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

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

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...

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Main Authors: 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
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-60975-z
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https://doi.org/10.1038/s41467-025-60975-z

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