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tocysts [3, 4] and can be maintained in cell culture indefinitely without loss ... POU transcription factor Oct-4, and stage-specific embryonic antigen 1 (SSEA-1) [8].
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Original A rticle Correlation of Murine Embryonic Stem Cell Gene Expression Profiles with Functional Measures of Pluripotency Lars Palmqvist,a Clive H. Glover,b Lien Hsu,c Min Lu,c Bolette Bossen,c James M. Piret,b,d R. Keith Humphries,a,e Cheryl D. Helgasonc,f Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada; bMichael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada; cDepartment of Cancer Endocrinology, BC Cancer Agency, Vancouver, British Columbia, Canada; Departments of dChemical and Biological Engineering, eMedicine, and fSurgery, University of British Columbia, Vancouver, British Columbia, Canada

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Key Words. Embryonic stem cells • Markers • Pluripotency • Leukemia inhibitory factor • Gene expression • Microarray

Abstract Global gene expression profiling was performed on murine embryonic stem cells (ESCs) induced to differentiate by removal of leukemia inhibitory factor (LIF) to identify genes whose change in expression correlates with loss of pluripotency. To identify appropriate time points for the gene expression analysis, the dynamics of loss of pluripotency were investigated using three functional assays: chimeric mouse formation, embryoid body generation, and colony-forming ability. A rapid loss of pluripotency was detected within 24 hours, with very low residual activity in all assays by 72 hours. Gene expression profiles of undifferentiated ESCs and ESCs cultured for 18 and 72 hours in the absence of LIF were determined using the Affymetrix GeneChip U74v2. In total, 473 genes were identified as significantly differentially expressed, with approximately one third having unknown biological

function. Among the 275 genes whose expression decreased with ESC differentiation were several factors previously identified as important for, or markers of, ESC pluripotency, including Stat3, Rex1, Sox2, Gbx2, and Bmp4. A significant number of the decreased genes also overlap with previously published mouse and human ESC data. Furthermore, several membrane proteins were among the 48 decreased genes correlating most closely with the functional assays, including the stem cell factor receptor c-Kit. Through identification of genes whose expression closely follows functional properties of ESCs during early differentiation, this study lays the foundation for further elucidating the molecular mechanisms regulating the maintenance of ESC pluripotency and facilitates the identification of more reliable molecular markers of the undifferentiated state. Stem Cells 2005;23:663–680

Introduction

without loss of their broad pluripotent differentiation capacity as determined by their ability to give rise to all three germ layers both in vitro and in vivo [2]. The more recent establishment of human ESC lines [5] has further increased the interest in ESCs because they raise hope of an unlimited source of cells for tissue engineering and cell therapies in the future. However, realization of this potential requires an increased knowledge of the

Embryonic stem cells (ESCs) are characterized by their ability to both self-renew and differentiate [1, 2]. However, the molecular mechanisms that regulate the decision between these two processes are poorly understood. Mouse ESCs were originally isolated from the inner cell mass (ICM) of preimplantation blastocysts [3, 4] and can be maintained in cell culture indefinitely

Correspondence: Cheryl D. Helgason, Ph.D., Department of Cancer Endocrinology, BC Cancer Agency, 675 West 10th Avenue, Vancouver, BC, Canada, V5Z 1L3. Telephone: 604-675-8011; Fax: 604-675-8183; e-mail: [email protected] Received July 13, 2004; accepted for publication January 13, 2005. ©AlphaMed Press 1066-5099/2005/$12.00/0 doi: 10.1634/stemcells.2004-0157

Stem Cells 2005;23:663–680 www.StemCells.com

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molecular mechanisms governing self-renewal and pluripotency to guide the development of processes that control the expansion and differentiation of stem cells ex vivo. Unlike hematopoietic stem cells, for which quantitative assays of stem cell potential have been defined and validated, no such assays currently exist for ESCs. In the murine system, selfrenewal is measured by the ability of mouse ESCs to continuously proliferate in culture while maintaining an undifferentiated colony morphology [6]. The most rigorous in vivo assay to establish functionality of cultured mouse ESCs is blastocyst injection and measurement of their ability to give rise to chimeric mice because it requires ESC contribution to all adult tissue, including germ cells [1]. However, injection of 10 to 15 ESCs into a single blastocyst does not provide a quantitative measure of stem cell potential at the single-ESC level. Two in vitro assays have been used extensively as surrogates for chimera formation when testing culture reagents or examining the consequences of genetic manipulation. The colony-forming cell (CFC) assay is used to determine the plating efficiency of ESC populations under various conditions and thus may be considered indicative of self-renewal potential. Formation of embryoid bodies (EBs) can be performed at a clonal level in vitro and reflects multilineage differentiation potential [7]. The correlation between these in vitro assays and chimera generation has not been determined. Assessment of pluripotency has also relied on the expression of selected molecular markers. For murine ESCs, these have included alkaline phosphatase, the POU transcription factor Oct-4, and stage-specific embryonic antigen 1 (SSEA-1) [8]. However, the correlation between marker expression and the various functional assays has not been extensively studied. Knowledge of the intricate mechanisms regulating ESC pluripotency and differentiation potential is currently limited to a few signaling pathways (i.e., leukemia inhibitory factor [LIF]) and regulatory factors (i.e., Oct-4 and Nanog). Thus, very little is known about the tolerance limits of different culture conditions for maintaining stem cell function during expansion or how these relate to altered gene expression patterns in ESCs. Identification of molecular markers that correlate with pluripotency would be invaluable to enrich for the desired cells, as well as to monitor their maintenance during expansion protocols. Achieving the goal of defining the core stem cell regulatory network requires a precise characterization of the functional capacities of the cells for which the transcriptional profile is described. In this study, we established gene expression profiles during early differentiation of the well-defined R1 ESC line [9] and correlated gene expression changes with both phenotypic and functional assessment of the same cells. Functional capacity was determined by blastocyst injections for chimeric mouse formation, EB assays, and CFC counts. Undifferentiated ESCs and ESCs cultured without LIF for 18 or 72 hours were chosen for gene-array analysis. We identified 473 unique genes as

Genetic Correlates of Murine ESC Pluripotency significantly differentially expressed during early ESC differentiation, and approximately one third of these have unknown biological function. Among the 275 genes whose expression decreased with ESC differentiation were several factors previously identified as important for, or markers of, ESC pluripotency, including Stat3, Rex1, Sox2, Gbx2, and Bmp4. A significant number of the decreased genes also overlapped with previously published mouse and human ESC data. Reverse transcription–polymerase chain reaction (RT-PCR) validation showed high correlation with the gene-array data, and several genes were also shown to have similar changes after LIF removal in two other murine ESC lines. Expression of the commonly used ESC markers Oct-4 and SSEA-1 was also examined in parallel with the functional assays. However, a close correlation was not observed. Interestingly, among a subset of 48 decreased genes that showed the closest correlation with the functional assays was the stem cell factor (SCF) receptor c-Kit that can be a useful marker of undifferentiated ESCs.

Materials and Methods Growth of the Mouse ESC Lines and Mouse Embryonic Fibroblasts The R1 [9], J1 [10], and EFC [11] ESCs were routinely maintained at 37°C humidified air with 5% CO 2 on a layer of irradiated mouse embryo fibroblasts (MEFs) and fed daily with a complete change of ESC maintenance medium consisting of high-glucose Dulbecco’s modified Eagle’s medium (DMEM) (all reagents obtained from StemCell Technologies Inc. [STI], Vancouver, British Columbia, Canada, unless otherwise indicated) supplemented with 15% ESC-tested fetal bovine serum (FBS), 0.1 mM nonessential amino acids, 2 mM glutamine, 1,000 U/ml LIF, 100 U/ml penicillin, 100 μg/ml streptomycin, and 100 μM monothioglycerol (MTG) (Sigma, Oakville, Ontario, Canada). For gene expression profiling, R1 ESCs were from passage 14 and had been frozen at 10 6 cells per vial. Cells were passaged every second day in maintenance cultures. To passage cells, a single-cell suspension was generated by treatment with 0.25% trypsin and 1 mM EDTA (T/E) (Invitrogen Life Technologies, Burlington, Ontario, Canada) for 5 minutes until cells detached from the culture vessel surface. T/E activity was then quenched with DMEM supplemented with 10% FBS. The cells were centrifuged at 1,200 rpm for 7 minutes and resuspended in ESC maintenance medium. Viable cells were plated at 1 × 10 6 per 100-mm dish on irradiated MEFs and cultured for a further 48 hours at 37°C, 5% CO2 before harvest for RNA isolation, differentiation, or functional assessment. In subsequent experiments, all cells used were within five passages of initial thawing. MEFs were maintained at 37°C humidified air with 5% CO2

Palmqvist, Glover, Hsu et al. in DMEM supplemented with 10% FBS, 100 U/ml penicillin, 100 μg/ml streptomycin, and 100 μM MTG. Cells to be irradiated were trypsinized, resuspended in 2 ml of medium, and exposed to 60 Gy from an x-ray source before replating for use as feeder cells. Alternatively, MEFs for RNA isolation were grown in ESC maintenance media, trypsinized, pelleted, and placed in Trizol reagent (Invitrogen).

