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Keywords:

  • Human cord blood;
  • Hematopoietic stem cells;
  • Microarray

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References
  10. Supporting Information

Human cord blood (CB)–derived CD133+ cells carry characteristics of primitive hematopoietic cells and proffer an alternative for CD34+ cells in hematopoietic stem cell (HSC) transplantation. To characterize the CD133+ cell population on a genetic level, a global expression analysis of CD133+ cells was performed using oligonucleotide microarrays. CD133+ cells were purified from four fresh CB units by immunomagnetic selection. All four CD133+ samples showed significant similarity in their gene expression pattern, whereas they differed clearly from the CD133+ control samples. In all, 690 transcripts were differentially expressed between CD133+ and CD133+ cells. Of these, 393 were increased and 297 were decreased in CD133+ cells. The highest overexpression was noted in genes associated with metabolism, cellular physiological processes, cell communication, and development. A set of 257 transcripts expressed solely in the CD133+ cell population was identified. Colony-forming unit (CFU) assay was used to detect the clonal progeny of precursors present in the studied cell populations. The results demonstrate that CD133+ cells express primitive markers and possess clonogenic progenitor capacity. This study provides a gene expression profile for human CD133+ cells. It presents a set of genes that may be used to unravel the properties of the CD133+ cell population, assumed to be highly enriched in HSCs.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References
  10. Supporting Information

Hematopoietic stem cells (HSCs), possessing self-renewing and differentiation potential, are required for the lifelong sustenance of a functional blood system. Stem cell transplantation is an established procedure in the treatment of hematological malignancies. Recently, stem cell transplantation has been used as a therapy for many nonhematological disorders, including immunodeficiency syndromes, inborn errors of metabolism, and autoimmune diseases [13]. More specific transplants consisting of selected HSCs are required for novel indications of stem cell transplantation, especially when human leukocyte antigen-identical sibling donors are not available. The use of T-cell depletion effectively diminishes graft-versus-host disease, and the depletion of B cells may prevent Epstein-Barr virus-associated lymphoproliferative disease [4, 5]. The number of primitive cells and their proliferation capacity are considered preferable parameters for the engraftment potential as compared with nucleated cellularity [6, 7]. To increase the number of cells used in transplantation and to promote ex vivo expansion of HSCs, a greater understanding of profitable cell populations is required.

Human cord blood (CB) is an excellent source of HSCs. Rapidly available CB unit serves as an alternative for patients without potential bone marrow (BM) donor. Lower risk of graft-versus-host disease and cytomegalovirus infection is associated with CB transplantation. The comparison of the gene expression profiles of HSCs from peripheral blood (PB), BM, and CB suggests that CB-derived HSCs also have the potential to differentiate into cells of nonhematopoietic lineages [8]. HSCs from different sources display unique characteristics in terms of key transcription factors and genes associated with cell cycle, homing, and apoptosis [810]. HSCs from CB express transcription factors not seen in HSCs from other sources. Overexpression of these transcription factors may inhibit differentiation and might explain the higher proliferation rate observed in CB-derived HSCs [8].

The CD34 antigen has been the most widely used marker for HSC enrichment. Although the reconstruction of the adaptive immune system has been demonstrated with human CB-derived CD34+ progenitor cells in mice [11], the CD34+ cell fraction is heterogeneous. The CD133 antigen provides a promising selection marker for HSC enrichment. CD133+ cells are considered to be highly noncommitted with the capacity to self-renew and differentiate. In addition, CD133+ cells have been shown to have a higher clonogenic capacity than CD34+/CD133 cells [12]. Most of the CD133+ cells are CD34+ -bright, whereas CD34+ -dim cells are CD133. A small population of CD34/CD133+ cells (0.2%) has been found in CB, demonstrating that CD133 expression is not necessarily associated with CD34 expression [13].

The CD133 molecule has been found on the surface of HSCs, neuronal stem cells, and embryonic stem cells (ESCs). Moreover, the expression of CD133 is related to several solid organ malignancies, including lung and brain cancers [14, 15]. A recent study demonstrates that only CD133+ cancer stem cells are capable of brain tumor initiation while they sustain the ability to self-renew and proliferate [15]. In addition, CB-derived CD133+ cells may be able to differentiate into endothelial and neuronal cells [16].

