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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.

A major stress response influenced by microRNAs (miRNAs) is senescence, a state of indefinite growth arrest triggered by sublethal cell damage. Here, through bioinformatic analysis and experimental validation, we identified miR-340-5p as a novel miRNA that foments cellular senescence. miR-340-5p was highly abundant in diverse senescence models, and miR-340-5p overexpression in proliferating cells rendered them senescent. Among the target mRNAs, miR-340-5p prominently reduced the levels of LBR mRNA, encoding lamin B receptor (LBR). Loss of LBR by ectopic overexpression of miR-340-5p derepressed heterochromatin in lamina-associated domains, promoting the expression of DNA repetitive elements characteristic of senescence. Importantly, overexpressing miR-340-5p enhanced cellular sensitivity to senolytic compounds, while antagonization of miR-340-5p reduced senescent cell markers and engendered resistance to senolytic-induced cell death. We propose that miR-340-5p can be exploited for removing senescent cells to restore tissue homeostasis and mitigate damage by senescent cells in pathologies of human aging.

Cells respond to many stressors by senescing, acquiring stable growth arrest, morphologic and metabolic changes, and a proinflammatory senescence-associated secretory phenotype. The heterogeneity of senescent cells (SnCs) and senescence-associated secretory phenotype are vast, yet ill characterized. SnCs have diverse roles in health and disease and are therapeutically targetable, making characterization of SnCs and their detection a priority. The Cellular Senescence Network (SenNet), a National Institutes of Health Common Fund initiative, was established to address this need. The goal of SenNet is to map SnCs across the human lifespan to advance diagnostic and therapeutic approaches to improve human health. State-of-the-art methods will be applied to identify, define and map SnCs in 18 human tissues. A common coordinate framework will integrate data to create four-dimensional SnC atlases. Other key SenNet deliverables include innovative tools and technologies to detect SnCs, new SnC biomarkers and extensive public multi-omics datasets. This Perspective lays out the impetus, goals, approaches and products of SenNet.

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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 and fibroblasts derived from premature aging diseases in vitro. 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. Evaluating corresponding medical records reveals that individuals with increased senescent cells have a significantly decreased rate of malignant neoplasms, lending support for the protective role of senescence in limiting cancer development. In sum, we introduce a novel predictor of cellular senescence and apply it to diagnostic medical images, indicating cancer occurs more frequently for those with a lower rate of senescence.

Little is known about tissue specific changes that occur with aging in humans. Using the description of 33 million histological samples we extract thousands of age- and mortality-associated features from text narratives that we call The Human Pathome (pathoage.com). Notably, we can broadly determine when pathological aging starts, indicating a sexual dimorphism with females aging earlier but slower and males aging later but faster. Using machine learning, we employ unsupervised topic-modelling to identify terms and themes that predict age and mortality. As a proof of principle, we cross reference these terms in PubMed to identify nintedanib as a potential aging intervention and show that nintedanib reduces markers of cellular senescence, reduces pro-fibrotic gene pathways in senescent cells and extends the lifespan of fruit flies. Our findings pave the way for expanded exploitation of population datasets towards discovery of novel aging interventions.