Human Lung Organoid Models of SARS-CoV-2 Infection

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: 3U19AI116484-05S1

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Key facts

  • Disease

  • Start & end year

  • Known Financial Commitments (USD)

  • Funder

    National Institutes of Health (NIH)
  • Principle Investigator

  • Research Location

    United States of America, Americas
  • Lead Research Institution

  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory


  • Special Interest Tags


  • Study Subject


  • Clinical Trial Details


  • Broad Policy Alignment


  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable


ABSTRACTPrimary human organoid models are an increasingly deployed platform for in vitro infectious disease modeling.The COVID-19 pandemic, engendered by the novel coronavirus SARS-CoV-2, represents a grave threat topublic health and physiologic in vitro infection models are therefore urgently needed. This supplement requestfor U19AI116484, Stanford Cooperative Center for Novel, Alternative Model Systems for Enteric Diseases(Stanford NAMSED), requests funding to create new models for SARS-CoV-2 infection using novel human lungorganoid technologies in collaboration with Dr. Ralph Baric at UNC, a recognized coronavirus authority. Thesestudies exploit SARS-CoV-2 infection of organoids using a feeder-free, chemically defined human lung organoidsystem (Calvin Kuo lab), lung organoids with integrated immune components (Calvin Kuo), methods for robustapical-basal inversion of lung organoid polarity (Manuel Amieva), BSL3 single cell RNA-seq (Catherine Blish)and SARS-CoV-2-GFP indicator strains and BSL3 facilities (Ralph Baric). The SARS-CoV-2 infection of lungorganoids will occur in BSL3 containment at both UNC and Stanford to compare apical versus basal infectionroutes, document how epithelial infection initiates secondary immune responses, and overall generate improved3D physiological models of SARS-CoV-2-GFP infection relevant to therapeutics screening.