Rotkevich Mikhail, Viana Carlotta, Neguembor Maria Victoria, Cosma Maria Pia
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, 08003, Spain.
Instituto de Biologia Molecular de Barcelona (IBMB), CSIC, Barcelona, 08028, Spain.
Cell Mol Life Sci. 2025 Aug 29;82(1):323. doi: 10.1007/s00018-025-05837-z.
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.
基因组的三维组织在调节基因表达、维持细胞特性以及介导对环境线索的反应中起着关键作用。超分辨率显微镜和基因组技术的进步使人们能够以前所未有的方式洞察纳米级分辨率下的染色质结构。然而，这些技术产生的数据的复杂性和数量需要创新的计算策略来进行有效的分析和解释。在本综述中，我们探讨深度学习在三维基因组组织分析中的变革性作用，强调深度学习模型如何被用于加强染色质研究中的图像重建、分割和动态跟踪。我们概述了深度学习增强方法，这些方法显著提高了图像的空间和时间分辨率，特别关注单分子定位显微镜。此外，我们讨论了深度学习对分割准确性的贡献及其在单细胞水平剖析染色质动力学的单粒子跟踪中的应用。这些进展辅以实现多模态整合和可解释性的框架，将染色质生物学的边界推向临床诊断和个性化医学。最后，我们讨论了新兴的临床应用，其中基于染色质成像的深度学习模型有助于疾病分层、药物反应预测和早期癌症检测。我们还讨论了数据稀疏性、模型可解释性的挑战，并提出了未来以更高精度和解码基因组功能的方向。
