<?xml version="1.0" encoding="UTF-8"?>
<STUDY_SET xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <STUDY center_name="GEO" alias="GSE165200" accession="SRP302575">
    <IDENTIFIERS>
      <PRIMARY_ID>SRP302575</PRIMARY_ID>
      <EXTERNAL_ID namespace="BioProject" label="primary">PRJNA693539</EXTERNAL_ID>
      <EXTERNAL_ID namespace="GEO">GSE165200</EXTERNAL_ID>
    </IDENTIFIERS>
    <DESCRIPTOR>
      <STUDY_TITLE>Adult Stem Cell-derived Complete Lung Organoid Models Emulate Lung Disease in COVID-19</STUDY_TITLE>
      <STUDY_TYPE existing_study_type="Transcriptome Analysis"/>
      <STUDY_ABSTRACT>SARS-CoV-2, the virus responsible for COVID-19, causes widespread damage in the lungs in the setting of an overzealous immune response whose origin remains unclear. We present a scalable, propagable, personalized, cost-effective adult stem cell-derived human lung organoid model that is complete with both proximal and distal airway epithelia. Monolayers derived from adult lung organoids (ALOs),  primary airway cells, or hiPSC-derived alveolar type-II (AT2) pneumocytes were infected with SARS-CoV-2 to create in vitro lung models of COVID-19. Infected ALO-monolayers best recapitulated the transcriptomic signatures in diverse cohorts of COVID-19 patient-derived respiratory samples. The airway (proximal) cells were critical for sustained viral infection, whereas distal alveolar differentiation (AT2?AT1) was critical for mounting the overzealous host immune response in fatal disease; ALO monolayers with well-mixed proximodistal airway components recapitulated both. Findings validate a human lung model of COVID-19 , which can be immediately utilized to investigate COVID-19 pathogenesis and vet new therapies and vaccines. Overall design: Human lung organoids were grown in standard culture, ALI and monolayer conditions. Human small airway epithelium and iPSC-derived alveolar epithelial type 2 cells were infected with SARS-CoV2.</STUDY_ABSTRACT>
      <CENTER_PROJECT_NAME>GSE165200</CENTER_PROJECT_NAME>
    </DESCRIPTOR>
  </STUDY>
</STUDY_SET>
