Scalable workflow for characterization of cell-cell communication in COVID-19 patients

  1. Yingxin Lin
  2. Lipin Loo
  3. Andy Tran
  4. David M. Lin
  5. Cesar Moreno
  6. Daniel Hesselson
  7. G. Gregory Neely
  8. Jean Y. H. Yang

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Abstract

COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable and scalable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use this to examine eight public single-cell RNA-seq datasets (six from peripheral blood mononuclear cells, one from bronchoalveolar lavage and one from nasopharyngeal), with a total of 211 individual samples. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate diverse communication patterns across individuals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients. Overall, this workflow can be generalized and scaled to combine multiple scRNA-seq datasets to uncover cell-cell interactions.

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  1. Version published to 10.1371/journal.pcbi.1010495
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    SciScore for 10.1101/2020.12.30.424641: (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
    [B] Liao dataset - The raw count matrices of single-cell RNA-seq data from bronchoalveolar lavage fluid was downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) under the accession number GSE145926.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Clustering: We group various pathways based on the similarity of intercellular communication patterns using hierarchical clustering with Euclidean distance and ward.D2 agglomerative method implemented in the function hclust in R.
    hclust
    suggested: (HCLUST, RRID:SCR_009154)
    Gene Ontology analysis: Differential gene expressions were identified using moderated t-statistics implemented in the R package limma (version 3.44.3).
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    The gene set over-representation analysis for the significant DE genes (top 100 genes selected) with biological process (BP) gene ontology is measured using the “enrichGO” function in the R package clusterProfiler (version 3.16.0) (43).
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)

    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.
    • 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.

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  3. Version published to 10.1101/2020.12.30.424641v1 on bioRxiv