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First published online June 13, 2005
Stem Cells Vol. 23 No. 8 September 2005, pp. 1154 -1169
doi:10.1634/stemcells.2004-0171; www.StemCells.com
© 2005 AlphaMed Press

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Transcriptional Profiling of Human Hematopoiesis During In Vitro Lineage-Specific Differentiation

Martina Komora, Saskia Güllera, Claudia D. Baldusb, Sven de Vosc, Dieter Hoelzera, Oliver G. Ottmanna, Wolf-K. Hofmanna

a Department of Hematology and Oncology, University Hospital, Frankfurt, Germany;
b Department of Hematology, Oncology and Transfusion Medicine, University Hospital Benjamin Franklin, Berlin, Germany;
c Division of Hematology/Oncology, UCLA School of Medicine, Los Angeles, California, USA

Key Words. Gene expression • Hematopoietic development • Hematopoietic stem cell • Microarray • Transcription

Correspondence: Wolf-K. Hofmann, M.D., Department of Hematology, Oncology and Transfusion Medicine, University Hospital Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany. Telephone: 49-30-8445-3421; Fax: 49-30-8445-4468; e-mail: W.K.Hofmann{at}charite.de


   ABSTRACT
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
To better understand the transcriptional program that a ccompanies orderly lineage-specific hematopoietic differentiation, we performed serial oligonucleotide microarray analysis of human normal CD34+ bone marrow cells during lineage-specific differentiation. CD34+ bone marrow cells isolated from healthy individuals were selectively stimulated in vitro with the cytokines erythropoietin (EPO), thrombopoietin (TPO), granulocyte colony-stimulating factor (G-CSF), and granulocyte macrophage colony-stimulating factor (GM-CSF). Cells from each of the lineages were harvested after 4, 7, and 11 days of culture for expression profiling. Gene expression was analyzed by oligonucleotide microarrays (HG-U133A; Affymetrix, Santa Clara, CA). Experiments were done in triplicates. We identified 258 genes that are consistently upregulated or downregulated during the course of lineage-specific differentiation within each specific lineage (horizontal change). In addition, we identified 52 genes that contributed to a specific expression profile, yielding a genetic signature specific for successive stages of differentiation within each of the three lineages. Analysis of horizontal changes selected 21 continuously upregulated genes for EPO-induced differentiation (including GTPase activator proteins RAP1GA1 and ARHGAP8, which regulate small Rho GTPases), 21 for G-CSF–induced/GM-CSF–induced differentiation, and 91 for TPO-induced differentiation (including DLK1, of which the role in normal hematopoiesis is not defined). During the lineage-specific differentiation, 58 (erythropoiesis), 30 (granulopoiesis), and 37 (thrombopoiesis) genes were significantly downregulated, respectively. The expression of selected genes was confirmed by real-time polymerase chain reaction. Our data encompass the first extensive transcriptional profile of human hematopoiesis during in vitro lineage-specific differentiation.


   INTRODUCTION
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Normal hematopoiesis is maintained by pluripotent, long-term repopulating stem cells that generate progenitors capable of differentiating into all three hematopoietic lineages. Only a few pluripotent hematopoietic stem cells possesses the ability to actively self-renew at any given time [1]. Hematopoietic differentiation is strictly regulated by several exogenous factors [2]. Lineage-specific growth factors, such as erythropoietin (EPO), granulocyte colony-stimulating factor (G-CSF), granulocyte macrophage colony-stimulating factor (GM-CSF), and thrombopoietin (TPO), induce proliferation and differentiation into functionally active peripheral blood (PB) cells by activating multiple genes in an orchestrated way [3]. Other growth factors, such as stem cell factor (SCF), the ligand of the Flt3/Flt2 receptor tyrosine kinase (FL), interleukin (IL)-1, IL-3, and IL-6, support growth and survival of primitive progenitor cells [4–6].

