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Genes to Cells (2009) 14, 243-260. doi:10.1111/j.1365-2443.2008.01268.x
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Transcriptional profiling of CD31+ cells isolated from murine embryonic stem cells

 Devi Mariappan1, Johannes Winkler1, Shuhua Chen1, Herbert Schulz2, Jürgen Hescheler1 and Agapios Sachinidis1,*

1 Center of Physiology and Pathophysiology, Institute of Neurophysiology, and Center of Molecular Medicine, University of Cologne (CMMC), Robert-Koch Str. 39, 50931 Cologne, Germany
2 Max-Delbrueck-Center for Molecular Medicine—MDC, Robert-Rössle Str. 10, 13092 Berlin, Germany


   Abstract
Top
 Abstract
Introduction
 Results
 Discussion
 Experimental procedures
 References
 
Identification of genes involved in endothelial differentiation is of great interest for the understanding of the cellular and molecular mechanisms involved in the development of new blood vessels. Mouse embryonic stem (mES) cells serve as a potential source of endothelial cells for transcriptomic analysis. We isolated endothelial cells from 8-days old embryoid bodies by immuno-magnetic separation using platelet endothelial cell adhesion molecule-1 (also known as CD31) expressed on both early and mature endothelial cells. CD31+ cells exhibit endothelial-like behavior by being able to incorporate DiI-labeled acetylated low-density lipoprotein as well as form tubular structures on matrigel. Quantitative and semi-quantitative PCR analysis further demonstrated the increased expression of endothelial transcripts. To ascertain the specific transcriptomic identity of the CD31+ cells, large-scale microarray analysis was carried out. Comparative bioinformatic analysis reveals an enrichment of the gene ontology categories angiogenesis, blood vessel morphogenesis, vasculogenesis and blood coagulation in the CD31+ cell population. Based on the transcriptomic signatures of the CD31+ cells, we conclude that this ES cell-derived population contains endothelial-like cells expressing a mesodermal marker BMP2 and possess an angiogenic potential. The transcriptomic characterization of CD31+ cells enables an in vitro functional genomic model to identify genes required for angiogenesis.


   Introduction
Top
 Abstract
 Introduction
Results
 Discussion
 Experimental procedures
 References
 
Vasculogenesis and angiogenesis are the two distinct mechanisms responsible for the formation of new blood vessels (Risau 1997). Vasculogenesis occurs during early embryonic development and starts with the formation of bipotential precursors, the hemangioblasts (Fong et al. 1999). These precursors differentiate into endothelial cells and primitive blood cells, and they together form a primitive vascular network. Angiogenesis is a complex and tightly controlled process where the formation of new blood vessels from the pre-existing blood vasculature occurs by the sprouting, splitting and remodeling of the vascular network (Risau 1997). Endothelial cells are centrally involved in each process, where they migrate, proliferate and assemble into tubes which is regulated by a secreted angiogenic mitogen, vascular endothelial growth factor (Breier et al. 1992).

Purified endothelial cells are of great interest as they could be useful for engineering of new blood vessels and for the treatment of myocardial ischemia. A potential source of cells for these applications are embryonic stem (ES) cells which have the capacity to generate all embryonic cell lineages, including endothelial cells (Doetschman et al. 1985; Risau 1995). ES cell-derived endothelial cell lines have been previously reported. Induced endothelial cells are immortalized by SV40 large T antigen (Gendron et al. 1996) or by polyoma middle T antigen (Balconi et al. 2000) or selected by Tie1 promoter-driven puromycin resistance gene expression (Marchetti et al. 2002). These cell lines show common properties of endothelial cells, and the latter cell line was shown to participate in tumor angiogenesis in vivo. ES cells were shown to spontaneously differentiate in vitro into the endothelial lineage, ultimately forming vascular structures in ES cell-derived embryoid bodies (EBs) (Vittet et al. 1996). In addition, advances in the understanding of angiogenesis have been translated to the development of drugs (Jain et al. 2006) targeting the control of angiogenesis in conditions such as cancer. Thus, the development of endothelial cell lines specified in various differentiation stages should be useful not only for vascular biology research but also for drug discovery. Finally, endothelial cells are required for practical application to vascular regeneration, in particular for the commercialization of vascular regeneration via the generation of transplantable cell products.

Although significant progress has been made towards the possible applications of ES-derived endothelial cells, the definition and identity of endothelial cells pose a major challenge because a definitive marker for endothelial cells remains obscure because of the heterogeneity of most markers that are also expressed in other mesodermal cell types such as hematopoietic cells. CD31 is the most widely used vascular marker, expressed by endothelial cells as well as by a subset of hematopoietic cells (Watt et al. 1995). Thus, CD31+ cells can be defined as endothelial-like, possessing the potential to differentiate into functional endothelial cells. The specific transcriptomic identity of the CD31+ cells is essential for characterizing the CD31+ cellular phenotypes and for the identification of the biological, physiological and functional processes occurring in these cells. We isolated endothelial-like cells from differentiating mouse ES cells using a CD31 antibody in conjunction with immuno-magnetic isolation. After characterization of the CD31+ cells by immunostaining and by functional assays, the complete transcriptome of the cells has been identified. Our microarray results clearly show an endothelial expression signature in the CD31+ population, specifically regulated signaling pathways and novel candidate genes possibly involved in vascular development.


