Comparing the genomes of different species can reveal how they are related to one another. Such comparative studies can also reveal how genomes are modified in species-specific ways to regulate gene activity. The genomes of humans and chimpanzees are very similar in sequence. It is therefore likely that differing patterns of gene regulation underlie many of the differences observed between the two species. However, only a few kinds of chimpanzee cell that can be grown in the laboratory are available for research; this lack of samples has limited the ability of researchers to perform such comparative studies.

One way around this problem is to use induced pluripotent stem cells (or iPSCs). IPSCs are created by exposing mature cells—for example, skin cells—to conditions and molecules that convert them into an embryonic-like state. This state—called ‘induced pluripotency’—allows the cells to be coaxed into becoming many different cell types that can be grown in the laboratory. But it is more difficult to establish high quality iPSCs from chimpanzees than it is from humans or mice.

Gallego Romero, Pavlovic et al. have now addressed this problem by creating iPSCs from skin cells taken from seven healthy chimpanzees. These cell lines were then analysed and compared to each other and to seven iPSC lines created from human cells. The chimpanzee iPSC lines were found to be much more similar to each other than the mature cells that were used to make them. Similar results were also observed for the human iSPCs, which likely reflects the conserved changes that take place when the genomes of mature cells are reprogrammed to pluripotency.

This high level of similarity between iPSCs from different individuals of the same species allowed Gallego Romero, Pavlovic et al. to discover many subtle differences in gene regulation between chimpanzees and humans. For example, over 4500 genes were found to be expressed differently in human and chimpanzee iPSCs, and over 3500 genomic regions had different patterns of certain DNA modifications that can help to regulate gene expression.

These newly created chimpanzee iPSC lines represent a valuable resource for comparative studies of gene regulation. In the future, this resource could help researchers to identify further differences in gene regulation between closely related primate species.

Comparative functional genomic studies of humans and other primates have been consistently hindered by a lack of samples (Gallego Romero et al., 2012). In spite of their clear potential to inform our understanding of both human evolution and disease, practical and ethical concerns surrounding working with non-human primates have constrained the field to using a limited set of cell types collected in a non-invasive or minimally invasive manner, primarily lymphoblastoid cell lines (LCLs) and fibroblasts. Comparative studies of any other primate tissue have been limited to using post-mortem (typically frozen) materials, thereby precluding most experimental manipulation and yielding primarily observational insights (see, e.g., Blekhman et al., 2008; Blekhman et al. 2010; Brawand et al., 2011).

An alternative has been to use model organisms in an attempt to recapitulate inter-primate regulatory differences. The typical approach involves the introduction of sequences of evolutionary interest into a model system, and then searching for spatial or temporal differences in gene expression that can be ascribed to the introduced sequence (Enard et al., 2009; Cotney et al., 2013). This is a difficult and challenging approach and, perhaps as a result, there are still only a handful of well-described examples of human-specific regulatory adaptations in primates (Prabhakar et al., 2008; McLean et al., 2011) and even fewer cases where the underlying regulatory mechanisms have been resolved (Rockman et al., 2005; Pollard et al., 2006). While these studies are useful and often informative, they also entail assumptions of functional conservation between the model system and the species of interest that may not necessarily be true (Gallego Romero et al., 2012).

Induced pluripotent stem cells (iPSCs) can provide a viable means of circumventing these concerns and limitations, at least with respect to the subset of phenotypes that can be studied in in vitro systems. Reprogramming somatic cell lines to a stable and self-sustaining pluripotent state (Takahashi and Yamanaka, 2006; Takahashi et al., 2007) has become routine practice for human and murine cell lines, but extension to other animals, especially non-human primates, is not yet widespread despite some exceptions (e.g., Ezashi et al., 2009; Ben-Nun et al., 2011; Nagy et al., 2011; Marchetto et al., 2013b). Instead, the broadest application of iPSCs to date has been the generation of lines derived from patients suffering from a variety of genetic disorders (Cohen and Melton, 2011; Israel et al., 2012; Liu et al., 2012; Merkle and Eggan, 2013; Wang et al., 2014), with the dual aims of providing a deeper understanding of disease phenotypes and developing new therapeutic avenues. These cell lines have been shown to display in vitro properties corresponding to relevant patient phenotypes observed in vivo, both as iPSCs and when differentiated into other pertinent cell types, supporting their utility in clinical applications; more generally, these properties also highlight the tantalizing flexibility of iPSCs as a means of exploring developmental and cell lineage determination pathways.

Thus, the development of an iPSC-based system for comparative genomic studies in primates will allow us to compare regulatory pathways and complex phenotypes in humans and our close evolutionary relatives using appropriate models for different tissues and cell types. This will be a powerful resource with which to examine the contribution of changes in gene regulation to human evolution and diversity. To demonstrate the validity of this approach, we have generated a panel of 7 chimpanzee iPSC lines that are fully characterized and comparable to human iPSC lines in their growth and differentiation capabilities.

We generated a panel of iPSC lines from seven chimpanzees through electroporation of episomal plasmids expressing OCT3/4 (also known as POU5F1), SOX2, KLF4, L-MYC, LIN28, and an shRNA targeting TP53 (Okita et al., 2011), as well as an in vitro-transcribed EBNA1 mRNA transcript (Howden et al., 2006; Chen et al., 2011) that promotes increased exogenous vector retention in the days following electroporation. Our chimpanzee panel is comprised of seven healthy individuals (4 female, 3 male, further details on these individuals are given in Supplementary file 1) ranging from 9 to 17 years old. Fibroblasts from 5 of the 7 individuals were purchased from the Coriell Institute for Medical Research, while the remaining two (C6, C7) were derived from 3 mm skin punch biopsies directly collected from animals at the Yerkes Primate Research Center of Emory University (see ‘Materials and methods’). All chimpanzee iPSC lines described in this publication are available fully and without restrictions to other investigators upon request to the corresponding authors.

Characterizing the chimpanzee iPSCs

The chimpanzee iPSC lines closely resemble human iPSC lines in morphology (Figure 1A; all images shown in main text are from chimpanzee line C4. Similar images of the other lines are available as Figure 1—figure supplements 1–5). All lines could be maintained in culture for at least 60 passages without loss of pluripotency or self-renewal capability using standard iPSC culture conditions, both on mouse embryonic fibroblast (MEF) feeder cells and in feeder-free conditions. The genomes of all our lines appeared to be cytogenetically stable; all exhibited normal karyotypes after reprogramming and more than 15 passages in culture, ruling out the presence of gross chromosomal abnormalities (Figure 1B, Figure 1—figure supplement 1).

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We confirmed nuclear expression of OCT3/4, SOX2 and NANOG in all lines by immunocytochemistry (Figure 1C; Figure 1—figure supplement 2). The pluripotent cells also express the surface antigens Tra-1-81 and SSEA4, while cells collected from the center of differentiating colonies expressed SSEA1 at levels comparable to differentiating colonies of human iPSC lines (Figure 1—figure supplement 3). To confirm that the observed expression of pluripotency-associated genes is of endogenous origin, we performed qPCR with primers designed to specifically amplify the endogenous OCT3/4, SOX2, NANOG and L-MYC transcripts (Figure 1D; all PCR primers used in this work are listed in Supplementary file 2). Indeed, we found no evidence of exogenous gene expression after 10 passages (Figure 1—figure supplement 4), and no traces of genomic integration or residual episomal plasmid retention after 15 passages (Figure 1E). These observations indicate that self-renewal in our chimpanzee iPSC lines is maintained solely through endogenous gene expression.

To confirm pluripotency and test the differentiation capabilities of our lines, we performed a number of assays. First, we generated embryoid bodies from all 7 chimpanzee iPSC lines and assayed their ability to spontaneously differentiate into the three germ layers by immunocytochemistry. All lines spontaneously gave rise to tissues from the three germ layers (Figure 2A; Figure 2—figure supplement 1). Second, we carried out directed differentiations to hepatocytes and cardiomyocytes in a subset of the lines using previously published protocols (see ‘Materials and methods’, Figure 2—figure supplement 2 and Video 1). Third, we performed teratoma formation assays in four of the lines using Fox Chase SCID-beige and CB17.Cg-Prkdc^scidLyst^bg-J/Crl immunodeficient male mice. All four iPSC lines were capable of generating tumours in mice, and all tumours examined contained tissues of endodermal, ectodermal and mesodermal origins (Figure 2B, Figure 2—figure supplement 3). To confirm the chimpanzee origin of these tissues, we extracted and performed Sanger sequencing on mitochondrial DNA from the tumours (Figure 2—figure supplement 4).

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Video 1

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Finaly, we characterized pluripotency in our lines through PluriTest, a bioinformatic classifier that compares the gene expression profiles of new lines to those obtained from a reference set of over 400 well-characterized human pluripotent and terminally differentiated lines (Müller et al., 2011), modified to accommodate data from both species. All chimpanzee lines have PluriTest pluripotency scores greater than the pluripotency threshold value of 20 (Figure 3A, Supplementary file 1). We also calculated PluriTest novelty scores for all samples. In human PSCs, novelty values above 1.67 are suggestive of chromosomal duplications or expression of differentiation-associated genes. Human PSCs with high novelty scores are typically either difficult to maintain and expand in culture (because they differentiate spontaneously at a high rate), or cannot be consistently differentiated to all three germ layers. All of our chimpanzee lines had novelty scores above the 1.67 threshold (Figure 3B). However, in contrast to human PSCs with high novelty scores, our chimpanzee lines can be both easily maintained in culture and differentiated into all three germ layer lineages, as demonstrated by the embryoid body and teratoma assays detailed above. We thus hypothesize that the observed high novelty scores are likely driven by inter-species gene regulatory differences that the PluriTest assay, which was trained exclusively on human samples, interpreted as abnormal gene expression.

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Interspecies analysis of gene expression and methylation data from iPSCs

To better examine gene expression and regulatory differences between human and chimpanzee iPSCs, we generated genome-wide RNA-sequencing and DNA methylation data (see ‘Materials and methods’) from all chimpanzee iPSC lines, as well as from 7 human iPSC lines also generated and validated in our laboratory. While all of the chimpanzee iPSCs were derived from fibroblast cell lines (Supplementary file 1), the human iPSCs were derived from both fibroblasts and immortalised LCLs from Caucasian and Yoruba individuals (see Supplementary file 1 for additional details). We designed the comparative study this way in order to demonstrate that regulatory differences between human and chimpanzee iPSCs cannot be explained by technical differences due to culturing conditions or the cell type of the somatic precursor cells used for reprograming.

