Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establishment of convolutional neural networks (CNN). Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Furthermore, it is a potential therapeutic target. Specific molecular markers are used to identify senescent cells. Moreover senescent cells show unique morphology, which can be identified. We develop a successful morphology-based CNN system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells by senescence probability output from pre-trained CNN optimised for the classification of cellular senescence, Deep Learning-Based Senescence Scoring System by Morphology (Deep-SeSMo). Deep-SeSMo correctly evaluates the effects of well-known anti-senescent reagents. We screen for drugs that control cellular senescence using a kinase inhibitor library by Deep-SeSMo-based drug screening and identify four anti-senescent drugs. RNA sequence analysis reveals that these compounds commonly suppress senescent phenotypes through inhibition of the inflammatory response pathway. Thus, morphology-based CNN system can be a powerful tool for anti-senescent drug screening.
Backgrounds: Deep learning is currently being applied to many complex tasks. The accuracy of image classification, in particular, has improved with the development of convolutional neural networks (CNNs). Cellular senescence is a hallmark of ageing and is highly important for the pathogenesis of aging-related diseases. Furthermore, cellular senescence has been identified as a potential therapeutic target. Specific molecular markers are widely used to identify senescent cells, but senescent cells also show unique morphology, which can be identified by CNN. Objective: We develop a strong-performing, morphology-based CNN system called Deep-SeSMo to identify senescent cells, and establish a novel quantitative scoring system to evaluate the probability that an endothelial cell is senescent. Methods and results: We induced cellular senescence in human umbilical vein endothelial cells (HUVECs) using three different stressors: ROS; an anti-cancer reagent; and replication stress, and demonstrated that CNN performed with high accuracy and generalizability. Then, we developed a “senescence score” based on the output of the pre-trained CNN, optimized it for the classification problem, and defined the overall average output probability calculated by the pre-trained CNN as the quantitative senescence score, namely Deep-SeSMo. Deep-SeSMo correctly evaluates the effects of well-known anti-senescent reagents; nicotinamide mononucleotide (NMN) and metrofmin. We screened for drugs that control cellular senescence using a kinase inhibitor library by Deep-SeSMo-based drug-screening and discovered four novel anti-senescent drugs. RNA sequence analysis revealed that those compounds commonly suppress senescent phenotypes through inhibition of the inflammatory response pathway. Conclusions: Using Deep-SeSMo, we demonstrated that the morphology-based CNN system can be a powerful tool for anti-senescent drug screening.
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