   Results
Top
 Abstract
 Introduction
 Results
Discussion
 Experimental procedures
 References
 
Development of CD31+ cells in differentiating EBs

To isolate CD31+ cells from mouse ES cells, their vasculogenic potential was first characterized by analyzing the expression of endothelial-specific genes during the in vitro differentiation of ES cells and by detection of vascular network-like structures in differentiating EBs. Murine 1G11 endothelial cells were used as a positive control for the expression of endothelial-specific genes. ES cells were differentiated into EBs by LIF removal and aggregation. At different time points during the differentiation process, endothelial-specific gene expression was analyzed by RT-PCR, including CD31, von Willebrand factor (vWF), Tie2, Tie1, VE-cadherin, ICAM2, Flt-1 and Flk-1. CD31, vWF and Tie2 were expressed in undifferentiated ES cells (see Fig. S1A), and get down-regulated or undetectable during the initial stages of differentiation and increased again at the later stages (days 6–12) of EB differentiation indicating an endothelial developmental process. Flk-1 and Flt-1 were expressed from early time points of EB differentiation, whereas VE-cadherin, Tie1 and ICAM2 transcripts appeared by day 6 and later all transcript levels remained high and constant (see Fig. S1A). With progression of time in culture, there was a decrease in the transcription of the pluripotency markers like Oct-3/4 (known as Pou5f1), Rex1 and Nanog and a parallel increase in the transcription of endothelial-specific genes (such as CD31 and VE-cadherin) in ES cells, indicating a progressive differentiation to endothelial cells (see Fig. S1A). Our results are consistent with previous reports showing that ES cells are able to spontaneously differentiate in vitro into the endothelial lineages, ultimately forming vascular structures in ES-derived EBs (Vittet et al. 1996). Previous studies have also shown that the endothelial markers were sequentially expressed, closely recapitulating endothelial cell differentiation in vivo during embryonic development (Vittet et al. 1996; Vailhe et al. 2001).

Endothelial-specific protein expression in developing EBs was analyzed by whole-mount immunostaining using an anti-CD31 antibody. To monitor endothelial differentiation of ES cells, EBs were stained with anti-CD31 and analyzed with confocal laser scanning microscopy at different time points. EBs containing several cell clusters positive for CD31 (see Fig. S1B) were observed at day 6 of differentiation. The staining in these clusters was observed at cell–cell contacts, and these cell clusters might represent the hemangioblasts (a common precursor for hematopoietic and endothelial cells). Network-like capillaries resembling a primitive vascular network were observed approximately 8–12 days (see Fig. S1B) during differentiation. The development of vascular network-like structures during the time course of differentiation was accompanied by the expression of VE-cadherin, ICAM2 and CD31 as determined by RT-PCR analysis (see Fig. S1A) and it is in agreement with previous studies (Vittet et al. 1996; Hirashima et al. 1999).

Isolation and characterization of CD31+ cells from mouse ES cells

Based on previous reports (Vittet et al. 1996; Levenberg et al. 2002) and our analysis of endothelial gene and protein expression, the most suitable method and time point for the isolation of endothelial cells were determined. We selected CD31 as a marker for ES cell-derived endothelial cells. During mouse ES cell differentiation, CD31 was expressed on the cell surface and 20%–25% of the cells were CD31+ by day 8 (Fig. 1A). As CD31 expression was observed in ES cells, a cell surface antigen SSEA-1 (stage-specific embryonic antigen) also expressed by undifferentiated ES cells (Cui et al. 2004) was used to deplete the undifferentiated ES cell population and the CD31+ cells were enriched by magnetic bead sorting (MACS) as described previously (Levenberg et al. 2002) to 95%–98% purity after two rounds of selection (Fig. 1A). To confirm their endothelial-like phenotype, CD31+ cells were cultured and assayed for the presence of endothelial markers, their capacity to uptake DiI-acetylated-LDL and the ability to form capillary-like structures.


Figure 1
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  		Figure 1  (A) Flow cytometric analysis carried out to determine the purity of the sorted cells. The purity of the sorted cells was analyzed using FACScan. The isolated cells show a purity approximately 98% compared to cells prior to selection (27%) which was stained with anti-CD31 conjugated with phycoerythrin. An appropriate isotype control was included. (B–D) Isolated CD31+ cells were cultured on gelatin-coated dishes and stained for endothelial-specific markers. Immunostaining demonstrates the expression of (B) von Willebrand factor (vWF) localized in the cytoplasm (green), (C) CD31 at the cell surface (red) and (D) VE-cadherin at inter-cellular junctions (red). (E) DiI-Ac-LDL staining demonstrates the characteristic feature of endothelial cells to take up acetylated LDL (low-density lipoprotein) in the cytosolic vesicles of the cells (red). Nuclei were counterstained with Hoechst (blue). (F) CD31+ cells when cultured on matrigel-coated plates form capillary tube-like structures after 24 h. Scale bar in B, C and E represents 100 µm and in D 200 µm.

