The Kinetic Status of Hematopoietic Stem Cell Subpopulations Underlies a Differential Expression of Genes Involved in Self-Renewal, Commitme
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《干细胞学杂志》
a Department of Biological Sciences, Biochemistry Section, University of Modena and Reggio Emilia, Modena, Italy;
b Institute of Hematology and Medical Oncology "L. & A. Seragnoli," University of Bologna, Italy
Key Words. CD34+ stem cells ? CD34– ? Hematopoietic stem cells ? Microarray ? Proliferation ? Self-renewal ? Differentiation ? Engraftment
Correspondence: Sergio Ferrari, M.D., Dipartimento di Scienze Biomediche, Sezione di Chimica Biologica, Università di Modena e Reggio Emilia, Via Campi 287, 41100 Modena, Italy. Telephone: 39-059-2055400; Fax: 39-059-2055410; e-mail: ferrari.sergio@unimo.it
ABSTRACT
A novel class of hematopoietic stem cells (HSCs) lacking the CD34 protein has been described in mice and humans . In vitro and in vivo studies have led to the hypothesis that HSCs exist in two functional states that can be distinguished by CD34 expression.CD34–cellsrepresentareservoirofkineticallyandfunction-ally resting HSCs that need to be activated to generate a CD34+ cell population with high proliferative and engraftment potential . Lin–CD34– cells seem to be mainly out of cycle and have minimal, if any, colony-forming and long-term culture-initiating cell (LTC-IC) ability. Conversely, most mobilized CD34+ cells are in G1 phase and retain clonogenic and LTC-IC activity . In vivo, Lin–CD34– cells derive from CD34+ progenitors and regain expression of CD34 on secondary transplantation .
The microarray technology has been recently used to find the correlations existing between the gene expression profile and the human HSC biology by the comparison of CD34+ HSCs obtained from different sources and subsets . Furthermore, the genome-wide analysis provides a valuable tool for examining how the genetic programs underlying the self-renewal and commitment are established in normal hematopoiesis.
Although some studies in mouse and in human have suggested that the CD34 expression correlates with cell proliferation , the relationship between the expression of CD34, cycling status, self-renewal, and lineage commitment is still poorly understood.
In this study, we attempted to address these issues by evaluating the gene expression profile of different subsets of peripheral blood hemopoietic stem/progenitor, Lin–CD34–, Lin–CD34+, and Lin+CD34+ cells. Our data indicate that the CD34–/CD34+ transition is associated with cell cycle recruitment, metabolic activation, and downregulation of growth-inhibitory pathways. The differential activation of these pathways leads to a strong differential expression of cyclins, CDKs, CDK inhibitors, and growth-arrest genes between CD34– and CD34+ cells. Moreover, CD34+ cells show a significant upregulation of self-renewal, commitment, and engraftment-related genes , whereas Lin–CD34– cells express preferentially genes related to quiescence and to neural, epithelial, and muscle differentiation.
MATERIALS AND METHODS
Global Transcriptome Changes in Lin–CD34–, Lin–CD34+, and Lin+CD34+
We assessed, in duplicate, the gene expression in all three hemopoietic stem/progenitor cell populations using Affymetrix HG-U95Av2 GeneChip array, representative of 12,625 transcripts.
All of the data have been deposited in the Gene Expression Omnibus MIAME-compliant public database at http://www.ncbi.nlm.nih.gov/geo. Lin–CD34– accession numbers are GSM25887 and GSM25888 (I and II replicate, respectively), Lin–CD34+ accession numbers are GSM25885 and GSM25886 (I and II replicate, respectively), and Lin+CD34+ accession numbers are GSM25883 and GSM25884 (I and II replicate, respectively).
The mRNA complexity significantly increased upon the acquisition of CD34 Ag expression; in fact, 5,348 versus 4,524 sequences are called present by Affymetrix MAS 5.0 absolute analysis algorithm in Lin–CD34+ and Lin–CD34– HSC, respectively; mRNA complexity increased also during Lin–CD34+ and Lin+CD34+ transition (6,128 versus 5,348 sequences called present in Lin+CD34+ and Lin–CD34+ HSCs, respectively). The most significant transcriptome changes were found between Lin–CD34– and Lin+CD34+ cells (supplementary online Table 1).
Clustering Analysis of Genes Differentially Expressed in Hematopoietic Stem/Progenitor Cells
A list of 2,720 changing and reliable genes that obtained filtering data as described in Materials and Methods was used for hierarchical clustering. The condition tree shows that the clustering algorithm hierarchically paired the two CD34+ population transcript profiles (Fig. 1).
Figure 1. Clustering of the 2,720 most changing genes. Clustering has been performed using an unsupervised approach and applying several clustering algorithms provided by GeneSpring. A combination of two hierarchical clustering analyses (gene tree and condition tree) is shown. The gene tree is shown on left; the condition tree is shown on top. Gene coloring was based on normalized signals as shown at the bottom of the figure.
GO Mapping of Differentially Expressed Genes
To identify whether the differentially expressed genes underlie a prevalent biological process, we uploaded the gene list of 2,720 changing and reliable genes onto MAPP Finder software. The prevalent categories in the biological process GO tree include protein biosynthesis, cell cycle, RNA metabolism, DNA replication and chromosome cycle, chromatin assembly/disassembly, tricarboxylic acid (TCA) cycle, DNA repair, oxidative phosphorylation, ubiquitin-dependent protein degradation, and transcription from Pol II promoter (supplementary online Table 2).