Preparation of ESCs for Differentiation ESCs were thawed and cultured for two passages over 96 hours as described above. To prepare cells for differentiation, they were harvested and washed as described above, resuspended in ESC maintenance medium, and preplated on tissue culture plates for 1 hour at 37°C, 5% CO2 to deplete contaminating MEFs. At the end of this preplating step, the nonadherent ESCs were discarded and the loosely adherent ESCs were collected by gently washing the surface of the tissue culture plate. Cells were pelleted by centrifugation, and viable cell numbers were determined. The frequency of contaminating MEFs in the undifferentiated (day 0) ESC samples was estimated to be less than 0.2% based on cell size during counting. A portion of the preplated ESCs was suspended at a density of 1 to 2 × 107 cells per 50 ml in liquid differentiation medium consisting of Iscove’s modified Dulbecco’s medium (IMDM), 15% FBS selected for its ability to support ESC differentiation, 2 mM glutamine, 150 μM MTG, and 40 ng/ml murine STI and plated into 4 × 100-mm Petri-style culture dishes (Falcon). Cells were cultured overnight (18 hours) at 37°C, 5% CO2. The following morning, EBs, both in suspension and loosely attached, were harvested and allowed to settle to the bottom of a 50-ml conical tube for approximately 10 minutes. The supernatant, containing mainly single cells, was removed, and the spontaneously pelleting EB fraction was collected by centrifugation at 1,200 rpm for 7 minutes. EBs were disrupted by incubation in T/E for 3 minutes at room temperature followed by passage through a 21-gauge needle to achieve single-cell suspensions. The cells were washed with 10% DMEM and suspended in 2 ml of IMDM to count. The remainder of the preplated ESCs was plated at a density of 104 cells per 35-mm low-adherence Petri dish in IMDM-based ES differentiation methylcellulose consisting of 0.9% methylcellulose, 15% FBS, 2 mM glutamine, 150 μM MTG, and 40 ng/ml murine SCF. Cultures were grown for 3 days and all EBs in each dish were harvested by carefully flooding the dish with IMDM and collecting the methylcellulose/EB solution. EBs were washed twice in DMEM plus 10% FBS to remove the residual methylcellulose and were then pooled and disrupted as described above.

Blastocyst Injection C57Bl/6J mice (used as blastocyst donors) and B6C3 F1 females (used as pseudopregnant blastocyst recipients) were purchased

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from the in-house breeding program at the BC Cancer Agency Animal Resource Center. All mice were maintained with sterilized food, water, and bedding. All protocols were conducted according to guidelines set forth by the Canadian Council for Animal Care and approved by the Animal Care Committee at the University of British Columbia. R1 ESCs were thawed and maintained for two passages on irradiated MEFs with daily feeding of maintenance medium. After the second passage, cells were harvested and subjected to 1 hour of preplating on plastic to deplete remaining MEFs. Cells were then plated onto gelatin-coated tissue culture dishes and fed with maintenance medium without LIF for the indicated lengths of time. To produce chimeras, 15 test cells (either ESCs or differentiated) were injected into 3.5-day blastocysts from C57Bl/6 mice as described previously [12] and implanted back into pseudopregnant recipient females to gestate normally. Coat color was used to identify chimerism of the resulting pups. Two independent experiments were performed.

Embryoid Body Formation Assays Single-cell suspensions were collected on day 0 or prepared, as outlined above, during differentiation. Defined numbers of cells (500 to 20,000, depending on time after LIF removal) were plated in 35-mm Petri-style dishes in the ESC differentiation methylcellulose medium described above to determine the efficiency of EB formation. EB numbers were determined microscopically after 5–6 days of culture, and colonies were qualitatively scored as large or small. Three independent experiments were performed. The percent EB formation efficiency was calculated by dividing the total number of EBs formed by the number of cells plated multiplied by 100.

CFC Assay Single-cell suspensions collected on day 0 or during differentiation were plated at various densities (500 to 20,000 cells per gelatinized 60-mm gridded tissue culture dish) to determine ESC CFC plating efficiency. Colonies were microscopically enumerated after 5–6 days of growth. To enable differential assessment of the colonies, the protocols outlined in the alkaline phosphatase detection kit (Sigma; 86-R) were modified for staining in 60-mm dishes. In brief, the medium was removed from the dishes, and 1 ml of room temperature fixative (prepared as per the Sigma protocol) was added for 30 seconds. The fixative was removed, and colonies were washed with 2 ml phosphate-buffered saline (PBS). Next, 1.5 ml alkaline dye mixture (prepared as per the Sigma protocol) was added; the dish was incubated in the dark at room temperature for 15 minutes. Finally, the dye mixture was removed and the colonies were covered with 2 ml PBS for microscopic evaluation. The numbers of stained (undifferentiated) versus unstained (differentiated) colonies were determined. Three independent experiments were performed. The percent CFC

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plating efficiency was calculated by dividing the total number of alkaline phosphatase–positive colonies by the number of cells plated multiplied by 100.

RNA Extraction and Array Hybridization Single-cell suspensions of test cells were prepared as described above and resuspended in Trizol (Invitrogen Life Technologies) at a density of 107 cells per ml. RNA was extracted following the manufacturer’s instructions. Standard Affymetrix amplification protocols were used to prepare probe RNA for Affymetrix arrays with 5 μg of starting total RNA. Biotin-labeled amplified RNA was fragmented, and hybridization cocktails were prepared according to the Affymetrix protocol. The mouse GeneChip (MG) U74v2 chips were hybridized on a GeneChip System (Affymetrix) at the Genome Science Centre, BC Cancer Agency, Vancouver, British Columbia, Canada, according to the manufacturer’s instructions. All experiments were performed in triplicate with the exception of the MEF samples, which were analyzed in duplicate. The MIAME (minimal information about a microarray experiment) guidelines were followed for data presentation [13].

Data Analysis The Affymetrix software MicroArray Suite 5.0 (MAS 5.0) was used to generate absolute expression estimates (absence/ presence calls) from the raw data. Software default thresholds were used to determine the present (P) or absent (A) calls (α1 = 0.04, α2 = 0.06, and τ = 0.015). The data obtained from MAS 5.0 were then normalized and further analyzed in the GeneSpring software version 6.2 (Silicon Genetics, Redwood City, CA). Per-chip normalization was done as follows: Values below 0.01 were set to 0.01, and then each measurement was divided by the 50th percentile of all measurements in that sample. Per-gene normalization was done as follows: Each gene was divided by the median of its measurements in all samples. If the median of the raw values was less than 10, then each measurement for that gene was divided by 10. We judged genes to be differentially expressed during ESC differentiation only when the difference in expression between two time points was at least twofold, the gene was identified by MAS 5.0 as present in two out of three replicates or present or marginal in all three replicates at the time point with the highest expression level, and the extent of difference in expression was statistically significant (p < .05 in a parametric Welsh analysis of variance [ANOVA] t-test). Classification of genes into functional categories was done by collecting annotations and keywords with the Onto-Express Tool (http://vortex.cs.wayne.edu:8080/ ontoexpress) [14], Affymetrix NetAffx (http://www.affymetrix. com/analysis/index.affx), and the Simplified Gene Ontology Tool included in the GeneSpring 6.2 Software. The GenMapp 2.0 software tool was used to analyze signaling pathways (http:// www.genmapp.org).

Genetic Correlates of Murine ESC Pluripotency

Quantitative RT-PCR RNA was isolated using Trizol, and the samples were then treated with DNase I (amplification grade) before RT-PCR according to the manufacturer’s recommendations (Invitrogen). Complementary DNA (cDNA) was generated by RT with random primers and the Superscript II enzyme and RNase inhibitor (Invitrogen). The RT reaction was incubated at 42ºC for 50 minutes followed by 15 minutes at 70ºC. The cDNA was stored at –20ºC for subsequent quantitative PCR analysis. Gene transcripts were quantified by real-time PCR using the iCycler apparatus (BioRad Inc., Hercules, CA) and were detected with SYBR Green as fluorochrome (IQ SYBR Green Supermix, BioRad Inc.). Gene sequences for primer design were obtained from the NCBI Reference Sequences database (http://www.ncbi.nlm.nih.gov/RefSeq/). Primers were chosen using the Primer3 software (http://www.broad.mit.edu/cgi-bin /primer/primer3_www.cgi), and the specificity of all primer pairs was tested with electronic PCR using the mouse genome and the mouse transcript database (http://www.ncbi.nlm.nih. gov/sutils/e-pcr/reverse.cgi). Primer sequences are provided in the supplemental material (supplementary online Table 1). Primers were ordered and synthesized at Invitrogen Life Technologies (http://www.invitrogen.com/). The relative expression changes were determined with the 2 –ΔΔCT method [15], and the housekeeping glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene transcript was used to normalize the results. PCR efficiency was tested for each primer pair by dilution series of cDNA to make sure that the efficiency was appropriate for the 2–ΔΔCT method (i.e., 95% or above). To identify amplification of any contaminating genomic DNA and ensure the specificity and the integrity of the PCR product, melt-curve analyses were performed on all PCR products. No products were obtained with real-time PCR from RNA samples when RT was omitted. Samples without template were included for each primer pair to identify contamination. Pearson’s correlation and Deming regression analysis were used to determine correlation and agreement, respectively, between the microarray and quantitative RT-PCR results using the Excel plug-in software Analyse-It v1.73.

