Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

  1. Divij Mathew
  2. Josephine R. Giles
  3. Amy E. Baxter
  4. Derek A. Oldridge
  5. Allison R. Greenplate
  6. Jennifer E. Wu
  7. Cécile Alanio
  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. Holly Ramage
  32. Sara Cherry
  33. Scott E. Hensley
  34. Sokratis A. Apostolidis
  35. Alexander C. Huang
  36. Laura A. Vella
  37. The UPenn COVID Processing Unit
  38. Michael R. Betts
  39. Nuala J. Meyer
  40. E. John Wherry
  41. Zahidul Alam
  42. Mary M. Addison
  43. Katelyn T. Byrne
  44. Aditi Chandra
  45. Hélène C. Descamps
  46. Yaroslav Kaminskiy
  47. Jacob T. Hamilton
  48. Julia Han Noll
  49. Dalia K. Omran
  50. Eric Perkey
  51. Elizabeth M. Prager
  52. Dana Pueschl
  53. Jennifer B. Shah
  54. Jake S. Shilan
  55. Ashley N. Vanderbeck

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Abstract

Coronavirus disease 2019 (COVID-19) has affected millions of people globally, yet how the human immune system responds to and influences COVID-19 severity remains unclear. Mathew et al. present a comprehensive atlas of immune modulation associated with COVID-19. They performed high-dimensional flow cytometry of hospitalized COVID-19 patients and found three prominent and distinct immunotypes that are related to disease severity and clinical parameters. Arunachalam et al. report a systems biology approach to assess the immune system of COVID-19 patients with mild-to-severe disease. These studies provide a compendium of immune cell information and roadmaps for potential therapeutic interventions.

Science , this issue p. eabc8511 , p. 1210

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  1. Version published to 10.1126/science.abc8511
  2. 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.

  3. Version published to 10.1101/2020.05.20.106401v1 on bioRxiv