TimeFlies: an snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging

Although there are multiple high-performing epigenetic aging clocks, few aging clocks based on gene expression exist, which allow direct prediction of age-associated genes. Existing transcriptomic clocks exhibit inconsistent performance and are limited in their ability to predict novel biomarkers. With the growing popularity of single-cell sequencing, there is a need for robust single-cell transcriptomic aging clocks. Moreover, clocks have yet to be applied to investigate the elusive phenomenon of sex differences in aging. We introduce TimeFlies, a pan-cell-type scRNA-seq aging clock for theDrosophila melanogasterhead. TimeFlies uses interpretable deep learning to classify donor age of cells based on gene expression. We identified key marker genes used in classification; lncRNAs were highly enriched among predicted biomarkers. The top biomarker gene across cell types is lncRNA:roX1, a regulator of X chromosome dosage compensation, a pathway previously identified as a top biomarker of aging in the mouse brain. We validated this finding experimentally, showing a decrease in survival probability in the absence of roX1in vivo. Furthermore, we trained sex-specific TimeFlies clocks and noted significant differences in model predictions and explanations between male and female clocks, suggesting that different pathways drive aging in males and females.