Summary Although lung disease is the primary clinical outcome in COVID-19 patients, how SARS-CoV-2 induces lung pathology remains elusive. Here we describe a high-throughput platform to generate self-organizing and commensurate human lung buds derived from hESCs cultured on micropatterned substrates. Lung buds resemble human fetal lungs and display proximodistal patterning of alveolar and airway tissue directed by KGF. These lung buds are susceptible to infection by SARS-CoV-2 and endemic coronaviruses and can be used to track cell type-specific cytopathic effects in hundreds of lung buds in parallel. Transcriptomic comparisons of infected lung buds and postmortem tissue of COVID-19 patients identified an induction of BMP signaling pathway. BMP activity renders lung cells more susceptible to SARS-CoV-2 infection and its pharmacological inhibition impairs infection by this virus. These data highlight the rapid and scalable access to disease-relevant tissue using lung buds that recapitulate key features of human lung morphogenesis and viral infection biology. Highlights • Derivation of self-organizing and commensurate lung buds on microchips from hESCs • Lung buds display in vivo -like proximo-distal patterning of lung tissue • Parallelized quantitative analysis of SARS-CoV-2 viral life cycle in lung buds • Identification of BMP signaling as a key mediator of SARS-CoV-2 infection via ACE2 Brivanlou and colleagues describe a high-throughput platform to generate self-organizing human lung buds from hESCs on microchips. Lung buds display in vivo -like proximo-distal patterning of lung tissue, and are amenable to high-throughput quantitative analysis of SARS-CoV-2 cell type-specific infection, transmission, and cytopathology. Transcriptomic comparisons with COVID-19 postmortem tissues identified the BMP signaling pathway as a key regulator of SARS-CoV-2 infection.
【저자키워드】 COVID-19, SARS-CoV-2, Endemic coronaviruses, lung development, lung organoids, BMP signaling, fetal lung, lung buds, lung differentiation, micropatterned hESCs,