 
First, the isolated cells were examined for their endothelial-like phenotype based on morphological observations. CD31+ cells appeared as spindle shaped flat cells (data not shown) which showed similar morphological characteristics to endothelial cells derived from other tissues (Chaturvedi & Sarkar 2006). The cells at confluence resembled a network-like structure (data not shown) consisting of intracellular spaces (referred to as lumen) thus, mimicking two-dimensional vasculogenesis. In order to characterize the phenotype of CD31+ cells, the cells were stained for endothelial-specific markers such as VE-cadherin, CD31 and vWF. In endothelial cells, CD31 is generally localized to the sites of cell–cell contact. VE-cadherin mediates cell–cell interactions through formation of adherens junctions, which are important in maintaining vascular integrity (Matsuyoshi et al. 1997). vWF is a large multimeric glycoprotein synthesized by endothelial cells that mediates platelet adhesion to subendothelium at the site of vascular injury. vWF appears to be expressed exclusively on endothelial cells where it shows a granular pattern of reactivity. It is also expressed in the cytoplasm of megakaryocytes. Previous report has demonstrated that the expression of vWF differs significantly between endothelial cells in different murine tissues (Yamamoto et al. 1998). In our experiments, only a small proportion of the cells stained positive for vWF showing a distinct granular pattern (Fig. 1B). Cells expressing CD31 (Fig. 1C) and VE-cadherin (Fig. 1D) were predominant, showing a typical junctional localization. Taken together, our results indicate that the isolated cells were able to retain their endothelial-specific antigens. Although CD31 is commonly expressed on endothelial cells, a previous study that used CD31 to isolate endothelial cells from ES cells have characterized an endothelial population that did not express CD31 antigen (Balconi et al. 2000). In summary, the above data indicate that the enriched population of CD31+ cells was predominantly endothelial in nature.

Besides immunostaining, these cells were further evaluated for their ability to take up Ac-LDL, one of the characteristic features of endothelial cells (Voyta et al. 1984). The staining pattern appeared to be more uniform than the immunostaining of the endothelial-specific antigen CD31 (Fig. 1C) and strong fluorescence was observed in the cytosolic vesicles of the cells, suggesting the efficient incorporation of DiI-Ac-LDL (Fig. 1E). Furthermore, to evaluate the ability of CD31+ cells to form vascular network-like structures, the cells were cultured on matrigel. When plated on matrigel, the cells organized and formed capillary-like structures within 24 h (Fig. 1F).

Detection of germ layer-specific and endothelial marker genes in CD31+ population

PCR conditions and cycle number were adjusted for each primer pair to assure amplification in the linear range. To assess the differentiation potential of CD31+ cells, we examined the gene expression of several lineage specific markers. The expression of an undifferentiated marker Fgf4 (Fig. 2A) was high in ES cells, whereas it remained undetectable during differentiation as well as in CD31+ cells. A primitive ectodermal marker, Fgf5 (Fig. 2A) was expressed in ES cells and 8-days old EBs at low levels, but absent in CD31+ cells. The early mesodermal marker T (Brachyury) was absent (Fig. 2A) but the late mesodermal marker BMP2 (Fig. 2A) was up-regulated in CD31+ cells. The endothelial-specific genes like CD31, vWF, ICAM2 and VE-cadherin were highly expressed in the CD31+ population (Fig. 2A). Interestingly, although a similar expression level of ICAM2 and CD31 was observed, the relative expression level of vWF was 12-fold higher in CD31+ cells than in the control 1G11 cells which is consistent with the previous study reporting that the expression of vWF differs significantly among endothelial cells in different mouse tissues (Yamamoto et al. 1998). Cardiac troponin C and the endodermal marker Foxa2 were down-regulated. A second endodermal marker Sox17 was highly up-regulated in CD31+ cells, but completely absent in the positive control (shown in Fig. 2A,B). Semi-quantitative RT-PCR analysis (Fig. 2B) was in accordance with the real-time PCR results. Recently, Matsui et al. have suggested a functional role of Sox17 and Sox18 in postnatal neovascularization of the liver, kidney and reproductive organs (Matsui et al. 2006). In summary, these results suggest that the CD31+ cells contain endothelial cells derived from BMP-2 expressing mesodermal population. No expression of markers for pluripotency or ectodermal differentiation was observed.


