Spatial, metabolic, and chemical imaging for enhanced characterization and optimal selection of CHO cell lines via multimodal label-free microscopy

Developing highly productive Chinese Hamster Ovary (CHO) cell lines is critical yet resource-intensive in the biopharmaceutical industry. Multimodal label-free microscopy has potential to enhance cell line selection through integration of system that combines Simultaneous Label-free Autofluorescence Multi-harmonic (SLAM) microscopy, Fluorescence Lifetime Imaging Microscopy (FLIM), and Coherent Anti-Stokes Raman Scattering (CARS) microscopy. Each modality offers unique insights into cellular structure, metabolism, and biochemistry. SLAM provides structural and metabolic analysis, FLIM maps metabolic cofactors like NAD(P)H and FAD, and CARS reveals chemical information. Various CHO cell lines were imaged using this system to generate multichannel, co-registered images. Deep learning models were developed to interpret this high content information, classifying CHO cell lines that were imaged over two passages, improving characterization and selection for biopharmaceutical production.