenerative diseases compared to, for example, hippocampal or cortical cocultures. There is considerable regional heterogeneity in the brain, not least with respect to the microglia that are a focus of this work (Tan et al., 2020). For example, cerebellar microglia are an outlier in terms of morphology, having a relatively un-ramified and compact morphology at baseline both in vivo and in vitro that may reflect different functions and responses to stimuli relative to their counterparts elsewhere (Balion et al., 2022; Colombo et al., 2022; Jebelli et al., 2015; Pampuscenko et al., 2021). Nonetheless, the work on uPA and SYK identified roles for these targets in cortical glial cultures as well, and some of these effects bore resemblance to those identified in the cocultures (such as SYK inhibition appearing to affect phagocytosis by both cortical microglia and microglia in coculture). This supports that the results from cerebellar cocultures may be more broadly relevant to microglia elsewhere in the brain. Another concern that is common to most primary cell cultures is the derivation from pre-/neo-natal rodents, which results in cells that are very young in comparison to the aged cells of the neurodegenerative conditions that are ultimately of interest. The cells here were also subjected to significant ‘culture-shock’ through the disruptive tissue dissociation process required for primary cell culture, and this is known to affect microglial state and function in particular (Cadiz et al., 2022). Further limitations of the primary cells used here include their batch-to-batch nature introducing variability in cell preparation, as was highlighted by variable baseline cell counts (particularly of neurons) throughout the work here. The variation not only affect total cell counts but also the proportion of cell types as well. Perhaps reflecting this, but also reflecting the challenges of using LPS, responses to LPS in the cocultures varied in strength considerably. In some cases, neuronal loss was near-total, whereas in others it was more moderate at around 50%. This variable sensitivity also appeared to affect the ‘type’ of death, with more aggressive neuronal loss being associated with a high number of dead cells while moderate neuronal loss was usually associated with relatively few dead cells. The latter phenotype is more in line with previous studies where the lack of dead cells suggested neuronal cell death by microglial phagocytosis, Chapter 8: Discussion Timothy James Yuji Birkle – November 2023 173 which was then confirmed (Brown and Neher, 2012; Fricker et al., 2012a, 2012b; Neher et al., 2014). The presence of many dead neurons, alongside profound neuronal loss, indicates that other neurotoxic mechanisms must be engaged, though these must still involve microglia given that all neuronal death can be prevented by microglial depletion (Birkle and Brown, 2023). The nature of these mechanisms is unclear, which limits the interpretation of some data from the cocultures such as the protective mechanism of hits identified in the high-content screen. It is likely that this acute neurotoxicity is a result of neurotoxic proinflammatory cytokines or reactive oxygen/nitrogen species released from LPS-activated microglia. Other concerns with the coculture model used here include their relatively poor characterisation compared to more recent models. Firstly, all primary cultures in this work use non-defined, serum-containing media, and this has frequently been highlighted as an issue for in vitro research. Serum is a poorly-defined mixture of components, though the serum components here were ordered from a consistent source and were also subject to a higher level of certification and quality control compared to most serum. Regardless, serum can affect neuronal electrophysiology and maturation even in the absence of effects on viability, and it may also activate microglia (Bardy et al., 2015; Bohlen et al., 2017; Timmerman et al., 2018). Additionally, the microglia used here have not been transcriptomically characterised; in the context of a microglial field in which diverse transcriptomic states have been identified in relation to different disease conditions (Paolicelli et al., 2022), this may be a limitation as no connection can be drawn between these microglia and those of specific diseases. However, it is important to note that the LPS-induced models used here did not aim to faithfully replicate specific disease conditions, but rather to be relevant to neuroinflammatory conditions more broadly. Moreover, functional analysis was prioritised over -omic characterisation. Other important limitations of LPS-induced neurodegenerative models have been discussed where relevant in the Results chapters, and in the Introduction (section 1.10.1). Crucially, though LPS may be a useful tool for studying inflammation in neurodegenerative diseases, it only models the inflammatory aspects of disease; moreover, the induced inflammatory states may be somewhat distinct from the inflammation induced in any specific neurodegenerative disease (Holtman et al., 2015; Sousa et al., 2018). Therefore, results from LPS-induced models usually cannot sensibly be considered for direct translation in isolation (Skrzypczak-Wiercioch and Sałat, 2022). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 174 Timothy James Yuji Birkle – November 2023 Finally, the question of the identity of the ‘Other’ cells in the cocultures used here remains unanswered. Their phase-contrast morphology and frequent association with groups of neurons strongly suggests that these are neurons at the early stages of cell death, but this cannot be certain without further investigation. These being cultures from developing brain tissue, it is possible that these cells are oligodendrocytes (or precursors thereof) or neuronal precursor cells. However, the relative scarcity of these cells suggests they may not be relevant to the findings in this work. 8.2.2 Image analysis The staining and image analysis approach developed and applied throughout this work enables accurate automated quantification of cells in neuron-glia cocultures, but also prohibits certain other analyses that may be beneficial. Firstly, the imaging and analysis captures the relatively coarse phenotypes of cell type counts and microglial morphology at the expense of finer detail on subcellular features. This is a result of both the staining and imaging; the use of live-cell staining was practical and effective, but live-cell stains are limited and do not exist for all cell types. Any future assays intending to visualise specific cellular targets while performing a similar analysis would need to optimise the general approach to work with fixed cells. Imaging was also performed at low magnification, which enables the capturing of large fields-of-view for robust cell counts but prevents good visualisation of important subcellular features such as neurite networks and the fine details of microglial processes. These decisions were made in the context of developing an assay for neurodegeneration where the phenotype of interest was overt changes in the numbers of different cell types. However, neurodegenerative research frequently focuses on detailed neuronal features such as neurite morphology and synapses, including recent work developing new neuron-glia cocultures (Bassil et al., 2021). With optimised immunocytochemistry protocols and more powerful imaging techniques, it may be possible to integrate analysis of broad phenotypes (perhaps using methods similar to those described here) with quantification of these important finer details. One other feature of these cocultures that currently lacks good analysis is the astrocyte population. Here, an astrocyte stain was omitted in favour of neuron and microglia stains because the latter cell types were at the heart of the neurodegenerative assay being used. Astrocytes could still be accurately counted using their distinctive nuclear morphology, a method that I validated in Chapter 4. However, the image Chapter 8: Discussion Timothy James Yuji Birkle – November 2023 175 analysis therefore completely lacked assessment of astrocyte morphology, for example, which may be a valuable phenotype to capture given that these cells are also reactive to inflammatory stimuli (Giovannoni and Quintana, 2020). This could be corrected in the future either using classic astrocyte markers such as GFAP if immunocytochemistry is optimised, or using the live-cell sulforhodamine 101 stain (though this stain has certain limitations) (Hülsmann et al., 2017; Rasmussen et al., 2016). Finally, the image analysis pipeline constructed here relies on machine learning methods, which can pose challenges for reproducibility (“Moving towards reproducible machine learning,” 2021). Cellpose is an openly-accessible deep learning-based segmentation approach for which pre-trained models are available (as used here), and the considerable documentation and validation of this approach is reassuring as to its effectiveness and reproducible output. Manually-trained classification models, on the other hand, are more prone to subjective bias in the initial training. Compounding this, the datasets gathered for model training are frequently not made available for checking and replication of this important analysis step. Here, efforts have been made to have all classifier models, and details on their training, publicly available. Furthermore, the UMAP analysis in Chapter 7 supports the objective relevance of the cell type classifications imposed on the data by myself (or rather, by a classifier that accurately replicates my own classification). 8.2.3 Orthogonal assays and treatments A consistent limitation in the studies presented here is the lack of orthogonal assays to confirm the results from neuroprotective inhibitors. In all studies, multiple inhibitors of the same cellular targets were used, and the similarity in effects between these inhibitors in some cases, particularly in relation to SYK, may be reassuring that the observed phenotypes are a result of on-target effects. This is most true where inhibitors are structurally dissimilar and thus unlikely to have the same off-target effects; such inhibitors were deliberately picked in the uPA and SYK studies and were frequently present in the high-content screen. However, the use of alternative small molecule inhibitors is not an orthogonal assay and cannot completely confirm any given effect. Experiments using genetic manipulation of the targets of interest would have complemented the inhibitor studies well, and these were attempted. For example, magnetofection is a transfection approach that has been reported to achieve good efficiencies in primary cultures and cocultures, which are generally challenging to STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 176 Timothy James Yuji Birkle – November 2023 transfect (Carrillo-Jimenez et al., 2018). However, it was not possible to optimise this within the time constraints of this work. One major hurdle was simply assessing transfection efficiency for any particular cell type within cocultures, as the standard bulk RT-qPCR measurement of RNA levels does not provide cell type discrimination. In addition, transfection attempts also appeared to activate microglia in cocultures, which is a well-known challenge given that microglia are immune cells that are highly sensitive to foreign nucleic acids (necessary to detect viral infections) (Smolders et al., 2018). This unacceptably interfered with microglia-mediated neurodegeneration assays. Other genetic manipulation approaches were not explored but could have included the use of transgenic animals to generate primary cultures (for example, with microglia- specific SYK knockout), or alternative transfection approaches. 