To prevent biases due to genetic divergence between the two species, we chose to restrict our gene expression analyses to a curated set of genes with one-to-one orthology between humans and chimpanzees (Blekhman et al., 2010; Blekhman, 2012). Following assessment of quality control metrics (see ‘Materials and methods’), we obtained normalised RPKM estimates for 12,171 genes that were expressed in at least 4 iPSC lines from either one of the species (see ‘Materials and methods’). We similarly restricted our DNA methylation analyses to a set of 335,307 high quality probes with a high degree of sequence conservation between humans and chimpanzees (as in Hernando-Herraez et al., 2013; see ‘Materials and methods’).

To examine broad patterns in the data, we used principal component analysis (PCA). We observed clear and robust separation of human and chimpanzee iPSC lines along the first principal component (PC) in both the gene expression and DNA methylation data (Figure 4A,B; regression of PC1 by species; p < 10⁻¹³ for the expression data; p < 10⁻¹² for the DNA methylation data). Within the human samples, PC2 appears to be driven by ethnicity, as we observe all Caucasian samples consistently clustering together despite their different cell types of origin (p = 0.005 for the association between PC2 and human ethnicity in the expression data, p = 0.044 in the DNA methylation data).

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We then analysed regulatory differences between the species by first focusing on the gene expression data. At an FDR of 1%, we identified 4609 genes (37.9%) as differentially expressed (DE) between the iPSCs of the two species (Supplementary file 3; see ‘Materials and methods’ for details). The majority of DE genes do not exhibit large inter-species fold-change differences in expression levels (Figure 4—figure supplements 1, 2). An analysis of functional annotation of the DE genes reveals that no Gene Ontology Biological Process terms (GO BP; Ashburner et al., 2000) are significantly overrepresented among these genes at an FDR of 5% (Supplementary file 4), although we identified 123 overrepresented terms if we limit our analysis to the 546 genes with absolute log₂ fold-change difference >2 (Supplementary file 4). Additionally, we tested for concordance between our list of DE genes and a list of 2730 genes that were previously classified as DE between human and non-human primate iPSC lines (Marchetto et al., 2013b). Given our stringent approach to consider orthologous genes, only 2081 (76%) genes could be analysed across the two studies. Of these, 1495 genes are detectably expressed in our lines, and 1079 (72.2%) are classified as DE between the species in both data sets (a highly significant enrichment; χ² p < 10⁻¹⁶). Expression trends within these DE genes are in the same direction in both data sets in 1060 of cases (98.24%).

Next, we used a similar approach to identify differentially methylated (DM) probes and regions between the iPSCs of both species (see ‘Materials and methods’). We identified 63,791 probes that are DM between the two species at an FDR of 1%, 26,554 of which have a mean intergroup β difference ≥0.1, our arbitrary effect size threshold for retaining probes for DM region (DMR) identification and downstream analyses. Of these, 10,460 probes could be further grouped into 3529 regions of 2 or more DM probes within 1 kb, which we designated DMRs; (Supplementary file 5); the numbers of probes and regions identified as DM at a range of mean interspecies β thresholds are given in Supplementary file 6.

In order to consider the DNA methylation and gene expression data jointly, we focused on a subset of 2348 DMRs that could be associated with a single Ensembl gene. Overall, these DMRs were associated with 2141 genes, of which 1350 were also detectably expressed in the iPSCs, and 558 (41.3%) were classified as DE between the species, a slightly higher proportion than expected by chance alone (p = 0.1). We further classified the DMRs as either ‘promoter’, ‘genic’ or ‘mixed’ depending on their position relative to annotated gene transcripts (see ‘Materials and methods’). The overall set of DMRs, as well as genic DMRs, are significantly associated with 4 and 79 GO BP terms respectively (FDR < 5%), including terms related to neurogenesis and skeletal system development. Enrichment of several terms related to neurogenesis and skeletal system development is likewise marginally significant amongst promoter and mixed DMRs (Supplementary file 7). However, the subset of inter-species DE genes that are also associated with DMRs are not significantly enriched with annotation for any GO BP or MF terms.

Comparative histone modification data

We used ChIP-seq to characterize the genome-wide distribution of two types of histone modifications (H3K27me3 and H3K27ac) in three of our chimpanzee iPSCs (see ‘Materials and methods’). We compared the chimpanzee data to histone modification data from three human iPSC lines from the Roadmap Epigenomics project (Figure 5). To do so, we downloaded raw sequence files from GEO and processed data from both species using the same pipeline (see ‘Materials and methods’). We identified ChIP-seq peaks using MACS or RSEG, as appropriate, and accounted for differences in genome sequence between the species as well as for incomplete power to identify peaks across species (see ‘Materials and methods’). To relate the ChIP-seq data to genes (and integrate over data from all peaks that are in proximity to a given gene), we then generated enrichment ChIP scores for a set of previously defined 26,115 orthologous transcription start sites (TSSs, from Zhou et al., 2014). The enrichment score (see ‘Materials and methods’ for details, also Supplementary file 8), reflects the ratio of mapped ChIP-seq read counts across all peaks within a 4 kb window centred on an orthologous TSS, relative to the genome-wide read count average after adjusting expectations based on the input control sample. We chose to classify as ‘enriched’ any region where the mean enrichment score across all three individuals in the species was larger than 1. This cut-off is arbitrary, but we confirmed that our qualitative results are robust by additionally testing enrichment cut-offs of 2, 5, and 10.

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Using this approach, we first examined genome-wide patterns of H3K27me3 enrichment in chimpanzee and human iPSCs. Overlap across the two species is considerably higher than expected by chance (Figure 5A, χ² p < 10⁻¹⁶), but it is somewhat unclear how to interpret this observation with respect to the expectation that human and chimpanzee iPSCs would have similar pluripotency potential. We thus focused on a set of 3913 genes (Li et al., 2013) previously annotated as bivalently modified in human PSCs—that is, genes known to be associated with both high H3K4me3 and H3K27me3, indicative of a ‘poised’ or ‘primed’ state (Bernstein et al., 2006). We expect the vast majority of these genes to also be associated with similar modifications in chimpanzee iPSCs. Only 2910 of the known bivalent genes were associated with clear orthologous TSSs and could be tested using our comparative H3K27me3 ChIP-seq data. Of these, 306 were not associated with the modification in either species, whereas of the 2604 genes that were associated with H3K27me3 in at least one species, 2368 (90.1%) were enriched for H3K27me3 in both species (Figure 5B, χ² p < 10⁻¹⁶).

We then examined H3K27ac enrichment patterns in both species. This mark is indicative of active promoters and gene transcription. Overall, we find good agreement between human and chimpanzee genes enriched for H3K27ac, with 95.8% human genes associated with the mark also enriched in chimpanzees (Figure 5C). However, there is a clear excess of genome-wide H3K27ac signal in chimpanzee iPSCs relative to humans, possibly due to an overall more sensitive ChIP enrichment in the chimpanzee samples (Figure 5—figure supplements 1, 2).

We proceeded by focusing on a list of 22 core pluripotency transcription factors (taken from Ng and Surani, 2011; Orkin and Hochedlinger, 2011), where we expect to find H3K27ac signal shared across the two species at a higher rate than in the genome-wide data, given the role of these factors in maintaining pluripotency. Due to our stringent requirements for establishing orthology, we were initially able to examine data from 14 of those genes; 11 of which were associated with H3K27ac in both species (Figure 5D)—one of the discrepancies is REX1 (also known as ZFP42), which we discuss further below. We extended our analysis to include the full set of 22 pluripotency transcription factors regardless of orthology, by testing solely for absence or presence of signal peaks identified by MACS (i.e., without considering enrichment scores; see ‘Materials and methods’). We again found a high overlap in H3K27ac enrichment across species, with 15 of the 22 genes associated with H3K27ac enrichment in both species (including the three master regulators of pluripotency, OCT4, SOX2, and NANOG; Figure 5—figure supplement 3). Of the remaining 7 genes, one (DAX1) was not found to be associated with H3K27ac in either species, four genes (ESSRB, KLF2, KLF4, and KLF5) were associated with H3K27ac only in chimpanzee (although this observation may reflect incomplete power to detect peaks in the human data), and only two genes (ZFX and REX1) were associated with H3K27ac in human but not in chimpanzee iPSCs.

REX1 may be dispensable for chimpanzee pluripotency

In order to further consider inter-species differences in the core pluripotency regulatory network, we examined expression levels in our chimpanzee and human iPSCs in the same list of 22 core pluripotency TFs described above. Expression values in all iPSC lines are shown in Figure 6A (see also Figure 6—figure supplement 1). Given the stringency of our interspecies analysis approach with respect to unique read mapping, we are unable to calculate RNA-seq-based expression estimates for six of these TFs, including OCT4 or NANOG, both of which have multiple pseudogenes that can confound mapping algorithms (however, as shown in Figure 1D, our qPCR results demonstrate that expression of those 2 genes is similar amongst all chimpanzee iPSC lines, and marginally higher than in our human iPSC control line). Of the 16 TFs with expression data for iPSCs from both species, 4 (E2F1, ESRRB, SALL4 and REX1) are DE between human and chimpanzee iPSCs at an FDR of 1%. Of these, ESRRB and REX1 are associated with absolute inter-species expression log₂ fold-changes >1. However, because ESRRB is expressed at very low levels across all samples (mean RPKM across all 14 samples = 0.47), we focused our subsequent analyses on REX1, which is expressed at low or undetectable levels in 6 of our 7 chimpanzee iPSCs (mean RPKM = 0.667), but at high levels in all human iPSC lines (mean RPKM = 180.58) and a single chimpanzee iPSC, C6 (Figure 6A). Our DNA methylation data is consistent with this gene expression pattern: all 10 probes located in the 5′ UTR or up to 1500 bp upstream from the REX1 TSS are highly methylated in the six chimpanzee lines (mean β across all promoter probes = 0.87), but exhibit intermediate or low levels of DNA methylation in all of the human iPSC lines and the REX1-expressing C6 line (Figure 6B); the entire region is a DMR (Supplementary file 5). Consistent with these findings, REX1 is also differentially enriched for H3K27ac signal in the two species—we identified no H3K27ac peaks at the REX1 TSS in the three chimpanzee lines, which did not include C6 (Figure 5D, Figure 5—figure supplement 3).