The aim of this study was to characterize CB-derived CD133+ cells on a genomic level and to provide a global gene expression profile of CD133+ cells. The clonogenic progenitor capacity of CD133+ cells was demonstrated, showing that they are highly noncommitted and hold the potential to differentiate into all cell types of the hematopoietic system. The expression analysis presented in this study focuses on transcripts that are associated with hematopoiesis and the cell cycle. The gene expression data bank of CD133+ cells may be used to study the pathogenesis of hematological diseases deriving from HSCs.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References
  10. Supporting Information

Cells

Umbilical CB was obtained from Helsinki Maternity Hospital and the Department of Obstetrics and Gynaecology, Helsinki University Central Hospital, Finland. All donors gave informed consent, and the study protocol was accepted by the ethical review board of the Helsinki University Central Hospital and Finnish Red Cross Blood Service. CB was collected in sterile collection bags (Cord Blood Collection system; Medsep Corporation, Covina, CA, http://www.medsep.com) containing citrate phosphate dextrose solution and was processed within 4–20 hours. All CB units tested negative for HIV, hepatitis C virus, hepatitis B virus, human T-cell lymphotropic virus, and syphilis. Mononuclear cells (MNCs) were isolated by Ficoll-Hypaque density gradient (Amersham Biociences, Piscataway, NJ, http://www.amersham.com). CD133+ cells were enriched through positive immunomagnetic selection using CD133 Cell Isolation Kit and magnetic cell sorting (MACS) affinity columns (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany, http://www.miltenyibiotec.com). CD133+ cells were subjected to two rounds of separation. CD133 cells from the same CB unit were collected for control purposes. Microarray analysis was performed using four separate CB units. In addition, six CB units were processed for quantitative real-time polymerase chain reaction (qRT-PCR) analysis.

Flow Cytometry

Immunomagnetically selected cells were labeled with phycoerythrin (PE)– and fluorescein isothiocyanate (FITC)–conjugated monoclonal antibodies (mAbs) to evaluate the purity of cell fractions. Labeling was carried out using CD133/2-PE (clone 293C3; Miltenyi Biotec) and CD45-FITC (clone 2D1; Becton, Dickinson and Company, Franklin Lakes, NJ, http://www.bd.com) in 50 μl of phosphate-buffered saline (PBS) at room temperature for 20 minutes. Isotype-identical mAbs IgG2b-PE and IgG1-FITC (Becton, Dickinson and Company) served as controls. Flow cytometry analysis was performed on FACSCalibur (Becton, Dickinson and Company) with a 488-nm blue argon laser. Fluorescence was measured using 530/30-nm (FITC) and 585/42-nm (PE) bandpass filters. Data were analyzed using the CellQuest software (BD Biosciences, San Jose, CA, http://www.bdbiosciences.com) and Windows Multiple Document Interface for Flow Cytometry, WinMDI version 2.8 (http://facs.scripps.edu/software.html).

Colony-Forming Unit Assay

Colony-forming unit (CFU) assay was performed using meth-ylcellulose, MethoCult GF H4434 with recombinant cytokines, and erythropoietin (StemCell Technologies, Vancouver, BC, Canada, http://www.stemcell.com). A total of 2 × 103 CD133 cells, 1 × 105 CD133 cells, or 1 × 105 MNCs were plated in duplicate and cultured for 14 days at 37°C with 5% carbon dioxide in a humidified atmosphere. Colonies were counted according to their morphological characteristics.

RNA Isolation

Total RNA from up to 2 × 107 pelleted cells was purified with RNeasy Mini Kit (Qiagen GmbH, Hilden, Germany, http://www1.qiagen.com) according to the manufacturer's instructions. Yield and quality of the RNA were measured by spectro-photometric analysis. Each sample was assessed for the integrity of RNA by discrimination of 18S and 28S ribosomal RNA on 1% agarose using ethidium bromide for visualization.