Figure 2
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  		Figure 2  Detection of germ layer- and endothelial-specific genes by real-time PCR and semi-quantitative RT-PCR. (A) mRNA levels were determined and the data were normalized to GAPDH (house keeping gene) and indicated as relative gene expression level. (B) mRNA expression profiles of CD31+ cells were compared to ES cells, 8-days old EBs, CD31-negative cells and 1G11 cells using semi-quantitative RT-PCR. GAPDH was used as an internal control.

 
Identification and annotation analysis of differentially expressed genes in CD31+ cells

RNA (isolated from three independent experiments) from CGR8 ES cells, 8-days old EBs, CD31+ cells, CD31-negative (referred to as CD31–) cells (depleted population) and the murine endothelial cell line 1G11 was also used as a template for hybridizations to Affymetrix MG 430 v2.0 oligonucleotide microarrays (Affymetrix UK Ltd., High Wycombe, UK). To identify gene ontology (GO) categories that are specifically enriched in the CD31+ cells, we initially compared gene expression profiles of CD31+ cells to control (8-days old) EBs containing a mixture of differentiated cells and to undifferentiated ES cells. We identified 273 transcripts that were differentially expressed in CD31+ cells (t-test P-value < 0.01 and fold change > 2). Our selection criteria resulted in only approximately 1.5% of differentially expressed transcripts in the CD31+ cells compared to ES cells and 8-days old EBs. Among these, 228 transcripts were up-regulated and 45 transcripts were down-regulated in the CD31+ population. We also compared gene expression profiles of CD31+ cells with the CD31– cells and ES cells. Using our selection criteria, 361 transcripts were differentially expressed, with 310 transcripts up-regulated and 51 transcripts down-regulated in the CD31+ population. Hierarchical clustering was carried out to give an overview of the differentially expressed transcripts (see Figs S2 and S3).

In order to validate the Affymetrix results we compared the relative expression levels as determined by qRT-PCR (Fig. 3A) with the relative expression levels determined in the microarray experiments. Results from the Affymetrix analyses correspond well with the results obtained by qRT-PCR and semi-quantitative RT-PCR, respectively. Moreover, changes of the expression levels of the genes in Fig. 2A obtained by the qRT-PCR correspond to the results obtained by the Affymetrix microarrays.


Figure 3
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  		Figure 3  (A) Validation of Affymetrix data by real time PCR analysis. The results obtained by both Affymetrix and real time PCR are expressed as percentage of the maximum fold change for individual gene. (B) Selected GO annotations (biological process, BP) of differentially expressed genes in CD31+ cells compared to 8-days old EBs and undifferentiated ES cells. The Genesis GO browser was used to visualize and identify GO BP categories of interest and extract corresponding lists of transcripts. Bar diagram shows the number of genes in the respective categories.

 
The Genesis GO browser (version 1.7.0, Sturn et al. 2002) was used to identify transcripts of interest belonging to the biological process categories adhesion, cell cycle, cell death, cell–cell signaling, cellular metabolism, development, stress response, signal transduction, transcription and transport. The bar diagram (Fig. 3B) shows the number of up- and down-regulated transcripts individually for each category. Interestingly, 17 genes associated with the cell cycle are up-regulated suggesting a high proliferative capacity of the cells. However, genes assigned to the cell death GO are also up-regulated suggesting an increased relevance of apoptotic processes within the CD31+ cell population. Programmed cell death is essential for the development, survival of multicellular organisms and also involved in the initiation of angiogenesis (Nor & Polverini 1999). Accordingly, 56 genes belonging to the GO category ‘development’ are also highly enriched in the CD31+ population. As expression of cell cycle-, cell death- and development-related genes are induced by soluble and transcription factors via specific signal transduction pathways it is not surprising that genes belonging to such GOs are enriched (Fig. 3B). Further detailed GO analysis has been carried out using the DAVID 2008 tools to identify enriched functional annotations in the categories Gene Ontology (level 5 or higher), KEGG (Kanehisa & Goto 2000) and Biocarta pathways <http://www.biocarta.com/genes/index.asp>. We identified multiple KEGG and Biocarta pathways over-expressed in CD31+ cells. Several GO categories associated with angiogenesis, blood vessel morphogenesis, vasculogenesis and blood coagulation were enriched among the annotations for transcripts up-regulated in the CD31+ population (Table 1A,B). Most of the transcripts in these categories have a well-documented association with endothelial differentiation, including ICAM2, Flk1, Flt1, VE-caherin, Tie1 and endoglin. Also among the genes up-regulated were the transcription factors GATA1, GATA2, Tal1 and Runx1 which are known to play a role in hematopoietic differentiation (Jaffredo et al. 2005). Consistent with our results and previous reports (for review see Schmeisser & Strasser 2002), discrimination between the hemangioblast and mature endothelial cells remains problematic because of the fact that the subsets of hematopoietic cells express markers similar to those of endothelial cells.


