A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells

Abstract

Induced pluripotent stem cells (iPSCs) derived from somatic cells (SCs) and embryonic stem cells (ESCs) provide promising resources for regenerative medicine and medical research, leading to a daily identification of new cell lines. However, an efficient system to discriminate the different types of cell lines is lacking. Here, we develop a quantitative system to discriminate the three cell types, iPSCs, ESCs, and SCs. The system consists of DNA-methylation biomarkers and mathematical models, including an artificial neural network and support vector machines. All biomarkers were unbiasedly selected by calculating an eigengene score derived from analysis of genome-wide DNA methylations. With 30 biomarkers, or even with as few as 3 top biomarkers, this system can discriminate SCs from pluripotent cells (PCs, including ESCs and iPSCs) with almost 100% accuracy. With approximately 100 biomarkers, the system can distinguish ESCs from iPSCs with an accuracy of 95%. This robust system performs precisely with raw data without normalization as well as with converted data in which the continuous methylation levels are accounted. Strikingly, this system can even accurately predict new samples generated from different microarray platforms and the next-generation sequencing. The subtypes of cells, such as female and male iPSCs and fetal and adult SCs, can also be discriminated with this method. Thus, this novel quantitative system works as an accurate framework for discriminating the three cell types, iPSCs, ESCs, and SCs. This strategy also supports the notion that DNA-methylation generally varies among the three cell types.

Variations in the Intragene Methylation Profiles Hallmark Induced Pluripotency

“…It shows the average errors of three types along with the standard errors of these estimates (in parenthesis), obtained by cross-validation. Remarkably, both types of classifiers and all intragene measures demonstrate a very good performance with the average accuracy above 95%, improving the previous 90% result, based on the artificial neural network and support vector machine implementations for CpG sites methylation data [17]. Instructively, all the considered intragene features perform in a very similar way with respect to classification quality, which could be understood as multimodal changes in gene methylation due to reprogramming.…”

“…Furthermore, there is an increasing evidence on the collective nature of such methylation markers, and the first successes due to the large scale machine learning analysis have been reported [17]. These studies, however, concentrated on the variations of methylation levels in separate CpG dinucleotides, which themselves do not characterize the aggregate changes to gene methylation and its coordinated variations in the groups of genes.…”

Improving Clock Prediction Algorithm for BDS-2/3 Satellites Based on LS-SVM Method

“…In order to build a suitable LS-SVM model, a sample dataset, the remaining clock residual after the removal of trend and periodic terms, is divided into a training sample dataset and a testing sample dataset in an appropriate way. Normally, 70% of sample data are employed for training and the rest are used for testing [28]. In the training processing, the training sample dataset is further divided into a number of successive multiple inputs, single output (MISO) groups, as shown in Table 3.…”

“…the rest are used for testing [28]. In the training processing, the training sample dataset is further divided into a number of successive multiple inputs, single output (MISO) groups, as shown in Table 3.…”

Wnt-3a Induces Epigenetic Remodeling in Human Dental Pulp Stem Cells

“…The epigenome of stem cells is different from the genome of differentiated cells. Stem cells present a genome predominantly in euchromatic conformation whereas the genome of somatic cells is more enriched in heterochromatin, with a higher amount of genes permanently silenced by cytosine methylation [27][28][29]. It is also known that DNA methylation levels are low in PSCs both in vitro and in vivo [24,30].…”