Microarray Analysis

Total RNA from each sample was used to prepare biotinylated target RNA, with minor modifications from the manufacturer's recommendations (http://www.affymetrix.com/support/technical/manual/expression_manual.affx). In brief, first-strand cDNA was generated from 100 ng of total RNA using a T7-linked oligo(dT) primer. After the first cDNA synthesis cycle, in vitro transcription was performed with unlabeled ribonucleotides. A second round of cDNA synthesis was then performed followed by in vitro transcription with biotinylated UTP and CTP (Enzo Biochem, Inc., Farmingdale, NY, http://www.enzo.com). Cleanup of double-stranded cDNA was performed using Pellet Paint Co-Precipitant (Novagen, Madison, WI, http://www.emdbiosciences.com/html/NVG/home.html) instead of Glycogen. Standard Affymetrix hybridization cocktail was added to 15 μg fragmented cRNA. After overnight hybridization using Affymetrix GeneChip Instrument System (Affymetrix, Santa Clara, CA, http://www.affymetrix.com), arrays were washed, stained with streptavidin-phycoerythrin, and scanned on an Affymetrix GeneChip Scanner 3000. All experiments were performed using Affymetrix Human Genome U133 Plus 2.0 oligonucleotide arrays (http://www.affymetrix.com/products/arrays/specific/hgu133plus.affx). The replicate results of hybridization data for CD133+ and CD133 cells were obtained from four individual CB units. Sample labeling and hybridization were carried out at the Finnish DNA Microarray Centre at Turku Centre for Biotechnology, Turku, Finland.

Statistical Analysis

Pearson correlation coefficient (m = 8, n = 54,612) was calculated for each sample pair using original signals values obtained from Operating Software detection algorithm. Pearson correlation was also calculated for fold-change values of microarray and qRT-PCR. The Pearson correlation coefficient, rik, between ith and kth samples, that is {y1i, y2i, …, yni} and {y1k, y2k, …, ynk}, respectively, is defined by

  • equation image

in which

  • equation image

are the mean and SD of the kth sample, respectively.

Preprocessing and Filtering of Microarray Data

The Affymetrix GeneChip Operating Software detection algorithm was used to determine the presence or absence of expression for each transcript. A transcript with either the detection call present or marginal was considered expressed. The complete gene expression data are available at http://qp01.novogroup.com/vpu. GeneChip Operating Software change algorithm was used to compare the CD133+ data against the CD133 data to detect and quantify changes in gene expression. The transcripts assigned with change call increased, decreased, marginally increased, or marginally decreased were considered differentially expressed. The direction of change (increased or decreased) was to be the same in all CD133+ samples, and the fold-change cutoff value was set to 3.

Clustering and Annotation

To identify and visualize the differences between the CD133+ and CD133 samples, two clustering algorithms, hierarchical clustering and self-organizing map (SOM) with the component plane representation, were applied [17, 18]. In hierarchical clustering, average and correlation were used as linkage and distance metric, respectively. Hierarchical clustering was performed for all eight CD133+ and CD133 samples. The SOM algorithm clusters transcripts having a similar expression profile in the same neuron of the component plane. Accordingly, transcripts clustered close to each other are similar whereas topologically distant transcripts have dissimilar expression pattern. The component plane representation also includes a unified-matrix (U-matrix) representation, which can be used to identify robust clusters consisting of several neurons [18]. For the SOM, the mean expression across four CD133+ and four CD133 samples resulting in two component planes was used. The SOM toolbox with Euclidean distance function, Gaussian neighborhood function, sheet SOM map with 15 × 9 neurons, and batch learning algorithm was applied for the SOM analysis [19]. Affymetrix GO Ontology Mining tool was employed to obtain molecular functions, biological processes, and cellular components for the transcripts in the clusters. The statistically significant hits were defined by χ2 test and the associated p value with the significance level at 5% (p < .05).

Gene Prioritization

To order the genes according to their discriminatory power, a stepwise gene selection algorithm was used [20]. Briefly, the algorithm computes mean and SD across CD133+ samples (μ+, σ+) and across CD133 samples (μ, σ). The weight for the ith gene is computed using signal-to-noise ratio [21]:

  • equation image

If a gene has a large magnitude weight, then the gene is strongly differentially expressed between CD133+ and CD133 samples, and variation in CD133+ and CD133 is low.

Quantitative qRT-PCR Analysis

To confirm the information obtained from the microarray data, 10 genes (CD133, CD34, KIT, SPINK2, NOTCH1, SOX4, TIE, CD2, CD14, and CD45) were subjected to qRT-PCR analysis using pools with three samples in each. Analysis was performed on two biological replicates. Total RNA was DNase-treated with DNA-free Kit (Ambion, Inc., Austin, TX, http://www.ambion.com), and reverse transcription was performed using High-Capacity cDNA Archive Kit with RNase Inhibitor Mix (Applied BioSystems, Foster City, CA, http://www.appliedbiosystems.com) in a final volume of 100 μl. Thermal cycling conditions for reverse transcription were 25°C for 10 minutes and 37° for 120 minutes on GeneAmp PCR System 9700 (Applied BioSystems).