Flow Cytometry Antibodies used for phenotype analysis included phycoerythrin -conjugated anti-CD117 (c-Kit, clone ACK45; BD Pharmingen, San Diego) as well as purified anti-SSEA-1 (Clone MC480; Chemicon International Inc., Temecula, CA), which was detected using a fluorescein isothiocyanate–conjugated antimouse immunoglobulin M antibody (BD Pharmingen). Singlecell suspensions collected on day 0 or prepared from differentiating ESCs were blocked for 10 minutes on ice with 5 μg/ml anti-mouse CD16/CD32 (Fc Block, BD Pharmingen) in PBS (STI) plus 2% FBS (PF). Cells were washed once with PF and then incubated on ice for 20 minutes with the primary monoclonal antibody. Cells were then washed once, incubated with

Palmqvist, Glover, Hsu et al. the secondary antibody if needed, washed again, and analyzed by flow cytometry using a FACSCalibur flow cytometer and CELLQuest software (BD Pharmingen). The forward- versus side-scatter profile was used to gate on viable cells, and an unstained sample was used to determine appropriate gating for quantification of expression. Cells to be stained for Oct-4 were resuspended in 100 μl of Hanks’ buffered saline solution (STI) plus 2% FBS (HF) and fixed with 100 μl of IntraPrep Permeabilization Reagent 1 (Immunotech, Westbrook, ME) for 15 minutes at room temperature. Cells were then washed with HF and permeabilized with IntraPrep Permeabilization Reagent 2 for 5 minutes before incubation with a 1:100 dilution of mouse anti-mouse Oct3/4 monoclonal antibody (Transduction Laboratories, Lexington, KY) for 15 minutes at room temperature. Cells were washed with HF before staining with allophycocyanin-labeled anti-mouse immunoglobulin G1 (BD Pharmingen). Samples were analyzed by flow cytometry as outlined above.

Comparison with Other Gene-Array Data Genes indicated as “decreased” in supplementary online Table 2 were split into three separate lists based on the statistical significance of the fold change between 0 and 18 hours, 0 and 72 hours, and 18 and 72 hours. Each of these three gene lists was compared with the following data tables: Bhattacharya et al. [16], supplementary online Table 2; Brandenberger et al. [17], supplementary online Table 2; Ivanova et al. [18], supplementary online Table 1 (genes marked as either I or D); Ramalho-Santos et al. [19], database S1; Sato et al. [20], database 1; Sharov et al. [21], dataset S7; Sperger et al. [22], supporting supplementary online Table 6; Kelly et al. [23], supplementary online Table 1; and Ginis et al. [24], supplementary online Table 3A. Comparison between [18] and [19] was performed on the basis of Affymetrix gene IDs. In [19], supplementary online Table 1 was refiltered as outlined in the original publication. Comparisons with human ES data sets [16, 17, 20, 22] was done by converting public ID references from the human data sets into murine Affymetrix codes using EnsMart (http://www.ensembl.org/Multi/martview). For [21], custom U codes were converted to Unigene codes using the translation tool available (http://lgsun.grc.nia.nih.gov/geneindex/). Groups F, I, L, and O were chosen because these were the lists that contained genes expressed in ESCs grown in LIF but not expressed in ESCs grown without LIF for 4 or 18 hours. For [23], Genbank codes were converted to Affymetrix codes using EnsMart. Following automated comparisons performed as described above, manual comparison between known genes was performed using gene names to ensure accurate comparison.

Results Pluripotency During Early Differentiation We hypothesized that loss of ESC pluripotency as defined by mea-

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surable functional readouts would correlate with significant alterations in gene expression. Identification of these gene expression changes would thus provide important insights into the genetic regulation of ESC pluripotency and facilitate the identification of new molecular markers of the undifferentiated ESC state. We relied on comparisons of the three measures of ESC potential (chimera formation, EB formation, and CFC assay) to select time points for analysis of gene expression profiles. Undifferentiated ESCs were cultured in medium containing LIF on irradiated primary MEF, which supply several as-yet unidentified factors that enhance the plating efficiency of the ESCs as well as assist in the maintenance of the undifferentiated state. Preplating of ESCs removes the MEF and enriches for ESCs with the capacity to contribute to the developing blastocyst. It has been suggested that those ESCs capable of loosely attaching to the feeder layer in a short (i.e., 1 hour) period of time have the highest likelihood of forming colonies (i.e., self-renewing) and thus may also be the most competent at contributing to the germ-line after blastocyst injection [25]. We thus elected to use only preplated, loosely adherent mouse ESCs for our analyses. The baseline activities for undifferentiated ESCs in the three different assays were first determined. Undifferentiated, preplated R1 ESCs yielded 100% chimeric pups after blastocyst injection (27 blastocysts injected; six born and analyzed in two independent experiments), 6.9% ± 1.0% of plated cells differentiated into EBs in the EB formation assay, and 12.50% ± 2.2% of plated cells gave rise to alkaline phosphatase–positive ESC colonies in the CFC assay. These results were within the normal range for the R1 cell line. ESC differentiation was then initiated by LIF removal and replating without MEF. The morphology of the ESCs before and after LIF removal is shown in Figure 1. At 18 and 24 hours after MEF and LIF removal, the ESCs looked very similar to one another and did not exhibit any clear signs of differentiation. Morphological differentiation was first apparent at 48 hours, and EBs could be seen after 72 hours. Despite the lack of appreciable phenotypic differentiation within the first 24 hours, there were significant changes in the functional properties of the ESCs. Cultured cells were harvested at various time points during this culture period and analyzed in the three different assays. The blastocyst injection assay showed a rapid decrease in the number of chimeras obtained after initiation of ESC differentiation (Fig. 2A). Only 28% of born pups (84 injected, 25 born and analyzed in two independent experiments) were chimeras when ESCs had differentiated for 24 hours, and less than 5% were chimeras after 72 hours of differentiation (66 injected, 29 born and analyzed in two independent experiments). The EB formation assay (Fig. 2B) showed approximately 5.5% ± 1.0%, 3.7% ± 0.3%, and 0.3% ± 0.1% readout at 18, 24, and 72 hours after LIF removal, respectively. In contrast, the frequency of cells replating in the CFC assay increased slightly during the first 24 hours but subsequently declined rapidly such that only 1.3% ± 0.3% retained this activity

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at 72 hours (Fig. 2C). In summary, all three assays showed clearly that most differentiation potential and self-renewing capacity was gone after 72 hours of differentiation. However, there was a high degree of variation during the first 24 hours amongst the in vitro and in vivo assays, with the EB formation assay correlating most closely with chimeric mouse formation. For the gene expression profiling, because both the EB formation and chimera assays showed a pronounced decrease within the first 18–24 hours, we elected to use the earlier time point, along with 72 hours of differentiation, to compare against the undifferentiated R1 ES. In parallel with the functional assays, the expression patterns of two markers commonly used to identify undifferentiated ESCs, the POU transcription factor Oct-4 and SSEA-1, were also analyzed to establish the level of correspondence in the readouts using each method. Although Oct-4 and SSEA-1 have been extensively used in ESC research, their expression patterns during ESC differentiation have not been studied in detail. Oct-4 is used as a marker for ESCs because of its requirement in ESC self-renewal [26]. The precise expression level of Oct-4 is important for determining ESC fates, and repression of Oct-4 induces loss of pluripotency and dedifferentiation to trophectoderm [26]. However, forced constitutive expression of Oct-4 cannot prevent ESC differentiation, and a less than twofold increase in expression actually causes differentiation into primitive endoderm and

Figure 1. Embryonic stem cell (ESC) morphology. Morphology of the R1 ESCs grown on mouse embryo fibroblasts (MEFs) with leukemia inhibitory factor (LIF) is shown. ESCs were followed for 72 hours after MEF and LIF removal, and pictures were taken at the indicated time points and with the indicated resolution.

