Longitudinal analysis reveals that delayed bystander CD8+ T cell activation and early immune pathology distinguish severe COVID-19 from mild disease

  1. Laura Bergamaschi
  2. Federica Mescia
  3. Lorinda Turner
  4. Aimee L. Hanson
  5. Prasanti Kotagiri
  6. Benjamin J. Dunmore
  7. Hélène Ruffieux
  8. Aloka De Sa
  9. Oisín Huhn
  10. Michael D. Morgan
  11. Pehuén Pereyra Gerber
  12. Mark R. Wills
  13. Stephen Baker
  14. Fernando J. Calero-Nieto
  15. Rainer Doffinger
  16. Gordon Dougan
  17. Anne Elmer
  18. Ian G. Goodfellow
  19. Ravindra K. Gupta
  20. Myra Hosmillo
  21. Kelvin Hunter
  22. Nathalie Kingston
  23. Paul J. Lehner
  24. Nicholas J. Matheson
  25. Jeremy K. Nicholson
  26. Anna M. Petrunkina
  27. Sylvia Richardson
  28. Caroline Saunders
  29. James E.D. Thaventhiran
  30. Erik J.M. Toonen
  31. Michael P. Weekes
  32. Berthold Göttgens
  33. Mark Toshner
  34. Christoph Hess
  35. John R. Bradley
  36. Paul A. Lyons
  37. Kenneth G.C. Smith

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Abstract

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  1. Version published to 10.1016/j.immuni.2021.05.010
  2. ScreenIT

    SciScore for 10.1101/2021.01.11.20248765: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    PBMCs were stained with 2ul of each antibody: anti-CD3-fluorescein isothiocyanate (FITC), clone UCHT1; anti-CD4-phycoerythrin (PE), clone RPA-T4; anti-CD8a-peridinin-chlorophyll
    anti-CD3-fluorescein
    suggested: None
    anti-CD4-phycoerythrin
    suggested: None
    anti-CD8a-peridinin-chlorophyll
    suggested: None
    Software and Algorithms
    SentencesResources
    FCS files generated were analysed using the Maxpar® Pathsetter™ software v2.0.45 (
    Maxpar®
    suggested: None
    Pathsetter™
    suggested: None
    Absolute cell numbers were calculated using the proportions of these immune cell populations within the parent populations determined by BD TruCount™.
    BD TruCount™
    suggested: None
    Data were analysed with FlowJo v10 (Becton Dickinson, Wokingham, UK).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Developed plates were read using an AID iSpot reader (Oxford Biosystems, Oxford, UK) and counted using AID EliSpot v7 software (Autoimmun Diagnostika GmbH, Strasberg, Germany).
    Oxford Biosystems
    suggested: (Science Exchange, RRID:SCR_010620)
    Read quality was assessed using FastQC v.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    0.6.4 (Babraham Bioinformatics, UK) and BBSplit (BBMap v.38.67(BBMap -
    BBMap
    suggested: (BBmap, RRID:SCR_016965)
    Alignment was performed using HISAT2 v.
    HISAT2
    suggested: (HISAT2, RRID:SCR_015530)
    Count matrices were generated using featureCounts (
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Rsubreads package - (Liao et al., 2019) and stored as a DGEList object (EdgeR package (Robinson et al., 2010) for further analysis.
    EdgeR
    suggested: (edgeR, RRID:SCR_012802)
    :

    Gene set enrichment analysis (GSEA) (Subramanian et al., 2005) was used to identify biological pathways enriched in COVID-19 severity groups relative to healthy controls.

    Gene set enrichment analysis
    suggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)
    Partial least squares discriminant analysis (PLS-DA) was conducted using the plsda() function from the package mixOmics (Rohart et al., 2017), a supervised method of sample discrimination whereby sample clustering is informed by group membership (here patient clusters 1 and 2).
    mixOmics
    suggested: (mixOmics, RRID:SCR_016889)
    Heat maps were created using the ComplexHeatmap package (Gu et al., 2016), with data scaled and centred prior to visualisation.
    ComplexHeatmap
    suggested: (ComplexHeatmap, RRID:SCR_017270)

    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 10.1101/2021.01.11.20248765 on medRxiv