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  		Table 1  Functional annotations that are enriched among transcripts up-regulated in CD31+ cells compared to ES cells and 8-days old EBs (A) or CD31 negative population (B). Count refers to the number of transcripts in the respective category. Modified Fishers exact P-value represents the enrichment in the annotation categories. Functional annotation chart was generated by DAVID tool
 
Signaling pathways enriched in the population of CD31+ endothelial cells

Signaling pathways such as calcium signaling belonging to signal transduction, T-cell receptor signaling, B-cell receptor signaling and leukocyte transendothelial migration playing a role in the immune system, focal adhesion molecules responsible for cell communication and cell adhesion molecules (CAMs) necessary for cell interactions were enriched in CD31+ cells.

Calcium signaling is one of the principal intracellular signaling mechanisms by which endothelial cells respond to external stimuli, such as fluid shear stress and ligand binding (Plank et al. 2006). In the present study, DNA microarray analysis has identified several genes (Plcg2, Itpr1, P2rx1, Mylk, Itpr2, Adcy9, Atp2a3 and CD38) enriched in CD31+ cells involved in calcium signaling (see Tables S1 and S2). CD38 is a multifunctional molecule involved in cell adhesion, activation, proliferation, adhesion, signal transduction and calcium signaling depending on the cellular environment.

Genes of CAMs enriched in CD31+ cells include CD40, MADCAM1, VE-cadherin, ESAM-1, f11 receptor, ICAM2, claudin5 and L-selectin ligand CD34 (see Tables S1 and S2). Cells interact with their extracellular environment through a variety of CAMs. CAMs are classified into four families which include integrins, cadherins, addressins (containing members of the immunoglobulin superfamily) and selectins. Members of each family have been detected in angiogenic blood vessels.

Immune recognition of endothelial cells has been implicated in a number of vascular diseases. Resting endothelial cells are efficient antigen presenting cells, capable of presenting intracellular antigens to the immune system. An increased expression level in the CD31+ population was observed for transcripts such as Nfatc1, Pik3r3, vav3, Jun and Fos involved in B- and T-cell receptor signaling pathways.

Biological process annotations enriched in the population of CD31+ endothelial cells

Endothelial cells are centrally involved in migrating, proliferating and assembling into tubes, processes that are regulated by secreted factors as well as by surrounding cells and extracellular matrix. A number of endothelial-specific markers were identified in the CD31+ population including flt-1, endomucin, Tie1, stabilin-1, Robo-4, Notch4, endoglin, ELK3, sox18 and plexin D1 (see Tables S1 and S2). These genes are involved in processes such as blood vessel development, angiogenesis and blood vessel morphogenesis with genes such as cadherin5, Foxc1, hey1 and lama4 being specifically involved in blood vessel development. Genes such as sox18, caveolin1, EGF-like domain 7, kinase insert domain protein receptor (flk1) and hey1 were identified to be involved in GO category vasculogenesis.

Transcripts such as thrombomodulin, coagulation factors (FV, FVIII and FX), coagulation factor 2 (thrombin) receptor-like 3, von Willebrand factor homologue, rab27a (member of ras oncogene family) and protein C receptor that were highly expressed in CD31+ cells were enriched in the GO category blood coagulation (see Tables S1 and S2).

Identification of transcripts down-regulated in CD31+ cells

Genes such as Dppa5 and Tdgf1/Cripto were highly expressed in ES cells (Fig. 4) and transcription factors such as Sox2 and Pou5f1 involved in maintaining pluripotency were down-regulated in CD31+ cells. ERBB3, v-erb-b2 erythroblastic leukemia viral oncogene homologue 3 (avian) down-regulated in CD31+ cells, was highly expressed in ES cells and reported to be enriched in human ES cells (Sperger et al. 2003). Another gene Matrilin3 down-regulated in CD31+ cells, was highly expressed in ES cells and CD31 negative cells was known to play a role in modulating chondrocyte differentiation during embryonic development (van der Weyden et al. 2006). We also compared the CD31+ population with 8-days old EBs and CD31– cells to examine the role of down-regulated genes and whether they are involved in the differentiation of other lineages such as cardiac or smooth muscle cells. The down-regulated genes included transcription factors such as Hand1, Hand2 and Tbx20 (Fig. 4) which are known to play an important role in cardiomyocyte differentiation (McFadden et al. 2005; Singh et al. 2005). Smooth muscle-related genes (Fig. 4), including {alpha}-SMA, caldesmon, tropomyosin and pdgfrb were down-regulated as well.


Figure 4
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  		Figure 4  Graphical representation of the down-regulated genes in CD31+ cells determined by microarray analysis. Expression of the down-regulated genes in CD31+ cells are represented in fold change and presented according to their relevant roles in pluripotency, smooth muscle differentiation and cardiac differentiation. Values are mean ± SD (n = 3).

