<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-22T06:01:23Z</responseDate><request verb="GetRecord" identifier="oai:www.repository.cam.ac.uk:1810/302393" metadataPrefix="uketd_dc">https://api.repository.cam.ac.uk/server/oai/request</request><GetRecord><record><header><identifier>oai:www.repository.cam.ac.uk:1810/302393</identifier><datestamp>2021-04-21T20:42:03Z</datestamp><setSpec>com_1810_224161</setSpec><setSpec>com_1810_256067</setSpec><setSpec>col_1810_224162</setSpec></header><metadata><uketd_dc:uketddc xmlns:uketd_dc="http://naca.central.cranfield.ac.uk/ethos-oai/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:uketdterms="http://naca.central.cranfield.ac.uk/ethos-oai/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://naca.central.cranfield.ac.uk/ethos-oai/2.0/ http://naca.central.cranfield.ac.uk/ethos-oai/2.0/uketd_dc.xsd">
   <dc:title>Tissue-specific adaptations of cell types</dc:title>
   <dc:identifier xsi:type="dcterms:DOI">10.17863/CAM.49464</dc:identifier>
   <dc:creator>Pires de Carvalho Gomes, Tomás</dc:creator>
   <uketdterms:authoridentifier xsi:type="uketdterms:ORCID">0000000313330191</uketdterms:authoridentifier>
   <uketdterms:advisor>Teichmann, Sarah Amalia</uketdterms:advisor>
   <dcterms:abstract>Cells are the building blocks of life, forming the vast diversity of tissues and organisms
in Nature. Across these, common cellular morphologies and functions have been
identified. High-throughput, multifactorial profiling of cells has grown exponentially
in recent years with the advent of single-cell RNA-sequencing (scRNA-seq),
increasingly unravelling cell diversity. Nonetheless, it is not yet known how different
environments affect cellular phenotypes.
The work presented on this Thesis reports on the transcriptional variation of
cell types across tissues, by use of single-cell RNA-sequencing. This technology,
developed in the last 10 years, has greatly impacted our ability to distinguish cellular
heterogeneity by their gene expression in various tissues or conditions.
Chapter 1 outlines the impact of single-cell RNA-sequencing in cell biology, presenting
the technology as the natural progression of lower throughput or low-resolution
methods. The chapter then shows how cellular heterogeneity can be deconstructed
by analysing this type of genomics data. It then expands on how individual datasets
can be used to build models of cell type identity for automatic annotation, ultimately
outlining the need to create a global cell type census of a whole organism. A cell
compendium like this should be useful for automatic annotation, as well as to obtain
a cross-tissue integrative overview of cell identity.
The same chapter also delves into the topic of heterogeneity in immune cells. Due
to the evolutionary pressure they are subject to and ubiquitous nature across the
organism, these are some of the most diverse cell types in multicellular organisms.
Chapter 2 presents a deconstruction of T-regulatory cells’ phenotypes in different
mouse and human tissues using single-cell RNA-sequencing. The analysis in this
chapter will show how these cells are structured in subpopulations, and how they
adapt when migrating between lymphoid and non-lymphoid tissues. It will also assess
the conservation of gene expression programmes for the same populations between
mouse and human.
The creation of a global cell type reference is an endeavour that can facilitate
analysis of new data, and reveal novel insights about cell and tissue biology. Several datasets have now been produced, and a method that can efficiently integrate them
and prepare them for use as a reference is necessary. Chapter 3 details the development
of such method, exploring its strengths and how it can be improved, in a
mouse dataset. Chapter 4 then applies this pipeline to a collection of human data,
and shows how cell types relate across tissues, as well as how the human reference
can be used in a practical case.
Lastly, Chapter 5 summarises all chapters, providing an overview on how single cell
sequencing has changed what we know about tissue biology, and how listing cell
types and compiling them as a functional reference can help future developments in
life sciences.</dcterms:abstract>
   <uketdterms:institution>University of Cambridge</uketdterms:institution>
   <dcterms:issued>2020-04-25</dcterms:issued>
   <dc:type>Thesis</dc:type>
   <uketdterms:qualificationlevel>Doctoral</uketdterms:qualificationlevel>
   <uketdterms:qualificationname>Doctor of Philosophy (PhD)</uketdterms:qualificationname>
   <dc:language>en</dc:language>
   <uketdterms:sponsor>Funded by the European Union’s H2020 research and innovation programme ‘ENLIGHT-TEN’ under Marie Skłodowska-Curie grant agreement no. 675395.</uketdterms:sponsor>
   <dcterms:isReferencedBy xsi:type="dcterms:URI">https://www.repository.cam.ac.uk/handle/1810/302393</dcterms:isReferencedBy>
   <dc:identifier xsi:type="dcterms:URI">https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/46887459-94fd-4926-847a-398889cffa44/download</dc:identifier>
   <uketdterms:checksum xsi:type="uketdterms:MD5">431e9b28e6c076285a505a681eeed93a</uketdterms:checksum>
   <dcterms:license>https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/aea3ef3c-9e56-453f-b8a3-d18dfd6b4b23/download</dcterms:license>
   <uketdterms:checksum xsi:type="uketdterms:MD5">87eda9de84448d1f82354d60eee3eb5f</uketdterms:checksum>
   <dc:rights>https://creativecommons.org/licenses/by-nc/4.0/</dc:rights>
   <dc:subject>single-cell</dc:subject>
   <dc:subject>transcriptomics</dc:subject>
   <dc:subject>tissue</dc:subject>
   <dc:subject>genomics</dc:subject>
   <dc:subject>immunology</dc:subject>
   <dc:subject>Treg</dc:subject>
   <dc:subject>classifier</dc:subject>
   <dc:subject>cell type</dc:subject>
</uketd_dc:uketddc>
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