Evidence map›Paper›PMID 41764479›Full record

ArticleEpigenetics & chromatin2026

Image-based epigenetic profiling with deep learning and high-speed super-resolution microscopy.

Yicheng Wang, Nur Syatila Ab Ghani, Munmee Dutta, Shungo Adachi, Kaoru Katoh, Masakazu Namihira, Toutai Mitsuyama, Yutaka Saito

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Article in Epigenetics & chromatin, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yicheng Wang *Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-0882, Chiba, Japan.
Nur Syatila Ab Ghani *Department of Data Science, School of Frontier Engineering, Kitasato University, 1-15-1 Kitazato, Minami-ku, Sagamihara, 252-0373, Kanagawa, Japan.
Munmee DuttaArtificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
Shungo AdachiDepartment of Proteomics, National Cancer Center Research Institute, Tsukiji 5-1-1, Chuo-ku, Tokyo, 104-0045, Japan.
Kaoru KatohExploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, 5-1 Higashiyama Myodaijicho, Okazaki, 444-8787, Aichi, Japan.
Masakazu NamihiraMolecular Biosystems Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, 305-8566, Ibaraki, Japan. m-namihira@aist.go.jp.
Toutai MitsuyamaArtificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
Yutaka SaitoGraduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-0882, Chiba, Japan. saito.yutaka@kitasato-u.ac.jp.

Funding

Japan Agency for Medical Research and Development JP19ak0101122Japan Society for the Promotion of Science 22H03691, 19K20409
6 · The paper itself

Abstract

backgroundComprehensive 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.

resultsHere 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.

conclusionsOur framework offers a generalizable strategy for image-based epigenetic profiling to uncover chromatin alterations in development, disease, and therapeutic response.

Indexed as

Deep LearningEpigenesis, GeneticEpigenomicsMicroscopyCCCTC-Binding FactorHEK293 CellsHistone Deacetylase InhibitorsHistonesHumansMethyl-CpG-Binding Protein 2Rett SyndromeCCCTC-Binding FactorCTCF protein, humanHistone Deacetylase InhibitorsHistonesMECP2 protein, humanMethyl-CpG-Binding Protein 2Deep learningHistone deacetylase inhibitorImage-based epigenetic profilingRett syndromeSuper-resolution microscopy

Identifiers

PMID41764479
PMCPMC13059569

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.