Abstract
AbstractCellular 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 generalizing our approach, the predictor recognizes higher rates of senescence in p21-positive and ethynyl-2’-deoxyuridine (EdU)-negative nuclei in tissues and shows an increasing rate of senescent cells with age in H&E-stained murine liver tissue and human dermal biopsies. Evaluating medical records reveals that higher rates of senescent cells correspond to decreased rates of malignant neoplasms and increased rates of osteoporosis, osteoarthritis, hypertension and cerebral infarction. In sum, we show that morphological alterations of the nucleus can serve as a deep learning predictor of senescence that is applicable across tissues and species and is associated with health outcomes in humans.
Funder
U.S. Department of Health & Human Services | NIH | Office of Strategic Coordination
Novo Nordisk Fonden
Lundbeckfonden
Ministry of Higher Education and Science | Forskerakademiet
Publisher
Springer Science and Business Media LLC
Subject
Neuroscience (miscellaneous),Geriatrics and Gerontology,Aging
Cited by
36 articles.
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