Abstract
The use of human induced pluripotent stem cells (iPSCs), used as an alternative to human embryonic stem cells (ESCs), is a potential solution to challenges, such as immune rejection, and does not involve the ethical issues concerning the use of ESCs in regenerative medicine, thereby enabling developments in biological research. However, comparative analyses from previous studies have not indicated any specific feature that distinguishes iPSCs from ESCs. Therefore, in this study, we established a linear classification-based learning model to distinguish among ESCs, iPSCs, embryonal carcinoma cells (ECCs), and somatic cells on the basis of their DNA methylation profiles. The highest accuracy achieved by the learned models in identifying the cell type was 94.23%. In addition, the epigenetic signature of iPSCs, which is distinct from that of ESCs, was identified by component analysis of the learned models. The iPSC-specific regions with methylation fluctuations were abundant on chromosomes 7, 8, 12, and 22. The method developed in this study can be utilized with comprehensive data and widely applied to many aspects of molecular biology research.
Highlights
The application of human induced pluripotent stem cells in medicine requires prior assessment of the cells with respect to quality, including identity, equivalence, and safety
If a model capable of discriminating between embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) can be constructed using supervised machine learning, the difference between the two cell types could be elucidated. Such a model could help identify the factors underlying the differences between ESCs and iPSCs, as well as enable visualization of these differences, which cannot be distinguished by the naked human eyes
The promoter regions of the pluripotencyassociated genes POU5F1, NANOG, SALL4, RAB25, and EPHA1 showed low levels of methylation, whereas those of Construction of a machine learning model for the classification of cell types The DNA methylation data of 385,683 CpG sites and information on the cell type of the training samples were used for machine learning (Fig. 2a)
Summary
The application of human induced pluripotent stem cells (iPSCs) in medicine requires prior assessment of the cells with respect to quality, including identity, equivalence, and safety. If a model capable of discriminating between ESCs and iPSCs can be constructed using supervised machine learning, the difference between the two cell types could be elucidated. Such a model could help identify the factors underlying the differences between ESCs and iPSCs, as well as enable visualization of these differences, which cannot be distinguished by the naked human eyes. Our machine learning-based analysis method and the identified epigenetic indices are useful for evaluating the therapeutic application of human iPSCs. We propose a new method for molecular analysis of the cells that combines comprehensive DNA methylation data and machine learning
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