Disruptions of these intricate sequences of transcriptional activation and suppression of multiple genes can cause hematological diseases, such as leukemias, myelodysplastic syndromes (MDS), or myeloproliferative syndromes (MPS). Elucidating the pattern and sequence of gene expression during normal hematopoietic cell development may help to unravel the disease-specific mechanisms in hematopoietic malignancies.

The first report describing sequential analysis of gene expression during hematopoietic differentiation in the mouse system has recently been published [7]. Transcriptional profiling of murine multipotent hematopoietic progenitor cells (factor-dependent cell-Paterson [FDCP]-mix cell line) during multilineage differentiation revealed several known and as-yet unknown genes that are differentially expressed in erythroid and neutrophil differentiation as well as genes involved in self-renewal and differentiation [7].

We have established an in vitro model that enables the detailed analysis of human hematopoietic differentiation originating from unstimulated, steady-state bone marrow CD34+ cells. Using this system, we have analyzed gene expression patterns of the three hematopoietic lineages at defined time points throughout lineage-specific differentiation. Because we used primary human CD34+ cells, the experiments were done in triplicates for each of the time points and each of the conditions to minimize individual changes in expression levels of approximately 22,500 genes that were analyzed.

Our data provide several new insights into understanding differentiation and proliferation of human stem and progenitor cells. In addition, the data should be particularly suitable for comparative analysis of gene expression in hematopoietic malignancies, to characterize pathomechanisms of diseases, and, finally, to find genetically defined therapeutic targets.


   MATERIALS AND METHODS
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
CD34+ Cell Selection
Heparinized bone marrow samples from different healthy individuals were obtained by aspiration from the posterior iliac crest after written informed consent. To lessen activation of the cells by technical manipulation, fresh bone marrow was processed immediately after the aspiration to select the CD34+ cells within the next 4 hours. Mononuclear cells were separated by density-gradient centrifugation through Ficoll-Hypaque (Biochrom, Berlin, Ger-many, http://www.biochrom.de). CD34+ cells were purified by high-gradient magnetic cell separation (MACS) (Miltenyi Biotec, Mönchengladbach, Germany, http://www.miltenyibiotec.com).

Suspension Cultures and Growth Factors
Cells were assayed in Iscove’s modified Dulbecco’s culture medium supplemented with 1 mmol glutamine, 1% penicillin and streptomycin, and 10% fetal calf serum (HyClone, Logan, UT, http://www.hyclone.com). At least 5 x 105 CD34+ cells were assayed in 12-well plates and incubated at 37°C and 5% CO2 in a fully humidified atmosphere in air. To induce lineage-specific differentiation, growth factors (R&D Systems, Wiesbaden-Nor-denstadt, Germany, http://www.rndsystems.com) were added as follows: for erythropoietic differentiation, SCF (50 ng/ml), Flt3-ligand (50 ng/ml), IL-3 (10 ng/ml), EPO (10 U/ml); for granulopoietic differentiation, SCF (50 ng/ml), Flt3-ligand (50 ng/ml), IL-3 (10 ng/ml), G-CSF, and GM-CSF (each, 10 ng/ml); for megakaryopoietic differentiation, SCF (50 ng/ml), Flt3-ligand (50 ng/ml), TPO (20 ng/ml). All growth factors were added at the beginning of culture. The cells were cultured in a final volume of 3 ml, in separate wells for each of the conditions and each of the time points. The cells were inspected daily for 14 days and harvested at predefined time points (days 4, 7, and 11). To increase the purity of cells belonging to the erythropoietic and megakaryopoietic lineages, respectively, for RNA extraction, lineage-specific cells were purified if necessary by immunomagnetic beads using the MACS system. Cells of the erythropoietic lineage have been selected by using CD71+ microbeads; cells of the megakaryopoietic lineage have been selected by using CD61+ microbeads.

Flow Cytometry and Cytospins
Freshly isolated CD34+ and cultured cells were characterized by dual-color immunofluorescence using fluorescein isothiocynate (FITC)– and phycoerythrin (PE)–conjugated human monoclonal antibody (anti-CD34 PE) using a FACScan (Becton, Dickinson and Company, Heidelberg, Germany, http://www.bd.com).

