Microcomputed tomography analysis The cross sectional volum

[jy9202]

Additional file 2, Figure S1B shows that 1,254, 344, and オーダー AP24534 98 of phosphorylation sites were on serine, threonine, and tyrosine, respectively. These results are comparable with the distribution of phosphorylation sites in HeLa carcinoma cells and in platelets, In order to further assess the nature of the phosphopro teomes identified in this study, we compared the assigned gene ontologies of the phosphoproteins with those proteins present in the whole human proteome, Each protein was classified according to the three different domains included in the gene ontology project, namely cellular component which describes the subcel lular localization of the identified protein, biological process which indicates the biological function to which the gene product contri butes; and molecular function which describes the elemental activities of a gene product at a molecular level, The distributions of these domains in our phospho protein database were compared to those in the SwissProt database, The data show that phosphoproteins were on the whole not biased towards any GO, in that the subcellular distribution of phosphoproteins identified were similar to those reported in the SwissProt database, The minor differences in the proportion of membrane proteins could be explained by the difficulty in solubilizing membrane proteins with buffers compatible with the rest of the workflow.



We also observed that phos phoproteins located in the purchase AT7519 cytosol cytoplasm and those with roles in translation, cell cycle regulation, and prolif eration were well represented relative to thpan Akt 阻害剤 e whole proteome, Other well represented phosphoproteins include those with protein kinase activity and other enzymes, Phosphoproteomics classified hematological cancer cells according to pathology We next asked whether quantitative phosphoproteomics could be used to classify hematological cell lines accord ing to their pathological origin. Normalized peptide intensities were used to calculate the overall fold differ ence for each phosphopeptide identified and quantified in our study. These data were then subjected to principal component analysis, When analyzing the nine cell lines together, inspection of the PCA outputs revealed that principal component 1 pro duced two clearly separated groups, one containing AML cell lines and another containing lymphoma and multiple myeloma cell lines, while principal component 2 separated cells derived from lymphoma from those that originated from multiple myeloma, We also analyzed the phosphorylation data of each cell line in three separate PCA plots corresponding to the three different cancer types.

Only data on the cell lines of each different malignancy were included in each particular PCA plot. These data, summarized in Figure 1B, revealed that cell lines within a given disease could also be separated based on their phosphoprotein con tent, indicating global differences in phosphorylation between cells of the same pathology with the exception of RL and DoHH2 lymphoma cell lines which could not be separated by PCA, The ability of phosphoproteomics to classify hemato logical cell lines was also assessed by unsupervised hier archical clustering.