Abstract
Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, bulk-cultured organoids physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in the microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for molecular profiling. Coupled with a deep learning image-processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity. A record of this paper's transparent peer review process is included in the supplemental information.
Original language | English (US) |
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Pages (from-to) | 764-776.e6 |
Journal | Cell Systems |
Volume | 14 |
Issue number | 9 |
DOIs | |
State | Published - Sep 20 2023 |
Externally published | Yes |
Keywords
- cell polarity
- deep learning
- genotype-to-phenotype
- high-throughput imaging
- microwell arrays
- organoids
- quantitative phenotyping
- single-organoid sequencing
- tumorigenesis
ASJC Scopus subject areas
- Pathology and Forensic Medicine
- Histology
- Cell Biology