OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics.
Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses. However, these metrics are difficult and labor-intensive to obtain for high-throug...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010584&type=printable |
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| author | Jonathan M Matthews Brooke Schuster Sara Saheb Kashaf Ping Liu Rakefet Ben-Yishay Dana Ishay-Ronen Evgeny Izumchenko Le Shen Christopher R Weber Margaret Bielski Sonia S Kupfer Mustafa Bilgic Andrey Rzhetsky Savaş Tay |
| author_facet | Jonathan M Matthews Brooke Schuster Sara Saheb Kashaf Ping Liu Rakefet Ben-Yishay Dana Ishay-Ronen Evgeny Izumchenko Le Shen Christopher R Weber Margaret Bielski Sonia S Kupfer Mustafa Bilgic Andrey Rzhetsky Savaş Tay |
| author_sort | Jonathan M Matthews |
| collection | DOAJ |
| description | Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses. However, these metrics are difficult and labor-intensive to obtain for high-throughput image datasets. Here, we present OrganoID, a robust image analysis platform that automatically recognizes, labels, and tracks single organoids, pixel-by-pixel, in brightfield and phase-contrast microscopy experiments. The platform was trained on images of pancreatic cancer organoids and validated on separate images of pancreatic, lung, colon, and adenoid cystic carcinoma organoids, which showed excellent agreement with manual measurements of organoid count (95%) and size (97%) without any parameter adjustments. Single-organoid tracking accuracy remained above 89% over a four-day time-lapse microscopy study. Automated single-organoid morphology analysis of a chemotherapy dose-response experiment identified strong dose effect sizes on organoid circularity, solidity, and eccentricity. OrganoID enables straightforward, detailed, and accurate image analysis to accelerate the use of organoids in high-throughput, data-intensive biomedical applications. |
| format | Article |
| id | doaj-art-566dc2ed98c6459aa44fb593a7fd1f73 |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-566dc2ed98c6459aa44fb593a7fd1f732025-08-20T03:12:32ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-11-011811e101058410.1371/journal.pcbi.1010584OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics.Jonathan M MatthewsBrooke SchusterSara Saheb KashafPing LiuRakefet Ben-YishayDana Ishay-RonenEvgeny IzumchenkoLe ShenChristopher R WeberMargaret BielskiSonia S KupferMustafa BilgicAndrey RzhetskySavaş TayOrganoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses. However, these metrics are difficult and labor-intensive to obtain for high-throughput image datasets. Here, we present OrganoID, a robust image analysis platform that automatically recognizes, labels, and tracks single organoids, pixel-by-pixel, in brightfield and phase-contrast microscopy experiments. The platform was trained on images of pancreatic cancer organoids and validated on separate images of pancreatic, lung, colon, and adenoid cystic carcinoma organoids, which showed excellent agreement with manual measurements of organoid count (95%) and size (97%) without any parameter adjustments. Single-organoid tracking accuracy remained above 89% over a four-day time-lapse microscopy study. Automated single-organoid morphology analysis of a chemotherapy dose-response experiment identified strong dose effect sizes on organoid circularity, solidity, and eccentricity. OrganoID enables straightforward, detailed, and accurate image analysis to accelerate the use of organoids in high-throughput, data-intensive biomedical applications.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010584&type=printable |
| spellingShingle | Jonathan M Matthews Brooke Schuster Sara Saheb Kashaf Ping Liu Rakefet Ben-Yishay Dana Ishay-Ronen Evgeny Izumchenko Le Shen Christopher R Weber Margaret Bielski Sonia S Kupfer Mustafa Bilgic Andrey Rzhetsky Savaş Tay OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics. PLoS Computational Biology |
| title | OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics. |
| title_full | OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics. |
| title_fullStr | OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics. |
| title_full_unstemmed | OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics. |
| title_short | OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics. |
| title_sort | organoid a versatile deep learning platform for tracking and analysis of single organoid dynamics |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010584&type=printable |
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