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<p>Quantifying biological aging is essential for understanding functional decline and evaluating anti-aging interventions. We present SkinAGE, a fast and resource-efficient transcriptome-based aging clock built using a deep neural network (DNN) trained on gene expression profiles of human dermal fibroblasts. SkinAGE accurately predicts cellular aging status across independent cohorts and a UVB-induced photoaging model. In 23rd-passage HFF-1 cells, the model assigned an average age score of 44, which increased by 24 units after UVB exposure, indicating enhanced transcriptomic senescence. Treatment with human embryonic stem cell-derived extracellular vesicles (hESC-EVs) reduced this score by an average of 21.2 units, suggesting a potent ameliorative effect on cellular aging. Transcriptomic analysis supported this, showing restoration of aging-related gene expression, including enrichment of cell cycle progression and p53 signaling pathways. Overall, SkinAGE provides a scalable, accurate, and cost-effective framework for quantifying cellular aging and assessing rejuvenation strategies.</p> Graphical abstract <p></p>

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A transcriptome-based tool for assessing skin aging reveals the ameliorative effect of stem cell-derived extracellular vesicles on UVB-induced aging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

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  • Jinpeng Xie,
  • Lihong Wang,
  • Shuaifei Zhao,
  • Jue Wang,
  • Yang Cheng,
  • Lei Xu,
  • Ting Kou,
  • Ying Chen,
  • Yuxiu Ying,
  • Xiaoshuang Xin,
  • Ting Zhang,
  • Xu Xu,
  • Dandan Lu,
  • Xiangyu Hu,
  • Yusu Zhang,
  • Chenyu Qiu,
  • Jun Wang,
  • Gu Cheng,
  • Jing Zhou,
  • Siyun Lei,
  • Qiqi Lyu,
  • Jingtao Li,
  • Yihuai Pan,
  • Tong Cao

摘要

Quantifying biological aging is essential for understanding functional decline and evaluating anti-aging interventions. We present SkinAGE, a fast and resource-efficient transcriptome-based aging clock built using a deep neural network (DNN) trained on gene expression profiles of human dermal fibroblasts. SkinAGE accurately predicts cellular aging status across independent cohorts and a UVB-induced photoaging model. In 23rd-passage HFF-1 cells, the model assigned an average age score of 44, which increased by 24 units after UVB exposure, indicating enhanced transcriptomic senescence. Treatment with human embryonic stem cell-derived extracellular vesicles (hESC-EVs) reduced this score by an average of 21.2 units, suggesting a potent ameliorative effect on cellular aging. Transcriptomic analysis supported this, showing restoration of aging-related gene expression, including enrichment of cell cycle progression and p53 signaling pathways. Overall, SkinAGE provides a scalable, accurate, and cost-effective framework for quantifying cellular aging and assessing rejuvenation strategies.

Graphical abstract