Potential modes of COVID-19 transmission from human eye revealed by single-cell atlas

  1. Kiyofumi Hamashima
  2. Pradeep Gautam
  3. Katherine Anne Lau
  4. Chan Woon Khiong
  5. Timothy A Blenkinsop
  6. Hu Li
  7. Yuin-Han Loh

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Abstract

There is a pressing urgency to understand the entry route of SARS-CoV-2 viruses into the human body. SARS-CoV-2 viruses enter through ACE2 receptors after the S proteins of the virus are primed by proteases such as TMPRSS2. Most studies focused on the airway epithelial and lung alveolar cells as the route of infection, while the mode of transmission through the ocular route is not well established. Here, we profiled the presence of SARS-CoV-2 receptors and receptor-associated enzymes at single-cell resolution of thirty-three human ocular cell types. We identified unique populations of corneal cells with high ACE2 expression, among which the conjunctival cells co-expressed both ACE2 and TMPRSS2, suggesting that they could serve as the entry points for the virus. Integrative analysis further models the signaling and transcription regulon networks involved in the infection of distinct corneal cells. Our work constitutes a unique resource for the development of new treatments and management of COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.09.085613: (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
    The expression profile of the selected genes was plotted on the bubble plot with the ‘DotPlot’ function in Seurat or violin plot with ‘geom_violin’ function in ggplot2 with the slight modification.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    MAGIC could restore missing values in the large sparse count matrix of single-cell RNA-seq.
    MAGIC
    suggested: (Magic, RRID:SCR_006406)
    The gene-set co-expressing with both ACE2 and TMPRSS2 was used in the gene-set enrichment analysis (GSEA) by Metascape [40] (http://metascape.org).
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    SCENIC is a powerful software which reconstructs a single-cell regulatory network based on the co-expression patterning between transcription factors (TFs) and potential target genes and the enrichment of the regulator’s binding motif.
    SCENIC
    suggested: (SCENIC, RRID:SCR_017247)
    Based on the exported NicheNet data, the network graph was created using Cytoscape [43] (version 3.7.1) with a Compound Spring Embedder layout.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    The proposed pathway of virus transmission via the route from the human eye was illustrated using BioRender (https://biorender.com).
    BioRender
    suggested: (Biorender, RRID:SCR_018361)
    Data availability: Single-cell RNA-seq data (human primary eyes) have been deposited in the Gene Expression Omnibus (GEO) under the accession code GSE147979, which will be released after publishing our previous studies (Gautam and Hamashima et al., manuscript in preparation).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)

    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: 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

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  2. Version published to 10.1101/2020.05.09.085613 on bioRxiv