Single-cell transcriptomic analysis of SARS-CoV-2 reactive CD4 + T cells

  1. Benjamin J. Meckiff
  2. Ciro Ramírez-Suástegui
  3. Vicente Fajardo
  4. Serena J Chee
  5. Anthony Kusnadi
  6. Hayley Simon
  7. Alba Grifoni
  8. Emanuela Pelosi
  9. Daniela Weiskopf
  10. Alessandro Sette
  11. Ferhat Ay
  12. Grégory Seumois
  13. Christian H Ottensmeier
  14. Pandurangan Vijayanand

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

The contribution of CD4 + T cells to protective or pathogenic immune responses to SARS-CoV-2 infection remains unknown. Here, we present large-scale single-cell transcriptomic analysis of viral antigen-reactive CD4 + T cells from 32 COVID-19 patients. In patients with severe disease compared to mild disease, we found increased proportions of cytotoxic follicular helper (T FH ) cells and cytotoxic T helper cells (CD4-CTLs) responding to SARS-CoV-2, and reduced proportion of SARS-CoV-2 reactive regulatory T cells. Importantly, the CD4-CTLs were highly enriched for the expression of transcripts encoding chemokines that are involved in the recruitment of myeloid cells and dendritic cells to the sites of viral infection. Polyfunctional T helper (T H )1 cells and T H 17 cell subsets were underrepresented in the repertoire of SARS-CoV-2-reactive CD4 + T cells compared to influenza-reactive CD4 + T cells. Together, our analyses provide so far unprecedented insights into the gene expression patterns of SARS-CoV-2 reactive CD4 + T cells in distinct disease severities.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.06.12.148916: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Cells were stimulated by the addition of individual virus-specific peptide pools (1 µg/ml) for 6 h in the presence of a blocking CD40 antibody (1 µg/ml; Miltenyi Biotec).
    CD40
    suggested: None
    For subsequent MACS-based enrichment of CD154+, cells were sequentially stained with fluorescence-labeled surface antibodies (antibody list in Table S2), Cell-hashtag TotalSeq(tm)-C antibody (0.5 µg/condition), and a biotin-conjugated CD154 antibody (clone 5C8; Miltenyi Biotec) followed by anti-biotin microbeads (Miltenyi Biotec)
    CD154
    suggested: None
    anti-biotin microbeads ( Miltenyi Biotec)
    suggested: None
    Analogous to enrichment for CD154+, CD137-expressing CD4+ memory T cells cells were positively selected by staining with biotin-conjugated CD137 antibody (clone REA765; Miltenyi Biotec) followed by anti-biotin MicroBeads and applied to a new MS column.
    CD137
    suggested: None
    anti-biotin
    suggested: None
    Software and Algorithms
    SentencesResources
    All flow cytometry data were analyzed using FlowJo software (version 10).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    The merged data was transferred to the R statistical environment for analysis using the package Seurat (v3.1.5) (Stuart et al., 2019).
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Single-cell differential gene expression analysis: Pairwise single-cell differential gene expression analysis was performed using the MAST package in R (v1.8.2) (Finak et al., 2015) after conversion of data to log2 counts per million (log2 CPM + 1).
    MAST
    suggested: (MAST, RRID:SCR_016340)

    Results from OddPub: Thank you for sharing your code.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.06.12.148916 on bioRxiv