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Stem Cells. Author manuscript; available in PMC Jul 5, 2007.
Published in final edited form as:
PMCID: PMC1906587
HALMS: HALMS128925
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A meta-analysis of human embryonic stem cells transcriptome integrated into a web-based expression atlas

Abstract

Microarray technology provides a unique opportunity to examine gene expression patterns in human embryonic stem cells (hESCs). We performed a meta-analysis of 38 original studies reporting on the transcriptome of hESCs. We determined that 1076 genes were found overexpressed in hESCs by at least 3 studies when compared to differentiated cell types, thus composing a “consensus hESC gene list”. Only one gene was reported by all studies: the homeodomain transcription factor POU5F1/OCT3/4. The list comprised other genes critical for pluripotency such as the transcription factors NANOG and SOX2, and the growth factors TDGF1/CRIPTO and Galanin. We show that CD24 and SEMA6A, two cell surface protein-coding genes from the top of the consensus hESC gene list, display a strong and specific membrane protein expression on hESCs. Moreover, CD24 labeling permits to purify by flow cytometry hESCs co-cultured on human fibroblasts. The “consensus hESC gene list” also included the FZD7 WNT receptor, the G protein-coupled receptor GPR19, and the HELLS helicase which could play an important role in hESCs biology. Conversely, we identified 783 genes downregulated in hESCs and reported in at least three studies. This “consensus differentiation gene list” included the IL6ST/GP130 LIF receptor. We created an online hESC expression atlas, (http://amazonia.montp.inserm.fr), to provide an easy access to this public transcriptome dataset. Expression histograms comparing hESC to a broad collection of fetal and adult tissues can be retrieved with this web tool for more than 15 000 genes.

Keywords: pluripotent stem cells, gene expression profiling, microarray analysis

Introduction

In the preimplantation mammalian embryo, the inner cell mass is able to differentiate into any cell type of the embryo proper. It has been recognized in mice since 1981 that embryonic stem cells (ESCs) with a prolonged proliferative capacity in vitro can be derived from the inner cell mass [1]. ESC line derivation from human embryos was reported in 1998 [2]. ESCs are pluripotent cells that can contribute to all tissues in vivo, and to the three primary germ layers as well as extraembryonic tissues in vitro. As pluripotency is maintained even after prolonged periods of culture, human ESCs (hESCs) have a great therapeutic potential in regenerative medicine. Careful molecular characterization of this unique cellular model of pluripotency should help to optimize and scale up in vitro manipulation of hESCs for clinical applications. Some genes specific to the very early stages of development and expressed in hESC lines such as POU5F1/OCT3/4, NANOG, REX1, SOX2, FGF4 and FOXD3 have already been identified [38]. However, the picture is far from complete and the molecular mechanisms involved in self-renewal and pluripotency are still under tight scrutiny [9, 10]. Moreover, extensive knowledge on hESCs should help in designing protocols for the isolation and production of other multi/pluripotent stem cells derived from tissues of adult individuals.

Microarrays are a major technical breakthrough that can monitor the expression of a whole genome in one experiment. Application of this technology to hESCs has largely contributed to our knowledge on the mechanisms underlying the maintenance of pluripotency of hESCs and their in vitro differentiation. Unfortunately, the datasets generated are heterogeneous both in accessibility (public databases or Supplemental data) and in the techniques used (variability in microarray design, sample labeling techniques, choice of control samples and computational tools). Despite our findings of 38 original publications reporting hESC transcriptome analyses, the disparities between datasets, the variety of sources, the substantial know-how needed for transcriptome data mining discourage most non-specialists to consult this information. Thus, large amount of data are very much underused, and lack alternative interpretations since only the conclusions reflecting the analysis carried out by the authors are presented. This situation has led to initiatives such as ONCOMINE in the field of cancer [11]. We present here the first effort in compiling all publicly available microarray data relating to hESCs. From the 38 original publications studying the hESC transcriptome, we identified genes that were consistently overexpressed in hESCs when compared to differentiated samples (“consensus hESC gene list”) and underexpressed in hESCs (“consensus differentiation gene list”) in different studies. These lists will further deepen our knowledge on this unique cell model of developmental biology. Concurrently, we created an on-line database, Amazonia!, which provides an easy access to this public transcriptome dataset.

