The genetic mechanisms governing human pre-implantation embryo development and the in vitro counterparts, human embryonic stem cells (hESCs), still remain incomplete. Previous global genome studies demonstrated that totipotent blastomeres from day-3 human embryos and pluripotent inner cell masses (ICMs) from blastocysts, display unique and differing transcriptomes. Nevertheless, comparative gene expression analysis has revealed that no significant differences exist between hESCs derived from blastomeres versus those obtained from ICMs, suggesting that pluripotent hESCs involve a new developmental progression. To understand early human stages evolution, we developed an undifferentiation network signature (UNS) and applied it to a differential gene expression profile between single blastomeres from day-3 embryos, ICMs and hESCs. This allowed us to establish a unique signature composed of highly interconnected genes characteristic of totipotency (61 genes), in vivo pluripotency (20 genes), and in vitro pluripotency (107 genes), and which are also proprietary according to functional analysis. This systems biology approach has led to an improved understanding of the molecular and signaling processes governing human pre-implantation embryo development, as well as enabling us to comprehend how hESCs might adapt to in vitro culture conditions.
Citation: Galan A, Diaz-Gimeno P, Poo ME, Valbuena D, Sanchez E, Ruiz V, et al. (2013) Defining the Genomic Signature of Totipotency and Pluripotency during Early Human Development. PLoS ONE 8(4): e62135. https://doi.org/10.1371/journal.pone.0062135
Editor: Ying Xu, University of Georgia, United States of America
Received: September 11, 2012; Accepted: March 19, 2013; Published: April 17, 2013
Copyright: © 2013 Galan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by Agreement between Institute of Health Carlos III (ISCIII), Regional Ministry of Health (GVA) and CIPF for the Research on Regenerative Medicine and FIS Project PI08-1134 from the ISCIII of the Spanish Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Totipotency and pluripotency are at the root of both embryo development and the stem cell field. Therefore, understanding the molecular mechanisms involved is crucial to understanding developmental biology as well as regenerative medicine. Systems biology focuses on complex interactions within biological systems, using a holistic perspective, with the main aim of integrating all knowledge into a model and discovering emergent properties and networks to make it function as a system , .
Blastomeres from human pre-implantation embryos up to day-3 of development are considered to be totipotent since they can give rise to a complete embryo , . From day 4 of development cells from the outside part of the embryo go on to form the trophectoderm, while the inside blastomeres generate the pluripotent inner cell mass (ICM) that will differentiate into mesoderm, ectoderm, and endoderm as well as the germ cells of the future human being –.
Human embryonic stem cells (hESCs) are pluripotent cells that have been artificially created and do not exist in nature. They were initially derived from the ICM cells of the human blastocyst – but can also be obtained from other developmental stages, including single blastomeres from 5- to 8-cell embryos , . hESCs represent an excellent model for regenerative medicine applications for the investigation of fundamental aspects of pluripotency. Indeed, the knowledge gathered from them was at the heart of the groundbreaking discovery of somatic cell reprogramming into a pluripotent state carried out by the overexpression of specific factors , .
For a short period of time the ICM is considered the paradigm of in vivo pluripotency. Indeed, for some time, cultured hESCs were considered to be equivalent to the ICM cells from which they were derived, although this concept was later revised . In this context, recent studies have revealed that hESCs originate from a post-ICM intermediate, a transient epiblast-like structure which has undergone X-inactivation in female cells . Furthermore, while blastomeres from day-3 embryos and the ICM share some biological similarities, they also exhibit significant differences as revealed by comparative gene expression analysis , .
Whole genome analyses are key to understanding the molecular mechanisms governing totipotency, and in vivo as well as in vitro pluripotency. Initial studies were performed by capturing a detailed view of hESC and ICM gene expression , –, and further amplification protocols allowed single cell microarray analysis, thus making the profiling of gene expression in single blastomeres possible , , , . Several differential gene expression studies have revealed that human blastomeres, ICM, and hESC signatures significantly differ , , , , suggesting the existence of independent developmental transcriptional signatures.
In this study, we aim to use these models from a systems biology perspective to investigate the inherent genomic signatures and networks governing human totipotency, and in vivo as well as in vitro pluripotency. Using this approach, we have also analyzed how pluripotent hESCs, regardless of their derivation source, might adapt to in vitro culture conditions.
Comparative whole genome expression profile of human blastomeres versus ICMs and hESCs
Human single blastomeres from day-3 embryos (6- to 8-cell stage; n = 41), ICM from human blastocysts (n = 2), three hESC lines derived from ICMs (VAL-5, -7, -8), and two hESC lines obtained from single blastomeres (VAL-10B, VAL-11B) were compared using genome-wide transcriptional analysis (Fig 1A). All hESC lines used in this study were derived in the same laboratory following the same protocol, and are fully characterized and registered (http://www.isciii.es/ISCIII/es/contenidos/fd-investigacion/fd-ejecucion/fd-programas-investigacion/fd-investigacion-terapia-celular-medicina-regenerativa/fd-banco-nacional-lineas-celulares/fd-lineas-celulares-disponibles/lineas-de-celulas-hES.shtml); VAL-5, -8 and -11B were XX, and VAL-7 and -10B were XY , , .
Differential gene expression profile was carried out in single blastomeres from day-3 embryos, ICM from blastocysts, and their derived hESC counterparts. Principal component analysis (PCA) of the whole transcriptome of single blastomeres from day-3 embryos, ICMs from blastocysts, and hESCs derived from both sources (B) and PCA of the UNS of blastomeres, ICMs, and hESCs (C). Samples in the same cluster category stay closer together than in any other sample. The most separate clusters are blastomeres and hESCs, and ICMs show an intermediate pattern which falls between both. ICM_origin and B_origin correspond to hESC derived from ICM and Blastomeres respectively.
In order to compare the transcriptional profiles among them, microarray data were normalized and a principal component analysis (PCA) was carried out to identify clustering patterns that identified different origins (Fig 1B). Blastomeres and ICMs clustered into two separate groups while hESCs from different sources (blastomeres and ICMs) grouped to form a third cluster, indicating the existence of three transcriptional patterns, two according to developmental origin (blastomere and ICM), and a third according to the hESC phenotype, regardless their derivation origin. PCA revealed the existence of gene expression signatures that are indicative of single blastomeres (totipotency) ICMs (in vivo pluripotency), and hESCs (in vitro pluripotency). The relative position of these three PCA clusters informs of the similarities between their gene expression signatures, and indicates that blastomere and hESC differ the most while ICM occupies an intermediate position (Fig 1B). Furthermore, the differential gene expression profile showed no significant differences between individual hESC lines regardless their origin of derivation.