Erythropoietic cells were characterized by staining with an anti-CD71 FITC antibody, in some cases by using a Glycophorin A antibody. Megakaryocytic cells were determined with an anti-CD61 FITC or anti-CD42b FITC antibody, and granulopoietic cells were analyzed with anti-CD15 FITC and anti-CD11b FITC antibodies (all antibodies from Becton, Dickinson and Company). Isotype-matched nonspecific antibodies were used as controls. Analysis gates were set to exclude dead cells and debris, with 5,000 viable cells analyzed per sample.

Twenty thousand cells were used for preparation of cell cytospins, which were stained by the Pappenheim (May-Grünwald-Giemsa) method. Morphology was analyzed by light microscopy.

RNA Isolation and Oligonucleotide Microarray Hybridization
Total RNA was extracted using TRIzol (Invitrogen, Grand Island, NY, http://www.invitrogen.com) according to the manufacturer’s protocol with minor modifications. To ensure that the gene expression that we measured by microarray assay was not affected by degradation of the RNA extracted from the purified cells, we used the Bioanalyzer 2100 system (Agilent, Waldbronn, Germany, http://www.chem.agilent.com) to evaluate the quality of the RNA.

Oligonucleotide microarray hybridization (HG-U133A; Affymetrix Inc., Santa Clara, CA, http://www.affymetrix.com) was performed as described previously [8]. To assay 100 ng of total RNA, the standard Affymetrix target amplification protocol was modified by using first-round cRNA product to generate a double-stranded cDNA that was then used for a second round of in vitro transcription for synthesis of the biotinylated cRNA.

For the granulopoietic differentiation, only two sets of samples were available because of the limited amount of RNA extracted from one experimental set, which was not sufficient for microarray hybridization.

The in vitro expansion of megakaryocytic cells is critical, resulting in a low number of differentiated cells due to the endomitotic cell division of megakarypoiesis. Due to the lack of sufficient cell numbers for RNA extraction, in the final microarray analysis, two samples of megakaryocytic differentiation (one at d4 and one at d11) are missing.

Data Analysis
Data analysis was performed with GeneSpring software version 4.2 (Silicon Genetics, Redwood City, CA, http://www.silicongenetics.com/cgi/SiG.cgi/index.smf) and Microarray Analysis Suites 5.0 (MAS 5.0; Affymetrix, Inc.). To identify genes that are continuously upregulated during the course of lineage-specific differentiation (horizontally change), statistical restrictions were used as follows: genes should be present (P) by the Affymetrix data analysis in at least two of three samples (all two samples, respectively) of the triplicates at all time points except at day 0 to be considered for the next analysis step; at each of the time points, expression should be increased at least 1.05-fold compared with the preceding time point. This strategy was used vice versa for downregulated genes. Secondly, we analyzed differentially expressed genes by comparing the expression at each of the specific time points between the three different hematopoietic lineages (vertical change). We required a minimum change of threefold in one hematopoietic lineage versus the other two. In addition, genes should be called present (P) at the particular time point and absent (A) at day 0 to exclude stem cell–specific genes.

We performed class membership prediction to identify genes with a characteristic expression pattern for each of the three hematopoietic lineages during hematopoiesis that could be used to classify the samples according to their growth factor stimulation and affiliated hematopoietic lineage (three classes: erythropoiesis, granulopoiesis, and megakaryopoiesis). Hierarchical clustering analysis by Spearman’s confidence correlation was used to identify gene clusters. The separation ratio was set at 0.5.

We generated Gene Ontology classifications for all differentially expressed genes during erythropoiesis, granulopoiesis, and megakaryopoiesis using the FatiGO (http://fatigo.bioinfo.cnio.es) Data Mining with Gene Ontology [9]. Annotations are available for a subset of genes (~68%) represented on the HG-U133A microarray. For this purpose, differentially expressed genes were classified according to their molecular function. Classifications of all three lineages were compared and resumed to generate homogenous groups and to reduce repeated nominations of specific genes. It should be noticed that the functional classification of a specific gene may be redundant, resulting from the assignment of one gene to more than one category.

