Dataset Information

Bioinformatics analysis of the biological changes involved in the osteogenic differentiation of human mesenchymal stem cells.

ABSTRACT: The mechanisms underlying the osteogenic differentiation of human bone marrow mesenchymal stem cells (hBMSCs) remain unclear. In the present study, we aimed to identify the key biological processes during osteogenic differentiation. To this end, we downloaded three microarray data sets from the Gene Expression Omnibus (GEO) database: GSE12266, GSE18043 and GSE37558. Differentially expressed genes (DEGs) were screened using the limma package, and enrichment analysis was performed. Protein-protein interaction network (PPI) analysis and visualization analysis were performed with STRING and Cytoscape. A total of 240 DEGs were identified, including 147 up-regulated genes and 93 down-regulated genes. Functional enrichment and pathways of the present DEGs include extracellular matrix organization, ossification, cell division, spindle and microtubule. Functional enrichment analysis of 10 hub genes showed that these genes are mainly enriched in microtubule-related biological changes, that is sister chromatid segregation, microtubule cytoskeleton organization involved in mitosis, and spindle microtubule. Moreover, immunofluorescence and Western blotting revealed dramatic quantitative and morphological changes in the microtubules during the osteogenic differentiation of human adipose-derived stem cells. In summary, the present results provide novel insights into the microtubule- and cytoskeleton-related biological process changes, identifying candidates for the further study of osteogenic differentiation of the mesenchymal stem cells.

SUBMITTER: Fan T

PROVIDER: S-EPMC7348183 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bioinformatics analysis of the biological changes involved in the osteogenic differentiation of human mesenchymal stem cells.

Fan Tingyu T Qu Rongmei R Yu Qinghe Q Sun Bing B Jiang Xin X Yang Yuchao Y Huang Xiaolan X Zhou Zhitao Z Ouyang Jun J Zhong Shizhen S Dai Jingxing J

Journal of cellular and molecular medicine 20200528 14

The mechanisms underlying the osteogenic differentiation of human bone marrow mesenchymal stem cells (hBMSCs) remain unclear. In the present study, we aimed to identify the key biological processes during osteogenic differentiation. To this end, we downloaded three microarray data sets from the Gene Expression Omnibus (GEO) database: GSE12266, GSE18043 and GSE37558. Differentially expressed genes (DEGs) were screened using the limma package, and enrichment analysis was performed. Protein-protein ...[more]

PMID: 32463168

Similar Datasets

Project description:Human bone marrow mesenchymal stem cells (hBMSCs) are widely used cell source for clinical bone regeneration. Achieving the greatest therapeutic effect is dependent on the osteogenic differentiation potential of the stem cells to be implanted. However, there are still no practical methods to characterize such potential non-invasively or previously. Monitoring cellular morphology is a practical and non-invasive approach for evaluating osteogenic potential. Unfortunately, such image-based approaches had been historically qualitative and requiring experienced interpretation. By combining the non-invasive attributes of microscopy with the latest technology allowing higher throughput and quantitative imaging metrics, we studied the applicability of morphometric features to quantitatively predict cellular osteogenic potential. We applied computational machine learning, combining cell morphology features with their corresponding biochemical osteogenic assay results, to develop prediction model of osteogenic differentiation. Using a dataset of 9,990 images automatically acquired by BioStation CT during osteogenic differentiation culture of hBMSCs, 666 morphometric features were extracted as parameters. Two commonly used osteogenic markers, alkaline phosphatase (ALP) activity and calcium deposition were measured experimentally, and used as the true biological differentiation status to validate the prediction accuracy. Using time-course morphological features throughout differentiation culture, the prediction results highly correlated with the experimentally defined differentiation marker values (R>0.89 for both marker predictions). The clinical applicability of our morphology-based prediction was further examined with two scenarios: one using only historical cell images and the other using both historical images together with the patient's own cell images to predict a new patient's cellular potential. The prediction accuracy was found to be greatly enhanced by incorporation of patients' own cell features in the modeling, indicating the practical strategy for clinical usage. Consequently, our results provide strong evidence for the feasibility of using a quantitative time series of phase-contrast cellular morphology for non-invasive cell quality prediction in regenerative medicine.

Dataset Information

Bioinformatics analysis of the biological changes involved in the osteogenic differentiation of human mesenchymal stem cells.

Publications

Bioinformatics analysis of the biological changes involved in the osteogenic differentiation of human mesenchymal stem cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets