Comparative evaluation of datasets derived from label-free quantitative secretome analysis related to lung cancer metastasis using IDEAL-Q, Progenesis LC-MS and MaxQuant softwares

• Main Authors: Juo-LingSun, 孫若齡
• Other Authors: Pao-Chi Liao
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/99797102263247549524

碩士 === 國立成功大學 === 環境醫學研究所 === 100 === In recent years, the proteomics quantitative analysis plays an important role in biomedical research, analyzing the differential protein expression help us to understand the biological changes or biomarkers discoveries in clinical applications. It is necessary to discover a rapid, highly reproducible, and accurate quantitative software to do label-free quantitative proteomics research. Here we used the real complex samples that are investigated in the present lab to compare the results of the quantitative softwares (IDEAL-Q, Progenesis LC-MS and MaxQuant). Briefly, the six biological replicates of secretome samples from non-small cell lung cancer cell lines CL1-0 and CL1-5, which with low and high invasive abilities, respectively, were collected and analyzed via LC-MS/MS. Approximately, 72.6% of total identified proteins revealed in more than half samples in each cell line. Additionally, 68.3 % proteins were present in both cells. The protein identified results show that the data set had high reproducibility of protein components and good data quality. And the three softwares also provide user-friendly interfaces to facilitate protein and peptide quantitation. Next we compared the quantitation results of the datasets based on the number of quantifiable peptides/proteins, the spiked internal standard protein, and the protein expression levels. Finally, we compare the label-free quantitation results of the softwares with previously published data using iTRAQ labeling. Both the two quantitative proteomics strategy, label-free and iTRAQ labeling quantitative analysis, showed a high consistency (〉 60%) of protein abundances.