Dexamethasone modulates immature neutrophils and interferon programming in severe COVID-19

  1. Sarthak Sinha
  2. Nicole L. Rosin
  3. Rohit Arora
  4. Elodie Labit
  5. Arzina Jaffer
  6. Leslie Cao
  7. Raquel Farias
  8. Angela P. Nguyen
  9. Luiz G. N. de Almeida
  10. Antoine Dufour
  11. Amy Bromley
  12. Braedon McDonald
  13. Mark R. Gillrie
  14. Marvin J. Fritzler
  15. Bryan G. Yipp
  16. Jeff Biernaskie

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Abstract

Although critical for host defense, innate immune cells are also pathologic drivers of acute respiratory distress syndrome (ARDS). Innate immune dynamics during Coronavirus Disease 2019 (COVID-19) ARDS, compared to ARDS from other respiratory pathogens, is unclear. Moreover, mechanisms underlying the beneficial effects of dexamethasone during severe COVID-19 remain elusive. Using single-cell RNA sequencing and plasma proteomics, we discovered that, compared to bacterial ARDS, COVID-19 was associated with expansion of distinct neutrophil states characterized by interferon (IFN) and prostaglandin signaling. Dexamethasone during severe COVID-19 affected circulating neutrophils, altered IFN active neutrophils, downregulated interferon-stimulated genes and activated IL-1R2 + neutrophils. Dexamethasone also expanded immunosuppressive immature neutrophils and remodeled cellular interactions by changing neutrophils from information receivers into information providers. Male patients had higher proportions of IFN active neutrophils and preferential steroid-induced immature neutrophil expansion, potentially affecting outcomes. Our single-cell atlas (see ‘Data availability’ section) defines COVID-19-enriched neutrophil states and molecular mechanisms of dexamethasone action to develop targeted immunotherapies for severe COVID-19.

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  1. Version published to 10.1038/s41591-021-01576-3
  2. ScreenIT

    SciScore for 10.1101/2021.04.18.440366: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Participants were required to have a definitive diagnosis and appropriate consent and samples collected within 72hrs of admission to the ICU in order to be included.
    IRB: This study was approved by the Conjoint Health Research Ethics Board (CHREB) at the University of Calgary (Ethics ID: REB20-0481) and is consistent with and the Declaration of Helsinki.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    IL-28a, IL-33, IP-10, LIF, MCP-1, MCP-2, MCP-3, MCP-4, MDC, MIP-1α, MIP-1β, MIP-1d, PDGF-AA, PDGF-AB/BB, RANTES, SDF-1 a+b, sCD40L, SCF,TARC, TGFa, TNFa, TNFb, TPO, TRAIL, TSLP, VEGF) and a 14 MilliPLEX soluble cytokine (sCD30, sEGFR, sgp130, sIL-1RI, sIL-1RII, sIL-2Ra, sIL-4R, sIL-6R, sRAGE, sTNF RI, sTNF RII, sVEGF R1, sVEGF R2, sVEGF R3) arrays (Millipore Sigma, Oakville, ON, Canada) on a Luminex Model 200 Luminometer (Luminex Corporation, Austin, TX)
    MCP-1
    suggested: None
    Software and Algorithms
    SentencesResources
    Cell density was quantified with a hemacytometer, cell viability was assessed with Trypan Blue staining (T8154; Sigma Aldrich), and 7500 live lymphocytes were transferred to a sterile 1.5 mL microcentrifuge tube.
    T8154; Sigma Aldrich
    suggested: None
    Sequencing reads were aligned using CellRanger 3.1.0 pipeline33 to the standard pre-built GRCh38 reference genome.
    CellRanger
    suggested: (SCIGA, RRID:SCR_021002)
    Boxplots comparing cell type composition were generated using the ggplot2 package.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Differential cell-cell interaction networks were reconstructed using the Connectome R toolkit v0.2.240 and CellChat v1.0.0 41.
    Connectome R toolkit
    suggested: None
    Differential edge list was passed through CircosDiff (a wrapper around the R package ‘circlize’) and CellChat’s netVisual_chord_gene to filter receptor-ligand edges and generate Circos plots.
    Circos
    suggested: (Circos, RRID:SCR_011798)
    Briefly, neutrophils were subsetted from scVelo-realigned Seurat object and processed using default and recommended parameters specified in SCENIC’s vignette (https://github.com/aertslab/SCENIC) using the hg19 RcisTarget reference.
    https://github.com/aertslab/SCENIC
    suggested: (SCENIC, RRID:SCR_017247)
    Rds’) was profiled using g:Profiler’s functional enrichment analysis and genes intersecting with the Interferon pathway were plotted using iRegulon (Cytoscape plugin)45. COVID Neutrophil Atlas: To enable intuitive exploration of single-cell datasets, a web portal (http://biernaskielab.ca/covid_neutrophil or http://biernaskielab.com/covid_neutrophil) was built using RShiny v1.1.0, shinyLP v.1.1.2, and shinythemes v.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    Results from OddPub: Thank you for sharing your data.

    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 10.1101/2021.04.18.440366 on bioRxiv