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• W2419046874 abstract "•Comprehensive portal for diverse iPSC, protocols, metadata, and genomic assays•Recurrent CNV occur during reprogramming, impact oncogenes and tumor suppressors•DNA methylation is influenced by cell of origin in iPSC•PSC X-chromosome inactivation impacts lineage differentiation outcomes The rigorous characterization of distinct induced pluripotent stem cells (iPSC) derived from multiple reprogramming technologies, somatic sources, and donors is required to understand potential sources of variability and downstream potential. To achieve this goal, the Progenitor Cell Biology Consortium performed comprehensive experimental and genomic analyses of 58 iPSC from ten laboratories generated using a variety of reprogramming genes, vectors, and cells. Associated global molecular characterization studies identified functionally informative correlations in gene expression, DNA methylation, and/or copy-number variation among key developmental and oncogenic regulators as a result of donor, sex, line stability, reprogramming technology, and cell of origin. Furthermore, X-chromosome inactivation in PSC produced highly correlated differences in teratoma-lineage staining and regulator expression upon differentiation. All experimental results, and raw, processed, and metadata from these analyses, including powerful tools, are interactively accessible from a new online portal at https://www.synapse.org to serve as a reusable resource for the stem cell community. The rigorous characterization of distinct induced pluripotent stem cells (iPSC) derived from multiple reprogramming technologies, somatic sources, and donors is required to understand potential sources of variability and downstream potential. To achieve this goal, the Progenitor Cell Biology Consortium performed comprehensive experimental and genomic analyses of 58 iPSC from ten laboratories generated using a variety of reprogramming genes, vectors, and cells. Associated global molecular characterization studies identified functionally informative correlations in gene expression, DNA methylation, and/or copy-number variation among key developmental and oncogenic regulators as a result of donor, sex, line stability, reprogramming technology, and cell of origin. Furthermore, X-chromosome inactivation in PSC produced highly correlated differences in teratoma-lineage staining and regulator expression upon differentiation. All experimental results, and raw, processed, and metadata from these analyses, including powerful tools, are interactively accessible from a new online portal at https://www.synapse.org to serve as a reusable resource for the stem cell community. Pluripotent stem cells (PSC) have been used to study human development, model disease, and generate cellular tools for regenerative medicine. Human embryonic stem cells (hESC) have been considered the functional, genetic, and epigenetic gold standard in the field (Thomson et al., 1998Thomson J.A. Itskovitz-Eldor J. Shapiro S.S. Waknitz M.A. Swiergiel J.J. Marshall V.S. Jones J.M. Embryonic stem cell lines derived from human blastocysts.Science. 1998; 282: 1145-1147Crossref PubMed Scopus (12344) Google Scholar). Methods of somatic cell reprogramming to generate induced PSC (iPSC) (Takahashi and Yamanaka, 2006Takahashi K. Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors.Cell. 2006; 126: 663-676Abstract Full Text Full Text PDF PubMed Scopus (18970) Google Scholar) are continually being improved and have enabled the generation of iPSC using a variety of somatic cell sources, gene combinations, and methodologies. However, due to the intensive resources required for iPSC generation and characterization, direct comparisons of iPSC generated using a wide range of technologies and cell sources from multiple independent laboratories have rarely been performed, making it unclear whether all methodologies produce iPSC with a similar quality and stability. A variety of studies have compared the expression profiles, pluripotentiality, and genetic and epigenetic stability of hESC and iPSC including lines generated using different strategies, distinct parental somatic cell types, or reprogramming methods (Bock et al., 2011Bock C. Kiskinis E. Verstappen G. Gu H. Boulting G. Smith Z.D. Ziller M. Croft G.F. Amoroso M.W. Oakley D.H. et al.Reference maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines.Cell. 2011; 144: 439-452Abstract Full Text Full Text PDF PubMed Scopus (743) Google Scholar, International Stem Cell Initiative et al., 2007International Stem Cell Initiative Adewumi O. Aflatoonian B. Ahrlund-Richter L. Amit M. Andrews P.W. Beighton G. Bello P.A. Benvenisty N. Berry L.S. et al.Characterization of human embryonic stem cell lines by the International Stem Cell Initiative.Nat. Biotechnol. 