Automated, high-throughput, quantitative characterization of organoid morphology evidences tunability and homogeneity in microfluidics-engineered organoids
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2024
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| author | Galan, Edgar A. |
| author_facet | Galan, Edgar A. |
| contents | <p>This document contains the working draft of a research manuscript prepared by Edgar A. Galan based on research he conducted at Tsinghua University. The manuscript includes original text and figures related to the development of microfluidics and image analysis technologies for organoid research. This version represents the state of the manuscript as of 19th of November, 2024. The final version may differ following further submission to a peer-reviewed journal. The associated code for this publication can be found at https://github.com/Edgar-Galan/organoid-profiler.<br><br>Abstract<br>Organoids are becoming essential tools for research in biology and interdisciplinary fields. However, they often exhibit high morphological heterogeneity, influencing organogenesis, drug uptake, and transcriptomic profiles. Microfluidics enables scalable and optimized fabrication of organoids, providing precise control over their composition and morphology in high-throughput. We generated datasets of >10,000 high-quality brightfield images of microfluidics-engineered mouse lung alveolar organoids and >460 whole-organoid live/dead fluorescence images with corresponding brightfield pairs, tracking individual organoids daily for ≥11 days. To analyze these large datasets, we developed Organoid Profiler, an open-source tool for organoid morphological analysis written in Python, which extracts 24 features from brightfield images and corrected intensity from fluorescence images without requiring manually labeled datasets. We demonstrate that microfluidics-engineered organoids can be fabricated with precise control over geometry and cell density, resulting in exceptional homogeneity and reproducibility, with intra- and inter-batch variations of <9% and ≤5% at day 0, and <20% and <14% across all days for area and aspect ratio. Datasets of 2,497 and 461 images of mouse liver organoids and hiPSC-derived brain organoids, respectively, were generated to demonstrate the generalizability of microfluidics-engineered organoids and Organoid Profiler. Finally, we validated our alveolar organoid model through immunohistochemistry, flow cytometry, and RNA sequencing to confirm functionality and tissue recapitulation. We offer these high-quality datasets, Organoid Profiler, and our protocol for microfluidics-engineered organoid fabrication for researchers to utilize.<br><br></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17446713 |
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| publishDate | 2024 |
| publisher | Zenodo |
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| spellingShingle | Automated, high-throughput, quantitative characterization of organoid morphology evidences tunability and homogeneity in microfluidics-engineered organoids Galan, Edgar A. Organoids Microfluidics Image processing <p>This document contains the working draft of a research manuscript prepared by Edgar A. Galan based on research he conducted at Tsinghua University. The manuscript includes original text and figures related to the development of microfluidics and image analysis technologies for organoid research. This version represents the state of the manuscript as of 19th of November, 2024. The final version may differ following further submission to a peer-reviewed journal. The associated code for this publication can be found at https://github.com/Edgar-Galan/organoid-profiler.<br><br>Abstract<br>Organoids are becoming essential tools for research in biology and interdisciplinary fields. However, they often exhibit high morphological heterogeneity, influencing organogenesis, drug uptake, and transcriptomic profiles. Microfluidics enables scalable and optimized fabrication of organoids, providing precise control over their composition and morphology in high-throughput. We generated datasets of >10,000 high-quality brightfield images of microfluidics-engineered mouse lung alveolar organoids and >460 whole-organoid live/dead fluorescence images with corresponding brightfield pairs, tracking individual organoids daily for ≥11 days. To analyze these large datasets, we developed Organoid Profiler, an open-source tool for organoid morphological analysis written in Python, which extracts 24 features from brightfield images and corrected intensity from fluorescence images without requiring manually labeled datasets. We demonstrate that microfluidics-engineered organoids can be fabricated with precise control over geometry and cell density, resulting in exceptional homogeneity and reproducibility, with intra- and inter-batch variations of <9% and ≤5% at day 0, and <20% and <14% across all days for area and aspect ratio. Datasets of 2,497 and 461 images of mouse liver organoids and hiPSC-derived brain organoids, respectively, were generated to demonstrate the generalizability of microfluidics-engineered organoids and Organoid Profiler. Finally, we validated our alveolar organoid model through immunohistochemistry, flow cytometry, and RNA sequencing to confirm functionality and tissue recapitulation. We offer these high-quality datasets, Organoid Profiler, and our protocol for microfluidics-engineered organoid fabrication for researchers to utilize.<br><br></p> |
| title | Automated, high-throughput, quantitative characterization of organoid morphology evidences tunability and homogeneity in microfluidics-engineered organoids |
| topic | Organoids Microfluidics Image processing |
| url | https://doi.org/10.5281/zenodo.17446713 |