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. 2011 Jul;13(6):661-74.
doi: 10.3109/14653249.2010.548379. Epub 2011 Jan 20.

Global transcriptome analysis of human bone marrow stromal cells (BMSC) reveals proliferative, mobile and interactive cells that produce abundant extracellular matrix proteins, some of which may affect BMSC potency

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Global transcriptome analysis of human bone marrow stromal cells (BMSC) reveals proliferative, mobile and interactive cells that produce abundant extracellular matrix proteins, some of which may affect BMSC potency

Jiaqiang Ren et al. Cytotherapy. 2011 Jul.

Abstract

Background aims: Bone marrow stromal cells (BMSC) are being used for immune modulatory, anti-inflammatory and tissue engineering applications, but the properties responsible for these effects are not completely understood. Human BMSC were characterized to identify factors that might be responsible for their clinical effects and biomarkers for assessing their quality.

Methods: Early passage BMSC prepared from marrow aspirates of seven healthy subjects were compared with three human embryonic stem cell (hESC) samples, CD34(+) cells from three healthy subjects and three fibroblast cell lines. The cells were analyzed with oligonucleotide expression microarrays with more than 35 000 probes.

Results: BMSC gene expression signatures of BMSC differed from those of hematopoietic stem cells (HSC), hESC and fibroblasts. Genes upregulated in BMSC were involved with cell movement, cell-to-cell signaling and interaction and proliferation. The upregulated genes most probably belonged to pathways for integrin signaling, integrin-linked kinase (ILK) signaling, NF-E2-related factor-2 (NFR2)-mediated oxidative stress response, regulation of actin-based motility by Rho, actin cytoskeletal signaling, caveolar-mediated endocytosis, clathrin-mediated endocytosis and Wingless-type MMTV integration site (Wnt/β catenin signaling. Among the most highly upregulated genes were structural extracellular matrix (ECM) proteins (α5 and β5 integrin chains, fibronectin and collagen type IIIα1 and Vα1) and functional EMC proteins [connective tissue growth factor (CTGF), transforming growth factor beta-induced protein (TGFBI) and A disintegrin and metalloproteinase (ADAM12)].

Conclusions: Global analysis of human BMSC suggests that they are mobile, metabolically active, proliferative and interactive cells that make use of integrins and integrin signaling. They produce abundant ECM proteins that may contribute to their clinical immune modulatory and anti-inflammatory effects.

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Figures

Figure 1
Figure 1. Global transcriptome analysis of BMSCs, HSCs and hESCs
BMSCs from 4 subjects, CD34+ peripheral blood HSCs from 3 subjects and 3 hESC samples were analyzed with oligonucleotide microarray with >35,000 probes. Panel A shows the unsupervised hierarchical clustering analysis of the 4,475 probes that were increased at least 2-fold in one sample and expressed by 80% of the samples. Panel B shows the hierarchical clustering analysis of 2,800 differentially expressed genes (F-test, p<0.05).
Figure 2
Figure 2. Pathway analysis of differentially expressed genes in BMSCs compared to HSCs
The canonical pathways significantly modulated by the genes differently expressed in BMSCs and HSCs are shown (Ingenuity Pathway Analysis, p < 0.05). The p value for each pathway is indicated by the bar and is expressed as −1 times the log of the p value. The line represents the ratio of the number of genes in a given pathway divided by the total number of genes that make up that pathway. The red bar indicated genes up-regulated in BMSCs and the green bar those up-regulated in HSCs. Only the 30 pathways with the most significant changes are shown.
Figure 3
Figure 3. Pathway analysis of differentially expressed genes in BMSCs compared to hESCs
The canonical pathways significantly modulated by the genes differently expressed in BMSCs and hESCs are shown (Ingenuity Pathway Analysis, p < 0.05). The p value for each pathway is indicated by the bar and is expressed as −1 times the log of the p value. The line represents the ratio of the number of genes in a given pathway divided by the total number of genes that make up that pathway. The red bar indicated genes up-regulated in BMSCs and the green bar those up-regulated in hESCs. Only the 30 pathways with the most significant changes are shown.
Figure 4
Figure 4. Comparison of the expression of selected gene between BMSCs, HSCs and hESCs
Quantitative real time PCR was used to measure gene expression levels in the 4 BMSC, 3 HSC and 3 hESC samples of TGFBI, CD44, CCL12, BMI1, VCAM1, IL6 and SELL.
Figure 5
Figure 5. Global transcriptome analysis of BMSCs and fibroblasts
BMSCs from 3 subjects and 3 fibroblast cell lines were analyzed with oligonucleotide microarray with >35,000 probes. Panel A shows the unsupervised hierarchical clustering analysis of the 4,944 probes that were increased at least 2-fold in one sample and were expressed by 80% of the samples. Panel B shows that hierarchical clustering analysis of 1,991 differentially expressed genes (F-test, p<0.05).
Figure 6
Figure 6. Pathway analysis of differentially expressed genes in BMSCs compared to Fibroblasts
The canonical pathways significantly modulated by the genes differently expressed in BMSCs and fibroblasts are shown (Ingenuity Pathway Analysis p < 0.05). The p value for each pathway is indicated by the bar and is expressed as −1 times the log of the p value. The line represents the ratio of the number of genes in a given pathway divided by the total number of genes that make up that pathway. The red bar indicated genes up-regulated in BMSCs and the green bar those up-regulated in fibroblasts. Only the 30 pathways with the most significant changes are shown.
Figure 7
Figure 7. Comparison of BMSCs, fibroblasts, HSCs and hESCs
Results of the gene expression analysis of 7 BMSCs, 3 fibroblast, 4 hESC and 4 HSCs samples were analyzed by multiple dimensional clustering (Partek Software). Since the transcriptome analysis of 3 fibroblast and 3 BMSC samples were perform at a different time than that of the 4 HSC, 4 hESC and 3 BMSC samples, genes whose expression differed due to a batch effect were removed by identifying genes whose expression differed between the two groups of BMSC samples and excluding them from the data set (p<0.05). A total of 4,300 genes were removed.

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