Other categories, not evidenced by GO mapping analysis, had been examined, such as cell adhesion, cytokine and hematopoietic growth factor receptors, and transcription factors.
Cell Cycle Regulator Gene Expression
The analysis of expression of cyclins, CDKs, cyclin-dependent kinase inhibitors (CDKNs), and growth-arrest genes led to the following results (Fig. 2A). First, CD34– cells were characterized by a preferential expression of growth arrest genes, such as Gas6, RGS2, ZFP36, ING1, PEDF, and LNK and of some CDKNs, such as CDKN1A (p21 waf-1) , CDKN2C (p18) and CDKN2D (p19) . This expression pattern was paralleled by a concomitant lower expression of cyclins and CDKs compared with CD34+ cells. Second, CD34+ cells preferentially expressed early G1 cyclins and CDKs (D2 and D3 cyclins, CDKs 4 and 6). Very low levels of late G1 or mitotic cyclins and associated CDKs were also detected. Moreover, some cell cycle–related genes, such as NFY and c-myb, which regulate the promoter activity of the human CD34 gene , were shown to be increased in the CD34–/ CD34+ transition (see below).
Figure 2. Expression of cell cycle regulators, growth factors receptors, and cytokines. Eisen tree map computed using the GeneSpring gene tree and the Pearson correlation equation on the modulated probe sets belonging to the following categories: (A) cell cycle, (B) growth factor receptors, and (C) cytokines. The signal-based coloring legend is shown at the bottom of the figure.
Differential Activation of Pathways Leading to Cell Proliferation or Growth Inhibition
To better characterize the molecular mechanisms regulating HSC proliferation or quiescence, we assessed whether growth factor receptors were differentially expressed in CD34+ and CD34– cells (Fig. 2B). The results obtained evidenced that distinct sets of receptors are preferentially expressed by CD34– (IL-17R, transforming growth factor -?R1, TGF?R2, IL-10RA, IL-10RB) or CD34+ cells (FLT3, MPL, IFNR2, EpoR).
We then analyzed the expression of genes involved in TPO, FLT3, IFN, IL-17, IL-10, and TGF? pathways by graphic visualization of gene expression using GeneMAPP software.
Our data showed that signal transducers, particularly those involved in Ras-mediated pathways, are constitutively expressed in all populations studied, whereas receptors, primary response genes, and inhibitors are differentially expressed as follows.
TPO Pathway ? This pathway is apparently upregulated in CD34+ cells, because TPO response genes, such as HOXB4, CBF?, Runx1, Nfe-2, Gata-2, Fli-1, CD41b, CD42b, and CD61, are upregulated in this cell population (Fig.3A). Consistently, CD34+cells gave origin to early and late megakaryocyte progenitors (i.e., CFU-MK and BFU-MK) in response to TPO, whereas CD34– cells did not show any colony-forming ability even after 7 days of culture (Figs. 4A, 4B). Interestingly, cultures of CD34– cells generated secondary CFU-MK after 21 days in serum-free medium in the presence of TPO.
Figure 3. Visualization of expression data on the maps of (A) TPO, (B) IL-10, and (C) IL-17 pathways. Genes are colored according to the absolute and comparative expression (Lin+CD34+ versus Lin–CD34– cells). The legend of the coloring criteria is reported on the right of the figure. Abbreviations: IL, interleukin; TPO, thrombopoietin.
Figure 4. Effects of TPO, IL-10, and IL-17 on Lin–CD34– and Lin–CD34+ cells. (A): TPO treatment. (a): TPO induces the clonogenic growth of CFU-MK and BFU-MK from freshly isolated CD34+ but not Lin–CD34– cells. (b): Similarly, 7 days of culture of Lin–CD34– cells onto irradiated murine stromal cells (M2-10B4), genetically engineered to produce G-CSF and IL-3 did not induce secondary colony formation in response to TPO. The results shown derive from six different experiments and are expressed as mean ± standard deviation. (c): Megakaryocyte progenitors (CFU-MK) became detectable after 19–21 days (day +21) of incubation of Lin–CD34– cells in serum-free liquid medium added with TPO. At this time point, 7% ± 3% of total cell population was represented by CD34+ cells (see text). (B): IL-10 treatment. Clonogenic efficiency of highly purified (a) Lin–CD34+ and (c) Lin–CD34– cells. The cells were cultured in semisolid medium for 14 days with GM-CSF, IL-3, EPO, and SCF. (b): Lin–CD34+ and (d) Lin–CD34– cells were cultured onto M2-10B4 stromal cells in the presence of a cytokine cocktail (FLT-3L, TPO, IL-11, and SCF) with or without IL-10 or in the presence of IL-10 alone. Subsequently, clonogenic assays in semisolid medium with GM-CSF, IL-3, EPO, and SCF were set up to assess the production of secondary CFU-C. The results are expressed as mean ± standard deviation of three different experiments. IL-10 showed no significant activity on clonogenic CD34+ cells (p = not significant), whereas the addition of IL-10 inhibited the secondary colony-forming activity of CD34– cells induced by cytokines in liquid cultures (p < .03). (C): IL-17 treatment. Clonogenic efficiency of highly purified (a) Lin–CD34+ and (c) Lin–CD34–cells. The cells were cultured in semisolid medium for 14 days with GM-CSF, IL-3, EPO, and SCF. (b): Lin–CD34+ and (d) Lin–CD34– cells were cultured onto M2-10B4 stromal cells in the presence of a cytokine cocktail (FLT-3L, TPO, IL-11, and SCF) with or without IL-17 or in the presence of IL-17 alone. Subsequently, clonogenic assays in semisolid medium with GM-CSF, IL-3, EPO, and SCF were set up to assess the production of secondary CFU-C. The results are expressed as mean ± standard deviation of three different experiments. Similarly to IL-10, IL-17 showed no activity on clonogenic CD34+ cells (p = not significant), whereas the study cytokine inhibited the secondary clonogenic activity of CD34– cells induced by cytokines in liquid cultures (p < .04). Abbreviations: CFU-MK, colony-forming unit megakaryocyte; EPO, erythropoietin; IL, interleukin; SCF, stem cell factor; TPO, thrombopoietin.
