Li Minghong, Yang Yurong, Wu Rucheng, Gong Haiyan, Yuan Zan, Wang Jixin, Long Erping, Zhang Xiaotong, Chen Yang
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.
Adv Sci (Weinh). 2025 Feb;12(8):e2406413. doi: 10.1002/advs.202406413. Epub 2025 Jan 7.
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.
染色质构象动力学涉及细胞内细胞核内连续且可逆的变化，这些变化参与调节基因表达、DNA复制和损伤修复等过程。本文介绍了SEE，这是一种人工智能（AI）方法，它利用自动编码器和变换器技术，通过单细胞RNA测序数据和有限数量的单细胞Hi-C图谱来分析染色质动力学。SEE用于研究不同尺度上的染色质动力学，能够检测（i）拓扑相关结构域（TADs）中的重排，以及（ii）基因位点处染色质相互作用的振荡。此外，SEE通过利用染色质构象的动态特征，有助于解释与疾病相关的单核苷酸多态性（SNPs）。总体而言，SEE提供了一种单细胞、高分辨率的方法，用于在发育和疾病背景下分析染色质动力学。
