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

Biology

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

D. Kusumoto, T. Seki, et al.

Deep-SeSMo is a groundbreaking deep learning system developed by Dai Kusumoto and colleagues for the identification and quantification of senescent cells. This research not only evaluates known anti-senescent agents but also uncovers four novel drugs that suppress senescence by targeting inflammatory pathways.

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Playback language: English
Abstract
This study presents Deep-SeSMo, a deep learning-based system for identifying senescent cells and quantifying senescence using morphology. Deep-SeSMo accurately evaluates known anti-senescent reagents and screens a kinase inhibitor library, identifying four novel anti-senescent drugs. RNA sequencing reveals these drugs suppress senescence by inhibiting the inflammatory response pathway.
Publisher
Nature Communications
Published On
Jan 11, 2021
Authors
Dai Kusumoto, Tomohisa Seki, Hiromune Sawada, Akira Kunitomi, Toshiomi Katsuki, Mai Kimura, Shogo Ito, Jin Komuro, Hisayuki Hashimoto, Keiichi Fukuda, Shinsuke Yuasa
Tags
Deep-SeSMo
senescent cells
deep learning
anti-senescent drugs
inflammatory response
RNA sequencing
kinase inhibitor

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