SEE: A Method for Predicting the Dynamics of Chromatin Conformation Based on Single-Cell Gene Expression

Minghong Li; Yurong Yang; Rucheng Wu; Haiyan Gong; Zan Yuan; Jixin Wang; Erping Long; Xiaotong Zhang; Yang Chen

doi:10.1002/advs.202406413

SEE: A Method for Predicting the Dynamics of Chromatin Conformation Based on Single-Cell Gene Expression

Adv Sci (Weinh). 2025 Jan 7:e2406413. doi: 10.1002/advs.202406413. Online ahead of print.

Authors

Minghong Li^{1

2}, Yurong Yang¹, Rucheng Wu¹, Haiyan Gong³, Zan Yuan¹, Jixin Wang⁴, Erping Long⁵, Xiaotong Zhang^{2

6}, Yang Chen¹

Affiliations

¹ State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
² Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
³ Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
⁴ School of Basic Medicine, Tsinghua University, Beijing, 100084, China.
⁵ The State Key Laboratory of Respiratory Health and Multimorbidity, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
⁶ Shunde Innovation School, University of Science and Technology Beijing, Foshan, 528399, China.

PMID: 39778075
DOI: 10.1002/advs.202406413

Abstract

The dynamics of chromatin conformation involve continuous and reversible changes within the nucleus of a cell, which participate in regulating processes such as gene expression, DNA replication, and damage repair. Here, SEE is introduced, an artificial intelligence (AI) method that utilizes autoencoder and transformer techniques to analyze chromatin dynamics using single-cell RNA sequencing data and a limited number of single-cell Hi-C maps. SEE is employed to investigate chromatin dynamics across different scales, enabling the detection of (i) rearrangements in topologically associating domains (TADs), and (ii) oscillations in chromatin interactions at gene loci. Additionally, SEE facilitates the interpretation of disease-associated single-nucleotide polymorphisms (SNPs) by leveraging the dynamic features of chromatin conformation. Overall, SEE offers a single-cell, high-resolution approach to analyzing chromatin dynamics in both developmental and disease contexts.

Keywords: chromatin dynamics; single cell Hi‐C; single cell RNA‐seq.

Abstract

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