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Mol Cell Proteomics. 2004 Jan;3(1):24-42. Epub 2003 Oct 13.

Complementary analysis of the Mycobacterium tuberculosis proteome by two-dimensional electrophoresis and isotope-coded affinity tag technology.

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  • 1Core Facility Protein Analysis, Max Planck Institute for Infection Biology, D-10117 Berlin, Germany.


Classical proteomics combined two-dimensional gel electrophoresis (2-DE) for the separation and quantification of proteins in a complex mixture with mass spectrometric identification of selected proteins. More recently, the combination of liquid chromatography (LC), stable isotope tagging, and tandem mass spectrometry (MS/MS) has emerged as an alternative quantitative proteomics technology. We have analyzed the proteome of Mycobacterium tuberculosis, a major human pathogen comprising about 4,000 genes, by (i) 2-DE and mass spectrometry (MS) and by (ii) the isotope-coded affinity tag (ICAT) reagent method and MS/MS. The data obtained by either technology were compared with respect to their selectivity for certain protein types and classes and with respect to the accuracy of quantification. Initial datasets of 60,000 peptide MS/MS spectra and 1,800 spots for the ICAT-LC/MS and 2-DE/MS methods, respectively, were reduced to 280 and 108 conclusively identified and quantified proteins, respectively. ICAT-LC/MS showed a clear bias for high M(r) proteins and was complemented by the 2-DE/MS method, which showed a preference for low M(r) proteins and also identified cysteine-free proteins that were transparent to the ICAT-LC/MS method. Relative quantification between two strains of the M. tuberculosis complex also revealed that the two technologies provide complementary quantitative information; whereas the ICAT-LC/MS method quantifies the sum of the protein species of one gene product, the 2-DE/MS method quantifies at the level of resolved protein species, including post-translationally modified and processed polypeptides. Our data indicate that different proteomic technologies applied to the same sample provide complementary types of information that contribute to a more complete understanding of the biological system studied.

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