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Fischer, D.S. ; Ansari, M. ; Wagner, K.I.* ; Jarosch, S.* ; Huang, Y.* ; Mayr, C. ; Strunz, M. ; Lang, N.J. ; D'Ippolito, E.* ; Hammel, M.* ; Mateyka, L.* ; Weber, S.* ; Wolff, L.S.* ; Witter, K.* ; Fernandez, I.E.* ; Leuschner, G.* ; Milger, K.* ; Frankenberger, M. ; Nowak, L.* ; Heinig-Menhard, K.* ; Koch, I. ; Stoleriu, M.-G. ; Hilgendorff, A. ; Behr, J.* ; Pichlmair, A.* ; Schubert, B. ; Theis, F.J. ; Busch, D.H. ; Schiller, H. B. ; Schober, K.

Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through 'reverse phenotyping'.

Nat. Commun. 12:4515 (2021)

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The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for 'reverse phenotyping'. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Receptor; Lymphocytes; Expression; Epitopes; Distinct; Linking; Naive

ISSN (print) / ISBN 2041-1723

e-ISSN 2041-1723

Journal Nature Communications

Quellenangaben Volume: 12, Issue: 1, Article Number: 4515

Publisher Nature Publishing Group

Publishing Place London

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)
Lung Health and Immunity (LHI)
Helmholtz AI - HMGU (HAI - HMGU)

Grants Joachim Herz Stiftung
German Center for Infection Research (DZIF)
German Research Foundation (DFG)
German Center for Lung Research (DZL)
Helmholtz Association
BMBF project Single Cell Genomics Network Germany
European Union's Horizon 2020 research and innovation program
Chan Zuckerberg Initiative
BMBF
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
German research foundation (DFG) fellowship through the Graduate School of Quantitative Biosciences Munich (QBM)
German National Network of University Medicine of the Federal Ministry of Education and Research (BMBF)