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.

使用人类诱导多能干细胞 (iPSCs) 作为人类胚胎干细胞 (ESCs) 的替代品，是应对免疫排斥等挑战的一种潜在解决方案，并且不涉及与使用 ESCs 相关的伦理问题再生医学，从而促进生物研究的发展。然而，先前研究的比较分析并未表明 iPSC 与 ESC 的任何特定特征。因此，在本研究中，我们建立了一个基于线性分类的学习模型，以根据它们的 DNA 甲基化谱来区分 ESC、iPSC、胚胎癌细胞 (ECC) 和体细胞。学习模型在识别细胞类型方面达到的最高准确率为 94.23%。此外，iPSC 的表观遗传特征与 ESC 不同，通过学习模型的成分分析来识别。具有甲基化波动的 iPSC 特异性区域在 7、8、12 和 22 号染色体上很丰富。本研究开发的方法可以利用全面的数据，广泛应用于分子生物学研究的许多方面。