Predicting age in single cells and low coverage DNA methylation data [scBS]

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 novel 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), and developed a robust single-cell DNA methylation age prediction model (scEpiAge-blood and also scEpiAge-liver). We find that our new scEpiAge 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 epigenetic 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. Overall design: Whole blood taken from wild type black 6 mice spanning a range of ages is processed using scM&T-seq to produce paired single-cell transcriptomes and single-cell methylomes.

衰老是指随时间推移，细胞、器官及生物体发生的各类变化不断累积，伴随整体功能的逐步衰退。在分子与细胞层面，生物年龄（biological age）这一概念已得到学界广泛认可，多种新型生物年龄生物标志物相继被鉴定，其中尤以基于表观遗传DNA甲基化的衰老时钟（epigenetic DNA-methylation based clocks）最为典型。随着单细胞DNA甲基化谱分析技术的问世，研究单个细胞生物年龄的可行性被提出；早期一项基于多取材于发育早期的有限单细胞数据集的概念验证研究，证实了该方法在解析生物体衰老变化与细胞衰老异质性方面的可行性与研究价值。
本研究中，我们从覆盖10至101周龄广泛年龄跨度的小鼠外周血样本中构建了大型单细胞DNA甲基化与配对转录组数据集，并开发了性能稳健的单细胞DNA甲基化年龄预测模型scEpiAge-blood与scEpiAge-liver。研究表明，我们所开发的新型scEpiAge模型可在涵盖极稀疏数据集在内的多款公开数据集上实现精准年龄预测，同时也可对单个细胞的年龄进行推断。值得注意的是，19%的单个细胞的表观遗传年龄分布宽度超出技术预期，这提示体内确实存在表观遗传年龄异质性，且该异质性可能与细胞间的功能差异密切相关。此外，我们还观察到不同主要血细胞类型之间的表观遗传衰老进程存在显著差异。本研究为优化单细胞与稀疏数据的表观遗传年龄预测工具提供了重要基础，同时凸显了细胞异质性在衰老过程中的关键研究价值。
实验整体设计：从覆盖不同年龄的野生型C57BL/6小鼠体内采集全血样本，通过scM&T-seq技术进行处理，以获取配对的单细胞转录组与单细胞甲基化组数据。