A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids

Alexandra Sockell; Wing Wong; Scott Longwell; Thy Vu; Kasper Karlsson; Daniel Mokhtari; Julia Schaepe; Yuan-Hung Lo; Vincent Cornelius; Calvin Kuo; David Van Valen; Christina Curtis; Polly M Fordyce

doi:10.1016/j.cels.2023.08.002

A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids

Cell Syst. 2023 Sep 20;14(9):764-776.e6. doi: 10.1016/j.cels.2023.08.002.

Authors

Affiliations

¹ Department of Genetics, Stanford University, Stanford, CA 94305, USA.
² Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA.
³ Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
⁴ Department of Biochemistry, UT Austin, Austin, TX 78712, USA.
⁵ Department of Biochemistry, Stanford University, Stanford, CA 94305, USA.
⁶ Department of Medicine, Stanford University, Stanford, CA 94305, USA.
⁷ Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125, USA.
⁸ Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA. Electronic address: cncurtis@stanford.edu.
⁹ Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: pfordyce@stanford.edu.

PMID: 37734323
DOI: 10.1016/j.cels.2023.08.002

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.

Keywords: cell polarity; deep learning; genotype-to-phenotype; high-throughput imaging; microwell arrays; organoids; quantitative phenotyping; single-organoid sequencing; tumorigenesis.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Cell Movement
Clustered Regularly Interspaced Short Palindromic Repeats*
Extracellular Matrix*
Genomics
Humans
Organoids

Abstract

Publication types

MeSH terms

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