Heterogeneity of neutrophils and inflammatory responses in patients with COVID-19 and healthy controls

  1. Jintao Xu
  2. Bing He
  3. Kyle Carver
  4. Debora Vanheyningen
  5. Brian Parkin
  6. Lana X. Garmire
  7. Michal A. Olszewski
  8. Jane C. Deng

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Abstract

Severe respiratory viral infections, including SARS-CoV-2, have resulted in high mortality rates despite corticosteroids and other immunomodulatory therapies. Despite recognition of the pathogenic role of neutrophils, in-depth analyses of this cell population have been limited, due to technical challenges of working with neutrophils. We undertook an unbiased, detailed analysis of neutrophil responses in adult patients with COVID-19 and healthy controls, to determine whether distinct neutrophil phenotypes could be identified during infections compared to the healthy state. Single-cell RNA sequencing analysis of peripheral blood neutrophils from hospitalized patients with mild or severe COVID-19 disease and healthy controls revealed distinct mature neutrophil subpopulations, with relative proportions linked to disease severity. Disruption of predicted cell-cell interactions, activated oxidative phosphorylation genes, and downregulated antiviral and host defense pathway genes were observed in neutrophils obtained during severe compared to mild infections. Our findings suggest that during severe infections, there is a loss of normal regulatory neutrophil phenotypes seen in healthy subjects, coupled with the dropout of appropriate cellular interactions. Given that neutrophils are the most abundant circulating leukocytes with highly pathogenic potential, current immunotherapies for severe infections may be optimized by determining whether they aid in restoring an appropriate balance of neutrophil subpopulations.

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  1. Version published to 10.3389/fimmu.2022.970287
  2. ScreenIT

    SciScore for 10.1101/2021.12.01.470817: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: All participants provided written informed consent for sample collection and subsequent analyses.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The top 10 feature genes for each type of neutrophil were presented in a bubble plot using the DotPlot function from the Seurat package.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    These feature genes were then mapped to human protein-protein interactions (PPIs) downloaded from the BioGRID database (version 4.4.197) using R.
    BioGRID
    suggested: (BioGrid Australia, RRID:SCR_006334)
    The bipartite plot of significant pathways, genes, and PPIs were presented using the Cytoscape tool (37)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    The KEGG pathways significantly enriched (adjusted P-value <0.05) in feature genes were identified for each cluster using clusterProfiler package.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    The top 3 significant pathways were shown in the bubble plot using ggplot2 package.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.12.01.470817 on bioRxiv