2007; 25: 803-816Crossref PubMed Scopus (869) Google Scholar, Müller et al., 2011Müller F.J. Schuldt B.M. Williams R. Mason D. Altun G. Papapetrou E.P. Danner S. Goldmann J.E. Herbst A. Schmidt N.O. et al.A bioinformatic assay for pluripotency in human cells.Nat. Methods. 2011; 8: 315-317Crossref PubMed Scopus (342) Google Scholar, Rouhani et al., 2014Rouhani F. Kumasaka N. de Brito M.C. Bradley A. Vallier L. Gaffney D. Genetic background drives transcriptional variation in human induced pluripotent stem cells.PLoS Genet. 2014; 10: e1004432Crossref PubMed Scopus (198) Google Scholar, Schlaeger et al., 2015Schlaeger T.M. Daheron L. Brickler T.R. Entwisle S. Chan K. Cianci A. DeVine A. Ettenger A. Fitzgerald K. Godfrey M. et al.A comparison of non-integrating reprogramming methods.Nat. Biotechnol. 2015; 33: 58-63Crossref PubMed Scopus (345) Google Scholar). However, these have been limited to a few variables, have multiple methods or laboratories collecting and processing samples, and typically employ a single genomics platform. “Multi-omics” analyses have proved to be essential in deciphering complex gene regulatory programs, as demonstrated by analyses of iPSC reprogramming transitional states (Clancy et al., 2014Clancy J.L. Patel H.R. Hussein S.M. Tonge P.D. Cloonan N. Corso A.J. Li M. Lee D.S. Shin J.Y. Wong J.J. et al.Small RNA changes en route to distinct cellular states of induced pluripotency.Nat. Commun. 2014; 5: 5522Crossref PubMed Scopus (46) Google Scholar, Lee et al., 2014Lee D.S. Shin J.Y. Tonge P.D. Puri M.C. Lee S. Park H. Lee W.C. Hussein S.M. Bleazard T. Yun J.Y. et al.An epigenomic roadmap to induced pluripotency reveals DNA methylation as a reprogramming modulator.Nat. Commun. 2014; 5: 5619Crossref PubMed Scopus (84) Google Scholar, Tonge et al., 2014Tonge P.D. Corso A.J. Monetti C. Hussein S.M. Puri M.C. Michael I.P. Li M. Lee D.S. Mar J.C. Cloonan N. et al.Divergent reprogramming routes lead to alternative stem-cell states.Nature. 2014; 516: 192-197Crossref PubMed Scopus (101) Google Scholar). The Progenitor Cell Biology Consortium (PCBC) of the National Heart, Lung and Blood Institute was founded to study iPSC reprogramming and differentiation and develop strategies to address the challenges presented by the transplantation of these cells. These questions include, but are not limited to: (1) Do iPSC consistently generate all three germ layers? (2) How prevalent is copy-number variation (CNV) in iPSC generated using different reprogramming methodologies? (3) Do different reprogramming methods affect global methylation, gene, splicing and microRNA (miRNA) expression profiles? (4) Can aberrant PSC gene regulation be identified on a global basis? (5) How do variables such as X-chromosome inactivation (XCI) affect iPSC quality, stability, and differentiation potential? To advance these goals, the PCBC developed a Central Cell Characterization Core and Bioinformatics Core to perform standardized and comprehensive characterization of iPSC generated using different somatic cell sources, methodologies, and vectors. The characterized iPSC are being made available through WiCell Research Institute. Using integrative analyses across genomic analysis platforms, we present comparative results on phenotype, genetics, epigenetics, and gene regulation for a diverse panel of iPSC and hESC. Standardized methods and strict control of reagents during cell culture, sample collection, and assay performance were used to evaluate the innate potential and limitations of these cells with fewer confounding factors. Our use of this uniform analytical methodology allowed us to discover candidate regulators of the fate of reprogrammed cells. To maximize the utility of this resource, we developed an interactive open data portal for access to the raw data, metadata, results, and protocols from these experiments for further analysis (https://www.synapse.org/PCBC). An overview of the study is presented in Figure 1. The evaluation of iPSC from multiple laboratories and methodologies required highly structured cell-line annotations and well-documented protocols to make comprehensive comparisons