Dissecting the common and compartment-specific features of COVID-19 severity in the lung and periphery with single-cell resolution

  1. Kalon J. Overholt
  2. Jonathan R. Krog
  3. Bryan D. Bryson

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Abstract

As the global COVID-19 pandemic continues to escalate, no effective treatment has yet been developed for the severe respiratory complications of this disease. This may be due in large part to the unclear immunopathological basis for the development of immune dysregulation and acute respiratory distress syndrome (ARDS) in severe and critical patients. Specifically, it remains unknown whether the immunological features of the disease that have been identified so far are compartment-specific responses or general features of COVID-19. Additionally, readily detectable biological markers correlated with strata of disease severity that could be used to triage patients and inform treatment options have not yet been identified. Here, we leveraged publicly available single-cell RNA sequencing data to elucidate the common and compartment-specific immunological features of clinically severe COVID-19. We identified a number of transcriptional programs that are altered across the spectrum of disease severity, few of which are common between the lung and peripheral immune environments. In the lung, comparing severe and moderate patients revealed severity-specific responses of enhanced interferon, A20/IκB, IL-2, and IL-6 pathway signatures along with broad signaling activity of IFNG, SPP1, CCL3, CCL8 , and IL18 across cell types. These signatures contrasted with features unique to ARDS observed in the blood compartment, which included depletion of interferon and A20/IκB signatures and a lack of IL-6 response. The cell surface marker S1PR1 was strongly upregulated in patients diagnosed with ARDS compared to non-ARDS patients in γδ T cells of the blood compartment, and we nominate S1PR1 as a potential marker for immunophenotyping ARDS in COVID-19 patients using flow cytometry.

HIGHLIGHTS

  • COVID-19 disease severity is associated with a number of compositional shifts in the cellular makeup of the blood and lung environments.

  • Transcriptional data suggest differentially expressed cell surface proteins as markers for COVID-19 immunophenotyping from BALF and PBMC samples.

  • Severity-specific features COVID-19 manifest at the pathway level, suggesting distinct changes to epithelia and differences between local and systemic immune dynamics.

  • Immune-epithelial cellular communication analysis identifies ligands implicated in transcriptional regulation of proto-oncogenes in the lung epithelia of severe COVID-19 patients.

  • Network analysis suggests broadly-acting dysregulatory ligands in the pulmonary microenvironment as candidate therapeutic targets for the treatment of severe COVID-19.

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    SciScore for 10.1101/2020.06.15.147470: (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
    Gene set enrichment analysis (GSEA) [67] was performed using the “fgsea” package (v. 1.12.0) [68] in R.
    Gene set enrichment analysis
    suggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)
    GSEA results were interpreted according to normalized enrichment score (NES) and an adjusted p-value, with a p<0.05 significance threshold.
    GSEA
    suggested: (SeqGSEA, RRID:SCR_005724)
    Transcription factor (TF) analysis was similarly conducted on the DEG list to query the regulation of TFs identified by the ENCODE and ChEA ChIP-X databases [71, 72].
    ENCODE
    suggested: (Encode, RRID:SCR_015482)
    ChEA
    suggested: (ChEA, RRID:SCR_005403)
    To specifically analyze signaling from mononuclear phagocytes and neutrophils, differentially expressed target genes ranking among the 250 most strongly predicted targets of the top 20 ligands were used, with the upper 25% of these targets according to regulatory potential visualized in circos plots.
    circos
    suggested: (Circos, RRID:SCR_011798)
    For each receiver cell type, ligands with positive Pearson correlation were filtered based on 1) either belonging to the list of top 20 ligands or having a Pearson correlation greater than 0.1, and 2) presence of the corresponding receptor in over 10% of cells (manually validated using CellPhoneDB [73]).
    CellPhoneDB
    suggested: (CellPhoneDB, RRID:SCR_017054)
    GO and TF analysis was conducted using the “EnrichR” package, and statistical significance was qualified using the EnrichR adjusted p-value based on Fisher’s exact test, with a significance threshold of p<0.05.
    EnrichR
    suggested: (Enrichr, RRID:SCR_001575)

    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
    NCT04324021TerminatedEfficacy and Safety of Emapalumab and Anakinra in Reducing H…

    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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  2. Version published to 10.1101/2020.06.15.147470 on bioRxiv