Although multiple high-performing epigenetic aging clocks exist, few are based directly on gene expression. Such transcriptomic aging clocks allow us to identify potential age-associated genes directly. However, most existing transcriptomic clocks model a subset of genes and are limited in their ability to predict novel biomarkers. With the growing application of single-cell sequencing, there is a need for robust single-cell transcriptomic aging clocks. Moreover, aging clocks have yet to be applied to investigate the elusive phenomenon of sex differences in aging. We introduce TimeFlies, a pan-cell-type snRNA-seq aging clock for the Drosophila melanogaster head. TimeFlies uses deep learning to classify the donor age of cells based on genome-wide gene expression profiles. Using explainability methods, we identified key marker genes contributing to the classification, with lncRNAs showing up as highly enriched among predicted biomarkers. lncRNA:roX1 and lncRNA:roX2 are top clock genes across cell types. Both are regulators of X chromosome dosage compensation, a pathway previously found to be significantly affected by aging in the mouse brain. We validated these findings experimentally in Drosophila, showing a decrease in survival when dosage compensation is inhibited in 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.
Tennant et al. (Tue,) studied this question.