For PCR, the template was added to PCR mix consisting of 12.5 μl TaqMan Universal PCR Master Mix containing Uracil N-glycosylase for PCR carry-over prevention, 1.25 μl of TaqMan Gene Expression Assays probe (Hs00156373_m1, Hs00195682_m1, Hs00174029_m1, Hs00221653_m1, Hs00413187_ m1, Hs00268388_s1, Hs00178500_m1, Hs01040181_m1, Hs0069122_g1, Hs00365634_g1, Hs99999905_m1; Applied Biosystems), and diethyl pyrocarbonate–treated water (Ambion, Inc.). Samples were assayed in triplicate in a total volume of 25 μl. The qRT-PCR thermal cycling conditions were as follows: an initial step at 50°C for 2 minutes for Uracil N-glycosylase activation; 95°C for 10 minutes; and 40 cycles of 15 seconds at 95°C and 1 minute at 60°C.

A standard curve for serial dilutions of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) template was similarly constructed. GAPDH was chosen as the internal control because its expression levels had no variance between the samples in the microarray analysis. Changes in fluorescence were monitored using the ABI PRISM 7000 Sequence Detection System (Applied BioSystems), and raw data were analyzed by Sequence Detection System 1.1 Software (Applied BioSystems). The relative standard curve method was used to balance the variation in the amount of cDNA and to compensate for different levels of inhibition during reverse transcription and PCR.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References
  10. Supporting Information

Quality Assessment

To ensure validity of the samples and preprocessed microarray data, several methods were used for quality assessment. The purity of positively selected CD133+ cells was more than 90% by flow cytometry, and the CD133 cell population was nearly 100% pure (Fig. 1). Generally, 105–106 CD133+ cells were recovered from a CB unit and the viability of selected cells was at least 99%. The integrity of total RNA was confirmed by spectrophotometry and agarose gel electrophoresis.

To ensure the uniformity and comparability of the biological replicates, their pair-wise relationships were defined by Pearson correlation coefficients. The Pearson correlation coefficients were calculated for all the data points, excluding Affymetrix control samples, thus 54,609 transcripts per array were compared. The consistency in all cases was high, but the correlation within CD133+ samples was stronger than the correlation between CD133+ and CD133 samples. The correlation coefficients between CD133+ replicates had a mean of 0.98 (range, 0.95–1.00). The correlation coefficients indicated significant similarity of the CD133+ samples. The correlation coefficients between CD133+ and CD133 samples reached an average of 0.78. In hierarchical clustering, the CD133+ and CD133 samples clustered at the opposite ends of the dendrogram. These results demonstrate that the CD133+ cells are much more similar to one another than to the CD133 cells from the same individual.

The microarray result for 10 selected genes was confirmed by qRT-PCR analysis. The average fold change was calculated for each gene and compared with the result from microarray analysis (supplemental online Table 1). The results correlated strongly (Pearson correlation coefficient, 0.95).

The Expression Profile of CD133+ Cells

The comparison of CD133+ and CD133 data sets resulted in 690 transcripts that were differentially expressed at least three- fold (supplemental online Table 2). In CD133+ cells, 393 of the transcripts were upregulated and 297 were downregulated. Annotation was found for 227 (58%) overexpressed transcripts, which encode molecules involved in biological processes ranging from metabolism to development (Fig. 2). A functional role was found for 221 (74%) of the underexpressed transcripts, the protein products of which participate in cell communication, immune response, organogenesis, apoptosis, and chemotaxis (Fig. 2).

Two different clustering methods were applied to the set of 690 transcripts passing the initial screening filter. Hierarchical cluster analysis showed moderate variation in expression within a transcript between replicates. The expression of genes encoding CD133, CD34, and other transmembrane proteins, such as FLT3, LAPTM4B, EBPL, and CRIM1, had minor variance in all four CD133+ samples. Other very similarly expressed transcripts were ANKRD28, several members of the HOX gene family, and transcripts encoding hypothetical proteins. Moreover, DKC1, BAALC, and JUP had minimal variation within CD133+ replicates. In contrast, slightly more variation was observed in the expression of KIT, a known stem cell marker.

