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Trial Lecture – time and place
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Adjudication committee
- First opponent: Assistant Professor William S. DeWitt, University of Washington, USA
- Second opponent: Senior Research Officer Katherine J. L. Jackson, Garvan Institute of Medical Research, Australia
- Third member and chair of the evaluation committee: Senior Researcher Cecilia Fahlquist-Hagert, University of Oslo
Chair of the Defence
Associate Professor Andreas Lossius, University of Oslo
Principal Supervisor
Professor Victor Greiff, University of Oslo
Summary
Adaptive immune receptors (AIRs) – T-cell receptors and antibodies – are proteins that play a key role in the immune system, as they can precisely recognize a broad array of antigens and thus help thei immune system respond to this particular threat. The set of all AIRs in an individual – a repertoire – determines the individual's ability to form immune responses towards various antigens and is in turn shaped by the environmental interactions through immunological memory.
In the last decades, technological advancements such as AIRR sequencing (determining the sequences of the genes that encode AIRs) and high-throughput immune receptor specificity screenings (determining which antigens receptors
target) generated large datasets. In parallel, the field of machine learning (ML) has seen a rapid development enabling training of large ML models on larger yet scale data. In combination, these developments revolutionized our understanding of AIRs and paved the way for new AIR- based diagnostics and precision therapeutics.
This thesis examines how the large-scale data can be leveraged to boost our understanding of AIR repertoires and not just individual receptors. First, it explores the systematic differences in the generation of antibody repertoires between subjects of various levels of similarity, showing how such differences can be separated from multiple potential sources of noise, and showing that even genetically identical subjects may have significantly different repertoires. Then, it introduces a method for utilizing widely available AIR repertoire metadata instead of scarce antigen annotations for individual AIRs to identify and generate immune status-associated AIR sequences. Last but not least, this work explores how the set of MHC alleles affects the TCR repertoire composition in mice, and develops methodology for estimation of
overlap between TCR repertoires that is robust to the stochasticity of AIR repertoires and experimental noises.
Additional information
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