Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning

Jin Komuro, Yuta Tokuoka, Tomohisa Seki, Dai Kusumoto, Hisayuki Hashimoto, Toshiomi Katsuki, Takahiro Nakamura, Yohei Akiba, Thukaa Kuoka, Mai Kimura, Takahiro Yamada, Keiichi Fukuda, Akira Funahashi, Shinsuke Yuasa

研究成果: Article査読

1 被引用数 (Scopus)


The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a crucial preliminary step in heart failure, but its treatment has not yet been fully successful. In this study, we established a system to evaluate cardiomyocyte hypertrophy using a deep learning-based high-throughput screening system and identified drugs that inhibit it. First, primary cultured cardiomyocytes from neonatal rats were stimulated by both angiotensin II and endothelin-1, and cellular images were captured using a phase-contrast microscope. Subsequently, we used a deep learning model for instance segmentation and established a system to automatically and unbiasedly evaluate the cardiomyocyte size and perimeter. Using this system, we screened 100 FDA-approved drugs library and identified 12 drugs that inhibited cardiomyocyte hypertrophy. We focused on ezetimibe, a cholesterol absorption inhibitor, that inhibited cardiomyocyte hypertrophy in a dose-dependent manner in vitro. Additionally, ezetimibe improved the cardiac dysfunction induced by pressure overload in mice. These results suggest that the deep learning-based system is useful for the evaluation of cardiomyocyte hypertrophy and drug screening, leading to the development of new treatments for heart failure.

ジャーナルBiochemical and Biophysical Research Communications
出版ステータスPublished - 2022 12月 3

ASJC Scopus subject areas

  • 生物理学
  • 生化学
  • 分子生物学
  • 細胞生物学


「Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。