Genetic Correlates of Murine ESC Pluripotency mesoderm [26]. Thus, a critical amount of Oct-4 is required to sustain stem cell self-renewal but is not sufficient to prevent differentiation. SSEA-1 is a glycoprotein expressed during early embryonic development and by undifferentiated ESCs. However, the precise role of SSEA-1 in pluripotency and self-renewal has not been defined. ESCs selected for expression of SSEA-1 and platelet endothelial cell adhesion molecule 1 are enriched for cells that differentiate predominantly into epiblast cells in chimeric embryos [27]. In the present study, protein expression of both SSEA-1 and Oct-4 remained relatively unchanged throughout the first 48 hours of differentiation (Fig. 2D). At 120 hours after LIF removal, 18.5% ± 1.1% of the cells retained expression of SSEA-1, whereas 40.6% ± 6.6% of the cells continued to express Oct-4. Thus there was no clear correlation between expression of these two markers and the various ESC functional assays.

Gene-Array Analysis Cultured R1 ESCs from matched passage numbers were collected in three separate experiments at 0, 18, and 72 hours of differentiation after LIF removal and were analyzed using the

Figure 2. Assays of embryonic stem cell (ESC) pluripotency. Comparison of in vitro and in vivo functional measures of ESC potential with expression of the commonly used ESC markers Oct-4 and stage-specific embryonic antigen 1 (SSEA-1). R1 ESCs were thawed, cultured, and assayed as outlined in Materials and Methods. The frequency of cells capable of (A) generating chimeric mice (summary of two independent experiments), (B) giving rise to embryoid bodies (summary of three independent experiments), or (C) giving rise to colonies in the colony-forming cell assay (summary of three independent experiments) is shown as a function of time after leukemia inhibitory factor removal. (D): Flow cytometric analysis (summary of three independent experiments) was used to determine the percentage of gated cells expressing cell-surface SSEA-1 and intracellular Oct-4 at the same time points. Data are the mean ± standard error of the mean for replicated experiments.

Palmqvist, Glover, Hsu et al. Affymetrix GeneChip MG-U74v2 array containing 36,767 different probe sets. Duplicate samples of the MEF feeders were also collected and analyzed to assess any possible contamination of the undifferentiated ESCs. Hybridization, scanning, and production of raw data files were performed according to standard protocols. The MAS 5.0 software was used for the initial scaling and expression analysis. The data were then normalized and further analyzed using the GeneSpring software. To validate the reproducibility and the overall variation of the data, hierarchical clustering analysis was performed. The data were first filtered for genes present in at least one of the three time points (present in at least two out of three replicates), resulting in 13,002 different probe sets. The average number of present genes for each time point was 12,818 (coefficient of variation [CV], 6%) in undifferentiated ESCs, 11,806 (CV, 4%) at 18 hours, and 12,297 (CV, 1%) at 72 hours after LIF removal. The number of expressed genes at the different time points was not statistically significantly different. Hierarchical clustering was applied to the reduced gene set on individual array samples (three replicates for each time point) using Pearson’s correlation and average linkage clustering as implemented in GeneSpring. The individual samples from the three experiments clustered tightly together according to their respective time points, as seen in Figure 3, indicating that the overall interexperimental variation was low. The clustering also reflects the temporal progression of the ESC differentiation. As expected, the first two time points (0 and 18 hours) clustered more closely together, whereas the 72-hour time point showed a more distinct expression pattern, resulting in a greater relative distance from the other two time points, consistent with the functional data (Fig. 2).

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Genes that were differentially expressed after LIF removal were determined using the following criteria: The difference in expression level between two time points was at least twofold, the gene was present in all three replicates at the time points with the highest abundance, and the extent of difference in expression was statistically significant (p < .05) in a parametric Welsh ANOVA ttest. With this approach, 473 unique genes were identified as significantly differentially expressed (275 decreased, 194 increased, and 4 both decreased and increased) during early ESC differentiation after LIF removal (complete gene lists in the supplemental data, supplementary online Table 2). Unigene and RefSeq IDs were used to exclude redundant probe sets on the array and to get the true number of affected genes. Only preplated, loosely adherent ESCs were used in our experiments, and visual assessment of MEF contamination of undifferentiated ESCs suggested it was consistently less than 1 in 500. However, even with these measures, we sought to exclude the possibility that contaminating MEF cells may have distorted the data. Duplicate samples of MEFs were therefore also analyzed on the U74v2 GeneChip. The data from both the MEF and the R1 ESC was scaled in MAS 5.0 and normalized in GeneSpring 6.2 before 1% of the raw value (five times more than the estimated contamination) obtained for the gene in MEF was subtracted from the value of the same gene in the undifferentiated R1 ESCs. The analysis steps to find differentially expressed genes were then performed as described above. Only genes detected as decreased during differentiation in the original analysis were reanalyzed. Genes whose expression increased during differentiation would not be affected by MEF contamination in a way that would give false-positive results. This evaluation indicates that none of the genes that showed a significant twofold or greater decrease in the initial analysis lost their significance after MEF subtraction (data not shown) and suggests that the level of MEF contamination was not sufficient to distort the ESC gene expression data.

Gene Expression Validation

Figure 3. Distance tree. Hierarchical clustering of the individual gene-array samples was carried out using Pearson’s correlation and average linking clustering to determine reproducibility and interexperimental variability. Relative distance values are shown.

The expression patterns of some genes previously implicated in maintaining ESC pluripotency and self-renewal were analyzed to validate the data and the approach to identify differentially expressed genes (Table 1). Mouse ESCs are usually cocultured on a feeder layer of irradiated MEFs. In addition to providing a matrix for attachment, MEFs produce LIF, required for propagation of pluripotent mouse ESCs [28]. LIF-null MEFs cannot support self-renewal [29]. However, LIF also needs to be complemented by fetal calf serum to block differentiation. LIF binds to the gp130 receptor that leads to activation of the transcription factor Stat3 [1]. Stat3 was clearly detected in undifferentiated R1 ESCs, and LIF removal decreased Stat3 expression with the most pronounced effect during the first 18 hours (49% reduction). This was also seen for two other genes involved in the LIF/gp130 pathway, the Oncostatin M receptor, Osmr, and the interleukin 6 sig-

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nal transducer, Il6st. A known Stat3 target gene, Pim1 [30], was also decreased significantly at the transcriptional level. Taken together, these observations confirm that the LIF/gp130/Stat3 pathway was rapidly shut down after MEF and LIF removal. The bone morphogenic proteins can act in combination with LIF to sustain self-renewal and preserve multilineage differentiation, chimera contribution, and germ line transmission properties [31]. Bmp4 transcript levels were significantly decreased during differentiation. However, the onset of the decrease was later than for Stat3 (between 18 and 72 hours, 80% reduction). The same was seen for Rex-1/Zfp42, known to be highly expressed in undifferentiated ESCs and downregulated after retinoic acid–induced differentiation [32, 33]. A 90% reduction in Rex-1/Zfp42 expression was seen between 18 and 72 hours after LIF removal. Transcript abundance of the Akp2 gene coding for alkaline phosphatase, commonly used as a marker for undifferentiated ESCs, was also decreased significantly (55% reduction between 18 and 72 hours). The POU transcription factor message, pou5f1, coding for Oct-4 was highly expressed in undifferentiated ESCs but was not

Table 1. Genes previously reported as enriched in undifferentiated ESCs or to be markers of ESC differentiation were compared with the genes identified as differentially expressed in the present study Expression relative to 0 hrs after LIF removal Gene symbol

Accession #

18 hrs

72 hrs

p value

Stat3

NM_011486

0.51

0.41

.0128

Osmr

NM_011019

0.40

0.18

.0321 .0015

changed significantly during the first 72 hours after LIF removal in keeping with the protein expression data (Fig. 2D). Transcript levels of the embryonal stem cell–specific gene 1 (Esg-1 or developmental pluripotency associated gene 5, Dppa5) did not change during differentiation. However, this observation is consistent with the observation that Esg-1 probably is a downstream target of Oct-4 and is downregulated slowly after Oct-4 suppression [34]. The abundance of FoxD3, a gene expressed early in mouse embryonic development, also remained unchanged during the first 72 hours after LIF removal. The SRY-box containing transcription factor Sox2 may act to maintain ESC pluripotency and is expressed in the ICM, epiblast, and germ cells, just like Oct-4 [35]. Sox2 was significantly decreased (60% reduction between 18 and 72 hours, p = .0136). Sox2, together with Oct-4, is involved in the regulation of fibroblast growth factor 4 (Fgf4), another factor confined to the ICM of the blastocyst [35]. Although Fgf4 transcripts were not significantly decreased (p = .09), it was reduced more than twofold (52% reduction between 18 and 72 hours). This was also the case for the newly discovered marker of ESC pluripotency Dppa3 [36], which decreased 78% between 0 and 72 hours (p = .073). Similarly, the regulatory factor Nanog, which can rescue ESCs from LIF/Stat3 dependence and maintain Oct-4 expression [37, 38], was not quite significantly decreased during the first 72 hours of differentiation (p = .07), and the reduction in expression was less than twofold (41%). Several genes indicative of ESC differentiation were increased after LIF removal. For example, the mesoderm marker Brachyury [39] increased 15fold between 18 and 72 hours (p = .003). The ectoderm marker Nestin [40] increased approximately threefold between 18 and 72 hours (p = 0.04), and the epithelial cell marker Prominin 1 [41] increased fivefold between 18 and 72 hours (p = .0010). In conclusion, most of these changes are consistent with the loss of ESC pluripotency as measured in all three assays and indicate that the thresholds used to define differentially expressed genes in this study are reasonable.

