Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches

Chung-Yueh Lien; Tseng-Tse Chen; En-Tung Tsai; Yu-Jer Hsiao; Ni Lee; Chong-En Gao; Yi-Ping Yang; Shih-Jen Chen; Aliaksandr A Yarmishyn; De-Kuang Hwang; Shih-Jie Chou; Woei-Chyn Chu; Shih-Hwa Chiou; Yueh Chien

doi:10.3390/cells12020211

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches

Cells. 2023 Jan 4;12(2):211. doi: 10.3390/cells12020211.

Authors

Chung-Yueh Lien¹, Tseng-Tse Chen², En-Tung Tsai³, Yu-Jer Hsiao^{3

4}, Ni Lee^{3

5}, Chong-En Gao^{3

6}, Yi-Ping Yang^{3

7}, Shih-Jen Chen^{3

8}, Aliaksandr A Yarmishyn³, De-Kuang Hwang^{3

8}, Shih-Jie Chou^{3

9}, Woei-Chyn Chu², Shih-Hwa Chiou^{3

8

9}, Yueh Chien^{3

9}

Affiliations

¹ Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 112303, Taiwan.
² Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan.
³ Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan.
⁴ School of Medicine, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan.
⁵ Institute of Anatomy and Cell Biology, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan.
⁶ Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan.
⁷ Institute of Food Safety and Health Risk Assessment, School of Pharmaceutical Sciences, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan.
⁸ Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 11217, Taiwan.
⁹ Institute of Pharmacology, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan.

Abstract

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells (iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells (iPSC-RPEs) to meet the demand of regeneration medicine. Since the production of iPSCs and iPSC-derived cell lineages generally requires massive and time-consuming laboratory work, artificial intelligence (AI)-assisted approach that can facilitate the cell classification and recognize the cell differentiation degree is of critical demand. In this study, we propose the multi-slice tensor model, a modified convolutional neural network (CNN) designed to classify iPSC-derived cells and evaluate the differentiation efficiency of iPSC-RPEs. We removed the fully connected layers and projected the features using principle component analysis (PCA), and subsequently classified iPSC-RPEs according to various differentiation degree. With the assistance of the support vector machine (SVM), this model further showed capabilities to classify iPSCs, iPSC-MSCs, iPSC-RPEs, and iPSC-RGCs with an accuracy of 97.8%. In addition, the proposed model accurately recognized the differentiation of iPSC-RPEs and showed the potential to identify the candidate cells with ideal features and simultaneously exclude cells with immature/abnormal phenotypes. This rapid screening/classification system may facilitate the translation of iPSC-based technologies into clinical uses, such as cell transplantation therapy.

Keywords: artificial intelligence; convolutional neural network; deep learning; induced pluripotent stem cells; retinal pigment epithelial cells; traditional machine learning.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence
Cell Differentiation
Deep Learning*
Humans
Induced Pluripotent Stem Cells*
Retinal Pigment Epithelium

Grants and funding

This study was funded by Ministry of Science and Technology (MOST) (MOST 111-2314-B-075-036-MY3; MOST 111-2320-B-075-007; MOST 111-2320-B-A49-028-MY3; MOST 111-2321-B-A49-009), National Science and Technology Council (NSTC 111-2634-F-006-012); Taipei Veterans General Hospital (V111B-025).