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

Abstract

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 the Drosophila melanogaster head. 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 roX1 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.

Graphical Abstract

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