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

作者：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 · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-60975-z · 被引用次数：6 · 研究领域：Telomeres, Telomerase, and Senescence、Genetics, Aging, and Longevity in Model Organisms

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. Aging leads to progressive loss of effective tissue regeneration, which has been linked to the accumulation of senescent cells. Here, the authors use machine learning to identify a stable nuclear morphometric phenotype that detects senescent cells across tissues and age, enabling their quantification and mapping in diverse environments.