Anti-senescent drug screening by deep learning-based morphology senescence scoring

Abstract

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.

A pan-cancer organoid platform for precision medicine

“…Further, the nuclei counts from experimental and generated fluorescence images as counted by threshold-based segmentation pipeline had high overall agreement (Figure S6B). While the RCA network demonstrated the potential to capture cytostatic drug phenotypes, to accurately predict specific cytostatic mechanisms, the RCA network can be adapted to train on specific stains such as senescence-associated beta-galactosidase activity in the case of drug-induced senescence (Kusumoto et al, 2021).…”

Vascular Endothelial Senescence: Pathobiological Insights, Emerging Long Noncoding RNA Targets, Challenges and Therapeutic Opportunities

“…It has been studied for many different applications, such as cancer diagnostics ( Kleppe et al, 2021 ), medical image analysis ( Wiestler and Menze, 2020 ), multi-omics and big data analyses ( Krassowski et al, 2020 ; Haemmig et al, 2021 ; Mahmud et al, 2021 ), long noncoding RNA research ( Alam et al, 2020 ), and protein structural modeling and design ( Gao et al, 2020 ). Recently, deep learning was employed to identify senescent ECs ( Kusumoto et al, 2021 ). The authors developed a quantitative scoring system to examine cellular senescence of ECs.…”

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

“…Understanding such genetics of disease allows health care professionals to recommend better treatments and provide more accurate diagnoses [21]. Recently DL has been successfully used to perform the classification of cellular senescence (a state in which human body cells can no longer divide and become a therapeutic target; a hallmark of aging) for finding drugs that control cellular senescence [22].…”