The COVID-19 immune landscape is dynamically and reversibly correlated with disease severity

  1. Hamid Bolouri
  2. Cate Speake
  3. David Skibinski
  4. S. Alice Long
  5. Anne M. Hocking
  6. Daniel J. Campbell
  7. Jessica A. Hamerman
  8. Uma Malhotra
  9. Jane H. Buckner
  10. the BRI COVID-19 Research Team

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

Despite a rapidly growing body of literature on COVID-19, our understanding of the immune correlates of disease severity, course and outcome remains poor. Using mass cytometry, we assessed the immune landscape in longitudinal whole blood specimens from 59 patients presenting with acute COVID-19, and classified based on maximal disease severity. Hospitalized patients negative for SARS-CoV-2 were used as controls. We found that the immune landscape in COVID-19 forms three dominant clusters, which correlate with disease severity. Longitudinal analysis identified a pattern of productive innate and adaptive immune responses in individuals who have a moderate disease course, whereas those with severe disease have features suggestive of a protracted and dysregulated immune response. Further, we identified coordinate immune alterations accompanying clinical improvement and decline that were also seen in patients who received IL-6 pathway blockade. The hospitalized COVID-19 negative cohort allowed us to identify immune alterations that were shared between severe COVID-19 and other critically ill patients. Collectively, our findings indicate that selection of immune interventions should be based in part on disease presentation and early disease trajectory due to the profound differences in the immune response in those with mild to moderate disease and those with the most severe disease.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.09.18.303420: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All healthy control samples were from healthy subjects in the BRI Immune-Mediated Disease Registry and Repository who had given written informed consent in accordance with the Declaration of Helsinki and according to the BRI Institutional Review Board-approved protocol IRB07109.
    IRB: All healthy control samples were from healthy subjects in the BRI Immune-Mediated Disease Registry and Repository who had given written informed consent in accordance with the Declaration of Helsinki and according to the BRI Institutional Review Board-approved protocol IRB07109.
    Randomizationnot detected.
    BlindingAll assays were run and analyzed in a blinded manner.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Specifically, gates for CD25, CD38, CD69, HLA-DR, PD-1 and PD-L1 were the same for all cell types where they were applied.
    PD-L1
    suggested: ATCC Cat# CRL-1882, RRID:CVCL_G248)
    Software and Algorithms
    SentencesResources
    Data was analyzed using a FlowJo software versions 10.6.0 and 10.6.1 (FlowJo LLC, Ashland, OR).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Caveats for our tocilizumab analysis include the small cohort size, and that all but one of these patients were treated with convalescent plasma prior to tocilizumab treatment. Therefore, it is possible that convalescent plasma acts synergistically with tocilizumab to cause the immune signature we identified. Interestingly and in contrast to tocilizumab, we saw no clear immune signature of convalescent plasma within 7 days, suggesting either our cohort was too small to see changes, the immune populations change after the times we analyzed, or convalescent plasma does not act at the level of blood leukocyte populations. It is clear that further investigation is needed to determine if tocilizumab has a therapeutic role in COVID-19, and in what patient population it would be useful and this may be determined in part by the character and trajectory of the immune landscape of the patient. The demographics of our COVID-19 patients were consistent with published case reports. African Americans and Hispanics were overrepresented in the severe COVID-19 group to the population of Washington State, which is consistent with reports from other states in the USA (10, 11). We also found that type 2 diabetes was more common in those with severe disease compared to moderate or mild disease. Notably, all groups have higher diabetes prevalence than the US or Washington rates (28); the highest prevalence in Washington state is among 65-74 year olds at 21.5%, which is more than doubled in the coh...

    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 10.1101/2020.09.18.303420 on bioRxiv