Automated iPSC culture with machine learning-enabled cell passaging control and automated differentiated cell detection

Discover how the CellXpress.ai^® Automated Cell Culture System transforms iPSC maintenance and expansion into a fully automated, walk-away workflow. This application note showcases a machine learning–enabled solution that monitors colony confluency and differentiation in real time, triggering automated passaging or excluding wells based on user-defined thresholds. By integrating liquid handling, imaging, and intelligent decision-making, the system reduces manual workload, improves consistency, and preserves stem cell quality.

Ideal for scaling up stem cell research, this approach ensures reproducibility, saves time and reagents, and empowers scientists to focus on discovery, not routine cell culture.