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Background: In recent years technology has emerged to describe the protein spots on two-dimensional gel electrophoresis. Numerous gel statistical analysis software has been developed and the impact of these initiatives on promoting proteomics is impressive. Proteomic analysis of massive data sets with having high variable need multivariate methods to provide the simultaneous analysis of multiple variables. In this study, the process of cellular differentiation and aging of stem cells to astrocytes studied by proteomics and gel analysis software and statistical applications were considered. Materials and Methods: Proteome of four groups (stem cells, young astrocytes, moderately differentiated astrocytes and old astrocytes) were analyzed by the software of Progenesis Same Spots. Cluster analysis, principal component analysis and analysis of power were used in the experimental groups. Results and Discussion: In bioinformatics and statistical analysis of two-dimensional gel electrophoresis technique were detected 940 protein spots with significant expression changes (p <0.05) in four groups that comparisons between groups suggest that the expression of new proteins and the silencing of certain proteins in the signaling pathway of cell differentiation and senescence. Clustering analysis of the expression of proteins can be divided into two 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 using statistical analysis software to quickly and easily done and significant expression changes induced differentiation and senescence on proteome level as well as evaluated with statistical analysis of clustering and PCA and determined indicators of changes.

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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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Introduction: IgA nephropathy is the most common cause of primary glomerulonep-hritis throughout the most of developed countries. Since the biopsy is the only way for diagnosis of IgA nephropathy, finding an easy and non-invasive method seems to be necessary for prognosis, diagnosis and treatment of this disease. In this study, it was attempted to find some urine candidate biomarkers that represent the progression of disease in patients with IgA nephropathy. Materials & Methods: Urine samples from 13 patients were collected and their prote-ome were extracted and analyzed with na-no-LC-MS/MS. The protein profile was obtained and those differential proteins bet-ween patients with advanced and mild disease states (based on the renal function eGFR) were determined using orthogonal projection to latent structure discriminant analysis (OPLS-DA) and the acquired data underwent bioinformatics analysis. Findings: A panel composed of 50 signi-ficant proteins was obtained in which 10 top candidate biomarkers were introdu-ced. Dermcidin and Osteopontin had highe-st variation amongst the proteins, so that they overrepresented and underrepresented, res-pectively. Complement system and inna-te immune response were introduced as the significantly important different processes between two groups of patients. Discussion & Conclusion: The introduced panel of urinary biomarkers can open a new insight to the mechanism of disease progr-ession and may be helpful as a non-invasive diagnosis method.