Predicting quality decay in continuously passaged mesenchymal stem cells by detecting morphological anomalies.

With rapid advances in cell therapy, technologies enabling both consistency and efficiency in cell manufacturing are becoming necessary. Morphological monitoring allows practical quality maintenance in cell manufacturing facilities, but relies heavily on human skill. For more reproducible and data-driven quality evaluation, image-based morphological analysis provides multiple advantages over manual observation. Our group has investigated the performance of multiple morphological parameters obtained from time-course images to non-invasively and quantitatively predict cellular quality using machine learning algorithms. Although such morphology-based computational models succeeded in early cell quality predictions, it was difficult to introduce our approach in cell manufacturing facilities owing to data variation issues. Since manufacturing facilities have fixed their protocol to minimize anomalies as much as possible, most accumulated data are normal, and anomalies are scarce. Thus, our morphological analysis had to adapt to such practical situation where it was difficult to observe a wide range of data variations, including both normal samples and anomalies, which is typically essential to improve most machine learning models' performance. In the present study, we introduce a practical morphological analysis concept by investigating the performance of anomalous quality decay discrimination during the continuous passaging of human mesenchymal stem cells (hMSCs). Combining the visualization method and asymmetric statistic discrimination, we describe an effective morphology-based, in-process quality monitoring concept to detect quality anomalies throughout cell culture process. Our results showed that the use of morphological parameters to reflect cellular population heterogeneity can predict hMSC quality decay within 6 h after seeding.