Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells

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.

Isotropically resolved label-free tomographic imaging based on tomographic moulds for optical trapping

“…Besides RI tomography, it is also possible to retrieve the 3D absorptivity maps of chromophores 43 . Fluorescence 13 and label-free nonlinear microscopy 44 can also be integrated for correlative volumetric analyses of live cells 45 . Ultimately, the isotropic super-resolution tomography is expected to complement the conventional super-resolution fluorescence microscopy 46 .…”

A Novel Peptide-MHC Targeted Chimeric Antigen Receptor T Cell Forms a T Cell-like Immune Synapse

“…First, our novel CAR recognising peptide-MHC formed a ‘classical’ ‘bulls-eye’ structure of SMAC proteins at the immune synapse, in contrast with evidence that CAR-protein interactions which display microclusters of Lck [ 25 ]. Interestingly, previous studies have also demonstrated that the choice of intracellular co-stimulation domain in the CAR (i.e., CD28 versus 41BB) does not influence immune synapse formation per se [ 58 ], but can influence the equilibrium of phosphorylation and dephosphorylation of the CAR-CD3z endodomain and directly influence CAR T cell activation [ 55 ]. Previous studies have also shown that varying the length of CARs [ 59 , 60 ] and also the numbers and location of ITAM CD3 [ 61 ] can also impact CAR performance.…”

Roadmap on Digital Holography-Based Quantitative Phase Imaging

“…Deep learning approaches have been used in image acquisition and reconstruction, including denoising and deblurring [112]. Reconstructed images have also been analyzed, with the aid of AI, for biological studies, including cell type classifications (Figure 8a) [113] and segmentation [114]. Recently, obtaining molecular information from unlabeled live cells has been realized by training a network architecture with RI and fluorescence image pairs in order to extract molecular information from unlabeled RI tomogram data (Figure 8b) [115].…”