Il6st

NM_010560

0.62

0.40

Pim1

NM_008842

1.08

0.36

.0106

Bmp4

NM_007554

0.99

0.40

.0051

Rex1/Zfp42

NM_009556

1.08

0.12

.0005

Akp2

NM_007431

1.24

0.55

.0168

Oct4

NM_013633

1.09

1.01

.4823

RT-PCR Validation

Sox2

NM_011443

0.90

0.41

.0136

Fgf4

NM_010202

1.39

0.67

.0902

Nanog

XM_132755

0.82

0.59

.0690

FoxD3

NM_010425

1.74

0.95

.5490

Dppa3

NM_139218

1.06

0.24

.0731 .1430

To further confirm the fidelity of the gene-array data, a set of 28 genes was selected and their transcript levels were tested using quantitative RT-PCR in R1 ESCs before and after LIF removal. The genes selected included some of the genes mentioned above, as well as other genes increased, decreased, or unchanged after LIF removal according to the gene-array results. The fold change was calculated between 0 and 18 hours, 18 and 72 hours, and 0 and 72 hours, respectively. The individual RT-PCR results can be found in the supplemental data (supplementary online Table 3). The analysis revealed a high degree of correlation between the gene-array data and the RT-PCR results (Pearson’s correlation, r = 0.87, Fig. 4A), with a tendency that the PCR results showed greater changes than the array suggested (proportional bias 1.2 in a Deming method comparison analysis, not shown).

Esg1/Dppa5

NM_025274

1.02

0.58

Brachyury

NM_009309

0.48

7.3

.0034

Nestin

NM_016701

0.97

2.78

.0384

Prominin 1

NM_008935

1.27

5.53

.0010

Relative expression levels compared with undifferentiated ESCs are listed. The p values for the most significant fold change from Welsh analysis of variance t-tests are listed. Abbreviations: ESC, embryonic stem cell; LIF, leukemia inhibitory factor.

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Figure 4. Correlation between gene array and quantitative reverse transcription–polymerase chain reaction (RT-PCR). (A): Pearson’s correlation analysis was done on the log ratio values obtained from the quantitative real-time RT-PCR and the gene-array fold changes (n = 84). (B, C, D): Comparison of quantitative real-time RT-PCR results from the three different embryonic stem cell lines are shown. Pearson’s correlation analysis was done on the log ratios of the values obtained with the 2 –ΔΔCT method (n = 45 in each comparison).

To determine if the gene expression changes observed in the R1 ESCs are general and more broadly observed, the expression patterns of 15 genes were followed for 72 hours after LIF removal in two other ESC lines, J1 [10] and EFC [11], and compared with the R1 ESC line using quantitative RT-PCR (Table 2). Overall, there was a good correlation amongst all three ESC lines (Figs. 4B–4D). Specifically, RT-PCR analysis was able to verify the expression changes of genes such as Lox, Ankrd1, and c-Kit that showed significantly decreased transcript levels after 18 hours in all three ESC lines tested. Similarly, Rex1, Sox2, Leftb, and Mtf2 were all changed in a similar fashion in the R1, J1, and EFC cell lines. The RT-PCR results also confirmed that Oct-4 transcript levels were not decreased significantly during the first 72 hours of differentiation in any of the cell lines, in agreement with the genearray results and the protein level observed (Fig. 2D). In contrast, although the decrease in Nanog transcripts was not quite significant in R1 ESCs according to the gene array, the RT-PCR results indicated a reduction. Nanog was reduced by 50%–70% after 72 hours in all three ESC lines according to the quantitative RT-PCR analysis but consistent with delayed reduction in Nanog upon differentiation (Table 2). Only two of the tested genes showed different expression patterns amongst the cell lines. The mesoderm marker Brachyury and the homeobox transcription factor Pbx3 were both increased during differentiation in the R1 and J1 ESCs, but not in the EFC cell line. This observation suggests that the various ESC lines might follow slightly altered differentiation pathways upon LIF removal.

Table 2. Comparison of relative expression levels obtained by quantitative real-time RT-PCR in the R1, J1, and EFC ESC lines during differentiation

Gene symbol Ankrd1 Lox Kit Leftb Pim1 Oct-4 Nanog Pim2 Rex1 Mtf2 Ptch Sox2 Hck Brachyury Pbx3

Accession # NM_013468 NM_010728 NM_021099 NM_010094 NM_008842 NM_013633 XM_132755 NM_138606 NM_009556 NM_013827 NM_008957 NM_011443 NM_010407 NM_009309 NM_016768

Expression relative to 0 hrs after LIF removal R1 ESC line J1 ESC line EFC ESC line 18 hrs 72 hrs 18 hrs 72 hrs 18 hrs 72 hrs 0.09 0.07 0.02 0.05 0.03 0.002 0.13 0.11 0.09 0.01 0.07 0.008 0.35 0.17 0.22 0.12 0.36 0.12 0.58 0.41 1.84 0.51 0.69 0.27 0.81 0.48 0.66 0.39 0.48 0.22 0.84 1.11 1.27 1.57 2.00 2.46 0.91 0.53 0.62 0.29 0.59 0.23 0.87 4.14 1.87 9.19 1.32 17.15 0.90 0.05 1.07 0.01 0.97 0.005 1.00 0.30 0.93 0.13 0.68 0.10 1.15 0.19 2.00 0.13 1.68 0.23 2.07 0.35 1.15 0.11 1.04 0.17 2.30 0.05 1.11 0.01 1.19 0.03 0.54 23.4 0.07 14.9 0.15 0.59 2.93 3.73 1.04 2.38 0.24 0.55

Relative expression levels were calculated between undifferentiated ESCs (0 hrs) and the 18- and 72-hr time points, respectively. The RT-PCR results were calculated with the 2 –ΔΔCT method. GAPDH was used as an endogenous control to normalize the data. Results represent the mean from two independent RT-PCR reactions. Abbreviations: ESC, embryonic stem cell; LIF, leukemia inhibitory factor; RT-PCR, reverse transcription–polymerase chain reaction.

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These validation analyses demonstrate that the early changes during differentiation uncovered by the gene-array analysis could be verified with an independent method and could in most cases be observed in multiple ESC lines. Taken together, they provide confidence in our approach to identify differentially expressed genes with a high likelihood of exhibiting true expression level changes during the loss of ESC pluripotency.

Functional Classification of Differentially Expressed Genes It is likely that genes showing similar functional properties and expression patterns form interacting networks that contribute to the phenotypic and functional characteristics of the cells of interest. Functional classification of the differentially expressed genes into appropriate biological processes was thus performed using the NetAffx and GeneOntology Express tools as well as the simplified gene ontology tool in the GeneSpring software package. The differentially expressed genes were separated into 13 main categories (Fig. 5A; see supplementary online Table 4 for complete lists). Many differentially expressed genes were, as expected, classified as being involved in development and differentiation (61 genes, 10%) or directly or indirectly involved in cell-cycle control and cell proliferation (32 genes, 5%). Furthermore, at least 81 genes (13%) were classified as being involved in intracellular signal transduction, cell–cell signaling, or response to external stimuli (supplementary online Table 4). A closer look at the signaling pathways affected during ESC differentiation revealed that the Yamaguchi sarcoma viral oncogene gene (Yes) was decreased significantly between 0 and 72 hours (60%). Yes has recently been shown to be regulated by LIF and to be important for ESC self-renewal [42]. Yes is a gene coding for an Src tyrosine kinase expressed in both mouse and human ESCs and is downregulated when these cells differentiate [42]. Another significantly decreased factor was the Notch ligand Jagged-1 (55% reduction between 0 and 18 hours), which has been implicated in hematopoietic stem cell self-renewal [43]. Jagged-1 is involved in embryonic vascular development, and a Jag1 knockout is embryonic lethal [44]. A signaling pathway that is of particular interest during ESC differentiation is the mitogen-activated protein kinase (MAPK) pathway. The self-renewal of ESCs is influenced by the MAPK pathway, in which expression of Erk and Shp-2 counteracts the proliferative effects of Stat3 and promotes differentiation [45]. According to the array results, the Erk gene (Mapk1 or Mapk3) and most other components in this pathway were already present in undifferentiated ESCs, and their expression levels did not change significantly at the transcription level during the first 72 hours of differentiation (data not shown). However, some genes in the MAPK pathway (e.g., Kras2 and Mapk12) had significantly increased transcript levels during differentiation, indicating an activation of the pathway (supplementary online Table 4). Fur-

