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

journal article·2025-02Open access

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

Advanced Science· Volume 12· Issue 8

Publisher page Open full text

Source abstract

Abstract

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.

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Research context

OpenAlex topics: Single-cell and spatial transcriptomics · Genomics and Chromatin Dynamics · Epigenetics and DNA Methylation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

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

Department of Computer Science and Technology University of Science and Technology Beijing Beijing 100083 China

Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing 100083 China

School of Basic Medicine Tsinghua University Beijing 100084 China

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

Shunde Innovation School University of Science and Technology Beijing Foshan 528399 China

Funding

National Key Research and Development Program of China

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