A Caltech Library Service

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

Sockell, Alexandra and Wong, Wing and Longwell, Scott and Vu, Thy and Karlsson, Kasper and Mokhtari, Daniel and Schaepe, Julia and Lo, Yuan-Hung and Cornelius, Vincent and Kuo, Calvin and Van Valen, David and Curtis, Christina and Fordyce, Polly M. (2022) A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids. . (Unpublished)

[img] PDF - Submitted Version
Creative Commons Attribution Non-commercial No Derivatives.

[img] PDF - Supplemental Material
Creative Commons Attribution Non-commercial No Derivatives.


Use this Persistent URL to link to this item:


Organoids are powerful experimental models for studying the ontogeny and progression of diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, organoids in bulk culture 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 microenvironment variability. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can be retrieved from their microwells for sequencing and 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.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper ItemData/Code
Sockell, Alexandra0000-0002-8725-9415
Longwell, Scott0000-0003-4064-0400
Karlsson, Kasper0000-0003-0162-1354
Mokhtari, Daniel0000-0003-4206-9471
Schaepe, Julia0000-0003-0416-0469
Lo, Yuan-Hung0000-0002-9704-3124
Van Valen, David0000-0001-7534-7621
Curtis, Christina0000-0003-0166-3802
Fordyce, Polly M.0000-0002-9505-0638
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. 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. Author 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. K.K. and Y.L. performed the organoid gene-editing. A.S., W.W., C.C., and P.M.F wrote the manuscript. Data Availability. Raw sequencing data for shallow WGS and dual ATAC/RNA sequencing is available on SRA with accession number PRJNA858865 at the NCBI Short Read Archive ( Other data, including image files for experiments #1-6, labeled training, validation, and test set data for deep learning model training, processed data files, and per-experiment and per-macrowell summary reports are available through OSF at The authors have declared no competing interest.
Funding AgencyGrant Number
Chan Zuckerberg InitiativeUNSPECIFIED
Record Number:CaltechAUTHORS:20230316-182900000.74
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120180
Deposited By: George Porter
Deposited On:17 Mar 2023 23:38
Last Modified:17 Mar 2023 23:38

Repository Staff Only: item control page