Profiling epigenetic age in single cells

Abstract

DNA methylation of a defined set of CpG dinucleotides emerged as a critical and precise biomarker of the aging process. Multi-variate machine learning models, known as epigenetic clocks, can exploit quantitative changes in the methylome to predict the age of bulk tissue with remarkable accuracy. However, intrinsic sparsity and digitized methylation in individual cells have so far precluded the assessment of aging in single cell data. Here, we present scAge, a probabilistic approach to determine the epigenetic age of single cells, and validate our results in mice. scAge tissue-specific and multi-cell type single cell clocks correctly recapitulate chronological age of the original tissue, while uncovering the inherent heterogeneity that exists at the single-cell level. The data suggest that while tissues age in a coordinated fashion, some cells age more or less rapidly than others. We show that individual embryonic stem cells exhibit an age close to zero, that certain stem cells in a tissue show a reduced age compared to their chronological age, and that early embryogenesis is associated with the reduction of epigenetic age of individual cells, the latter supporting a natural rejuvenation event during gastrulation. scAge is both robust against the low coverage that is characteristic of single cell sequencing techniques and is flexible for studying any cell type and vertebrate organism of interest. This study demonstrates for the first time the potential for accurate epigenetic age profiling at single-cell resolution.