8.3 Future directions 8.3.1 Automated image analysis Particularly during the screening project, the analysis developed here generated a rich dataset that was used to assess the overall phenotype of coculture with respect to all measured features (counts of each cell type and microglial morphology). The ability to perform such analysis may be one of the unique advantages of this approach; previous methods often capture considerable detailed information on neuronal health, for example, but at the expense of information on glial populations. However, this concept could be taken further. As discussed in the Limitations (section 8.2), analysis of astrocytes is largely lacking due to the absence of an astrocyte stain, which may be possible to rectify. Even from the current protocol, additional detailed morphology information about microglia could be generated and perhaps combined into condensed morphology metrics using principal component analysis or other dimensionality reduction. There is also further information available for neurons such as neurite density, despite the relatively low magnification of the imaging used, and these could be incorporated into a multivariate assessment of overall phenotype as well. Finally, measurements of other important features could be derived separately from imaging data but similarly incorporated into a multivariate analysis. ELISA or MSD assays could be used to quantify the release of pro- and anti-inflammatory cytokines from treated cocultures in parallel, and it would further be possible to spectroscopically determine media pH for a rough readout on glycolytic response. Chapter 8: Discussion Timothy James Yuji Birkle – November 2023 177 As an alternative to generating more measurements of specific image features, it may now be possible to use more generalised machine learning frameworks such as contrastive learning to identify phenotypes and phenotypic changes within images of neuron-glia cocultures (Kobayashi et al., 2022; H. Zhang et al., 2023). In such methods, images themselves (rather than derived measurements) would be the raw material for learning patterns in the data, and this would be an unbiased approach to identifying important phenotypes. 8.3.2 uPA As discussed in Chapter 5, further study of uPA in microglia would benefit from more rigorous validation and, in particular, it would be exciting to thoroughly test the role of uPA in microglial proliferation by using inactivated uPA or fragments thereof to conclusively test whether proteolysis and/or specific binding events underlie any effect. As noted previously, using orthogonal approaches to confirm or exclude the tentative findings here would be essential, too. If confirmed, then it would be interesting to test the hypothesis that the roles of uPA in microglial phagocytosis and proliferation could arise from common underlying biology, for example a role for uPA in regulating cell adhesion. This hypothesis is supported by the uPA-uPAR system controlling cell adhesion in other cells (De Lorenzi et al., 2016; Ferraris et al., 2014; Hillig et al., 2008; Madsen et al., 2007), and also by the microglial morphology data here where uPA (or uPA-uPAR) inhibitors reduced or prevented LPS-induced microglial shape change. Changes to cell adhesion would certainly modify morphology and phagocytosis in theory, and could affect proliferation given the known feedback between adhesion pathways and proliferative signalling (Jones et al., 2019; Moreno-Layseca and Streuli, 2014). 8.3.3 SYK Future work on SYK will need to deliberately investigate its putative positive and negative roles in Alzheimer’s disease within the same study. To my knowledge, SYK has been studied in P301S tauopathy mice and in 5xFAD amyloid pathology mice (Ennerfelt et al., 2022; Schweig et al., 2019), but not in mixed pathology mice such as a hAPP/hTau model (Lippi et al., 2018). Assessing SYK knockout or inhibition in such mice (specifically, its beneficial or detrimental effects over time/disease) could be particularly informative. In terms of the in vitro work presented here, further research would ideally identify the molecular mechanisms controlling each identified effect of STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 178 Timothy James Yuji Birkle – November 2023 SYK inhibition, particularly which receptors are involved. It is sensible to hypothesise that the effect of SYK inhibition on reducing microglial numbers is due to blockade of CSF1R signalling, and that the reduction of phagocytosis is through blockage of signalling from TREM2 or CR3; however, this needs specific testing. Finally, the work here was unable to thoroughly investigate the role of SYK in controlling microglial metabolism. Given that TREM2 has an immunometabolic role in these cells (Cosker et al., 2021; Piers et al., 2020), and that metabolism can in turn affect disease-relevant phenotypes such as inflammation and phagocytosis (Lauro and Limatola, 2020), the possibility of SYK as a druggable target for influencing microglial metabolism should be explored. 8.3.4 High-content screening Many of the possible methodological improvements to the high-content screen coincide with the noted possibilities for future coculture image analysis methods. However, it will also be essential to conduct similar screens in the future using iPSC-derived cells (as discussed in Chapter 7), and all live-cell stains used here are reported to be effective in human cell models (Baldassari et al., 2022; Er et al., 2015; Rizo et al., 2022; Scaroni et al., 2022; Victor et al., 2022). While using small molecule inhibitors may well be of interest from a drug repurposing perspective, the fact that they often hit multiple cellular targets (which may be both known and unknown) complicates interpretation of the data. With optimised transfection protocols, arrayed CRISPR or siRNA screens could become an effective alternative for future studies, and this would circumvent some of these difficulties. Finally, the screen here identified steroid signalling as a particularly interesting pathway to examine further with respect to microglia-mediated neurodegeneration. It would be exciting to follow-up this finding with detailed study, including full characterisation of the inflammatory and transcriptomic state of microglia in which steroid signalling has been altered. In doing so, it will also be important to study any sex effects, as this screen was conducted using mixed-sex cultures in which baseline steroid signalling may be non-physiological. 8.4 Implications and conclusions Overall, this work has focused on the detrimental effects of microglial activity and added to existing work on microglia-mediated neurodegeneration. Both uPA and SYK are elevated in the diseased or neurodegenerating brain (Mehra et al., 2016; Chapter 8: Discussion Timothy James Yuji Birkle – November 2023 179 Sierksma et al., 2020), and the studies here suggest that they can contribute to disease progression under inflammatory conditions. The high-content screen also suggested other targets for further study including steroid signalling pathways. Collectively, this work finds that all these targets may be of interest for microglia-targeted therapies against neurodegenerative diseases, and particularly those diseases where microglia adopt a strongly proinflammatory phenotype and in which microglia-mediated neuronal damage has been observed. Importantly, this work has focused on targets that are readily targetable pharmaceutically: uPA has been extensively studied as a target for cancer treatment, and the inhibitor prodrug upamostat was FDA-approved for pancreatic cancer treatment in 2017; SYK has also been of interest for diverse diseases, and the first SYK inhibitor was FDA-approved in 2018 for the treatment of chronic immune thrombocytopenia; and the high-content screen used an annotated library of small molecules, therefore by definition near-exclusively identifying ‘druggable’ targets (exceptions being where library compounds were simply endogenous molecules activating certain receptors, rather than being drugs themselves). Much more research will be required to determine the viability of these pathways for neurodegenerative disease treatment, however. Both uPA and SYK are broadly important proteins for diverse physiological functions, and it may be that specific microglial mechanisms downstream of these proteins would be better-suited for selectively targeting detrimental microglial activity in disease. There is interest in SYK as a therapeutic target for Alzheimer’s disease currently, but this is also contentious (Wang and Colonna, 2023). The literature on SYK and the TREM2 pathway in which it sits (though SYK also acts in many other signalling pathways) is mixed and, like microglia, these proteins are not simply good or bad for disease progression (as was discussed further in Chapter 6). This mirrors a common theme in translational microglia research: harnessing microglia for therapy will not only require the right targets, but also targeting the right stage of disease at the right time. Finally, this work has validated image analysis methods that will hopefully encourage and enable more work using neuron-glia cocultures, at a time when these systems are increasingly needed to accurately model complex neurodegenerative biology. Though similar methods are available through commercial platforms, the protocols and analysis approaches used here are open-source and freely available to all researchers. The possibility of further use of these methods in screening is particularly exciting, as the information obtained for all cell types can be used to prioritise hits and STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 180 Timothy James Yuji Birkle – November 2023 filter out those that have undesirable effects despite showing positive results according to the primary outcome measure. 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Endocrinology 140, 3843–3852. https://doi.org/10.1210/endo.140.8.6907 STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 246 Timothy James Yuji Birkle – November 2023 10 APPENDICES APPENDIX 1 – I’M INFECTED, EAT ME! ............................................................................ 247 APPENDIX 2 – SUPPORTING INFORMATION FOR CHAPTER 4............................................ 260 APPENDIX 3 – SUPPORTING INFORMATION FOR CHAPTER 7............................................ 263 Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 247 APPENDIX 1 – I’M INFECTED, EAT ME! This appendix is a direct reproduction of the following published mini-review: Birkle, T., & Brown, G. C. (2021). I'm Infected, Eat Me! Innate Immunity Mediated by Live, Infected Cells Signaling To Be Phagocytosed. Infection and immunity, 89(5), e00476- 20. https://doi.org/10.1128/IAI.00476-20. This publication was written in collaboration with my supervisor, Guy Brown. I produced the initial manuscript, but there were then substantial revisions and additions made throughout the text by both myself and by Guy Brown. Therefore, this should not be considered solely as my own work. Abstract Innate immunity against pathogens is known to be mediated by: barriers to pathogen invasion, activation of complement, recruitment of immune cells, immune cell phagocytosis of pathogens, death of infected cells, and activation of the adaptive immunity via antigen presentation. Here, we propose and review the support for a novel mode of innate immunity whereby live, infected host cells induce phagocytes to phagocytose the infected cell, thereby potentially reducing infection. We discuss evidence that host cells, infected by virus, bacteria, or other intracellular pathogens: i) release nucleotides and chemokines as find-me signals, ii) expose on their surface phosphatidylserine and calreticulin as eat-me signals, iii) release and bind opsonins to induce phagocytosis, and iv) downregulate don’t-eat-me signals CD47, MHC1 and sialic acid. As long as the pathogens of the host cell are destroyed within the phagocyte, then infection can be curtailed, and if antigens from the pathogens are cross-presented by the phagocyte then an adaptive response would also be induced. Phagocytosis of live, infected cells may thereby mediate innate immunity. