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  • Induced pluripotent stem cells iPSCs have the

    2018-10-22

    Induced pluripotent stem melk pathway (iPSCs) have the characteristics of human embryonic stem cells (hESCs), and many studies have investigated the similarities between iPSCs and hESCs, including genome stabilities, transcriptome (Chin et al., 2009; Guenther et al., 2010; Newman and Cooper, 2010; Wang et al., 2011) and histone modifications (Guenther et al., 2010), and DNA methylation (Bock et al., 2011; Doi et al., 2009; Kim et al., 2010; Lister et al., 2011; Ohi et al., 2011; Ruiz et al., 2012). These studies revealed both similarities and differences in the properties of iPSCs and hESCs. DNA methylation in iPSCs has been reported to acquire irregular methylation patterns while retaining some memory of somatic cells during the reprogramming process, thus exhibiting a methylation profile unique to iPSCs (Bock et al., 2011; Doi et al., 2009; Kim et al., 2010; Lister et al., 2011; Ohi et al., 2011). However, because these previous studies differed in the quantitation techniques, genome coverage, and sample sizes employed, it remains contentious whether iPSCs possess a methylation signature that can be used to distinguish iPSCs from hESCs.
    Results
    Discussion Up to now, it was not clear whether hiPSCs have distinct transcriptomes and methylomes when compared with hESCs. Although one initial study reported the presence of iPSC-specific gene expression in a small number of iPSCs (Chin et al., 2009), several other studies argued that, at least on the individual gene-expression level, there are large variations among separate data sets (Guenther et al., 2010; Newman and Cooper, 2010). Recognizing the limitations for analyses based on individual genes, we previously utilized weighted gene coexpression network analysis (WGCNA) to identify functional modules that are distinct between iPSCs and ESCs (Wang et al., 2011). We further showed that one of these functional modules was inversely correlated with the level of DNA methylation in gene promoters, suggesting specific methylation changes in the hiPSCs. However, the module (n = 751 genes) had a small overlap (2 out of 66) with the signature genes identified in this study (TCERG1L and TSPYL5). Because iPSCs exhibit a significant increase in genome-wide methylation when compared with parental somatic cells, we suspected that de novo methylation plays an important role in establishing a unique iPSC methylation signature. By comparing methylation patterns in mutant ICF-iPSCs, we indeed found some altered methylation signatures, suggesting that DNMT3B contributes to de novo methylation during reprogramming. In particular, we identified five signature CpGs (out of the 82 CpG signature sites) that undergo DNMT3B-mediated de novo methylation. This conclusion was also extended to hypermethylation signatures identified by others (Lister et al., 2011; Ruiz et al., 2012). Our methylation signature is different from what was previously identified by either microarray or high-throughput sequencing analysis. Earlier studies suffered primarily from limited sample sizes due to the costly approach required to measure genome-wide DNA methylation levels on a comprehensive scale. Several previous studies using RRBS attempted to verify reported signatures in the literature and found a lack of reproducibility (Bock et al., 2011; Ziller et al., 2011), arguing instead for variations in iPSCs. Because RRBS covers ∼10% of human CpG sites and is biased toward regions of high CpG density, it is possible that the method could not fully detect the regions that were consistently different in iPSCs. A more recent study by Ruiz et al. (2012) using the bisulfite sequencing padlock probe (BSPP) system identified nine signature genes that distinguish hESCs from hiPSCs. On average, BSPP covers 500,000 CpGs in the human genome (∼1% of all human CpG sites); however, these sites have low overlap with the Infinium 27k array (∼25% shared sites within 100 bp). Moreover, when we compared our list of signature CG sites with other signatures in the literature, we found minimal overlap (Doi et al., 2009; Lister et al., 2011). Nevertheless, it is still unclear whether this low overlap is due to incompatible coverage or lack of sample size for robust delineation of an accurate signature. For example, Lister et al. (2011) initially identified hundreds of CG-DMRs in iPSCs, only a small fraction of which could be confirmed in multiple cell lines, suggesting that the number of sites is gradually reduced as the sample size becomes larger. By contrast, although we identified a methylation signature using 25 cell lines, we were able to validate these signatures in 249 other samples, demonstrating that our signature comprises a core set of CpG sites that can reliably distinguish iPSCs, hESCs, and somatic cells. Overall, we suggest that although a definitive signature whereby a given site is always differentially methylated between the two cell types may not exist, a panel of CpG sites representing loci that tend to be differentially methylated is sufficient to segregate iPSCs and hESCs. Thus, this panel of CpG methylation signatures in iPSCs may be useful as a molecular biomarker for classifying iPSCs in the future.