SARS-CoV-2 receptor ACE2 and TMPRSS2 are predominantly expressed in a transient secretory cell type in subsegmental bronchial branches

  1. Soeren Lukassen
  2. Robert Lorenz Chua
  3. Timo Trefzer
  4. Nicolas C. Kahn
  5. Marc A. Schneider
  6. Thomas Muley
  7. Hauke Winter
  8. Michael Meister
  9. Carmen Veith
  10. Agnes W. Boots
  11. Bianca P. Hennig
  12. Michael Kreuter
  13. Christian Conrad
  14. Roland Eils

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Abstract

The SARS-CoV-2 pandemic affecting the human respiratory system severely challenges public health and urgently demands for increasing our understanding of COVID-19 pathogenesis, especially host factors facilitating virus infection and replication. SARS-CoV-2 was reported to enter cells via binding to ACE2, followed by its priming by TMPRSS2. Here, we investigate ACE2 and TMPRSS2 expression levels and their distribution across cell types in lung tissue (twelve donors, 39,778 cells) and in cells derived from subsegmental bronchial branches (four donors, 17,521 cells) by single nuclei and single cell RNA sequencing, respectively. While TMPRSS2 is expressed in both tissues, in the subsegmental bronchial branches ACE2 is predominantly expressed in a transient secretory cell type. Interestingly, these transiently differentiating cells show an enrichment for pathways related to RHO GTPase function and viral processes suggesting increased vulnerability for SARS-CoV-2 infection. Our data provide a rich resource for future investigations of COVID-19 infection and pathogenesis.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.13.991455: (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

    Software and Algorithms
    SentencesResources
    Processed data in the form of count tables and metadata tables containing patient ID, sex, age, smoking status, cell type and QC metrics for each cell are available on FigShare (https://doi.org/10.6084/m9.figshare.11981034.v1) and Mendeley Data (
    FigShare
    suggested: (FigShare, RRID:SCR_004328)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This resource certainly comes with limitations (see below). However, we believe the unprecedented depth of our analysis on a single cell level will provide a valuable resource for future mechanistic studies and target mining for pulmonary host factors that are involved in facilitating virus entry and replication, ultimately leading to defining genes that are of urgent interest for studying transcriptional changes in COVID-19 patients or the SARS-CoV-2 pathogenicity in general. This data set comprises 16 individuals and a total of 57,229 cells. Thus, this large single-cell and nuclei expression resource enables the analysis of weakly expressed genes such as ACE2 and identification of rare cell types and the transient secretory cell type, for which our data showed a particularly high level of potential vulnerability for SARS-CoV-2 infection assuming that ACE2 is the receptor that is likely to be crucial for its cell entry. Although we strongly believe that the here presented data is well suited as reference data set to study SARS-CoV-2 infection on the single cell transcriptional level, we are also well aware of potential limitations of our data. First, the here presented data is derived from individuals that have no infection history with SARS-CoV-2. However, we deem our data clinically meaningful as our patient cohort is representative for those being affected by SARS-CoV-2 associated disease. The patient cohort analyzed here is in line with recently published data on charact...

    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.

  2. ScreenIT

    SciScore for 10.1101/2020.03.13.991455: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementEXPERIMENTAL MODEL AND SUBJECT DETAILS Human lung tissues and bronchial branches All subjects gave their informed consent for inclusion before they participated in the study .Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableAlthough the first reported cases suggested higher infection rates for males [ Chen et al. ( 2020) , Huang et al. ( 2020)] , no significant differences in the infection rate of males and females were found with increasing numbers of COVID-19 patients [ Wang et al. ( 2020b) , Brussow ( 2020) , Zhang et al. ( 2020a)] .

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Lukassen , Chua , Trefzer , Kahn , Schneider et al. ( 2020 ) 11 SARS-CoV-2 was shown to comprise a FURIN cleavage site that is absent in SARSCoV [ Coutard et al. ( 2020) , Wu et al. ( 2020b)] .
    SARSCoV
    suggested: None
    ACKNOWLEDGEMENTS Cryopreserved surgical lung tissues from patients were kindly provided from the Lung Biobank Heidelberg , a member of the accredited Tissue Bank of the National Center for Tumor Diseases ( NCT ) Heidelberg , the BioMaterialBank Heidelberg and the Biobank platform of the German Center for Lung Research ( DZL) .
    BioMaterialBank
    suggested: None
    Afterwards , ALI cultures were characterized for their proper differentiation using Uteroglobin/CC10 ( #RD181022220-01 , BioVendor)
    BioVendor
    suggested: (BioVendor Laboratory Medicine, SCR_005143)
    Pictures were assembled using Photoshop CS6 ( Adobe) .
    Photoshop
    suggested: (Adobe Photoshop, SCR_014199)
    Low-quality cells were removed during pre-processing using Seurat version 3.0.0 ( https://github.com/satijalab/seurat ) based on the following Lukassen , Chua , Trefzer , Kahn , Schneider et al. ( 2020 ) 20 criteria: ( a ) >200 or , depending on the sample , <6000 – 9000 genes ( surgical lung tissues)/ <3000 – 5000 genes ( ALI cultures) , ( b ) <15 % mitochondrial reads ( Supp . Fig . 7) .
    Seurat
    suggested: (SEURAT, SCR_007322)
    QUANTIFICATION AND STATISTICAL ANALYSIS Statistical analyses Statistical analyses were performed using R and Python 3.7.1 with scipy 0.14.1 and statsmodels 0.9.0 .
    Python
    suggested: (IPython, SCR_001658)
          <div style="margin-bottom:8px">
        <div><b>scipy</b></div>
        <div>suggested: (SciPy, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008058">SCR_008058</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Gene set enrichment analysis were performed using Metascape Zhou et al. ( 2019) ] on the KEGG , Canonical pathways , GO , Reactome , and Corum databases .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Metascape</b></div>
        <div>suggested: (Metascape, <a href="https://scicrunch.org/resources/Any/search?q=SCR_016620">SCR_016620</a>)</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>KEGG</b></div>
        <div>suggested: (KEGG, <a href="https://scicrunch.org/resources/Any/search?q=SCR_012773">SCR_012773</a>)</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>Corum</b></div>
        <div>suggested: (CORUM, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002254">SCR_002254</a>)</div>
      </div>
    </td></tr></table>

    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).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.03.13.991455v3 on bioRxiv
  4. Version published to 10.1101/2020.03.13.991455v2 on bioRxiv
  5. Version published to 10.1101/2020.03.13.991455v1 on bioRxiv