Abstract
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.
Highlights
Understanding the immune response at the cellular scale requires knowledge regarding interactions between immune cells and their microenvironment
In order to perform Optical diffraction tomography (ODT) experiments in our study, we employed an experimental setup which is based on off-axis holography equipped with a high-speed illumination scanner using a digital micromirror device (DMD; DLP6500FLQ DLP 0.65 1080 p Type A DMD, Texas Instrument) (Figure 1)
The resultant off-axis hologram was recorded by a CMOS camera (FL3-U3-13Y3M-C, FLIR Systems, Inc, USA) synchronized with the DMD to record 49 holograms of the sample illuminated at different angles
Summary
Understanding the immune response at the cellular scale requires knowledge regarding interactions between immune cells and their microenvironment. Such fluorescence-based techniques have the advantage of chemical specificity They are limited due to photo-bleaching and photo-toxicity, which necessitates the use of complementary label-free, rapid three-dimensional (3D) microscopy methods to assess long-term dynamic changes in IS morphologies (Skylaki et al, 2016). It is effective but is too laborious and difficult for time-resolved volumetric segmentation To overcome this barrier, automatic segmentation has been developed based on basic algorithms that include intensity thresholding, filtering, morphological operations, region accumulation, and deformable models (Dimopoulos et al, 2014). Automatic segmentation has been developed based on basic algorithms that include intensity thresholding, filtering, morphological operations, region accumulation, and deformable models (Dimopoulos et al, 2014) These methods often result in poor segmentation, for adjoining cell segmentations, which occur in immune responses. The results suggest that DeepIS offers a new analytical approach to immunological research
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
