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

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 and Michael N. Wosczyna ()
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Sahil A. Mapkar: New York University Grossman School of Medicine
Sarah A. Bliss: New York University Grossman School of Medicine
Edgar E. Perez Carbajal: New York University Grossman School of Medicine
Sean H. Murray: New York University Grossman School of Medicine
Zhiru Li: New York University Grossman School of Medicine
Anna K. Wilson: New York University Grossman School of Medicine
Vikrant Piprode: New York University Grossman School of Medicine
You Jin Lee: New York University Grossman School of Medicine
Thorsten Kirsch: New York University Grossman School of Medicine
Katerina S. Petroff: New York University Grossman School of Medicine
Fengyuan Liu: New York University Grossman School of Medicine
Michael N. Wosczyna: New York University Grossman School of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-60975-z

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