Longitudinal immune profiling reveals key myeloid signatures associated with COVID-19

  1. Elizabeth R. Mann
  2. Madhvi Menon
  3. Sean Blandin Knight
  4. Joanne E. Konkel
  5. Christopher Jagger
  6. Tovah N. Shaw
  7. Siddharth Krishnan
  8. Magnus Rattray
  9. Andrew Ustianowski
  10. Nawar Diar Bakerly
  11. Paul Dark
  12. Graham M. Lord
  13. Angela Simpson
  14. Timothy Felton
  15. Ling-Pei Ho
  16. NIHR Respiratory TRC
  17. Marc Feldmann
  18. CIRCO
  19. John R. Grainger
  20. Tracy Hussell

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Abstract

Longitudinal analysis of the immune response in patients with COVID-19 identifies a myeloid signature associated with severe disease.

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  1. Version published to 10.1126/sciimmunol.abd6197
  2. ScreenIT

    SciScore for 10.1101/2020.06.13.20127605: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Informed consent was obtained for each patient.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableUsing this strategy peripheral blood samples were collected from 18 females and 9 males (age range 28-69).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Samples were acquired on an LSRFortessa cell analyzer (Becton Dickinson) and analysed using FlowJo (TreeStar).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Groups were compared using an unpaired Mann-Whitney test for healthy individuals versus COVID-19 patients, Kruskal-Wallis test for multiple comparisons, or Spearman’s rank correlation coefficient test for correlation of separate parameters within the COVID-19 patient group, using Prism 8 software (GraphPad).
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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:
    There are, of course, limitations to any study of samples during a viral pandemic for which there is no vaccine. However, we believe that these do not reduce the importance of the messages derived from our study. A longitudinal analysis in real time for phenotypic, functional and soluble markers naturally limits the number of patients interrogated. In-depth analysis of smaller cohorts however, is necessary to gain insight into mechanism and is of interest to the pharmaceutical industry. In addition to the limitations of control subjects addressed above, the only other is that patients may not accurately define the onset of symptoms. As data is plotted per patient, however, this does not affect the interpretation of the results. There are clinical implications of our data, as far as it has been analysed. Using non-steroidal anti-inflammatory drugs (NSAIDs) remains controversial24 and our study would suggest it is not desirable, as it would compound the already low COX-225. Since most of the pathogenic mechanisms involve myeloid cells, neutrophils and monocytes, it would be advantageous to reduce their influx to the lung once lung pathology is established. Relevant strategies include inhibition of the complement anaphylatoxin C5a26 or IL-8 (CXCL8), which are strong chemoattractants for many immune cells, including neutrophils27. Antagonism of CXCR2 that mobilises neutrophil and monocyte from the bone marrow, neutrophil elastase inhibitors and inhibition of G-CSF, IL-23 and IL-1...

    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 10.1101/2020.06.13.20127605v1 on medRxiv