possible. Metadata standards were developed to capture the origin of each line, starting cell type, donor demographics, and reprogramming parameters (derivation method, vector type, reprogramming genes, culture conditions). These metadata were provided by the originating laboratory and confirmed and augmented with in vitro genetic and experimental characterization of the line. RNA sequencing (RNA-seq) was performed at an acceptable depth to facilitate accurate gene-expression quantification (Supplemental Experimental Procedures). To facilitate use of the protocols, genomic analyses, and metadata produced through this effort, we developed a sophisticated interactive data portal, the interface of which is exemplified in Figure 1. In addition to integrated provenance annotations for every raw data file, script, or processed result file, data can be queried through an interactive heatmap viewer that displays and inter-relates gene expression, DNA methylation, and miRNA expression for queried genes, pathways, and gene signatures produced in the analyses described here. These signatures have been further propagated into ToppGene (Chen et al., 2009Chen J. Bardes E.E. Aronow B.J. Jegga A.G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization.Nucleic Acids Res. 2009; 37: W305-W311Crossref PubMed Scopus (1785) Google Scholar) for interactive queries. Synapse IDs are included to access the resources, data, metadata, ontologies, and other information through the Synapse online repository. The data from the first 64 lines (58 iPSC and 6 hESC) enrolled in the study are presented here with their characteristics outlined in Figure 2A (details in syn2767694). All lines completed a standardized screen to ensure they met a basic set of criteria. This included self-renewal in defined feeder-free conditions, expression of markers of pluripotency and a lack of expression of markers of differentiation, a normal karyotype, and the ability to grow sufficient quantities of cells for the analyses (Tables S2 and S3; Figure S1). Overall, 6 hESC and 35 iPSC (64%) met these criteria and 23 iPSC did not (36%) (Table S4). Abnormal karyotypes were observed in seven lines (Table S5), with karyotypes for all lines available (syn2679104). The most consistent flow cytometry anomalies were TRA-1-81 and TRA-1-60 below 90% or an increase in SSEA-1 above 5% (Figure 2B). Due to contamination, difficulty in expanding cells, and/or abnormal karyotype, not all lines were included in functional pluripotency assays. Pluripotency was evaluated in a teratoma assay on 49 lines. Forty-six of the lines met the screening criteria outlined in Table S3 and 45 of these lines generated teratomas. Three lines did not meet the PSC screening criteria with decreased expression of self-renewal markers and increased differentiation in culture (SC12-021, SC12-023, and SC14-082), and all three successfully generated teratomas. All teratomas were scored by a clinical pathologist, and representatives of all three embryonic germ layers were identified in all tumors (detailed information is available at Synapse syn2882785). We performed immunostaining analysis on teratomas from a subset of lines to confirm pluripotency (muscle-specific actin [MSA], neurofilament, and α-fetoprotein) and OCT4 to evaluate the presence of undifferentiated PSC (Figure S1). This included two lines that did not meet the screening criteria and independent iPSC from the same donor as controls (Table S7), and three teratomas that had regions histopathologically classified as poorly differentiated as well as independent teratomas generated from the same lines (Table S8). The immunostaining confirmed pluripotency in all tumors (Figure S1). OCT4 staining was observed in one teratoma with a poorly differentiated region (SC12-034), although other teratomas from this line were fully differentiated and did not have OCT-4-stained regions. Two teratomas from other lines (SC11-014 and SC11-0013) with poorly differentiated regions did not have OCT4 immunoreactivity, although we did not have adjacent sections for staining (Table S8). Genetic stability was evaluated between independent lines with common donors by CNV SNP microarrays. Although two SNP genotyping arrays were used, all lines derived from a single donor were run on the same platform (see Experimental Procedures). Variations were observed in all lines and on all chromosomes (Figure S2). Excluding human leukocyte antigen-associated regions, 724 non-benign or clinically