At that time point, phenotypic analysis demonstrated the presence of CD34+ cells (7% ± 3% of the total population) deriving from CD34– HSCs (Fig. 4A, panel c). Although we have no formal evidence, it is conceivable that functional response to TPO is associated with the acquisition of the CD34 Ag.
IL-10 Pathway ? IL-10 inhibits cell-cycle progression of HSCs and progenitor cells acting through STAT1/STAT3 activation . Analysis of the genes involved in this pathway showed that both receptor isoforms (IL-10RA and IL-10RB) and IL-10 primary response genes, such as CDKN1A/p21 and CDKN2D/p19, were preferentially expressed in CD34– cells (Fig. 3B). Clonogenic assays on Lin– cells demonstrated a minimal activity of IL-10 on CD34+ cells (Figs. 4B, panel b, 4B, panel b), whereas the addition of IL-10 inhibited the secondary colony-forming activity of CD34– cells induced by cytokines in liquid cultures (Fig. 4B, panel d) (p < .03). At this stage, the percentage of CD34+ cells deriving from CD34– HSCs after 7 days in liquid culture was 6% ± 2%, with no significant difference between IL-10–treated and control samples.
IL-17 Pathway ? As reported in Figure 2, IL-17 receptor is specifically expressed by CD34– cells. Moreover, some IL-17 primary response genes (i.e., ICAM-1 and IL-8) are preferentially expressed in the same cell population (Fig. 3C). Consistent with these findings, we did not observe any significant activity of IL-17 on CD34+ cells (Figs. 4C, panel a, 4C, panel b) (p = not significant). In addition, we demonstrated for the first time the inhibitory effect of IL-17 on the secondary clonogenic activity of CD34– cells induced by cytokines in liquid cultures (Fig. 4C, panel d) (p < .04).
In this set of experiments, the percentage of CD34+ cells originating from CD34– cells after 7 days was 4% ± 2%, with no difference between IL-17–treated and control samples.
Finally, gene expression data suggest that FLT3 and IFN pathways are active mainly in CD34+ cells, whereas TGF? exerts its inhibitory effect preferentially on CD34– cells (supplementary online Fig. 3).
Metabolic Activation of CD34+ Cells
Genes involved in DNA replication, such as DNA polymerases, topoisomerases, and minichromosome maintenance (MCM), were preferentially expressed in CD34+ cells, particularly in Lin+CD34+ cells (supplementary online Fig. 4). These data are consistent with the kinetic status of the three analyzed cell populations; in fact, CD34+ cells are mainly in G1 phase of cell cycle and synthesize the enzyme components for the subsequent S phase.
Genes involved in DNA repair (base excision repair, nucleotide excision repair, mismatch repair, and double-strand break repair) exhibited a preferential expression in CD34+ cells, particularly in Lin+CD34+ (supplementary online Fig. 5). Again these results are consistent with the kinetic and differentiation status of CD34– and CD34+ cells . The global expression analysis of genes involved in RNA splicing, capping, and polyadenilation showed that the process of RNA maturation is mainly active in CD34+ cells (supplementary online Fig. 6). Moreover, combined analysis of the expression of genes codifying for ribosomal proteins demonstrated that these transcripts, already present in CD34– cells, undergo a remarkable induction in Lin–CD34+ and a slight decrease in the subsequent transition to Lin+CD34+ cells (supplementary online Fig. 7A). These data are in keeping with previous studies describing the increase of ribosome biogenesis during the G0/G1 transition . The expression of genes involved in protein translation and modification was increased in CD34+ cells, particularly in Lin+CD34+ cells (supplementary online Figs. 7B, 7C).
Genes codifying for proteins involved in oxidative phosphorylation and TCA cycle processes showed a prevalent expression in CD34+ cells, especially in Lin+CD34+ (supplementary online Figs. 8A, 8B). These data are consistent with the already described activation of oxidative phosphorylation and TCA cycle in early G1 phase of the cell cycle .
Self-Renewal Capacity
Analysis of TF expression indicated that most genes involved in self-renewal process were upregulated in CD34+ cells (Fig. 5A). Among them, HOXA5, HOXA9, HOXA10, HOXB2, HOXB5, Meis1, and PBX2 are preferentially expressed by CD34+ cells.
Figure 5. Expression of transcription regulators and differentiation markers. Eisen tree map computed using the GeneSpring gene tree and the Pearson correlation equation on the modulated probe sets belonging to the following categories: (A) transcription factors, (B) transcription activators and repressors, and (C) differentiation markers. The signal-based coloring legend is shown at the bottom of the figure.
