A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation

In this study, based on live-cell bright-field images, we developed a strategy, harnessing different machine learning models. This strategy can identify cell lineage non-invasively, modulate the differentiation process in real-time, and optimize the differentiation protocol, improving the invulnerability in PSC-to-functional cell differentiation. Pluripotent stem cells present the ability to differentiate into many types of cells in vitro, which could be used for cell therapy, disease modeling, and drug development.

One of the main problems in PSC-derived cell production, is the instability among cell lines and batches. It often leads to multiple repeated experiments, consuming significant time and labor. Currently, state-of-the-art microscopic technologies could support long-term time-lapse, high-throughput image acquisition on live cells.

Meanwhile, the fast evolving machine learning method is being increasingly applied in cellular image analysis, which is opening possibilities to recognize specific cellular constituents or cell images during differentiation in cell culture. This method could hopefully be applied to standardized other cell fate induction systems like organoid differentiation, direct transdifferentiation, or cellular programming, especially for unstable systems requiring multiple steps and complex inducers. Also, our strategy is compatible with other technologies, which promisingly can be integrated into a self-adaptive and closed-loop system for full-automatic processing of PSC differentiation in vitro.