Comparative Undifferentiation Network Signature (UNS) expression profile of human blastomeres versus ICMs and hESCs
To narrow down the genes and networks implicated in totipotency and pluripotency, we created an Undifferentiation Network Signature (UNS) composed of 266 genes characteristic of early human embryo developmental stages (blastomeres and ICM) and of hESCs. This selection was initially composed of 191 genes selected from published reports from our group and others that included the most characteristic undifferentiation markers, namely NANOG, POU5F1, SOX2, GDF3, SOX2, DNMT3A, etc. , , , –. Following this, an additional gene network analysis in the literature and databases (including GeneCards and iHOP) identified another 75 genes that highly interact with the genes included in our initial signature cohort, which were added with the purpose of understanding the gene pathways governing pluripotency (see Material and Methods). These connectors included transcription factors such as WT1, TP53, and BMP family members, adhesion molecules such as CDH1, or the transcription cofactor EP300. Housekeeping genes such as GAPDH and ACTB, and structural proteins like NES and VIM were also found in the connectors list, as well as several members of the MAP Kinase signaling pathway. Our final UNS included 266 genes composed of the most significant undifferentiation markers plus other markers which strongly interact with them, which overall, were involved in signaling pathways such as Activin/TGFB/BMP, Protein Kinases, FGF, Wnt, Retinoic Acid, Rho/Ras; transcriptional modulators related with telomerase activity; cell cycle, proliferation and self-renewal; cell adhesion, protein binding, and transport. Some of these genes are also related to epigenetic modifications, metabolism, cytoskeleton, or gamete differentiation. The detailed UNS gene list, including name, function, localization, global and specific pathways in which they are involved, as well as their relative expression in the models investigated is presented in Table S1.
Similar to the whole genome approach, we performed PCA to analyze interactions among the UNS transcriptional profiles which identified three distinct groups corresponding to single blastomeres, ICMs, and hESCs independently of their source of derivation (Fig 1C). As expected, the differences between these three groups was better captured by analyzing the UNS alone, as indicated by a larger variance on the first principal component (PC1) of the UNS PCA (39.02%) in comparison to the explained variance of PC1 in the global transcriptome analysis (29,11%) (Fig 1).
Next, we grouped genes according to the differential expression level (up- or down- regulation) at each of the three cell types (single blastomeres, ICM and hESC). From the complete UNS, 189 genes showed significant differences between hESCs, ICMs and/or blastomeres (Fig 2A; clusters 1 to 6), while 77 genes did not (Fig 2B; cluster 7). Interestingly, the differential gene expression analysis revealed no significant differences between hESC lines originated from different sources of derivation, meaning that although single blastomere and ICM cells have very different gene expression profiles, their derived hESCs are transcriptionally equivalent, which points to a convergent adaptation to the in vitro culture conditions. Furthermore, the fact that 189 out of 268 UNS genes correspond to one cell-type defined cluster indicates that the great majority of the UNS genes are specific of a particular developmental stage (Fig 2A), highlighting the relevance of the cell-type specific gene signature.
(A) Expression patterns with significant differences are grouped in six clusters according to over-expression or down-regulation after comparative gene expression analysis, giving rise to the totipotency signature (TS) from blastomeres (Clusters 1 and 2), the in vivo pluripotency signature (IVVPS) (Clusters 3 and 4), and the in vitro pluripotency signature (IVTPS). Red box means up-regulated genes, and blue box means down-regulated (Clusters 5 and 6). (B) Genes from the UNS with no significant differences in expression are also included (Cluster 7) and are represented in green. Connector genes are shown in bold. The criteria for statistically significant values was a p-value cutoff of <0.05. Complete data are described in Table S1.
Totipotency Gene Signature (TS)
Differential gene expression of single blastomeres isolated from day-3 embryos versus ICMs and hESCs revealed a unique transcriptome signature that conforms to a totipotency signature (TS). The putative gene expression profile related to totipotency includes a group of 17 up-regulated genes and a group of 44 significantly down-regulated markers when compared to ICMs and hESCs. Up-regulated genes are mainly involved in cell adhesion (n = 2), cell cycle (n = 5), and regulation of transcription (n = 6) (e.g. CCNB1, GBX2, WT1, FOXD3 and UTF1), whereas those down-regulated are involved in cell cycle (n = 8), signaling pathways (n = 11) and metabolism (n = 5) (e.g. BMP2 or GAPDH), and mainly consisted of genes involved in cell proliferation (n = 10) and activation of transcription (n = 6) (e.g. GABRB3, LIN28, ZFP42, TOP2A, and RPL), as shown in Fig 2 and Table S2.
Differential gene expression results obtained for the TS were run using the web tool Cytoscape, an open source bioinformatics software platform for visualizing molecular interaction networks and biological pathways which integrates these networks with annotations, gene expression profiles and other data of interest . Cytoscape analysis revealed 31 gene interactions of within the TS (Fig 3A) representing several networks. Interestingly, the transcription factors UTF1, ZFP42, and FOXD3 directly connected in the nucleus, and the latter two also combined with the cytoplasmic proliferation molecule LIN28. Furthermore, FOXD3 and DPPA4 also directly interacted with the cell adhesion molecule GJA1. This protein located in the plasma membrane also showed direct interaction with signaling molecules in the cytoplasm and in the extracellular matrix, similar to the extracellular matrix protein (ECMP) or BMP2 where other members of the FGF/activin and protein kinase pathways convey. Moreover, other genes proven to play key roles in undifferentiation such as WT1, have also been shown to directly interact with the cytoskeleton molecule KRT18 which in turn directly binds to the adhesion molecule CEACAM and to molecules which play key roles in cell function like the housekeeping gene GAPDH and the ribosomal markers which form part of the TS (Fig 3A).
Significant genes of each signature were represented according to their gene function and specific role in the cell, localization and type of interaction between them. Node border color refers to cell localization, node shape to general function and node color to specific function in the cell. Edge color refers to physical interactions, biochemical interactions or to both; when not specified, a functional interaction is assumed. Upstream arrow (red) means up-regulation versus the other categories, and downstream arrow (blue) means down-regulation versus the other categories. Microarray data values represented here are shown in Fig S1 and S2.
Moreover, all the components are tightly interconnected conforming to a global network where most components, from different locations in the cell, are eventually related. All TS components are extensively described in Table S2.
In Vivo Pluripotency Signature (IVVPS)
The In vivo pluripotency signature (IVVPS) is composed of 21 genes that are significantly differentially regulated in ICMs versus single blastomeres and/or hESCs. Seventeen are over-expressed, including the protein membrane transporter ABCG2, the imprinting/methylation genes H19 and DNMT3L, structural components like ACTB and KRT8, transcriptional factors like GDF3 and HMGB2, cell adhesion molecules such as MUC15, signaling components like IL6 and SMAD4 and the receptor PPARG which is involved in important biological functions. Among the four genes down-regulated in ICMs versus blastomeres and hESCs are the cell cycle markers such as GTSE1, the transcription factor HMGB2, the heat shock protein HSPB1, and the transcriptional regulator EP300 (Fig 2 and Table S3).
The Cytoscape analysis revealed that 11 markers interacted in the IVVPS (Fig 3B). All of them are up-regulated except the nuclear gene, HSPB1 which is involved in mitosis. The methylation gene DNMT3L physically interacts with the transcription factor HDAC1, and directly binds to SMAD4. This protein kinase exhibits functional interaction with the transcription factor NFKB1 and physical and biochemical interactions with the cofactor EP300. NFKB1 directly interacts with the ABC transporter ABCG2 which is also related to the extracellular interleukin, IL6, and they both join with the cytoplasmic cytoskeleton marker KRT8. The up-regulated gene KRT8 functionally binds to the structural gene ACTB, and to the heat shock protein HSPB1, which is down-regulated in the in vivo pluripotency signature. All of these genes connect to finally form an intrinsic network describing the pluripotency signature in vivo (Fig 3B).
