Schmidt F, Fields HF, Purwanti Y, Milojkovic A, Salim S, Wu KX, Simoni Y, Vitiello A, MacLeod DT, Nardin A, Newell EW, Fink K, Wilm A, Fehlings M. In-depth analysis of human virus-specific CD8
+ T cells delineates unique phenotypic signatures for T cell specificity prediction.
Cell Rep 2023;
42:113250. [PMID:
37837618 DOI:
10.1016/j.celrep.2023.113250]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/21/2023] [Accepted: 09/26/2023] [Indexed: 10/16/2023] Open
Abstract
Following viral infection, the human immune system generates CD8+ T cell responses to virus antigens that differ in specificity, abundance, and phenotype. A characterization of virus-specific T cell responses allows one to assess infection history and to understand its contribution to protective immunity. Here, we perform in-depth profiling of CD8+ T cells binding to CMV-, EBV-, influenza-, and SARS-CoV-2-derived antigens in peripheral blood samples from 114 healthy donors and 55 cancer patients using high-dimensional mass cytometry and single-cell RNA sequencing. We analyze over 500 antigen-specific T cell responses across six different HLA alleles and observed unique phenotypes of T cells specific for antigens from different virus categories. Using machine learning, we extract phenotypic signatures of antigen-specific T cells, predict virus specificity for bulk CD8+ T cells, and validate these predictions, suggesting that machine learning can be used to accurately predict antigen specificity from T cell phenotypes.
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