Non-Invasive, Label-free Image Approaches to Predict Multimodal Molecular Markers in Pluripotency Assessment

We detail an innovative non-invasive technique to predict gene and protein expression in pluripotent stem cells using advanced bright-field microscopy. The method employs machine learning algorithms to classify cells based on brightfield images, avoiding the need for traditional staining and manual annotation. Our approach uses DeepLearning technology to predict the gene expression (qPCR, RNA-seq) and protein expression (immunostaining, Flowcytometry) of cells from brightfield microscopy images. It provides a robust tool for non-destructive and continuous monitoring of the pluripotency status of stem cells, which will greatly advance regenerative medicine. It will be an approach that will contribute significantly to the manufacturing process of cellular products, especially where non-destructive and continuous monitoring is required.