In Vitro Pluripotency Signature (IVTPS)
In vitro pluripotency signature (IVTPS) is composed of 107 genes from the UNS which show significant differences between hESCs (derived from single blastomeres or isolated ICMs) versus these cellular sources prior to manipulation. This list of genes contains the most significant pluripotency and self-renewal markers, including the core pluripotency transcriptional genes, namely POU5F1 (OCT4), NANOG, and SOX2. The list also contains some other important transcription factors (HMGB1, KLF4, MYC, SALL2 etc.), the telomerase related genes, TERT and TERF1, ‘de novo’ methylation markers (DNMT3A, DNMT3B), genes belonging to the most representative pluripotency signaling pathways such as Activin/TGFB/BMP (BMP2, LEFTY1, LEFTY2, TGFB1), Wnts (WNT5A, WNT6, TDGFB1), and ribosomal genes (RPL14, RPL7, RPS24), among others (Fig 2). The complete list of the 107 genes constituting the hESC signature is detailed in Table S4 along with their main characteristics and properties. Although the majority were represented by previously reported genes, more than 30% of them are new interacting molecules, including the cytoskeleton proteins VIM and NES, the transcription factors TP53 and FOS, and cell proliferation proteins AR and CXCL12 (Table S4). All these genes show a high degree of interaction, as represented by Cytoscape analysis, where only PIM2 remains unbound (Fig S2). Differential gene expression analysis revealed that almost 80% of the genes that conform to the IVTPS are over-expressed in hESCs when compared to blastomeres and ICM, suggesting transcriptional machinery activation.
Network analysis was also run for the most representative undifferentiating markers, highlighting the IVTPS and the presence of interacting markers that form a straight network, which may help us to understand and complete their signaling pathways (Fig 3C), and confirming previous studies in which direct interactions between transcription factors as POU5F1, NANOG, SOX2 and MYC are reported , , . Cytoscape analysis shows how an adhesion interacting molecule located on the plasma membrane, CDH1, functionally interacts with the most important transcription factors that characterize pluripotency and self-renewal in the nucleus, namely POU5F1, NANOG, TERT, TGFB1, SOX2 (up-regulated in hESCs versus single blastomeres and isolated ICMs), and MYC and KLF4 (down-regulated in hESCs versus single blastomeres and ICMs), with two components of the Wnt signaling network (WNT5A and FZD7) in the nucleus, and with TDGF1, from the same signaling network but in the extracellular space. Thus, these interactions describe new functional interactions and putative regulatory networks controlling hESCs in vitro. DPPA5, is up-regulated in single blastomeres and ICM cells and directly binds with POU5F1 and NANOG, suggesting that direct regulation may occur. The ‘novo methylation’ gene DNMT3A physically joins to the down-regulated transcription factor MYC and to DNMT3B in the nucleus. Interestingly DNMT3B functionally interacts with the up-regulated transcription factor SOX2, the telomerase related gene TERT1 in the nucleus, with NOTCH1 in the plasma membrane, and with TDGF1 in the extracellular space (Fig 3C). These results show a complex and intricate network, in which multiple components with different functions and from several different signaling pathways are connected in different parts of the cell.
Signatures Comparison and Functional Enrichment Analysis
Gene signatures from each category, TS, IVVPS, and IVTPS obtained from the UNS gene expression clustering analysis were further analyzed. The distribution of the connector genes in each signature was also studied. Interestingly, almost 70% of interacting genes were shown to be present in any of the three signatures, suggesting a putative active role in controlling each signaling process, and confirming that these new molecules are facilitators present in all the signatures we have identified (Fig 2).
Functional enrichment allows a statistical approach for genes belonging to a Gene Ontology (GO) category. The functional enrichment of UNS was achieved by comparing gene signatures between the UNS versus the complete genome, each signature versus the UNS, each signature versus the complete genome, and between them using the functional enrichment tool FatiGo, from the Babelomics platform. The functional enrichment analysis of the UNS versus complete genome comparison resulted in a very large number of enriched terms, even applying a very restrictive p value (p value<0.005); 1,630 Biological Process (BP), 452 Molecular Function (MF), and 155 Cell Component (CC) GO terms, as well as 121 KEGG pathways. To visualize and summarize functional results among signatures GOslim GOA gene distribution terms were obtained, representing each signature compared to the whole genome (Fig 4).
GO slim GOA analysis of the three ontologies are represented separately: (A) Molecular Functions (MF); (B) Biological Process (BP); and (C) Cellular Components (CC). Each GO term from a GOslim subset is represented on the x-axis, and gene content in percentage related to each gene signature is compared on the y-axis. The asterisk marks the over-represented significant terms (GOSlim GOA adjusted-p-value <0.05) in gene signatures after Fisher exact test genome comparison.
The IVTPS showed the highest number of significant terms in the whole GOslim GOA search term analysis, MF like those mentioned as antioxidant, catalytic, channel or electron carrier activities were present only in this signature, while some other MF such as kinases, signal transducers and molecules with structural activities were also found to be enriched (Fig 4A). Some enriched BP terms included cellular membrane fusion, and extracellular structure organization, reaction to external or internal stimuli (behavior) and biosynthetic processes, among others (Fig 4B). Furthermore, CC cohort analysis also showed that terms that were mainly related with cell surface, extracellular space and proteinatious extracellular matrix components (Fig 4C) were enriched. GO terms analysis was in concordance with IVTPS molecules and their expression pattern, in which many of up-regulated genes were described as playing key roles not only in transcription but also in signaling and secretion (Fig 2 and 3), and might also play a role for in vitro culture survival. When GOslim was applied for down-regulated genes in the IVTPS, all 33 terms were found significant for IVTPS as GATA2 and GATA3 are present in all terms, thus non supporting differential information. The IVVPS was also enriched in enzyme and structural molecular activity regulating genes as well as those involved in transporter activity (Fig 4A). In terms of the BP cohort, IVVPS genes were enhanced in processes related to cellular component movement, secretion, multi-organism processes and cell death (Fig 4B). CC analysis showed a protein distribution occupying chromosomes, cell surface and extracellular space (Fig 4C), which correlates with the genes included in the IVVPS signature which included mainly up-regulated transcription factors, cytoskeleton components, and a gene transporter (Fig 2 and 3). No significant terms were been found for down-regulated genes in the IVVPS in the GOslim GOA analysis. Finally, TS components were mainly enhanced in activities related with structural molecules, oxidoreductases and kinases in the MF analysis (Fig 4A); no specific BP molecules were over-represented compared to IVVPS and IVTPS, although multi-organism process, cell component movement and behavior BP components were enhanced (Fig 4B). CC analysis of the TS showed a general distribution throughout all the categories excluding the cell surface (Fig 4C). When down-regulated genes in TS signature were functionally analyzed, two terms, structural molecule activity and chromosome were found to be enriched.
