Journal
JOURNAL OF SEPARATION SCIENCE
Volume 29, Issue 18, Pages 2793-2801Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/jssc.200600290
Keywords
independent component analysis; multivariate data mining; non-gel; pattern recognition; principal component analysis
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Mass spectrometry (MS) has become a powerful tool for the quantitative analysis of complex protein samples. A high-throughput strategy for the comparative analysis of multiple protein samples with high complexity becomes more and more important. Two strategies, spectral count and peak intensity, for label-free MS analysis of prefractionated complex mixtures have been described recently to be useful for quantitation. Here we compare both strategies for rapid and quantitative 1-D shotgun LC/MS/MS analyses of highly complex protein mixtures using silica-based monolithic columns. First, we validated linearity and sensitivity of these methods by spiking varying amounts of an internal standard protein in a complex plant protein extract. Secondly, quantitative data of proteins of Medicago truncatula nodules were visualized with independent components analysis using data either obtained from spectral count or peak integration performed with commercial software. Spectral count showed apparent advantages over peak integration because several peptides per protein are automatically averaged, the linear dynamic range of quantitation increases in complex matrices and the number of quantified proteins surpasses the number of proteins using peak integration. Thus, for the need of rapid comparative analysis of highly complex protein samples, spectral count enables sample pattern recognition and identification of biomarkers in nongel based proteomic studies.
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