Single cell DNA methylation ageing in mouse blood

表观遗传学 DNA甲基化 老化 生物 甲基化 转录组 生物年龄 细胞 计算生物学 电池类型 DNA 基因 遗传学 基因表达 进化生物学
作者
Marc Jan Bonder,Stephen J. Clark,Felix Krueger,Siyuan Luo,João Agostinho de Sousa,Aida M. Hashtroud,Thomas M. Stubbs,Anne‐Katrien Stark,Steffen Rulands,Oliver Stegle,Wolf Reik,Ferdinand von Meyenn
出处
期刊: [Cold Spring Harbor Laboratory]
被引量:7
标识
DOI:10.1101/2023.01.30.526343
摘要

ABSTRACT Ageing is the accumulation of changes and overall decline of the function of cells, organs and organisms over time. At the molecular and cellular level, the concept of biological age has been established and biomarkers of biological age have been identified, notably epigenetic DNA-methylation based clocks. With the emergence of single-cell DNA methylation profiling methods, the possibility to study biological age of individual cells has been proposed, and a first proof-of-concept study, based on limited single cell datasets mostly from early developmental origin, indicated the feasibility and relevance of this approach to better understand organismal changes and cellular ageing heterogeneity. Here we generated a large single-cell DNA methylation and matched transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages (10-101 weeks of age). We observed that the number of genes expressed increased at older ages, but gene specific changes were small. We next developed a robust single cell DNA methylation age predictor (scEpiAge), which can accurately predict age in a broad range of publicly available datasets, including very sparse data and it also predicts age in single cells. Interestingly, the DNA methylation age distribution is wider than technically expected in 19% of single cells, suggesting that epigenetic age heterogeneity is present in vivo and may relate to functional differences between cells. In addition, we observe differences in epigenetic ageing between the major blood cell types. Our work provides a foundation for better single-cell and sparse data epigenetic age predictors and highlights the significance of cellular heterogeneity during ageing. Highlights - Model to estimate DNA methylation age in single cells - Large multi-omics dataset of single cells from murine blood - Epigenetic age deviations from chronological age are greater than technical expected from technical variability - Number of genes expressed increases with chronological and epigenetic age
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