Every gene list comparison was also analyzed for pluripotency versus the UNS, totipotency versus the UNS and pluripotency versus totipotency. No significant results were found (adjusted p-value <0.1) in any of these comparisons, with the exception of the TS versus IVTPS, where the cell surface term (GO: 0009986) was over-represented (adjusted-p value = 0.054629), and included the 16 following genes: ACVR2B, BRCA1, CAV1, CD44, GATA2, GATA3, HSPD1, ITGA5, ITGA6, KLF4, RARA, RELA, TDGF1, TGFB1, THY1, TNF. These results concur with previous results, strongly supporting the concept of hESC culture adaptation.
hESCs can be derived from different sources, which include single blastomeres from day-3 embryos, and the ICM from blastocysts. These different developmental origins led us to hypothesize that different using starting material from different sources could result in hESC that have distinctive gene expression profiles. With that purpose, we took a genome wide approach to compare the genome expression profiles of human blastomeres versus ICMs and hESCs. PCA identified three transcriptional patterns, two according to developmental origin (blastomere or ICM), and the third corresponding to hESCs, regardless their derivation origin.
Thus, the concept of totipotency (TS), in vivo and in vitro pluripotency (IVVPS and IVTPS) was further investigated using a systems biology approach, and with the help of an undifferentiation network signature (UNS). The UNS was created to elucidate global signaling pathways that control each developmental stage and was formed by compiling genes that had previously been reported to play key roles in undifferentiation (n = 191) and combining them with genes that strongly interact with them, termed as “connectors” (n = 75). Comparative gene expression analysis was applied to the UNS and confirmed our previous PCA results, allowing us to establish a gene clustering unique to each developmental signature. When functional enrichment was applied to our UNS, a large number of general GO functions were identified, which is expected for such a signature created from bibliographic and database resources. Connector molecules are mostly signaling and housekeeping genes that play a fundamental role in basal cellular functions. These connectors represent the 20%, 33%, and 31% of the TS, IVPS and IVTPS respectively, also indicating the relevance in defining the function of the each stage specific functions.
The TS was created from samples originating from single blastomeres from day-3 embryos, which resulted in a specific signature constituted by 61 genes; 28% of which were up-regulated and principally comprised cell cycle markers, signaling components, and transcription factors, such as GBX2 and UTF1 both implicated in maintaining an undifferentiated state cost commonly by gene repression , ; 72% were down-regulated, including the transcription factors WT1 and FOXD3 , . Down-regulated markers also included transcription factors involved in cell transcription, proliferation, pluripotency and telomerase activity such as GABRB3, IFITM2, ZFP42 and PRKDC , , , RPL4, RPL7A, LIN28 , and genes involved in cell metabolism such as GAPDH and IMPDH2 , . Furthermore, network analysis revealed a highly interconnected association with more than 50% of the TS genes showing at least one interaction, and in which transcription factors, signaling, translational, and structural components, as well as molecules involved in metabolism and cell adhesion were interconnected. This low expression profile may be indicative of a transient developmental stage in which single blastomeres may be preparing for later developmental changes, and is in concordance with GO analysis in which several MF and BP terms are enriched, albeit in a lower proportion than in the other gene signatures discussed.
The ICM gene expression profile is represented by the IVVPS, and is constituted by very few genes (n = 21), 80% of them were over-represented and 20% were down-regulated. Most of the up-regulated markers play key roles in the cell, such as MCM10 which is involved in the initiation of genome replication  and DNMT3L which stimulates de novo methylation and also mediates transcriptional and epigenetic repression through its interaction with HDAC1 , , and the cytoskeleton components ACTB and KRT8. The ABC transporter, ABCG2, is also included in this group, and is notable for its selectivity in separating putative stem cells by cell sorter assays , . Down-regulated genes included the heat shock protein HSPB1 which inhibits translation , and the recently described pluripotency cofactor, EP300 . Most of the markers mentioned above show physical, biochemical and functional interactions that may control the in vivo pluripotency of ICM cells. Furthermore, functional analysis showed that the CC components included chromosome, cell surface and extracellular space terms enriched in the IVVPS, supporting the notion of an enhanced role for cell membrane transport and trafficking in ICM cells. This has also been confirmed by similar enrichment in the BP category cellular components also involved in movement, multi-organism process, secretion and cell death, and the MF enzyme regulatory, motor, signal transducer and transporter activity GO terms.
Finally, the IVTPS is unique to hESC, and is composed of the most numerous group of genes in our UNS (n = 107). Of these, 78% were up-regulated and 22% down-regulated. Up-regulated genes in hESCs versus ICMs and blastomeres included the most significant markers of pluripotency and cellular immortality characterisation, namely the transcriptional core NANOG, POU5F1 (OCT4), and SOX2, and telomerase related TERT1 and TERF1, as well as Activin/Nodal signaling markers such as TGFB1, LEFTY1 and LEFTY2, Wnt signaling such as WNT5A and WNT6, adhesion molecules such as THY1, ribosomal genes involved in cell proliferation (RPL6, RPL14), and the transcriptional repressor TP53 , , , , . The DNA methyltransferases, DNMT3A and DNMT3B, which play key roles in regulating gene expression and chromatin structure ,  were up-regulated in hESCs, as shown in previous studies, and confirmed by these results , , , while the enzyme catalysing their activity, DNMT3L, was up-regulated in the IVVPS. KLF4 and MYC genes, which have been used for reprogramming somatic cells in order to obtain induced pluripotent stem cells (iPSCs) , , have been found to be down-regulated in hESCs when compared to single blastomeres and ICMs, suggesting that these genes may be necessary in exceedingly high levels for the induction of pluripotency, and at lower levels during propagation. Indeed, it has been reported that MYC participates in facilitating undifferentiation by stimulating gene expression and proliferation , .
Network analysis revealed a high degree of interconnection between all IVTPS cohort members. Indeed, only one of the 107 genes in the signature showed no interaction with any other gene. Most members showed 4 or 5 interactions with other transcription factors, molecules involved in signaling, adhesion, metabolism or translation (Fig S2). In fact, previous studies have reported that the core transcription factors OCT4, SOX2, and NANOG may control reprogramming both by positively regulating their own promoters, forming an interconnected self-regulatory loop, and secondly by co-occupying and activating the expression of genes necessary for maintaining an undifferentiated stage, while contributing to the repression of genes that code for differentiation routes , , . In general terms, it has been described that biological networks consist of highly connected nodes called hubs, which if removed would lead to fragmentation of the network , . Some of the genes that constitute hubs receive extensive inputs. For example, the enhancer region of the Oct4 gene is bound by at least 14 transcription factors in mouse ESCs (mESCs) , and our networking analysis reveals that human OCT4 (POU5F1) shows direct interactions with 29 markers of the in vitro pluripotency signature, 16 from the nucleus (GATA3, KLF4, MIKI67, SOX2, ESR1, MYC, CCND1, HMGB1, RELA, POU2F1, TERT, TERF1, DPPA5, NR6A1, RUVBL1, TP53), 3 from cytoplasm (NES, VIM, GAS6), 5 from the plasma membrane (FZD5, NOTCH1, THY1, CDH1, CD44), and 5 from the extracellular space (LEFTY2, TDGF1, FN1, EGFR, TNF) (Fig S2). These interactions serve as key regulators in the enhancement of transcription, as genes bound by more transcription factors tend to be more actively transcribed . This model could be additionally applied to human gene regulation, in which the more transcription factors forming the hub are occupied, the higher the gene expression, especially when combined with the idea that the key pluripotency-associated factors may self-regulate their own expression . In the same context, TERT1 and TERF1, which mediate self-renewal and cellular immortality, have also been shown to be up-regulated in hESCs, as previously demonstrated with TERF1 in mice .
