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

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.

Deep learning models for cancer stem cell detection: a brief review

“…Improvements in microscopy, computational capabilities, and data analysis have enabled high-throughput, high-content approaches from endpoint 2D microscopy images to 3D microscopy images. This approach has been engaged in various cell biology research activities (28)(29)(30), specifically for stem cell characterization (31-33) and differentiation pattern (34)(35)(36)(37), disease modeling (38, 39), and drug screening and discovery (40,41). The advantages of this technique are that it is non-invasive, high-throughput, and consistent, which can save time and costs once automated.…”

HLA-Homozygous iPSC-Derived Mesenchymal Stem Cells Rescue Rotenone-Induced Experimental Leber’s Hereditary Optic Neuropathy-like Models In Vitro and In Vivo

“…iPSCs that can be derived from human skin or peripheral blood possess self-renewal and differentiation capabilities that are similar to embryonic stem cells (ESCs), without the accompanying ethical concerns [11,12]. These characteristics allow researchers to utilize iPSCs to generate a variety of lineage cells after defined differentiation, iPSCs highly useful for translational medicine [13][14][15][16][17]. Both iPSCs and ESCs confer the distinct advantage of enabling unlimited MSC production, thus surmounting the constraints associated with donor availability in conventional MSC-based therapeutic approaches.…”