Nuclear morphology is a deep learning biomarker of cellular senescence

Indra Heckenbach, Garik V. Mkrtchyan, Michael Ben Ezra, Daniela Bakula, Jakob Sture Madsen, Malte Hasle Nielsen, Denise Oró, Brenna Osborne, Anthony J. Covarrubias, M. Laura Idda, Myriam Gorospe, Laust Mortensen, Eric Verdin, Rudi Westendorp, Morten Scheibye-Knudsen*

*Corresponding author for this work

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70 Citations (Scopus)
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Abstract

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 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.

Original languageEnglish
JournalNature Aging
Volume2
Issue number8
Pages (from-to)742-755
Number of pages14
ISSN2662-8465
DOIs
Publication statusPublished - 2022

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