An Appearance Data‐Driven Model Visualizes Cell State and Predicts Mesenchymal Stem Cell Regenerative Capacity

Mesenchymal stem cells (MSCs) are widely used in treating various diseases. However, lack of a reliable evaluation approach to characterize the potency of MSCs has dampened their clinical applications. Here, a function‐oriented mathematical model is established to evaluate and predict the regenerative capacity (RC) of MSCs. Processed by exhaustive testing, the model excavates four optimal fitted indices, including nucleus roundness, nucleus/cytoplasm ratio, side‐scatter height, and ERK1/2 from the given index combinations. Notably, three of them except ERK1/2 are cell appearance‐associated features. The predictive power of the model is validated via screening experiments of these indices by predicting the RC of newly enrolled and chemical inhibitor‐treated MSCs. Further RNA‐sequencing analysis reveals that cell appearance‐based indices may serve as major indicators to visualize the results of integration‐weighted signals in and out of cells and reflect MSC stemness. In general, this study proposes an appearance data‐driven predictive model for the RC and stemness of MSCs. An appearance data‐driven mathematical model which excavates four optimal fitted indices including nucleus roundness, nucleus/cytoplasm ratio, side‐scatter height, and ERK1/2 is constructed to evaluate the regenerative capacity of mesenchymal stem cells (MSCs) in vitro. The predictable model advances the application of MSCs in basic and translational studies, and also provides a promising approach to characterize the other MSC potencies.