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Published September 20, 2023 | Published
Journal Article Open

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

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.

Copyright and License

© 2023 Published by Elsevier.

Acknowledgement

The authors thank the Cell Sciences Imaging Facility and Genetics Bioinformatics Service Center at Stanford University for assistance in confocal microscopy and computational resources, respectively. This work was supported by the NIH Director's Pioneer Award (DP1CA238296) to C.C. and the NIH Director's New Innovator Award (DP2GM123641) to P.M.F. C.C. and P.M.F. are Chan Zuckerberg Biohub Investigators. We thank members of the Curtis and Fordyce lab for helpful feedback on this manuscript.

Contributions

A.S., W.W., K.K., C.C., and P.M.F. conceptualized the initial research idea. A.S. and W.W. performed the microwell experiments. A.S. tested and made the microwell devices and performed image-processing bioinformatic analysis. W.W. performed organoid-picking experiments, made single-organoid sequencing libraries, and performed bioinformatic analysis. S.L. and D.M. set up the microscope, performed initial testing, and implemented the organoid-picking platform. T.V. performed manual labeling of deep-learning training and test data. V.C. performed initial growth and cell seeding experiments. D.V.V., J.S., and A.S. set up the deep-learning pipeline, and P.M.F. wrote code to quantify cell growth from processed images. K.K. and Y.-H.L. performed the organoid gene editing. A.S., W.W., C.C., and P.M.F. wrote the manuscript.

Data Availability

All sequencing raw data, including shallow whole-genome sequencing, dual bulk ATAC/RNA sequencing, and single-cell RNA sequencing data have been deposited at dbGAP and are publicly available as of the date of publication. The accession number is provided in the key resources table. Other data (image files for all experiments; labeled training, validation, and test set data for deep learning model training; processed data files; and per-experiment and per-macrowell summary reports) have been deposited at OSF and are publicly available as of the date of publication. The DOI is provided in the key resources table.

All original code has been deposited on GitHub and is publicly available as of the date of the publication. The DOIs are listed in the key resources table.

Any additional information required to reanalyze the data reported in this paper is available from the lead contacts upon request.

Conflict of Interest

Unrelated to this study, C.C. is a stockholder in Illumina and DeepCell, and an advisor to DeepCell, Genentech, Bristol Myers Squibb, 3T Biosciences, NanoString, and ResistanceBio. Unrelated to this study, C.K. is a founder and stockholder for Surrozen Inc, Mozart Therapeutics, and NextVivo. Unrelated to this study, P.M.F. is an advisor to Evozyne. C.C. and P.M.F. are on the advisory board of the journal. W.W., A.S., C.C., and P.M.F. have filed a provisional patent (Patent Application No. 63/380.196) related to this work.

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Additional details

Created:
November 2, 2023
Modified:
January 9, 2024