Image-based early predictions of functional properties in cell manufacturing

Effective cell manufacturing is essential to realizing the full potential of cell-based therapies but faces a multitude of challenges. One of the major challenges is the identification of critical quality attributes (CQAs), especially ones that enable early predictions of functional properties of the final products. The main goal of this study is to develop machine learning models for early predictions of the functional properties of mesenchymal stromal/stem cells(MSCs) in cell manufacturing. Deep learning models are trained and tested for image-based prediction of functional property-Collagen II expression after chondrogenic differentiation-of MSCs cells. During the MSC expansion, images of culturing wells were collected daily in the first six days, and the Collagen II level was assayed at the end of differentiation, following expansion. For each day, a deep learning model was trained with images from a specific experimental condition, and each model was tested with images from the same condition and also from other conditions. The trained neural network models showed 70-90 percent accuracy. Most of the models across different days and conditions show high consistency, especially models trained with images past day 2 of cell culture. Such consistency suggests that models are picking up similar features in predicting chondrogenesis capability. Our study highlighted the potential of deep neural network models used for early predictions of the functional properties of MSCs in cell manufacturing.