Real-Time Polymerase Chain Reaction
To confirm the microarray results, real-time polymerase chain reaction (PCR) analysis was performed to measure expression of selected genes as follows (common gene name, Affymetrix annotationon HG-U133A): RAP1GA1, 203911_at; PTGS2, 204748_at; IRC1, 217078_s_at; DLK1, 209560_s_at; PBX1,205253_at. Total RNA (0.02–1.29 µg) was processed directly to cDNA by reverse transcription (RT) using SuperScrip II (Invitrogen) according to the manufacturer’s protocol in a total volume of 20 µl. Comparative real-time RT-PCR assays [10, 11] were performed for each sample in triplicate in a final reaction volume of 25 µl. GPI and the gene of interest (GOI) were coamplified in the same tube using 2 µl cDNA, 1 x universal master mix (Applied Biosystems, Foster City, CA, http://www.appliedbiosystems.com), 250 nM human GPI probe (VIC-labeled) with 600 nM each of the GPI forward and reverse primers, and 250 nM of GOI probe (6-FAM labeled) with 900 nM each of the GOI forward and reverse primers. Final concentrations of primers and probes were chosen based on optimization experiments. Probes were labeled with quencher TAMRA (6-carboxy-tetramethyl-rhodamine) at the 3' end. Amplification was carried out at 50°C for 2 minutes, 95°C for 10 minutes, followed by 40 PCR cycles at 95°C for 15 seconds and 60°C for 1 minute. All reactions were done in MicroAmp optical 96-well plates using an ABI Prism 7700 sequence detection system (Applied Biosystems). Sequences of primers and probes of the GOI are available from Applied Biosystems upon request. The comparative cycle thresh-old (CT) method was used to determine the relative expression levels of the genes. The threshold cycles for the GOI and GPI were determined, and the cycle number difference ({Delta}CT = GPI – GOI) was calculated for each replicate. Relative gene expression values were calculated using the mean of {Delta}CT from the three replicates, that is µ({Delta}CT) = ({sum}{Delta}CT)/3, and expressed as 2µ({Delta}CT).


   RESULTS
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
In Vitro Differentiation of CD34+ Cells
CD34+ cells of healthy individuals were plated on different culture conditions for lineage-specific differentiation to generate erythropoietic, granulopoietic, and megakaryopoietic cells (Fig. 1Go). Lineage-specific cells were characterized by morphology and by immunophenotype as described. Cells from each of the donors were cultured separately, and RNA was prepared from each of the conditions for microarray hybridization.



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  		Figure 1. Morphology of CD34+ cells and cells generated by in vitro differentiation. Typical morphology is detectable in erythropoiesis (day 4, proerythroblasts; day 7, erythroblasts; day 11, normoblasts), granulopoiesis (incremental organization of nucleus resulting in polymorph nuclear cells), and megakaryopoiesis (increasing polyploidy). Abbreviations: EPO, erythropoietin; FL, ligand of the Flt3/Flt2 receptor tyrosine kinase; G-CSF, granulocyte colony-stimulating factor; GM-CSF, granulocyte macrophage colony-stimulating factor; IL-3, interleukin-3; SCF, stem cell factor; TPO, thrombopoietin.

 
On day 4 of culture, cell numbers increased markedly under conditions for erythropoiesis and granulopoiesis (>threefold). Because of the endomitotic development of megakaryocytic cells, the proliferation of these cells was slow compared with erythropoietic and granulopoietic cells. Table 1Go shows a summary of cell numbers and the purities of the lineage-specific cells after positive selection used for gene expression analysis.


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  		Table 1. Summary of cell numbers, purities of the in vitro–differentiated cells, and the "present" calls (P) of the hybridized microarrays
 
Oligonucleotide Microarrays
Gene expression in cells sampled at days 0, 4, 7, and 11 of hematopoietic differentiation was analyzed by oligonucleotide microarrays (HG-U133A).