significant CNV from 529 unique genomic loci were identified (syn3105726). Although not significant, lines reprogrammed with integrating vectors trended toward a higher frequency of clinically significant CNV (58%) compared with non-integrating vectors (41%). Our study included different iPSC generated from the same donor sample and reprogramming methods, thereby enabling the direct evaluation of the CNV present in the donor versus those induced during reprogramming and culture. We observed CNV that were specific to the donor, and others present among multiple genetically distinct iPSC (Figure S2). We identified lines generated from the same donor samples that had variable CNV (Table S6), with some donors having higher frequencies of CNV than others (such as D001, 2, 3, 4, and 9). We discovered 102 non-benign CNV shared by at least two distinct donors, with 83 of these CNV variably present in two or more distinct samples from a common donor. Two donors (D004 and D003) were solely responsible for 46 of these CNV, while 26 were recurrent among multiple donors (Figure S2C). A more stringent analysis considering CNV shared among at least three donors identified a set of 31 frequently affected genomic loci, suggesting that they occurred during iPSC reprogramming or that the starting samples were mosaic (Abyzov et al., 2012Abyzov A. Mariani J. Palejev D. Zhang Y. Haney M.S. Tomasini L. Ferrandino A.F. Rosenberg Belmaker L.A. Szekely A. Wilson M. et al.Somatic copy number mosaicism in human skin revealed by induced pluripotent stem cells.Nature. 2012; 492: 438-442Crossref PubMed Scopus (295) Google Scholar, McConnell et al., 2013McConnell M.J. Lindberg M.R. Brennand K.J. Piper J.C. Voet T. Cowing-Zitron C. Shumilina S. Lasken R.S. Vermeesch J.R. Hall I.M. et al.Mosaic copy number variation in human neurons.Science. 2013; 342: 632-637Crossref PubMed Scopus (405) Google Scholar, Young et al., 2012Young M.A. Larson D.E. Sun C.W. George D.R. Ding L. Miller C.A. Lin L. Pawlik K.M. Chen K. Fan X. et al.Background mutations in parental cells account for most of the genetic heterogeneity of induced pluripotent stem cells.Cell Stem Cell. 2012; 10: 570-582Abstract Full Text Full Text PDF PubMed Scopus (161) Google Scholar) (Figure 2). Comparison of the CNV and RNA-seq data identified 19 non-benign and clinically significant CNV that overlap with differentially expressed genes in a manner consistent with the detected duplication or deletion (syn2731183) (Figure 2C). This included 88 downregulated genes in deleted regions, 79 of which correspond to the frequently observed X-chromosome mosaic monosomy. Among 26 upregulated genes in duplicated regions, a duplication of 20q11.21 corresponded to the upregulation of nine overlapping genes, including four (ID1, BCL2L1, HM13, and TPX2) previously shown to promote hESC survival or oncogenic potential (Nguyen et al., 2014Nguyen H.T. Geens M. Mertzanidou A. Jacobs K. Heirman C. Breckpot K. Spits C. Gain of 20q11.21 in human embryonic stem cells improves cell survival by increased expression of Bcl-xL.Mol. Hum. Reprod. 2014; 20: 168-177Crossref PubMed Scopus (66) Google Scholar). We also found compatible regulation of the cancer susceptibility genes FYN (6q21 duplication), ERCC2 (19q13.32 duplication), and NIN (14q22.1 duplication), as well as the tumor-suppressor genes FOXO4, BEX1, SRPX, EDA2R, GPC3 (X monosomy), ING2 (4q35.1 deletion), and EIF4E3 (3p13 deletion) (Osborne et al., 2013Osborne M.J. Volpon L. Kornblatt J.A. Culjkovic-Kraljacic B. Baguet A. Borden K.L. eIF4E3 acts as a tumor suppressor by utilizing an atypical mode of methyl-7-guanosine cap recognition.Proc. Natl. Acad. Sci. USA. 2013; 110: 3877-3882Crossref PubMed Scopus (74) Google Scholar). These results are consistent with these CNV conferring a survival or proliferative advantage. To understand the molecular determinants of PSC quality as a function of reprogramming method and somatic cell origin, we performed mRNA, miRNA, and methylation profiling on iPSC and hESC with profiles from hESC-derived embryoid bodies (EB) as a control. Relative to EB, hESC and iPSC were largely indistinguishable from each other at the global gene-expression level by both hierarchical clustering and principal component analysis (PCA) (syn3107554). Greater variability was observed from analogous DNA methylation and miRNA profiles (Figure S3). However, restricting the analysis to genes with varying expression only in PSC identifies donor, sex, reprogramming