The expression of HOXB4, recently described as a key regulator of TPO-induced HSC self-renewal , was sixfold upregulated in Lin–CD34+ and ninefold upregulated in Lin+CD34+ compared with Lin–CD34– cells. HOXB4 expression was detected only by real-time quantitative PCR (RTQPCR), because the HOXB4 probe set is not represented on HG-U95Av2 array. GATA-2 and Bmi1, key factors for HSC self-renewal , and LMO2, CBF?, and CUTL1, known regulators of early hematopoiesis , were found to be expressed in all cell populations, particularly in CD34+ cells. Conversely, ID1 and ID2 (inhibitors of cell differentiation) resulted in downregulation of CD34+ cells. Genes belonging to the WNT and NOTCH pathways were expressed at very low levels in all cell populations.
Lineage Commitment Capacity
The expression analysis of TFs involved in all hematopoietic lineage differentiation (Fig. 5A) evidenced that several genes increased in CD34–/CD34+ transition: GATA-1, PU.1/SPI1 , and HOXA5 (myeloid commitment); GATA-1, LMO2, TAL-1, LDB1, and TCF3 (erythroid commitment); GATA-1, CBF?, GATA-2, FLI1, and NF-E2 (megakaryocytic commitment); GATA-1 and C/EBP? (eosinophil commitment), and PU.1 and C/EBP? (neutrophil commitment).
Although transcripts of TFs involved in monocyte (ICSBP1 , EGR-1 , HOXA10 ) and lymphoid (Ikaros, GATA-3 ) commitment were always detectable, variations of their expression levels did not correlate with the differentiation degree of the analyzed cell populations (Fig. 5A).
The upregulation of lineage-commitment TFs in CD34+ cells was associated with the induction of a large number of intracellular and surface markers belonging to the monocytic (CD14, ACO2, TMSF7), granulocytic (CD16, LILRA3, LILRB3, MMP9, CSF3R, CD32), lymphoid (CD69, CD19, CD164, CD58, TRB), megakaryocytic (PF4, F2R, VEGF, CD31, CD41, CD151, GP1BB), and erythroid (KLF1, RUVLB1, RUVLB2, GYPC) differentiation lineages (Fig. 5C).
Transcriptional activators, such as topoisomerase, helicases, acetyltransferase, and chromatin remodeling proteins (SMARCA2, SMARCD2, SMARCA4, SMARCC2, and SMARCC1) were mainly expressed in CD34+ cells; conversely, transcriptional repressors were preferentially expressed in Lin–CD34– (Fig. 5B).
Engraftment Capacity
Previous reports demonstrated that human CD34+ cells have a significantly greater engraftment potential than CD34– cells when transplanted in irradiated nonobese diabetic/severe combined immunodeficiency (NOD/SCID) mice . Our molecular analysis showed that the expression of genes belonging to the cell adhesion category is higher in CD34+ cells (supplementary online Fig. 9). Furthermore, genes specifically involved in the homing and engraftment of HSCs in the BM were preferentially expressed in CD34+ cells. In fact, Lin–CD34+ cells showed a higher expression of VLA-4, VLA-5, and SELPLG compared with Lin–CD34– cells (Fig. 6). Interestingly, CD34– cells exhibited higher levels of CXCR4, but also of RGS1 and RGS13, that function as negative regulators of CXCR4 activity by the inhibition of trimeric G proteins (Fig. 6). Taken together, these observations support the view that CD34+ cells have higher engraftment capacity in primary recipients of xenogenic transplant compared with CD34– cells.
Figure 6. Schematic view of the engraftment pathway. Genes are colored according to the absolute and comparative expression (Lin–CD34+ versus Lin–CD34– cells). The legend of the coloring criteria is reported at the bottom of the figure.
Differential Expression of Nonhematopoietic Markers
Global expression analysis of nonhematopoietc markers, such as epithelial, neural, and muscle tissue markers, revealed their preferential expression in CD34– cells (Fig. 7). The expression of these genes strongly decreases during the CD34–/CD34+ transition and becomes undetectable in terminally differentiated cells (R. Manfredini et al., unpublished data). Among these genes, the more differentially expressed were the epithelial markers CDH1 (E-cadherin) and K5 type II keratin (KRT5), the neural marker dopamine receptor 4 (DRD4), and the muscle marker tropomyosin 2, beta (TMP2).
Figure 7. Expression of nonhemopoietic markers. Eisen tree map computed using the GeneSpring gene tree and the Pearson correlation equation on the modulated probe sets belonging to the following categories: (A) neural markers, (B) muscle markers, and (C) epithelial markers. The signal-based coloring legend is shown at the bottom of the figure.
Real-Time Quantitative PCR Validation of Differential Expressed Genes
To confirm microarray data, we carried out a TaqMan RTQPCR analysis on a validation set containing 77 transcripts selected among the differentially expressed genes with greatest biological significance. TaqMan data were uploaded onto GeneSpring software as described in Materials and Methods and analyzed together with the array data. Supplementary online Figure 10 shows a gene tree and condition tree computed onto the validation set gene list using the Spearman correlation. Almost all of the analyzed genes showed the same expression pattern with both analyses.