The SOM was constructed using mean values of 690 differentially expressed genes between the CD133+ and CD133 samples (Fig. 3). The mean value was used to determine the similar expression behavior common to all CD133+ samples. The SOM revealed four prominent clusters of genes distinguishing CD133+ and CD133 cell populations. The clusters are illustrated by the U-matrix.

SOM clusters 1 and 2 represented upregulated genes, and clusters 3 and 4 comprised downregulated genes. In cluster 1, the association to a biological process was attained for 88 (57%) of the transcripts. The significantly represented biological processes were primary metabolism, cell proliferation, and regulation of transcription. In cluster 2, a functional role was found for 69 (59%) of the transcripts. The most significant functional categories were transcription and development. Cluster 3 contained a group of downregulated genes associated with cell communication and immune response. Annotation was found for 86 (76%) of the genes in cluster 3. In addition, cluster 4 contained a number of genes whose protein products participate in signal transduction and response to stimulus. Moreover, the phosphorylation– and protein modification–related genes were downregulated. Cluster 4 contained 64 (70%) transcripts with known biological function.

In the SOM component plane, the most prominent finding was that known HSC markers CD133, CD34, and KIT had similar expression patterns and clustered into the same neuron. Interestingly, this neuron also contained the gene for SPINK2, expressed by 77-fold in microarray analysis. The markedly high expression of SPINK2 was confirmed by qRT-PCR (fold change 196). The role of SPINK2 is poorly understood but its expression is seen in human BM CD34+ cells and testicle tissues (http://genome.ucsc.edu/cgi-bin/hgNear).

CD133+ Cell–Enriched Genes

Altogether, 22,764 (42%) of the 54,675 transcripts on the arrays were expressed in one or more of the CD133+ samples. On each CD133+ array, a similar number of transcripts was expressed with maximum variance of 0.8%. Upregulation was seen in 6178 (11%) transcripts in at least one CD133+ sample. Each individual CD133+ sample had a similar number of unique gene expressions. The common expression pattern for all four CD133+ samples encompassed 2285 upregulated transcripts. Of these, 2034 (89%) transcripts were overexpressed at least twofold. The 2285 transcripts common for all CD133+ samples included genes whose protein products participate in cell communication, development, response to endogenous stimulus, chromosome organization, and biogenesis. Also, genes associated with RNA processing and mRNA metabolism were significantly overexpressed. Annotated biological process was found for 1399 (61%) of the transcripts.

The expression of 257 transcripts was seen in CD133+ samples only (Fig. 4A; supplemental online Table 3). These transcripts were absent in CD133 samples. Annotation was found for 155 (60%) transcripts. The most significantly represented biological processes among this set were DNA metabolism, cell proliferation, and regulation of transcription (Fig. 4B; Table 1). The transcripts expressed in CD133+ cells contained only 32 genes encoding potential integral membrane proteins that may serve as markers for HSCs (supplemental online Table 4). In addition, the 257 transcripts common for CD133+ samples were ranked using a gene prioritization method. The gene coding for transmembrane protein LAPTM4B, overexpressed by 26-fold, got the highest weight value in prioritization.

Cell Cycle

The expression data were surveyed to establish the cell cycle state of CD133+ cells. The expression of GATA2 (fold change, 7.0) and N-MYC (fold change, 15) that keep the HSCs in undifferentiated state was significantly elevated in CD133+ cells [22, 23]. The downregulation of these genes would initiate the cell cycle. DST (fold change, 5.3) and PLAGL1 (fold change, 9.1), which support cell cycle arrest, were upregulated as well. A cell cycle inhibitor and negative regulator of proliferation, NME1, was overexpressed in CD133+ cells by 3.7-fold.

Most of the CB-derived HSCs have been shown to be in G0 [24]. However, factors promoting the G1 phase, such as CDK6 (fold change, 10) and BCAT1 (fold change, 19), were overexpressed along with CDK4 (fold change, 3.9), which acts in the G1/S transition. The negative regulator of CDK4 and CDK6, p18, was underexpressed by 5.1-fold. Moreover, the overexpression of BMI-1 was observed by 2.8-fold. BMI-1 enhances the cell cycle by inhibiting p16, the negative regulator of the cell cycle. As expected, p16 was not expressed in CD133+ cells.

