COVID-19 severity correlates with airway epithelium–immune cell interactions identified by single-cell analysis

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Abstract

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  1. SciScore for 10.1101/2020.04.29.20084327: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: We obtained a signed informed consent for inclusion in this study.
    IACUC: The study was approved by the institutional ethics committee of the Charité - Universitâtsmedizin Berlin (EA2/066/20) and conducted in accordance with the Declaration of Helsinki.
    RandomizationNote that the here presented cohort does not represent the patient distribution admitted to Charité with regards to sex, age, or COVID-19 severity as the patients were randomly chosen based on their presence in the hospital and willingness to donate samples for this study.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableIn addition to the COVID-19 patients, our cohort included one Influenza B patient (#4, male, 37 years) with respiratory symptoms (cough, dyspnea) and a hospitalization duration of four days.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Pre-processing and data analysis: The raw 3’ single cell RNA sequencing data were processed using CellRanger version 3.1.0 (10X Genomics).
    CellRanger
    suggested: None
    The filtered expression matrices were loaded into Seurat version 3.1.4.9012 (https://github.com/satijalab/seurat), where further filtering was done to remove cells with less than 200 genes expressed or more than 15% mitochondrial transcripts.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Cell-cell interactions were calculated using CellPhoneDB version 2.1.2 [Efremova et al. (2020), https://github.com/Teichlab/cellphonedb] on data subsampled according to the following strategy: in order to decrease the impact of samples with high cell numbers, all samples were randomly down-sampled to the size of the smallest sample.
    CellPhoneDB
    suggested: (CellPhoneDB, RRID:SCR_017054)
    In order to identify genes that are significantly regulated as the cells differentiate along the cell-to-cell distance trajectory, we used differentialGeneTest() function implemented in Monocle2 [Qiu et al, 2017].
    Monocle2
    suggested: (Monocle2, RRID:SCR_016339)
    In addition, count and metadata tables containing patient ID, sex, age, cell type and QC metrics for each cell will be available at FigShare.
    FigShare
    suggested: (FigShare, RRID:SCR_004328)

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04359901Active, not recruitingSarilumab for Patients With Moderate COVID-19 Disease
    NCT04322773TerminatedAnti-il6 Treatment of Serious COVID-19 Disease With Threaten…
    NCT04311697CompletedIntravenous Aviptadil for Critical COVID-19 With Respiratory…


    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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