DISCUSSION
R.M.L. and S.F. contributed equally to this study. This work was funded by MURST-COFIN 2002, Associazione Italiana per la Ricerca sul Cancro, and the University of Bologna (funds for selected topics).
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b Institute of Hematology and Medical Oncology "L. & A. Seragnoli," University of Bologna, Italy
Key Words. CD34+ stem cells ? CD34– ? Hematopoietic stem cells ? Microarray ? Proliferation ? Self-renewal ? Differentiation ? Engraftment
Correspondence: Sergio Ferrari, M.D., Dipartimento di Scienze Biomediche, Sezione di Chimica Biologica, Università di Modena e Reggio Emilia, Via Campi 287, 41100 Modena, Italy. Telephone: 39-059-2055400; Fax: 39-059-2055410; e-mail: ferrari.sergio@unimo.it
ABSTRACT
A novel class of hematopoietic stem cells (HSCs) lacking the CD34 protein has been described in mice and humans . In vitro and in vivo studies have led to the hypothesis that HSCs exist in two functional states that can be distinguished by CD34 expression.CD34–cellsrepresentareservoirofkineticallyandfunction-ally resting HSCs that need to be activated to generate a CD34+ cell population with high proliferative and engraftment potential . Lin–CD34– cells seem to be mainly out of cycle and have minimal, if any, colony-forming and long-term culture-initiating cell (LTC-IC) ability. Conversely, most mobilized CD34+ cells are in G1 phase and retain clonogenic and LTC-IC activity . In vivo, Lin–CD34– cells derive from CD34+ progenitors and regain expression of CD34 on secondary transplantation .
The microarray technology has been recently used to find the correlations existing between the gene expression profile and the human HSC biology by the comparison of CD34+ HSCs obtained from different sources and subsets . Furthermore, the genome-wide analysis provides a valuable tool for examining how the genetic programs underlying the self-renewal and commitment are established in normal hematopoiesis.
Although some studies in mouse and in human have suggested that the CD34 expression correlates with cell proliferation , the relationship between the expression of CD34, cycling status, self-renewal, and lineage commitment is still poorly understood.
In this study, we attempted to address these issues by evaluating the gene expression profile of different subsets of peripheral blood hemopoietic stem/progenitor, Lin–CD34–, Lin–CD34+, and Lin+CD34+ cells. Our data indicate that the CD34–/CD34+ transition is associated with cell cycle recruitment, metabolic activation, and downregulation of growth-inhibitory pathways. The differential activation of these pathways leads to a strong differential expression of cyclins, CDKs, CDK inhibitors, and growth-arrest genes between CD34– and CD34+ cells. Moreover, CD34+ cells show a significant upregulation of self-renewal, commitment, and engraftment-related genes , whereas Lin–CD34– cells express preferentially genes related to quiescence and to neural, epithelial, and muscle differentiation.
MATERIALS AND METHODS
Global Transcriptome Changes in Lin–CD34–, Lin–CD34+, and Lin+CD34+
We assessed, in duplicate, the gene expression in all three hemopoietic stem/progenitor cell populations using Affymetrix HG-U95Av2 GeneChip array, representative of 12,625 transcripts.
All of the data have been deposited in the Gene Expression Omnibus MIAME-compliant public database at http://www.ncbi.nlm.nih.gov/geo. Lin–CD34– accession numbers are GSM25887 and GSM25888 (I and II replicate, respectively), Lin–CD34+ accession numbers are GSM25885 and GSM25886 (I and II replicate, respectively), and Lin+CD34+ accession numbers are GSM25883 and GSM25884 (I and II replicate, respectively).
The mRNA complexity significantly increased upon the acquisition of CD34 Ag expression; in fact, 5,348 versus 4,524 sequences are called present by Affymetrix MAS 5.0 absolute analysis algorithm in Lin–CD34+ and Lin–CD34– HSC, respectively; mRNA complexity increased also during Lin–CD34+ and Lin+CD34+ transition (6,128 versus 5,348 sequences called present in Lin+CD34+ and Lin–CD34+ HSCs, respectively). The most significant transcriptome changes were found between Lin–CD34– and Lin+CD34+ cells (supplementary online Table 1).
Clustering Analysis of Genes Differentially Expressed in Hematopoietic Stem/Progenitor Cells
A list of 2,720 changing and reliable genes that obtained filtering data as described in Materials and Methods was used for hierarchical clustering. The condition tree shows that the clustering algorithm hierarchically paired the two CD34+ population transcript profiles (Fig. 1).
Figure 1. Clustering of the 2,720 most changing genes. Clustering has been performed using an unsupervised approach and applying several clustering algorithms provided by GeneSpring. A combination of two hierarchical clustering analyses (gene tree and condition tree) is shown. The gene tree is shown on left; the condition tree is shown on top. Gene coloring was based on normalized signals as shown at the bottom of the figure.
GO Mapping of Differentially Expressed Genes
To identify whether the differentially expressed genes underlie a prevalent biological process, we uploaded the gene list of 2,720 changing and reliable genes onto MAPP Finder software. The prevalent categories in the biological process GO tree include protein biosynthesis, cell cycle, RNA metabolism, DNA replication and chromosome cycle, chromatin assembly/disassembly, tricarboxylic acid (TCA) cycle, DNA repair, oxidative phosphorylation, ubiquitin-dependent protein degradation, and transcription from Pol II promoter (supplementary online Table 2).