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The REX1 genes codes for a transcription factor present in all placental mammal species, which has long been established as a marker of pluripotency in human and mouse PSCs (Brivanlou et al., 2003). Multiple publications have suggested that this gene plays an important role in maintaining pluripotency and inhibiting differentiation into the three primary tissue germ layers (Masui et al., 2008; Scotland et al., 2009; Son et al., 2013), with multiple mechanisms of action having been proposed. However, REX1-knockout mouse ESC lines can give rise to chimeric animals, and homozygous F2 REX1 null mice are viable (Masui et al., 2008), suggesting that REX1 may not be indispensable for murine pluripotency. In humans, loss of REX1 expression in ESCs following shRNA knockdown has been associated with a rapid loss of pluripotency, as well as a decrease in glycolytic activity and a lack of observable mature mesodermal structures in teratoma formation assays (Son et al., 2013).

To determine the consequences of a lack of REX1 expression in chimpanzee iPSCs, we considered gene expression data from all human iPSC lines and the 6 chimpanzee iPSC lines that do not express REX1. We asked whether there is an excess of DE genes among those thought to be directly regulated by, or downstream of, REX1 (Figure 6C,D; see ‘Materials and methods’), but failed to find enrichment in all categories except for genes associated with GO term BP:0006096, glycolysis, where 19 of 34 testable genes were DE at an FDR of 1% between the two species (p < 0.01 from 100,000 permutations). The direction of this effect ran contrary to previous reports, however, with genes highlighted by Son et al. (2013) as downregulated following REX1 knockdown, such as PGAM1 or LDHA, having significantly higher expression in chimpanzee iPSCs than in human iPSCS (Figure 6D). Furthermore, the REX1-expressing line C6 is not an outlier amongst the other chimpanzee iPSC lines (Figure 6D), suggesting that the observed inter-species regulatory differences cannot be attributed to differences in REX1 expression between the species.

We note that both the teratomas and EBs generated from chimpanzee iPSC lines that do not express REX1 gave rise to mature structures from all three germ layers similar to those observed in REX1-expressing line C6 (Figure 2—figure supplements 1, 3). Furthermore, and consistent with our observations, REX1 is either absent or expressed at low levels in one replicate of either of the two retrovirally reprogrammed bonobo (Pan paniscus, sister species to chimpanzees) iPSC lines generated by Marchetto et al. (2013b), although it is expressed in both replicates of both chimpanzee iPCS lines from the same group (Figure 6—figure supplement 2). Together, these findings suggest that that the variable loss of REX1 expression in chimpanzee and bonobo iPSCs does not impair pluripotency, and that its regulatory functions of in humans may be being fulfilled in chimpanzee iPSCs by other regulatory mechanisms.

Comparison of iPSCs and other tissues

We collected RNA-sequencing data from all cell lines used to generate both the chimpanzee and human iPSCs (Supplementary file 6). Following quality control and normalisation steps, we obtained RPKM values for 13,147 genes across all 28 iPSC and precursor samples (see ‘Materials and methods’). We also obtained DNA methylation profiles from all samples at the same 335,307 probes described above. PCA of both data sets show that the first PC was significantly associated with tissue type in both data sets (p < 10⁻²⁷ for the expression data; p < 10⁻¹⁷ for the DNA methylation data; see Figure 7 and Supplementary file 9), while human and chimpanzee samples are separated by species along PC2 (p = 0.001 for the expression data; p < 10⁻⁴ for the methylation data). However, given the absence of chimpanzee LCLs in our dataset, it is not possible to determine whether the separation is driven by tissue type, species, or both.

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Overall, chimpanzee iPSCs have significantly higher levels of DNA methylation compared to the somatic lines they were generated from (p < 10⁻¹⁵; Figure 7—figure supplement 1), an observation that extends to all genomic features we tested (Figure 7—figure supplement 2); similar observations have been previously made in human PSCs (Bock et al., 2011; Nazor et al., 2012). Remarkably, both DNA methylation and gene expression levels in iPSCs are relatively homogeneous within species, far more so than in their corresponding precursor cells (Figure 6B,D; p < 10⁻¹⁴ when comparing overall pairwise distances within all chimpanzee iPSCs and within all chimpanzee fibroblasts in the methylation data; p < 10⁻⁹ for the same comparison in the gene expression data). DNA methylation levels in iPSCs also have significantly reduced coefficients of variation relative to their precursor lines (range of CVs for chimpanzee iPSCs = 0.78–0.80, for chimpanzee fibroblasts = 0.87–0.90; p < 10⁻⁰⁶). We observed the same pattern in the human data, although in this case the multiple somatic origins of the cell lines of origin contribute to the higher level of variation.

We then performed analyses of gene expression and DNA methylation differences in the combined iPSC and somatic precursor dataset. First, we carried out a comparison of the iPSCs and the precursor cells within each species (see ‘Materials and methods’) and classified 9235 genes as DE between chimpanzee fibroblasts and the corresponding iPSCs. In humans the number of DE genes is 7765 if we consider all iPSC lines and their somatic precursors, 8087 if we only consider those derived from LCLs (n = 5), and 5489 if we only consider those derived from fibroblasts (n = 2; Supplementary file 10). Similarly, we identified 18,029 DMRs between chimpanzee fibroblasts and iPSCs, and 12,078 DMRs between all human somatic precursors and all human iPSCs (Supplementary files 11, 12). No GO categories are significantly overrepresented in any of these data sets.

Next, we focused on a comparison of inter-species differences in gene expression and DNA methylation levels across cell types. Following joint normalisation and modelling of data from all samples (see ‘Materials and methods’), we classified 5663 genes as DE between the chimpanzee precursor fibroblasts and the collection of human precursor LCLs and fibroblasts, as well as 84,747 DM probes and 9107 DMRs (always at an FDR of 1%). Most of these regulatory differences, however, reflect variation across cell types rather than across species (6324 genes and 70,312 probes are DE or DM between the human fibroblasts and LCLs, respectively). We thus considered only data from the fibroblast precursors in the two species. Only 2 of the human iPSCs were reprogrammed from fibroblasts, leading to a loss in power; we were nonetheless able to identify 1236 DE genes and 25,456 DM probes between human and chimpanzee fibroblasts, and 1118 DE genes and 16,392 DM probes between the corresponding iPSCs of the two species. None of these gene sets were significantly enriched for functional annotations using GO BP terms. Although the overlap of inter-species DE genes and DM probes between the iPSCs and the precursors is considerable (13.6% of DE genes and 11.8% of DM probes), a large number of regulatory differences are only observed between the iPSC lines of the two species (Figure 7—figure supplement 3). This observation is robust with respect to different approaches to normalising and modelling the data (Figure 7—figure supplement 4), strongly suggesting that many of the differences we observe between our chimpanzee and human iPSC lines may be intrinsic features of the pluripotent state in these two species.

iPSCs have the potential to transform our understanding of the biology of non-model organisms and facilitate functional comparative studies. To this end, we have generated a panel of 7 fully characterized chimpanzee iPSCs. All lines are capable of spontaneously giving rise to the three tissue germ layers in vitro and in vivo and meet all currently established criteria for pluripotency. The chimpanzee iPSC lines provide a tantalising avenue for investigating how changes in gene expression and regulation underlie the architecture of complex phenotypic traits in humans and our closest living relatives (Gallego Romero et al., 2012; Marchetto et al., 2013a). In particular, we believe that through the use of directed differentiation protocols, functional studies could be performed in cell types where strong a priori hypotheses support a role for selective pressure underlying inter-species divergence (e.g., liver, heart, kidney [Blekhman et al., 2008, 2010]). In that sense, we hope that this panel of cell lines will be a useful tool to researchers interested in overcoming current limitations of comparative studies in primates. To that purpose, all chimpanzee iPSC described in this publication the panel are available fully and without restrictions to other investigators upon request to the corresponding authors.

Other groups have previously generated pluripotent stem cells from primates (Liu et al., 2008; Chan et al., 2010; Tomioka et al., 2010; Wu et al., 2010; Ben-Nun et al., 2011; Deleidi et al., 2011; Okamoto and Takahashi, 2011; Wu et al., 2012; Hong et al., 2014; Wunderlich et al., 2014). Indeed, a recent publication (Marchetto et al., 2013b) reported the generation of two chimpanzee and two bonobo (P. paniscus) iPSC lines through the use of retroviral vectors. However, in the course of our work we have found that retroviral vector silencing in chimpanzee iPSCs was not as stable as in human iPSC lines generated at the same time using the same method (see ‘Materials and methods’ and Figure 1—figure supplement 5). Our use of episomal vectors circumvents this problem, and more broadly the problems of both random exogenous gene reactivation and disruption of the host genome through retroviral integration (Sommer et al., 2012).

More generally, while the sum total of primate PSC generation efforts so far has resulted in a sizable number of lines being established from various donors and species, these have been generated through various reprogramming protocols and source cell types. We have generated iPSCs from a panel of seven individuals using a consistent protocol and cell type of origin. Given the panel size, it is powerful enough to robustly detect inter-species differences in gene expression, splicing and regulation. The fact that our panel contains both female and male lines also allows for future studies of sex-specific differences in gene expression in various cell types. Indeed, we have previously shown that this can be accomplished using as few as six individuals from each species (Blekhman et al., 2010).

Beyond its future applications, however, our panel has already yielded insights into the pluripotent state in chimpanzees and humans. On the one hand, both at the transcriptional and epigenetic level, our iPSCs are remarkably homogeneous both within and between species, significantly more so than their precursors cells. This finding aligns with our current understanding of the reprogrammed pluripotent state as a complex, highly regulated state (Jaenisch and Young, 2008), deviations from which are likely to result in loss of pluripotency and lineage commitment. Additional support for this notion was provided by the strong overlap in H3K27me3 signal between the two species, especially in known bivalent genes. It is remarkable that we have been able to observe this considerable conserved chromatin signature despite the obvious confounding technical batch effect in these comparative data.