The S phase was demonstrated by high expression of genes encoding minichromosome maintenance proteins crucial in DNA replication. Known S-phase inducers MCM2 (fold change, 3.1), MCM5 (fold change, 4.2), MCM6 (fold change 2.5), and MCM7 (fold change, 2.8) were upregulated. Interestingly, CDK2AP1, a suppressor of DNA replication, was overexpressed by fourfold and CDKN2D, needed in S phase, was underexpressed by 20-fold. However, the low expression of CDKN2D refers to G1 phase [25]. No known transcripts encoding molecules acting in G2 phase or G2/M transition were seen. Several transcripts coding for molecules with ubiquitin-protein ligase activity, such as SH3MD2, UHRF1, ZNRF1, EDD, and TIF1, were overexpressed more than threefold. Many cell cycle regulatory molecules are controlled by ubiquitin-mediated pro- teolysis to regulate the number of cells in each phase of the cell cycle [26]. Genes associated with mitosis, such as SKB1, STAG1, ANAPC7, and MPHOSPH9, were overexpressed by 2.6-fold, 1.6-fold, 2.6-fold, and 3.1-fold, respectively. These data suggest that a portion of CD133+ cells are cycling.

Hematopoiesis

The expression of genes associated with self-renewal and differentiation was studied to unravel the hematopoietic state of CD133+ cells. Several HSC-associated genes were overexpressed: CD133 by 60-fold, CD34 by 13-fold, KIT by 26-fold, TIE by 3.2-fold, SCA-1 by 2.1-fold, MEIS1 by 10-fold, and ANGPT1 by 12-fold. Genes supporting self-renewal, such as GATA2, MPLV, STAT5A, and TCF7L2, were upregulated by 7.0-fold, 12-fold, 1.9-fold, and 3.3-fold, respectively. Hox genes, thought to be involved in HSC regulation, were also highly upregulated. The expression of HOXA9 (fold change, 130) induces stem cell expansion, and HOXA5 (fold change, 10) and HOXA10 (fold change, 3.7) are specific to the long-term repopulating population of HSCs [27, 28]. Upregulation of GATA2 and other transcription factors supporting self-renewal may account for the differentiation arrest and support the more primitive nature of CB-derived HSCs [8]. The previously reported early markers for hematopoeitic progenitors, BAALC and C17 [2931], were expressed in CD133+ cells only. The BAALC gene was overexpressed by 33-fold. The expression of BAALC has been shown in brain tissue, yet its functional role is unknown [30]. The overexpression of C17, a gene coding for an extracellular molecule with signal transduction activity, was 15-fold.

AML1, overexpressed by 2.5-fold in CD133+ cells, may also support HSC self-renewal although it has been characterized as an early differentiation marker of the myeloid lineage. The other early myeloid differentiation gene, PU.1, was absent. GATA1, which affects erythropoiesis, and PAX5, which promotes B-precursor development, were both absent. No change in expression of GFI1 leading to T-lymphoid differentiation was detected. NFE2, required for HSCs determination to mega-karyocyte and erythrocyte lineage, was downregulated.

The expression of lineage-determination markers glycophorin-A, CD38, CD7, CD33, CD56, CD16, CD3, or CD2 was undetected in CD133+ cells. The expression of CD45 was seen in CD133+ cells, but it was downregulated. The CD45 antigen is abundant in lymphoid cells, covering approximately 10% of the cell surface. The gene expression results suggest a naive state for the CD133+ cell population, containing long-term and short-term repopulating HSCs as well as early progenitors with myeloid and lymphoid lineage potential.

CFU assay was used to identify primitive hematopoietic cells from CD133+, CD133, and MNC fractions by stimulating them to express their developmental potential (supplementary online Table 5). Total CFU (CFU-TOT) number was determined as the sum of granulocyte-erythroid-macrophage-megakaryocyte (CFU-GEMM), granulocyte-macrophage (CFU-GM), erythroid (CFU-E), and burst-forming erythroid (BFU-E) colonies (Fig. 5). CFU-TOT counts were 80, 0.58, and 1.09 per 1000 cells for CD133+, CD133, and MNC populations, respectively. The highest proportion of CD133+ cells formed CFU-GM colonies (58%) and CFU-GEMM colonies (38%). BFU-E represented 4.2% of the colonies, yet CFU-E colonies were not observed. Taken together, CD133+ is a valid selection marker for HSC enrichment. The clonogenic progenitor capacity of CD133+ cells demonstrates that they are highly noncommitted and hold the potential to differentiate into all cells in the hematopoietic system.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References
  10. Supporting Information

