Automating high-throughput screens using patient-derived colorectal cancer organoids
To address some of the hurdles associated with the use of patient-derived organoids (PDOs) in large-scale screens, such as assay reproducibility and scalability, a semi-automated bioprocess was developed for the controlled production of standardized PDOs at scale.
From this, we developed an end-to-end, automated workflow starting with assay-ready colorectal cancer organoids that included establishment in culture, maintenance, and screening. In addition, automation protocols were set up for routine monitoring, in culture, pre- and post-treatment and compound effects were monitored over time. Organoid growth and development was analyzed using a deep learning-based image segmentation model which automated the segmentation of the organoids. Using this approach, we tracked the effects of various compounds on colorectal organoid size, morphology, texture, and additional morphological and phenotypic readouts. Overall, our results show the superior potential of PDOs vs. other tissues in both precision medicine and high-throughput drug discovery applications when using automation with high-content imaging.
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