technology, and originating laboratory as the major driving covariates by hierarchical clustering (Figure 3A ). These differences did not clearly associate with the passage number of the profiled PSC. Of interest, H9 cells (D025) analyzed greater than ten passages apart displayed a highly variable signature with the higher passage more similar to EB. Likewise, one of two mesenchymal stem cell-derived iPSC from the same donor and laboratory (D017) exhibited a similar EB-like signature. Neither the D017 nor the H9 samples displayed apparent global DNA methylation differences, demonstrating the utility of distinct genomic platforms in assessing PSC quality (Figure S3). To identify differences associated with major cell-line variables, we performed all possible pairwise comparisons from each metadata category for gene expression, splicing, miRNA, and DNA methylation (syn3094629). We identified 355 differentially expressed genes from these comparisons and 3,451 differential methylated DNA probes. As expected, laboratory of origin accounted for the largest number of differences, likely because several iPSC derivation protocols and cell types of origin were largely unique to a single laboratory (e.g., RNA-based reprogramming, stromal priming) and could therefore mask handling or other technical differences between laboratories. The major distinguishing reprogramming variables from the methylation analyses were cell type of origin (1,427 probes), method of reprogramming (1,346 probes), and sex (520 probes). Clustering of these methylation profiles readily distinguished lines based on both sex and abnormal karyotypes (Figure 3B), while PCA segregated samples based on cell of origin (Figure 3C). Although these samples consistently segregated by cell of origin independently of the donor sex, these differences could not be directly attributed to blood and fibroblast somatic methylation profile differences (data not shown). To examine the impact on possible pathways, we looked at the enrichment of our discovered reprogramming regulated genes among gene ontology (GO) terms for each of the different measurement platforms (Figure 4A ). The most prominent pathway level effects were found in the methylation comparisons with hESC for a wide array of biological comparisons and tested reprogramming variables. We observed consistent regulation of inflammatory and immune response, ion homeostasis, and regulation of cell proliferation gene sets, particularly among all iPSC compared with hESC, among the different profiling technologies. To determine whether differential methylation might be a source of observed gene-expression differences, we compared the expression profiles of these differentially regulated genes and probes, based on common gene annotations (e.g., promoter, body, or UTR location of the probe). This analysis indicates that ∼21% of all differentially methylated probes correspond to gene-expression changes in the PSC, while ∼43% of all differentially expressed genes appear to be due to underlying differential DNA methylation (Pearson ρ < −0.5). Only negative correlations were considered from these analyses. Taken together, these data suggest that while iPSC are largely similar to hESC at the level of gene expression, observed differences are frequently correlated with changes in DNA methylation. Among DNA-methylation profiles, comparison of all iPSC to hESC yielded 180 differentially methylated sites, with 52% of these anti-correlated with gene expression (n = 93). A more relaxed analysis (non-adjusted moderated t test p < 0.05) of unique donor samples indicated that methylation probes largely segregated by donor and cell of origin when subjected to hierarchical clustering (Figure 4B). In agreement with previously published studies, DPP6, TMEM132C, and PTPRT were among the most differentially methylated loci between iPSC and hESC (Figure 4C). In addition, we found that several interesting gene loci were hypomethylated (FRG1B, SLC6A5) and hypermethylated (PTPRN2, LINC00939, CBLN4, MC3R, NFIC, EIF3D, CLSTN2, AX747064, and OR1A2) in iPSC. Genes hypermethylated in iPSC were associated with neuronal differentiation and genomic targets of the polycomb repressive complex 2 (PRC2) (ToppGene). The most highly differentially expressed iPSC versus hESC gene, the paternally imprinted PEG3, was also anti-correlated with DNA-methylation probes (Pearson ρ < −0.98) (Figure 4D). A close examination of the expression of core pluripotency factors across