 

   Discussion
Top
 Abstract
 Introduction
 Results
 Discussion
Experimental procedures
 References
 
In order to define the transcriptome of cells involved in vasculogenesis and angiogenesis, microarray expression profiling of CD31+ cells in comparison to undifferentiated ES cells and differentiated populations was carried out. Mouse ES cells serve as an effective model for the isolation of CD31+ cells and to study the different steps involved in endothelial differentiation. CD31 is a member of the immunoglobulin gene superfamily, which is highly expressed on the surface of endothelial cells, at moderate levels on leukocytes and platelets (Sheibani et al. 1999) and also on hematopoietic stem cells (Baumann et al. 2004).

By comparing the CD31+ cells against undifferentiated ES cells and differentiated population (8-days old EBs), we generated a dataset represented by 273 transcripts that are differentially expressed in CD31+ cells. Our microarray analysis of CD31+ cells clearly shows an endothelial expression signature in the CD31+ population, specifically regulated signaling pathways and novel candidate genes possibly involved in vascular development. A transcript such as PCTAIRE-motif protein kinase2 with no pre-defined functions in vascular differentiation was identified. Comparative bioinformatic analysis reveals an enrichment of the gene ontology (GO) categories angiogenesis, blood vessel morphogenesis, vasculogenesis and blood coagulation in the CD31+ cell population. Transcripts such as ICAM2, Flk1, Flt1, VE-caherin, Tie1 and endoglin in these categories have a well-documented association with endothelial differentiation, including (Garlanda & Dejana 1997).

Comparison with endothelial gene signatures

A previous study used a similar microarray approach to define a gene signature for endothelial cells (flk1+ cells) isolated from mouse ES cells (Wang et al. 2006). In the above study, flk1+ cells were isolated from differentiated EBs at different time points and the differentially expressed genes were identified in comparison to the flk1– cells. We compared the differentially expressed genes in the flk1+ population to our list of genes differentially expressed in CD31+ cells. Analysis of the results revealed that some similarities exist with our list. Among the 70 genes differentially expressed in the flk1+ population, 38 genes were found in our list and the remaining genes were absent. This observation could reflect either differences in the time points and the markers used to define and purify endothelial cells or differences in the microarrays used. As shown in Table 2, the expression level of the genes up-regulated in flk1+ cells at 84 h was down-regulated at later time points whereas the expression level in CD31+ cells for most of the genes appeared to be low when compared to the flk1+ cells at 8 days. However, genes down-regulated during endothelial differentiation showed a similar pattern in both flk-1+ and CD31+ populations.


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  		Table 2  Comparison of endothelial gene signatures between the previously reported Flk-1+ (Wang et al. 2006) and CD31+ cells
 
While this manuscript was in preparation, Nikolova-Krstevski et al. (2008) has reported the identification of genes expressed during early and late stages of endothelial differentiation in CD144 (VE-cadherin) positive cells isolated from mouse ES cells. Genes such as Robo4, Flt1 Sox 18, Elk3, CD31, VE-cadherin and ERG are enriched in both CD144 and CD31 positive cells. In our study we have identified several novel transcripts that were highly expressed in CD31+ population. For several of these mouse genes, zebrafish orthologues were identified. Results of a morpholino knockdown of one candidate gene in the zebrafish embryos suggest a major role for this gene in the vascular development (data not shown). Further studies are under way to characterize this novel gene on the molecular level.

Relevance of CD31+ cells as a model for drug discovery

The angiogenic potential of CD31+ cells was demonstrated by the matrigel assay, where the cells were able to form tubular capillary-like structures. The angiogenic potential of the CD31+ cells mimics the endothelial cells under disease conditions such as cancer (Jain et al. 2006). This offers a huge potential to use these cells for identifying potential drug molecules targeting growth factor receptors or critical enzymes that are involved in metabolic or signal transduction pathways thereby inhibiting the tubular capillary-like structures of CD31+ cells.

A more sophisticated in vitro model allowing identification of drugs with anti-angiogenic potential could use tumor cells (either as a monolayer or spheroids) in co-cultures with CD31+ cells. Under these conditions, angiogenic factors released by tumor cells would activate the CD31+ cells to proliferate, migrate and form new capillaries within the tumor cells that are essential for supply of nutrients under in vivo conditions thus allowing tumor expansion.

In conclusion our transcriptomic analysis defines gene signatures for the CD31+ population and provides an insight into the genes involved in signaling pathways as well as an analysis of the cellular and molecular mechanisms underlying vascular development in mouse ES cells. Gain-of-function and loss-of-function analysis will determine which of these transcripts play key roles in vascular development. This might help to identify new targets and new potential drugs for anti-cancer therapy.