As an indicator of high-quality hybridization results, we determined the value of expressed genes (P) of every array analyzed by MAS 5.0. The mean value of present calls (P-calls) was 10,074 out of 22,500 (44%; Table 1Go) and therefore above 25% in all of our experiments, as required by MIAME (minimum information about a microarray experiment) for a high-quality microarray experiment [12].

Horizontal Analysis of Gene Expression During Lineage-Specific Differentiation

Characterization of Genes Known to Be Lineage-Specific   As a confirmation of the lineage-specific in vitro differentiation, we identified continuously upregulated genes that are already known to be associated with specific programs of hematopoietic differentiation. These genes were not expressed in CD34+ cells. Figure 2Go shows the expression of specific genes for the erythrocytic (e.g., ANK, D9S57E, CD35), granulocytic (e.g., IRC1, FCGR2B), and megakaryocytic (e.g., SERPINE1, THBD, PDGF1, and CD42C) lineage in the course of differentiation.



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  		Figure 2. Confirmation of the in vitro differentiation of CD34+ cells by microarray analysis. During lineage-specific differentiation, the expression of well-known marker genes for each hematopoietic lineage analyzed increased continuously. Expression of erythrocytic (ANK, erythrocytic Ankyrin 1; Z39IG, immunoglobulin superfamily protein; CD35, complement component [3b/4b] receptor 1, including Knops blood group system; D9S57E, tropomodulin), granulocytic (IRC1, leukocyte membrane antigen; FCGR2B, Fc fragment of immunoglobulin G, low-affinity IIb, receptor for CD32) and mega-karyocytic marker genes (SERPINE1, serine [or cysteine] proteinase inhibitor, clade E [nexin, plasminogen activator inhibitor]; PHS1, prostaglandin-endoperoxide synthase 1; THBD, thrombomodulin; MSR1, macrophage scavenger receptor 1; PDGF1, platelet-derived growth factor alpha; FCGR2A, receptor for CD32; CD42C, glycoprotein Ib [platelet]) is shown.

 

Genes Strongly Associated with Specific Hematopoietic Lineages   To investigate genes that are strongly associated with lineage-specific differentiation, we generated lists containing genes with continuously increasing or decreasing expression in each hematopoietic lineage. Continuously regulated genes of the hematopoietic lineages were hierarchically clustered (Figs. 3A–3CGo).



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  		Figure 3. Cluster analysis of genes associated with (A) erythropoiesis, (B) granulopoiesis, and (C) megakaryopoiesis. One cluster of continuously upregulated and another cluster of continuously down-regulated genes for each of the three hematopoietic lineages are shown. Color code: blue, low expression; red, high expression. The intensity of the color reflects the reliability of the expression data.

 
Only a few genes increased or decreased continuously during erythropoietic (21 up, 58 down), granulopoietic (21 up, 30 down), and megakaryocytic differentiation (91 up, 37 down). Nine genes are regulated in both erythropoietic and granulopoietic cells (e.g., CD86 antigen, immunoglobulin superfamily protein, solute carrier family 7), three genes are regulated in erythropoiesis and megakaryopoiesis (FLJ20748, CD86, EST: 217678_AT), and three genes are regulated during granulopoietic and megakaryopoietic differentiation (potassium large-conductance calcium-activated channel, CD86, MGC5528). Only one gene is continuously regulated in all three differentiation programs (CD86), suggesting that it may be involved in commitment or differentiation of the hematopoietic stem cell. Table 2Go lists genes with continuously increasing expression in erythropoiesis. Table 3Go shows continuously decreasing genes in erythropoiesis. The data set for all lineages (continuously upregulated and downregulated genes) is given in the supplemental material (supplemental online Tables 1Go–6GoGoGoGoGo). In addition, the complete expression data of all samples will be available for downloading at http://knm1.ibe.med.uni-muenchen.de/kn_home/WKH/index.htm.