Materials and Methods

Lists of genes differentially expressed

Analyzing 38 original studies using transcriptome analysis to study hESC, we were able to collect 20 lists of transcripts that were upregulated in hESCs compared to differentiated cell types and 11 lists of transcripts that were downregulated (see Supplemental Tables S1, S2 and S3). We only selected transcripts lists that provided a fold ratio of the mean expression in hESCs to that in differentiated cells. Each list was mapped to Unigene build 176. When the mean value of expression in differentiated cells was zero, which occurred with ESTs and SAGE, the hESC/differentiation ratio was arbitrarily set at 50. Only genes with a fold ratio greater or equal to 2 (“hESC” genes), or lower or equal to 0.5 (“differentiation” genes) were selected. The Gene Ontology annotation analysis was carried out using the Fatigo+ tool on the Babelomics website (http://babelomics.bioinfo.cipf.es) using gene symbols [12]. Only annotation with a false discovery rates (FDR) adjusted P-value < 0.05 were considered significant.

Integrating Affymetrix GeneChip datasets obtained from distinct studies

In order to compare the transcriptome of hESCs to that of differentiated cell populations, we built an expression compendium by combining the U133A (Affymetrix, Santa Clara, USA) microarray data from 8 publications [1320]. Indeed, we and others have shown that the GeneChip system (Affymetrix) allows direct comparison between datasets obtained in different centers, provided that the same chip and the same normalization are used [10, 21, 22]. The number of samples amounted to 217, including 24 hESC samples (11 different hESC lines). All samples were normalized before analysis with the GCOS 1.2 software (Affymetrix), using the “global scaling” method with a TGT value set to 100. The “ detection call” can either be “present”, when the perfect match probes are significantly more hybridized than the mismatch probes, “absent” when both perfect match and mismatch probes display a similar fluorescent signal, or “marginal” when the probeset does neither comply to the present nor to the absent call criteria. When several probesets measured the same gene, only the probeset with the maximal number of present detection call across all samples was selected. This step reduced the list of probesets to 14 074. This dataset is available as Supplemental Table S4 and can be accessed on our website http://amazonia.montp.inserm.fr.

Hierarchical clustering

Hierarchical clustering was carried out on the detection call data with the CLUSTER and TREEVIEW software packages [23]. The value 1 was assigned to present calls, −1 to absent calls and 0 to marginal calls. This matrix was clustered without further mathematical transformation. Only genes were clustered, i.e. the order of the samples is the order used on the Amazonia! website, and is based on grouping according to the embryonic germ layer origin of the sample.

hESC lines and karyotype

The list of hESC lines used and their respective karyotype are listed in Table 1.

Table 1
hESC: laboratory of origin and karyotype

hESC culture

After approval from the French Ministry of Research, the French Ministry of Health and the Agence de la Biomédecine, HUES1 and HUES3 hESCs were imported from Douglas Melton’s laboratory (Harvard University, MA, USA) and cultured as described [24]. Cells were passaged every 3–4 days enzymatically with 0.25% trypsin/EDTA (Invitrogen, Cergy Pontoise, France) and cultured in knockout Dulbecco’s modified essential medium (Invitrogen) without plasmanate, with 10% KO-SR (Invitrogen), 2 mM L-glutamine, 1x nonessential amino acids, 0.05 mM β-mercaptoethanol, 10 ng/ml FGF2 (Abcys, Paris, France). Medium was replaced daily. HUES1 and HUES3 were cultured on murine embryonic fibroblasts (MEF) obtained from E13 ICR mice embryos (Harlan, Gannat, France) or on human foreskin fibroblasts (hFF) in porcine skin gelatin coated (Sigma-Aldrich, St. Quentin. Fallavier, France) 6 wells dishes. The hFF cell lines SM1 and SM3 were derived from respectively 75 and 3 years old patients undergoing foreskin reduction. Informed consent was obtained from the patient or the patient’s parents. MEF were cultured in DMEM with 10% fetal calf serum (FCS) and hFF in DMEM with 20% FCS. MEF and hFF were mitotically inactivated by mitomycin-C (2h at 10 μg/ml). These hESC expressed POU5F1, TRA-1-60, TRA-1-81, displayed phosphatase alkaline activity, and were able to differentiate into embryoid bodies that expressed differentiation markers of astrocytic lineage (GFAP) or endodermal lineage (alpha-feto protein) (see Supplemental Figure S1 and data not shown).

