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, Cindy Tatiana Báez-Becerra, Silvia Cho, Chumo Chen, Jackson Willier, Stella Maris Cao, Jonathan Rink, Jasper Lee, Ariana Barcoma, Jessica Arzavala, Nora Emerson, Yuancheng Ryan Lu, Bing Ren, M. Margarita Behrens, Joseph R. Ecker
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.