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. We developed scAge, a probabilistic approach to determine the epigenetic age of single cells, and validated our results in mice. scAge tissue-specific and multi-cell type single cell clocks correctly recapitulated the chronological age of the original tissue, while uncovering the inherent heterogeneity that exists at the single-cell level. The data suggested that while cells in a tissue age in a coordinated fashion, some cells age more or less rapidly than others. We showed that individual embryonic stem cells exhibit an age close to zero, that certain stem cells in a tissue showed a reduced age compared to their chronological age, and that early embryogenesis is associated with the reduction of epigenetic age in 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 mammalian organism of interest. We demonstrate the potential for accurate epigenetic age profiling at single-cell resolution.