Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea.
Elife. 2020 Dec 17;9:e49023. doi: 10.7554/eLife.49023.
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.
免疫突触（IS）是 T 细胞与专业抗原呈递细胞之间的细胞-细胞连接。由于 IS 的形成是启动抗原特异性免疫反应的关键步骤，因此已经使用了各种活细胞成像技术，其中大多数依赖于荧光显微镜来研究 IS 的动态。然而，与荧光成像相关的固有局限性，如光漂白和光毒性，阻止了对 IS 动态的高频长期评估。在这里，我们提出并通过光学衍射层析和基于深度学习的分割相结合的方法，实验验证了一种用于 IS 动力学的无标记、体积和自动评估方法。该方法能够自动、定量地分析与 IS 动力学相关的形态和生化参数的 IS 动力学的时空变化，为免疫研究提供了新的选择。
