Abstract
Nuclear Morphology is a Deep Learning Biomarker of Senescence Across Tissues and Species
Highlights
43 44 Cellular senescence is widely recognized as a fundamental process in aging, both as a primary causal factor in the decline of tissue homeostasis and as a consequence of other aging processes such as inflammation and DNA damage 1–3
Trained on fibroblasts maintained in cell culture, the classifier achieves 541 very accurate results, which was confirmed by applying it to independent cell lines
The base model trained on ionizing radiation (IR) and replicative senescence (RS) can identify either type along with senescence induced by doxorubicin, indicating that the predictor has identified features found in multiple types related to DNA damage
Summary
43 44 Cellular senescence is widely recognized as a fundamental process in aging, both as a primary causal factor in the decline of tissue homeostasis and as a consequence of other aging processes such as inflammation and DNA damage 1–3. 60 We present deep learning models that can predict cellular senescence with high accuracy based on nuclear morphology These methods can further distinguish between multiple types of senescence, including radiation-induced damage and replicative exhaustion. We evaluated the predictor on mouse astrocytes and neurons and found it indicates increased senescence in cells subjected to ionizing radiation, confirming its relevance to different cell types and organisms. These methods were further applied to H&E-stained mouse liver tissue, where we found an increasing rate of senescence with age.
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