(1067-D) Transform the complexities of 3D cell culture into a reliable and translatable science: Automation of 3D organoid culture and organoid analysis.
Tuesday, February 6, 2024
2:00 PM – 3:00 PM EST
Location: Exhibit Halls AB
Abstract: Attrition in the therapeutic pipeline can often be attributed to lack of translational efficacy from the pre-clinical phase to the clinic. Organoids show a great promise as a game-changer in disease modeling and drug screening, since they better resemble tissue structure and functionality, also showing more predictive response to drugs. However, challenges associated with the practical adoption of organoids, such as assay complexity, reproducibility, ability to scale up have limited their widespread adoption as a primary screening method in drug discovery. To alleviate the bottlenecks that come with labor-intensive manual protocols we developed a cell culture automation solution CellXpress.ai. The instrument enables automation of entire organoid culture for prolonged complex workflows. CellXpress.ai provides media exchanges, plating, passaging, monitoring organoids, end point assay and complex image analysis. It contains functional components including automated imager, liquid handler, and incubator, connected by AI- powered software. Development of cell cultures monitored by periodic imaging and analysis, which can trigger automatic decisions to initiate passaging, end-po int assay, or troubleshooting steps. Here we present results from automation of several commonly used organoid protocols, including culture of 3D organoids in matrix domes, or in the low attachment plates. Healthy intestinal organoids, and patient-derived colorectal cancer organoids were cultured, passaged, and expanded in Matrigel domes (24 well). Organoids were cultured with automated media exchanges and monitoring by imaging every 48h. After 7-10 days organoids were automatically collected, purified from Matrigel and dispersed, then mixed with fresh Matrigel and re-pated. Organoids self-organized and developed complex crypt structures. Organoids were monitored in transmitted light, and Machine learning-based image analysis allowed to determine organoids number, size (by area) and complexity (by number of crypts). For endpoint assay (96well), organoids were stained for viability markers. We monitored concentration and time-dependent effects of the panel of anti-cancer compounds on healthy intestinal organoids (toxicity evaluation).
Cell culture process automation powered by imaging and AI -controlled decision making has a great potential to bring 3D biology into another level, allowing to increase throughput and reproducibility, and enabling variety of high throughput drug discovery and precision medicine applications.