Single cell profiling of COVID-19 patients: an international data resource from multiple tissues

  1. Chan Zuckerberg Initiative Single-Cell COVID-19 Consortia
  2. Esteban Ballestar
  3. Donna L. Farber
  4. Sarah Glover
  5. Bruce Horwitz
  6. Kerstin Meyer
  7. Marko Nikolić
  8. Jose Ordovas-Montanes
  9. Peter Sims
  10. Alex Shalek
  11. Niels Vandamme
  12. Linos Vandekerckhove
  13. Roser Vento-Tormo
  14. Alexandra Chloe Villani

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Abstract

In late 2019 and through 2020, the COVID-19 pandemic swept the world, presenting both scientific and medical challenges associated with understanding and treating a previously unknown disease. To help address the need for great understanding of COVID-19, the scientific community mobilized and banded together rapidly to characterize SARS-CoV-2 infection, pathogenesis and its distinct disease trajectories. The urgency of COVID-19 provided a pressing use-case for leveraging relatively new tools, technologies, and nascent collaborative networks. Single-cell biology is one such example that has emerged over the last decade as a powerful approach that provides unprecedented resolution to the cellular and molecular underpinnings of biological processes. Early foundational work within the single-cell community, including the Human Cell Atlas, utilized published and unpublished data to characterize the putative target cells of SARS-CoV-2 sampled from diverse organs based on expression of the viral receptor ACE2 and associated entry factors TMPRSS2 and CTSL (Muus et al., 2020; Sungnak et al., 2020; Ziegler et al., 2020). This initial characterization of reference data provided an important foundation for framing infection and pathology in the airway as well as other organs. However, initial community analysis was limited to samples derived from uninfected donors and other previously-sampled disease indications. This report provides an overview of a single-cell data resource derived from samples from COVID-19 patients along with initial observations and guidance on data reuse and exploration.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.20.20227355: (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

    Antibodies
    SentencesResources
    One million of cells was stained with the CITE-seq antibody mix containing >250 barcoded antibodies (TotalSeq™-A, BioLegend)
    TotalSeq™-A
    suggested: None
    For instance, the naive Tcell populations could be resolved from high level of CCR7, and also from having levels of antibody_CD45RA as opposed to antibody_CD45RO, which is high in other CD4+ Tcells.
    CCR7
    suggested: None
    antibody_CD45RA
    suggested: None
    antibody_CD45RO
    suggested: None
    Software and Algorithms
    SentencesResources
    The supernatant was removed and the BAL fluid cells were counted and subsequently processed fresh for CITEseq/scRNAseq.
    CITEseq/scRNAseq
    suggested: None
    Genes associated with G1 and S cell stage, which are provided in the Seurat package (Stuart et al. 2019), were excluded and the remaining genes were downsampled to 5000 genes using scvi native method.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    The alignment, quantification and preliminary cell calling were carried out via the STARsolo functionality of STAR 2.7.3a, with the cell calling subsequently refined with Cell Ranger 3.0.2’s version of EmptyDrops (Lun et al., 2019).
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Cell Ranger
    suggested: (Cell Ranger , RRID:SCR_017344)

    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.
    • No funding statement was detected.
    • 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.

  2. Version published to 10.1101/2020.11.20.20227355 on medRxiv