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Publications

Journal Article Preprint Epublish 2 citations

PubMed: 40501778 DOI: 10.1101/2025.05.31.657155

Diverse modes of T cell receptor sequence convergence define unique functional and cellular phenotypes.

bioRxiv

Authors: Stefan Schattgen, Kasi Vegesana, William D Hazelton, Anastasia Minervina, Sebastiaan Valkiers, Kamil Slowikowski, Neal Smith, Alexandra-ChloƩ Villani, Paul G Thomas, Philip Bradley

Affiliations

  • Department of Host-Microbe Interactions, St. Jude Children's Research Hospital, Memphis, TN, USA.
  • Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
  • Department of Informatics, University of Antwerp, Antwerp, Belgium.
  • Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA. Harvard Medical School, Boston, MA, USA.
  • Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
  • Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA. Institute for Protein Design, Department of Biochemistry, University of Washington, Seattle, WA, USA.

Abstract

Single-cell techniques allow concurrent study of gene activity and T cell receptor (TCR) sequences, identifying connections between TCR structure and cell traits. Expanding on our CoNGA software, we present a "metaCoNGA" analysis of 6 million T cells from 91 diverse studies, mapping TCR sequence similarity across tissues and diseases. This approach exposes shared TCR features within specific T cell subsets, including those associated with infection, cancer, and autoimmunity. We introduce a method to identify T cell groups with similar gene expression and biased TCR amino acid composition, providing a systematic framework for classifying diverse unconventional T cells, including KIR+ CD8+ T cells, CD4+ regulatory T cells, and subsets of NKT and MAIT cells. A new TCR clustering approach identifies thousands of convergent TCR sequence clusters hypothesized to target shared antigens. These clusters show coherent gene expression, highlighting the role of antigen exposure in shaping T cell behavior. Finally, we provide a tool for users to merge new data with this resource and rapidly identify T cell features in their data sets. This resource empowers investigations into the complex relationship between TCR sequence and T cell function in human health.

MeSH

Cells, Genes, Phenotype, T-Lymphocytes, Gene Expression, Receptors, Antigen, T-Cell