Rapid, label-free, volumetric, and automated assessment in microscopy is necessary to assess the dynamic interactions between lymphocytes and their targets through the immunological synapse (IS) and the relevant immunological functions. However, attempts to realize the automatic tracking of IS dynamics have been stymied by the limitations of imaging techniques and computational analysis methods. Here, we demonstrate the automatic three-dimensional IS tracking by combining optical diffraction tomography and deep-learning-based segmentation. The proposed approach enables quantitative spatiotemporal analyses of IS regarding morphological and biochemical parameters related to its protein densities, offering a novel complementary method to fluorescence microscopy for studies in immunology.
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