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

Pluripotent stem cell (PSC) technologies have been widely used in drug discovery, disease modeling, and regenerative medicine. However, available PSC-to-functional cell differentiation systems are impeded by problems of severe line-to-line and batch-to-batch variability. Precise control of cell differentiation in real time is therefore important. In this protocol, we describe a non-invasive and intelligent strategy that overcomes the variability in cell differentiation by using bright-field image-based machine learning. Taking PSC-to-cardiomyocyte differentiation as an example, this methodology provides detailed information for control of the initial PSC state, early assessment and intervention in differentiation conditions, and elimination of the misdifferentiated cell contamination, together realizing consistently high-quality differentiation from PSCs to functional cells. In principle, this strategy can be extended to other cell differentiation or reprogramming systems with multiple steps to support cell manufacturing, as well as to further our understanding of the mechanisms during cell fate conversion.