  Embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs)
derived from somatic cells (SCs) 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 cell lines is lacking.
Here, we developed a quantitative system to discriminate the three cell types,
iPSCs, ESCs and SCs. The system contains 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 ESCs and iPSCs with almost 100% accuracy, and 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 system.
Thus, this quantitative system works as a novel general and accurate framework
for discriminating the three cell types, iPSCs, ESCs, and SCs and this strategy
supports the notion that DNA-methylation generally varies among the three cell
types.