All these results suggest that hESCs might adapt to cell culture conditions, by activating a vast number of transcription factors, signaling, cell adhesion, cell proliferation, and translation molecules. This putative in vitro adaptation is supported by functional analysis which revealed a high number of MF category GO search terms in the IVTPS cohort, corresponding to cellular transport, signaling and enzymatic activities, and general molecular and structural function. Enriched BP category terms included those related to stimuli response, cellular component movement and secretion, as well as metabolic processes, extracellular structure organization and cellular membrane fusion. Enhanced CC group terms were generally distributed evenly over GO term cohorts, although cell surface and extracellular matrix and space terms were enhanced. All these enriched terms encompass genes that are mainly related to the cell adhesion and interaction necessary for hESCs to form their characteristic colony structure in culture for adapting to in vitro cell culture conditions. Therefore, hESCs display a common gene profile, independent of the source of derivation, which is different from that of single blastomeres, and ICM cells. These results concur with recent reports showing that hESCs derived from different stages of embryo development exhibit very little difference in their gene expression profiles, maintain a similar pluripotent phenotype, and that the slight differences observed are probably due to differences in derivation and culture procedures . In our case, variability due to experimental procedures was minimized, thus leading us to conclude that there were no significant differences between hESCs from different developmental sources. These results could be of great importance to the understanding of human embryo development, pluripotency, and reprogramming of somatic cells to iPSCs.
Overall, in this paper we present a defined gene signature for blastomeres (TS), hESCs (IVTPS), and ICMs (IVVPS). Network analysis allowed us to establish biochemical, physical, and functional interactions between the genes that segregated to each expression cluster, which may define each developmental regulation stage. This was enabled by a systems biology approach, which allows the integration of massive data from biological databases with classical molecular biology information in a cellular and physiological context that accurately approximates the real situation occurring in biological processes . This new perspective is resulting in a revolution in classical molecular biology approaches and will enable us to continue to elucidate complex processes and pathways, such as those related with the beginning of human embryo development.
Materials and Methods
Transcriptional profiles from Day-3 embryos single blastomeres, and from ICM cells  were compared to the hESC signature obtained from single blastomeres from Day-3 embryos, and to ICM cells from blastocysts, namely: VAL-10b and VAL-11b; and VAL-5, -7, and -8, respectively , ,  (Fig 1A).
Permission for this Project was granted by the Spanish Authority, Instituto de Salud Carlos III on December 13th 2006 for the project entitled “Derivation of human embryonic stem cells (hESC) of therapeutic grade in Spain.” Human embryos frozen at different stages at the Instituto Valenciano de Infertilidad (IVI) were donated for this work according to the Spanish law 45/2003. The donors were asked to sign a specific consent form for stem cell derivation as indicated in the Real Decreto 2132/2004. All hESCs used in this work have been characterised, published and registered in the Spanish Stem Cell Bank (www. isciii/htdocs/terapia/terapia_bancocelular.jsp) and are available worldwide. This work was performed in the Valencian Node of the Spanish Stem Cell Bank, at the Centro Investigación Principe Felipe (CIPF).
Derivation of hESCs
From whole embryos.
Donated frozen embryos were thawed using an Embryo Thaw Kit (Vitrolife, Kungsbacka, Sweden) according to the manufacturer's instructions. Pronuclear stage and day-two embryos was transferred to IVF and CCM medium 1∶1 (Vitrolife). Thawed day-3 embryos were transferred to CCM medium (Vitrolife) and cultured for an additional 2 to 3 days. At the blastocyst stage, the zona pellucida was removed by treatment with Tyrode's acid solution, after which the embryos were sequentially washed in CCM and HES medium.
Zona-free blastocysts were cultured on irradiated human foreskin fibroblasts (ATTC) in multiwell cell culture plates (Beckton, Dickinson and Company, Erembodegem, Belgium) in 80% Knockout DMEM (Gibco/BRL, Paisley, Scotland, UK), 20% Knockout SR (Gibco/BRL), 1 mM glutamine (Sigma, St. Louis, MO), 0.1 mM β-Mercaptoethanol (Sigma), 1% non-essential amino acids stock (Gibco/BRL), 20 ng/ml of human basic Fibroblast Growth Factor (h-bFGF) (Invitrogen, Life Technologies, Carlsbad, CA) and 0.5% Penicillin-Streptomycin (Sigma). The plate was incubated at 37°C and 5% CO2 with no manipulation for 3 days. After this period, the medium was changed every 48 h and the culture was maintained for 2–3 weeks, until outgrowth with hESC morphology appeared. The outgrowth was dissociated mechanically avoiding the areas corresponding to trophoectoderm. The isolated fragments were re-plated in a new well containing new irradiated feeder cells and fresh HES medium. The medium was changed every 48 h and the growth of colonies with hESC morphology was checked under microscope. When several colonies were expanded the cryopreservation and characterisation of the new cell line was performed. This process was followed for VAL-5, -8, and -9 , .
From Inner Cell Masses isolated with a laser.
Once the embryo was thawed and cultured until blastocyst stage, the inner cell mass (ICM) was isolated using a micromanipulator. The holding micropipettes (Humagen, Charlottesville, VA) were put in the micromanipulator on the inverted Microscope. The blastocyst was placed in a drop of GPGD medium (Vitrolife) supplemented with 5% Human Serum Albumin (Vitrolife) in a micromanipulation plate (Becton, Dickinson, and Company) and was held with holding pipettes from both sides, trying to localise the ICM at 9 o'clock position. The ICM was separated from trophectoderm by laser shots cutting perpendicularly to the pipettes, from up to down as near as possible to ICM whilst avoiding its damage. When both parts were separated, the zona pellucida was separated by careful pipetting. The isolated ICM was seeded on irradiated human foreskin fibroblasts (ATTC) and the protocol described above was followed. This protocol was followed for VAL-7 .
From single blastomeres.
Donated frozen day-3 human embryos were thawed and incubated in CCM medium (Vitrolife) for at least 3 h under standard culture conditions (37°C, 5% CO2). Then, the single blastomere was removed from the embryo by a biopsy procedure similar to that it used in pre-implantation genetic diagnosis of genetic defects. The biopsied embryo was transferred to CCM medium (Vitrolife), cultured for additional 2–3 days and at blastocyst stage was cryopreserved.
The biopsied blastomere was transferred into a drop of CCM medium covered with mineral oil (Sigma) and cultured for 24 h. The isolated blastomere was transferred onto irradiated human foreskin fibroblasts (ATTC) in drops of CCM medium (Vitrolife) supplemented with 10 µg/ml human Laminin (Sigma) and covered with mineral oil (Sigma), this was referred to as day 0. From day 3, the medium drop containing the attached blastomere was refreshed daily by replacing 1/3 of the volume with CCM medium supplemented with human Laminin (Sigma; 10 µg/ml) and 25 ng/ml of h-bFGF (Invitrogen, Life Technologies, Carlsbad, CA). From day 5, CCM medium was replaced with standard hESC medium (80% KnockOut-DMEM, 20% KnockOut Serum Replacement, 25 ng/ml bFGF) enriched with 10% FCS, and replaced in drops on a daily basis. When an initial hESC colony was detected, it was dissected one or two days later within the same drop. The procedure was repeated in approximately five days. After the second dissection, small hESC clumps were transferred into a 4-well dish (Nunc) with freshly seeded irradiated human feeders. The following day, the FCS was withdrawn from the medium and replaced with the standard serum-free hESC medium. This process was followed for the derivation of VAL-10B and VAL-11B .