On the other hand, we were also able to identify over 4500 genes that are DE between human and chimpanzee iPSCs, as well as over 3500 DMRs between the two species. These numbers are greater than what has been previously observed in comparisons of other tissues across humans and chimpanzees with similar sample sizes (Blekhman et al., 2008, 2010). We believe that the reasons for this difference are likely to primarily stem from increased power to detect DE genes and DMRs in our sample relative to previous work. Given the small amount of intra-species variability we observed in RNA-seq and methylation relative to other tissues, we expect to have greater power to detect small, statistically significantly inter-species differences that would have been missed by studies that consider more variable tissue samples. This notion is supported by the fact that the fraction of genes with log FC < 2 we detect as DE between human and chimpanzee iPSCs is greater than in other comparison we have performed with any other tissue (Figure 4—figure supplement 2). Though small in magnitude, we expect that a subset of these regulatory differences may be biologically relevant (e.g., we find that inter-species regulatory differences in methylation levels are enriched in regions associated with developmental processes; Supplementary files 4, 5).

We specifically highlighted an inter-species difference in REX1 expression levels. This gene is considered an indispensible pluripotency marker in human and mouse PSCs, but our observations suggest that it may not be the case in chimpanzees. Although only one chimpanzee iPSC line expresses REX1, we were unable to identify any systematic differences between our human and chimpanzee iPSCs that would indicate a reduction in pluripotency. We also examined REX1 expression levels in 73 human iPSC lines generated in-house from Caucasian individuals using the Illumina HT12v4 array (Figure 6—figure supplement 3). All lines had PluriTest pluripotency scores >20, yet 3 of 73 lines (4.1%) showed levels of REX1 expression that were indistinguishable from background signal, suggesting that REX1 may not be expressed in these individuals despite their high pluripotency scores. We also examined methylation status at the REX1 locus in previously published human ESCs and iPSCs from Ziller et al. (2011), and found that although all ESC lines examined exhibited consistent levels of low methylation at the REX1 promoter, human iPSC lines analysed in exhibited either hemi- or hyper-methylated REX1 promoter regions (Figure 6—figure supplement 4). In the absence of publicly available REX1 expression data from either of the hiPSC lines with hypermethylated promoters we cannot be certain that the gene is not expressed in these lines, but the combination of these findings with our observations above and previous literature suggest that REX1 may be important in regulation and maintenance of pluripotency in ESCs, but not necessarily so in iPSCS.

Additionally, in chimpanzees, the REX1 gene has undergone multiple deletions and insertions relative to the human sequence, most significantly a 647 bp insertion in its first intron, and two insertions in the 3′ UTR region of approximately 300 bp each that may disrupt the local regulatory landscape; the gene has also been duplicated, with a second copy retrotransposed into chromosome 14; none of these changes are shared with gorillas or orang-utans. Although it is currently unclear whether some or all of these changes are also present in the bonobo, these findings might explain why we observed low or no REX1 expression more frequently in chimpanzees than in humans, and suggests that the gene may not be necessary for maintaining pluripotency in the Pan lineage.

PSCs have been used to study developmental pathways in vitro (e.g., Paige et al., 2012; Rada-Iglesias et al., 2012; Wamstad et al., 2012; Xie et al., 2013). Although optimization of existing differentiation protocols will likely be necessary for application in the chimpanzee system, our panel of iPSCs makes it possible to carry out comparative developmental studies between humans and chimpanzees, and firmly test the hypothesis that changes in gene regulation and expression, especially during development, underlie phenotypic differences between closely related species, especially primates (Britten and Davidson, 1971; King and Wilson, 1975; Jacob, 1977; Carroll, 2005, 2008). In addition, we should be able to recreate and test the effect of inter-species regulatory changes in the correct cell type and species environment, enabling studies that cannot otherwise be performed in humans and non-human primates. The use of panels of iPSCs including lines from both humans and non-human primates will thus allow us to gain unique insights into the genetic and regulatory basis for human-specific adaptations.

1. 1. Gallego Romero I
   2. Pavlovic BJ
   3. Hernando-Herraez I
   4. Banovich NE
   5. Kagan CL
   6. Burnett JE
   7. Huang CH
   8. Mitrano A
   9. Chavarria CI
   10. Ben-Nun IF
   11. Li Y
   12. Sabatini K
   13. Leonardo TR
   14. Parast M
   15. Marques-Bonet T
   16. Laurent LC
   17. Loring JF
   18. Gilad Y

   (2014) Generation of a Panel of Induced Pluripotent Stem Cells From Chimpanzees: a Resource for Comparative Functional Genomics (RNA-Seq)

   Publicly available at the NCBI Gene Expression Omnibus (Accession no: GSE60996).

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60996
2. 1. Gallego Romero I
   2. Pavlovic BJ
   3. Hernando-Herraez I
   4. Banovich NE
   5. Kagan CL
   6. Burnett JE
   7. Huang CH
   8. Mitrano A
   9. Chavarria CI
   10. Ben-Nun IF
   11. Li Y
   12. Sabatini K
   13. Leonardo TR
   14. Parast M
   15. Marques-Bonet T
   16. Laurent LC
   17. Loring JF
   18. Gilad Y

   (2014) Generation of a Panel of Induced Pluripotent Stem Cells From Chimpanzees: a Resource for Comparative Functional Genomics (methylation array)

   Publicly available at the NCBI Gene Expression Omnibus (Accession no: GSE61342).

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61342
3. 1. Gallego Romero I
   2. Pavlovic BJ
   3. Hernando-Herraez I
   4. Zhou X
   5. Ward MC
   6. Banovich NE
   7. Kagan CL
   8. Burnett JE
   9. Huang CH
   10. Mitrano A
   11. Chavarria CI
   12. Ben-Nun IF
   13. Li Y
   14. Sabatini K
   15. Leonardo TR
   16. Parast M
   17. Marques-Bonet T
   18. Laurent LC
   19. Loring JF
   20. Gilad Y

   (2015) Generation of a Panel of Induced Pluripotent Stem Cells From Chimpanzees: a Resource for Comparative Functional Genomics

   Publicly available at the NCBI Gene Expression Omnibus (Accession no: GSE69919).

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE69919

1. 1. Marchetto MC
   2. Narvaiza I
   3. Denli AM
   4. Benner C
   5. Gage FH

   (2013) Differential LINE-1 retrotransposition in induced pluripotent stem cells between humans and great apes

   Publicly available at the NCBI Gene Expression Omnibus (Accession no: GSE47626).

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE47626
2. 1. Roadmap Epigenomics Consortium Kundaje
   2. Meuleman A
   3. Ernst W
   4. Bilenky J
   5. Yen M
   6. Heravi-Moussavi A
   7. Kheradpour A
   8. Zhang P
   9. Wang JZ

   et al. (2015) NIH Epigenomics Roadmap Initiative

   Publicly available at the NCBI Gene Expression Omnibus (Accession no: PRJNA34535).

   http://www.ncbi.nlm.nih.gov/bioproject/PRJNA34535

1. 1. Alexa A
   2. Rahnenführer J
   3. Lengauer T

   (2006) Improved scoring of functional groups from gene expression data by decorrelating GO graph structure

   Bioinformatics 22:1600–1607.

   https://doi.org/10.1093/bioinformatics/btl140

   • Google Scholar
2. 1. Ashburner M
   2. Ball CA
   3. Blake JA
   4. Botstein D
   5. Butler H
   6. Cherry JM
   7. Davis AP
   8. Dolinski K
   9. Dwight SS
   10. Eppig JT
   11. Harris MA
   12. Hill DP
   13. Issel-Tarver L
   14. Kasarskis A
   15. Lewis S
   16. Matese JC
   17. Richardson JE
   18. Ringwald M
   19. Rubin GM
   20. Sherlock G

   (2000) Gene ontology: tool for the unification of biology

   The Gene Ontology Consortium. Nature Genetics 25:25–29.

   https://doi.org/10.1038/75556

   • Google Scholar
3. 1. Ben-Nun IF
   2. Montague SC
   3. Houck ML
   4. Tran HT
   5. Garitaonandia I
   6. Leonardo TR
   7. Wang YC
   8. Charter SJ
   9. Laurent LC
   10. Ryder OA
   11. Loring JF

   (2011) Induced pluripotent stem cells from highly endangered species

   Nature Methods 8:829–831.

   https://doi.org/10.1038/nmeth.1706

   • Google Scholar
4. 1. Benjamini Y
   2. Hochberg Y

   (1995)

   Controlling the false discovery rate: a practical and powerful approach to Multiple Testing

   Journal of the Royal Statistical Society B 57:289–300.

   • Google Scholar
5. 1. Bernstein BE
   2. Mikkelsen TS
   3. Xie X
   4. Kamal M
   5. Huebert DJ
   6. Cuff J
   7. Fry B
   8. Meissner A
   9. Wernig M
   10. Plath K
   11. Jaenisch R
   12. Wagschal A
   13. Feil R
   14. Schreiber SL
   15. Lander ES

   (2006) A bivalent chromatin structure marks key developmental genes in embryonic stem cells

   Cell 125:315–326.

   https://doi.org/10.1016/j.cell.2006.02.041

   • Google Scholar
6. 1. Blekhman R

   (2012)

   A database of orthologous exons in primates for comparative analysis of RNA-seq data

   Nature Precedings.

