Natural killer cell immunotypes related to COVID-19 disease severity

  1. Christopher Maucourant
  2. Iva Filipovic
  3. Andrea Ponzetta
  4. Soo Aleman
  5. Martin Cornillet
  6. Laura Hertwig
  7. Benedikt Strunz
  8. Antonio Lentini
  9. Björn Reinius
  10. Demi Brownlie
  11. Angelica Cuapio
  12. Eivind Heggernes Ask
  13. Ryan M. Hull
  14. Alvaro Haroun-Izquierdo
  15. Marie Schaffer
  16. Jonas Klingström
  17. Elin Folkesson
  18. Marcus Buggert
  19. Johan K. Sandberg
  20. Lars I. Eriksson
  21. Olav Rooyackers
  22. Hans-Gustaf Ljunggren
  23. Karl-Johan Malmberg
  24. Jakob Michaëlsson
  25. Nicole Marquardt
  26. Quirin Hammer
  27. Kristoffer Strålin
  28. Niklas K. Björkström
  29. The Karolinska COVID-19 Study Group

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Abstract

The NK cell activation landscape in acute SARS-CoV-2 infection is associated with COVID-19 disease severity.

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  1. Version published to 10.1126/sciimmunol.abd6832
  2. ScreenIT

    SciScore for 10.1101/2020.07.07.20148478: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: The study was approved by the Swedish Ethical Review Authority and all patients gave informed consent.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableHealthy controls were SARS-CoV-2 IgG seronegative at time of inclusion, median age was 50-59 years, and 11 out of 17 were male (65%).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Flow cytometry data analysis: FCS3.0 files were exported from the FACSDiva and imported into FlowJo v.
    FACSDiva
    suggested: (BD FACSDiva Software, RRID:SCR_001456)
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Certain figures were generated in R (versions 3.6.0 and 3.6.1) with packages factoextra (v1.0.5), RColorBrewer (v1.1-2), ggplot2 (v3.2.1 and v3.3.0), tidyr (v.1.0.2), randomcoloR (v.1.1.0.1), reshape2 (v.1.4.3), viridis (v.0.5.1), and pheatmap (v.10.12).
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Data in h5 format was read using Seurat (v3.1.5) then filtered for zero-variance genes and size-factor normalized using scater v1.12.2/scran v1.12.1.
    scater
    suggested: (scater, RRID:SCR_015954)
    Pairwise differential expression of genes detected in at least 20% of cells was performed using MAST (v1.10.0) and genes with an FDR < 10−3 in any comparison were clustered into six clusters (determined by gap statistic) by k-means clustering of Z-scores.
    MAST
    suggested: (MAST, RRID:SCR_016340)
    Gene ontology enrichment of gene clusters was performed using PANTHER overrepresentation tests (release 20200407) using gene ontology database 2020-03-23.
    PANTHER
    suggested: (PANTHER, RRID:SCR_004869)
    Data was visualized using ComplexHeatmap (v2.0.0) and ggplot2 (v3.2.1).
    ComplexHeatmap
    suggested: (ComplexHeatmap, RRID:SCR_017270)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Statistical analysis: Data was analyzed in GraphPad Prism v8.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Significant PhenoGraph clusters (P ≤ 0.05) were determined by Chi-Square goodness-of-fit tests comparing the relative abundance of each categorical group in each individual PhenoGraph cluster relative to input.
    PhenoGraph
    suggested: (Phenograph, RRID:SCR_016919)
    Supplementary Figure 1: NK cell differentiation in COVID-19 disease Supplementary Figure 2: NK cell activation in COVID-19 disease Supplementary Figure 3: KIRs and NK cell education in COVID-19 disease Supplementary Figure 4: Adaptive NK cell expansions in COVID-19 Supplementary Figure 5: Strategy for UMAP analysis and representative marker expression Supplementary Figure 6: Selected PhenoGraph clusters and their markers are expressed differentially across clinical parameter-defined patient groups Supplementary Figure 7: Correlations between CD56bright NK cell arming, NK cell phenotype, and soluble factors in COVID-19 Supplementary Table 1: Clinical characteristics of Covid-19 patients Supplementary Table 2: Clinical laboratory results of Covid-19 patients Supplementary Table 3: Flow cytometry panel Supplementary Table 4: Gene ontology analysis of DEGs from scRNAseq analysis of BAL NK cells in COVID-19 Supplementary Table 5: KIR-ligand typing of the study cohort Supplementary Table 6A: Clinical laboratory results of all patients with and without adaptive NK cell expansions Supplementary Table 6B: Clinical laboratory results of severe patients with and without adaptive NK cell expansions Supplementary Table 7: Analysis of the observed distribution of PhenoGraph clusters across clinical parameter-defined groups Supplementary Table 8A: Literature-curated interactions from International Molecular Exchange Consortium (IMEx) interactom database Supplementary Table 8B: Nodes and related degrees and betweenness Supplementary Table 8C: Overview of the KEGG pathways
    KEGG
    suggested: (KEGG, RRID:SCR_012773)

    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 10.1101/2020.07.07.20148478v1 on medRxiv