Profiling epigenetic age in single cells and in a high-throughput manner

摘要

Abstract DNA methylation of a defined set of CpG dinucleotides emerged as a critical and precise biomarker of the aging process. Multivariate machine learning models, known as epigenetic clocks, can exploit quantitative changes in the methylome to predict the age of biological samples with high accuracy. However, existing clocks have only been built on and applied to bulk samples, obscuring the inherent heterogeneity that exists at the level of single cells. We have recently developed a novel, robust, and flexible epigenetic clock framework capable of imputation-free profiling of biological age in single cells, enabling for the first time the investigation of the individual aging trajectories of cells. We validated our method on some tissues and cell types in mice and observed strong correlations and low error rates. We seek to further improve and refine our method, as well as to apply it to additional tissues in mice, specifically in the context of lifespan-extending interventions. We will also extend our single-cell clock method to human samples, enabling the novel assessment of epigenetic aging of individual human cells. In addition, we propose to develop epigenetic age profiling via low-cost and high-throughput methodologies for assessing biological age in standard conditions and in response to certain longevity treatments. These studies will make critical advances in the aging field, enabling both low-cost and scalable epigenetic age profiling of samples as well as dissection of the single-cell epigenomic aging landscape in both mice and humans.