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 248 Timothy James Yuji Birkle – November 2023 Introduction Mammalian cells can be infected by a variety of pathogenic agents including bacteria, viruses, fungi, protozoa and prions. Intracellular niches within host cells are attractive for many such pathogens, providing the metabolic building blocks and protection from host immune surveillance that are often essential for their propagation – not to mention the obligate reliance of viral infections on the host cell’s genetic and/or translational machinery. Such infections can overrun host cells, exploiting their resources to spread from one cell to another, and between organisms, harming or killing their hosts. To limit this, the mammalian innate immune system can detect such infections and attempt to eliminate or clear the pathogen as quickly as possible, independently of any adaptive immune response that may eventually develop. If this fails, the infected cell may trigger its own cell death. However, while this stops infection of the dying cell, it may aid spread to other cells. Here we hypothesise and review the evidence that infected cells may, in some circumstances, directly induce phagocytosis of themselves without undergoing cell death, thereby being eaten alive and mediating pathogen clearance. This hypothesis is outlined in Figure A1. Pathogen detection Infections can be detected when pathogen-associated molecular patterns (PAMPs) activate any of a variety of host cell pathogen recognition receptors (PRRs) (Takeuchi and Akira, 2010). PAMPs represent structural motifs common to many pathogens which Find-me Phagocyte Eat-me Don’t- eat-me Pathogen (bacteria, virus, prion, protozoa, fungi) Infection Live, infected host cell Host innate immune response Intracellular pathogen Phagocytosis of live, infected cell: limits infection & may cross-present antigens Phagocytosis Phagocytosis Opsonin Figure A1. Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 249 are therefore useful for the immune system to detect, including bacterial lipopolysaccharide (LPS), flagellin and viral genetic material. This drives an innate immune response, including upregulation of complement factors, release of microbiocidal agents, cytokine signalling, and the activity of natural killer cells and phagocytes. Some of these innate responses are essential prior to development of adaptive responses (Tosi, 2005). Cell surface PRRs, including toll-like receptors (TLRs) and Dectins, detect extracellular PAMPs and usually instigate general proinflammatory signalling within and between cells (Franz and Kagan, 2017; Kagan and Barton, 2015; Kumar et al., 2011). By contrast, intracellular PRRs sense intracellular PAMPs, indicative of more serious intracellular invasion. Thus, these RIG-I-like receptors (RLRs), NOD-like receptors (NLRs) and AIM2-like receptors (ALRs) drive more dramatic cellular events, including cell death. Pathogens generally stimulate multiple PRRs and indeed other pathways too, giving multiple potential outcomes for the cell (Franz and Kagan, 2017; Kagan and Barton, 2015; Kumar et al., 2011). Host cell death induced by infection Infection may induce cell death of the host cell as a protective response to deprive pathogens of intracellular niches and curtail their replicative cycles (Jorgensen et al., 2017). We briefly outline host cell death induced by infection here for the purpose of comparison to our hypothesis. Apoptosis is mediated by caspases and Bcl-2 homologous proteins, and causes nuclear condensation, membrane blebbing and cell shrinkage. Cellular functions are shut down and phosphatidylserine (PS) exposure is used to signal for phagocytic engulfment in a process known as efferocytosis (Boada- Romero et al., 2020). Apoptosis and other forms of cell death have been hypothesised to be intrinsically anti-microbial by killing the infected host cell and thereby limiting replication of the pathogen, and in some cases killing the pathogen (Jorgensen et al., 2017). However, the subsequent engulfment of infected cells by phagocytes is now considered the main cause of microbial death instead, via acidification, reactive oxygen species (ROS), and enzymatic degradation within phagolysosomes (Martin et al., 2012; Nainu et al., 2017; Yang et al., 2012). In contrast to most host cells, phagocytes are professional killers, armed with dedicated pathogen killing mechanisms including the NADPH oxidase, inducible nitric oxide synthase, and peroxidases (Yang et al., 2012). Thus, apoptosis itself, as opposed to the subsequent phagocytosis, may have limited anti-microbial activity. In both mice and Drosophila, inhibition of phagocytosis STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 250 Timothy James Yuji Birkle – November 2023 exacerbates viral infections, indicating that phagocytosis (rather than apoptosis) of infected cells is central to viral immunity (Nainu et al., 2017; Watanabe et al., 2005). In addition, apoptosis is normally strongly anti-inflammatory and inhibits antigen presentation by phagocytes, suppressing both innate and adaptive responses to pathogens (Birge et al., 2016); though, it should be noted that such apoptosis can be pro-inflammatory under certain circumstances (Peterson et al., 2017; Weng et al., 2014). Furthermore, apoptotic cells often detach from the extracellular matrix and from other cells, and breakup into soluble apoptotic bodies that may spread infection (Davis and Ramakrishnan, 2009; Kranich et al., 2010; Wyllie et al., 1980). Thus, apoptosis may be the opposite of what is required to fight an infection. In contrast to apoptosis, necrotic forms of cell death, such as necroptosis and pyroptosis, are inherently lytic and induce rupture of the plasma membrane prior to phagocytosis of the cell. They therefore promote inflammation through release of cytosolic proinflammatory mediators and damage-associated molecular patterns (DAMPs). Necrotic death of the cell protects against infection by depriving the pathogen of its host cell, and pyroptosis in particular may trap microbes within the cell corpse or release peptides that directly kill bacteria (Jorgensen et al., 2016; Liu et al., 2016). However, lytic cell death may also promote spread of infection by releasing the live pathogen (Hybiske and Stephens, 2008; Uwamahoro et al., 2014). For example, mycobacterial infections can result in both programmed and secondary (subsequent to apoptosis) necrosis of infected cells, thus allowing lytic release and spread of the mycobacteria (Berg et al., 2016; Roca et al., 2019). For this reason, many pathogens actively induce host cell necrosis themselves through production of proteases, phospholipases, and cytolysins, in order to leave the host cell (Flieger et al., 2018). Thus, some pathogens encourage and exploit host cell death, while other pathogens instead block it, indicating that host cell death may limit their infections (Chung et al., 2017; Fisher et al., 2017; Lamkanfi and Dixit, 2010; Moore et al., 1994). However, clearing pathogen-infected cells prior to host cell death is potentially better than afterwards, as it may: act sooner; avoid dispersal of the pathogen; be more likely to kill the pathogen; invoke less aggressive inflammation than necrotic cell death; and it may enable the cross-presentation of microbial antigens at earlier stages of infection. Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 251 Phagocytosis of live cells It used to be thought that host cells were only phagocytosed when dead or dying. However, it is now clear that host cells can be phagocytosed alive in a wide variety of contexts, including macrophage phagocytosis of viable neutrophils, neurons and tumour cells (Chao et al., 2012; Feng et al., 2018, 2015; Lagasse and Weissman, 1994; Vilalta and Brown, 2018). This generally results in death of the engulfed cell, resulting in a type of cell death we have termed ‘phagoptosis’: cell death due to phagocytosis (Brown and Neher, 2012). This raises the possibility that infected cells are eaten alive, as alluded to in a recent excellent review on macrophage phagocytosis (Lemke, 2019). Below, we will review the evidence that this occurs, and that infected cells release find-me signals, eat- me signals and opsonins which induce this phagocytosis. These signals regulate the phagocytosis of live and dead host cells as well as pathogens (Karaji and Sattentau, 2017; Ravichandran, 2010; Stuart and Ezekowitz, 2005). Analogous to our hypothesis that innate immunity against infection is partly mediated by phagocytosis of live, infected cells, is the relatively recent discovery that innate immunity against cancer is partly mediated by phagocytosis of live cancer cells by host phagocytes (Chao et al., 2012; Feng et al., 2018, 2015). This has led to increasing interest in the field of phagocytosis of live cells and the development of multiple experimental treatments promoting host phagocytosis of live cancer cells (Cham et al., 2020). In addition, it has been found that certain cancer treatments promote antigen presentation by the phagocytes engulfing the cancer cell (Garg et al., 2016). Thus, there is a clear precedent for our hypothesis that innate immunity against infection is partly mediated by phagocytosis of live, infected cells, and that this might promote an adaptive response via antigen presentation. Find-me signals Find-me signals are chemoattractants released by cells to guide phagocytes to their location, facilitating their engulfment, and include nucleotides and chemokines (Ravichandran, 2010). ATP is released as a ‘danger’ signal by injured, stressed or infected cells, either passively due to cellular damage or actively through mechanisms such as pannexin hemichannels or vesicular exocytosis (Dosch et al., 2018; Elliott et al., 2009). For STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 252 Timothy James Yuji Birkle – November 2023 example, HeLa, COS-7 and T84 cells infected with E. coli released ATP (Crane et al., 2002) via Toll-like receptor-mediated exocytosis, and this ATP stimulated macrophage phagocytosis and reduced bacterial loads in vivo (Ren et al., 2014). Macrophages infected by Leishmania donovani also released ATP, but via pannexin-1 channels (Basu et al., 2020). Finally, one notable study found that in the brain, live herpes virus- infected neurons were found to release ATP, which recruited microglia that then phagocytosed the live, infected cells and limited the infection (Fekete et al., 2018). This study directly demonstrates our hypothesis. The nucleotide UDP is another find-me signal that is also released from E. coli- infected mice and lipopolysaccharide-treated macrophages via gap junctions, which reduces bacterial loads in vivo (J. Qin et al., 2016; Zhang et al., 2011). Vesicular stomatitis virus infection of macrophages causes similar release alongside upregulation of the UDP receptor P2Y6, leading to reduced viral infection in mouse models; infection was decreased by addition of UDP and increased by P2Y6 knockout in vitro and in vivo (Li et al., 2014). Chemotaxis to UDP-releasing macrophages may in some cases be mediated by the chemokine MCP-1 (Zhang et al., 2011). Notably, data from other studies show that UDP released from stressed cells may also stimulate phagocytosis of such cells (Koizumi et al., 2007; Neher et al., 2014). Alongside nucleotides, chemokines are another extremely common class of find-me signal. The CC chemokines macrophage inflammatory protein-1α (MIP1α) and macrophage chemoattractant protein-1 (MCP-1) are upregulated before cell death in macrophages infected by influenza, hepatitis C virus, and the bacterium O. tsutsugamushi (Cho et al., 2000; Hofmann et al., 1997; Liu et al., 2017). Additionally, other infections such as by influenza, and human rhinovirus have been shown to elicit CXCL10 release from live host epithelial and alveolar type II cells, which may guide macrophages chemotaxis (Shelfoon et al., 2016; Wang et al., 2011; Xuan et al., 2015). Other chemokines known to recruit macrophages include: CXCL8/12, CCL3/4/13/19/21/24/25 and XCL2 (Xuan et al., 2015). Thus, find-me signals can be released by live, infected cells, thereby attracting phagocytes. However, it should be appreciated that different host cell types differ in their intrinsic capacity to detect pathogens and release find-me signals such as chemokines in response. For example, though many cell types will be capable of chemokine release (and as discussed here, they may be likely to activate