Rapid and Accurate Identification of Cell Phenotypes of Different Drug Mechanisms by Using Single-Cell Fluorescence Images Via Deep Learning.

Journal: Analytical chemistry
PMID: 37162406

Abstract

Identification of a drug mechanism is vital for drug development. However, it often resorts to the expensive and cumbersome omics methods along with complex data analysis. Herein, we developed a methodology to analyze organelle staining images of single cells using a deep learning algorithm (TL-ResNet50) for rapid and accurate identification of different drug mechanisms. Based on the organelle-related cell morphological changes caused by drug action, the constructed deep learning model can fast predict the drug mechanism with a high accuracy of 92%. Further analysis reveals that drug combination at different ratios can enhance a certain mechanism or generate a new mechanism. This work would highly facilitate clinical medication and drug screening.

Authors

  • Xue-Wei Zhang
    Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China.
  • Yan-Fei Yang
    Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China.
  • Gong-Xiang Qi
    Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China.
  • Fu-Heng Zhai
    Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China.
  • Teng Fei
    School of Resource and Environmental Sciences, Wuhan University, Wuhan, China.
  • Jian-Hua Wang
    Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China.
  • Yong-Liang Yu
    Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China. Electronic address: yuyl@mail.neu.edu.cn.
  • Shuai Chen
    State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.

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