all PSC identified a large number of genes correlated and anti-correlated with NANOG and MYC (Figure 4E). Genes coexpressed with NANOG and MYC were enriched in negative regulators of differentiation, stem cell maintenance, and positive regulation of cell proliferation, while anti-correlated genes were enriched in experimentally observed ectoderm differentiation upregulated genes (ToppGene). To test whether these differences could be related to PSC quality and increased passaging, we compared the expression of these same genes with hESC from a previously described single-cell RNA-seq dataset (Figure 4E) (Yan et al., 2013Yan L. Yang M. Guo H. Yang L. Wu J. Li R. Liu P. Lian Y. Zheng X. Yan J. et al.Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells.Nat. Struct. Mol. Biol. 2013; 20: 1131-1139Crossref PubMed Scopus (1042) Google Scholar). Clustering of both early (passage 0) and late (passage 10) single-cell hESC confirmed that NANOG and MYC high lines were most similar to early-passage hESC. Among the 64 lines evaluated, 41 underwent genomics characterization, with five unstable lines included as controls. These 46 lines comprised five cell-of-origin groups, five reprogramming vector types, and five distinct gene combinations. Comprehensive pairwise comparisons of all metadata categories across each genomics platform highlighted a large number of genes (syn3106206), splicing variants (syn3106266), methylation probes (syn3106255), and miRNAs (syn3106244) strongly associated with one or more of these variables (Figure 5). To our knowledge, few of these molecular differences have previously been reported. Many of the most significant differences were observed among differentially methylated probes (Figures 5A and S4A). For example, SOX2 was hypermethylated in retroviral lines relative to all hESC and nearly all iPSC. Reciprocal differences in gene expression were frequently observed for these and many other differentially methylated genes. For all genomic analyses, the small number of unique donors available for certain reprogramming methods limited the power of our analysis. However, the availability of a small number of iPSC derived from the same donor with different methods provides additional confirmation of our findings. For example, differentially expressed retroviral and lentiviral associated genes (e.g., TXNRD2, JUN, UCP2, and HIST1H2BF; Figures 5B and S4B) were consistently observed from uniquely reprogrammed lines from a single donor (D007). Notably, these genes are involved in multiple pathways related to oxidative stress. Differential expression of multiple genes affecting cell growth and differentiation (ID2, ID4, JAG1, IGFBP5, and GLT1D1) were observed with OSK-L-l-p53KD, relative to other gene reprogramming combinations or hESC. In unstable lines, decreased expression of crucial PSC genes (ZFP42 and TRIM6) was associated with increased promoter and gene methylation of these genes. Using a 96-gene qPCR panel we verified differential expression for multiple genes where corresponding probes were present (e.g., ZFP42; Figure S4C). In total, 41 miRNAs were statistically associated with at least one reprogramming variable. Among these, we observed three miRNAs (miR-92b, miR-30c-1, miR-30c-2) with predicted mRNA targets that were differentially expressed in a reciprocal manner (GO-Elite) (Figure 5C). Five of the 41 regulated miRNAs were also anti-correlated with methylation probes (miR-141, miR-130b, miR-191, miR-660, miR-548f-1), suggesting regulation in part by DNA methylation. Alternative splicing and promoter usage was evaluated in our RNA-seq data. Comparison of hESC and hESC-derived EBs identified 129 alternative exon events with a false discovery rate p < 0.05 (syn3106284), including many well-validated events (in MBD2, DNMT3B, SLK, ADD3, MARK3, FYN, NUMB, NAV2, and NFYA) (Gopalakrishna-Pillai and Iverson, 2011Gopalakrishna-Pillai S. Iverson L.E. A DNMT3B alternatively spliced exon and encoded peptide are novel biomarkers of human pluripotent stem cells.PLoS One. 2011; 6: e20663Crossref PubMed Scopus (15) Google Scholar, Lu et al., 2014Lu Y. Loh Y.H. Li H. Cesana M. Ficarro S.B. Parikh J.R. Salomonis N. Toh C.X. Andreadis S.T. Luckey C.J. et al.Alternative splicing of MBD2 supports self-renewal in human pluripotent stem cells.Cell Stem Cell. 2014; 15: 92-101Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar, Salomonis et al., 2009Salomonis N. Nelson B. Vranizan K. Pico A.R. Hanspers K. Kuchinsky A. Ta