RRC ID 89958
Author Wang Y, Ab Ghani NS, Dutta M, Adachi S, Katoh K, Namihira M, Mitsuyama T, Saito Y.
Title Image-based epigenetic profiling with deep learning and high-speed super-resolution microscopy.
Journal Epigenetics Chromatin
Abstract 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 image-based epigenetic profiling framework that combines high-speed super-resolution microscopy with deep learning. 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 showed compartment switching and lamina-associated domain alterations consistent with the image-based features. 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 image-based epigenetic profiling to uncover chromatin alterations in development, disease, and therapeutic response.
Volume 19(1)
Published 2026-2-28
DOI 10.1186/s13072-026-00662-5
PII 10.1186/s13072-026-00662-5
PMID 41764479
PMC PMC13059569
MeSH CCCTC-Binding Factor / metabolism Deep Learning* Epigenesis, Genetic* Epigenomics* / methods HEK293 Cells Histone Deacetylase Inhibitors / pharmacology Histones / metabolism Humans Methyl-CpG-Binding Protein 2 / genetics Microscopy* / methods Rett Syndrome / genetics
Resource
Human and Animal Cells 293T(RCB2202) 409B2(HPS0076) HPS3042 HPS3049 HPS3084