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

  1. David S. Fischer
  2. Meshal Ansari
  3. Karolin I. Wagner
  4. Sebastian Jarosch
  5. Yiqi Huang
  6. Christoph H. Mayr
  7. Maximilian Strunz
  8. Niklas J. Lang
  9. Elvira D’Ippolito
  10. Monika Hammel
  11. Laura Mateyka
  12. Simone Weber
  13. Lisa S. Wolff
  14. Klaus Witter
  15. Isis E. Fernandez
  16. Gabriela Leuschner
  17. Katrin Milger
  18. Marion Frankenberger
  19. Lorenz Nowak
  20. Katharina Heinig-Menhard
  21. Ina Koch
  22. Mircea G. Stoleriu
  23. Anne Hilgendorff
  24. Jürgen Behr
  25. Andreas Pichlmair
  26. Benjamin Schubert
  27. Fabian J. Theis
  28. Dirk H. Busch
  29. Herbert B. Schiller
  30. Kilian Schober

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Abstract

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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  1. Version published to 10.1038/s41467-021-24730-4
  2. ScreenIT

    SciScore for 10.1101/2020.12.07.20245274: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.12.07.20245274v1 on medRxiv