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Molecular & Cellular Proteomics 6:1621-1637, 2007.
© 2007 by The American Society for Biochemistry and Molecular Biology, Inc.

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Technology

Mascot File Parsing and Quantification (MFPaQ), a New Software to Parse, Validate, and Quantify Proteomics Data Generated by ICAT and SILAC Mass Spectrometric Analyses

Application To the Proteomics Study of Membrane Proteins from Primary Human Endothelial Cells ^*,S

David Bouyssié^,, Anne Gonzalez de Peredo^,,¶, Emmanuelle Mouton, Renaud Albigot, Lucie Roussel^||, Nathalie Ortega^||, Corinne Cayrol^||, Odile Burlet-Schiltz, Jean-Philippe Girard^|| and Bernard Monsarrat

From the Laboratoire de Protéomique et Spectrométrie de Masse des Biomolécules and ^|| Laboratoire de Biologie Vasculaire, Equipe Labellisée "Ligue 2006," Institut de Pharmacologie et de Biologie Structurale, CNRS UMR 5089, 205 route de Narbonne, 31077 Toulouse, France

Proteomics strategies based on nanoflow (nano-) LC-MS/MS allow the identification of hundreds to thousands of proteins in complex mixtures. When combined with protein isotopic labeling, quantitative comparison of the proteome from different samples can be achieved using these approaches. However, bioinformatics analysis of the data remains a bottleneck in large scale quantitative proteomics studies. Here we present a new software named Mascot File Parsing and Quantification (MFPaQ) that easily processes the results of the Mascot search engine and performs protein quantification in the case of isotopic labeling experiments using either the ICAT or SILAC (stable isotope labeling with amino acids in cell culture) method. This new tool provides a convenient interface to retrieve Mascot protein lists; sort them according to Mascot scoring or to user-defined criteria based on the number, the score, and the rank of identified peptides; and to validate the results. Moreover the software extracts quantitative data from raw files obtained by nano-LC-MS/MS, calculates peptide ratios, and generates a non-redundant list of proteins identified in a multisearch experiment with their calculated averaged and normalized ratio. Here we apply this software to the proteomics analysis of membrane proteins from primary human endothelial cells (ECs), a cell type involved in many physiological and pathological processes including chronic inflammatory diseases such as rheumatoid arthritis. We analyzed the EC membrane proteome and set up methods for quantitative analysis of this proteome by ICAT labeling. EC microsomal proteins were fractionated and analyzed by nano-LC-MS/MS, and database searches were performed with Mascot. Data validation and clustering of proteins were performed with MFPaQ, which allowed identification of more than 600 unique proteins. The software was also successfully used in a quantitative differential proteomics analysis of the EC membrane proteome after stimulation with a combination of proinflammatory mediators (tumor necrosis factor-, interferon-, and lymphotoxin /ß) that resulted in the identification of a full spectrum of EC membrane proteins regulated by inflammation.

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