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Integrative Multivariate Analysis of Mouse Liver Acini https://doi.org/10.17863/CAM.114617 Cornelius, Mercedes Barnes, crispin Hepatocytes are the primary constituents of liver cells, performing numerous functions vital for sustaining life. The microenvironment of these cells is not homogeneous; specifically, two cell types known as periportal and pericentral hepatocytes in the liver are region-specific. This is due to the differential distribution of oxygen, nutrients, and hormones, which results in distinct gene expression patterns and the spatial separation of metabolic functions, recognized as liver zonation. The factors that govern this spatial phenotypic heterogeneity remain inadequately understood and hepatic pathologies persist as major contributors to global mortality rates. This research aimed to address this gap by defining the relationship between the spatial positioning of hepatocytes and lipid droplets, mitochondria, and fibrotic structures. Additionally, it sought to identify phenotypic identities that regulate mitochondria, lipid droplet, and fibrotic activity in different regions of the liver. A unique methodology where live dynamic imaging of a living Mus musculus’ (mice) liver was performed. To perform intravital microscopy (IVM), the methodology required the creation of a 3D printed platform called RHIT (Reusable Hepatic Imaging Tool) that enabled seamless integration into IVM protocols and stabilized the tissue for dynamic and acute imaging for metabolic studies of mice—a previously unrecognized technique in IVM. Additionally, an advanced pipeline named Systematic Observation of Neighboring Cells (VISION) was created. After revisiting liver zonation with VISION, novel insights were observed. The quantification of putative individual hepatocytes was identified for the first time. Key findings include the identification of hepatocyte phenotypes and their dietary responses, revealing significant variations in lipid droplet accumulation under different dietary conditions. Multivariate analysis identified previously undefined phenotypes in hepatocytes and distinguished hepatocytes in an unbiased, highly discriminatory manner. This study provides a deeper understanding of hepatocyte behavior, offering insights applicable across nucleated cells and organs, with potential benefits for pathology and diagnostics. University of Cambridge 2024-04-30 Thesis Doctoral Doctor of Philosophy (PhD) eng https://www.repository.cam.ac.uk/handle/1810/378009 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/456ac176-f27c-428f-849e-7680dca8ac29/download 0510016f5f2790fd138aa7d9a3276f5f https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/b267dd72-ae84-4e88-bff8-d2600f240d6d/download 87eda9de84448d1f82354d60eee3eb5f http://purl.org/NET/rdflicense/allrightsreserved health liver machine learning multivariate analysis physcial biology physical characterization of complex systems single-cell analysis