Culture and maintenance of undifferentiated hESCs
All hESC lines were cultured and maintained on irradiated human foreskin fibroblasts (ATTC) in multiwell cell culture plates (Becton, Dickinson, and Company, Erembodegem, Belgium) in 80% Knockout DMEM (Gibco/BRL, Paisley, Scotland, UK), 20% Knockout SR (Gibco/BRL), 1 mM glutamine (Sigma, St. Louis, MO), 0.1 mM β-Mercaptoethanol (Sigma), 1% non-essential amino acids stock (Gibco/BRL), containing 10 ng/ml of bFGF (Invitrogen, Life Technologies, Carlsbad, CA). The colonies were incubated at 37°C with 5% CO2, and the medium was changed every 48 h.
The colonies were mechanically dissected into clumps every 4 to 5 days and transferred to dishes containing new inactivated human foreskin feeder cells.
Preparation of RNA for microarray analysis
The RNA from hESCs was isolated using Zymo Research's Mini RNA Isolation Kit™ (Zymo Research Corporation) following the manufactureŕs recommendations. A total of 100,000 cells per VAL line were used for the RNA extraction destined for microarray analyses.
RNA was quantified by spectrometry (NanoDrop ND1000, NanoDrop Technologies, Wilmington, Delaware USA) and the quality confirmed using an RNA 6000 Nano Kit and Bioanalyzer (Agilent Technologies, Palo Alto, California USA) assay. 480 ng of total RNA was used to produce Cyanine 3-CTP-labeled cRNA using the One-Color Quick Amp Labelling Kit (Agilent p/n 5190-0442) according to the manufacturer's instructions. Following ‘One-Color Microarray-Based Gene Expression Analysis’ protocol Version 5.7 (Agilent p/n G4140-90040), 3 µg of labelled cRNA was hybridised with the Whole Human Genome Oligo Microarray Kit (Agilent p/n G2519F-014850) containing 41,000+ unique human genes and transcripts. Arrays were scanned in an Agilent Microarray Scanner (Agilent G2565BA) and data were extracted using Agilent Feature Extraction Software 9.5.3 following the Agilent protocol GE1-v5_95_Feb07 and the QC Metric Set GE1_QCMT_Jan08.
Microarrays from single biopsied blastomeres, ICMs, and hESCs from both sources are deposited under GEO numbers GSE22032 and GSE42520.
Gene expression data was analysed using the Limma Bioconductor package . The Normexp function was applied for background correction, quantile normalization  was used to standardize across arrays and multiple testing adjustment of p-values was done according to Benjamini and Hochberg's methodology .
For gene network analysis, an undifferentiation network signature (UNS) was created using genes previously reported in the literature to be characteristic of undifferentiation (n = 191) , , , – which were combined with genes that strongly interact with them, referred to as connectors (n = 75). These interaction data were obtained from the iHOP database  and GeneCards V3 database . Connector genes were included in the UNS only if they had at least 15 interaction partners within the initial 191 undifferentiation gene list. Data processing was done using in house R scripts. The UNS was further visualized and analyzed in Cytoscape  where genes were labeled according to cell localization, molecular function, and the signaling or metabolomic pathways they are involved in. This functional data was obtained from BioMart database.
Functional enrichment was performed with FatiGO  a widely used SEA implementation, which is included in the Babelomics  web-based package using the Gene Ontology (GO) , and KEGG Pathways  vocabularies. GO term annotations for the genes in the microarray were taken from the Ensembl database (release 55), and KEGG Pathway annotations were obtained from the KEGG web page. GO slims are cut-down versions of the GO ontologies which contain a subset of the terms in the whole GO ontology library. They give a broad overview of the ontology content without the specific fine grain details of the terms which is particularly useful for giving a summary of the GO annotation results. The GOslim options were selected in FatiGO in order to summarize functional enrichment comparison among signatures. For functional enrichment analysis, GO slim GOA, from FatiGO enrichment tool from Babelomics, was eventually used. GOslim GOA is composed by 33 terms, and all are used, hence no level needs to be selected.
Validation by real-time quantitative PCR (qPCR)
cDNA retro-transcription was performed with the MMLV enzyme contained in the Advantage™ RT-for-PCR kit (Clontech, Takara, Japan) using oligo (dT)18 as primers. 0.5–1 µg of total RNA was subjected to initial denaturalisation, retro-transcription for 60 min at 42°C and final enzyme inactivation at 70°C for 10 min.
For qPCR experiments, 150 ng of synthesised cDNA of each VAL line studied was included . Experiments were performed in duplicate, and each lot of experiments included an internal positive control and a negative water control. 2 µl of cDNA was added to each qPCR reaction which was carried out using the LightCycler FastStart PLUS Master SYBR Green (Roche) in a LightCycler 2.0 (Roche). qPCR cycles consisted of one denaturalization step at 95°C for 10 min, one amplification step of 40 cycles of 95°C 10 secs, 59°C, 6 secs, and 72°C 10 sec, and a melting curve to assess amplicon specificity. Validation assays were performed for DPPA5, HMGB1, MYC, POU5F1, RPL14, and RPL19 genes in non-amplified samples. Results are shown in Fig S3.
Cytoscape analysis showing microarray data value representation of: (A) the totipotency signature (TS), (B) the in vivo pluripotency signature (IVVPS), and (C) the selected in vitro pluripotency signature (IVTPS).
(A) Cytoscape analysis of all gene markers showing any interaction from the in vitro pluripotency signature. Node border color refers to cell localization, node shape to general function, and node color to specific function in the cell. Edge color refers to physical interactions, biochemical interactions or to both; when not specified functional interaction is assumed. Upstream arrow (red) means up-regulation versus single blastomeres and ICM, and downstream arrow (blue) means down-regulation versus blastomeres and ICMs. (B) Microarray data value representations of the in vitro pluripotency signature markers showing any interactions.
Validation of microarray results by real-time quantitative PCR. (A) Results obtained from qPCR analysis performed on non-amplified blastomeres, ICMs, and hESCs (VAL-5,-7, -8, 10B, -11B) for DDPA5, HMGB1, MYC, POU5F1, RPL14 and RPL19. RPS24 were used as references. (B) Microarray data corresponding to genes analyzed.
Gene list of the Undifferention Network Signature (UNS). Genes were added for their high interaction ratios and the databases used are indicated. Gene name, description, cell localization, and main and specific function in the cell are also specified. Normalized data values for each category, single blastomeres from day-3 human embryos, ICMs from blastocysts, hESCs derived from single blastomeres, and hESCs derived from ICMs. Normalized data are log-transformed expression values. The criteria for statistically significant values was a p-value cutoff of <0.05
Gene list constituting the totipotency signature (TS). Gene name, description, cell localization, and main and specific function in the cell are also specified. Normalized data values for each category, single blastomeres from day-3 embryos, ICMs from blastocysts, hESCs derived from single blastomeres, and hESCs derived from ICMs. Normalized data are log-transformed expression values. The criteria for statistically significant values was a p-value cutoff of <0.05.