The HS293 and HS235 hESC lines were cultured in the Department of Obstetrics and Gynecology, (CLINTEC) at the Karolinska University Hospital as described [25]. Briefly, hESC were cultured on hFF (CRL-2429; American Type Culture Collection, Manassas, VA) mitotically inactivated by irradiation (40 Gy), in KO-DMEM 20% knockout SR, 2 mM Glutamax, 0.5% penicillin–streptomycin, 1% nonessential amino acids, 0.5 mM β-mercaptoethanol (all from Gibco Invitrogen Corporation, Paisley, Scotland), 1% insulintransferrin-selenium (Sigma-Aldrich) and 8 ng/mL of bFGF (R&D Systems, Oxford, UK). For karyotype analysis, hESC cells were treated with 75ng/ml final Karyomax Colcemid (Invitrogen) for 1h, trypsinized, incubated in 0.0375M KCl for 20 min, and fixed in fresh 3:1 methanol/acetic acid solution. 1 to 2 spreads were counted for chromosome number and 12 to 16 banding patterns were analyzed at 300–500 bands resolution.

Flow cytometry analysis

Human ES cells and fibroblasts were dissociated with trypsin (0.25%)-EDTA (1mM) (GIBCO) for 3 min. Cells were then washed with PBS and incubated for 30 min at 4°C in PBS with the corresponding monoclonal antibody (MAb): anti-CD24 MAb conjugated to phycoerythrin (PE) (dilution 1:50) (clone ALB9, Immunotech, Marseille, France) and/or anti-CD44 MAb conjugated to fluorescein isothiocyanate (FITC) (dilution 1:50) (clone J-173, Immunotech). After PBS washes, cells were suspended in Facsflow (Becton Dickinson, San Jose, CA) and fluorescence was analyzed with a FACSCalibur flow cytometer (Becton Dickinson) or sorted with a FACSAria cell sorter (Becton Dickinson). Appropriate isotype controls were included in all analyses.

Immunofluorescence

hESCs cultured on coverslips were fixed for 20 min in 4% paraformaldehyde and washed three times in PBS. Cells were permeabilized with 0.1% Triton X-100. After blocking at room temperature for 60 min in PBS with 5% donkey serum (S30, Chemicon international, Temecula, CA), cells were incubated for 1 hour at room temperature with primary antibody diluted in PBS with 5% donkey serum: POU5F1/OCT3/4 (sc 9081, Santa Cruz Biotechnology, Santa Cruz, CA; 1:300) and SEMA6A (AF1146, R&D, Abington, United Kingdom; 1:50). Cells were washed three times in PBS and incubated for 1 hour at room temperature with Alexa Fluor® 488 Donkey anti-Rabbit (A-11034; Molecular Probes; 1:1000) and Cy3 Donkey anti-Goat (Jackson ImmunoResearch; USA; 1:400) secondary antibodies, for POU5F1 and SEMA6A respectively. Unbound antibodies were removed by three washes in PBS. Hoechst staining was added to the first wash (Sigma, 5 μg/ml).

Results

Compiling hESC expression profiles

As of October 1st, 2006, we identified 38 original studies, 1protocol description and 4 reviews analyzing the transcriptome of hESCs (Table 2). The original studies used various hESCs, control cells and gene expression analysis techniques (summarized in Supplemental Table S1). 28 different hESC lines were used and transcriptome techniques included microarrays (7 different types of chips), ESTs scanning, SAGE, MPSS and Illumina beads. One study compared chromosome immuno-precipitation (ChIP) chip data with transcriptome data in hESCs [10]. Nevertheless, some common features emerged such as the frequent investigation of the H1, H9, and BG01 hESCs (in 12, 12 and 11 studies respectively), and the use of the GeneChip (Affymetrix) microarray system (in 16 studies) (see supplemental table S1).

