An open resource for T cell phenotype changes in COVID-19 identifies IL-10-producing regulatory T cells as characteristic of severe cases

  1. Julika Neumann
  2. Teresa Prezzemolo
  3. Lore Vanderbeke
  4. Carlos P. Roca
  5. Margaux Gerbaux
  6. Silke Janssens
  7. Mathijs Willemsen
  8. Oliver Burton
  9. Pierre Van Mol
  10. Yannick Van Herck
  11. CONTAGIOUS co-authors
  12. Joost Wauters
  13. Els Wauters
  14. Adrian Liston
  15. Stephanie Humblet-Baron

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The pandemic spread of the novel coronavirus SARS-CoV-2 is due, in part, to the immunological properties of the host-viral interaction. The clinical presentation varies greatly from individual to individual, with asymptomatic carriers, mild to moderate-presenting patients and severely affected patients. Variation in immune response to SARS-CoV-2 may underlie this clinical variation. Using a high dimensional systems immunology platform, we have analyzed the peripheral blood compartment of 6 healthy individuals, 23 mild-to-moderate COVID-19 patients and 20 severe COVID-19 patients. We identify distinct immunological signatures in the peripheral blood of the mild-to-moderate and severe COVID-19 patients, including T cell lymphopenia, more consistent with peripheral hypo-than hyper-immune activation. Unique to the severe COVID-19 cases was a large increase in the proportion of IL-10-secreting regulatory T cells, a lineage known to possess anti-inflammatory properties in the lung. Annotated data is openly available ( https://flowrepository.ors/experiments/2713 ) with clinical correlates, as a systems immunology resource for the COVID-19 research community.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.31.20112979: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Briefly, between March 27th and April 17th 2020, healthy volunteers and adult COVID-19 patients were recruited at COVID-19 hospitalization ward at UZ Leuven (Leuven, Belgium) after informed consent.
    IRB: All procedures were approved by the UZ Leuven Ethical Committee (protocol study number S63881).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Cells were then washed twice with PBS (Fisher Scientific) and stained with live/dead marker (fixable viability dye eFluor780, eBioscience) and fluorochrome-conjugated antibodies against surface markers: anti-CD14 (TuK4), anti-CCR7 (G043H7) (eBioscience); anti-CD3 (REA613) (Miltenyi Biotec); anti-CD4 (SK3), anti-CD8 (SK1), anti-PD1 (EH12.1), anti-CD45RA (HI100) (all from BD Biosciences); anti-CD25 (
    anti-CD14
    suggested: None
    anti-CCR7
    suggested: (Fluidigm Cat# 3159003, RRID:AB_2714155)
    anti-CD3
    suggested: (BD Biosciences Cat# 341134, RRID:AB_647412)
    REA613
    suggested: None
    anti-CD4
    suggested: (Cell Sciences Cat# 873.018.050, RRID:AB_10052620)
    anti-CD8
    suggested: (Thermo Fisher Scientific Cat# MA1-42127, RRID:AB_2537279)
    anti-PD1
    suggested: None
    anti-CD45RA
    suggested: None
    anti-CD25
    suggested: None
    Software and Algorithms
    SentencesResources
    ), anti-CTLA-4 (BNI3), anti-GATA3 (L50-823) (all from BD Biosciences); anti-IL-4 (MP4-25D2), anti-TNFα (Mab11), anti-FOXP3 (206D) (all from BioLegend).
    BD Biosciences)
    suggested: None
    The complete set of FCS files used for the COVID-19 cytokine immune phenotyping has been deposited on FlowRepository and annotated in accordance with the MIFlowCyt standard.
    FlowRepository
    suggested: (FLOWRepository, RRID:SCR_013779)
    Flow cytometry analysis: The concatenated dataset was analyzed through successive FlowSOM clustering and tSNE representation after exporting similar event numbers for each sample per condition group and then subsampling equal event numbers per condition.
    FlowSOM
    suggested: (FlowSOM, RRID:SCR_016899)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Key limitations in our study are the study size and the cross-sectional nature. The data presented here rely on a small number of patients without longitudinal follow-up, and lacking asymptomatic patients to screen the full spectrum of the COVID-19 immune signature. Study expansion and follow-up are ongoing to correct these deficits, and identify key components of the immune system responsible for a better protection against SARS-CoV-2. The other major limitation of the current work is the focus on T cells. With the limited phenotype changes observed here, a more comprehensive study of systemic myeloid cell changes in COVID-19 would be justified. Altogether, our study highlights the absence of a strong anti-viral response against SARS-CoV-2 across mild to severe COVID-19 patients, and the elevated presence of anti-inflammatory IL-10-producing regulatory T cells in the severely affected patients. This data suggests that a route to normalization of anti-SARS-CoV-2 immunity is critical in the attempt to cure these patients. Finally, with the intent to share and exchange knowledge to accelerate research on this pandemic, our data are available on the following open access repository for further investigation: https://flowrepository.org/experiments/2713.

    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 10.1101/2020.05.31.20112979 on medRxiv
  3. Version published to 10.1002/cti2.1204