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  		Table 2. Genes continuously regulated during erythropoietic differentiation with continuously increasing expression
 

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  		Table 3. Genes continuously regulated during erythropoietic differentiation with continuously decreasing expression
 

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  		Table 4. Genes differentially expressed in erythropoiesis
 

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  		Table 5. Genes differentially expressed in granulopoiesis
 

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  		Table 6. Genes differentially expressed in megakaryopoiesis
 
Vertical Analysis of Differentially Expressed Genes
A vertical analysis was performed to identify genes that are differentially expressed in a specific hematopoietic lineage at a defined time point. The analysis of genes expressed in cells on specific lineage-directed differentiation conditions that show a greater than threefold change at any defined time point compared with cells in the remaining conditions (e.g., erythropoiesis versus granulopoiesis and megakaryopoiesis) resulted in lists of differentially expressed genes for each of the hematopoietic lineages. Tables 4Go–6GoGo show highly differentially expressed genes for each lineage (A, erythropoiesis; B, granulopoiesis; C, megakaryopoiesis). The specific time point of expression is indicated. We could not detect genes common to all three hematopoietic lineages, supporting the lineage-specificity of the identified genes.

Class Membership Prediction
The maximal number of genes to predict the class membership was found to be 53 using five neighbors. We defined this set of genes to be able to classify a particular sample according to its lineage. Figure 4Go represents the results of hierarchical clustering with Spearman’s confidence correlation of 25 samples of in vitro–differentiated CD34+ cells. Three subclusters indicate the affiliation to one of the three hematological lineages. One misclassification (sample E_d04_2) occurred. In Table 7Go, the predictive genes are listed according to the appearance in the cluster.



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  		Figure 4. Identification of genes expressed in differentiating CD34+ bone marrow cells, which are significantly correlated with each of the three analyzed hematopoietic lineages. Results represent the analysis by hierarchical clustering with Spearman’s confidence correlation of 25 samples of in vitro–differentiated CD34+ bone marrow cells. Fifty-three genes were selected to predict the class membership of each of the samples. The vertical list contains each of the samples. The horizontal list displays the 53 genes. Three clusters corresponding to the three hematological lineages were found, as indicated by the black bars. One misclassification occurred (sample ID: E_d04_2). For granulopoietic and megakaryopoietic differentiation, three and two samples are missing in the analysis, respectively. Color code: blue, low expression; red, high expression. The intensity of the color reflects the reliability of the expression data.

 

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  		Table 7. Genes that are predictive for the lineages of human in vitro–differentiating hematopoietic cells (see Fig. 4Go)
 
Confirmation of Expression of Continuously Regulated Genes During In Vitro Hematopoietic Differentiation by Real-Time PCR
To confirm the expression data from the oligonucleotide micro-array studies, we analyzed the expression of a selection of six significantly regulated genes (two in each lineage) in 25 samples by real-time PCR. The gene for PTGS2 was analyzed in erythropoietic and granulopoietic samples. All experiments were done in triplicate. The variance between the triplicates was less than 5%. The results were normalized to the expression of GPI in each of the samples. Figure 5Go summarizes the expression data measured by real-time PCR for the selected genes. We could confirm our microarray results for both the continuously upregulated and downregulated genes not only for the single time points but also for the course of gene expression during hematopoietic differentiation.



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  		Figure 5. Validation of gene expression pattern of continuously regulated genes during lineage-specific differentiation by real-time polymerase chain reaction (PCR). The expression of a selection of six genes in 25 samples was analyzed by real-time PCR. Two genes per hematopoietic lineage were analyzed in samples of the appropriate lineage (the gene for PTGS2 was analyzed in erythropoietic and granulopoietic samples). We found with one exception (IRC1 should be upregulated in G) that for both the continuously upregulated and downregulated genes, the real-time PCR results are similar to the results detected by microarray analysis. X-axis, days; Y-axis, GPI-normalized expression levels as detected by real-time PCR.

 
Gene Ontology Analysis
Categorization of differentially expressed genes in all three hematopoietic lineages according to the molecular function yielded seven functional groups (genes with binding, catalytic, signal transducer, transcription regulator, structural molecule, enzyme regulator, and transporter activity; Fig. 6Go).