Table 2
43 original studies or reviews analyzing hESC transcriptome

Meta-analysis of genes differentially expressed between hESCs and non-pluripotent cells

One main objective of large scale gene expression analyses of hESCs is to identify the set of genes that are overexpressed in this unique cell type (“hESC” genes) or underexpressed (“differentiation” genes). We reasoned that bona fide “hESC” or “differentiation” genes would be repeatedly uncovered by independent groups, regardless of the hESC lines, the control cells, the assay format or the statistical method that had been used. We collected, from these 38 original studies, 20 lists of transcripts overexpressed in hESCs (“hESC genes”) and 11 lists of genes underexpressed in hESCs (“differentiation genes”).

The 20 lists of hESC genes comprised 5567 different genes. As illustrated in Figure 1A, we observed a marked heterogeneity between these lists, with only 1076 genes found overexpressed in hESCs by three or more independent studies, 48 genes by 10 or more studies and only 1 gene by all 20 lists (see Supplemental Table S2). The 48 genes found overexpressed in at least 10 studies are listed in Table 3. Of note, the pivotal ES transcription factor POU5F1/OCT3/4 is the one gene found by all 20 lists, whereas genes found in at least 10 studies include the transcription factors NANOG and SOX2, and the growth factors TDGF1/CRIPTO and Galanin (GAL) that are known to be highly expressed by hESCs. Thus, according to hESC transcriptome analyses published to date, this list of 1076 genes found overexpressed in hESC cells by three or more studies can be viewed as a “consensus hESC gene list”.

Figure 1Figure 1
Meta-analysis of published “hESC” and “differentiation” gene lists
Table 3
48 genes overexpressed in hESCs compared to differentiated cell types in at least 10 studies

In order to get further insights into this hESC list, we built an expression compendium by combining the data from 5 publications using the U133A GeneChip microarray to analyze hESC transcriptome and 3 publications providing the transcriptome of various normal fetal and adult tissues (see material and method and Supplemental Table S4). This compendium included the gene profiling of 24 hESC samples and more than 190 various fetal and adult tissues samples. A heat map was generated for the “consensus hESC gene list” in this expression compendium based on the detection call provided by the GCOS 1.2 software. The detection call is a way to evaluate whether a gene is expressed or not in a given sample. Hierarchical clustering (Figure 1C) delineated four major clusters of genes: cluster (a) was a group of 40 genes specifically detected in hESCs (“hESC specific” genes, Table 4), including expected genes such as POU5F1/OCT3/4, NANOG, TDGF1, LIN28, CLDN6, GDF3, DNMT3A, but also genes such as CYP26A1, HELLS or GPR19, cluster (b) featured genes that were detected in both hESCs and CNS samples such as the GABA receptors GABRB3 and GABRA5, and the growth factor FGF13, cluster (c) genes detected in samples characterized by a high mitotic index such as SKP2, MYC, the cyclin CCNA2 and the MCM genes MCM2, MCM5, MCM6 and MCM7, cluster (d) genes overexpressed in hESCs but also expressed in a majority of the tissues included in this dataset such as PGK1, HSPA9B, and the ribosomal genes RPLP0, RPL6, RPL7 and RPL24. The complete lists of genes composing these clusters are available as Supplemental Table S5. The expression histograms of the 40 hESC specific genes are shown in Supplemental Figure S2. These results show that, though all 1076 genes of the “consensus hESC gene list” have been found overexpressed in hESCs compared to non-hESC samples by at least three different studies, 40 genes are indeed hESC specific (cluster a) but most are nonetheless also expressed in adult tissues to various extent.

Table 4
40 genes specifically expressed in hESCs (cluster “a” in Figure 1C)

The 11 lists of “differentiation” genes summed up to 4798 genes and we noted a similar heterogeneity as in the “hESC” lists (Supplemental Table S3). Out of these 4798 genes, only 783 were found underexpressed in hESCs by at least 3 different studies, composing a “consensus differentiation gene list”, three genes, lumican, collagen 1A1 and 3A1, by nine studies and none by all 11 studies (Figure 1B). Table 5 shows 30 selected genes found by at least 6 studies, which include the bone morphogenetic proteins (BMP) 1 and 4, the keratins 7 and 18, insulin-like growth factor 2 (IGF2), Heart and neural crest derivatives expressed 1 (HAND1) and the transducing chain IL6ST/GP130. The complete “consensus differentiation gene list” can be found in Supplemental Table S3.

