生物
表观遗传学
染色质
DNA甲基化
转座因子
转录组
遗传学
染色质重塑
表观遗传学
组蛋白
甲基化
细胞生物学
基因
基因表达调控
衰老的大脑
基因表达
异染色质
计算生物学
后生
神经科学
转录因子
基因调控网络
细胞
表观基因组
进化生物学
基因组
基因组学
CpG站点
作者
Qiurui Zeng,Wenliang Wang,Wei Tian,A. Klein,Anna Bartlett,Hanqing Liu,Joseph R. Nery,Rosa G. Castanon,Julia Osteen,Nicholas D. Johnson,Wubin Ding,Huaming Chen,Jordan Altshul,Mia Kenworthy,Cynthia Valadon,William S. Owens,Zhanghao Wu,Maria Luisa Amaral,Nathan R. Zemke,Yuru Song
出处
期刊:Cell
[Cell Press]
日期:2026-03-11
卷期号:189 (7): 2148-2166.e27
被引量:3
标识
DOI:10.1016/j.cell.2026.02.015
摘要
Aging is a major risk factor for neurodegenerative diseases, yet the underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types. We observed that transposable element (TE) methylation alone distinguished age groups, showing cell-type-specific genome-wide demethylation. Chromatin conformation analysis demonstrated age-related increases in topologically associated domain (TAD) boundary strength with enhanced accessibility at CCCTC-binding factor (CTCF) binding sites. Spatial transcriptomics across 895,296 cells revealed regional heterogeneity during aging within identical cell types. Finally, we developed deep-learning models that reliably predict age-related gene expression changes using multi-modal epigenetic features, providing mechanistic insights into gene regulation. Age-related comparisons use a 2-month baseline reflecting the late-adolescent/early-young adult stage. This dataset advances our understanding of brain aging and offers potential translational applications.
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