Emergence of Low-density Inflammatory Neutrophils Correlates with Hypercoagulable State and Disease Severity in COVID-19 Patients

  1. Samantha M Morrissey
  2. Anne E Geller
  3. Xiaoling Hu
  4. David Tieri
  5. Elizabeth A Cooke
  6. Chuanlin Ding
  7. Matthew Woeste
  8. Huang-ge Zhange
  9. Rodrigo Cavallazi
  10. Sean P Clifford
  11. James Chen
  12. Maiying Kong
  13. Corey T Watson
  14. Jiapeng Huang
  15. Jun Yan

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel viral pathogen that causes a clinical disease called coronavirus disease 2019 (COVID-19). Approximately 20% of infected patients experience a severe manifestation of the disease, causing bilateral pneumonia and acute respiratory distress syndrome. Severe COVID-19 patients also have a pronounced coagulopathy with approximately 30% of patients experiencing thromboembolic complications. However, the etiology driving the coagulopathy remains unknown. Here, we explore whether the prominent neutrophilia seen in severe COVID-19 patients contributes to inflammation-associated coagulation. We found in severe patients the emergence of a CD16IntCD44lowCD11bInt low-density inflammatory band (LDIB) neutrophil population that trends over time with changes in disease status. These cells demonstrated spontaneous neutrophil extracellular trap (NET) formation, phagocytic capacity, enhanced cytokine production, and associated clinically with D-dimer and systemic IL-6 and TNF-α levels, particularly for CD40+ LDIBs. We conclude that the LDIB subset contributes to COVID-19-associated coagulopathy (CAC) and could be used as an adjunct clinical marker to monitor disease status and progression. Identifying patients who are trending towards LDIB crisis and implementing early, appropriate treatment could improve all-cause mortality rates for severe COVID-19 patients.

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    SciScore for 10.1101/2020.05.22.20106724: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study Participants and Clinical Data: The Institutional Review Board at University of Louisville approved the present study and written informed consent was obtained from either subjects or their legal authorized representatives (IRB No. 20. 0321).
    Consent: Study Participants and Clinical Data: The Institutional Review Board at University of Louisville approved the present study and written informed consent was obtained from either subjects or their legal authorized representatives (IRB No. 20. 0321).
    Randomizationnot detected.
    BlindingAll COVID-19 patients were followed by the research team daily and the clinical team was blinded to findings of the research analysis to avoid potential bias.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    CyTOF Data Analysis: CyTOF data was analyzed using a combination of the Cytobank software package(49) and the CyTOF workflow(50), which consists of suite of packages(51) (52-55) available in R (r-project.org).
    Cytobank
    suggested: (Cytobank, RRID:SCR_014043)
    For analysis conducted within the CyTOF workflow, FlowJo Workspace files were imported and parsed using functions within flowWorkspace(52) and CytoML(53).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    The analyses were carried out in the Statistical software R (https://www.r-project.org/) and Prism version 10.
    https://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    Prism
    suggested: (PRISM, RRID:SCR_005375)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 40, 29 and 30. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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 10.1101/2020.05.22.20106724v1 on medRxiv