Other categories, not evidenced by GO mapping analysis, had been examined, such as cell adhesion, cytokine and hematopoietic growth factor receptors, and transcription factors.
Cell Cycle Regulator Gene Expression
The analysis of expression of cyclins, CDKs, cyclin-dependent kinase inhibitors (CDKNs), and growth-arrest genes led to the following results (Fig. 2A). First, CD34– cells were characterized by a preferential expression of growth arrest genes, such as Gas6, RGS2, ZFP36, ING1, PEDF, and LNK and of some CDKNs, such as CDKN1A (p21 waf-1) , CDKN2C (p18) and CDKN2D (p19) . This expression pattern was paralleled by a concomitant lower expression of cyclins and CDKs compared with CD34+ cells. Second, CD34+ cells preferentially expressed early G1 cyclins and CDKs (D2 and D3 cyclins, CDKs 4 and 6). Very low levels of late G1 or mitotic cyclins and associated CDKs were also detected. Moreover, some cell cycle–related genes, such as NFY and c-myb, which regulate the promoter activity of the human CD34 gene , were shown to be increased in the CD34–/ CD34+ transition (see below).
Figure 2. Expression of cell cycle regulators, growth factors receptors, and cytokines. Eisen tree map computed using the GeneSpring gene tree and the Pearson correlation equation on the modulated probe sets belonging to the following categories: (A) cell cycle, (B) growth factor receptors, and (C) cytokines. The signal-based coloring legend is shown at the bottom of the figure.
Differential Activation of Pathways Leading to Cell Proliferation or Growth Inhibition
To better characterize the molecular mechanisms regulating HSC proliferation or quiescence, we assessed whether growth factor receptors were differentially expressed in CD34+ and CD34– cells (Fig. 2B). The results obtained evidenced that distinct sets of receptors are preferentially expressed by CD34– (IL-17R, transforming growth factor -?R1, TGF?R2, IL-10RA, IL-10RB) or CD34+ cells (FLT3, MPL, IFNR2, EpoR).
We then analyzed the expression of genes involved in TPO, FLT3, IFN, IL-17, IL-10, and TGF? pathways by graphic visualization of gene expression using GeneMAPP software.
Our data showed that signal transducers, particularly those involved in Ras-mediated pathways, are constitutively expressed in all populations studied, whereas receptors, primary response genes, and inhibitors are differentially expressed as follows.
TPO Pathway ? This pathway is apparently upregulated in CD34+ cells, because TPO response genes, such as HOXB4, CBF?, Runx1, Nfe-2, Gata-2, Fli-1, CD41b, CD42b, and CD61, are upregulated in this cell population (Fig.3A). Consistently, CD34+cells gave origin to early and late megakaryocyte progenitors (i.e., CFU-MK and BFU-MK) in response to TPO, whereas CD34– cells did not show any colony-forming ability even after 7 days of culture (Figs. 4A, 4B). Interestingly, cultures of CD34– cells generated secondary CFU-MK after 21 days in serum-free medium in the presence of TPO.
Figure 3. Visualization of expression data on the maps of (A) TPO, (B) IL-10, and (C) IL-17 pathways. Genes are colored according to the absolute and comparative expression (Lin+CD34+ versus Lin–CD34– cells). The legend of the coloring criteria is reported on the right of the figure. Abbreviations: IL, interleukin; TPO, thrombopoietin.
Figure 4. Effects of TPO, IL-10, and IL-17 on Lin–CD34– and Lin–CD34+ cells. (A): TPO treatment. (a): TPO induces the clonogenic growth of CFU-MK and BFU-MK from freshly isolated CD34+ but not Lin–CD34– cells. (b): Similarly, 7 days of culture of Lin–CD34– cells onto irradiated murine stromal cells (M2-10B4), genetically engineered to produce G-CSF and IL-3 did not induce secondary colony formation in response to TPO. The results shown derive from six different experiments and are expressed as mean ± standard deviation. (c): Megakaryocyte progenitors (CFU-MK) became detectable after 19–21 days (day +21) of incubation of Lin–CD34– cells in serum-free liquid medium added with TPO. At this time point, 7% ± 3% of total cell population was represented by CD34+ cells (see text). (B): IL-10 treatment. Clonogenic efficiency of highly purified (a) Lin–CD34+ and (c) Lin–CD34– cells. The cells were cultured in semisolid medium for 14 days with GM-CSF, IL-3, EPO, and SCF. (b): Lin–CD34+ and (d) Lin–CD34– cells were cultured onto M2-10B4 stromal cells in the presence of a cytokine cocktail (FLT-3L, TPO, IL-11, and SCF) with or without IL-10 or in the presence of IL-10 alone. Subsequently, clonogenic assays in semisolid medium with GM-CSF, IL-3, EPO, and SCF were set up to assess the production of secondary CFU-C. The results are expressed as mean ± standard deviation of three different experiments. IL-10 showed no significant activity on clonogenic CD34+ cells (p = not significant), whereas the addition of IL-10 inhibited the secondary colony-forming activity of CD34– cells induced by cytokines in liquid cultures (p < .03). (C): IL-17 treatment. Clonogenic efficiency of highly purified (a) Lin–CD34+ and (c) Lin–CD34–cells. The cells were cultured in semisolid medium for 14 days with GM-CSF, IL-3, EPO, and SCF. (b): Lin–CD34+ and (d) Lin–CD34– cells were cultured onto M2-10B4 stromal cells in the presence of a cytokine cocktail (FLT-3L, TPO, IL-11, and SCF) with or without IL-17 or in the presence of IL-17 alone. Subsequently, clonogenic assays in semisolid medium with GM-CSF, IL-3, EPO, and SCF were set up to assess the production of secondary CFU-C. The results are expressed as mean ± standard deviation of three different experiments. Similarly to IL-10, IL-17 showed no activity on clonogenic CD34+ cells (p = not significant), whereas the study cytokine inhibited the secondary clonogenic activity of CD34– cells induced by cytokines in liquid cultures (p < .04). Abbreviations: CFU-MK, colony-forming unit megakaryocyte; EPO, erythropoietin; IL, interleukin; SCF, stem cell factor; TPO, thrombopoietin.