Table 5
30 selected genes underexpressed in hESCs compared to differentiated cell types and found by at least 6 studies

We compared functional Gene Ontology (GO) annotations of the hESC genes to the differentiation genes. Several functional annotations were more represented in each category (Figure 1D). There were significantly more genes involved in “metabolism”, “mitosis”, “RNA splicing”, “nuclear pore”, “DNA repair” in the hESC gene list, reflecting the intense proliferation, DNA replication and DNA remodeling taking place in these cells. Conversely, GO annotations such as “organ development”, “skeletal development”, “extracellular matrix”, “cell adhesion”, “cell communication”, “integral to plasma membrane”, “signal transduction”, were significantly more frequent among genes upregulated in differentiated tissues, in agreement with the idea that hESC differentiation mimics early organogenesis, which is associated with the development of complex cell-cell communication and cell-extra cellular matrix (ECM) interactions.

CD24 and SEMA6A are hESC markers

We next inquired whether we could find, among the hESC genes, new markers that may be useful to identify, isolate and qualify hESCs in vitro. We focused on two cell surface hESC genes: CD24 and SEMA6A. These two genes have been found to be overexpressed in hESCs by respectively nine and fifteen studies, and are therefore good candidates as new hESC markers.

CD24 is a sialoglycoprotein known to be expressed on mature granulocytes and B cells subpopulations [26]. In addition to these hematopoietic cell types, microarray data from the expression compendium evidenced a high CD24 mRNA expression in keratinocytes, pancreas and thyroid, whereas most neural tissues, muscle, liver and testis did not express CD24 (Figure 2A). Upon differentiation of hESCs into embryoid bodies (EBs), CD24 expression is markedly downregulated (Figure 2B). Importantly, human foreskin fibroblasts (hFF) samples did not express CD24 (Figure 2C, 2F). Hence, we investigated whether CD24 could discriminate hESCs from fibroblasts in culture. We analyzed by flow cytometry the hESC lines HUES1, HUES3, HS293 and HS235 [24, 25] cultured on hFF and evidenced two distinct cell populations, one CD24+ and one CD24− (Figure 2E). To demonstrate that the CD24+ population corresponded to hESCs and the CD24− population to hFF, we took advantage of CD44, a strong fibroblast marker not expressed by hESCs (Figures 2D, 2G). Double staining of hESCs cultured on hFF showed that these two markers were expressed in a mutually exclusive manner and delimited a hESC CD24+CD44− population that did not overlap with the fibroblast CD24−CD44+ population (Figure 2H). Using CD24/CD44 labeling, we were able to separate hESC from fibroblasts and obtain pure hESC populations that recovered the cell sorting procedure and grew in vitro while retaining POU5F1 and cardinal cell surface markers of pluripotency expression (Supplemental Figure S3 and data not shown).

Figure 2Figure 2
CD24 and SEMA6A are two new hESC markers

SEMA6A is a class 6 semaphorin [27], i.e. transmembrane with cytoplasmic domain, known to be expressed in developing neural tissue. We recently reported the expression of this semaphorin in cumulus oophorus cells [28]. Comparison of RNA expression in hESCs and normal adult tissues showed that in addition to hESCs, SEMA6A was also expressed at high level in adult samples from the central and peripheral nervous system and placenta (Figure 2I). As for CD24, SEMA6A mRNA is downregulated upon EB differentiation and is not detected in human fibroblasts (Figure 2J and K). Immunofluorescence analysis showed a membrane localization of SEMA6A on hESCs, in contrast to POU5F1/OCT3/4 which had a strict nuclear localization (Figure 2L-M). In summary, we showed that CD24 and SEMA6A have indeed a preferential expression in hESCs, that this expression is confirmed at the protein level, and is declining upon EB differentiation. Thus these markers can be used to discriminate hESCs from feeder fibroblasts