Genetic Correlates of Murine ESC Pluripotency thermore, the gene coding for Grb2-associated binder 1 (Gab1) was significantly decreased (58% reduction between 18 and 72 hours, p = .0035). Gab1 binds to Shp-2 and is believed to suppress the MAPK pathway in ESCs; also, increased synthesis of Gab1 together with Oct-4 may suppress induction of differentiation [1]. Although most of the factors involved in the MAPK pathway are primarily regulated at the posttranscriptional level, the fact that transcripts for many pathway members were detected in undifferentiated ESCs suggests that they harbor the required components to quickly respond to signals that promote differentiation. The Wnt signaling pathway is important for maintenance of pluripotency in undifferentiated ESCs, and a recent study also showed that activation of the Wnt pathway by pharmacological inhibition of Gsk-3 is able to maintain pluripotency in both human and mouse ESCs [46]. Most components of the Wnt signaling pathway could be detected in both undifferentiated and differentiated ESCs, but some factors were also significantly changed

Figure 5. Annotation of differentially expressed genes. All genes differentially expressed during embryonic stem cell differentiation were classified according to (A) biological process and (B) cellular component. Some genes are classified in more than one category, resulting in the total number of genes indicated in the figures being greater than the total number of differentially expressed genes.

Palmqvist, Glover, Hsu et al. during differentiation (e.g., Fzd5, Wnt3, and CyclinD3; supplementary online Table 4). However, overall there was no dramatic change at the transcription level of several important factors belonging to this pathway during the first 72 hours of differentiation (e.g., β-catenin, Gsk-3, and Axin; data not shown). The Hedgehog signaling pathway plays a critical role during development and has been extensively studied in different species and tissues [47, 48]. In this pathway, two receptors, Ptch and Ptch2, and two transcription factors, Gli1 and Gli2, were decreased significantly during ESC differentiation. Furthermore, at least one suggested downstream target of this pathway, the Bmp4 gene already discussed above, had significantly decreased transcription, providing additional evidence that this pathway might be involved in maintaining ESC self-renewal or pluripotency. Importantly, out of 473 differentially expressed genes, the largest group consisted of 173 genes or expressed sequence tags with unknown biological function. Further analyses of the genes within this group whose expression changes closely correlate with loss of pluripotency may reveal novel mechanisms involved in ESC maintenance. Of the 275 genes significantly decreased during ESC differentiation, 48 genes showed a more than twofold decrease in transcript levels within the first 18 hours and continued to decrease or remained at a low level of expression until 72 hours (Table 3). Changes in the transcript levels for these genes correlated well with the functional assays, especially the chimera generation and EB formation assays (Fig. 1). At least half of them have been identified as ESC-enriched or to be downregulated during differentiation in previous studies [17–23] (see below), but several of these genes have unknown biological function or have not previously been implicated in ESC maintenance (e.g., Lox, Tnc, and Jag1; Table 3).

Molecular Markers of Pluripotency We hypothesized that classification of differentially expressed genes according to cellular component, in combination with functional analyses, may lead to the identification of new markers that more accurately reflect ESC potential. Classification of the significantly changed genes according to cellular component is shown in Figure 5B (complete list provided in supplementary online Table 5). At least 60 gene products (11%) were considered localized to the plasma membrane and might therefore be good candidates as markers for undifferentiated ESCs. Genes increased during differentiation were also included in this list of possible ESC markers because they can be used to detect the onset of differentiation or for negative selection strategies for isolating undifferentiated ESCs (Table 4). Protein expression analyses of some of these have already been conducted in relation to ESC differentiation, i.e., Cd9, Cd44, and Osmr [49–51]. Additional gene products of interest include the adhesion molecule Vcam1 and the hedgehog signaling pathway receptors Ptch and Ptch2. One gene of special interest encodes the SCF recep-

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tor c-Kit. According to the gene-array results, transcript level of the c-Kit gene was one of the most significant changes during differentiation (54% reduction between 0 and 18 hours and 75% reduction between 0 and 72 hours; p = .0016; Table 3), which also correlated well with the functional assays (Fig. 1). Furthermore, this finding was also verified in the R1, J1, and EFC ESC lines by quantitative RT-PCR (Table 2). No c-Kit ligand/SCF was added to the culture medium used in these experiments, and SCF was not detected in the undifferentiated R1 ESCs, although it was indeed present at high levels in the MEF according to the genearray results (data not shown). Because c-Kit is expressed on several pluripotent cell types, including germ cells and hematopoietic stem cells, and antibodies are commercially available, we were able to compare the results of the array and RT-PCR experiments with protein expression levels measured by flow cytometry (Fig. 6). This analysis revealed that expression of c-Kit on the cell surface of differentiating ESCs closely paralleled the loss of functional potential as measured using the EB formation assay (Fig. 7). At later time points, the correlation was not as close (i.e., EB formation capacity continued to decrease while protein expression remained unchanged), suggesting the emergence of differentiated cell types expressing c-Kit protein.

Comparison with Previously Published Gene-Array Data Sets Gene expression profiling to determine regulatory factors and signaling pathways present in ESCs has been performed extensively in recent years [16–24, 34]. It has been hypothesized that the undifferentiated state is conferred on various stem cell populations through the use of similar molecular mechanisms. Initial support for this hypothesis came from experiments comparing the gene expression profiles of multiple stem cell populations [18, 19, 34]. However, further analysis of available data indicated minimal overlap between different published stem cell–associated gene sets [52, 53]. The discrepancy observed amongst these studies likely arises in large part from significant differences in the strategies used to identify the stem cell profile. However, true differences in stem cell biology probably also exist among stem cells taken from different tissues (e.g., ESCs, neural stem cells, or hematopoietic stem cells). Some studies have relied on comparison of ESCs to terminally differentiated tissues [20, 23, 34] to establish a list of genes enriched in ESCs. However, this strategy is likely to miss genes involved in pluripotency that are transiently turned off early during the differentiation process but not uniquely expressed in the stem cell population. Another strategy used has been to compare stem cell populations arising from the same tissue source across species. An example is the comparison of mouse with human ESCs. Although mouse and human ESCs are both derived from preimplantation blastocysts, they differ in responsiveness to extrinsic signals and in expression of surface markers; for example, LIF cannot sustain self-renewal of human

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674

ESCs, even in the presence of serum, suggesting the existence of other signaling pathways essential for self-renewal in human

ESCs [5]. The evidence that human and mouse ESCs share a common core molecular program is also somewhat conflicting [16,

Table 3. Genes decreasing during ESC differentiation after LIF removal and most closely correlating with the loss of pluripotency

Gene symbol Accession # Cell adhesion Col1a1 NM_007742 Vcam1 NM_011693 Thbs1 NM_011580 Cell growth and maintenance Acta2 NM_007392 Cav NM_007616 Emp1 NM_010128 S100a6 NM_011313 Akap2 NM_009649 Metabolism Prss23 NM_029614 Dnajb9 NM_013760 Abca1 NM_013454 Eif2s2 NM_026030 Transcription Ankrd1 NM_013468 Fos NM_010234 Fosb NM_008036 Fosl2 NM_008037 AA408868 NM_030612 Klf4 NM_010637 Bcl3 NM_033601 Tbx3 NM_011535 Aebp2 NM_009637 Egr1 NM_007913 Mllt2h NM_133919 Cell development and differentiation Cd44 NM_009851 Serpine1 NM_008871 Inhba NM_008380 Csf1 NM_007778 Signal transduction Ccl2 NM_011333 Apbb1ip NM_019456 Socs3 NM_007707 Osmr NM_011019 Mras NM_008624 Jag1 NM_013822 Kit NM_021099 Fzd5 NM_022721 Unknown biological process Lox NM_010728 Fbln2 NM_007992 Tnc NM_011607 BB120430 AI847445 Bgn NM_007542 C730049F20Rik AI840339 Sdpr NM_138741 Hnrph1 NM_021510 Timp2 NM_011594 AI504685 AI504685 C85523 C85523 AI115454 NM_175345 5730501N20Rik AI882080