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  		Figure 6. Classification of differentially expressed genes of normal hematopoiesis according to gene ontology. Genes were annotated according to FatiGO Data Mining with Gene Ontology. Annotations are available for a subset of genes (~68%) represented on the HG-U133A microarray. The functional classification of a specific gene may be redundant, resulting from the assignment of one gene to more than one category.

 
Genes with binding (E, 36%; versus G, 37%; versus M, 38%), catalytic (E, 26%; versus G, 19%; versus M, 19%), and signal transducer activity (E, 22%; versus G, 29%; versus M, 23%) were comparable between the three hematopoietic lineages. The percentage of genes performing transcription regulator activity or structure molecule activity (E, 2%; versus G, 7%; versus M, 3%; E, 6%; versus G, 2%; versus M, 1%) was slightly elevated in the granulopoietic or erythropoietic lineage, whereas genes functioning as enzyme regulators (E, 4%; versus G, 3%; versus M, 6%) and transporters (E, 4%; versus G, 3%; versus M, 10%) were overrepresented in the megakaryopoietic lineage. Further, it should be noted that the functional classification of a specific gene may be redundant, resulting from the assignment of one gene to more than one category.


   DISCUSSION
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
To identify sets of genes that enable the underlying pathomechanisms and therefore potential new therapeutic targets to be elucidated [13–15], several groups have performed gene expression profiling in CD34+ cells from patients with different hematopoietic malignancies. Gene expression in normal human hematopoietic stem cells has been characterized [16–19], but mainly static expression analysis was performed on selected human CD34+ cells. These studies have described genes involved in stem cell maintenance and renewal [20, 21]. Analysis of the transcriptional program of normal differentiating hematopoietic cells is available in part for erythropoietic cells [22–25], megakaryopoietic cells [26–28], and PB granulocytes [29]. Serial analysis of gene expression was performed on selected CD15+ cells (PB) [30], neutrophil gene expression [31–34], or granulopoietic differentiation in cell lines [35]. However, a comprehensive transcriptional profile of the differentiating hematopoietic stem cell into the erythropoietic, granulopoietic, and megakaryopoietic lineage was not available.

Because CD34+ cells are a heterogeneous population, using such cells for in vitro analyses is always a compromise. To our knowledge, at this time it is not possible to perform genome-wide microarray hybridization from the very small number of cells we would have with more selected hematopoietic progenitor cells (e.g., CD34+CD33– cells). On the other hand, by using restrictions for data analysis as described, we were able in the present study to show that it is possible to detect (and to validate) transcriptional programs that are associated with in vitro hematopoiesis.

Recently, a detailed expression profile of hematopoietic differentiation in mouse cells using the FDCP-Mix system was published [7]. Bruno et al. investigated the dynamic transcriptional profile of murine hematopoietic progenitor cells in the context of self-renewal and multilineage differentiation. Following the same intention, we have performed serial gene expression profiling in human differentiating CD34+ cells by oligonucleotide microarrays. The experiments presented here were done in triplicates from different healthy individuals to minimize individual changes in serial gene expression analysis. Our finding that several genes known to be lineage-specific were indeed restricted to the corresponding in vitro differentiation conditions could confirm the validity of our approach.

Erythropoietic Differentiation
During stimulation with EPO, the expression of two GTPase activator proteins, RAP1GA1 (RAP1, GTPase-activating protein 1) and ARHGAP8 (Rho GTPase-activating protein 8), increases continuously. These GTPase activator proteins convert their substrates to the putatively inactive GDP-bound state, thereby interrupting the associated signaling pathways. RAP1GA1 inactivates RAP1, a Ras-related protein implicated in several biological processes that was identified as an antagonist of Ras [36]. ARHGAP8, also known as BPGAP1, specifically binds to RhoA, a GTPase involved in the regulation of actin cytoskeleton organization, membrane trafficking, gene expression, and cell proliferation [37].

