Integrative epigenomic profiling by high-speed super-resolution imaging and deep learning reveals chromatin state biomarkers

Background Comprehensive profiling of epigenetic states is essential for understanding gene regulation and disease mechanisms. Sequencing-based methods such as ChIP-seq, Hi-C, and RNA-seq provide genome-wide views of histone modifications and 3D genome organization, but lack spatial resolution within single nuclei. Results Here we present an integrative framework that combines high-speed super-resolution microscopy with deep learning for image-based epigenetic profiling. Using models of (i) histone deacetylase inhibition in HEK293T cells and (ii) Rett syndrome iPS cells carrying MECP2 mutations, our approach accurately discriminated their epigenetic states (99.6% and 96.1% accuracy, respectively) and identified the nuclear periphery as a hotspot of H3K27ac and CTCF redistribution. Sequencing-based analyses confirmed compartment switching and lamina-associated domain alterations consistent with the image-based biomarkers. These results demonstrate that high-speed super-resolution imaging, when combined with deep learning, provides a powerful tool for epigenetic profiling. Conclusions Our framework offers a generalizable strategy for integrating imaging and genomics to uncover chromatin-level mechanisms in development, disease, and therapeutic response.