Expression relative to 0 hrs after LIF removal, 18 hrs p value

Expression relative to 0 hrs after LIF removal, 72 hrs

p value

Reference

0.05 0.08 0.11

.00793 .03805 .01728

0.08 0.08 0.13

.0278 .0344 .0203

18 — 17

0.13 0.20 0.22 0.38 0.45

.05130 .02880 .10000 .11300 .13100

0.10 0.23 0.09 0.16 0.28

.0472 .0254 .0381 .0186 .0159

18–20 18, 19 18, 19 — 18, 22

0.36 0.38 0.42 0.43

.04408 .06820 .14900 .00736

0.46 0.31 0.05 0.17

.1740 .0216 .0060 .0170

18, 19 17 22 —

0.04 0.06 0.25 0.26 0.27 0.32 0.38 0.41 0.44 0.49 0.50

.01611 .03985 .02309 .02917 .01029 .00361 .00266 .01023 .00747 .02355 .28300

0.07 0.11 0.33 0.31 0.50 0.05 0.38 0.30 0.43 0.44 0.32

.0657 .0017 .0094 .2830 .0310 .0021 .1570 .0214 .0002 .0258 .0277

19 — — — — 23 — 18, 19 — 22 —

0.08 0.11 0.32 0.36

.00931 .00070 .03052 .03371

0.19 0.05 0.29 0.49

0.04301 0.06000 0.02966 0.07110

— 18, 19 — —

0.04 0.18 0.22 0.40 0.43 0.45 0.46 0.47

.06990 .04105 .00055 .10500 .04385 .04943 .09390 .08780

0.02 0.22 0.15 0.18 0.07 0.47 0.23 0.31

.0279 .0664 .0055 .0321 .00001 .0411 .0016 .0228

18, 19, 22 22 22 22 — — 22 18, 20

0.02 0.14 0.14 0.14 0.15 0.16 0.18 0.33 0.41 0.44 0.45 0.48 0.49

.00272 .01975 .02558 .01359 .01494 .05340 .12300 .04185 .03587 .01515 .07180 .05960 .01931

0.06 0.09 0.09 0.18 0.10 0.22 0.11 0.33 0.46 0.34 0.38 0.31 0.45

.0100 .0054 .0134 .0224 .0071 .0361 .0278 .2670 .0298 .0004 .0449 .0344 .0068

18, 19, 21 18, 19 18, 19, 21, 22 — 18, 19 — — — — — — — —

Relative expression ratios compared with undifferentiated ESCs and p values from Welsh analysis of variance t-tests are also shown. The reference numbers indicate other studies that have identified the gene to be ESC-enriched or downregulated during ESC differentiation. Abbreviations: ESC, embryonic stem cell; LIF, leukemia inhibitory factor.

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Table 4. Differentially expressed genes with plasma membrane localization Expression relative to 0 hrs after LIF removal Genes symbol Decreased Vcam1 Cd44 Cav Emp1 Csf1 Osmr Abca1 Jag1 Kit Fzd5 Il6st Ly75 Icam1 Gjb3 Cd9 Slc2a3 9430079M16Rik Ppap2a Gja7 Abcb1a Enah Ptch Akp2 Slc29a1 Trfr Fgfr2 Utrn Ptch2 Ak1 Cd1d1 Adam19 1600025H15Rik Ltb Increased Enpp2 Dgka Cldn4 Cldn6 Btla St14 Igsf9 Podxl Itga8 Ddr1 Cldn7 Spint1 Met Rnf128 Slc39a8 Tap1 Prom1 Calcr Sfrp2 Tapbp Perp Cd24a Dlk1 Cask Gp38 Amot Lrp8

Accession #

18 hrs

p 72 hrs value

NM_011693 NM_009851 NM_007616 NM_010128 NM_007778 NM_011019 NM_013454 NM_013822 NM_021099 NM_022721 NM_010560 NM_013825 NM_010493 NM_008126 NM_007657 NM_011401 NM_175414 NM_008903 NM_008122 NM_011076 NM_010135 NM_008957 NM_007431 NM_022880 NM_011638 NM_010207 NM_011682 NM_008958 NM_021515 NM_007639 NM_009616 NM_028064 NM_008518

0.08 0.08 0.20 0.22 0.36 0.40 0.42 0.45 0.46 0.47 0.62 0.80 0.81 0.83 0.99 1.00 1.00 1.05 1.10 1.10 1.21 1.22 1.24 1.37 1.46 1.48 1.49 1.53 1.53 1.66 1.82 1.88 2.29

0.08 0.19 0.23 0.09 0.49 0.18 0.05 0.47 0.23 0.31 0.40 0.31 0.31 0.46 0.45 0.44 0.49 0.49 0.22 0.35 0.31 0.27 0.55 0.44 0.68 0.67 0.59 0.38 0.66 0.45 0.45 0.83 0.85

.0344 .0430 .0254 .0302 .0337 .0321 .0060 .0411 .0016 .0228 .0058 .0350 .0188 .0423 .0345 .0027 .0310 .0041 .0376 .0201 .0074 .0012 .0168 .0150 .0390 .0293 .0013 .0054 .0304 .0291 .0020 .0062 .0202

NM_015744 NM_016811 NM_009903 NM_018777 NM_177584 NM_011176 NM_033608 NM_013723 NM_001001309 NM_007584 NM_016887 NM_016907 NM_008591 NM_023270 NM_026228 NM_013683 NM_008935 NM_007588 NM_009144 NM_009318 NM_022032 NM_009846 NM_010052 NM_009806 NM_010329 NM_153319 NM_053073

3.10 2.49 2.36 2.14 2.03 1.73 1.55 1.54 1.51 1.50 1.48 1.35 1.35 1.34 1.33 1.31 1.27 1.18 1.18 1.08 1.03 0.90 0.82 0.79 0.79 0.76 0.47

9.13 0.95 1.60 3.40 14.27 2.45 2.22 2.54 2.81 2.39 5.97 2.50 2.18 2.51 2.32 2.73 5.53 6.05 2.28 2.21 2.39 2.73 1.77 1.91 1.63 4.10 2.26

.0118 .0079 .0472 .0081 .0034 .0319 .0050 .0019 .0298 .0264 .00001 .0138 .0311 .0453 .0208 .0074 .0010 .0338 .0150 .0202 .0269 0 .0291 .0052 .0323 .0073 .0255

Relative expression levels compared with undifferentiated embryonic stem cells are listed. The p values for the most significant fold change from Welsh analysis of variance t-tests are listed. Abbreviation: LIF, leukemia inhibitory factor.

20, 24]. However, the molecular mechanisms that confer pluripotency are likely evolutionary conserved, and thus comparison of mouse and human ESC gene-array data might still be informative. The gene expression data reported here were compared with nine different gene-array data sets, comprising four studies on

Figure 6. Fluorescence-activated cell sorting profile. Undifferentiated R1 embryonic stem cells (ESCs) (0 hour) and R1 ESCs differentiated 18 and 72 hours after leukemia inhibitory factor removal were analyzed with flow cytometry using c-Kit antibody. Abbreviations: FSC, forward scatter; SSC, side scatter.

Figure 7. Correlation between c-Kit expression and embryoid body formation. Expression of c-Kit (ó) was monitored at the indicated times during embryonic stem cell differentiation using flow cytometry, and expression levels were plotted as percent gated cells versus time (right axis). Embryoid body (EB)–forming cell frequency (°) determined on the same cell populations is also shown (percent EB formation indicated on the left axis).

Genetic Correlates of Murine ESC Pluripotency

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genes from our data and genes identified as either ESC enriched or downregulated during ESC differentiation in the other data sets, despite the wide variety of experimental designs and cell lines used. Of the genes identified as significantly decreased after LIF removal in our study, 60% (164 of 275) were found in at least one other data set, and 28% (78 of 275) were found in at least two other data sets. There was greater similarity with the murine data (129 of 275 or 47% were found in at least one other murine data

murine ESCs [18, 19, 21, 23], four studies on human ESCs [16, 17, 20, 22], and one study comparing human and mouse ESCs [24] (summarized in Table 5). Only genes downregulated during differentiation after LIF removal were used in the comparisons because most other studies only report ESC-enriched or ESCspecific genes. Complete gene lists derived from these comparisons can been found in supplementary online Table 6. Overall, there was a large overlap between the downregulated

Table 5. Summary of the published gene expression studies in mouse and human ESCs that were used for comparison Technology platform Affymetrix MGU74v2

Number Gene list used of genes Supplementary 2,270 online Table 1 (refiltered according to author’s criteria)

Undifferentiated ESCs cultured on gelatin D3 in the presence of LIF; expression compared with ESCs differentiated for 96 hrs by treatment with 1 μM retinoic acid

Atlas Mouse cDNA expression arrays

Table 1

17

6

Ramalho-Santos et al. [19]

Undifferentiated ESCs grown on irradiated C57/Bl6 fibroblasts for two passages before RNA isolation; expression compared with differentiated bone marrow and tissue from the lateral ventricles on the brain

Affymetrix MGU74v2

Database S1

1,787

82 (85)

Sharov et al. [21]

Expression profiles of 36 tissues from early R1 development and adult stem cells, including undifferentiated ESCs and ESCs differentiated for 4 or 18 hrs in the absence of fibroblasts and LIF

cDNA library construction

Data set S7, 260 groups F, I, L, and O, with comparable annotation

Reference Ivanova et al. [18]