hESC transcriptome data visualization through an open access web interface

We developed a website, Amazonia! (http://amazonia.montp.inserm.fr), to allow the scientific community access to these public data. This website is dedicated to the visualization of large, publicly available, human transcriptome data (Le Carrour et al., manuscript submitted). A main topic of this website is human embryonic stem cells. Data are visualized as expression histograms with a color code facilitating the recognition of cell type. Genes are accessed either by key words or through lists of genes. Most interestingly, when data were obtained using the same platform format, sample labeling and data normalization, it was possible to combine different experiments in one single virtual experiment. The U133A expression compendium comprises for example more than 200 different samples from 8 publications including hESCs and normal adult tissues. Thus, Amazonia! provides the expression profile of about 15 000 different genes in about 100 different tissues types or purified cell populations including 11 hESCs (H1, H9, HS181, HS235, HS237, FES21, FES22, FES29, FES30, ,I6, HES-2). Figure 3A illustrates this feature of our website with the expression histograms of five hESC specific genes (POU5F1, NANOG, GPR19, Helicase lymphoid-specific (HELLS) and the cytochrome P450 CYP26A1), two genes expressed in nonlineage-differentiated cells (HAND1 and IGF2), two factors highly expressed by human fibroblasts and smooth muscle that may contribute to the supporting properties of fibroblast to hESC culture (Gremlin (GREM1) and Matrix metalloproteinase 1 (MMP1)), one hematopoietic marker (CD45), one central nervous system marker (Glial fibrillary acidic protein (GFAP)) and one ubiquitously expressed gene (Ribosomal protein L3 (RPL3)).

Figure 3Figure 3
Expression of selected genes using Amazonia!

Another important feature of Amazonia! is the possibility to compare on the same web page the expression of a gene of interest in various datasets. Figure 3B shows that the expression of Frizzled 7 (FZD7) was markedly upregulated in hESCs, was downregulated during non-lineage differentiation into EBs, and was also highly expressed in embryonal carcinoma and Yolk sac carcinoma samples. The combination of these three histograms evidences a preferential FZD7 mRNA expression in normal and malignant embryonic cells, suggesting that FZD7 may play a major role in these pluripotent cell types.

In order to facilitate access for the scientific community to the lists of genes from published transcriptome analyses, we implemented a list manager in our Amazonia! website. Thus, one can access these lists straightforwardly and obtain an expression histogram for each gene in various public transcriptome collections. This feature is of particular interest to challenge a list of genes, for example a “hESC” gene list, with other hESC datasets or even with non-hESC datasets such as cancer datasets. Indeed, once a gene is selected, the user can navigate between various thematic pages, switching for instance from the “stem cells” page to the “leukemia and lymphoma”, “lung cancer” or other pages. Using this feature of the Amazonia! website, we could observe that the sialoglycoprotein CD24 was also highly expressed in acute lymphoblastic leukemia, lung cancer and glioma samples when compared to the corresponding normal samples (Figure 3C and data not shown).

Discussion

Human ESCs are remarkable by their ability both to self renew and to generate virtually any kind of cell type, hence carrying many hopes for cell therapy. It is anticipated that genome wide expression analyses, by providing an extensive molecular taxonomy, will help understanding this unique cell model of stem cell pluripotency. Transcriptome results can be viewed as a non-biased, genome wide, expression catalog. Many groups have published the transcriptome of hESCs, but, as a direct consequence of the massive data generated, access to this data on a routine basis was precluded for most researchers. Therefore, the construction of a database collecting publicly available hESC transcriptome and accessible through a user friendly interface is of utmost interest for the hECS researchers’ community. We found 38 original studies or reviews analyzing the transcriptome of hESCs. Expression data and gene lists extracted from these studies were included into Amazonia! and are now readily accessible. Interestingly, the frequent use of the U133A oligonucleotide microarrays allowed us to construct a virtual expression dataset of about 15 000 different genes in more than 200 tissue samples from various origins, including 24 hESC samples. Hence, the expression of each gene in hESCs is directly contrasted to that of normal fetal and adult tissues, as illustrated in Figure 3A.

