Lung epithelial stem cells express SARS-CoV-2 entry factors: implications for COVID-19

  1. Anna A. Valyaeva
  2. Anastasia A. Zharikova
  3. Artem S. Kasianov
  4. Yegor S. Vassetzky
  5. Eugene V. Sheval

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Abstract

SARS-CoV-2 can infiltrate the lower respiratory tract, resulting in severe respiratory failure and a high death rate. Normally, the airway and alveolar epithelium can be rapidly reconstituted by multipotent stem cells after episodes of infection. Here, we analyzed published RNA-seq datasets and demonstrated that cells of four different lung epithelial stem cell types express SARS-CoV-2 entry factors, including Ace2 . Thus, stem cells can be potentially infected by SARS-CoV-2, which may lead to defects in regeneration capacity partially accounting for the severity of SARS-CoV-2 infection and its consequences.

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  1. Version published to 10.1101/2020.05.23.107334v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.05.23.107334: (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.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Since the standard cell clustering approach failed to separate the Hopx+/Igfbp2- cell subpopulation from the bulk AT1 cells, as well as Axin2+ cells from AT1 cells, subclusters of interest were identified by the absolute expression of marker genes: Hopx and Igfbp2 for AT1 cells and Axin2 and Sftpc for AT2 cells.
    AT1
    suggested: RRID:CVCL_WX69)
    Software and Algorithms
    SentencesResources
    Raw pair-end reads were quality and adapter trimmed with BBDuk (minlen=31 qtrim=r trimq=20 ktrim=r k=25 mink=11 hdist=1) from the BBTools suite, and FastQC was used for quality control.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Then the reads were pseudoaligned to the mouse transcriptome (obtained from GRCm38 primary genome assembly and GENCODE gene annotation version M24 (https://www.gencodegenes.org/mouse/release_M24.html)) using kallisto38 with default parameters.
    GENCODE
    suggested: (GENCODE, RRID:SCR_014966)
    Differential gene expression analysis was carried out using the DESeq2 R package44.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Volcano plots were generated using the ggplot2 R package45.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Raw gene counts from the GSE118891 dataset were converted to TPM values (gene lengths calculated as the union of all gene exons were obtained from Ensembl (v91) gene annotation) and log2-transformed.
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    After the data were normalized by the means of the Seurat package (NormalizeData function with default parameters), linear dimensional reduction (PCA) was conducted based on 2000 HVGs (identified by the FindVariableFeatures function with default parameters).
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)

    Results from OddPub: Thank you for sharing your code.

    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

    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 10.1101/2020.05.23.107334v1 on bioRxiv