this when infected), other populations simply will not be. By contrast, other signals that regulate Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 253 phagocytosis, such as certain eat-me signals and don’t-eat-me signals as we will now discuss, are likely to be observed more ubiquitously given their broader physiological importance to most, if not all, cell types. Eat-me signals: phosphatidylserine and calreticulin An eat-me signal is a signal on a cell inducing a phagocyte to phagocytose the cell. The most well-studied such signal is the membrane phospholipid phosphatidylserine. Though usually confined to the internal face of the plasma membrane by ATP- dependent flippases, once externalised by scramblases phosphatidylserine can be bound by phagocytic receptors on phagocytes to induce phagocytosis. Phosphatidylserine exposure was once thought to only occur during apoptosis, where caspase activity deactivates the flippases that maintain phosphatidylserine asymmetry and activates the scramblase XKR8 (Boada-Romero et al., 2020). However, phosphatidylserine exposure is now known to occur on viable cells in a variety of circumstances, such as immune activation, oxidative stress, or calcium elevation (Elliott et al., 2005, 2009; Fadeel et al., 1998; Jitkaew et al., 2009; Shlomovitz et al., 2019; Tyurina et al., 2007). Phosphatidylserine exposure on viable cells is mediated by calcium-activated scramblases, such as transmembrane protein 16F (TMEM16F), and is reversible once the cytosolic calcium returns to normal (Suzuki et al., 2010). Importantly, phosphatidylserine exposure on viable cells is sufficient to induce phagocytosis of such cells (Jitkaew et al., 2009; Tyurina et al., 2007; Y. Zhang et al., 2020). In addition, cells can undergo reversible apoptosis, insufficient to induce cell death alone, but sufficient to induce phagocytosis of the live cell (Gong et al., 2019; Hornik et al., 2016). Live infected cells can expose phosphatidylserine too, and thereby be subject to this phagocytic clearance. For example, live HIV-infected cells were shown to externalise phosphatidylserine, which induced macrophages to phagocytose these cells, mediated by the phagocytic receptor MerTK and the phosphatidylserine-binding opsonins Gas6 and Protein S (Chua et al., 2018). In another demonstration of this process, infection of human cells by the bacterium Chlamydia was also shown to cause rapid and reversible phosphatidylserine exposure on host cells, dependent on calcium elevation and independent of apoptosis, which then induced macrophages to phagocytose the live, infected cells (Goth and Stephens, 2001). In yet another example, infection of mouse brain with adenovirus (modified as a vector) was shown to cause phosphatidylserine exposure on live brain cells, with subsequent phagocytosis by STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 254 Timothy James Yuji Birkle – November 2023 microglia of the infected cells observed in vivo using 2-photon imaging (Tufail et al., 2017). Mechanistically, this was via calcium activation of phospholipid scramblase 1 (PLSCR1), with the consequent phosphatidylserine exposure on infected cells inducing microglial phagocytosis via the phagocytic receptor MerTK, resulting in clearance of the infected cells. PLSCR1 is known to be induced by viral infection and to mediate the antiviral response of cells to many different viruses by multiple mechanisms (Kodigepalli and Nanjundan, 2015; Lu et al., 2007); this study by Tufail and colleagues may add induction of phosphatidylserine exposure and subsequent live, infected cell phagocytosis to that list. As a final consideration, enveloped viruses normally have phosphatidylserine on their surface, and may thereby cause infected cells themselves to exhibit surface phosphatidylserine when the virus enters or leaves the host cell (Amara and Mercer, 2015; Younan et al., 2018). Another well-known eat-me signal is calreticulin, which can be exposed on the surface of viable, stressed or dying cells, and induces phagocytosis of such cells by the LDL receptor-related protein (LRP1 receptor) on phagocytes (Gardai et al., 2005; Hornik et al., 2016; Wang and Song, 2020). Calreticulin normally functions as a chaperone in the endoplasmic reticulum, but can be released onto the cell surface, or indeed secreted, as a result of endoplasmic reticulum stress, inflammation, or infection (Jeffery et al., 2011; Williams, 2006; Zhu and Newkirk, 1994). For example, Mycobacterium tuberculosis and cytomegalovirus infections cause calreticulin exposure on the surface of infected cells (Jo et al., 2017; Zhu and Newkirk, 1994). Inflammatory activated macrophages release calreticulin, and plasma calreticulin levels are increased in sepsis patients (Byrne et al., 2013; Feng et al., 2018, 2015; Xu et al., 2019). This extracellular calreticulin can act as an opsonin, binding both the target cell and the phagocytic receptor LRP1 on the phagocyte to stimulate engulfment (Basu et al., 2001; Gardai et al., 2005; Ogden et al., 2001). In contrast to exposed phosphatidylserine, which generally inhibits inflammation and antigen presentation, phagocytosis of calreticulin-exposed cells stimulates antigen presentation (Obeid et al., 2007; Panaretakis et al., 2009; Tesniere et al., 2010). Thus, phagocytosis of infected cells exposing calreticulin is more likely to result in cross-presentation of antigens. Don’t-eat-me signals: CD47, sialic acid and MHC-I Counterbalancing eat-me signals, don’t-eat-me signals are surface expressed molecules which discourage phagocytosis by a potential phagocyte. The best-understood example Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 255 is CD47, a plasma membrane-localised protein expressed ubiquitously on host cells which, by activating SIRPα receptor on phagocytes, inhibits engulfment; blocking CD47 thereby increases phagocytosis of viable cells (Gardai et al., 2005; W. Zhang et al., 2020). During malaria, Plasmodium parasites preferentially infect young CD47hi red blood cells (Banerjee et al., 2015). These cells lose CD47 over time, leading to their eventual phagocytic turnover, so infecting CD47hi red blood cells allows the parasite to complete that stage of its life cycle prior to this clearance. However, the host cells fight back; infected red blood cells downregulate CD47 levels to expedite their phagocytic removal (Ayi et al., 2016; Turrini et al., 1992). This is therefore an example of an infected host cell downregulating a don’t-eat-me signal to enable phagocytosis of the infected cell and thereby reduce infection. Sialic acid residues on cell surface glycoproteins and glycolipids also act as a don’t-eat-me signal, whereas removal of these residues (desialylation) promotes phagocytosis of the cell (Meesmann et al., 2010). Infection can induce desialylation of host cells; for example, influenza infection induces a rapid decrease in surface sialic acid residues on live, infected cells (Cho et al., 2016; Nita-Lazar et al., 2015); phagocytes can then phagocytose these cells (Watanabe et al., 2004, 2002). MHC-I is another don’t-eat-me signal present on most healthy host cells to prevent them being phagocytosed (Barkal et al., 2018). MHC-I is downregulated by many virally infected cells to prevent MHC-I-mediated antigen presentation, but this MHC-I downregulation may then promote phagocytosis of the infected host cell, providing effective immunity. Opsonins: MFG-E8, Gal-3, antibodies and complement Opsonins are normally soluble extracellular proteins which, when bound to cells, stimulate phagocytes to phagocytose such opsonin-tagged cells. Opsonins often bind eat-me signals, such as phosphatidylserine, and act as transcellular bridges to phagocytic receptors on phagocytes. For example, galectin-3 can bridge target cells and phagocytes through its carbohydrate binding domains (Caberoy et al., 2012; Karlsson et al., 2009; Nomura et al., 2017). Galectin-3 expression and secretion is upregulated upon infection, including influenza and pneumococcal infections of epithelia (Farnworth et al., 2008; Nita-Lazar et al., 2015). Additionally, as noted above, live HIV-infected cells expose phosphatidylserine and bind the phosphatidylserine-binding opsonins Gas6 and Protein S, which then stimulate the phagocytic receptor MerTK, resulting in STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 256 Timothy James Yuji Birkle – November 2023 phagocytosis of the HIV-infected cells by macrophages (Chua et al., 2018). MFG-E8 is yet another phosphatidylserine-binding opsonin, which can mediate microglial phagocytosis of phosphatidylserine-exposing live neurons (Fricker et al., 2012a). This appears to help remove prion protein-infected neurons in the brain, such that MFG-E8 knockout mice exhibit accelerated prion disease (Kranich et al., 2010). IgG antibodies are classical opsonins, which bind antigens on the pathogen and activate Fcγ receptors on the phagocyte, resulting in phagocytosis. Infected cells might also bind such antibodies, either because: i) pathogen antigens are on the host cell surface as part of the pathogen’s cell cycle, for example during entry or exit from the cell, or ii) pathogen antigens are displayed by the host cell together with MHC-I. Antibody-dependent cellular phagocytosis (ADCP) of live, infected cells is well- established, and in some cases may mediate immunity (Bakkouri et al., 2011; Chung et al., 2007; Cortjens et al., 2017; Tay et al., 2019). Whether pathogen antigens displayed by host MHC-I can bind antibodies is unclear but would require some specific mechanism to prevent it. If antibodies do bind to these displayed pathogen antigens, then they should result in phagocytosis of live, infected cells. The complement system targets infected cells, causing either lysis through formation of a membrane attack complex, or opsonisation of the target through deposition of opsonins C1q, C3b, iC3b and C4b (Merle et al., 2015a, 2015b). C1q can bind phosphatidylserine or calreticulin on host cells, and C3b binds desialylated surfaces and stimulates phagocytosis via complement receptors such as CR1, CR3, and CR4 (Mevorach et al., 1998; Ogden et al., 2001; Païdassi et al., 2008; Verneret et al., 2014). Infected cells cause complement activation at their surface, which enables their phagocytosis by phagocytes, without host cell lysis (Sölder et al., 1989; Stanley et al., 1984; Strijp et al., 1988). Exemplifying this, West Nile virus infection of neurons induces complement tagging of the neurons, resulting in complement-mediated phagocytosis of the live neurons’ synapses by microglia both in culture and in vivo (Vasek et al., 2016). Antigen presentation Antigens from pathogen-infected cells can be cross presented with MHC-I to T cells by dendritic cells that have phagocytosed the infected cells (Albert et al., 1998; Kurts et al., 2010; Schaible et al., 2003). Cross presentation by dendritic cells also occurs with non- lytic virus and intracellular bacterial infections, suggesting that the death of infected Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 257 cells may not be required (Belz et al., 2005). Thus, while signals from dying cells may promote cross-presentation when phagocytosed by antigen-presenting cells, these signals may also be present on infected cells (Yatim et al., 2017). This is supported by the finding that incubation of live, virus-infected cells with dendritic cells leads to dendritic cell presentation of viral antigens to T-cells (Flechsig et al., 2011; Ramirez and Sigal, 2002). Similarly, upon phagocytosis of viable neutrophils, dendritic cells can cross-present antigens from bacteria, yeast or cancer cells that the neutrophil has previously phagocytosed (Alfaro et al., 2011; Megiovanni et al., 2006). Thus, it is possible that phagocytosis of live, infected host cells by antigen presenting cells may result in presentation of pathogen antigens, resulting in adaptive immunity, but this would need to be tested directly. Alternatively, phagocytosis of just part of an infected cell, for example by merocytophagy or trogocytosis, might also be used to induce adaptive immunity in a very similar manner; however, while the evidence for such pathogen transfer is now convincing, it is less clear whether such material can thereafter be used for antigen presentation, and this would certainly depend on the pathogen species (Steele et al., 2016, 2019). Resistance by pathogens If phagocytosis of live, infected cells is an important mediator of immunity, then we might expect resistance mechanisms to have developed in rapidly evolving pathogens such as viruses. Indeed, there is evidence that diverse pathogens express their own products or manipulate host gene expression to inhibit general phagocytosis, though we note that this interference is not necessarily a response to live, infected cell phagocytosis specifically (Uribe-Querol and Rosales, 2017). For example, HIV-1 encodes Tat and Nef proteins, which inhibit phagocytosis of infected cells by macrophages (Debaisieux et al., 2015; Torre et al., 2002); and the human cytomegalovirus (HCMV) expresses the protein UL-18, which mimics the don’t-eat-me signal MHC-I α-chain to inhibit phagocytosis of the infected host cells (Willcox et al., 2003). In addition, many viruses, including SARS-CoV-2, cause upregulation of CD47 or express CD47 mimics, inhibiting phagocytosis of the infected cells (Cameron et al., 2005; Cham et al., 2020; Y.