Gene list constituting the in vivo pluripotency signature (IVVPS). Gene name, description, cell localization, and main and specific function in the cell are also specified. Normalized data values for each category, single blastomeres from day-3 embryos, ICMs from blastocysts, hESCs derived from single blastomeres, and hESCs derived from ICMs. Normalized data are log-transformed expression values. The criteria for statistically significant values was a p-value cutoff of <0.05.
Gene list constituting the in vitro pluripotency signature (IVTPS). Gene name, description, cell localization, and main and specific function in the cell are also specified. Normalized data values for each category, single blastomeres from 6- and 8-cell embryos, ICMs from blastocysts, hESCs derived from single blastomeres, and hESCs derived from ICMs. Normalized data are log-transformed expression values. The criteria for statistically significant values was a p-value cutoff of <0.05.
We want to acknowledge the help received from all members of the former Valencia Node of the National Stem Cell Bank from the CIPF, Dr. Irene Cervelló from FIVI, Dr. Susana Rodríguez-Navarro and the members of the mRNA transport lab from the CIPF for their support in the elaboration of this paper.
Conceived and designed the experiments: AG AC CS. Performed the experiments: AG MEP DV ES VR. Analyzed the data: AG PD AC DM. Contributed reagents/materials/analysis tools: DM JD. Wrote the paper: AG CS.
- 1. Noble D (2006) Systems biology and the heart. Biosystems 83: 75–80.
- 2. Bu Z, Callaway DJ (2011) Proteins move! Protein dynamics and long-range allostery in cell signaling. Adv Protein Chem Struct Biol 83: 163–221.
- 3. Braude P, Bolton V, Moore S (1988) Human gene expression first occurs between the four- and eight-cell stages of preimplantation development. Nature 332: 459–461.
- 4. Van de Velde H, Cauffman G, Tournaye H, Devroey P, Liebaers I (2008) The four blastomeres of a 4-cell stage human embryo are able to develop individually into blastocysts with inner cell mass and trophectoderm. Hum Reprod 23: 1742–1747.
- 5. Giritharan G, Ilic D, Gormley M, Krtolica A (2011) Human embryonic stem cells derived from embryos at different stages of development share similar transcription profiles. PLoS One 6: e26570.
- 6. Edwards RG, Hansis C (2005) Initial differentiation of blastomeres in 4-cell human embryos and its significance for early embryogenesis and implantation. Reprod Biomed Online 11: 206–218.
- 7. Niakan KK, Han J, Pedersen RA, Simon C, Pera RA (2012) Human pre-implantation embryo development. Development 139: 829–841.
- 8. Reubinoff BE, Pera MF, Fong CY, Trounson A, Bongso A (2000) Embryonic stem cell lines from human blastocysts: somatic differentiation in vitro. Nat Biotechnol 18: 399–404.
- 9. Simon C, Escobedo C, Valbuena D, Genbacev O, Galan A, et al. (2005) First derivation in Spain of human embryonic stem cell lines: use of long-term cryopreserved embryos and animal-free conditions. Fertil Steril 83: 246–249.
- 10. Thomson JA, Itskovitz-Eldor J, Shapiro SS, Waknitz MA, Swiergiel JJ, et al. (1998) Embryonic stem cell lines derived from human blastocysts. Science 282: 1145–1147.
- 11. Valbuena D, Galan A, Sanchez E, Poo ME, Gomez E, et al. (2006) Derivation and characterization of three new Spanish human embryonic stem cell lines (VAL -3 -4 -5) on human feeder and in serum-free conditions. Reprod Biomed Online 13: 875–886.
- 12. Chung Y, Klimanskaya I, Becker S, Li T, Maserati M, et al. (2008) Human embryonic stem cell lines generated without embryo destruction. Cell Stem Cell 2: 113–117.
- 13. Ilic D, Giritharan G, Zdravkovic T, Caceres E, Genbacev O, et al. (2009) Derivation of human embryonic stem cell lines from biopsied blastomeres on human feeders with minimal exposure to xenomaterials. Stem Cells Dev 18: 1343–1350.
- 14. Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, et al. (2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131: 861–872.
- 15. Yu J, Vodyanik MA, Smuga-Otto K, Antosiewicz-Bourget J, Frane JL, et al. (2007) Induced pluripotent stem cell lines derived from human somatic cells. Science 318: 1917–1920.
- 16. Reijo Pera RA, DeJonge C, Bossert N, Yao M, Hwa Yang JY, et al. (2009) Gene expression profiles of human inner cell mass cells and embryonic stem cells. Differentiation 78: 18–23.
- 17. O'Leary T, Heindryckx B, Lierman S, van Bruggen D, Goeman JJ, et al. (2012) Tracking the progression of the human inner cell mass during embryonic stem cell derivation. Nat Biotechnol 30: 278–282.
- 18. Galan A, Montaner D, Poo ME, Valbuena D, Ruiz V, et al. (2010) Functional genomics of 5- to 8-cell stage human embryos by blastomere single-cell cDNA analysis. PLoS One 5: e13615.
- 19. Hervouet E, Vallette FM, Cartron PF (2009) Dnmt3/transcription factor interactions as crucial players in targeted DNA methylation. Epigenetics 4: 487–499.
- 20. Abeyta MJ, Clark AT, Rodriguez RT, Bodnar MS, Pera RA, et al. (2004) Unique gene expression signatures of independently-derived human embryonic stem cell lines. Hum Mol Genet 13: 601–608.
- 21. Adewumi O, Aflatoonian B, Ahrlund-Richter L, Amit M, Andrews PW, et al. (2007) Characterization of human embryonic stem cell lines by the International Stem Cell Initiative. Nat Biotechnol 25: 803–816.
- 22. Assou S, Le Carrour T, Tondeur S, Strom S, Gabelle A, et al. (2007) A meta-analysis of human embryonic stem cells transcriptome integrated into a web-based expression atlas. Stem Cells 25: 961–973.
- 23. Bhattacharya B, Miura T, Brandenberger R, Mejido J, Luo Y, et al. (2004) Gene expression in human embryonic stem cell lines: unique molecular signature. Blood 103: 2956–2964.
- 24. Gonzalez-Roca E, Garcia-Albeniz X, Rodriguez-Mulero S, Gomis RR, Kornacker K, et al. (2010) Accurate expression profiling of very small cell populations. PLoS One 5: e14418.
- 25. Kurimoto K, Yabuta Y, Ohinata Y, Saitou M (2007) Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nat Protoc 2: 739–752.
- 26. Abbosh PH, Zhang S, Maclennan GT, Montironi R, Lopez-Beltran A, et al. (2008) Germ cell origin of testicular carcinoid tumors. Clin Cancer Res 14: 1393–1396.
- 27. Vassena R, Boue S, Gonzalez-Roca E, Aran B, Auer H, et al. (2011) Waves of early transcriptional activation and pluripotency program initiation during human preimplantation development. Development 138: 3699–3709.
- 28. Aguilar-Gallardo C, Poo M, Gomez E, Galan A, Sanchez E, et al. (2010) Derivation, characterization, differentiation, and registration of seven human embryonic stem cell lines (VAL-3, -4, -5, -6M, -7, -8, and -9) on human feeder. In Vitro Cell Dev Biol Anim 46: 317–326.
