Principal Investigator: Profiling epigenetic age in single cells
People age at very different rates. This is controlled by genetics, lifestyle, and environmental factors, among many others. Within this context, it has become clear that chronological age, the number of years one has been alive, is an imperfect measure of the biological aging process. In the quest to trace biological aging more accurately in a heterogenous population, scientists have been working to develop robust “biomarkers” of aging, which are molecular markers that enable tracking the aging process in tissues and organisms. Although a variety of markers have been used, DNA methylation (chemical modifications on the DNA that alter the activity of a cell) has shown to be the most accurate at predicting biological age. However, all previous methods relied on profiling bulk samples (i.e., millions of cells mixed together), which only provides an average measure of biological age and inherently obscures the heterogeneity that exists between cells. Here, we developed the first method to profile biological age from epigenetic (methylation) data at single-cell resolution. This enables tracking how individual cells age, and how different cells respond to promising longevity or rejuvenation therapies. We open a new avenue to investigate biological aging at the highest resolution possible: the cell.
DNA methylation dynamics emerged as a promising biomarker of mammalian aging, with multivariate machine learning models (‘epigenetic clocks’) enabling measurement of biological age in bulk tissue samples. However, intrinsically sparse and binarized methylation profiles of individual cells have so far precluded the assessment of aging in single-cell data. Here, we report scAge, a statistical framework for epigenetic age profiling at single-cell resolution, and validate our approach in mice. Our method recapitulates the chronological age of tissues, while uncovering heterogeneity among cells. We show accurate tracking of the aging process in hepatocytes, demonstrate attenuated epigenetic aging in muscle stem cells, and track age dynamics in embryonic stem cells. We also use scAge to reveal, at the single-cell level, a natural and stratified rejuvenation event occurring during early embryogenesis. We provide our framework as a resource to enable exploration of diverse epigenetic aging trajectories at single-cell resolution.
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