-T. Lee et al., 2016; Tal et al., 2020; Z.-H. Wang et al., 2020). STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 258 Timothy James Yuji Birkle – November 2023 Conclusion We have outlined above a wide range of evidence that live, infected host cells signal to be phagocytosed, and that this may contribute to limiting infection. In Table A1, we list a number of studies with direct evidence of macrophage phagocytosis of live, infected cells, using a range of pathogens and model systems. We hope that the explicit articulation of the hypothesis here (and illustrated in Fig. A1), with discussion of the accumulating supporting literature, will promote awareness of this potentially common innate immune mechanism and encourage its rigorous testing. Such research would involve answering the following questions. Firstly, are host cells infected with the pathogen of interest phagocytosed alive to an extent sufficient to limit infection in vivo? Secondly, does blocking the phagocytosis of infected cells by phagocytes in vivo increase the spread of infection by various pathogens? And thirdly, does phagocytosis of live, infected cells lead to presentation of pathogen antigens and effective adaptive immunity? Author, year, and reference Infectious agent Infected cella Phagocytea Chua et al., 2018 HIV-1 CD4+ T cells Human MDM Ayi et al., 2016 P. falciparum Human RBC Murine and human BMDM/MDM Baxter et al., 2014 HIV-1 Human CD4+ T cells Human MDM Fekete et al., 2018 Neurotropic herpesvirus Murine neurons Murine microglia Tufail et al., 2017 Adenovirus Murine neurons Murine microglia Goth & Stephens, 2001 C. trachomatis, C. pneumoniae Human neutrophils Human MDM If the hypothesis is true, then there may be translational applications. For example, treatment with specific opsonins (or treatments that increase opsonin production) may enhance phagocytosis of infected cells. Treatments boosting phagocyte numbers or expression of specific phagocytic receptors may similarly bolster an innate immune response of this kind, as would antibody treatments that block don’t-eat-me signals such as CD47. Moreover, targeting the phagocytosis of live, infected cells would Table A1. aRBC, red blood cells; MDM, monocyte-derived macrophages; BMDM, bone marrow-derived macrophages Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 259 represent the ability to target a disease earlier on during productive infection than other treatments might be able to, and potentially in a way that actively aids the host adaptive immune response simultaneously. Finally, it should be recognised that a better understanding of live cell phagocytosis in this context may have relevance to other fields, including oncology and neuropathology as we have mentioned during this review. For example, cancer treatments targeting live cell phagocytosis are in development, and this approach may benefit from a broader understanding of the process if we are to design new treatments to push the innate immune system above and beyond its usual capacity. More topically, there is some evidence to suggest that live cell phagocytosis can become dysregulated during hyperinflammation, including under the conditions of a cytokine storm as can be invoked upon infections including by SARS-CoV-2 (Ishidome et al., 2017). Therefore, while we have discussed live, infected cell phagocytosis in depth here, we wish to emphasise how relevant the underlying mechanisms might be to a considerably broader array of highly impactful human diseases. STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 260 Timothy James Yuji Birkle – November 2023 APPENDIX 2 – SUPPORTING INFORMATION FOR CHAPTER 4 Table 4. All cell measurements produced by the CellProfiler pipeline Feature Excluded from classifier? Reason 1 AreaShape_Area 2 AreaShape_BoundingBoxArea 3 AreaShape_BoundingBoxMaximum_X x Location data irrelevant 4 AreaShape_BoundingBoxMaximum_Y x Location data irrelevant 5 AreaShape_BoundingBoxMinimum_X x Location data irrelevant 6 AreaShape_BoundingBoxMinimum_Y x Location data irrelevant 7 AreaShape_Center_X x Location data irrelevant 8 AreaShape_Center_Y x Location data irrelevant 9 AreaShape_Compactness 10 AreaShape_ConvexArea 11 AreaShape_Eccentricity 12 AreaShape_EquivalentDiameter 13 AreaShape_EulerNumber x 0 variance feature (all objects have EulerNumber = 1) 14 AreaShape_Extent 15 AreaShape_FormFactor 16 AreaShape_MajorAxisLength 17 AreaShape_MaxFeretDiameter 18 AreaShape_MaximumRadius 19 AreaShape_MeanRadius 20 AreaShape_MedianRadius 21 AreaShape_MinFeretDiameter 22 AreaShape_MinorAxisLength 23 AreaShape_Orientation 24 AreaShape_Perimeter 25 AreaShape_Solidity 26 Intensity_IntegratedIntensityEdge_DR7 27 Intensity_IntegratedIntensityEdge_Hoechst 28 Intensity_IntegratedIntensityEdge_IB4 29 Intensity_IntegratedIntensityEdge_NeuO 30 Intensity_IntegratedIntensity_DR7 31 Intensity_IntegratedIntensity_Hoechst 32 Intensity_IntegratedIntensity_IB4 33 Intensity_IntegratedIntensity_NeuO 34 Intensity_LowerQuartileIntensity_DR7 35 Intensity_LowerQuartileIntensity_Hoechst 36 Intensity_LowerQuartileIntensity_IB4 37 Intensity_LowerQuartileIntensity_NeuO 38 Intensity_MADIntensity_DR7 39 Intensity_MADIntensity_Hoechst 40 Intensity_MADIntensity_IB4 41 Intensity_MADIntensity_NeuO 42 Intensity_MassDisplacement_DR7 43 Intensity_MassDisplacement_Hoechst 44 Intensity_MassDisplacement_IB4 45 Intensity_MassDisplacement_NeuO 46 Intensity_MaxIntensityEdge_DR7 47 Intensity_MaxIntensityEdge_Hoechst 48 Intensity_MaxIntensityEdge_IB4 49 Intensity_MaxIntensityEdge_NeuO 50 Intensity_MaxIntensity_DR7 51 Intensity_MaxIntensity_Hoechst 52 Intensity_MaxIntensity_IB4 53 Intensity_MaxIntensity_NeuO 54 Intensity_MeanIntensityEdge_DR7 55 Intensity_MeanIntensityEdge_Hoechst 56 Intensity_MeanIntensityEdge_IB4 57 Intensity_MeanIntensityEdge_NeuO 58 Intensity_MeanIntensity_DR7 59 Intensity_MeanIntensity_Hoechst 60 Intensity_MeanIntensity_IB4 61 Intensity_MeanIntensity_NeuO 62 Intensity_MedianIntensity_DR7 63 Intensity_MedianIntensity_Hoechst 64 Intensity_MedianIntensity_IB4 65 Intensity_MedianIntensity_NeuO 66 Intensity_MinIntensityEdge_DR7 67 Intensity_MinIntensityEdge_Hoechst 68 Intensity_MinIntensityEdge_IB4 69 Intensity_MinIntensityEdge_NeuO 70 Intensity_MinIntensity_DR7 71 Intensity_MinIntensity_Hoechst 72 Intensity_MinIntensity_IB4 73 Intensity_MinIntensity_NeuO 74 Intensity_StdIntensityEdge_DR7 75 Intensity_StdIntensityEdge_Hoechst 76 Intensity_StdIntensityEdge_IB4 77 Intensity_StdIntensityEdge_NeuO 78 Intensity_StdIntensity_DR7 79 Intensity_StdIntensity_Hoechst 80 Intensity_StdIntensity_IB4 81 Intensity_StdIntensity_NeuO 82 Intensity_UpperQuartileIntensity_DR7 83 Intensity_UpperQuartileIntensity_Hoechst 84 Intensity_UpperQuartileIntensity_IB4 85 Intensity_UpperQuartileIntensity_NeuO 86 Location_CenterMassIntensity_X_DR7 x Location data irrelevant 87 Location_CenterMassIntensity_X_Hoechst x Location data irrelevant 88 Location_CenterMassIntensity_X_IB4 x Location data irrelevant 89 Location_CenterMassIntensity_X_NeuO x Location data irrelevant 90 Location_CenterMassIntensity_Y_DR7 x Location data irrelevant 91 Location_CenterMassIntensity_Y_Hoechst x Location data irrelevant 92 Location_CenterMassIntensity_Y_IB4 x Location data irrelevant 93 Location_CenterMassIntensity_Y_NeuO x Location data irrelevant 94 Location_CenterMassIntensity_Z_DR7 x Location data irrelevant 95 Location_CenterMassIntensity_Z_Hoechst x Location data irrelevant 96 Location_CenterMassIntensity_Z_IB4 x Location data irrelevant 97 Location_CenterMassIntensity_Z_NeuO x Location data irrelevant 98 Location_Center_X x Location data irrelevant 99 Location_Center_Y x Location data irrelevant 100 Location_Center_Z x Location data irrelevant 101 Location_MaxIntensity_X_DR7 x Location data irrelevant 102 Location_MaxIntensity_X_Hoechst x Location data irrelevant 103 Location_MaxIntensity_X_IB4 x Location data irrelevant 104 Location_MaxIntensity_X_NeuO x Location data irrelevant 105 Location_MaxIntensity_Y_DR7 x Location data irrelevant 106 Location_MaxIntensity_Y_Hoechst x Location data irrelevant 107 Location_MaxIntensity_Y_IB4 x Location data irrelevant 108 Location_MaxIntensity_Y_NeuO x Location data irrelevant 109 Location_MaxIntensity_Z_DR7 x Location data irrelevant 110 Location_MaxIntensity_Z_Hoechst x Location data irrelevant 111 Location_MaxIntensity_Z_IB4 x Location data irrelevant 112 Location_MaxIntensity_Z_NeuO x Location data irrelevant 113 Parent_ShrunkenNuclei x Parent data irrelevant 114 RadialDistribution_FracAtD_DR7_1of4 115 RadialDistribution_FracAtD_DR7_2of4 116 RadialDistribution_FracAtD_DR7_3of4 117 RadialDistribution_FracAtD_DR7_4of4 118 RadialDistribution_FracAtD_Hoechst_1of4 119 RadialDistribution_FracAtD_Hoechst_2of4 120 RadialDistribution_FracAtD_Hoechst_3of4 121 RadialDistribution_FracAtD_Hoechst_4of4 122 RadialDistribution_FracAtD_IB4_1of4 123 RadialDistribution_FracAtD_IB4_2of4 124 RadialDistribution_FracAtD_IB4_3of4 125 RadialDistribution_FracAtD_IB4_4of4 126 RadialDistribution_FracAtD_NeuO_1of4 127 RadialDistribution_FracAtD_NeuO_2of4 128 RadialDistribution_FracAtD_NeuO_3of4 129 RadialDistribution_FracAtD_NeuO_4of4 130 RadialDistribution_MeanFrac_DR7_1of4 131 RadialDistribution_MeanFrac_DR7_2of4 132 RadialDistribution_MeanFrac_DR7_3of4 133 RadialDistribution_MeanFrac_DR7_4of4 134 RadialDistribution_MeanFrac_Hoechst_1of4 135 RadialDistribution_MeanFrac_Hoechst_2of4 136 RadialDistribution_MeanFrac_Hoechst_3of4 137 RadialDistribution_MeanFrac_Hoechst_4of4 138 RadialDistribution_MeanFrac_IB4_1of4 139 RadialDistribution_MeanFrac_IB4_2of4 140 RadialDistribution_MeanFrac_IB4_3of4 141 RadialDistribution_MeanFrac_IB4_4of4 142 RadialDistribution_MeanFrac_NeuO_1of4 143 RadialDistribution_MeanFrac_NeuO_2of4 144 RadialDistribution_MeanFrac_NeuO_3of4 145 RadialDistribution_MeanFrac_NeuO_4of4 146 RadialDistribution_RadialCV_DR7_1of4 147 RadialDistribution_RadialCV_DR7_2of4 148 RadialDistribution_RadialCV_DR7_3of4 149 RadialDistribution_RadialCV_DR7_4of4 150 RadialDistribution_RadialCV_Hoechst_1of4 151 RadialDistribution_RadialCV_Hoechst_2of4 152 RadialDistribution_RadialCV_Hoechst_3of4 153 RadialDistribution_RadialCV_Hoechst_4of4 154 RadialDistribution_RadialCV_IB4_1of4 155 RadialDistribution_RadialCV_IB4_2of4 156 RadialDistribution_RadialCV_IB4_3of4 157 RadialDistribution_RadialCV_IB4_4of4 158 RadialDistribution_RadialCV_NeuO_1of4 159 RadialDistribution_RadialCV_NeuO_2of4 160 RadialDistribution_RadialCV_NeuO_3of4 161 RadialDistribution_RadialCV_NeuO_4of4 Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 261 Feature Excluded from classifier? Reason 1 AreaShape_Area 2 AreaShape_BoundingBoxArea 3 AreaShape_BoundingBoxMaximum_X x Location data irrelevant 4 AreaShape_BoundingBoxMaximum_Y x Location data irrelevant 5 AreaShape_BoundingBoxMinimum_X x Location data irrelevant 6 AreaShape_BoundingBoxMinimum_Y x Location data irrelevant 7 AreaShape_Center_X x Location data irrelevant 8 AreaShape_Center_Y x Location data irrelevant 9 AreaShape_Compactness 