Attrition in the therapeutic pipeline is often linked to the gap in translational efficacy between the pre-clinical phase and clinical outcomes. Organoids offer strong potential for improving disease modeling and drug screening, as they more accurately replicate tissue architecture and function, and tend to yield drug responses that better predict clinical results.
Image Credit: metamorworks/Shutterstock.com
Despite these advantages, practical hurdles - such as complex assay workflows, challenges with reproducibility, and scalability limitations - have slowed their broader integration as a primary screening tool in drug discovery.
To address the inefficiencies associated with labor-intensive manual protocols, the CellXpress.ai™ Automated Cell Culture System has been developed. This advanced workstation automates the entire organoid culture process, accommodating prolonged and complex workflows. This enables a completely hands-off process, reduced model variability and the scale up of organoid production.
The CellXpress.ai system facilitates media exchange, plating, passaging, organoid monitoring, endpoint assay execution, and complex, AI-powered image analysis and decision making..
In this article, results from the automation of several widely utilized organoid protocols are presented, including the culture of three-dimensional organoids in matrix domes.
Healthy intestinal organoids were cultured, passaged and expanded in Matrigel® domes (24-well format). Organoids underwent automated media exchange and were monitored using machine learning-assisted imaging every 24 hours.
After 5–6 days, organoids were automatically collected, purified from Matrigel, dispersed, mixed with fresh Matrigel, and re-plated.
Subsequently, organoids self-organized and developed intricate crypt structures. Monitoring was conducted using transmitted light, and machine learning-based image analysis was employed to quantify organoid number, size (by area) and optical density.
For endpoint assays in 96-well plates, organoids were stained for viability markers and assessed for concentration- and time-dependent effects of compounds on healthy intestinal organoids (toxicity evaluation) or patient-derived colorectal cancer organoids (drug screening).
Automated cell culture systems, guided by imaging and machine learning-driven decision-making, hold significant promise for advancing 3D biology. These technologies can enhance both throughput and reproducibility, making them well-suited for accelerating drug discovery and improving disease modeling efforts.
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Acknowledgments
Produced from material originally authored by Oksana Sirenko, Astrid Michlmayr, Emilie Keidel, Marco Lindner, Jeff McMillan, Angeline Lim, Zhisong Tong, Bruce Gonzaga and Felix Spira from Molecular Devices.
About Molecular Devices UK Ltd
Molecular Devices is one of the world’s leading providers of high-performance bioanalytical measurement systems, software and consumables for life science research, pharmaceutical and biotherapeutic development. Included within a broad product portfolio are platforms for high-throughput screening, genomic and cellular analysis, colony selection and microplate detection. These leading-edge products enable scientists to improve productivity and effectiveness, ultimately accelerating research and the discovery of new therapeutics. Molecular Devices is committed to the continual development of innovative solutions for life science applications. The company is headquartered in Silicon Valley, California, with offices around the globe. For more information, please visit www.moleculardevices.com.
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