Unearthing the hESC gene panoply may help to define what makes hESCs unique. To achieve this goal, most studies compared hESC transcriptome data to that of more differentiated cell types and obtained lists of genes over- or underexpressed in hESCs. Comparison of different transcriptome surveys of hESCs gave us the unique opportunity to identify genes that were identified by several authors as differentially expressed in hESCs. However, a striking heterogeneity was observed between the 20 lists of hESC genes, and only 48 genes were found to be highly enriched in hESCs in at least ten or publications, among 5567 genes found in at least one. Some of these differences may be explained by platform-to-platform or lab-to-lab variability, but this is likely not the main explanation as suggested by transcriptome platform comparisons [22]. Rather, the differences between the hESC cells lines used, the control samples, the specific caveats of each transcriptome analysis technique and the statistical methodologies likely contribute to these disparities (see Supplemental Table S2). For instance, the homeobox transcription factor NANOG, which is universally expressed in hESC, was not reported by Sperger et al. because no probe for NANOG was present on their microarray [29], nor was it reported by Brandenberger et al. because the differential regulation of NANOG in their in vitro differentiation model did not met their stringent statistical criteria [30]. ZFP42 (the human homolog of murine rex1) was never listed by MPSS studies because its MPSS signature has repeat sequences [31]. Another pitfall impacting on differentially expressed genes lists comparisons and contributing to the “small intersection” problem [32] is that in order to be at the intersection of 20 lists, a gene must have fulfilled 20 times the statistical filter, which it does with a probability equal to the product of the probabilities of each test. A way to circumvent this difficulty would be to obtain the raw data from these studies and to apply specific statistical tests [33]. However, the raw data were not available for many studies analyzed here, which prevented us from applying this approach in our study. Nevertheless, the 1076 hESC genes list provides the opportunity to the scientific community to examine the genes that have found over- or underexpressed in hESC by several authors. These lists provide further molecular insight into the biology of this unique stem cell model and are now starting points for many new research directions in the field of hESC. In the future, it will be interesting to extend this list by investigating additional hESC lines. The collection of additional transcriptome data is ongoing and we will update the database according to new publications on the hESC genome expression.

As one can easily notice by brownsing the “consensus hESC gene list” in the U133A “embryonic and adult samples” dataset, we found very few genes that are completely hESC specific. Indeed, most genes found overexpressed in hESCs are also expressed in other tissues (Figure 1C), and only 40 genes, grouped in cluster (a), comprised genes which expression was not detected in most other tissues (see supplemental Figure S2). Of note, genes can only be labeled as hESC “specific” in respect of the different tissues and cell populations that have been tested. As new cell types will be investigated, some of these genes may be found expressed in these new samples and thereby cease to be specific to hESCs. Specificity also depends on the sensitivity of the assay investigating expression of a given gene. For example, POU5F1/OCT3/4 has been reported to be expressed in germinal cells and even in bone marrow by RT-PCR assays [34, 35]. By contrast, microarray analysis show that this gene, clearly expressed at a high level in hESCs and teratocarcinoma samples, is neither detected by this technique in testis, ovary, bone marrow samples, nor in a pure oocyte population (Figure 3A and unpublished data). These observations suggest that the properties of hESCs, comprising self-renewal, unrestrained proliferation and pluripotency, are mediated by the expression of few specific genes, if any, together with genes which individually are not hESC specific, but whose combined expression is specific to hESCs. The “consensus hESC gene list” should encompass many of those genes contributing to the embryonic stem cell characteristics.

