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

摘要 一组已定义的CpG二核苷酸的DNA甲基化成为一种关键和精确的生物标志物 老化的过程。被称为表观遗传时钟的多变量机器学习模型可以利用 甲基组的定量变化预测生物样品的年龄具有较高的准确性。 然而，现有的时钟只是建立在批量样本上并应用于批量样本，掩盖了固有的 存在于单细胞水平上的异质性。我们最近开发了一种新颖、健壮、 灵活的表观遗传时钟框架，能够在单细胞中免归因性地描绘生物年龄， 首次实现了对细胞个体老化轨迹的调查。我们验证了我们的 方法对小鼠某些组织和细胞类型进行检测，发现相关性强，错误率低。我们 寻求进一步改进和改进我们的方法，并将其应用于小鼠的其他组织， 特别是在延长寿命的干预措施方面。我们还将延长我们的单细胞时钟 方法对人类样本，使个体人类细胞的表观遗传老化的新评估成为可能。 此外，我们建议通过低成本和高通量来开发表观遗传年龄分布 在标准条件下和对某些寿命作出反应时评估生物年龄的方法 治疗。这些研究将在老龄化领域取得重大进展，使低成本和 样本的可扩展表观遗传年龄分析以及单细胞表观基因组老化的解剖 无论是在老鼠身上还是在人类身上。

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.