Abstract
We propose and experimentally validate a label-free, volumetric, and automated assessment method of immunological synapse dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed approach enables automatic and quantitative spatiotemporal analyses of immunological synapse kinetics regarding morphological and biochemical parameters related to the total protein densities of immune cells, thus providing a new perspective for studies in immunology.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Posted February 04, 2019.
Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of chimeric antigen receptor T cells
