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Nuclear morphology is a deep learning biomarker of cellular senescence

15 Aug 2022

Abstract excerpt

Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack of exclusive or universal markers. Using neural networks, we predict senescence from the nuclear morphology of human fibroblasts with up to 95% accuracy, and investigate murine astrocytes, murine neurons, and fibroblasts with premature aging in culture. After...

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PMID: 37118134
DOI: 10.1038/s43587-022-00263-3

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