Abstract
The 3D organization of the genome plays a critical role in regulating gene expression, maintaining cellular identity, and mediating responses to environmental cues. Advances in super-resolution microscopy and genomic technologies have enabled unprecedented insights into chromatin architecture at nanoscale resolution. However, the complexity and volume of data generated by these techniques necessitate innovative computational strategies for effective analysis and interpretation. In this review, we explore the transformative role of deep learning in the analysis of 3D genome organization, highlighting how deep learning models are being leveraged to enhance image reconstruction, segmentation, and dynamic tracking in chromatin research. We provide an overview of deep learning-enhanced methodologies that significantly improve spatial and temporal resolution of images, with a special focus on single-molecule localization microscopy. Furthermore, we discuss deep learning's contribution to segmentation accuracy, and its application in single-particle tracking for dissecting chromatin dynamics at the single-cell level. These advances are complemented by frameworks that enable multimodal integration and interpretability, pushing the boundaries of chromatin biology into clinical diagnostics and personalized medicine. Finally, we discuss emerging clinical applications where deep learning models, based on chromatin imaging, aid in disease stratification, drug response prediction, and early cancer detection. We also address the challenges of data sparsity, model interpretability and propose future directions to decode genome function with higher precision and impact.