Nuclear morphology is a deep learning biomarker of senescence across tissues and species

Abstract

Cellular senescence is a critical component of aging and many age-related diseases, but understanding its role in human health is challenging in part due to the lack of exclusive or universal markers. Using neural networks, we achieve high accuracy in predicting senescence state and type from the nuclear morphology of DAPI-stained human fibroblasts, murine astrocytes, murine neurons, and fibroblasts derived from premature aging diseases in culture. After generalizing this approach, the predictor recognizes an increasing rate of senescent cells with age in H&E-stained murine liver tissue and human dermal biopsies, suggesting that alterations in nuclear morphology is a universal feature of senescence. Evaluating corresponding medical records reveals that individuals with a higher rate of senescent cells have a significantly decreased rate of malignant neoplasms, lending support for the protective role of senescence in limiting cancer development. Additionally, we find a positive association with lower significance for other conditions, including osteoporosis, osteoarthritis, hypertension, cerebral infarction, hyperlipidemia, and hypercholesteremia. In sum, we introduce a predictor of cellular senescence based on nuclear morphology that is applicable across tissues and species and is associated with health outcomes in humans.