The gene expression profile of human HSCs, especially CD34+ cells, has been reported from various sources [810, 27, 3234]. This study characterizes the gene expression profile of CB-derived cells selected using CD133, a marker thought to be specific for HSCs. Altogether, 42% of the transcripts on the arrays were expressed in one or more of the CD133+ samples. The great number of expressed transcripts in CD133+ cells may be due to the open chromatin structure of HSCs [9, 35, 36]. In all, 690 transcripts were found to be differentially expressed between CD133+ and CD133 cells. Among these were many genes encoding known stem cell markers and genes coding for hematopoietic regulators. The genes encoding mature hematopoietic markers were not expressed in CD133+ cells, whereas their expression was detected in CD133 cells.

Hierarchical cluster analysis presented a set of 537 transcripts with differential expression between CD133+ and CD133 cells. The expression pattern of these transcripts was similar within all CD133+ and CD133 samples, and the level of expression was uniform. Some transcripts showed variation in their expression level between biological replicates even though the direction of change was the same. The variance of expression level in CB-derived HSCs is known to be higher than in HSCs from other sources [8]. The higher individual variance may be explained by the unique birth event in each case.

SOM clustering demonstrated that the biological processes associated with upregulated or downregulated genes were divergent. SOM clustering segregated genes into separate neurons, providing sets of genes with a similar gene expression pattern. The genes associated with cell growth and maintenance, transcriptional activity, and cell cycle were significantly overexpressed in CD133+ cells. The emphasized activity of these biological processes is known to be representative of hematopoietic progenitor cells [10, 34, 37]. In contrast, the CD133 cell fraction displayed a significantly elevated number of genes whose protein products participate in immune response and reaction to stimulus, corresponding to the expression pattern of mature blood cells.

SOM analysis was performed on the 690 differentially expressed genes. It revealed that SPINK2 had a similar expression pattern with known HSC markers CD133, CD34, and KIT. The increased expression of SPINK2 has recently been described in CB-derived CD34+ CD133+ cells [37]. The decreased expression of SPINK2 in testis has been shown to be associated with infertility [38]. Similarly, CD133 has been suggested to take part in the formation of spermatozoa and thereby have a significant role in male fertility [39]. CD133 expression is assumed to affect the formation of lamellipodia, enabling HSC migration [32].

In this study, the main focus of the expression analysis was on genes related to cell cycle and hematopoiesis. A number of differentially expressed genes involved in these processes were identified in CD133+ cells. According to the literature, most of the CD133+ cells reside in the G0/G1 state of the cell cycle [40, 41]. However, certain enhancers of cell cycle and S-phase inducers were upregulated in CD133+ cells, suggesting that a portion of these cells may be cycling. Genes supporting self-renewal and differentiation arrest were highly expressed in CD133+ cells. The expression pattern of CD133+ cells alludes to proliferation activity.

Furthermore, the expression of genes encoding cell adhesion molecules related to functionally important processes in HSC migration and homing was examined. Among the 690 differentially expressed genes, 11 that encode adhesion molecules were upregulated in CD133+ cells. The overexpression of these genes (CD34, IL-18, JUP, DST, COL5A1, TRO, DSG2, ITGA9, SEPP1, PKD2, and VAV3) is also associated with cell cycle arrest and response to external stress. The 16 downregulated genes associated with cell adhesion encoded known mature cell markers, such as CD2 and CD36. Several genes encoding chemokines and integrins were downregulated. The low or undetectable expression of genes associated with migration probably relates to CB as the source of the CD133+ cells, as the CB microenvironment differs from that of BM. The engraftment potential of CB-derived HSCs is known to be delayed compared with other sources of HSCs [42]. The gene coding for VLA-4, needed for HSC homing, was upregulated. The upregulation of VLA-4 has been shown to be crucial to HSC engraftment in mice [43]. CB-derived HSCs have higher long-term engraftment capacity, and their engraftment potential is significantly greater as compared with BM and PB [8, 44].

A set of 257 transcripts, expressed solely in CD133+ cells, was found. This set encompassed several genes coding for putative integral membrane proteins. The expression and localization of these proteins cannot be deduced from the present data and are a subject of further investigations. Of the common genes expressed in CD133+ cells, LAPTM4B got the highest weight value in gene prioritization. The overexpression of LAPTM4B has been detected in mouse and human ESCs, HSCs, and neuronal stem cells by several independent studies [27, 45, 46]. LAPTM4B has no known biological function, but some observations link its upregulation to certain cancer cell lines and poor differentiation of human hepatocellular carcinoma tissues [47]. For 125 of the 257 transcripts, a biological function could not be found. These novel genes may serve as the basis for further studies on HSC regulation.

