Single cell DNA methylation ageing in mouse blood

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.

Differential Responses of Dynamic and Entropic Aging Factors to Longevity Interventions

“…This study utilized single-cell methylation data, as provided by the authors of [12]. Samples from individuals younger than 5 months and cells that did not meet the methylation quality control (QC) criteria were excluded from the analysis.…”

“…The authors express their gratitude to Bohan Zhang and Vadim Gladyshev from Harvard Medical School for facilitating access to the data from [19], and to Ferdinand von Meyenn, Stephen Clark, and Wolf Reik from the ETH and Altos labs for providing early access to the data from [12].…”

“…To validate this interpretation on the level of individual cells, we analyzed single-cell DNAm (scDNAm) data from [12], calculating mutual information (MI) between methylation sites to assess codependence (detailed in Materials and Methods). This nonnegative measure is exactly zero if the sites change their states independently(see, e.g., [18]).…”

“…In this Letter, we followed [11] and utilized PCA to obtain a semi-quantitative understanding of the dynamics of methylation profiles associated with aging, as well as their response to lifelong and transient interventions in mice. This was done using bulk DNA methylation (DNAm) data from [4] and newly available single cell DNAm (scDNAm) data from [12]. We produced evidence suggesting that the leading aging signature in the bulk DNAm data from mice characterizes the dynamics of a large cluster of tightly-synchronized age-dependent features exhibiting an exponential dependence on age with the exponent matching the exponent of the Gompertz mortality law.…”

Development of a novel epigenetic clock resistant to changes in immune cell composition

“…While the resulting measure is cell-type independent, it becomes challenging to biologically interpret as the underlying signal is derived from a mixture of CpG sites that can be either cell type-independent or cell type-dependent. Other modern approaches include the development of single-cell epigenetic clocks 18,19 , though the underlying technology will require further maturing before it can match the sensitivity and accuracy of bulk measurement-based clocks.…”