{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/391775"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/391775","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Decoding Human T Cell Immunity With Single-Cell Multi-Omics","abstract":"In this work, I explore human adaptive immunity through single-cell omics by jointly analysing T cell gene expression and T cell receptor (TCR) sequence modalities. Across several longitudinal infectious disease studies, I identify transient T cell states after infection and link these phenotypes with specific TCR sequences. In the first chapter, I provide a comprehensive overview of the field's state-of-the-art, encompassing experimental single T cell profiling methods, relating key findings from prior single-cell studies, and computational tools for antigen specificity prediction. Throughout the next chapters, I introduce Cell2TCR, an open-source framework for cross-donor TCR sequence comparison, and make the case for convergent clonal selection after infection through the discovery and characterisation of TCR motifs. Next, I link TCR motifs to antigen specificity using experimental T cell specificity databases and validate the findings with in-house data in the context of several viral infections, by integration with public single-cell data, and by comparing to bulk sequencing samples. I demonstrate the framework's utility for motif discovery in a controlled human infection study as well as in the context of community infections within an at-risk cohort, and make it compatible with human gamma-delta T cell and murine T cell responses. Furthermore, I show that TCR motifs exhibit T cell compartment preference and strong donor major histocompatibility complex (MHC) restriction, consistent with TCR-peptide-MHC recognition principles. In the last results chapter, I present a T cell atlas comprising 3,6 million T cell transcriptomes, and with paired TCR sequences for about half of them, which has been curated from 23 datasets of various infectious diseases. I outline a strategy to propose novel peptide-MHC combinations for a given disease context like infection, integrate 3D structure-informed model ranking into the process and evaluate it using internally generated antigen specificity data. Taken together, this research represents a significant step towards de novo prediction of T cell antigen specificity, paving the way for large-scale TCR repertoire interpretation and deliberate TCR engineering.","abstract_html":"In this work, I explore human adaptive immunity through single-cell omics by jointly analysing T cell gene expression and T cell receptor (TCR) sequence modalities. Across several longitudinal infectious disease studies, I identify transient T cell states after infection and link these phenotypes with specific TCR sequences. In the first chapter, I provide a comprehensive overview of the field's state-of-the-art, encompassing experimental single T cell profiling methods, relating key findings from prior single-cell studies, and computational tools for antigen specificity prediction. Throughout the next chapters, I introduce Cell2TCR, an open-source framework for cross-donor TCR sequence comparison, and make the case for convergent clonal selection after infection through the discovery and characterisation of TCR motifs. Next, I link TCR motifs to antigen specificity using experimental T cell specificity databases and validate the findings with in-house data in the context of several viral infections, by integration with public single-cell data, and by comparing to bulk sequencing samples. I demonstrate the framework's utility for motif discovery in a controlled human infection study as well as in the context of community infections within an at-risk cohort, and make it compatible with human gamma-delta T cell and murine T cell responses. Furthermore, I show that TCR motifs exhibit T cell compartment preference and strong donor major histocompatibility complex (MHC) restriction, consistent with TCR-peptide-MHC recognition principles. In the last results chapter, I present a T cell atlas comprising 3,6 million T cell transcriptomes, and with paired TCR sequences for about half of them, which has been curated from 23 datasets of various infectious diseases. I outline a strategy to propose novel peptide-MHC combinations for a given disease context like infection, integrate 3D structure-informed model ranking into the process and evaluate it using internally generated antigen specificity data. Taken together, this research represents a significant step towards de novo prediction of T cell antigen specificity, paving the way for large-scale TCR repertoire interpretation and deliberate TCR engineering.","abstract_has_math":false,"creators":["Dratva, Lisa Marietta"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Teichmann, Sarah"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-06-11","date_published":"2025-06-11","updated_at":"2026-07-24T01:33:03Z","subjects":["Immunology","Single-cell","T cell","TCR","Antigen specificity","Genomics"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/bfaa24e5-f0d3-4f54-9e32-7c94a485327d/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.122780","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Teichmann, Sarah"]},{"key":"dc:creator","label":"Author","values":["Dratva, Lisa Marietta"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-06-11"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/391775"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Immunology","Single-cell","T cell","TCR","Antigen specificity","Genomics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/bfaa24e5-f0d3-4f54-9e32-7c94a485327d/download","http://purl.org/NET/rdflicense/allrightsreserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.122780"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/0042efaa-db77-4c98-83bc-55f556f2cbbb/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this work, I explore human adaptive immunity through single-cell omics by jointly analysing T cell gene expression and T cell receptor (TCR) sequence modalities. 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I demonstrate the framework's utility for motif discovery in a controlled human infection study as well as in the context of community infections within an at-risk cohort, and make it compatible with human gamma-delta T cell and murine T cell responses. Furthermore, I show that TCR motifs exhibit T cell compartment preference and strong donor major histocompatibility complex (MHC) restriction, consistent with TCR-peptide-MHC recognition principles. In the last results chapter, I present a T cell atlas comprising 3,6 million T cell transcriptomes, and with paired TCR sequences for about half of them, which has been curated from 23 datasets of various infectious diseases. I outline a strategy to propose novel peptide-MHC combinations for a given disease context like infection, integrate 3D structure-informed model ranking into the process and evaluate it using internally generated antigen specificity data. 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In the first chapter, I provide a comprehensive overview of the field's state-of-the-art, encompassing experimental single T cell profiling methods, relating key findings from prior single-cell studies, and computational tools for antigen specificity prediction. Throughout the next chapters, I introduce Cell2TCR, an open-source framework for cross-donor TCR sequence comparison, and make the case for convergent clonal selection after infection through the discovery and characterisation of TCR motifs. Next, I link TCR motifs to antigen specificity using experimental T cell specificity databases and validate the findings with in-house data in the context of several viral infections, by integration with public single-cell data, and by comparing to bulk sequencing samples. I demonstrate the framework's utility for motif discovery in a controlled human infection study as well as in the context of community infections within an at-risk cohort, and make it compatible with human gamma-delta T cell and murine T cell responses. Furthermore, I show that TCR motifs exhibit T cell compartment preference and strong donor major histocompatibility complex (MHC) restriction, consistent with TCR-peptide-MHC recognition principles. In the last results chapter, I present a T cell atlas comprising 3,6 million T cell transcriptomes, and with paired TCR sequences for about half of them, which has been curated from 23 datasets of various infectious diseases. I outline a strategy to propose novel peptide-MHC combinations for a given disease context like infection, integrate 3D structure-informed model ranking into the process and evaluate it using internally generated antigen specificity data. 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