Meta-analysis of COVID-19 single-cell studies confirms eight key immune responses

  1. Manik Garg
  2. Xu Li
  3. Pablo Moreno
  4. Irene Papatheodorou
  5. Yuelong Shu
  6. Alvis Brazma
  7. Zhichao Miao

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Abstract

Several single-cell RNA sequencing (scRNA-seq) studies analyzing immune response to COVID-19 infection have been recently published. Most of these studies have small sample sizes, which limits the conclusions that can be made with high confidence. By re-analyzing these data in a standardized manner, we validated 8 of the 20 published results across multiple datasets. In particular, we found a consistent decrease in T-cells with increasing COVID-19 infection severity, upregulation of type I Interferon signal pathways, presence of expanded B-cell clones in COVID-19 patients but no consistent trend in T-cell clonal expansion. Overall, our results show that the conclusions drawn from scRNA-seq data analysis of small cohorts of COVID-19 patients need to be treated with some caution.

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  1. Version published to 10.1038/s41598-021-00121-z
  2. ScreenIT

    SciScore for 10.1101/2021.01.24.427089: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 21. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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. ScreenIT

    SciScore for 10.1101/2021.01.24.427089: (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

    Software and Algorithms
    SentencesResources
    According to Wilk et al., dropEst, samtools and STAR were used for the reads mapping.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Zhang dataset We downloaded the raw fastq files of the Zhang dataset​3 from the GSA (Genome Sequence Archive) database under the accession number HRA000150.
    Genome Sequence Archive
    suggested: None
    The gene module scores for HLA class II and ISG signature were calculated for each individual cell using sc.tl.score_genes function of scanpy​39​ v1.5.1. 2020​9 (TCR only) were analyzed separately using the python library pyvdj​40 v0.1.2.
    python
    suggested: (IPython, RRID:SCR_001658)
    The boxplots and barplots were generated using R-package ggplot2​41 v3.3.2, ggpubr​42 v0.4.0.999, rstatix​42,43​ v0.6.0.999 and tidyverse​44​ v1.3.0.
    R-package
    suggested: None

    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: Please consider improving the rainbow (“jet”) colormap used on page 21. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

  4. Version published to 10.1101/2021.01.24.427089v1 on bioRxiv