Experimental design Cell line Undifferentiated ESCs grown on fibroblasts CCE in the presence of LIF, cultured for one passage on gelatin before RNA extraction; expression compared with differentiated bone marrow and fetal liver cells

Kelly et al. [23]

Overlap 90 (97)

16 (18)

Bhattacharya et al. [16] Expression profiles of five ESC lines grown GE01, GE09, Custom oligo- Supplementary on fibroblasts was compared with 8-day-old BG01, nucleotide glass online Table 2 EB outgrowths BG02, TE06 arrays

92

5

Brandenberger et al. [17]

Expression profiles of three undifferentiH1, H7, H9 ated ESC lines compared with EBs (8 days of differentiation), prehepatocytes (induced by addition of DMSO), and preneural cells (induced by addition of retinoic acid)

Massively parallel signature sequencing

Supplementary online Table 2

532

19 (20)

Sato et al. [20]

Undifferentiated ESCs compared with ESCs induced to differentiated neurons for 3 wks

Affymetrix HGU133A

Database 1

918

39 (40)

Sperger et al. [22]

Undifferentiated ESCs compared with mul- H1, H7, H9, tiple cell lines, including germ cell tumor H13, H14 and other differentiated cell lines

Custom cDNA arrays

Supplementary online Table 6

1,760

45 (51)

Ginis et al. [24]

Comparison between human and murine undifferentiated ESCs

RT-PCR/focused Table 3A functional microarrays

13

2

H1

Human H1, mouse D3

Supplementary online Table 2 was split into three separate lists based on the statistical significance of the fold change between 0 and 18 hours, 0 and 72 hours, and 18 and 72 hours. Each of these three gene lists was compared with the gene lists indicated. The experimental design, cell line, and technology used are shown along with the number of genes in the original publication and the number of genes that overlapped with the 275 significantly decreased genes in our data. Numbers in parentheses represent the actual overlap with the 298 probe sets used in the analysis representing the 275 unique genes. Abbreviations: DMSO, dimethyl sulfoxide; EB, embryoid body; ESC, embryonic stem cell; LIF, leukemia inhibitory factor; RT-PCR, reverse transcription–polymerase chain reaction.

Palmqvist, Glover, Hsu et al. set) than with the human data (75 of 275 or 27% were found in at least one other human data set). Genes that we identified as differentially expressed between 0 and 18 hours were the least represented in other published data (41% between 0 and 18 hours compared with 67% between 18 and 72 hours and 63% between 0 and 72 hours), possibly because our approach to use early time points during differentiation for defining differential expression has not been widely used. Only two other studies included comparable early time points in their study [21, 23]. Within individual data sets, the degree of overlap depended primarily on the number of genes in the starting list (Table 5). The data set generated by Kelly et al. [23] stood out because of its high degree of overlap (35%), which can potentially be explained by the similarity in the two studies in terms of experimental design and the similarity of the genetic background of the ESCs used. However, this encompasses relatively few genes (6 out of 17 genes). Furthermore, many genes that we report as decreased during the first 72 hours were reported as also being present in human ESCs by Sato et al. [20]. However, they were not identified as differentially expressed in that study. This discrepancy could possibly be due to the later time point (3 weeks) used for determining differential expression in that study (data not shown). No single gene was found enriched in every human and mouse ESC data set. Jade1, Leftb, and Smarcad1 were the most commonly identified genes in other data sets (i.e., in six of nine other data sets). Leftb is a transforming growth factor-beta family member, which is expressed on the left side of developing mouse embryos and is implicated in left-right determination [54]. Jade1 was recently identified as a gene involved in anteroposterior axis development [55]. Smarcad1 has previously been identified as a marker of preimplantation embryos [56]. The gene coding for Tenascin-C was downregulated within the first 18 hours after LIF removal (Table 3) and was also commonly observed as differentially expressed in other data sets (i.e., in four of eight data sets). Tenascin, also known as hexabrachion and cytotactin, is an extracellular matrix protein with a spatially and temporally restricted tissue distribution that is tightly regulated during embryonic development and in adult tissue remodeling [57]. Other commonly identified markers of undifferentiated ESCs were also found in this comparison. Sox2, Rex1, Bmp4, and Gbx2 were all observed in at least three other data sets. This also included the gene Lox, coding for lysyl oxidase, found in three other murine data sets [18, 19, 21]. It was in fact one of the most pronounced and rapidly downregulated genes detected (Table 3). At least one study identified c-Kit as enriched in ESCs, intriguingly in human ESCs [22]. Overall the comparison with previously published data indicated some intriguing similarities but also showed that most genes do not overlap in pairwise comparisons. This underlines the many differences that might arise when different sources of cells, culture conditions, microarray technique platforms, and data analysis tools are used and emphasizes the need to correlate

677

expression changes with rigorous measures of ESC competence and differentiation potential.

Discussion The present study is distinct from previous studies in two important aspects. First and most important, only a selected population of germ line–competent ESCs, grown under carefully controlled, optimized culture conditions, was used to establish the gene expression profiles. It is likely that substantial variations in gene expression arise in response to culture variables. Considering the multiplicity of culture variables that can be important for the biological heterogeneity of cell populations and their gene expression profiles, remarkably little information has been provided about the conditions used to generate the cells for the gene expression profiles reported thus far [16–24, 34]. Second, few of the previous studies addressing questions about a shared or common stem cell gene expression signature among different types of stem cells, or even different ESC lines, have involved correlative functional assays to assess the pluripotency and self-renewal capacity of the cells of interest. By combining the gene expression profiling data with assays measuring ESC pluripotency and selfrenewal, it should be possible to more precisely define the genes critical for specifying these properties. We combined defined culture and differentiation conditions with various measures of ESC pluripotency to determine optimal times for gene expression analysis using high-density oligonucleotide microarray. The results showed that the functional capacity of ESCs declines rapidly under differentiation conditions, with the most pronounced changes in function occurring during the first 18–24 hours of differentiation. The gene expression results were subsequently used to find genes whose expression correlated with loss of pluripotency and that could explain loss of this capacity during differentiation or serve as markers for pluripotent ESCs. Analysis of the gene-array data revealed that 473 genes were differentially expressed during the first 72 hours of ESC differentiation, suggesting they are potentially important for the maintenance of ESC pluripotency and self-renewal. Important roles in the maintenance of undifferentiated ESCs have previously been demonstrated for several of the downregulated factors such as Stat3, Rex1, Sox2, Gbx2, and Bmp4. Intriguingly, one third of the differentially expressed genes have not been characterized in terms of involvement in biological processes. More important, a refined list of 48 genes whose transcript levels closely correlated with the functional assays (i.e., with early and persistent decreases in transcript levels after LIF removal) contains several genes with unknown function or genes that have not previously been suggested to play a role in ESC maintenance (Table 3). These genes may be novel candidates to play critical roles in the regulation of ESC pluripotency and self-renewal. Several of the genes in this list have also been found in previous gene expression studies in

Genetic Correlates of Murine ESC Pluripotency

678

human or mouse ESCs (e.g., Tnc and Lox; Table 3). Furthermore, several promising candidate markers for pluripotent ESCs were identified from the gene-array analysis (Table 4). The changes in transcript levels observed for one gene, c-Kit, were also verified at the protein level and showed good correlation with functional measures of ESC pluripotency (Fig. 7). This finding was verified in two other murine ESC lines (Table 2). Two other commonly used ESC markers, Oct-4 and SSEA-1, were also analyzed in parallel with the functional assays used in this study but showed poor correlation with the outcome in these (Fig. 2D). Further studies are needed to determine if our candidates (e.g., c-Kit) are more reliable markers of, and useful to enrich for, undifferentiated pluripotent ESCs. Additional work is also required to determine the functional significance of these observations for ESC maintenance. It is also important to note in the context of these and other genes of interest that global gene expression profiling does not discriminate between changes arising at the level of transcription versus mRNA stability. Also, changes in transcript levels in a subset of cells may go undetected, and likewise subtle changes might arise from changes in just a small proportion of the cells. Transcriptional profiling of various stem cell populations has been used to determine the types of regulatory factors and signaling pathways present in pluripotent cells, including ESCs [16–24, 34]. Comparing genes that were significantly decreased in our analysis with both murine and human data sets [16, 17, 20, 22] gave a large overlap with 176 of 298 genes identified in at least one of these other studies. Of note, 139 of these have been identified in other murine ESC studies. This analysis was not able to distinguish biological differences between human and murine ESCs because of the way it was performed. Undue emphasis was placed on the findings reported in this publication by comparing all publications with data. The level of overlap between our data and other murine studies was higher than the overlap with other human studies, but the difference can probably be accounted for by the increased difficulty of gene comparison between species. However, additional ESC gene expression profiling carried out in an analogous stringent man-

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