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<p>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 <i>Drosophila melanogaster</i> 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:<i>roX1</i> and lncRNA:<i>roX2</i> 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 <i>Drosophila</i>, 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.</p>

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An snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Nikolai Tennant,
  • Ananya Pavuluri,
  • Gunjan Singh,
  • Kaitlyn Cortez,
  • Kate O’Connor-Giles,
  • Erica Larschan,
  • Ritambhara Singh

摘要

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.