Document Type
Article
Publication Date
12-18-2023
Original Citation
Mukashyaka P,
Kumar P,
Mellert D,
Nicholas S,
Noorbakhsh J,
Brugiolo M,
Courtois E,
Anczuków O,
Liu E,
Chuang J.
High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos. Nat Commun. 2023;14(1):8406.
Keywords
JGM, Humans, Neoplasms, Organoids, Cell Proliferation, Neural Networks, Computer
JAX Source
Nat Commun. 2023;14(1):8406.
ISSN
2041-1723
PMID
38114489
DOI
https://doi.org/10.1038/s41467-023-44162-6
Grant
Research reported in this publication was supported by NIH grant R01CA230031, JC and NIH/NCI Cancer Center Support Grant P30 CA034196.
Abstract
Three-dimensional (3D) organoid cultures are flexible systems to interrogate cellular growth, morphology, multicellular spatial architecture, and cellular interactions in response to treatment. However, computational methods for analysis of 3D organoids with sufficiently high-throughput and cellular resolution are needed. Here we report Cellos, an accurate, high-throughput pipeline for 3D organoid segmentation using classical algorithms and nuclear segmentation using a trained Stardist-3D convolutional neural network. To evaluate Cellos, we analyze ~100,000 organoids with ~2.35 million cells from multiple treatment experiments. Cellos segments dye-stained or fluorescently-labeled nuclei and accurately distinguishes distinct labeled cell populations within organoids. Cellos can recapitulate traditional luminescence-based drug response of cells with complex drug sensitivities, while also quantifying changes in organoid and nuclear morphologies caused by treatment as well as cell-cell spatial relationships that reflect ecological affinity. Cellos provides powerful tools to perform high-throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.
Comments
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