Alignment and statistical difference analysis of complex peptide data sets generated by multidimensional LC-MS.

Abstract

A method for high-resolution proteomics analyses of complex protein mixtures is presented using multidimensional HPLC coupled to MS (MDLC-MS). The method was applied to identify proteins that are differentially expressed during fruit ripening of tomato. Protein extracts from red and green tomato fruits were digested by trypsin. The resulting highly complex peptide mixtures were separated by strong cation exchange chromatography (SCX), and subsequently analyzed by RP nano-LC coupled to quadrupole-TOF MS. For detailed quantitative comparison, triplicate RP-LC-MS runs were performed for each SCX fraction. The resulting data sets were analyzed using MetAlign software for noise and data reduction, multiple alignment and statistical variance analysis. For each RP-LC-MS chromatogram, up to 7000 mass components were detected. Peak intensity data were compared by multivariate and statistical analysis. This revealed a clear separation between the green and red tomato samples, and a clear separation of the different SCX fractions. MS/MS spectra were collected using the data-dependent acquisition mode from a selected set of differentially detected peptide masses, enabling the identification of proteins that were differentially expressed during ripening of tomato fruits. Our approach is a highly sensitive method to analyze proteins in complex mixtures without the need of isotope labeling.