Determining the age of single cells using scMLEAge

1 Abstract Aging is a complex biological process marked by a gradual decline in physiological function that contributes to increased vulnerability to disease and mortality. Numerous studies have investigated the cellular and molecular aspects of aging at single-cell resolution, yet the heterogeneity of cellular aging in an individual remains poorly understood. To enhance our ability to study aging at the single cell level, we developed a statistical framework to predict the age of individual cells based on their transcriptomic profiles. Our Bayesian approach estimates the most likely age of a cell given its read counts. We applied the model to data from Tabula Muris Senis and examined organ- and cell-type-specific transcriptomic signatures of aging. Compared with standard regression-based methods, our framework achieved higher predictive accuracy. We show that scMLEAge is a powerful tool for dissecting the cellular heterogeneity of aging and age-related functional decline. Competing Interest Statement The authors have declared no competing interest. Footnotes We updated the name to avoid confusion and added some new analysis to further confirm our results are valid.