Decoding HLA-restricted human T-cell immunity through a population-scale HLA–TCR atlas

PROJECT OVERVIEW

T cells are central to adaptive immunity, recognising antigenic peptides presented by highly polymorphic human leukocyte antigen (HLA) molecules through diverse T-cell receptors (TCRs). Variation in HLA genes is the strongest genetic risk factor for many immune-mediated diseases, including inflammatory bowel disease, ankylosing spondylitis, infectious diseases and cancer, yet the mechanisms by which HLA polymorphism shapes T-cell repertoire selection and immune responses remain poorly understood. Although recent advances in single-cell sequencing now enable simultaneous profiling of immune cell states and paired TCR repertoires, the vast majority of existing datasets lack HLA genotype information, preventing direct investigation of the relationship between antigen presentation, T-cell selection and immune function.

This project will use SARS-CoV-2 as an initial test case to establish and validate computational approaches for identifying functional and clinically relevant HLA–TCR interactions before extending these analyses to a broader range of immune-mediated diseases, including autoimmune and infectious diseases. Building on our recently developed method which accurately infers classical HLA genotypes directly from single-cell RNA sequencing data, the student will develop a population-scale HLA–TCR atlas at single-cell resolution by integrating HLA genotype, paired TCR repertoires and immune cell phenotypes across large existing datasets.

The successful candidate will receive interdisciplinary training spanning computational biology, statistical genetics, immunogenetics and single-cell multiomics, while working closely with collaborators and co-supervisors in immunology and clinical medicine. The project will integrate computational analyses with contemporary understanding of HLA biology and antigen presentation to uncover the mechanisms by which HLA variation shapes T-cell repertoire selection and adaptive immune responses across health and disease.

KEYWORDS

HLA, T-cell receptor, single-cell multiomics, adaptive immunity, immunogenetics

TRAINING OPPORTUNITIES

This project is well suited to a student with a background in statistical genetics, or a background in statistical modelling or machine learning who is interested in developing applied knowledge in the biological sciences. The successful candidate will be benefit from supervision by a team of scientists with key expertise in statistical genetics, immunology, and single cell genomics.

You will be based in the Kennedy Institute of Rheumatology, world-leading centres in genomics and inflammatory biology. Training will be provided in data science techniques including statistical data analysis and visualisation with R and Python, the writing of computational pipelines with Python/NextFlow, and the use of high-performance compute clusters. The student will gain expertise in analysing cutting-edge sequencing datasets including single-cell transcriptomics and immune repertoire analysis; statistical genetics and HLA immunogenetics; machine learning and multi-omic integration.

The Kennedy Institute is a world-renowned research centre and has a vibrant PhD program with weekly journal club, seminars, student symposia, weekly internal institute presentations and training. A core curriculum of lectures will provide a solid foundation of a broad range of subjects including data analysis, statistical methods, and immunology summer school. In additional to institutional support, the successful applicant will benefit from being part of the University of Oxford college system. Students will also have the opportunity to work closely with both computational, experimental and clinical scientists.

KEY PUBLICATIONS

• Ng ES, et al. Defining the genetic determinants of CD8+ T cell receptor repertoire in the context of immune checkpoint blockade. Science Advances. 2025.
• Mentzer AJ, et al. Human leukocyte antigen alleles associate with COVID-19 vaccine immunogenicity and risk of breakthrough infection. Nature. 2023.
• Ahern DJ, et al. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. Cell. 2022.
• Ishigaki K, et al. HLA autoimmune risk alleles restrict the hypervariable region of T cell receptors. Nature Genetics. 2022.
• Ahern DJ, et al. Single-cell HLA inference enables population-scale mapping of HLA–TCR associations in immune-mediated disease. bioRxiv. 2025.

THEMES

Computational biology, Clinical translation and experimental medicine, Interacting systems, Tissue repair and remodelling

CONTACT INFORMATION OF ALL SUPERVISORS

Yang Luo

Alexander Mentzer

Hannah Kockelbergh