10 AreaShape_ConvexArea 11 AreaShape_Eccentricity 12 AreaShape_EquivalentDiameter 13 AreaShape_EulerNumber x 0 variance feature (all objects have EulerNumber = 1) 14 AreaShape_Extent 15 AreaShape_FormFactor 16 AreaShape_MajorAxisLength 17 AreaShape_MaxFeretDiameter 18 AreaShape_MaximumRadius 19 AreaShape_MeanRadius 20 AreaShape_MedianRadius 21 AreaShape_MinFeretDiameter 22 AreaShape_MinorAxisLength 23 AreaShape_Orientation 24 AreaShape_Perimeter 25 AreaShape_Solidity 26 Intensity_IntegratedIntensityEdge_DR7 27 Intensity_IntegratedIntensityEdge_Hoechst 28 Intensity_IntegratedIntensityEdge_IB4 29 Intensity_IntegratedIntensityEdge_NeuO 30 Intensity_IntegratedIntensity_DR7 31 Intensity_IntegratedIntensity_Hoechst 32 Intensity_IntegratedIntensity_IB4 33 Intensity_IntegratedIntensity_NeuO 34 Intensity_LowerQuartileIntensity_DR7 35 Intensity_LowerQuartileIntensity_Hoechst 36 Intensity_LowerQuartileIntensity_IB4 37 Intensity_LowerQuartileIntensity_NeuO 38 Intensity_MADIntensity_DR7 39 Intensity_MADIntensity_Hoechst 40 Intensity_MADIntensity_IB4 41 Intensity_MADIntensity_NeuO 42 Intensity_MassDisplacement_DR7 43 Intensity_MassDisplacement_Hoechst 44 Intensity_MassDisplacement_IB4 45 Intensity_MassDisplacement_NeuO 46 Intensity_MaxIntensityEdge_DR7 47 Intensity_MaxIntensityEdge_Hoechst 48 Intensity_MaxIntensityEdge_IB4 49 Intensity_MaxIntensityEdge_NeuO 50 Intensity_MaxIntensity_DR7 51 Intensity_MaxIntensity_Hoechst 52 Intensity_MaxIntensity_IB4 53 Intensity_MaxIntensity_NeuO 54 Intensity_MeanIntensityEdge_DR7 55 Intensity_MeanIntensityEdge_Hoechst 56 Intensity_MeanIntensityEdge_IB4 57 Intensity_MeanIntensityEdge_NeuO 58 Intensity_MeanIntensity_DR7 59 Intensity_MeanIntensity_Hoechst 60 Intensity_MeanIntensity_IB4 61 Intensity_MeanIntensity_NeuO 62 Intensity_MedianIntensity_DR7 63 Intensity_MedianIntensity_Hoechst 64 Intensity_MedianIntensity_IB4 65 Intensity_MedianIntensity_NeuO 66 Intensity_MinIntensityEdge_DR7 67 Intensity_MinIntensityEdge_Hoechst 68 Intensity_MinIntensityEdge_IB4 69 Intensity_MinIntensityEdge_NeuO 70 Intensity_MinIntensity_DR7 71 Intensity_MinIntensity_Hoechst 72 Intensity_MinIntensity_IB4 73 Intensity_MinIntensity_NeuO 74 Intensity_StdIntensityEdge_DR7 75 Intensity_StdIntensityEdge_Hoechst 76 Intensity_StdIntensityEdge_IB4 77 Intensity_StdIntensityEdge_NeuO 78 Intensity_StdIntensity_DR7 79 Intensity_StdIntensity_Hoechst 80 Intensity_StdIntensity_IB4 81 Intensity_StdIntensity_NeuO 82 Intensity_UpperQuartileIntensity_DR7 83 Intensity_UpperQuartileIntensity_Hoechst 84 Intensity_UpperQuartileIntensity_IB4 85 Intensity_UpperQuartileIntensity_NeuO 86 Location_CenterMassIntensity_X_DR7 x Location data irrelevant 87 Location_CenterMassIntensity_X_Hoechst x Location data irrelevant 88 Location_CenterMassIntensity_X_IB4 x Location data irrelevant 89 Location_CenterMassIntensity_X_NeuO x Location data irrelevant 90 Location_CenterMassIntensity_Y_DR7 x Location data irrelevant 91 Location_CenterMassIntensity_Y_Hoechst x Location data irrelevant 92 Location_CenterMassIntensity_Y_IB4 x Location data irrelevant 93 Location_CenterMassIntensity_Y_NeuO x Location data irrelevant 94 Location_CenterMassIntensity_Z_DR7 x Location data irrelevant 95 Location_CenterMassIntensity_Z_Hoechst x Location data irrelevant 96 Location_CenterMassIntensity_Z_IB4 x Location data irrelevant 97 Location_CenterMassIntensity_Z_NeuO x Location data irrelevant 98 Location_Center_X x Location data irrelevant 99 Location_Center_Y x Location data irrelevant 100 Location_Center_Z x Location data irrelevant 101 Location_MaxIntensity_X_DR7 x Location data irrelevant 102 Location_MaxIntensity_X_Hoechst x Location data irrelevant 103 Location_MaxIntensity_X_IB4 x Location data irrelevant 104 Location_MaxIntensity_X_NeuO x Location data irrelevant 105 Location_MaxIntensity_Y_DR7 x Location data irrelevant 106 Location_MaxIntensity_Y_Hoechst x Location data irrelevant 107 Location_MaxIntensity_Y_IB4 x Location data irrelevant 108 Location_MaxIntensity_Y_NeuO x Location data irrelevant 109 Location_MaxIntensity_Z_DR7 x Location data irrelevant 110 Location_MaxIntensity_Z_Hoechst x Location data irrelevant 111 Location_MaxIntensity_Z_IB4 x Location data irrelevant 112 Location_MaxIntensity_Z_NeuO x Location data irrelevant 113 Parent_ShrunkenNuclei x Parent data irrelevant 114 RadialDistribution_FracAtD_DR7_1of4 115 RadialDistribution_FracAtD_DR7_2of4 116 RadialDistribution_FracAtD_DR7_3of4 117 RadialDistribution_FracAtD_DR7_4of4 118 RadialDistribution_FracAtD_Hoechst_1of4 119 RadialDistribution_FracAtD_Hoechst_2of4 120 RadialDistribution_FracAtD_Hoechst_3of4 121 RadialDistribution_FracAtD_Hoechst_4of4 122 RadialDistribution_FracAtD_IB4_1of4 123 RadialDistribution_FracAtD_IB4_2of4 124 RadialDistribution_FracAtD_IB4_3of4 125 RadialDistribution_FracAtD_IB4_4of4 126 RadialDistribution_FracAtD_NeuO_1of4 127 RadialDistribution_FracAtD_NeuO_2of4 128 RadialDistribution_FracAtD_NeuO_3of4 129 RadialDistribution_FracAtD_NeuO_4of4 130 RadialDistribution_MeanFrac_DR7_1of4 131 RadialDistribution_MeanFrac_DR7_2of4 132 RadialDistribution_MeanFrac_DR7_3of4 133 RadialDistribution_MeanFrac_DR7_4of4 134 RadialDistribution_MeanFrac_Hoechst_1of4 135 RadialDistribution_MeanFrac_Hoechst_2of4 136 RadialDistribution_MeanFrac_Hoechst_3of4 137 RadialDistribution_MeanFrac_Hoechst_4of4 138 RadialDistribution_MeanFrac_IB4_1of4 139 RadialDistribution_MeanFrac_IB4_2of4 140 RadialDistribution_MeanFrac_IB4_3of4 141 RadialDistribution_MeanFrac_IB4_4of4 142 RadialDistribution_MeanFrac_NeuO_1of4 143 RadialDistribution_MeanFrac_NeuO_2of4 144 RadialDistribution_MeanFrac_NeuO_3of4 145 RadialDistribution_MeanFrac_NeuO_4of4 146 RadialDistribution_RadialCV_DR7_1of4 147 RadialDistribution_RadialCV_DR7_2of4 148 RadialDistribution_RadialCV_DR7_3of4 149 RadialDistribution_RadialCV_DR7_4of4 150 RadialDistribution_RadialCV_Hoechst_1of4 151 RadialDistribution_RadialCV_Hoechst_2of4 152 RadialDistribution_RadialCV_Hoechst_3of4 153 RadialDistribution_RadialCV_Hoechst_4of4 154 RadialDistribution_RadialCV_IB4_1of4 155 RadialDistribution_RadialCV_IB4_2of4 156 RadialDistribution_RadialCV_IB4_3of4 157 RadialDistribution_RadialCV_IB4_4of4 158 RadialDistribution_RadialCV_NeuO_1of4 159 RadialDistribution_RadialCV_NeuO_2of4 160 RadialDistribution_RadialCV_NeuO_3of4 161 RadialDistribution_RadialCV_NeuO_4of4 STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 262 Timothy James Yuji Birkle – November 2023 Table 5. Top 20 cell measurements used for classification during assay development Rank Feature 1 Intensity_StdIntensity_Hs_Rescaled 2 Intensity_MaxIntensity_Hs_Rescaled 3 Intensity_IntegratedIntensity_Hs_Rescaled 4 AreaShape_Perimeter 5 Intensity_UpperQuartileIntensity_Hs_Rescaled 6 AreaShape_MajorAxisLength 7 AreaShape_MaxFeretDiameter 8 AreaShape_BoundingBoxArea 9 Intensity_IntegratedIntensity_IB4_Rescaled 10 Intensity_UpperQuartileIntensity_NeuO_Rescaled 11 Intensity_MADIntensity_Hs_Rescaled 12 Intensity_MeanIntensity_IB4_Rescaled 13 Intensity_UpperQuartileIntensity_IB4_Rescaled 14 Intensity_MADIntensity_NeuO_Rescaled 15 AreaShape_Area 16 AreaShape_EquivalentDiameter 17 AreaShape_ConvexArea 18 Intensity_MedianIntensity_IB4_Rescaled 19 Intensity_IntegratedIntensity_NeuO_Rescaled 20 Intensity_MaxIntensityEdge_IB4_Rescaled Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 263 APPENDIX 3 – SUPPORTING INFORMATION FOR CHAPTER 7 Table 6. Names of all compounds in the screen Further information, including annotated targets, can be found via the publication at doi: 10.1016/j.isci.2024.109454. Name CHEMBL_ID 1 ((E)-Styrylsulfanyl)-acetic acid CHEMBL127222 2 (-)-Lobeline CHEMBL122270 3 (+)-Cloprostenol CHEMBL37853 4 (1H-Benzoimidazol-2-yl)-(3,4-dichlorobenzyl)amine CHEMBL456444 5 (4-bromophenyl)((2R,3S,4S)-2-ethyl-3-methyl-4-(phenylamino)-3,4-dihydroquinolin-1(2H)-yl)methanoneCHEMBL454910 6 (E)-N'-(1-(2-hydroxyphenyl)ethylidene)-3-(morpholinosulfonyl)benzohydrazide CHEMBL3104350 7 (E)-N-(p-tolyl)cinnamamide CHEMBL2336359 8 1,6-dibromonaphthalen-2-yl dimethylcarbamate CHEMBL2071652 9 1-Benzyl-N-{[1-(4-fluorophenyl)cyclopentyl]methyl}piperidine-4-carboxamide CHEMBL1819501 10 2-(2-oxo-2-phenylethyl)malononitrile CHEMBL2206686 11 2-(4-Chloro-phenyl)-2,3a-dihydro-pyrazolo[4,3-c]quinolin-3-one (CGS-9896) CHEMBL20042 12 2-(naphthalen-2-yloxy)-1-(4-(2-phenylacetyl)piperazin-1-yl)ethanone CHEMBL3262876 13 2-[2-Benzoylamino-3-(2-fluoro-phenyl)-acryloylamino]-3-methyl-butyric acid CHEMBL188414 14 2-Phenylacetylamino-4,7-dihydro-5H-thieno[2,3-c]pyridine-3,6-dicarboxylic acid diethyl ester CHEMBL114535 15 3-(2,6-dichloro-phenyl)-5-methyl-isoxazole-4-carboxylic acid (4-diethylamino-phenyl)-amide CHEMBL178668 16 4-Bromo-N-(5-cyclopropyl-1H-pyrazol-3-yl)-benzamide CHEMBL115319 17 4-methoxybenzyl 6-methyl-2-oxo-4-phenyl-1,2,3,4-tetrahydropyrimidine-5-carboxylate CHEMBL1489246 18 5-((1H-indol-3-yl)methylene)-1-(naphthalen-1-yl)pyrimidine-2,4,6(1H,3H,5H)-trione CHEMBL402364 19 5-((4-(4-chlorophenoxy)phenyl)amino)-5-oxo-3-phenylpentanoic acid CHEMBL3634312 20 6-(2-chloro-4-fluoro-phenylsulfamoyl)-cyclohex-1-enecarboxylic acid ethyl ester CHEMBL426184 21 6-(4-Chlorophenyl)pyrimidine-4-carboxylic Acid CHEMBL3407899 22 6-Bromo-1H-benzoimidazole-4-carboxylic acid (1-aza-bicyclo[2.2.2]oct-3-yl)-amide CHEMBL356521 23 6-chloromelatonin CHEMBL34730 24 7-hydroxy-4-methyl-3-propyl-2H-chromen-2-one CHEMBL591811 25 Acetaminophen CHEMBL112 26 Aclidinium bromide CHEMBL551466 27 ACTINONIN CHEMBL308333 28 Adamantane-1-carboxylic Acid(3-methyl-3H-benzothiazol-2-ylidine)hydrazide CHEMBL598126 29 Adiphenine Hydrochloride CHEMBL555654 30 AGI-6780 CHEMBL3392845 31 Alfuzosin CHEMBL709 32 Alpelisib CHEMBL2396661 33 Am-251 CHEMBL285932 34 Amiodarone CHEMBL633 35 Anastrozole CHEMBL1399 36 Apixaban CHEMBL231779 37 Azd1981 CHEMBL1914489 38 AZD3759 CHEMBL3623290 39 Balicatib CHEMBL371064 40 Bendamustine CHEMBL487253 41 Bestatin CHEMBL29292 42 Bifonazole CHEMBL277535 43 Bisphenol A CHEMBL418971 44 BVT-14225 CHEMBL341324 45 BX-795 CHEMBL577784 46 C10315510 CHEMBL1085463 47 CAFFEIC ACID CHEMBL145 48 Caramiphen CHEMBL61946 49 CCT128930 CHEMBL263664 50 CFI-400945 CHEMBL3408947 51 Chelerythrine Chloride CHEMBL258893 52 CHEMBL3233842::US9346814, Cmpd No 1, Example 1 CHEMBL3233842 53 CID755673 CHEMBL1450770 54 cilostamide CHEMBL34431 55 CITCO CHEMBL458603 56 Conivaptan CHEMBL1755 57 CP-724714 CHEMBL483321 58 Dexmedetomidine CHEMBL778 59 Diclofenac CHEMBL139 60 digitoxigenin CHEMBL1453 61 Diltiazem CHEMBL23 62 DMBI CHEMBL328710 63 DMH1 CHEMBL2385597 64 Doxazosin CHEMBL707 65 eliglustat CHEMBL2110588 66 Enalapril CHEMBL578 67 Entinostat CHEMBL27759 68 Epinephrine CHEMBL679 69 Erlotinib CHEMBL553 70 Ethinyl Estradiol CHEMBL691 71 ethyl 4-(2-(4-oxo-3-phenyl-3,4,6,7-tetrahydrothieno[3,2-d]pyrimidin-2-ylthio)acetamido)benzoate CHEMBL2323234 72 Finasteride CHEMBL710 73 fipronil CHEMBL101326 74 Floxuridine CHEMBL917 75 Flunarizine CHEMBL30008 76 Fluoxetine CHEMBL41 77 Fulvestrant CHEMBL1358 78 Gbr-12935 CHEMBL26320 79 GPR39-C3 CHEMBL3342358 80 GR-135531 CHEMBL504585 81 GSK 2830371 CHEMBL3613749 82 GSK2194069 CHEMBL3646801 83 GSK343 CHEMBL2204995 84 GW549034X CHEMBL365286 85 GW7074 CHEMBL72365 86 Hemicholinium-3 CHEMBL1209714 87 Imidacloprid CHEMBL406819 88 Indapamide CHEMBL406 89 IPATASERTIB CHEMBL2177390 90 Isoproterenol CHEMBL434 91 Ispinesib CHEMBL2347651 92 JNJ-42153605 CHEMBL2179319 93 JTE-013 CHEMBL1368758 94 K02288 CHEMBL1230714 95 kb NB 142-70 CHEMBL1672571 96 Ketorolac CHEMBL469 97 Ki16425 CHEMBL361501 98 Ku-55933 CHEMBL222102 99 L-36526 CHEMBL9387 100 L-694458 CHEMBL310871 101 L-701324 CHEMBL31741 102 L-838417 CHEMBL373250 103 Labetalol Hydrochloride CHEMBL1200323 104 levocabastine CHEMBL1615438 105 Linagliptin CHEMBL237500 106 Losmapimod CHEMBL1088752 107 Mazindol CHEMBL781 108 Medroxyprogesterone Acetate CHEMBL717 109 Melatonin CHEMBL45 110 Methylscopolamine CHEMBL376897 111 miglustat CHEMBL1029 112 MK-3207 CHEMBL1910936 113 Mtep CHEMBL292065 114 N-(3-chlorophenyl)-2-(6-(1,3-dioxoisoindolin-2-yl)benzo[d]thiazol-2-ylthio)acetamide CHEMBL1173377 115 N-(3-cyanophenyl)picolinamide CHEMBL3609736 116 N-(4-(1,1,1,3,3,3-hexafluoro-2-hydroxypropan-2-yl)phenyl)-N-methylbenzamide CHEMBL379225 117 N-[2-(Dimethylamino)ethyl]-12-oxo-12H-benzo[g]pyrido[2,1-b]-quinazoline-4-carboxamide CHEMBL3289398 