# Organoid Core B

> **NIH NIH U19** · STANFORD UNIVERSITY · 2020 · $900,000

## Abstract

ABSTRACT
Primary 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 to
public health and physiologic in vitro infection models are therefore urgently needed. This supplement request
for 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 lung
organoid technologies in collaboration with Dr. Ralph Baric at UNC, a recognized coronavirus authority. These
studies exploit SARS-CoV-2 infection of organoids using a feeder-free, chemically defined human lung organoid
system (Calvin Kuo lab), lung organoids with integrated immune components (Calvin Kuo), methods for robust
apical-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 lung
organoids will occur in BSL3 containment at both UNC and Stanford to compare apical versus basal infection
routes, document how epithelial infection initiates secondary immune responses, and overall generate improved
3D physiological models of SARS-CoV-2-GFP infection relevant to therapeutics screening.

## Key facts

- **NIH application ID:** 10178731
- **Project number:** 3U19AI116484-05S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** CALVIN J KUO
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $900,000
- **Award type:** 3
- **Project period:** 2020-06-05 → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10178731

## Citation

> US National Institutes of Health, RePORTER application 10178731, Organoid Core B (3U19AI116484-05S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10178731. Licensed CC0.

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