   • Google Scholar
7. 1. Blekhman R
   2. Marioni JC
   3. Zumbo P
   4. Stephens M
   5. Gilad Y

   (2010) Sex-specific and lineage-specific alternative splicing in primates

   Genome Research 20:180–189.

   https://doi.org/10.1101/gr.099226.109

   • Google Scholar
8. 1. Blekhman R
   2. Oshlack A
   3. Chabot AE
   4. Smyth GK
   5. Gilad Y

   (2008) Gene regulation in primates evolves under tissue-specific selection pressures

   PLOS Genetics 4:e1000271.

   https://doi.org/10.1371/journal.pgen.1000271

   • Google Scholar
9. 1. Bock C
   2. Kiskinis E
   3. Verstappen G
   4. Gu H
   5. Boulting G
   6. Smith ZD
   7. Ziller M
   8. Croft GF
   9. Amoroso MW
   10. Oakley DH
   11. Gnirke A
   12. Eggan K
   13. Meissner A

   (2011) Reference Maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines

   Cell 144:439–452.

   https://doi.org/10.1016/j.cell.2010.12.032

   • Google Scholar
10. 1. Brawand D
    2. Soumillon M
    3. Necsulea A
    4. Julien P
    5. Csárdi G
    6. Harrigan P
    7. Weier M
    8. Liechti A
    9. Aximu-Petri A
    10. Kircher M
    11. Albert FW
    12. Zeller U
    13. Khaitovich P
    14. Grützner F
    15. Bergmann S
    16. Nielsen R
    17. Pääbo S
    18. Kaessmann H

    (2011) The evolution of gene expression levels in mammalian organs

    Nature 478:343–348.

    https://doi.org/10.1038/nature10532

    • Google Scholar
11. 1. Britten RJ
    2. Davidson EH

    (1971) Repetitive and non-repetitive DNA sequences and a specualation on the origins of evolutionary novelty

    The Quarterly Review of Biology 46:111–138.

    https://doi.org/10.1086/406830

    • Google Scholar
12. 1. Brivanlou AH
    2. Gage FH
    3. Jaenisch R
    4. Jessell T
    5. Melton D
    6. Rossant J

    (2003) Stem cells. Setting standards for human embryonic stem cells

    Science 300:913–916.

    https://doi.org/10.1126/science.1082940

    • Google Scholar
13. 1. Burridge PW
    2. Matsa E
    3. Shukla P
    4. Lin ZC
    5. Churko JM
    6. Ebert AD
    7. Lan F
    8. Diecke S
    9. Huber B
    10. Mordwinkin NM
    11. Plews JR
    12. Abilez OJ
    13. Cui B
    14. Gold JD
    15. Wu JC

    (2014) Chemically defined generation of human cardiomyocytes

    Nature Methods 11:855–860.

    https://doi.org/10.1038/nmeth.2999

    • Google Scholar
14. 1. Cain CE
    2. Blekhman R
    3. Marioni JC
    4. Gilad Y

    (2011) Gene expression differences among primates are associated with changes in a histone epigenetic modification

    Genetics 187:1225–1234.

    https://doi.org/10.1534/genetics.110.126177

    • Google Scholar
15. 1. Carroll SB

    (2005) Evolution at two levels: on genes and form

    PLOS Biology 3:e245.

    https://doi.org/10.1371/journal.pbio.0030245

    • Google Scholar
16. 1. Carroll SB

    (2008) Evo-devo and an expanding evolutionary synthesis: a genetic theory of morphological evolution

    Cell 134:25–36.

    https://doi.org/10.1016/j.cell.2008.06.030

    • Google Scholar
17. 1. Chan AWS
    2. Cheng PH
    3. Neumann A
    4. Yang JJ

    (2010) Reprogramming huntington monkey skin cells into pluripotent stem cells

    Cellular Reprogramming 12:509–517.

    https://doi.org/10.1089/cell.2010.0019

    • Google Scholar
18. 1. Chen G
    2. Gulbranson DR
    3. Hou Z
    4. Bolin JM
    5. Ruotti V
    6. Probasco MD
    7. Smuga-Otto K
    8. Howden SE
    9. Diol NR
    10. Propson NE
    11. Wagner R
    12. Lee GO
    13. Antosiewicz-Bourget J
    14. Teng JM
    15. Thomson JA

    (2011) Chemically defined conditions for human iPSC derivation and culture

    Nature Methods 8:424–429.

    https://doi.org/10.1038/nmeth.1593

    • Google Scholar
19. 1. Chen KG
    2. Mallon BS
    3. Hamilton RS
    4. Kozhich OA
    5. Park K
    6. Hoeppner DJ
    7. Robey PG
    8. McKay RD

    (2012) Non-colony type monolayer culture of human embryonic stem cells

    Stem Cell Research 9:237–248.

    https://doi.org/10.1016/j.scr.2012.06.003

    • Google Scholar
20. 1. Cheng X
    2. Ying L
    3. Lu L
    4. Galvão AM
    5. Mills JA
    6. Lin HC
    7. Kotton DN
    8. Shen SS
    9. Nostro MC
    10. Choi JK
    11. Weiss MJ
    12. French DL
    13. Gadue P

    (2012) Self-renewing endodermal progenitor lines generated from human pluripotent stem cells

    Cell Stem Cell 10:371–384.

    https://doi.org/10.1016/j.stem.2012.02.024

    • Google Scholar
21. 1. Cohen DE
    2. Melton D

    (2011) Turning straw into gold: directing cell fate for regenerative medicine

    Nature Reviews. Genetics 12:243–252.

    https://doi.org/10.1038/nrg2938

    • Google Scholar
22. 1. Cotney J
    2. Leng J
    3. Yin J
    4. Reilly SK
    5. DeMare LE
    6. Emera D
    7. Ayoub AE
    8. Rakic P
    9. Noonan JP

    (2013) The evolution of lineage-specific regulatory activities in the human embryonic limb

    Cell 154:185–196.

    https://doi.org/10.1016/j.cell.2013.05.056

    • Google Scholar
23. 1. Deleidi M
    2. Hargus G
    3. Hallett P
    4. Osborn T
    5. Isacson O

    (2011) Development of histocompatible primate-induced pluripotent stem cells for neural transplantation

    Stem cells 29:1052–1063.

    https://doi.org/10.1002/stem.662

    • Google Scholar
24. 1. Du P
    2. Kibbe WA
    3. Lin SM

    (2008) Lumi: a pipeline for processing Illumina microarray

    Bioinformatics 24:1547–1548.

    https://doi.org/10.1093/bioinformatics/btn224

    • Google Scholar
25. 1. Edgar RC

    (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput

    Nucleic Acids Research 32:1792–1797.

    https://doi.org/10.1093/nar/gkh340

    • Google Scholar
26. 1. Enard W
    2. Gehre S
    3. Hammerschmidt K
    4. Hölter SM
    5. Blass T
    6. Somel M
    7. Brückner MK
    8. Schreiweis C
    9. Winter C
    10. Sohr R
    11. Becker L
    12. Wiebe V
    13. Nickel B
    14. Giger T
    15. Müller U
    16. Groszer M
    17. Adler T
    18. Aguilar A
    19. Bolle I
    20. Calzada-Wack J
    21. Dalke C
    22. Ehrhardt N
    23. Favor J
    24. Fuchs H
    25. Gailus-Durner V
    26. Hans W
    27. Hölzlwimmer G
    28. Javaheri A
    29. Kalaydjiev S
    30. Kallnik M
    31. Kling E
    32. Kunder S
    33. Mossbrugger I
    34. Naton B
    35. Racz I
    36. Rathkolb B
    37. Rozman J
    38. Schrewe A
    39. Busch DH
    40. Graw J
    41. Ivandic B
    42. Klingenspor M
    43. Klopstock T
    44. Ollert M
    45. Quintanilla-Martinez L
    46. Schulz H
    47. Wolf E
    48. Wurst W
    49. Zimmer A
    50. Fisher SE
    51. Morgenstern R
    52. Arendt T
    53. de Angelis MH
    54. Fischer J
    55. Schwarz J
    56. Pääbo S

    (2009) A humanized version of Foxp2 affects cortico-basal ganglia circuits in mice

    Cell 137:961–971.

    https://doi.org/10.1016/j.cell.2009.03.041

    • Google Scholar
27. 1. Ezashi T
    2. Telugu BP
    3. Alexenko AP
    4. Sachdev S
    5. Sinha S
    6. Roberts RM

    (2009) Derivation of induced pluripotent stem cells from pig somatic cells

    Proceedings of the National Academy of Sciences of USA 106:10993–10998.

    https://doi.org/10.1073/pnas.0905284106

    • Google Scholar
28. 1. Gallego Romero I
    2. Ruvinsky I
    3. Gilad Y

    (2012) Comparative studies of gene expression and the evolution of gene regulation

    Nature Reviews Genetics 13:505–516.

    https://doi.org/10.1038/nrg3229

    • Google Scholar
29. 1. Gentleman RC
    2. Carey VJ
    3. Bates DM
    4. Bolstad B
    5. Dettling M
    6. Dudoit S
    7. Ellis B
    8. Gautier L
    9. Ge Y
    10. Gentry J
    11. Hornik K
    12. Hothorn T
    13. Huber W
    14. Iacus S
    15. Irizarry R
    16. Leisch F
    17. Li C
    18. Maechler M
    19. Rossini AJ
    20. Sawitzki G
    21. Smith C
    22. Smyth G
    23. Tierney L
    24. Yang JY
    25. Zhang J

    (2004) Bioconductor: open software development for computational biology and bioinformatics

    Genome Biology 5:R80.

    https://doi.org/10.1186/gb-2004-5-10-r80

    • Google Scholar
30. 1. Gilad Y
    2. Rifkin Sa
    3. Bertone P
    4. Gerstein M
    5. White KP

    (2005) Multi-species microarrays reveal the effect of sequence divergence on gene expression profiles

    Genome Research 15:674–680.

    https://doi.org/10.1101/gr.3335705

    • Google Scholar
31. 1. Hernando-Herraez I
    2. Prado-Martinez J
    3. Garg P
    4. Fernandez-Callejo M
    5. Heyn H
    6. Hvilsom C
    7. Navarro A
    8. Esteller M
    9. Sharp AJ
    10. Marques-Bonet T

    (2013) Dynamics of DNA methylation in recent human and great ape evolution

    PLOS Genetics 9:e1003763.

    https://doi.org/10.1371/journal.pgen.1003763

    • Google Scholar
32. 1. Hong SG
    2. Winkler T
    3. Wu C
    4. Guo V
    5. Pittaluga S
    6. Nicolae A
    7. Donahue RE
    8. Metzger ME
    9. Price SD
    10. Uchida N
    11. Kuznetsov SA
    12. Kilts T
    13. Li L
    14. Robey PG
    15. Dunbar CE