   Experimental procedures
Top
 Abstract
 Introduction
 Results
 Discussion
 Experimental procedures
References
 
Materials

All cell culture media, supplements, DNAse I and SuperscriptTM II Reverse Transcriptase were purchased from Invitrogen (Karlsruhe, Germany). JumpStart REDTaq ReadyMix, endothelial cell growth supplement (ECGS) and heparin were purchased from Sigma (Munich, Germany). QuadroMACSTM Separator, LS columns and microbeads (Rat anti-mouse IgM and Anti-phycoerythrin microbeads) were obtained from Miltenyi Biotech (Bergisch Gladbach, Germany). The murine 1G11 pulmonary endothelial cell line was a kind gift from Dr A. Vecchi (Dong et al. 1997). SSEA-1 (480) mouse monoclonal and vWF (H-300) rabbit polyclonal antibodies were purchased from Santa Cruz Biotech (Heidelberg, Germany), phycoerythrin-conjugated or unlabelled Rat Anti-mouse CD31/PECAM-1 antibody were obtained from Biozol (Eching, Germany), purified Rat Anti-Mouse CD144 (VE-Cadherin) Monoclonal from Becton Dickinson (Heidelberg, Germany) and secondary antibodies were purchased from Dianova (Hamburg, Germany).

ES cell culture and differentiation

The CGR8 murine ES cell line (ECACC 95011018) was derived from the 129/Ola mouse strain (Mountford et al. 1994). Cells were cultured on gelatin-coated dishes without feeder cells in Glasgow Minimum Essential Medium (GMEM) supplemented with 10% FBS, 2 mM L-glutamine, 50 µM β-ME and 100 U/mL LIF. ES cells were kept in a humidified 5% CO2 atmosphere at 37 °C and maintained at 70% confluency to retain an undifferentiated phenotype. For suspension culture, ES cells were seeded at a density of 2–3 x 106 cells per 10 cm bacteriological dish containing 15 mL medium without LIF. The cells aggregate into clusters and are termed embryoid bodies (EBs). On day 2, EBs were distributed to three to four new bacteriological dishes containing fresh medium. Again at day 4, EBs in each dish were split into three to four new bacteriological dishes containing fresh medium.

Isolation of endothelial cells from ES cells using magnetic beads

Endothelial cells were separated from 8-days old EBs by magnetic cell sorting using positive selection. Briefly, 8-days old EBs were dissociated by 1 mg/mL collagenase B (PAA Laboratories, Coelbe, Germany) treatment and filtered through a 40-µM cell strainer. The cells were washed and resuspended in 500 µL of binding buffer (phosphate buffered saline containing 0.5% bovine serum albumin and 2 mM EDTA) containing mouse monoclonal anti-SSEA1 antibody for 30 min at 4 °C. The cells were washed and labeled with Rat anti-Mouse IgM microbeads and the magnetic separation was carried out according to the manufacturer's instructions using the LS column and QuadroMACSTM Separator. The SSEA-1 negative cells were collected, washed and then labeled with phycoerythrin-conjugated Rat Anti-mouse CD31/PECAM-1 antibody. The CD31+ endothelial cells were separated using the anti-PE microbeads and separation was carried out as described above. CD31+ cells were passed again through a new column to obtain a pure population. CD31+ cells were collected, washed and cultured on gelatine-coated dishes in IMDM containing 20% FBS, 1% non-essential amino acids (vol/vol), 2 mM L-glutamine and 100 mM β-ME, 50 µg/mL endothelial cell growth supplements and 100 µg/mL heparin. Viability of the cells was determined by trypan blue dye exclusion. To estimate the purity of the isolated CD31+ cells, cells were read on a FACScan (BD Biosciences) and the data analysis was carried out using the CELLQUEST software (BD Biosciences).

Immunofluorescence staining

EBs were routinely examined for the presence of endothelium-like structures by whole-mount immunostaining with Rat Anti-mouse CD31/PECAM-1 antibody and observed using confocal laser scanning microscopy (Carl Zeiss, Jena, Germany). Cells isolated by MACS were cultured on gelatin-coated coverslips for immunostainings. The cells were either fixed in ice-cold methanol : acetone (7 : 3) or ice-cold acetone at –20 °C for 1 h and washed with 0.01% Triton X-100 in PBS. Blocking against unspecific antibody binding was carried out with 10% fat free milk powder dissolved in PBS (blocking solution) for 1 h at RT. After blocking, cells were washed and incubated with primary antibody diluted in blocking solution for overnight at 4 °C. Cells were rinsed with 0.01% Triton X-100 in PBS and incubated with species-specific secondary antibodies diluted in blocking solution for 1 h at RT. Nuclei were counterstained with Hoechst 33324 (Molecular Probes, Carlsbad, CA) at 37 °C for 20–30 min. Finally cells were washed and imaged on a Zeiss Axiovert 200 fluorescence microscope.

Uptake of fluorescent-labeled low-density lipoprotein

For DiI-Ac-LDL labeling, the medium was removed from subconfluent culture of endothelial cells and replaced with medium containing fluorescent-labeled 1,1'-dioctadecyl-3,3,3',3'-tetramethyl-indocarbocyanine perchlorate low-density lipoprotein (DiI-Ac-LDL) (Molecular Probes, Carlsbad, CA) at 10 µg/mL and incubated at 37 °C for 4 h. The cells were washed with PBS and nuclei were counterstained with Hoechst 33324 at 37 °C for 20–30 min. Finally cells were washed and imaged on a Zeiss Axiovert 200 fluorescence microscope (Carl Zeiss, Jena, Germany).