118 NA CHEMBL1892019 119 NA CHEMBL3640646 120 NA CHEMBL201885 121 NA CHEMBL2153461 122 NA CHEMBL225155 123 NA CHEMBL3109630 124 NA CHEMBL1463659 125 NA CHEMBL1232461 126 NA CHEMBL1081262 127 NA CHEMBL1093059 128 NA CHEMBL1231795 129 NA CHEMBL1235110 130 NA CHEMBL148342 131 NA CHEMBL1490019 132 NA CHEMBL1566489 133 NA CHEMBL158897 134 NA CHEMBL1609104 135 NA CHEMBL1645408 136 NA CHEMBL1834657 137 NA CHEMBL191513 138 NA CHEMBL201945 139 NA CHEMBL2064531 140 NA CHEMBL2140523 141 NA CHEMBL223001 142 NA CHEMBL223496 143 NA CHEMBL2391541 144 NA CHEMBL593763 145 NA CHEMBL2397317 146 NA CHEMBL2413519 147 NA CHEMBL2420781 148 NA CHEMBL2426364 149 NA CHEMBL269197 150 NA CHEMBL274548 151 NA CHEMBL293277 152 NA CHEMBL3188597 153 NA CHEMBL3314003 154 NA CHEMBL445990 155 NA CHEMBL467854 156 NA CHEMBL473384 157 NA CHEMBL492884 158 NA CHEMBL507614 159 NA CHEMBL97771 160 NA CHEMBL146735 161 NA CHEMBL119247 162 NA CHEMBL1258123 163 NA CHEMBL566315 164 NA CHEMBL491125 165 NA CHEMBL3263577 166 NA CHEMBL488817 167 NA CHEMBL95632 168 NA CHEMBL1770297 169 NA CHEMBL1739063 170 NA CHEMBL2093893 171 NA CHEMBL1814749 172 NEFLAMAPIMOD CHEMBL119385 173 NICARDIPINE CHEMBL1484 174 Nifedipine CHEMBL193 175 Norepinephrine CHEMBL1437 176 Odanacatib CHEMBL481611 177 OICR-9429 CHEMBL3798846 178 Olaparib CHEMBL521686 179 Ondansetron CHEMBL46 180 OTS964 CHEMBL3672369 181 P2X7_017 CHEMBL2094213 182 Palosuran Sulfate CHEMBL1164032 183 PD004451 CHEMBL1235237 184 PD-0325901 CHEMBL507361 185 PF-04991532 CHEMBL2165620 186 PF-05180999 CHEMBL3092562 187 PFI-3 CHEMBL3752911 188 PK-THPP CHEMBL2324344 189 PND-1186 CHEMBL3040440 190 Pranlukast CHEMBL21333 191 P-Toluenesulfonamide CHEMBL574 192 Pyrimethamine CHEMBL36 193 Rac-Ibipinabant CHEMBL158784 194 Reparixin CHEMBL191413 195 Rolipram CHEMBL63 196 ROTENONE CHEMBL429023 197 Rutaecarpine CHEMBL85139 198 SAR405 CHEMBL3622372 199 Saxagliptin CHEMBL385517 200 SB-271046 CHEMBL431298 201 SB-431542 CHEMBL440084 202 SID121283615 CHEMBL3144739 203 SID124896949 CHEMBL1870314 204 SID22414094 CHEMBL1387347 205 SID24808306 CHEMBL1543754 206 Sk&F-64139 CHEMBL287837 207 SKF-38393 CHEMBL542700 208 Sr-12813 CHEMBL458767 209 SU11274 CHEMBL261641 210 Tandutinib CHEMBL124660 211 tebanicline CHEMBL430497 212 terahydrocytisine CHEMBL3094062 213 tiagabine CHEMBL1027 214 Tilorone CHEMBL47298 215 Tipiracil CHEMBL235668 216 Tozadenant CHEMBL2105747 217 Trametinib CHEMBL2103875 218 Triamcinolone CHEMBL1451 219 Uridine CHEMBL100259 220 US8815951, 533 CHEMBL3661365 221 US9034907, 1 CHEMBL3703751 222 Vercirnon CHEMBL2178578 223 VERUBECESTAT CHEMBL3301601 224 Vesatolimod CHEMBL2424780 225 VX-72 CHEMBL1090090 226 XL888 CHEMBL2204502 227 Zafirlukast CHEMBL603 https://doi.org/10.1016%2Fj.isci.2024.109454 STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 264 Timothy James Yuji Birkle – November 2023 Name CHEMBL_ID 1 ((E)-Styrylsulfanyl)-acetic acid CHEMBL127222 2 (-)-Lobeline CHEMBL122270 3 (+)-Cloprostenol CHEMBL37853 4 (1H-Benzoimidazol-2-yl)-(3,4-dichlorobenzyl)amine CHEMBL456444 5 (4-bromophenyl)((2R,3S,4S)-2-ethyl-3-methyl-4-(phenylamino)-3,4-dihydroquinolin-1(2H)-yl)methanoneCHEMBL454910 6 (E)-N'-(1-(2-hydroxyphenyl)ethylidene)-3-(morpholinosulfonyl)benzohydrazide CHEMBL3104350 7 (E)-N-(p-tolyl)cinnamamide CHEMBL2336359 8 1,6-dibromonaphthalen-2-yl dimethylcarbamate CHEMBL2071652 9 1-Benzyl-N-{[1-(4-fluorophenyl)cyclopentyl]methyl}piperidine-4-carboxamide CHEMBL1819501 10 2-(2-oxo-2-phenylethyl)malononitrile CHEMBL2206686 11 2-(4-Chloro-phenyl)-2,3a-dihydro-pyrazolo[4,3-c]quinolin-3-one (CGS-9896) CHEMBL20042 12 2-(naphthalen-2-yloxy)-1-(4-(2-phenylacetyl)piperazin-1-yl)ethanone CHEMBL3262876 13 2-[2-Benzoylamino-3-(2-fluoro-phenyl)-acryloylamino]-3-methyl-butyric acid CHEMBL188414 14 2-Phenylacetylamino-4,7-dihydro-5H-thieno[2,3-c]pyridine-3,6-dicarboxylic acid diethyl ester CHEMBL114535 15 3-(2,6-dichloro-phenyl)-5-methyl-isoxazole-4-carboxylic acid (4-diethylamino-phenyl)-amide CHEMBL178668 16 4-Bromo-N-(5-cyclopropyl-1H-pyrazol-3-yl)-benzamide CHEMBL115319 17 4-methoxybenzyl 6-methyl-2-oxo-4-phenyl-1,2,3,4-tetrahydropyrimidine-5-carboxylate CHEMBL1489246 18 5-((1H-indol-3-yl)methylene)-1-(naphthalen-1-yl)pyrimidine-2,4,6(1H,3H,5H)-trione CHEMBL402364 19 5-((4-(4-chlorophenoxy)phenyl)amino)-5-oxo-3-phenylpentanoic acid CHEMBL3634312 20 6-(2-chloro-4-fluoro-phenylsulfamoyl)-cyclohex-1-enecarboxylic acid ethyl ester CHEMBL426184 21 6-(4-Chlorophenyl)pyrimidine-4-carboxylic Acid CHEMBL3407899 22 6-Bromo-1H-benzoimidazole-4-carboxylic acid (1-aza-bicyclo[2.2.2]oct-3-yl)-amide CHEMBL356521 23 6-chloromelatonin CHEMBL34730 24 7-hydroxy-4-methyl-3-propyl-2H-chromen-2-one CHEMBL591811 25 Acetaminophen CHEMBL112 26 Aclidinium bromide CHEMBL551466 27 ACTINONIN CHEMBL308333 28 Adamantane-1-carboxylic Acid(3-methyl-3H-benzothiazol-2-ylidine)hydrazide CHEMBL598126 29 Adiphenine Hydrochloride CHEMBL555654 30 AGI-6780 CHEMBL3392845 31 Alfuzosin CHEMBL709 32 Alpelisib CHEMBL2396661 33 Am-251 CHEMBL285932 34 Amiodarone CHEMBL633 35 Anastrozole CHEMBL1399 36 Apixaban CHEMBL231779 37 Azd1981 CHEMBL1914489 38 AZD3759 CHEMBL3623290 39 Balicatib CHEMBL371064 40 Bendamustine CHEMBL487253 41 Bestatin CHEMBL29292 42 Bifonazole CHEMBL277535 43 Bisphenol A CHEMBL418971 44 BVT-14225 CHEMBL341324 45 BX-795 CHEMBL577784 46 C10315510 CHEMBL1085463 47 CAFFEIC ACID CHEMBL145 48 Caramiphen CHEMBL61946 49 CCT128930 CHEMBL263664 50 CFI-400945 CHEMBL3408947 51 Chelerythrine Chloride CHEMBL258893 52 CHEMBL3233842::US9346814, Cmpd No 1, Example 1 CHEMBL3233842 53 CID755673 CHEMBL1450770 54 cilostamide CHEMBL34431 55 CITCO CHEMBL458603 56 Conivaptan CHEMBL1755 57 CP-724714 CHEMBL483321 58 Dexmedetomidine CHEMBL778 59 Diclofenac CHEMBL139 60 digitoxigenin CHEMBL1453 61 Diltiazem CHEMBL23 62 DMBI CHEMBL328710 63 DMH1 CHEMBL2385597 64 Doxazosin CHEMBL707 65 eliglustat CHEMBL2110588 66 Enalapril CHEMBL578 67 Entinostat CHEMBL27759 68 Epinephrine CHEMBL679 69 Erlotinib CHEMBL553 70 Ethinyl Estradiol CHEMBL691 71 ethyl 4-(2-(4-oxo-3-phenyl-3,4,6,7-tetrahydrothieno[3,2-d]pyrimidin-2-ylthio)acetamido)benzoate CHEMBL2323234 72 Finasteride CHEMBL710 73 fipronil CHEMBL101326 74 Floxuridine CHEMBL917 75 Flunarizine CHEMBL30008 76 Fluoxetine CHEMBL41 77 Fulvestrant CHEMBL1358 78 Gbr-12935 CHEMBL26320 79 GPR39-C3 CHEMBL3342358 80 GR-135531 CHEMBL504585 81 GSK 2830371 CHEMBL3613749 82 GSK2194069 CHEMBL3646801 83 GSK343 CHEMBL2204995 84 GW549034X CHEMBL365286 85 GW7074 CHEMBL72365 86 Hemicholinium-3 CHEMBL1209714 87 Imidacloprid CHEMBL406819 88 Indapamide CHEMBL406 89 IPATASERTIB CHEMBL2177390 90 Isoproterenol CHEMBL434 91 Ispinesib CHEMBL2347651 92 JNJ-42153605 CHEMBL2179319 93 JTE-013 CHEMBL1368758 94 K02288 CHEMBL1230714 95 kb NB 142-70 CHEMBL1672571 96 Ketorolac CHEMBL469 97 Ki16425 CHEMBL361501 98 Ku-55933 CHEMBL222102 99 L-36526 CHEMBL9387 100 L-694458 CHEMBL310871 101 L-701324 CHEMBL31741 102 L-838417 CHEMBL373250 103 Labetalol Hydrochloride CHEMBL1200323 104 levocabastine CHEMBL1615438 105 Linagliptin CHEMBL237500 106 Losmapimod CHEMBL1088752 107 Mazindol CHEMBL781 108 Medroxyprogesterone Acetate CHEMBL717 109 Melatonin CHEMBL45 110 Methylscopolamine CHEMBL376897 111 miglustat CHEMBL1029 112 MK-3207 CHEMBL1910936 113 Mtep CHEMBL292065 114 N-(3-chlorophenyl)-2-(6-(1,3-dioxoisoindolin-2-yl)benzo[d]thiazol-2-ylthio)acetamide CHEMBL1173377 115 N-(3-cyanophenyl)picolinamide CHEMBL3609736 116 N-(4-(1,1,1,3,3,3-hexafluoro-2-hydroxypropan-2-yl)phenyl)-N-methylbenzamide CHEMBL379225 117 N-[2-(Dimethylamino)ethyl]-12-oxo-12H-benzo[g]pyrido[2,1-b]-quinazoline-4-carboxamide CHEMBL3289398 118 NA CHEMBL1892019 119 NA CHEMBL3640646 120 NA CHEMBL201885 121 NA CHEMBL2153461 122 NA CHEMBL225155 123 NA CHEMBL3109630 124 NA CHEMBL1463659 125 NA CHEMBL1232461 126 NA CHEMBL1081262 127 NA CHEMBL1093059 128 NA CHEMBL1231795 129 NA CHEMBL1235110 130 NA CHEMBL148342 131 NA CHEMBL1490019 132 NA CHEMBL1566489 133 NA CHEMBL158897 134 NA CHEMBL1609104 135 NA CHEMBL1645408 136 NA CHEMBL1834657 137 NA CHEMBL191513 138 NA CHEMBL201945 139 NA CHEMBL2064531 140 NA CHEMBL2140523 141 NA CHEMBL223001 142 NA CHEMBL223496 143 NA CHEMBL2391541 144 NA CHEMBL593763 145 NA CHEMBL2397317 146 NA CHEMBL2413519 147 NA CHEMBL2420781 148 NA CHEMBL2426364 149 NA CHEMBL269197 150 NA CHEMBL274548 151 NA CHEMBL293277 152 NA CHEMBL3188597 153 NA CHEMBL3314003 154 NA CHEMBL445990 155 NA CHEMBL467854 156 NA CHEMBL473384 157 NA CHEMBL492884 158 NA CHEMBL507614 159 NA CHEMBL97771 160 NA CHEMBL146735 161 NA CHEMBL119247 162 NA CHEMBL1258123 163 NA CHEMBL566315 164 NA CHEMBL491125 165 NA CHEMBL3263577 166 NA CHEMBL488817 167 NA CHEMBL95632 168 NA CHEMBL1770297 169 NA CHEMBL1739063 170 NA CHEMBL2093893 171 NA CHEMBL1814749 172 NEFLAMAPIMOD CHEMBL119385 173 NICARDIPINE CHEMBL1484 174 Nifedipine CHEMBL193 175 Norepinephrine CHEMBL1437 176 Odanacatib CHEMBL481611 177 OICR-9429 CHEMBL3798846 178 Olaparib CHEMBL521686 179 Ondansetron CHEMBL46 180 OTS964 CHEMBL3672369 181 P2X7_017 CHEMBL2094213 182 Palosuran Sulfate CHEMBL1164032 183 PD004451 CHEMBL1235237 184 PD-0325901 CHEMBL507361 185 PF-04991532 CHEMBL2165620 186 PF-05180999 CHEMBL3092562 187 PFI-3 CHEMBL3752911 188 PK-THPP CHEMBL2324344 189 PND-1186 CHEMBL3040440 190 Pranlukast CHEMBL21333 191 P-Toluenesulfonamide CHEMBL574 192 Pyrimethamine CHEMBL36 193 Rac-Ibipinabant CHEMBL158784 194 Reparixin CHEMBL191413 195 Rolipram CHEMBL63 196 ROTENONE CHEMBL429023 197 Rutaecarpine CHEMBL85139 198 SAR405 CHEMBL3622372 199 Saxagliptin CHEMBL385517 200 SB-271046 CHEMBL431298 201 SB-431542 CHEMBL440084 202 SID121283615 CHEMBL3144739 203 SID124896949 CHEMBL1870314 204 SID22414094 CHEMBL1387347 205 SID24808306 CHEMBL1543754 206 Sk&F-64139 CHEMBL287837 207 SKF-38393 CHEMBL542700 208 Sr-12813 CHEMBL458767 209 SU11274 CHEMBL261641 210 Tandutinib CHEMBL124660 211 tebanicline CHEMBL430497 212 terahydrocytisine CHEMBL3094062 213 tiagabine CHEMBL1027 214 Tilorone CHEMBL47298 215 Tipiracil CHEMBL235668 216 Tozadenant CHEMBL2105747 217 Trametinib CHEMBL2103875 218 Triamcinolone CHEMBL1451 219 Uridine CHEMBL100259 220 US8815951, 533 CHEMBL3661365 221 US9034907, 1 CHEMBL3703751 222 Vercirnon CHEMBL2178578 223 VERUBECESTAT CHEMBL3301601 224 Vesatolimod CHEMBL2424780 225 VX-72 CHEMBL1090090 226 XL888 CHEMBL2204502 227 Zafirlukast CHEMBL603 Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 265 Name CHEMBL_ID 1 ((E)-Styrylsulfanyl)-acetic acid CHEMBL127222 2 (-)-Lobeline CHEMBL122270 3 (+)-Cloprostenol CHEMBL37853 4 (1H-Benzoimidazol-2-yl)-(3,4-dichlorobenzyl)amine CHEMBL456444 5 (4-bromophenyl)((2R,3S,4S)-2-ethyl-3-methyl-4-(phenylamino)-3,4-dihydroquinolin-1(2H)-yl)methanoneCHEMBL454910 6 (E)-N'-(1-(2-hydroxyphenyl)ethylidene)-3-(morpholinosulfonyl)benzohydrazide CHEMBL3104350 7 (E)-N-(p-tolyl)cinnamamide CHEMBL2336359 8 1,6-dibromonaphthalen-2-yl dimethylcarbamate CHEMBL2071652 9 1-Benzyl-N-{[1-(4-fluorophenyl)cyclopentyl]methyl}piperidine-4-carboxamide CHEMBL1819501 10 2-(2-oxo-2-phenylethyl)malononitrile CHEMBL2206686 11 2-(4-Chloro-phenyl)-2,3a-dihydro-pyrazolo[4,3-c]quinolin-3-one (CGS-9896) CHEMBL20042 12 2-(naphthalen-2-yloxy)-1-(4-(2-phenylacetyl)piperazin-1-yl)ethanone CHEMBL3262876 13 2-[2-Benzoylamino-3-(2-fluoro-phenyl)-acryloylamino]-3-methyl-butyric acid CHEMBL188414 14 2-Phenylacetylamino-4,7-dihydro-5H-thieno[2,3-c]pyridine-3,6-dicarboxylic acid diethyl ester CHEMBL114535 15 3-(2,6-dichloro-phenyl)-5-methyl-isoxazole-4-carboxylic acid (4-diethylamino-phenyl)-amide CHEMBL178668 16 4-Bromo-N-(5-cyclopropyl-1H-pyrazol-3-yl)-benzamide CHEMBL115319 17 4-methoxybenzyl 6-methyl-2-oxo-4-phenyl-1,2,3,4-tetrahydropyrimidine-5-carboxylate CHEMBL1489246 18 5-((1H-indol-3-yl)methylene)-1-(naphthalen-1-yl)pyrimidine-2,4,6(1H,3H,5H)-trione CHEMBL402364 19 5-((4-(4-chlorophenoxy)phenyl)amino)-5-oxo-3-phenylpentanoic acid CHEMBL3634312 20 6-(2-chloro-4-fluoro-phenylsulfamoyl)-cyclohex-1-enecarboxylic acid ethyl ester CHEMBL426184 21 6-(4-Chlorophenyl)pyrimidine-4-carboxylic Acid CHEMBL3407899 22 6-Bromo-1H-benzoimidazole-4-carboxylic acid (1-aza-bicyclo[2.2.2]oct-3-yl)-amide CHEMBL356521 23 6-chloromelatonin CHEMBL34730 24 7-hydroxy-4-methyl-3-propyl-2H-chromen-2-one CHEMBL591811 25 Acetaminophen CHEMBL112 26 Aclidinium bromide CHEMBL551466 27 ACTINONIN CHEMBL308333 28 Adamantane-1-carboxylic Acid(3-methyl-3H-benzothiazol-2-ylidine)hydrazide