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<p>Cellular senescence is an irreversible state of cell cycle arrest with a complex role in tissue repair, aging, and disease. However, inconsistencies in identifying cellular senescence have led to varying conclusions about their functional significance. We developed a machine learning-based approach that uses nuclear morphometrics to identify senescent cells at single-cell resolution. By applying unsupervised clustering and dimensional reduction techniques, we built a robust pipeline that distinguishes senescent cells in cultured systems, freshly isolated cell populations, and tissue sections. Here we show that this method reveals dynamic, age-associated patterns of senescence in regenerating skeletal muscle and osteoarthritic articular cartilage. Our approach offers a broadly applicable strategy to map and quantify senescent cell states in diverse biological contexts, providing a means to readily assess how this cell fate contributes to tissue remodeling and degeneration across lifespan.</p>

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Nuclear morphometrics coupled with machine learning identifies dynamic states of senescence across age

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月7日

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  • Sahil A. Mapkar,
  • Sarah A. Bliss,
  • Edgar E. Perez Carbajal,
  • Sean H. Murray,
  • Zhiru Li,
  • Anna K. Wilson,
  • Vikrant Piprode,
  • You Jin Lee,
  • Thorsten Kirsch,
  • Katerina S. Petroff,
  • Fengyuan Liu,
  • Michael N. Wosczyna

摘要

Cellular senescence is an irreversible state of cell cycle arrest with a complex role in tissue repair, aging, and disease. However, inconsistencies in identifying cellular senescence have led to varying conclusions about their functional significance. We developed a machine learning-based approach that uses nuclear morphometrics to identify senescent cells at single-cell resolution. By applying unsupervised clustering and dimensional reduction techniques, we built a robust pipeline that distinguishes senescent cells in cultured systems, freshly isolated cell populations, and tissue sections. Here we show that this method reveals dynamic, age-associated patterns of senescence in regenerating skeletal muscle and osteoarthritic articular cartilage. Our approach offers a broadly applicable strategy to map and quantify senescent cell states in diverse biological contexts, providing a means to readily assess how this cell fate contributes to tissue remodeling and degeneration across lifespan.