Deep learning predicts senescence based on DAPI staining.

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. Senescent cells are typically identified by a combination of senescence-associated markers, and the phenotype is heterogeneous. Here, using deep neural networks, Heckenbach et al. show that nuclear morphology can be used to predict cellular senescence in images of tissues and cell cultures.

Who wants to live forever? Until recently, the quest to slow ageing or even reverse it was the stuff of legends – or scams. But, today, an evidence-based race to delay or prevent ageing is energising scientists worldwide. Scientists say there are already a number of things we can do to extend life and health, while promising that current and ongoing large-scale trials of drugs and other interventions mean the once-mythical goal of healthy, longer-lived lives is not far away. "Death is inevitable but ageing is not," said Dr Nir Barzilai, founding director of the Institute for Aging Research at the Albert Einstein College of Medicine, New York.