    (2014) Path to the clinic: assessment of iPSC-based cell therapies in vivo in a nonhuman primate model

    Cell Reports 7:1298–1309.

    https://doi.org/10.1016/j.celrep.2014.04.019

    • Google Scholar
33. 1. Howden SE
    2. Wardan H
    3. Voullaire L
    4. McLenachan S
    5. Williamson R
    6. Ioannou P
    7. Vadolas J

    (2006) Chromatin-binding regions of EBNA1 protein facilitate the enhanced transfection of Epstein-Barr virus-based vectors

    Human Gene Therapy 17:833–844.

    https://doi.org/10.1089/hum.2006.17.833

    • Google Scholar
34. 1. Israel MA
    2. Yuan SH
    3. Bardy C
    4. Reyna SM
    5. Mu Y
    6. Herrera C
    7. Hefferan MP
    8. Van Gorp S
    9. Nazor KL
    10. Boscolo FS
    11. Carson CT
    12. Laurent LC
    13. Marsala M
    14. Gage FH
    15. Remes AM
    16. Koo EH
    17. Goldstein LS

    (2012) Probing sporadic and familial Alzheimer's disease using induced pluripotent stem cells

    Nature 482:216–220.

    https://doi.org/10.1038/nature10821

    • Google Scholar
35. 1. Jacob F

    (1977) Evolution and tinkering

    Science 196:1161–1166.

    https://doi.org/10.1126/science.860134

    • Google Scholar
36. 1. Jaenisch R
    2. Young R

    (2008) Stem cells, the molecular circuitry of pluripotency and nuclear reprogramming

    Cell 132:567–582.

    https://doi.org/10.1016/j.cell.2008.01.015

    • Google Scholar
37. 1. King MC
    2. Wilson A

    (1975) Evolution at two levels in humans and chimpanzees

    Science 188:107–116.

    https://doi.org/10.1126/science.1090005

    • Google Scholar
38. 1. Kocher TD
    2. Thomas WK
    3. Meyer A
    4. Edwards SV
    5. Paabo S
    6. Villablanca FX
    7. Wilson AC

    (1989) Dynamics of mitochondrial DNA evolution in animals: amplification and sequencing with conserved primers

    Proceedings of the National Academy of Sciences of USA 86:6196–6200.

    https://doi.org/10.1073/pnas.86.16.6196

    • Google Scholar
39. 1. Law CW
    2. Chen Y
    3. Shi W
    4. Smyth GK

    (2014) Voom: precision weights unlock linear model analysis tools for RNA-seq read counts

    Genome Biology 15:R29.

    https://doi.org/10.1186/gb-2014-15-2-r29

    • Google Scholar
40. 1. Li H
    2. Durbin R

    (2009) Fast and accurate short read alignment with Burrows-Wheeler transform

    Bioinformatics 25:1754–1760.

    https://doi.org/10.1093/bioinformatics/btp324

    • Google Scholar
41. 1. Li Q
    2. Lian S
    3. Dai Z
    4. Xiang Q
    5. Dai X

    (2013) BGDB: a database of bivalent genes

    Database 2013:bat057.

    https://doi.org/10.1093/database/bat057

    • Google Scholar
42. 1. Lian X
    2. Zhang J
    3. Azarin SM
    4. Zhu K
    5. Hazeltine LB
    6. Bao X
    7. Hsiao C
    8. Kamp TJ
    9. Palecek SP

    (2013) Directed cardiomyocyte differentiation from human pluripotent stem cells by modulating Wnt/β-catenin signaling under fully defined conditions

    Nature Protocols 8:162–175.

    https://doi.org/10.1038/nprot.2012.150

    • Google Scholar
43. 1. Liao Y
    2. Smyth GK
    3. Shi W

    (2013) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

    Bioinformatics 30:923–930.

    https://doi.org/10.1093/bioinformatics/btt656

    • Google Scholar
44. 1. Liu H
    2. Zhu F
    3. Yong J
    4. Zhang P
    5. Hou P
    6. Li H
    7. Jiang W
    8. Cai J
    9. Liu M
    10. Cui K
    11. Qu X
    12. Xiang T
    13. Lu D
    14. Chi X
    15. Gao G
    16. Ji W
    17. Ding M
    18. Deng H

    (2008) Generation of induced pluripotent stem cells from adult rhesus monkey fibroblasts

    Cell Stem Cell 3:587–590.

    https://doi.org/10.1016/j.stem.2008.10.014

    • Google Scholar
45. 1. Liu J
    2. Koscielska KA
    3. Cao Z
    4. Hulsizer S
    5. Grace N
    6. Mitchell G
    7. Nacey C
    8. Githinji J
    9. McGee J
    10. Garcia-Arocena D
    11. Hagerman RJ
    12. Nolta J
    13. Pessah IN
    14. Hagerman PJ

    (2012) Signaling defects in iPSC-derived fragile X premutation neurons

    Human Molecular Genetics 21:3795–3805.

    https://doi.org/10.1093/hmg/dds207

    • Google Scholar
46. 1. Lund RJ
    2. Närvä E
    3. Lahesmaa R

    (2012) Genetic and epigenetic stability of human pluripotent stem cells

    Nature Reviews. Genetics 13:732–744.

    https://doi.org/10.1038/nrg3271

    • Google Scholar
47. 1. Ma H
    2. Morey R
    3. O'Neil RC
    4. He Y
    5. Daughtry B
    6. Schultz MD
    7. Hariharan M
    8. Nery JR
    9. Castanon R
    10. Sabatini K
    11. Thiagarajan RD
    12. Tachibana M
    13. Kang E
    14. Tippner-Hedges R
    15. Ahmed R
    16. Gutierrez NM
    17. Van Dyken C
    18. Polat A
    19. Sugawara A
    20. Sparman M
    21. Gokhale S
    22. Amato P
    23. Wolf DP
    24. Ecker JR
    25. Laurent LC
    26. Mitalipov S

    (2014) Abnormalities in human pluripotent cells due to reprogramming mechanisms

    Nature 511:177–183.

    https://doi.org/10.1038/nature13551

    • Google Scholar
48. Book

    1. Marchetto MC
    2. Muotri AR
    3. Gage FH

    (2013a)

    Proposing a model for studying primate development using induced pluripotent stem cells

    In: Gage FH, Christen Y, editors. Programmed cells from basic neuroscience to therapy. Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 31–39.

    • Google Scholar
49. 1. Marchetto MC
    2. Narvaiza I
    3. Denli AM
    4. Benner C
    5. Lazzarini TA
    6. Nathanson JL
    7. Paquola AC
    8. Desai KN
    9. Herai RH
    10. Weitzman MD
    11. Yeo GW
    12. Muotri AR
    13. Gage FH

    (2013b) Differential L1 regulation in pluripotent stem cells of humans and apes

    Nature 503:525–529.

    https://doi.org/10.1038/nature12686

    • Google Scholar
50. 1. Masui S
    2. Ohtsuka S
    3. Yagi R
    4. Takahashi K
    5. Ko MS
    6. Niwa H

    (2008) Rex1/Zfp42 is dispensable for pluripotency in mouse ES cells

    BMC Developmental Biology 8:45.

    https://doi.org/10.1186/1471-213X-8-45

    • Google Scholar
51. 1. McLean CY
    2. Reno PL
    3. Pollen AA
    4. Bassan AI
    5. Capellini TD
    6. Guenther C
    7. Indjeian VB
    8. Lim X
    9. Menke DB
    10. Schaar BT
    11. Wenger AM
    12. Bejerano G
    13. Kingsley DM

    (2011) Human-specific loss of regulatory DNA and the evolution of human-specific traits

    Nature 471:216–219.

    https://doi.org/10.1038/nature09774

    • Google Scholar
52. 1. Meisner LF
    2. Johnson JA

    (2008) Protocols for cytogenetic studies of human embryonic stem cells

    Methods 45:133–141.

    https://doi.org/10.1016/j.ymeth.2008.03.005

    • Google Scholar
53. 1. Merkle FT
    2. Eggan K

    (2013) Modeling human disease with pluripotent stem cells: from genome association to function

    Cell Stem Cell 12:656–668.

    https://doi.org/10.1016/j.stem.2013.05.016

    • Google Scholar
54. 1. Müller FJ
    2. Schuldt BM
    3. Williams R
    4. Mason D
    5. Altun G
    6. Papapetrou EP
    7. Danner S
    8. Goldmann JE
    9. Herbst A
    10. Schmidt NO
    11. Aldenhoff JB
    12. Laurent LC
    13. Loring JF

    (2011) A bioinformatic assay for pluripotency in human cells

    Nature Methods 8:315–317.

    https://doi.org/10.1038/nmeth.1580

    • Google Scholar
55. 1. Nagy K
    2. Sung HK
    3. Zhang P
    4. Laflamme S
    5. Vincent P
    6. Agha-Mohammadi S
    7. Woltjen K
    8. Monetti C
    9. Michael IP
    10. Smith LC
    11. Nagy A

    (2011) Induced pluripotent stem cell lines derived from equine fibroblasts

    Stem Cell Reviews 7:693–702.

    https://doi.org/10.1007/s12015-011-9239-5

    • Google Scholar
56. 1. Nazor KL
    2. Altun G
    3. Lynch C
    4. Tran H
    5. Harness JV
    6. Slavin I
    7. Garitaonandia I
    8. Müller FJ
    9. Wang YC
    10. Boscolo FS
    11. Fakunle E
    12. Dumevska B
    13. Lee S
    14. Park HS
    15. Olee T
    16. D'Lima DD
    17. Semechkin R
    18. Parast MM
    19. Galat V
    20. Laslett AL
    21. Schmidt U
    22. Keirstead HS
    23. Loring JF
    24. Laurent LC

    (2012) Recurrent variations in DNA methylation in human pluripotent stem cells and their differentiated derivatives

    Cell Stem Cell 10:620–634.

    https://doi.org/10.1016/j.stem.2012.02.013

    • Google Scholar
57. 1. Ng HH
    2. Surani MA

    (2011) The transcriptional and signalling networks of pluripotency

    Nature Cell Biology 13:490–496.