CHEMBL598126 29 Adiphenine Hydrochloride CHEMBL555654 30 AGI-6780 CHEMBL3392845 31 Alfuzosin CHEMBL709 32 Alpelisib CHEMBL2396661 33 Am-251 CHEMBL285932 34 Amiodarone CHEMBL633 35 Anastrozole CHEMBL1399 36 Apixaban CHEMBL231779 37 Azd1981 CHEMBL1914489 38 AZD3759 CHEMBL3623290 39 Balicatib CHEMBL371064 40 Bendamustine CHEMBL487253 41 Bestatin CHEMBL29292 42 Bifonazole CHEMBL277535 43 Bisphenol A CHEMBL418971 44 BVT-14225 CHEMBL341324 45 BX-795 CHEMBL577784 46 C10315510 CHEMBL1085463 47 CAFFEIC ACID CHEMBL145 48 Caramiphen CHEMBL61946 49 CCT128930 CHEMBL263664 50 CFI-400945 CHEMBL3408947 51 Chelerythrine Chloride CHEMBL258893 52 CHEMBL3233842::US9346814, Cmpd No 1, Example 1 CHEMBL3233842 53 CID755673 CHEMBL1450770 54 cilostamide CHEMBL34431 55 CITCO CHEMBL458603 56 Conivaptan CHEMBL1755 57 CP-724714 CHEMBL483321 58 Dexmedetomidine CHEMBL778 59 Diclofenac CHEMBL139 60 digitoxigenin CHEMBL1453 61 Diltiazem CHEMBL23 62 DMBI CHEMBL328710 63 DMH1 CHEMBL2385597 64 Doxazosin CHEMBL707 65 eliglustat CHEMBL2110588 66 Enalapril CHEMBL578 67 Entinostat CHEMBL27759 68 Epinephrine CHEMBL679 69 Erlotinib CHEMBL553 70 Ethinyl Estradiol CHEMBL691 71 ethyl 4-(2-(4-oxo-3-phenyl-3,4,6,7-tetrahydrothieno[3,2-d]pyrimidin-2-ylthio)acetamido)benzoate CHEMBL2323234 72 Finasteride CHEMBL710 73 fipronil CHEMBL101326 74 Floxuridine CHEMBL917 75 Flunarizine CHEMBL30008 76 Fluoxetine CHEMBL41 77 Fulvestrant CHEMBL1358 78 Gbr-12935 CHEMBL26320 79 GPR39-C3 CHEMBL3342358 80 GR-135531 CHEMBL504585 81 GSK 2830371 CHEMBL3613749 82 GSK2194069 CHEMBL3646801 83 GSK343 CHEMBL2204995 84 GW549034X CHEMBL365286 85 GW7074 CHEMBL72365 86 Hemicholinium-3 CHEMBL1209714 87 Imidacloprid CHEMBL406819 88 Indapamide CHEMBL406 89 IPATASERTIB CHEMBL2177390 90 Isoproterenol CHEMBL434 91 Ispinesib CHEMBL2347651 92 JNJ-42153605 CHEMBL2179319 93 JTE-013 CHEMBL1368758 94 K02288 CHEMBL1230714 95 kb NB 142-70 CHEMBL1672571 96 Ketorolac CHEMBL469 97 Ki16425 CHEMBL361501 98 Ku-55933 CHEMBL222102 99 L-36526 CHEMBL9387 100 L-694458 CHEMBL310871 101 L-701324 CHEMBL31741 102 L-838417 CHEMBL373250 103 Labetalol Hydrochloride CHEMBL1200323 104 levocabastine CHEMBL1615438 105 Linagliptin CHEMBL237500 106 Losmapimod CHEMBL1088752 107 Mazindol CHEMBL781 108 Medroxyprogesterone Acetate CHEMBL717 109 Melatonin CHEMBL45 110 Methylscopolamine CHEMBL376897 111 miglustat CHEMBL1029 112 MK-3207 CHEMBL1910936 113 Mtep CHEMBL292065 114 N-(3-chlorophenyl)-2-(6-(1,3-dioxoisoindolin-2-yl)benzo[d]thiazol-2-ylthio)acetamide CHEMBL1173377 115 N-(3-cyanophenyl)picolinamide CHEMBL3609736 116 N-(4-(1,1,1,3,3,3-hexafluoro-2-hydroxypropan-2-yl)phenyl)-N-methylbenzamide CHEMBL379225 117 N-[2-(Dimethylamino)ethyl]-12-oxo-12H-benzo[g]pyrido[2,1-b]-quinazoline-4-carboxamide CHEMBL3289398 118 NA CHEMBL1892019 119 NA CHEMBL3640646 120 NA CHEMBL201885 121 NA CHEMBL2153461 122 NA CHEMBL225155 123 NA CHEMBL3109630 124 NA CHEMBL1463659 125 NA CHEMBL1232461 126 NA CHEMBL1081262 127 NA CHEMBL1093059 128 NA CHEMBL1231795 129 NA CHEMBL1235110 130 NA CHEMBL148342 131 NA CHEMBL1490019 132 NA CHEMBL1566489 133 NA CHEMBL158897 134 NA CHEMBL1609104 135 NA CHEMBL1645408 136 NA CHEMBL1834657 137 NA CHEMBL191513 138 NA CHEMBL201945 139 NA CHEMBL2064531 140 NA CHEMBL2140523 141 NA CHEMBL223001 142 NA CHEMBL223496 143 NA CHEMBL2391541 144 NA CHEMBL593763 145 NA CHEMBL2397317 146 NA CHEMBL2413519 147 NA CHEMBL2420781 148 NA CHEMBL2426364 149 NA CHEMBL269197 150 NA CHEMBL274548 151 NA CHEMBL293277 152 NA CHEMBL3188597 153 NA CHEMBL3314003 154 NA CHEMBL445990 155 NA CHEMBL467854 156 NA CHEMBL473384 157 NA CHEMBL492884 158 NA CHEMBL507614 159 NA CHEMBL97771 160 NA CHEMBL146735 161 NA CHEMBL119247 162 NA CHEMBL1258123 163 NA CHEMBL566315 164 NA CHEMBL491125 165 NA CHEMBL3263577 166 NA CHEMBL488817 167 NA CHEMBL95632 168 NA CHEMBL1770297 169 NA CHEMBL1739063 170 NA CHEMBL2093893 171 NA CHEMBL1814749 172 NEFLAMAPIMOD CHEMBL119385 173 NICARDIPINE CHEMBL1484 174 Nifedipine CHEMBL193 175 Norepinephrine CHEMBL1437 176 Odanacatib CHEMBL481611 177 OICR-9429 CHEMBL3798846 178 Olaparib CHEMBL521686 179 Ondansetron CHEMBL46 180 OTS964 CHEMBL3672369 181 P2X7_017 CHEMBL2094213 182 Palosuran Sulfate CHEMBL1164032 183 PD004451 CHEMBL1235237 184 PD-0325901 CHEMBL507361 185 PF-04991532 CHEMBL2165620 186 PF-05180999 CHEMBL3092562 187 PFI-3 CHEMBL3752911 188 PK-THPP CHEMBL2324344 189 PND-1186 CHEMBL3040440 190 Pranlukast CHEMBL21333 191 P-Toluenesulfonamide CHEMBL574 192 Pyrimethamine CHEMBL36 193 Rac-Ibipinabant CHEMBL158784 194 Reparixin CHEMBL191413 195 Rolipram CHEMBL63 196 ROTENONE CHEMBL429023 197 Rutaecarpine CHEMBL85139 198 SAR405 CHEMBL3622372 199 Saxagliptin CHEMBL385517 200 SB-271046 CHEMBL431298 201 SB-431542 CHEMBL440084 202 SID121283615 CHEMBL3144739 203 SID124896949 CHEMBL1870314 204 SID22414094 CHEMBL1387347 205 SID24808306 CHEMBL1543754 206 Sk&F-64139 CHEMBL287837 207 SKF-38393 CHEMBL542700 208 Sr-12813 CHEMBL458767 209 SU11274 CHEMBL261641 210 Tandutinib CHEMBL124660 211 tebanicline CHEMBL430497 212 terahydrocytisine CHEMBL3094062 213 tiagabine CHEMBL1027 214 Tilorone CHEMBL47298 215 Tipiracil CHEMBL235668 216 Tozadenant CHEMBL2105747 217 Trametinib CHEMBL2103875 218 Triamcinolone CHEMBL1451 219 Uridine CHEMBL100259 220 US8815951, 533 CHEMBL3661365 221 US9034907, 1 CHEMBL3703751 222 Vercirnon CHEMBL2178578 223 VERUBECESTAT CHEMBL3301601 224 Vesatolimod CHEMBL2424780 225 VX-72 CHEMBL1090090 226 XL888 CHEMBL2204502 227 Zafirlukast CHEMBL603 STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 266 Timothy James Yuji Birkle – November 2023 Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 267 Table 7. Top 20 cell measurements used for classification in the screen Rank Feature 1 Intensity_UpperQuartileIntensity_Hoechst 2 Intensity_IntegratedIntensity_Hoechst 3 AreaShape_Perimeter 4 Intensity_MaxIntensity_Hoechst 5 Intensity_UpperQuartileIntensity_NeuO 6 Intensity_MADIntensity_DR7 7 Intensity_StdIntensity_Hoechst 8 Intensity_MADIntensity_Hoechst 9 Intensity_MeanIntensity_Hoechst 10 Intensity_MedianIntensity_DR7 11 AreaShape_ConvexArea 12 AreaShape_MaxFeretDiameter 13 Intensity_MedianIntensity_NeuO 14 Intensity_UpperQuartileIntensity_DR7 15 Intensity_MedianIntensity_Hoechst 16 Intensity_MADIntensity_IB4 17 Intensity_MeanIntensity_DR7 18 RadialDistribution_MeanFrac_DR7_4of4 19 Intensity_MeanIntensityEdge_IB4 20 Intensity_MeanIntensity_NeuO STUDYING MICROGLIA-MEDIATED NEURODEGENERATION USING COCULTURES 268 Timothy James Yuji Birkle – November 2023 Table 8. Compounds and targets affecting microglial counts in the absence of LPS Ordered by decreasing microglial number. Predicted (LS) mean diff. = mean normalised microglia count after DMSO LPS- treatment minus after compound LPS- treatment. -1 represents increase in microglia number equal to when treated with LPS (approximately doubling), 1 = decrease in microglia number equal to inverse of change when treated with LPS. Targets: Gene symbols of known targets for each compound. Default on-target mechanism of action (MOA) is inhibition; suffixed * indicates agonism and # indicates binding. Highlighted target-MOAs are those which are repeated amongst all target-MOAs of the compounds. ID C H EM B L ID P red icted (LS) m ea n d iff. 9 5 .0 0 % C I o f d iff. Su m m ary A d j. p valu e N am e Targets 157 C H EM B L2 4 2 4 7 8 0 -2 .7 8 1 -3 .1 8 1 to -2 .3 8 0 **** <0 .0 0 0 1 V esato lim o d TLR 7 * 167 C H EM B L1 8 7 0 3 1 4 -0 .7 1 1 5 -1 .1 1 2 to -0 .3 1 1 2 **** <0 .0 0 0 1 SID 1 2 4 8 9 6 9 4 9 IN SR # TD P 1 124 C H EM B L1 4 6 7 3 5 -0 .4 5 3 7 -0 .8 5 4 0 to -0 .0 5 3 4 2 * 0 .0 1 0 9 H TR 1 B H TR 1 D 210 C H EM B L2 1 0 3 8 7 5 0 .4 2 2 3 0 .0 2 2 0 3 to 0 .8 2 2 6 * 0 .0 2 7 6 Tram etin ib M A P 2 K 1 M A P 2 K 2 A B C B 1 152 C H EM B L3 6 7 2 3 6 9 0 .4 6 9 1 0 .0 6 8 8 6 to 0 .8 6 9 4 ** 0 .0 0 6 6 O TS9 6 4 P B K N EK 1 161 C H EM B L2 2 0 4 5 0 2 0 .4 8 6 6 0 .0 8 6 3 2 to 0 .8 8 6 9 ** 0 .0 0 3 7 X L8 8 8 H SP 9 0 A A 1 H SP 9 0 A B 1 26 C H EM B L1 2 3 5 1 1 0 0 .4 9 6 0 .0 9 5 7 6 to 0 .8 9 6 3 ** 0 .0 0 2 7 SYK M A P K 1 M A P K 3 G P 6 105 C H EM B L6 0 3 0 .5 1 7 2 0 .1 1 7 0 to 0 .9 1 7 5 ** 0 .0 0 1 2 Zafirlu kast C YSLTR 1 C YSLTR 2 M A P K 1 4 31 C H EM B L1 4 5 1 0 .5 4 2 5 0 .1 4 2 3 to 0 .9 4 2 8 *** 0 .0 0 0 5 Triam cin o lo n e N R 3 C 1 * N FK B 1 N FE2 L2 96 C H EM B L5 0 7 3 6 1 0 .5 8 7 8 0 .1 8 7 5 to 0 .9 8 8 0 **** <0 .0 0 0 1 P D -0 3 2 5 9 0 1 M A P 2 K 1 B R A F M A P 2 K 2 M A P K 1 M A P 2 K 5 M ea n (D M SO LP S- n o rm alised m icro glia co u n t - Trea tm en t LP S- n o rm alised m icro glia co u n t) In h ib itio n is d efau lt m o d e o f actio n ; * in d icates ago n ism ; # in d icates b in d in g Th erefo re 0 = n o ch an ge, n egative = in crea sed m icro glia, p o sitive = d ecrea sed m icro glia Chapter 10: Appendices Timothy James Yuji Birkle – November 2023 269 Table 9. Compounds and targets affecting microglial counts in the presence of LPS Ordered by decreasing microglial number. Predicted (LS) mean diff. = mean normalised microglia count after DMSO LPS+ treatment minus after compound LPS+ treatment. -1 represents increase in microglia number twice as much as when treated with LPS (approximately doubling), 1 = decrease in microglia number equal back to LPS-untreated levels. Targets: Gene symbols of known targets for each compound. Default on-target mechanism of action (MOA) is inhibition; suffixed * indicates agonism and # indicates binding. Highlighted target-MOAs are those which are repeated amongst all target-MOAs of the compounds. ID C H EM B L ID Pred icted (LS) m ean diff. 95.00% C I o f diff. Sum m ary A dj. p value N am e Targets 214 C H EM B L426184 -1.19 -1.590 to -0.7895 **** <0 .0001 TA K-242/C LI-095/resato rvid TLR 4 24 C H EM B L41 -0.9716 -1.372 to -0.5713 **** <0 .0001 Fluo xetine SLC 6A 4 A D R A 2A H TR 1A * H TR 1B * H TR 1D * H TR 1F* C YP2C 19 H TR 2A * H TR 2C * SLC 6A 2 A C H E N ET SIG M A R 1 C YP2D 6 111 C H EM B L707 -0.8986 -1.299 to -0.4982 **** <0 .0001 D o xazo sin A D R A 1B A D R A 1D A D R A 1A SLC 6A 3 H TR 2B A D R A 2C H TR 2C A D R A 2A H TR 4 28 C H EM B L1358 -0.8631 -1.263 to -0.4627 **** <0 .0001 Fulvestrant PG R ESR 1 ESR 2 ESR R A EPH X2 N R 1H 4 139 C H EM B L373250 -0.7039 -1.104 to -0.3035 **** <0 .0001 L-838417 G A B R A 3* G A B R B 3* G A B R G 2* G A B R A 2* G A B R A 1* G A B R A 5* 29 C H EM B L1399 -0.6383 -1.039 to -0.2379 **** <0 .0001 A nastro zo le C YP19A 1 114 C H EM B L717 -0.5941 -0.9945 to -0.1938 **** <0 .0001 M ed ro xypro gestero ne A cetate PG R * A R * N R 3C 1* A KR 1C 3 SH B G # ESR 1* ESR 2* 128 C H EM B L1235237 -0.5466 -0.9470 to -0.1463 *** 0.0004 PD 004451 PD E10A 77 C H EM B L36 -0.4958 -0.8961 to -0.09540 ** 0.0027 Pyrim etham ine D H FR SLC 47A 1 5 C H EM B L139 -0.4904 -0.8908 to -0.09008 ** 0.0032 D iclo fen ac PTG S1 PTG S2 C XC L8 C XC R 1 79 C H EM B L385517 -0.4517 -0.8520 to -0.05132 * 0.0116 Saxagliptin D PP4 D PP9 D PP8 67 C H EM B L277535 -0.446 -0.8463 to -0.04560 * 0.0138 B ifo nazo le C YP17A 1 C YP51A 1 C YP3A 4 90 C H EM B L469 -0.4419 -0.8422 to -0.04152 * 0.0157 Keto ro lac PTG S1 PTG S2 PTG D S IN H A EPH A 2 210 C H EM B L2103875 0.4283 0.02798 to 0.8287 * 0.0233 Tram etinib M A P2K1 M A P2K2 A B C B 1 167 C H EM B L1870314 0.4376 0.03727 to 0.8380 * 0.0178 SID 124896949 IN SR # TD P1 219 C H EM B L429023 0.4682 0.06789 to 0.8686 ** 0.0068 R O TEN O N E M T-N D 4 M TO R H TR 6# A R C YB A M T-N D 1 N D U FA B 1 N FE2L2 107 C H EM B L63 0.4885 0.08813 to 0.8888 ** 0.0035 R o lipram PD E4A PD E4B PD E4C PD E4D 31 C H EM B L1451 0.5402 0.1398 to 0.9405 *** 0.0005 Triam cino lo ne N R 3C 1* N FKB 1 N FE2L2 14 C H EM B L1232461 1.118 0.6279 to 1.609 **** <0 .0001 B R D 4 B R D 3 B R D 2 B R D T 96 C H EM B L507361 1.155 0.7547 to 1.555 **** <0 .0001 PD -0325901 M A P2K1 B R A F M A P2K2 M A PK1 M A P2K5 161 C H EM B L2204502 1.255 0.8548 to 1.655 **** <0 .0001 XL888 H SP90A A 1 H SP90A B 1 152 C H EM B L3672369 1.477 1.077 to 1.877 **** <0 .0001 O TS964 PB K N EK1 M ean (D M SO LPS+ no rm alised m icro glia co unt - Treatm en t LPS+ no rm alised m icro glia co unt) Inhibitio n is default m o de o f actio n; * indicates ago nism ; # indicates binding Therefo re 0 = no change, negative = increased m icro glia, po sitive = decreased m icro glia