- 29. Galan A, Simon C (2012) Human embryonic stem cells derived in xeno-free conditions. Methods Mol Biol 873: 13–32.
- 30. Assou S, Cerecedo D, Tondeur S, Pantesco V, Hovatta O, et al. (2009) A gene expression signature shared by human mature oocytes and embryonic stem cells. BMC Genomics 10: 10.
- 31. Boyer LA, Lee TI, Cole MF, Johnstone SE, Levine SS, et al. (2005) Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122: 947–956.
- 32. Saitou M, Yabuta Y, Kurimoto K (2008) Single-cell cDNA high-density oligonucleotide microarray analysis: detection of individual cell types and properties in complex biological processes. Reprod Biomed Online 16: 26–40.
- 33. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27: 431–432.
- 34. Chen X, Xu H, Yuan P, Fang F, Huss M, et al. (2008) Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 133: 1106–1117.
- 35. Loh YH, Wu Q, Chew JL, Vega VB, Zhang W, et al. (2006) The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells. Nat Genet 38: 431–440.
- 36. Kristensen DM, Nielsen JE, Skakkebaek NE, Graem N, Jacobsen GK, et al. (2008) Presumed pluripotency markers UTF-1 and REX-1 are expressed in human adult testes and germ cell neoplasms. Hum Reprod 23: 775–782.
- 37. Lin X, Swaroop A, Vaccarino FM, Murtha MT, Haas M, et al. (1996) Characterization and sequence analysis of the human homeobox-containing gene GBX2. Genomics 31: 335–342.
- 38. Hewitt SM, Fraizer GC, Saunders GF (1995) Transcriptional silencer of the Wilms' tumor gene WT1 contains an Alu repeat. J Biol Chem 270: 17908–17912.
- 39. Pan G, Li J, Zhou Y, Zheng H, Pei D (2006) A negative feedback loop of transcription factors that controls stem cell pluripotency and self-renewal. FASEB J 20: 1730–1732.
- 40. Pinto AR, Li H, Nicholls C, Liu JP (2011) Telomere protein complexes and interactions with telomerase in telomere maintenance. Front Biosci 16: 187–207.
- 41. Shi W, Wang H, Pan G, Geng Y, Guo Y, et al. (2006) Regulation of the pluripotency marker Rex-1 by Nanog and Sox2. J Biol Chem 281: 23319–23325.
- 42. Peng S, Chen LL, Lei XX, Yang L, Lin H, et al. (2011) Genome-wide studies reveal that Lin28 enhances the translation of genes important for growth and survival of human embryonic stem cells. Stem Cells 29: 496–504.
- 43. Ercolani L, Florence B, Denaro M, Alexander M (1988) Isolation and complete sequence of a functional human glyceraldehyde-3-phosphate dehydrogenase gene. J Biol Chem 263: 15335–15341.
- 44. Zalkin H, Dixon JE (1992) De novo purine nucleotide biosynthesis. Prog Nucleic Acid Res Mol Biol 42: 259–287.
- 45. Park JH, Bang SW, Jeon Y, Kang S, Hwang DS (2008) Knockdown of human MCM10 exhibits delayed and incomplete chromosome replication. Biochem Biophys Res Commun 365: 575–582.
- 46. Verdin E, Dequiedt F, Kasler HG (2003) Class II histone deacetylases: versatile regulators. Trends Genet 19: 286–293.
- 47. Vinayagam A, Stelzl U, Foulle R, Plassmann S, Zenkner M, et al. (2011) A directed protein interaction network for investigating intracellular signal transduction. Sci Signal 4: rs8.
- 48. Cervello I, Gil-Sanchis C, Mas A, Delgado-Rosas F, Martinez-Conejero JA, et al. (2010) Human endometrial side population cells exhibit genotypic, phenotypic and functional features of somatic stem cells. PLoS One 5: e10964.
- 49. Zhou S, Schuetz JD, Bunting KD, Colapietro AM, Sampath J, et al. (2001) The ABC transporter Bcrp1/ABCG2 is expressed in a wide variety of stem cells and is a molecular determinant of the side-population phenotype. Nat Med 7: 1028–1034.
- 50. Cuesta R, Laroia G, Schneider RJ (2000) Chaperone hsp27 inhibits translation during heat shock by binding eIF4G and facilitating dissociation of cap-initiation complexes. Genes Dev 14: 1460–1470.
- 51. Young RA (2011) Control of the embryonic stem cell state. Cell 144: 940–954.
- 52. Bhattacharya B, Puri S, Puri RK (2009) A review of gene expression profiling of human embryonic stem cell lines and their differentiated progeny. Curr Stem Cell Res Ther 4: 98–106.
- 53. Chen CC, Zhong S (2008) Inferring gene regulatory networks by thermodynamic modeling. BMC Genomics 9 Suppl 2S19.
- 54. Kafri T, Ariel M, Brandeis M, Shemer R, Urven L, et al. (1992) Developmental pattern of gene-specific DNA methylation in the mouse embryo and germ line. Genes Dev 6: 705–714.
- 55. Vassena R, Dee Schramm R, Latham KE (2005) Species-dependent expression patterns of DNA methyltransferase genes in mammalian oocytes and preimplantation embryos. Mol Reprod Dev 72: 430–436.
- 56. Huntriss J, Hinkins M, Oliver B, Harris SE, Beazley JC, et al. (2004) Expression of mRNAs for DNA methyltransferases and methyl-CpG-binding proteins in the human female germ line, preimplantation embryos, and embryonic stem cells. Mol Reprod Dev 67: 323–336.
- 57. Richards M, Tan SP, Tan JH, Chan WK, Bongso A (2004) The transcriptome profile of human embryonic stem cells as defined by SAGE. Stem Cells 22: 51–64.
- 58. Rahl PB, Lin CY, Seila AC, Flynn RA, McCuine S, et al. (2010) c-Myc regulates transcriptional pause release. Cell 141: 432–445.
- 59. Ng HH, Surani MA (2011) The transcriptional and signalling networks of pluripotency. Nat Cell Biol 13: 490–496.
- 60. Varela E, Schneider RP, Ortega S, Blasco MA (2011) Different telomere-length dynamics at the inner cell mass versus established embryonic stem (ES) cells. Proc Natl Acad Sci U S A 108: 15207–15212.
- 61. Westerhoff HV, Palsson BO (2004) The evolution of molecular biology into systems biology. Nat Biotechnol 22: 1249–1252.
- 62. Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3: Article3.
- 63. Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19: 185–193.
- 64. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B Methodological 57: 289–300.
- 65. Hoffmann R, Valencia A (2004) A gene network for navigating the literature. Nat Genet 36: 664.
- 66. Harel A, Dalah I, Pietrokovski S, Safran M, Lancet D (2011) Omics data management and annotation. Methods Mol Biol 719: 71–96.
- 67. Al-Shahrour F, Minguez P, Tarraga J, Medina I, Alloza E, et al. (2007) FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Res 35: W91–96.
- 68. Medina I, Carbonell J, Pulido L, Madeira SC, Goetz S, et al. (2010) Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res 38 Suppl: W210–213
- 69. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25–29.
- 70. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38: D355–360.
- 71. Galan A, Simon C (2010) Monitoring stemness in long-term hESC cultures by real-time PCR. Methods Mol Biol 584: 135–150.