    https://doi.org/10.1038/ncb0511-490

    • Google Scholar
58. 1. Okamoto S
    2. Takahashi M

    (2011) Induction of retinal pigment epithelial cells from monkey iPS cells

    Investigative Ophthalmology & Visual Science 52:8785–8790.

    https://doi.org/10.1167/iovs.11-8129

    • Google Scholar
59. 1. Okita K
    2. Matsumura Y
    3. Sato Y
    4. Okada A
    5. Morizane A
    6. Okamoto S
    7. Hong H
    8. Nakagawa M
    9. Tanabe K
    10. Tezuka K
    11. Shibata T
    12. Kunisada T
    13. Takahashi M
    14. Takahashi J
    15. Saji H
    16. Yamanaka S

    (2011) A more efficient method to generate integration-free human iPS cells

    Nature Methods 8:409–412.

    https://doi.org/10.1038/nmeth.1591

    • Google Scholar
60. 1. Orkin SH
    2. Hochedlinger K

    (2011) Chromatin connections to pluripotency and cellular reprogramming

    Cell 145:835–850.

    https://doi.org/10.1016/j.cell.2011.05.019

    • Google Scholar
61. 1. Paige SL
    2. Thomas S
    3. Stoick-Cooper CL
    4. Wang H
    5. Maves L
    6. Sandstrom R
    7. Pabon L
    8. Reinecke H
    9. Pratt G
    10. Keller G
    11. Moon RT
    12. Stamatoyannopoulos J
    13. Murry CE

    (2012) A temporal chromatin signature in human embryonic stem cells identifies regulators of cardiac development

    Cell 151:221–232.

    https://doi.org/10.1016/j.cell.2012.08.027

    • Google Scholar
62. 1. Paradis E
    2. Claude J
    3. Strimmer K

    (2004) APE: analyses of phylogenetics and evolution in R language

    Bioinformatics 20:289–290.

    https://doi.org/10.1093/bioinformatics/btg412

    • Google Scholar
63. 1. Pollard KS
    2. Salama SR
    3. Lambert N
    4. Lambot MA
    5. Coppens S
    6. Pedersen JS
    7. Katzman S
    8. King B
    9. Onodera C
    10. Siepel A
    11. Kern AD
    12. Dehay C
    13. Igel H
    14. Ares M Jr
    15. Vanderhaeghen P
    16. Haussler D

    (2006) An RNA gene expressed during cortical development evolved rapidly in humans

    Nature 443:167–172.

    https://doi.org/10.1038/nature05113

    • Google Scholar
64. 1. Prabhakar S
    2. Visel A
    3. Akiyama JA
    4. Shoukry M
    5. Lewis KD
    6. Holt A
    7. Plajzer-Frick I
    8. Morrison H
    9. Fitzpatrick DR
    10. Afzal V
    11. Pennacchio LA
    12. Rubin EM
    13. Noonan JP

    (2008) Human-specific gain of function in a developmental enhancer

    Science 321:1346–1350.

    https://doi.org/10.1126/science.1159974

    • Google Scholar
65. Book

    1. R Development Core Team

    (2013)

    R: A language and environment for statistcal computing

    VIenna, Austria: R Foundation for Scientific Computing.

    • Google Scholar
66. 1. Rada-Iglesias A
    2. Bajpai R
    3. Prescott S
    4. Brugmann Sa
    5. Swigut T
    6. Wysocka J

    (2012) Epigenomic annotation of enhancers predicts transcriptional regulators of human neural crest

    Cell Stem Cell 11:633–648.

    https://doi.org/10.1016/j.stem.2012.07.006

    • Google Scholar
67. 1. Rana P
    2. Anson B
    3. Engle S
    4. Will Y

    (2012) Characterization of human-induced pluripotent stem cell-derived cardiomyocytes: bioenergetics and utilization in safety screening

    Toxicological Sciences 130:117–131.

    https://doi.org/10.1093/toxsci/kfs233

    • Google Scholar
68. 1. Risso D
    2. Schwartz K
    3. Sherlock G
    4. Dudoit S

    (2011) GC-content normalization for RNA-Seq data

    BMC Bioinformatics 12:480.

    https://doi.org/10.1186/1471-2105-12-480

    • Google Scholar
69. 1. Roadmap Epigenomics Consortium
    2. Kundaje A
    3. Meuleman W
    4. Ernst J
    5. Bilenky M
    6. Yen A
    7. Heravi-Moussavi A
    8. Kheradpour P
    9. Zhang Z
    10. Wang J
    11. Ziller MJ
    12. Amin V
    13. Whitaker JW
    14. Schultz MD
    15. Ward LD
    16. Sarkar A
    17. Quon G
    18. Sandstrom RS
    19. Eaton ML
    20. Wu YC
    21. Pfenning AR
    22. Wang X
    23. Claussnitzer M
    24. Liu Y
    25. Coarfa C
    26. Harris RA
    27. Shoresh N
    28. Epstein CB
    29. Gjoneska E
    30. Leung D
    31. Xie W
    32. Hawkins RD
    33. Lister R
    34. Hong C
    35. Gascard P
    36. Mungall AJ
    37. Moore R
    38. Chuah E
    39. Tam A
    40. Canfield TK
    41. Hansen RS
    42. Kaul R
    43. Sabo PJ
    44. Bansal MS
    45. Carles A
    46. Dixon JR
    47. Farh KH
    48. Feizi S
    49. Karlic R
    50. Kim AR
    51. Kulkarni A
    52. Li D
    53. Lowdon R
    54. Elliott G
    55. Mercer TR
    56. Neph SJ
    57. Onuchic V
    58. Polak P
    59. Rajagopal N
    60. Ray P
    61. Sallari RC
    62. Siebenthall KT
    63. Sinnott-Armstrong NA
    64. Stevens M
    65. Thurman RE
    66. Wu J
    67. Zhang B
    68. Zhou X
    69. Beaudet AE
    70. Boyer LA
    71. De Jager PL
    72. Farnham PJ
    73. Fisher SJ
    74. Haussler D
    75. Jones SJ
    76. Li W
    77. Marra MA
    78. McManus MT
    79. Sunyaev S
    80. Thomson JA
    81. Tlsty TD
    82. Tsai LH
    83. Wang W
    84. Waterland RA
    85. Zhang MQ
    86. Chadwick LH
    87. Bernstein BE
    88. Costello JF
    89. Ecker JR
    90. Hirst M
    91. Meissner A
    92. Milosavljevic A
    93. Ren B
    94. Stamatoyannopoulos JA
    95. Wang T
    96. Kellis M

    (2015) Integrative analysis of 111 reference human epigenomes

    Nature 518:317–330.

    https://doi.org/10.1038/nature14248

    • Google Scholar
70. 1. Rockman MV
    2. Hahn MW
    3. Soranzo N
    4. Zimprich F
    5. Goldstein DB
    6. Wray GA

    (2005) Ancient and recent positive selection transformed opioid cis-regulation in humans

    PLOS Biology 3:e387.

    https://doi.org/10.1371/journal.pbio.0030387

    • Google Scholar
71. 1. Schmidt D
    2. Wilson MD
    3. Spyrou C
    4. Brown GD
    5. Hadfield J
    6. Odom DT

    (2009) ChIP-seq: using high-throughput sequencing to discover protein-DNA interactions

    Methods 48:240–248.

    https://doi.org/10.1016/j.ymeth.2009.03.001

    • Google Scholar
72. 1. Scotland KB
    2. Chen S
    3. Sylvester R
    4. Gudas LJ

    (2009) Analysis of Rex1 (zfp42) function in embryonic stem cell differentiation

    Developmental Dynamics 238:1863–1877.

    https://doi.org/10.1002/dvdy.22037

    • Google Scholar
73. 1. Smyth GK

    (2004)

    Linear models and empirical bayes methods for assessing differential expression in microarray experiments

    Statistical Applications in Genetics and Molecular Biology, 3, Article3, 10.2202/1544-6115.1027.

    • Google Scholar
74. 1. Sommer CA
    2. Christodoulou C
    3. Gianotti-Sommer A
    4. Shen SS
    5. Sailaja BS
    6. Hezroni H
    7. Spira A
    8. Meshorer E
    9. Kotton DN
    10. Mostoslavsky G

    (2012) Residual expression of reprogramming factors affects the transcriptional program and epigenetic signatures of induced pluripotent stem cells

    PLOS ONE 7:e51711.

    https://doi.org/10.1371/journal.pone.0051711

    • Google Scholar
75. 1. Son MY
    2. Choi H
    3. Han YM
    4. Cho YS

    (2013) Unveiling the critical role of REX1 in the regulation of human stem cell pluripotency

    Stem Cells 31:2374–2387.

    https://doi.org/10.1002/stem.1509

    • Google Scholar
76. 1. Song Q
    2. Smith AD

    (2011) Identifying dispersed epigenomic domains from ChIP-Seq data

    Bioinformatics 27:870–871.

    https://doi.org/10.1093/bioinformatics/btr030

    • Google Scholar
77. 1. Takahashi K
    2. Tanabe K
    3. Ohnuki M
    4. Narita M
    5. Ichisaka T
    6. Tomoda K
    7. Yamanaka S

    (2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors

    Cell 131:861–872.

    https://doi.org/10.1016/j.cell.2007.11.019

    • Google Scholar
78. 1. Takahashi K
    2. Yamanaka S

    (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors

    Cell 126:663–676.

    https://doi.org/10.1016/j.cell.2006.07.024

    • Google Scholar
79. 1. Teschendorff AE
    2. Marabita F
    3. Lechner M
    4. Bartlett T
    5. Tegner J
    6. Gomez-Cabrero D
    7. Beck S

    (2013) A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data

    Bioinformatics 29:189–196.

    https://doi.org/10.1093/bioinformatics/bts680

    • Google Scholar
80. 1. Tohyama S
    2. Hattori F
    3. Sano M
    4. Hishiki T
    5. Nagahata Y
    6. Matsuura T
    7. Hashimoto H
    8. Suzuki T
    9. Yamashita H
    10. Satoh Y
    11. Egashira T
    12. Seki T
    13. Muraoka N
    14. Yamakawa H
    15. Ohgino Y
    16. Tanaka T
    17. Yoichi M
    18. Yuasa S
    19. Murata M
    20. Suematsu M
    21. Fukuda K

