Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells.
Moosung LeeYoung-Ho LeeJinyeop SongGeon KimYoungJu JoHyunSeok MinChan Hyuk KimYong Keun ParkPublished in: eLife (2020)
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.
Keyphrases
- deep learning
- label free
- high resolution
- high frequency
- single cell
- immune response
- machine learning
- convolutional neural network
- artificial intelligence
- single molecule
- cell therapy
- transcranial magnetic stimulation
- stem cells
- dendritic cells
- nitric oxide
- energy transfer
- quantum dots
- case report
- inflammatory response
- cell proliferation
- toll like receptor
- cell death