Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions

  1. Divij Mathew
  2. Josephine R. Giles
  3. Amy E. Baxter
  4. Allison R. Greenplate
  5. Jennifer E. Wu
  6. Cécile Alanio
  7. Derek A. Oldridge
  8. Leticia Kuri-Cervantes
  9. M. Betina Pampena
  10. Kurt D’Andrea
  11. Sasikanth Manne
  12. Zeyu Chen
  13. Yinghui Jane Huang
  14. John P. Reilly
  15. Ariel R Weisman
  16. Caroline A.G. Ittner
  17. Oliva Kuthuru
  18. Jeanette Dougherty
  19. Kito Nzingha
  20. Nicholas Han
  21. Justin Kim
  22. Ajinkya Pattekar
  23. Eileen C. Goodwin
  24. Elizabeth M. Anderson
  25. Madison E. Weirick
  26. Sigrid Gouma
  27. Claudia P. Arevalo
  28. Marcus J. Bolton
  29. Fang Chen
  30. Simon F. Lacey
  31. Scott E. Hensley
  32. Sokratis Apostolidis
  33. Alexander C. Huang
  34. Laura A. Vella
  35. The UPenn COVID Processing Unit
  36. Michael R. Betts
  37. Nuala J. Meyer
  38. E. John Wherry

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Abstract

COVID-19 has become a global pandemic. Immune dysregulation has been implicated, but immune responses remain poorly understood. We analyzed 71 COVID-19 patients compared to recovered and healthy subjects using high dimensional cytometry. Integrated analysis of ∼200 immune and >30 clinical features revealed activation of T cell and B cell subsets, but only in some patients. A subgroup of patients had T cell activation characteristic of acute viral infection and plasmablast responses could reach >30% of circulating B cells. However, another subgroup had lymphocyte activation comparable to uninfected subjects. Stable versus dynamic immunological signatures were identified and linked to trajectories of disease severity change. These analyses identified three “immunotypes” associated with poor clinical trajectories versus improving health. These immunotypes may have implications for therapeutics and vaccines.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.20.106401: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Patients, subjects, and clinical data collection: Patients admitted to the Hospital of the University of Pennsylvania with a SARS-CoV2 positive result were screened and approached for informed consent within 3 days of hospitalization.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Other statistical analysis was performed using Prism software (GraphPad).
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    High dimensional data analysis of flow cytometry data: viSNE and FlowSOM analysis were performed on Cytobank (https://cytobank.org).
    FlowSOM
    suggested: (FlowSOM, RRID:SCR_016899)
    Cytobank
    suggested: (Cytobank, RRID:SCR_014043)
    Resulting scores were hierarchically clustered using the hclust package in R.
    hclust
    suggested: (HCLUST, RRID:SCR_009154)

    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: 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 10.1101/2020.05.20.106401 on bioRxiv