Self-organized stem cell-derived human lung buds with proximo-distal patterning and novel targets of SARS-CoV-2

  1. E.A. Rosado-Olivieri
  2. B. Razooky
  3. H.-H. Hoffmann
  4. R. De Santis
  5. C.M. Rice
  6. A.H Brivanlou

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the global COVID-19 pandemic and the lack of therapeutics hinders pandemic control 1–2 . Although lung disease is the primary clinical outcome in COVID-19 patients 1–3 , how SARS-CoV-2 induces tissue pathology in the lung remains elusive. Here we describe a high-throughput platform to generate tens of thousands of self-organizing, nearly identical, and genetically matched human lung buds derived from human pluripotent stem cells (hPSCs) cultured on micropatterned substrates. Strikingly, in vitro -derived human lung buds resemble fetal human lung tissue and display in vivo -like proximo-distal coordination of alveolar and airway tissue differentiation whose 3D epithelial self-organization is directed by the levels of KGF. Single-cell transcriptomics unveiled the cellular identities of airway and alveolar tissue and the differentiation of WNT hi cycling alveolar stem cells, a human-specific lung cell type 4 . These synthetic human lung buds are susceptible to infection by SARS-CoV-2 and endemic coronaviruses and can be used to track cell type-dependent susceptibilities to infection, intercellular transmission and cytopathology in airway and alveolar tissue in individual lung buds. Interestingly, we detected an increased susceptibility to infection in alveolar cells and identified cycling alveolar stem cells as targets of SARS-CoV-2. We used this platform to test neutralizing antibodies isolated from convalescent plasma that efficiently blocked SARS-CoV-2 infection and intercellular transmission. Our platform offers unlimited, rapid and scalable access to disease-relevant lung tissue that recapitulate key hallmarks of human lung development and can be used to track SARS-CoV-2 infection and identify candidate therapeutics for COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.06.425622: (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

    Antibodies
    SentencesResources
    The primary antibodies used were as follows: rabbit anti-SOX9 (Millipore; AB5535; 1:250), goat anti-SOX2 (R&D Systems; AF2018; 1:250), rabbit anti-NKX2.1 (Abcam; ab76013; 1:200), mouse anti-Acetylated Tubulin (Sigma; T7451; 1:1000), goat anti-TP63 (R&D Systems; BAF1916; 1:250), rabbit anti-proSPC (Seven Hills; WRAB-9337; 1:500), mouse anti-HOPX (Santa Cruz; sc-398703; 1:250)
    anti-SOX9
    suggested: (Millipore Cat# AB5535, RRID:AB_2239761)
    anti-SOX2
    suggested: (Antibodies-Online Cat# ABIN411586, RRID:AB_10851124)
    anti-NKX2.1
    suggested: (Abcam Cat# ab76013, RRID:AB_1310784)
    anti-Acetylated Tubulin
    suggested: (Sigma-Aldrich Cat# T7451, RRID:AB_609894)
    anti-TP63
    suggested: (LSBio (LifeSpan Cat# LS-C575-100, RRID:AB_611010)
    anti-proSPC
    suggested: None
    anti-HOPX
    suggested: (Santa Cruz Biotechnology Cat# sc-398703, RRID:AB_2687966)
    To detect infected cells for HCoV-229E, HCoV-OC43 and HCoV-NL63, a mouse monoclonal anti-dsRNA antibody (Scicons: catalog no. 10010500) was used under similar conditions.
    anti-dsRNA
    suggested: (Millipore Cat# MABE1134, RRID:AB_2819101)
    Experimental Models: Cell Lines
    SentencesResources
    To detect infected cells for HCoV-229E, HCoV-OC43 and HCoV-NL63, a mouse monoclonal anti-dsRNA antibody (Scicons: catalog no. 10010500) was used under similar conditions.
    HCoV-NL63
    suggested: RRID:CVCL_RW88)
    All viruses were amplified at 33 °C in Huh-7.5 cells to generate a P1 stock.
    Huh-7.5
    suggested: RRID:CVCL_7927)
    Software and Algorithms
    SentencesResources
    , rabbit anti-nucleocapsid SARS-CoV-2 (GeneTex; GTX135357; 1;1000); rabbit anti-Active Caspase-3 (R&D Systems; AF835; 1:250); mouse anti-Mucin 5AC (Abcam; ab3649; 1:250); human anti-Spike SARS-CoV-2 (Robbiani et al., 2020; 1:1,000); anti-HNF-3BETA/FOXA2 (Neuromics; GT15186; 1:200); mouse J2 dsRNA (SCICONS; 1;1,000),
    Neuromics
    suggested: (Neuromics, RRID:SCR_013518)
    Three-dimensional visualization and image processing was performed in ImageJ.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    Imaging analysis: Analysis of confocal Z-stacks was performed using CellProfiler v4.0.4 (https://cellprofiler.org/).
    CellProfiler
    suggested: None
    https://cellprofiler.org/
    suggested: (CellProfiler Image Analysis Software, RRID:SCR_007358)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 16. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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. Version published to 10.1101/2021.01.06.425622 on bioRxiv