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

Dai Kusumoto, Tomohisa Seki, Hiromune Sawada, Akira Kunitomi, Toshiomi Katsuki, Mai Kimura, Shogo Ito, Jin Komuro, Hisayuki Hashimoto, Keiichi Fukuda, Shinsuke Yuasa

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


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.

Original languageEnglish
Article number257
JournalNature communications
Issue number1
Publication statusPublished - 2021 Dec 1

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • General
  • Physics and Astronomy(all)


Dive into the research topics of 'Anti-senescent drug screening by deep learning-based morphology senescence scoring'. Together they form a unique fingerprint.

Cite this