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Label-free multimodal optical bioimaging allows non-perturbative profiling of biological samples based on their intrinsic optical molecular properties. In this study, we utilized SLAM and FLIM microscopy to identify CHO cell lines with favorable process performance for the production of therapeutic monoclonal antibodies and proteins. Here, a single-cell analysis pipeline was developed to quantitatively characterize CHO cell lines based on their phenotypes. To perceive the rich information in the multi-modal bioimages, a custom-built multi-task deep neural network was built, which can extract features from different aspects of the optical and molecular properties of the sample. This work demonstrated the potential of ML-assisted multi-modal optical imaging in the identification of cell lines with desirable characteristics for biopharmaceutical production at earlier time points.
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Jindou Shi, Alexander Ho, Corey E. Snyder, Eric Chaney, Janet E. Sorrells, Aneesh Alex, Remben Talaban, Darold R. Spillman Jr., Marina Marjanovic, Steve R. Hood, Stephen A. Boppart, "Machine learning-assisted label-free multimodal optical bioimaging for biopharmaceutical CHO cell line characterization," Proc. SPIE PC12371, Multimodal Biomedical Imaging XVIII, PC123710D (6 March 2023); https://doi.org/10.1117/12.2648499