    (2013) Distinct metabolic flow enables large-scale purification of mouse and human pluripotent stem cell-derived cardiomyocytes

    Cell Stem Cell 12:127–137.

    https://doi.org/10.1016/j.stem.2012.09.013

    • Google Scholar
81. 1. Tomioka I
    2. Maeda T
    3. Shimada H
    4. Kawai K
    5. Okada Y
    6. Igarashi H
    7. Oiwa R
    8. Iwasaki T
    9. Aoki M
    10. Kimura T
    11. Shiozawa S
    12. Shinohara H
    13. Suemizu H
    14. Sasaki E
    15. Okano H

    (2010) Generating induced pluripotent stem cells from common marmoset (Callithrix jacchus) fetal liver cells using defined factors, including Lin28

    Genes to Cells 15:959–969.

    https://doi.org/10.1111/j.1365-2443.2010.01437.x

    • Google Scholar
82. 1. Trapnell C
    2. Pachter L
    3. Salzberg SL

    (2009) TopHat: discovering splice junctions with RNA-Seq

    Bioinformatics 25:1105–1111.

    https://doi.org/10.1093/bioinformatics/btp120

    • Google Scholar
83. 1. Wamstad JA
    2. Alexander JM
    3. Truty RM
    4. Shrikumar A
    5. Li F
    6. Eilertson KE
    7. Ding H
    8. Wylie JN
    9. Pico AR
    10. Capra JA
    11. Erwin G
    12. Kattman SJ
    13. Keller GM
    14. Srivastava D
    15. Levine SS
    16. Pollard KS
    17. Holloway AK
    18. Boyer LA
    19. Bruneau BG

    (2012) Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac lineage

    Cell 151:206–220.

    https://doi.org/10.1016/j.cell.2012.07.035

    • Google Scholar
84. 1. Wang G
    2. McCain ML
    3. Yang L
    4. He A
    5. Pasqualini FS
    6. Agarwal A
    7. Yuan H
    8. Jiang D
    9. Zhang D
    10. Zangi L
    11. Geva J
    12. Roberts AE
    13. Ma Q
    14. Ding J
    15. Chen J
    16. Wang DZ
    17. Li K
    18. Wang J
    19. Wanders RJ
    20. Kulik W
    21. Vaz FM
    22. Laflamme MA
    23. Murry CE
    24. Chien KR
    25. Kelley RI
    26. Church GM
    27. Parker KK
    28. Pu WT

    (2014) Modeling the mitochondrial cardiomyopathy of Barth syndrome with induced pluripotent stem cell and heart-on-chip technologies

    Nature Medicine 20:616–623.

    https://doi.org/10.1038/nm.3545

    • Google Scholar
85. 1. Wu Y
    2. Mishra A
    3. Qiu Z
    4. Farnsworth S
    5. Tardif SD
    6. Hornsby PJ

    (2012) Nonhuman primate induced pluripotent stem cells in regenerative medicine

    Stem Cells International 2012:767195.

    https://doi.org/10.1155/2012/767195

    • Google Scholar
86. 1. Wu Y
    2. Zhang Y
    3. Mishra A
    4. Tardif SD
    5. Hornsby PJ

    (2010) Generation of induced pluripotent stem cells from newborn marmoset skin fibroblasts

    Stem Cell Research 4:180–188.

    https://doi.org/10.1016/j.scr.2010.02.003

    • Google Scholar
87. 1. Wunderlich S
    2. Kircher M
    3. Vieth B
    4. Haase A
    5. Merkert S
    6. Beier J
    7. Göhring G
    8. Glage S
    9. Schambach A
    10. Curnow EC
    11. Pääbo S
    12. Martin U
    13. Enard W

    (2014) Primate iPS cells as tools for evolutionary analyses

    Stem Cell Research 12:622–629.

    https://doi.org/10.1016/j.scr.2014.02.001

    • Google Scholar
88. 1. Xie W
    2. Schultz MD
    3. Lister R
    4. Hou Z
    5. Rajagopal N
    6. Ray P
    7. Whitaker JW
    8. Tian S
    9. Hawkins RD
    10. Leung D
    11. Yang H
    12. Wang T
    13. Lee AY
    14. Swanson SA
    15. Zhang J
    16. Zhu Y
    17. Kim A
    18. Nery JR
    19. Urich MA
    20. Kuan S
    21. Yen CA
    22. Klugman S
    23. Yu P
    24. Suknuntha K
    25. Propson NE
    26. Chen H
    27. Edsall LE
    28. Wagner U
    29. Li Y
    30. Ye Z
    31. Kulkarni A
    32. Xuan Z
    33. Chung WY
    34. Chi NC
    35. Antosiewicz-Bourget JE
    36. Slukvin I
    37. Stewart R
    38. Zhang MQ
    39. Wang W
    40. Thomson JA
    41. Ecker JR
    42. Ren B

    (2013) Epigenomic analysis of multilineage differentiation of human embryonic stem cells

    Cell 153:1134–1148.

    https://doi.org/10.1016/j.cell.2013.04.022

    • Google Scholar
89. 1. Zhang Y
    2. Liu T
    3. Meyer CA
    4. Eeckhoute J
    5. Johnson DS
    6. Bernstein BE
    7. Nusbaum C
    8. Myers RM
    9. Brown M
    10. Li W
    11. Liu XS

    (2008) Model-based analysis of ChIP-Seq (MACS)

    Genome Biology 9:R137.

    https://doi.org/10.1186/gb-2008-9-9-r137

    • Google Scholar
90. 1. Zhou X
    2. Cain CE
    3. Myrthil M
    4. Lewellen N
    5. Michelini K
    6. Davenport ER
    7. Stephens M
    8. Pritchard JK
    9. Gilad Y

    (2014) Epigenetic modifications are associated with inter-species gene expression variation in primates

    Genome Biology 15:547.

    https://doi.org/10.1186/s13059-014-0547-3

    • Google Scholar
91. 1. Ziller MJ
    2. Müller F
    3. Liao J
    4. Zhang Y
    5. Gu H
    6. Bock C
    7. Boyle P
    8. Epstein CB
    9. Bernstein BE
    10. Lengauer T
    11. Gnirke A
    12. Meissner A

    (2011) Genomic distribution and inter-sample variation of non-CpG methylation across human cell types

    PLOS Genetics 7:e1002389.

    https://doi.org/10.1371/journal.pgen.1002389

    • Google Scholar

Author details

1. Irene Gallego Romero

   Department of Human Genetics, University of Chicago, Chicago, United States

   Contribution

   IGR, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Contributed equally with

   Bryan J Pavlovic

   For correspondence

   ireneg@uchicago.edu

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-1613-8998

2. Bryan J Pavlovic

   Department of Human Genetics, University of Chicago, Chicago, United States

   Contribution

   BJP, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Contributed equally with

   Irene Gallego Romero

   Competing interests

   The authors declare that no competing interests exist.

3. Irene Hernando-Herraez

   Institut de Biologia Evolutiva (CSIC/UPF), Barcelona, Spain

   Contribution

   IH-H, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Xiang Zhou

   Department of Biostatistics, University of Michigan, Ann Arbor, United States

   Contribution

   XZ, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Michelle C Ward

   Department of Human Genetics, University of Chicago, Chicago, United States

   Contribution

   MCW, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Nicholas E Banovich

   Department of Human Genetics, University of Chicago, Chicago, United States

   Contribution

   NEB, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Courtney L Kagan

   Department of Human Genetics, University of Chicago, Chicago, United States

   Contribution

   CLK, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Jonathan E Burnett

   Department of Human Genetics, University of Chicago, Chicago, United States

   Contribution

   JEB, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Constance H Huang

   Department of Human Genetics, University of Chicago, Chicago, United States

   Contribution

   CHH, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Amy Mitrano

    Department of Human Genetics, University of Chicago, Chicago, United States

    Contribution

    AM, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Claudia I Chavarria

    Department of Human Genetics, University of Chicago, Chicago, United States

    Contribution

    CIC, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Inbar Friedrich Ben-Nun

    Center for Regenerative Medicine, Department of Chemical Physiology, The Scripps Research Institute, La Jolla, United States

    Present address

    Lonza Walkersville, Inc., Walkersville, United States

    Contribution

    IFB-N, Conception and design, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

13. Yingchun Li

    1. Department of Pathology, University of California San Diego, San Diego, United States
    2. Sanford Consortium for Regenerative Medicine, La Jolla, United States

    Contribution

    YL, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

14. Karen Sabatini

    1. Center for Regenerative Medicine, Department of Chemical Physiology, The Scripps Research Institute, La Jolla, United States
    2. Sanford Consortium for Regenerative Medicine, La Jolla, United States
    3. Department of Reproductive Medicine, University of California San Diego, San Diego, United States

    Contribution

    KS, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

15. Trevor R Leonardo

    1. Center for Regenerative Medicine, Department of Chemical Physiology, The Scripps Research Institute, La Jolla, United States
    2. Sanford Consortium for Regenerative Medicine, La Jolla, United States
    3. Department of Reproductive Medicine, University of California San Diego, San Diego, United States

    Contribution

    TRL, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

16. Mana Parast

    1. Department of Pathology, University of California San Diego, San Diego, United States
    2. Sanford Consortium for Regenerative Medicine, La Jolla, United States

    Contribution

    MP, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

17. Tomas Marques-Bonet

    1. Institut de Biologia Evolutiva (CSIC/UPF), Barcelona, Spain
    2. Centro Nacional de Análisis Genómico (CNAG-CRG), Barcelona, Spain

    Contribution

    TM-B, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

18. Louise C Laurent

    1. Sanford Consortium for Regenerative Medicine, La Jolla, United States
    2. Department of Reproductive Medicine, University of California San Diego, San Diego, United States

    Contribution

    LCL, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

19. Jeanne F Loring

    Center for Regenerative Medicine, Department of Chemical Physiology, The Scripps Research Institute, La Jolla, United States

    Contribution

    JFL, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

20. Yoav Gilad

    Department of Human Genetics, University of Chicago, Chicago, United States

    Contribution

    YG, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    For correspondence

    gilad@uchicago.edu

    Competing interests

    The authors declare that no competing interests exist.

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