At that time point, phenotypic analysis demonstrated the presence of CD34+ cells (7% ± 3% of the total population) deriving from CD34– HSCs (Fig. 4A, panel c). Although we have no formal evidence, it is conceivable that functional response to TPO is associated with the acquisition of the CD34 Ag.
IL-10 Pathway ? IL-10 inhibits cell-cycle progression of HSCs and progenitor cells acting through STAT1/STAT3 activation . Analysis of the genes involved in this pathway showed that both receptor isoforms (IL-10RA and IL-10RB) and IL-10 primary response genes, such as CDKN1A/p21 and CDKN2D/p19, were preferentially expressed in CD34– cells (Fig. 3B). Clonogenic assays on Lin– cells demonstrated a minimal activity of IL-10 on CD34+ cells (Figs. 4B, panel b, 4B, panel b), whereas the addition of IL-10 inhibited the secondary colony-forming activity of CD34– cells induced by cytokines in liquid cultures (Fig. 4B, panel d) (p < .03). At this stage, the percentage of CD34+ cells deriving from CD34– HSCs after 7 days in liquid culture was 6% ± 2%, with no significant difference between IL-10–treated and control samples.
IL-17 Pathway ? As reported in Figure 2, IL-17 receptor is specifically expressed by CD34– cells. Moreover, some IL-17 primary response genes (i.e., ICAM-1 and IL-8) are preferentially expressed in the same cell population (Fig. 3C). Consistent with these findings, we did not observe any significant activity of IL-17 on CD34+ cells (Figs. 4C, panel a, 4C, panel b) (p = not significant). In addition, we demonstrated for the first time the inhibitory effect of IL-17 on the secondary clonogenic activity of CD34– cells induced by cytokines in liquid cultures (Fig. 4C, panel d) (p < .04).
In this set of experiments, the percentage of CD34+ cells originating from CD34– cells after 7 days was 4% ± 2%, with no difference between IL-17–treated and control samples.
Finally, gene expression data suggest that FLT3 and IFN pathways are active mainly in CD34+ cells, whereas TGF? exerts its inhibitory effect preferentially on CD34– cells (supplementary online Fig. 3).
Metabolic Activation of CD34+ Cells
Genes involved in DNA replication, such as DNA polymerases, topoisomerases, and minichromosome maintenance (MCM), were preferentially expressed in CD34+ cells, particularly in Lin+CD34+ cells (supplementary online Fig. 4). These data are consistent with the kinetic status of the three analyzed cell populations; in fact, CD34+ cells are mainly in G1 phase of cell cycle and synthesize the enzyme components for the subsequent S phase.
Genes involved in DNA repair (base excision repair, nucleotide excision repair, mismatch repair, and double-strand break repair) exhibited a preferential expression in CD34+ cells, particularly in Lin+CD34+ (supplementary online Fig. 5). Again these results are consistent with the kinetic and differentiation status of CD34– and CD34+ cells . The global expression analysis of genes involved in RNA splicing, capping, and polyadenilation showed that the process of RNA maturation is mainly active in CD34+ cells (supplementary online Fig. 6). Moreover, combined analysis of the expression of genes codifying for ribosomal proteins demonstrated that these transcripts, already present in CD34– cells, undergo a remarkable induction in Lin–CD34+ and a slight decrease in the subsequent transition to Lin+CD34+ cells (supplementary online Fig. 7A). These data are in keeping with previous studies describing the increase of ribosome biogenesis during the G0/G1 transition . The expression of genes involved in protein translation and modification was increased in CD34+ cells, particularly in Lin+CD34+ cells (supplementary online Figs. 7B, 7C).
Genes codifying for proteins involved in oxidative phosphorylation and TCA cycle processes showed a prevalent expression in CD34+ cells, especially in Lin+CD34+ (supplementary online Figs. 8A, 8B). These data are consistent with the already described activation of oxidative phosphorylation and TCA cycle in early G1 phase of the cell cycle .
Self-Renewal Capacity
Analysis of TF expression indicated that most genes involved in self-renewal process were upregulated in CD34+ cells (Fig. 5A). Among them, HOXA5, HOXA9, HOXA10, HOXB2, HOXB5, Meis1, and PBX2 are preferentially expressed by CD34+ cells.
Figure 5. Expression of transcription regulators and differentiation markers. Eisen tree map computed using the GeneSpring gene tree and the Pearson correlation equation on the modulated probe sets belonging to the following categories: (A) transcription factors, (B) transcription activators and repressors, and (C) differentiation markers. The signal-based coloring legend is shown at the bottom of the figure.
