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Application of Non Linear Statistics (Principal Component Analysis) in Data Analysis of Differentiation Stem Cell to Astrocyte
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H Zali   , M Rezaee tavirani * 1, A Seied khani nahal , Sh Moradi |
| 1- , rezaei.tavirani@ibb.ut.ac.ir |
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Abstract: (16402 Views) |
| Background: The combination of univariate and multivariate statistics can identify significant biological changes in protein expression between experimental groups. One of the most common statistical methods that help to analyze two-dimensional gel electrophoresis is principal component analysis. In this study, the differentiation of stem cells to astrocytes is study by proteomics and cell proteome of two groups will be analyzed by principal components analysis (PCA) by statistical software.
METHODS: Bone marrow aspirates from healthy donors and isolated mononuclear cell. Cells in 10% DMEM with low glucose, glutamine, streptomycin and penicillin in CO2 5% and moisture 98% were incubated at 37 º c. For differentiation of these cells into astrocytes, cells exposed to retinoic acid, cAMP, PGF, PDGF, NGF. Stem cells and astrocyte cell proteom were extracted and separated by two dimensional electrophoresis. The gels were stained using silver staining and scanned gels were analyzed statistically by using the Bioinformatics analysis software.
Results and Discussion: Bioinformatics and statistical analysis of two-dimensional gel electrophoresis technique is shown that 774 protein spots were detected in the two groups. Comparisons between groups suggest that the expression of new proteins and the silencing of certain proteins in the signaling pathway of cell differentiation. Clustering analysis of the expression of proteins can be divided into three main clusters indicate that there are clusters of proteins with similar expression which these proteins can provide similar performance in terms of testing or indicate its presence in the same biological pathway. PCA analysis confirmed the clustering results showed that the protein has been classified according to the test conditions. Finally, we can conclude that the differentiation makes a significant change in the level of expression of the proteome and statistical analysis like clustering and PCA can be considered as good and revealed indicators of changes. |
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| Keywords: Proteomics, Principal components analysis, Astrocytes |
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Full-Text [PDF 445 kb]
(4952 Downloads)
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Type of Study: Research |
Subject:
Medical microbiology
Received: 2013/03/18 | Accepted: 2013/05/13 | Published: 2013/05/13
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