Transcriptome approaches have several limitations. Each technique has its own technical limits, but this meta-analysis partly circumvented these by the simultaneous analysis of different, complementary methods. Another drawback is that by looking at the transcriptome, we consider gene expression at the RNA level only, eluding all forms of post-transcriptional regulation. It will therefore be important in the future to look the differential expression of the genes described in the hESC and differentiation lists at these hESC and differentiation gene lists at the protein level. For that matter, this meta-analysis provides a list of pertinent genes for further protein validation. In line with this proposition, we chose to investigate more thoroughly two cell surface protein-encoding genes, which may serve as new hESC markers: CD24 and SEMA6A. Though these two genes have been found to be overexpressed in hESCs by 9 and 15 studies, respectively, we found that hESCs shared this RNA expression with several adult tissues as substantiated by microarray results (Figure 2). However, purification of hESCs requires only distinguishing them from differentiating stem cells and from the co-cultured feeder cells. CD24 expression is low in differentiating hESCs, and is absent in human fibroblasts. We were thus able to purify by flow cytometry pure hESC populations. This provides a new tool to isolate highly enriched populations of hESCs for subsequent experiments, including microarray analysis. We observed that CD24 was also highly expressed on various malignant cell types, as previously reported [36, 37]. Since CD24 is a ligand for P-selectin, it has been suggested that CD24 could be important in the dissemination of tumor cells by facilitating the interaction with endothelial cells [38]. The role of CD24 in hESCs is less clear because P-selectin is not expressed by human fibroblasts nor by hESCs themselves (data not shown), suggesting that CD24 may have other molecular functions. Semaphorins have been initially identified for their role in neuronal guidance as chemorepellents, but it is becoming clear that this large family of genes also plays important roles in organogenesis, vascularization, angiogenenesis and B lymphocyte signaling. We show here a clear protein expression of semaphorin 6A in hESCs, with an overexpression in hESCs compared to many other cells types, suggesting a functional role for this transmembrane molecule in cell-to-cell interaction or signaling in hESCs.

The clustering of the “consensus hESC gene list” based on the “detection call” identified a cluster of 40 genes with a high specificity in hESCs, with no expression in most samples from more than 100 different fetal and adult cell tissues (Table 4). In addition to genes clearly expected such as POU5F1/OCT3/4, NANOG or TDGF1, this cluster included genes whose hESC specificity had been overlooked. For example the expression of the G protein-coupled receptor 19 (GPR19) is restricted to hESCs and, if functional, may offer a possibility for in vitro intervention on proliferation or pluripotency of hESCs. Other hESC specific genes comprise the cytochrome P450 CYP26A1 which is responsible for retinoic acid degradation [39] or the helicase HELLS which is expressed at a moderate level in a few lymphoid samples but most importantly in hESCs and could be involved in DNA strand separation, including replication, repair, recombination, and transcription [40] (Figure 3A). Our meta-analysis also spotted additional interesting genes such as FZD7 which was identified as the frizzled receptor preferentially expressed on hESCs. Based on this expression, we hypothesize that FZD7 could be a major WNT receptor in hESCs. Thus, FZD7 could contribute to the pluripotency signal mediated by WNT previously reported in hESCs [41]. Regarding the “consensus differentiation gene list”, many genes are clearly related to differentiation such as collagen and keratin genes. We note that the transducing chain IL6ST/GP130, which is necessary to convey the signals from the IL-6 growth factor family including LIF, is expressed at a very low level in hESCs as compared to most differentiated adult tissues. This is in line with the largely accepted view that LIF signaling is dispensable for pluripotency in hESCs [2].

Conclusion

We analyzed 38 publications studying the hESC transcriptome. We propose a “consensus hESC gene list” and a “consensus differentiation gene list” that identifies the genes found respectively up- or downregulated in hESCs compared to differentiated samples by at least three publications. We provide the first tool to directly visualize the expression of most human genes in hESCs, and provide direct comparison with their expression in many normal and malignant tissues. This tool may be considered as the first hESC expression Atlas online. By providing an easy access to this large public data, we hope that Amazonia! will help boosting the translation of these invaluable expression information into biological applications.

Supplementary Material

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S7

S8

Acknowledgments

We are grateful to the various labs that gave free access to their complete transcriptome data, in agreement with the MIAME recommendations [42]. We thank Ned Lamb and Cyril Berthenet for support with informatics (IGH de Montpellier), and Hassan Boukhaddaoui for cell imaging (Montpellier RIO Imaging). We are grateful to Isabelle Rodde-Astier, Antoine Héron and Valérie Duverger (MacoPharma) for decisive support for this project. We thank Geneviève Lefort, Marie Ponset and Franck Pellestor for assistance in hESC karyotyping.

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