L. Mercola M. Conklin B.R. Alternative splicing in the differentiation of human embryonic stem cells into cardiac precursors.PLoS Comput. Biol. 2009; 5: e1000553Crossref PubMed Scopus (76) Google Scholar) (Figure S5A), suggesting that these data are reliable for more in-depth evaluation. A total of 77 alternative exons were significant in a pairwise comparison of all major reprogramming or cell-of-origin variables in PSC. Manual examination of highly differential but non-significant splicing events suggest that many are valid, but detected with lower sensitivity due to reduced sequencing depth (Figures 5D and S5B). A significant potential confounder in this dataset is donor sex difference. A total of 520 probes were differentially methylated between male and female donors, the majority of which were localized to allosomes (457 probes) (Figure 6A ). Similarly, most differentially expressed genes between male and females were also localized to allosomes (43 out of 60), as were differentially expressed miRNA (4 out of 7). Predicted mRNA targets (GO-Elite) of one X-chromosomal miRNA (miR-18b) were enriched among male versus female RNA upregulated genes (Figure 6B). This miRNA was also found to be anti-correlated to its own DNA-methylation probes, suggesting that it is regulated by DNA methylation. Genes associated with autosomal differential DNA methylation were enriched for PRC2 factors and targets of the PRC2 transcription factor Suz12 (Figures S6B and S6C). Only one DNA-methylation-regulating gene, MECP2, was itself differentially methylated between females and males. This is consistent with prior studies that have identified MECP2 as a target of X inactivation (Vallot et al., 2015Vallot C. Ouimette J.F. Makhlouf M. Feraud O. Pontis J. Come J. Martinat C. Bennaceur-Griscelli A. Lalande M. Rougeulle C. Erosion of X chromosome inactivation in human pluripotent cells initiates with XACT coating and depends on a specific heterochromatin landscape.Cell Stem Cell. 2015; 16: 533-546Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar). In mouse ESC and human somatic cells, aberrant loss of XIST expression and corresponding breakdown of normal XCI has been associated with reduced developmental and increased oncogenic potential. In human PSC, XIST expression is required for the initiation of XCI but not for XCI maintenance. Multiple classes of female PSC have been described including those which only undergo XCI upon differentiation (class I), those that already have undergone XCI (class II and III), and PSC that have lost XIST during culture and have undergone eroded XCI (class III) (Hall et al., 2008Hall L.L. Byron M. Butler J. Becker K.A. Nelson A. Amit M. Itskovitz-Eldor J. Stein J. Stein G. Ware C. et al.X-inactivation reveals epigenetic anomalies in most hESC but identifies s" @default.
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• W2419046874 title "Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consortium" @default.
• W2419046874 cites W1970545305 @default.
• W2419046874 cites W1971453554 @default.
• W2419046874 cites W1975207448 @default.
• W2419046874 cites W1978609319 @default.
• W2419046874 cites W1987523847 @default.
• W2419046874 cites W1991107962 @default.
• W2419046874 cites W1992603073 @default.
• W2419046874 cites W2001125421 @default.
• W2419046874 cites W2007439698 @default.
• W2419046874 cites W2008485310 @default.
• W2419046874 cites W2014208702 @default.
• W2419046874 cites W2040737778 @default.
• W2419046874 cites W2041118365 @default.
• W2419046874 cites W2046607138 @default.
• W2419046874 cites W2052159842 @default.
• W2419046874 cites W2058068800 @default.
• W2419046874 cites W2058988392 @default.
• W2419046874 cites W2063135302 @default.
• W2419046874 cites W2075362559 @default.
• W2419046874 cites W2084201375 @default.
• W2419046874 cites W2086943348 @default.
• W2419046874 cites W2087699799 @default.
• W2419046874 cites W2088470456 @default.
• W2419046874 cites W2093068860 @default.
• W2419046874 cites W2101870168 @default.
• W2419046874 cites W2110492802 @default.
• W2419046874 cites W2124649657 @default.
• W2419046874 cites W2125987139 @default.
• W2419046874 cites W2128128648 @default.
• W2419046874 cites W2129164922 @default.
• W2419046874 cites W2139149201 @default.
• W2419046874 cites W2140729960 @default.
• W2419046874 cites W2152426454 @default.
• W2419046874 cites W2152627215 @default.
• W2419046874 cites W2165064122 @default.
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