In vitro tubule formation assay

To test the ability of the isolated endothelial cells to form capillary-like structures in vitro, cells were cultured on matrigel (Becton Dickinson, Heidelberg, Germany). Matrigel is composed of extracellular matrix proteins derived from Engelbreth–Holm–Swarm mouse sarcoma. Matrigel coating on 24-well plates was carried out according to the manufacturer's recommendations. Cells were seeded at a density of 2–3 x 105 cells per well containing 0.5 mL of medium. Plates were photographed 24 h post-plating using phase contrast microscopy (Carl Zeiss, Jena, Germany).

Semi-quantitative RT-PCR analysis

Total RNA was extracted using the RNAqueous kit (Ambion, Austin, TX). The RNA quality was assessed by agarose–formaldehyde gel electrophoresis. A total of 2 µg RNA was treated with DNase I to remove the contaminating genomic DNA, before reverse transcription into cDNA by SuperscriptTM II Reverse Transcriptase and random primers according to the manufacturer's specifications. For PCR amplification, cDNAs were amplified using JumpStart REDTaq ReadyMix and 0.3 µM of each primer. All amplifications were carried out using an Eppendorf PCR cycler under conditions optimized for each target sequence. Amplification included an initial denaturation step at 94 °C for 2 min, followed by 25–30 cycles of denaturation at 94 °C for 35 s, annealing at 60 °C for 45 s and elongation at 72 °C for 45 s followed by a final extension of 72 °C for 10 min. The PCR products were analyzed by gel electrophoresis. Gene-specific primer sequences are provided in Table 3.


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  		Table 3  Details of primers used for semi-quantitative RT-PCR and quantitative real-time PCR
 
Quantitative real-time PCR

Total RNA and cDNA synthesis were carried out as described above. Validation of the Affymetrix data was carried out by quantitative real-time PCR analysis using the ABI Prism 7500 Fast Sequence Detection System (Applied Biosystems, Foster City, CA). For PCR amplification, cDNAs were amplified using QuantiTect SYBR Green PCR Master Mix (Qiagen, Hilden, Germany) and 0.3 µM of each primer pair. Amplification was carried out starting with an initial step for 2.0 min at 50 °C, 10 min template denaturation/hot start step at 95 °C, followed by 40 cycles (95 °C for 15 s, 58 °C for 30 s and 60 °C for 45 s). Quantitative PCR analysis for each sample was carried out in triplicates. GAPDH was used as an internal control. Relative gene expression values were obtained by normalizing CT (threshold cycle) values of the target genes in comparison with CT values of the housekeeping gene (GAPDH) using the {Delta}{Delta}CT method.

Microarray expression analysis

Total RNA was isolated from five different samples: CGR8 undifferentiated ES cells, 8-days old CGR8 EBs, CD31+ cells, CD31– cells and 1G11 cells (endothelial control), using the RNAqueous kit. The RNA quality was assessed by agarose-formaldehyde gel electrophoresis. The RNA was processed and hybridized on the Mouse Genome 430 Version 2 Array (Affymetrix, High Wycombe, UK). The image data were analyzed with GCOS 1.4 using Affymetrix default analysis settings. After RMA normalization (Irizarry et al. 2003), a parametric ANOVA (F-test) and pair-wise comparisons using the Student's t-test (unpaired, assuming unequal variances) were carried out. The cluster analysis was carried out using CLUSTER version 3.0 (Eisen et al. 1998) applying mean centering and normalization of genes before average linkage clustering with uncentered correlation. Dendrograms were generated using Java TREEVIEW (Saldanha 2004).

Functional annotation

Lists of differentially expressed genes (commonly greater than twofold change and P-value lower than 0.01) were analyzed using the DAVID 2008 bioinformatics resource (Dennis et al. 2003) to identify enriched functional annotations such as biological processes and signaling pathways as previously described (Doss et al. 2007). Generally a single gene can participate in multiple KEGG or Biocarta pathways and GO categories.


   Acknowledgements
 
This work was supported by a grant from the European Commission (6th Framework Programme, Thematic Priority: Life sciences, Genomics and Biotechnology for Health, Contract No.: FunGenES LSHG-CT-2003-503494). We thank Dr Annunciata Vecchi, (Istituto Clinico Humanitas, Italy) for providing the murine endothelial cell line 1G11.


   Footnotes
 
Communicated by: Moshe Yaniv

* Correspondence: a.sachinidis{at}uni-koeln.de


   References
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 Abstract
 Introduction
 Results
 Discussion
 Experimental procedures
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Received: 13 August 2008
Accepted: 10 November 2008




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