The Research On The Protein Quantitative Data Analysis Methods Based On Stable Isotope Labeling And Mass Spectrometry

In the post-genomic era, with the continual advance of experimental technology and research strategy, proteomic research has been deeply promoted, and quantitative proteomics research has become a hot topic. Quantitative proteomics is not only committed to high-throughput and high accurately identify all the expressed proteins in organizations or cells, but also determinate their relative or absolute abundance. Large-scale protein quantitative technology based on stable isotope labeling and mass spectrometry, was widely used in quantitative proteomic research, with high-throughput, high accuracy, and other advantages. For the high-throughput quantitative mass spectrometry experiments, the experimental data analysis in particular the quantitative analysis is important, moreover the data analysis methods strongly dependent on the experimental technique. Choose the right algorithm is very important to get accurate results, and the algorithms need to be constantly updated with the advancement of experiment technology. In order to expand the dynamic range of protein quantification, there has been the experiment strategy which used gel electrophoresis separation technique to pre-separate the samples before quantitative analysis of samples. In the process of the data analysis produced by this method, we found "lost of quantitative information" and "one protein, different slices" problems, which result in quantitative information lost or inaccurate result, and specific algorithms are needed to deal with these two issues. "lost of quantitative information" problem although has been referred in current literature, but has not proposed proper solutions. For the special nature of the problem, we for the first time propose a solution which use accurate mass and time strategy to find missing quantitative information. Although "sum and average" method can solve "one protein, different slices" problem, but the result differed with the real value. In this paper, we propose the "bottom integrate" approach which can solve this problem, and with exactly the same as the real value. Application results show that these two issues have been successfully solved, and ultimately get accurate, reliable and comprehensive quantitative results. Finally, based on the 1.0 version of quantitative software MSAQ developed in our laboratory, we develop the 2.0 version, which can solve the two issues and has been successfully applied in a number of biological cases.