Int J Stem Cells.  2019 Mar;12(1):84-94. 10.15283/ijsc18102.

Effect of Substrate Topography and Chemistry on Human Mesenchymal Stem Cell Markers: A Transcriptome Study

• Zhang B
• Kasoju N
• Li Q
• Ma J
• Yang A
• Cui Z
• Wang H
• Ye H

• KMID: 2447227
• DOI: http://doi.org/10.15283/ijsc18102

Abstract

BACKGROUND AND OBJECTIVES
The International Society for Cellular Therapy (ISCT) proposed a set of minimal markers for identifying human mesenchymal stromal cells (hMSCs) in 2007. Since then, with the growing interest of better characterising hMSCs, various additional surface markers have been proposed. However, the impact of how culture conditions, in particular, the culture surface, vary the expression of hMSC markers was overlooked.
METHODS AND RESULTS
In this study, we utilized the RNA sequencing data on hMSCs cultured on different surfaces to investigate the variation of the proposed hMSC biomarkers. One of the three ISCT proposed positive biomarker, CD90 was found to be significantly down regulated on hMSCs culture on fibrous surfaces when compared to flat surfaces. The detected gene expression values for 177 hMSCs biomarkers compiled from the literature are reported here. Correlation and cluster analysis revealed the existence of different biomarker communities that displayed a similar expression profile. We found a list of hMSCs biomarkers which are the least sensitive to a change in surface properties and another list of biomarkers which are found to have high sensitivity to a change in surface properties.
CONCLUSIONS
This study demonstrated that substrate properties have paramount effect on altering the expressions of hMSCs biomarkers and the proposed list of substrate-stable and substrate-sensitive biomarkers would better assist in the population characterisation. However, proteomic level analysis would be essential to confirm the observations noted.

Keyword

Human mesenchymal stromal cells; Surface markers; Cell biomaterial interactions; Next generation sequencing; Quality control; Regenerative medicine

MeSH Terms

Biomarkers
Chemistry*
Gene Expression
Humans*
Mesenchymal Stromal Cells*
Quality Control
Regenerative Medicine
Sequence Analysis, RNA
Surface Properties
Transcriptome*
Biomarkers

Figure

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  Fig. 1 Expression levels of ISCT recommended hMSCs surface markers: (a) CD90 and CD105 showed substrate-sensitive response whereas CD73 showed substrate-stable response. CD90 and CD105 showed variations on expression levels that were higher than that of CD73. (b) The FPKM values for CD90 were plotted against the FPKM values of CD73 and CD105, line of best fit and R-square is shown in the figure.

• 

  Fig. 2 Genes correlated with the three positive ISCT recommended hMSCs markers: The total number of correlated genes with CD73 (334) was less than that with CD90 (464) and CD105 (467). CD90 and CD105 share over 84% of their individually correlated genes, whereas, CD7 3has no common genes with the other two markers.

• 

  Fig. 3 Substrate induced changes in expression of 177 hMSCs biomarkers compiled from the literature: Dendrogram shows that the expression levels of the biomarkers were significantly altered with respect to changes in topography in comparison with chemistry induced changes.

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  Fig. 4 Correlation analysis of gene expression levels of 177 hMSCs biomarkers compiled from the literature: (a) overall observation with reference to three ISCT markers highlighted and (b) first-degree-connections of ISCT markers suggested correlation between CD90 and CD105 including its neighbors but no correlation with that of CD73 and its neighbors. A negative correlation between two genes is shown by a red line and a positive correlation is shown by a green line. The size of the node represents the mean FPKM percentile for that particular gene.

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  Fig. 5 Cluster analysis of the 177 compiled hMSCs markers: (a) network modularity determination figure showing the configuration of 48 communities resulted in the highest modularity. (b) Gene frequency graph, the frequency (number) of genes that was allocated into the different size of communities, as a result of the optimal cluster configuration. (c) The clustered network consists of the circular subplots; each of the parameter circles represents a community/cluster. Each green line represents the positive correlation of the gene expressions. The interactions within each community are noticeably more than the interactions with other communities. The size of the node represents the average expression level of that gene. The three positive ISCT markers are highlighted in grey. The top three stable or variably expressed genes in each community are highlighted in (c) and the gene names in panel (d), in blue and red, respectively.

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  Fig. 6 Identification of hMSCs biomarkers that respond to changes in substrate topography: The number of differentially regulated genes (>for up-regulation and

Reference

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