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  3. A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems

A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems

  • Cell Discov. 2023 Jun 6;9(1):53. doi: 10.1038/s41421-023-00543-1.
Xiaochun Yang # 1,  Daichao Chen # 2,  Qiushi Sun # 3,  Yao Wang 2,  Yu Xia 4,  Jinyu Yang 4,  Chang Lin 5,  Xin Dang 1,  Zimu Cen 1,  Dongdong Liang 2,  Rong Wei 2,  Ze Xu 6,  Guangyin Xi 1,  Gang Xue 7,  Can Ye 2,  Li-Peng Wang 6,  Peng Zou 5 7,  Shi-Qiang Wang 6,  Pablo Rivera-Fuentes 8,  Salome Püntener 8 9,  Zhixing Chen 7 10,  Yi Liu 11,  Jue Zhang 12 13,  Yang Zhao 14 15
Affiliations

Affiliations

  • 1 State Key Laboratory of Natural and Biomimetic Drugs, MOE Key Laboratory of Cell Proliferation and Differentiation, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, China.
  • 2 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
  • 3 Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.
  • 4 College of Engineering, Peking University, Beijing, China.
  • 5 College of Chemistry and Molecular Engineering, Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China.
  • 6 State Key Laboratory of Membrane Biology, College of Life Sciences, Peking University, Beijing, China.
  • 7 Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
  • 8 Department of Chemistry, University of Zurich, Zurich, Switzerland.
  • 9 Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédéral de Lausanne, Lausanne, Switzerland.
  • 10 Institute of Molecular Medicine, National Biomedical Imaging Center, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, College of Future Technology, Peking University, Beijing, China.
  • 11 Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China. yiliu@bjtu.edu.cn.
  • 12 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China. zhangjue@pku.edu.cn.
  • 13 College of Engineering, Peking University, Beijing, China. zhangjue@pku.edu.cn.
  • 14 State Key Laboratory of Natural and Biomimetic Drugs, MOE Key Laboratory of Cell Proliferation and Differentiation, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, China. yangzhao@pku.edu.cn.
  • 15 Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China. yangzhao@pku.edu.cn.
  • # Contributed equally.
PMID: 37280224 DOI: 10.1038/s41421-023-00543-1
Abstract

The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), PSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial PSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 Inhibitor that can further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize PSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications.

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