Predicting productivity in CHO cells based on co-occurrence of metabolism-related features in simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy

Efficient cell line development is crucial for optimizing biopharmaceutical production. We demonstrate the potential of SLAM and FLIM microscopy to optimize this process by correlating metabolism-related features with measured productivity in early CHO cell passages. Eight CHO cell lines were imaged using SLAM and FLIM microscopy, and a pipeline was developed to classify the cells. A linear SVM achieved 95% accuracy in predicting productivity. Important features and their channel affiliations were identified, revealing optical metabolic characteristics from NAD(P)H and FAD associated with productivity. SLAM features correlated with growth and viability, while FLIM features correlated with protein production, highlighting the importance of multimodal label-free imaging.