A distinct innate immune signature marks progression from mild to severe COVID-19

  1. Stéphane Chevrier
  2. Yves Zurbuchen
  3. Carlo Cervia
  4. Sarah Adamo
  5. Miro E. Raeber
  6. Natalie de Souza
  7. Sujana Sivapatham
  8. Andrea Jacobs
  9. Esther Bachli
  10. Alain Rudiger
  11. Melina Stüssi-Helbling
  12. Lars C. Huber
  13. Dominik J. Schaer
  14. Jakob Nilsson
  15. Onur Boyman
  16. Bernd Bodenmiller

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Abstract

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  1. Version published to 10.1016/j.xcrm.2020.100166
  2. ScreenIT

    SciScore for 10.1101/2020.08.04.236315: (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
    All healthy controls were tested for SARS-CoV-2 specific IgA and IgG antibodies and all were below the diagnostic reference value.
    SARS-CoV-2 specific IgA and IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    Mass cytometry data analysis: Upon pre-processing, a subset of 1,000 randomly selected cells from each sample were exported as FCS files and loaded on Cytobank.
    Cytobank
    suggested: (Cytobank, RRID:SCR_014043)
    Data were displayed using the ggplot2 R package or the plotting functions of CATALYST (Nowicka et al., 2019).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Heatmaps were generated based on the pheatmap package.
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Clustering analysis of the myeloid and neutrophil subsets was performed using the R implementation of PhenoGraph run on all samples simultaneously, with the parameter k, defining the number of nearest neighbors, set to 100 (Levine et al., 2015).
    PhenoGraph
    suggested: (Phenograph, RRID:SCR_016919)
    The principal component analysis to identify the variations in the data described by the cluster frequencies or the combination of cluster frequencies and cytokine levels was performed based on the FactoMineR package.
    FactoMineR
    suggested: (FactoMineR, RRID:SCR_014602)
    Statistical analysis: The statistical analysis was performed using GraphPad Prism (version 8.4.3, GraphPad Software, La Jolla California USA) and R software (version 4.0.1) using the package “mgcv”.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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: We detected the following sentences addressing limitations in the study:
    This raises a caveat of our study, as we analyzed only peripheral blood samples. Another limitation is that only four patients were analyzed longitudinally, whereas the cellular trajectories during the acute infection rely on samples collected from multiple individuals who presented at different times after symptom onset. However, our time course analysis also highlights the importance of the sampling time point in analyzing the immune response. Notably, the paired sample analysis confirmed the patterns observed at the cohort level, providing strong support to the pseudo-time analysis. In summary, our systems-level analysis of the innate immune response to SARS-CoV-2 shows that there are profound changes in the peripheral monocyte compartment that are largely similar in cases of mild and severe disease. However, the patients with severe symptoms have a markedly stronger inflammatory phenotype throughout the disease course and most prominently show a distinct innate signature at later stages of the disease. These results provide evidence for a strong inflammatory response to SARS-CoV-2 infection, further supporting investigation of targeted anti-inflammatory interventions in severe cases of COVID-19 (Merad and Martin, 2020). The distinct time-dependent change in immune signatures indicate that specific interventions might benefit from precise timing to maximize therapeutic efficacy (Lang et al., 2020).

    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 10.1101/2020.08.04.236315 on bioRxiv