When comparing the expression data of CD133+ cells with published data on HSCs from CB, the highest similarity was seen with CD34+ CD133+ cells [37]. Among the differentially expressed genes found in CD34+ CD133+ cells, 28 were over-expressed also in the CD133+ cell population. In addition, 14 common genes between CD34+ CD38 Lin and CD133+ cell populations were found [9]. CD34+ CD38+ cells showed similarity to CD133 cells, having 10 genes in common [32]. The common genes are listed in Table 2. RBPMS and FLJ14054 were identified in all four studies. The expression of RBPMS has been shown in heart, prostate, intestine, and ovary, but its expression level is low in skeletal muscle, spleen, thymus, brain, and peripheral leukocytes [48]. RBPMS has been suggested to have tissue-specific alternative splicing and may play a role in RNA metabolism [48]. A few ESC-related stem cell markers, such as DNMT3B, DNMT3A, and DPPA4, were overexpressed in CD133+ cells as well [46, 4953]. Transcriptional evidence of ESC-related genes is a sign of the primitive nature of CB-derived CD133+ cells. CB-derived CD133+ cells have been shown to have nonhematopoietic differentiation potential with the capacity to develop into endothelial and neuronal cells [16]. Comparison of different cell populations, based on published data, is troublesome due to differences in the cell populations, platforms, and preprocessing methods used. Furthermore, the nomenclature of genes is inconstant. To find true overlap between different data sets, unprocessed data should be used.

This study provides a global gene expression profile for human CB-derived CD133+ cells. The clonogenic progenitor activity of CD133+ cells was demonstrated, showing that the CD133+ cell fraction is an excellent source of HSCs. The gene expression profile of CD133+ cells may be used to study the pathogenesis of hematological disorders and development of malignancies. An improved understanding of CD133+ cells furthers their potential in therapeutic applications. This study provides additional information regarding previous HSC gene expression analyses. Combining all published data would bring the scientific community closer to unraveling the riddle of HSCs.

Table Table 1.. The genes representing the most significant biological processes in CD133+ cells
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Table Table 2.. Common genes between the present study and published data on cord blood–derived hematopoietic stem cells
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Figure Figure 1.. Purity assessment of CD133+ and CD133 cell fractions by flow cytometry. CD133+ and CD133 cell populations were defined by first gating on forward and side scatter properties, excluding platelets and debris. Subsequent gates were set to exclude >99% of control cells labeled with isotype-specific antibody. Percentages indicating the purity of isolated cell fractions are shown for both plots. Abbreviations: PE, phycoerythrin; SSC, side scatter.

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Figure Figure 2.. Biological processes represented by the differentially expressed genes in CD133+ cells.

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Figure Figure 3.. Classification of CD133+ and CD133 samples by mean self-organizing map (SOM) analysis. (A): The four clusters determined by unified matrix (U-matrix). (B): Mean SOM component planes for CD133+ and CD133 samples.

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Figure Figure 4.. Common transcripts expressed in CD133+ cells. (A): Schematic representation of intersections and differences in CD133+ cells. Only transcripts expressed in CD133+ cells but absent in CD133 cells were included. (B): Categorization of common genes expressed in CD133+ cells based on Gene Ontology annotation.

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Figure Figure 5.. Clonogenic progenitor cell capacity of CD133+, CD133, and MNC populations. Abbreviations: CFU, colony-forming unit; MNC, mononuclear cell.

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Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References
  10. Supporting Information

We thank the staff of the Finnish Red Cross Blood Service Cord Blood Bank. We also acknowledge Miina Miller for technical help with microarray analysis and Sirkka Mannelin for help with CFU assay. Tuija Kekarainen is acknowledged for help with flow cy-tometry analysis and for valuable comments on the manuscript.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
SC050185SuppTable1.pdf60KSupplemental Table 1
SC050185SuppTable2.pdf1157KSupplemental Table 2
SC050185SuppTable3.pdf422KSupplemental Table 3
SC050185SuppTable4.pdf19KSupplemental Table 4
SC050185SuppTable5.pdf13KSupplemental Table 5

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