Single-cell landscape of immunological responses in patients with COVID-19

  1. Ji-Yuan Zhang
  2. Xiang-Ming Wang
  3. Xudong Xing
  4. Zhe Xu
  5. Chao Zhang
  6. Jin-Wen Song
  7. Xing Fan
  8. Peng Xia
  9. Jun-Liang Fu
  10. Si-Yu Wang
  11. Ruo-Nan Xu
  12. Xiao-Peng Dai
  13. Lei Shi
  14. Lei Huang
  15. Tian-Jun Jiang
  16. Ming Shi
  17. Yuxia Zhang
  18. Alimuddin Zumla
  19. Markus Maeurer
  20. Fan Bai
  21. Fu-Sheng Wang

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1038/s41590-020-0762-x
  2. ScreenIT

    SciScore for 10.1101/2020.07.23.217703: (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
    Enrichment analysis for the functions of the DEGs was conducted using the Metascape webtool (www.metascape.org).
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    Gene sets were derived from the GO Biological Process Ontology.
    GO Biological
    suggested: None

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are several limitations in this study. For example, it was very difficult to obtain the immune cells in bronchoalveolar lavage fluid due to biosafety reasons during the outbreak of COVID-19 when we performed this study. Also, the sample size is comparatively small. Therefore, future studies with longitudinal samples from more COVID-19 patients may help to determine the cause-and-effect relationships between immune characteristic of different cell types and disease outcome. Taken together, this integrated, multi-cellular description in our study lays the foundation for future characterization of the complex, dynamic immune responses to SARS-CoV-2 infection. The transcriptomic data, coupled with detailed TCR- and BCR-based lineage information, can serve as a rich resource for deeper understanding of peripheral lymphocytes in COVID-19 patients and pave the way for rationally designed therapies as well as development of SARS-CoV-2-specific vaccines.

    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 10.1101/2020.07.23.217703v1 on bioRxiv