The expression of HOXB4, recently described as a key regulator of TPO-induced HSC self-renewal , was sixfold upregulated in Lin–CD34+ and ninefold upregulated in Lin+CD34+ compared with Lin–CD34– cells. HOXB4 expression was detected only by real-time quantitative PCR (RTQPCR), because the HOXB4 probe set is not represented on HG-U95Av2 array. GATA-2 and Bmi1, key factors for HSC self-renewal , and LMO2, CBF?, and CUTL1, known regulators of early hematopoiesis , were found to be expressed in all cell populations, particularly in CD34+ cells. Conversely, ID1 and ID2 (inhibitors of cell differentiation) resulted in downregulation of CD34+ cells. Genes belonging to the WNT and NOTCH pathways were expressed at very low levels in all cell populations.
Lineage Commitment Capacity
The expression analysis of TFs involved in all hematopoietic lineage differentiation (Fig. 5A) evidenced that several genes increased in CD34–/CD34+ transition: GATA-1, PU.1/SPI1 , and HOXA5 (myeloid commitment); GATA-1, LMO2, TAL-1, LDB1, and TCF3 (erythroid commitment); GATA-1, CBF?, GATA-2, FLI1, and NF-E2 (megakaryocytic commitment); GATA-1 and C/EBP? (eosinophil commitment), and PU.1 and C/EBP? (neutrophil commitment).
Although transcripts of TFs involved in monocyte (ICSBP1 , EGR-1 , HOXA10 ) and lymphoid (Ikaros, GATA-3 ) commitment were always detectable, variations of their expression levels did not correlate with the differentiation degree of the analyzed cell populations (Fig. 5A).
The upregulation of lineage-commitment TFs in CD34+ cells was associated with the induction of a large number of intracellular and surface markers belonging to the monocytic (CD14, ACO2, TMSF7), granulocytic (CD16, LILRA3, LILRB3, MMP9, CSF3R, CD32), lymphoid (CD69, CD19, CD164, CD58, TRB), megakaryocytic (PF4, F2R, VEGF, CD31, CD41, CD151, GP1BB), and erythroid (KLF1, RUVLB1, RUVLB2, GYPC) differentiation lineages (Fig. 5C).
Transcriptional activators, such as topoisomerase, helicases, acetyltransferase, and chromatin remodeling proteins (SMARCA2, SMARCD2, SMARCA4, SMARCC2, and SMARCC1) were mainly expressed in CD34+ cells; conversely, transcriptional repressors were preferentially expressed in Lin–CD34– (Fig. 5B).
Engraftment Capacity
Previous reports demonstrated that human CD34+ cells have a significantly greater engraftment potential than CD34– cells when transplanted in irradiated nonobese diabetic/severe combined immunodeficiency (NOD/SCID) mice . Our molecular analysis showed that the expression of genes belonging to the cell adhesion category is higher in CD34+ cells (supplementary online Fig. 9). Furthermore, genes specifically involved in the homing and engraftment of HSCs in the BM were preferentially expressed in CD34+ cells. In fact, Lin–CD34+ cells showed a higher expression of VLA-4, VLA-5, and SELPLG compared with Lin–CD34– cells (Fig. 6). Interestingly, CD34– cells exhibited higher levels of CXCR4, but also of RGS1 and RGS13, that function as negative regulators of CXCR4 activity by the inhibition of trimeric G proteins (Fig. 6). Taken together, these observations support the view that CD34+ cells have higher engraftment capacity in primary recipients of xenogenic transplant compared with CD34– cells.
Figure 6. Schematic view of the engraftment pathway. Genes are colored according to the absolute and comparative expression (Lin–CD34+ versus Lin–CD34– cells). The legend of the coloring criteria is reported at the bottom of the figure.
Differential Expression of Nonhematopoietic Markers
Global expression analysis of nonhematopoietc markers, such as epithelial, neural, and muscle tissue markers, revealed their preferential expression in CD34– cells (Fig. 7). The expression of these genes strongly decreases during the CD34–/CD34+ transition and becomes undetectable in terminally differentiated cells (R. Manfredini et al., unpublished data). Among these genes, the more differentially expressed were the epithelial markers CDH1 (E-cadherin) and K5 type II keratin (KRT5), the neural marker dopamine receptor 4 (DRD4), and the muscle marker tropomyosin 2, beta (TMP2).
Figure 7. Expression of nonhemopoietic markers. Eisen tree map computed using the GeneSpring gene tree and the Pearson correlation equation on the modulated probe sets belonging to the following categories: (A) neural markers, (B) muscle markers, and (C) epithelial markers. The signal-based coloring legend is shown at the bottom of the figure.
Real-Time Quantitative PCR Validation of Differential Expressed Genes
To confirm microarray data, we carried out a TaqMan RTQPCR analysis on a validation set containing 77 transcripts selected among the differentially expressed genes with greatest biological significance. TaqMan data were uploaded onto GeneSpring software as described in Materials and Methods and analyzed together with the array data. Supplementary online Figure 10 shows a gene tree and condition tree computed onto the validation set gene list using the Spearman correlation. Almost all of the analyzed genes showed the same expression pattern with both analyses.
DISCUSSION
R.M.L. and S.F. contributed equally to this study. This work was funded by MURST-COFIN 2002, Associazione Italiana per la Ricerca sul Cancro, and the University of Bologna (funds for selected topics).
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