During erythropoietic differentiation, genes associated with the protooncogene c-myc are continuously downregulated, including Mina53 (myc-induced nuclear antigen with a molecular mass of 53 kDa), which is involved in cell proliferation and is directly induced by c-myc [38, 39]. FAP 48 (FKBP-associated protein, 48 kDa) is essential for normal development of the vascular system and has an antiproliferative effect on Daisy T cells [40]. The candidate tumor suppressor gene MXI1 (Max-interacting protein 1) negatively regulates Myc oncoprotein activity and is upregulated by overexpression of FAP48 [40].

Granulopoietic Differentiation
During granulopoietic differentiation, the expression of two G protein–coupled receptors, GPR86 (G protein–coupled receptor 86) and EDG2 (endothelial differentiation, lysophosphatic acid G protein–coupled receptor 2), is continuously upregulated.

GPR86, also called P2Y13, is a G-coupled receptor with high affinity to ADP. This orphan receptor is involved in the inhibition of adenylyl cyclase and the stimulation of mitogen-activated protein kinases (ERK1 and ERK2) and is supposed to be implicated in hematopoiesis and the immune system [41].

EDG2, also called lysophosphatic acid (LPA1) receptor, is a G protein–coupled receptor that mediates diverse cellular activities like morphological changes, cell proliferation, and survival [42]. It was shown to activate Rac, resulting in formation of lamellopodia, cell spreading, and migration [42].

Megakaryopoietic Differentiation
As a confirmation of the approach, we and others detected increasing expression of several genes associated with homeostasis and platelets during megakaryopoietic differentiation (e.g., CD61, thrombospondin, and vascular endothelial growth factor C) [43]. In our study, genes like DLK1 (delta-like 1 homologue Drosophila) and the epidermal growth factor (EGF) receptor pathway substrate 8 (EPS8) that are associated with EGF are upregulated during megakaryopoietic differentiation. DLK1 is a member of the EGF-like family involved in cell differentiation and participates in the differentiation control of several cell types, including adipocytes, small-cell lung cancer lines, stem cells, B cells, and adrenal gland cells [44–47]. Interestingly, it was found to be overexpressed in MDS [48]. EPS8 enhances EGF-dependent mitogenic signals upon binding to EGF receptor and participates in the mediation of signals from Ras to Racs.

Pre–B-cell leukemia transcription factor 1 (PBX1) belongs to the TALE/PBX homeobox family of transcription factors and is homologous to mouse Meis1 (myeloid ecotropic viral integration site 1). This nonactivating protein, which could be a repressor, binds to the sequence 5'-ATCAATCAA-3' and is converted into a potent transcriptional activator by the (1;19) translocation in pre–B-cell leukemia and in acute myelogenous leukemia. PBX1, which increases during megakaryopoiesis, is involved in the activation of the promoter of platelet factor 4 gene, which is important for megakaryocytic differentiation [49] and was identified to be upregulated in megakaryocytes [43].

Commitment Signature
A specific gene expression profile for all of the three hematopoietic lineages was generated by class membership analysis. We found a set of 53 genes that can be used to define the transcriptional program during hematopoietic differentiation. To confirm this profile, hierarchical clustering based on expression data of 53 selected genes enabled us to discriminate between unstimulated CD34+ cells and cells differentiating to one of the three specific hematopoietic lineages according to their gene expression profiles for each of the single bone marrow samples.

The knowledge about the specific transcriptional program in normal hematopoiesis may contribute to further understanding the complex process of hematopoietic stem cell development. It can be used to search for alterations in gene expression in any kind of hematological malignancies as well as in other diseases affecting hematopoiesis.

Nevertheless, these data can function as a starting point for examining changes in hematopoietic malignancies to define new pathophysiological pathways that can possibly be used for the strategy of target-specific treatment in the near future.


   ACKNOWLEDGMENTS
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
This work was supported by the Deutsche Forschungsgemeinschaft (HO 2207/3-1), the BMBF Competence Network Leukemias, and the German Genome Research Network.


   REFERENCES
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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Received July 30, 2004; accepted for publication April 3, 2005.



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