In the production of biotherapeutics, Chinese hamster ovary (CHO) cells are known as the gold standard. One challenge in the development of these cell lines is the identification of high expressing, yet stable CHO cells. Here we apply simultaneous label-free autofluorescence multi-harmonic (SLAM) microscopy to four CHO cell lines of varying levels of productivity and stability. With the assistance of machine learning, we were able to classify the CHO cell lines into their respective categories with an accuracy of 85%. Application of this CHO cell characterization technology to upstream bioprocessing can potentially improve workflows such as high-throughput screening and monitoring.
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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