Idioma:

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

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

Nature communications, 2021-01, Vol.12 (1), p.257-10, Article 257 [Periódico revisado por pares]

England: Nature Publishing Group

Texto completo disponível

Citações Citado por

Enviar para

Título:
Anti-senescent drug screening by deep learning-based morphology senescence scoring
Autor: Kusumoto, Dai ; Seki, Tomohisa ; Sawada, Hiromune ; Kunitomi, Akira ; Katsuki, Toshiomi ; Kimura, Mai ; Ito, Shogo ; Komuro, Jin ; Hashimoto, Hisayuki ; Fukuda, Keiichi ; Yuasa, Shinsuke
Assuntos: Age related diseases ; Aging ; Artificial neural networks ; beta-Galactosidase - metabolism ; Cell Shape ; Cellular communication ; Cellular Senescence ; Cytology ; Deep Learning ; Drug development ; Drug Evaluation, Preclinical ; Drug screening ; Drugs ; Endothelial cells ; Enzyme inhibitors ; Evaluation ; Human Umbilical Vein Endothelial Cells - cytology ; Human Umbilical Vein Endothelial Cells - drug effects ; Human Umbilical Vein Endothelial Cells - metabolism ; Humans ; Hydrogen Peroxide - toxicity ; Image classification ; Inflammation ; Inflammatory response ; Kinases ; Machine learning ; Morphology ; Neural networks ; Neural Networks, Computer ; Nucleotide sequence ; Pathogenesis ; Phenotypes ; Reagents ; Ribonucleic acid ; RNA ; Screening ; Senescence ; Sequence analysis ; Task complexity ; Therapeutic targets
É parte de: Nature communications, 2021-01, Vol.12 (1), p.257-10, Article 257
Descrição: 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.
Editor: England: Nature Publishing Group
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Anterior Resultado 17 Avançar Ir para próxima página

Realização: Logos de Redes Sociais:

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

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

England: Nature Publishing Group

Buscando em bases de dados remotas. Favor aguardar.