Christine C. Wu

Despite advances in metabolic and postmetabolic labeling methods for quantitative proteomics, there remains a need for improved label-free approaches. This need is particularly pressing for workflows that incorporate affinity enrichment at the peptide level, where isobaric chemical labels such as isobaric tags for relative and absolute quantitation and tandem mass tags may prove problematic or where stable isotope labeling with amino acids in cell culture labeling cannot be readily applied. Skyline is a freely available, open source software tool for quantitative data processing and proteomic analysis. We expanded the capabilities of Skyline to process ion intensity chromatograms of peptide analytes from full scan mass spectral data (MS1) acquired during HPLC MS/MS proteomic experiments. Moreover, unlike existing programs, Skyline MS1 filtering can be used with mass spectrometers from four major vendors, which allows results to be compared directly across laboratories. The new quantitative and graphical tools now available in Skyline specifically support interrogation of multiple acquisitions for MS1 filtering, including visual inspection of peak picking and both automated and manual integration, key features often lacking in existing software. In addition, Skyline MS1 filtering displays retention time indicators from underlying MS/MS data contained within the spectral library to ensure proper peak selection. The modular structure of Skyline also provides well defined, customizable data reports and thus allows users to directly connect to existing statistical programs for post hoc data analysis. To demonstrate the utility of the MS1 filtering approach, we have carried out experiments on several MS platforms and have specifically examined the performance of this method to quantify two important post-translational modifications: acetylation and phosphorylation, in peptide-centric affinity workflows of increasing complexity using mouse and human models.

To quantify proteins on a global level from mammalian tissue, a method was developed to metabolically introduce 15N stable isotopes into the proteins of Rattus norvegicus for use as internal standards. The long-term metabolic labeling of rats with a diet enriched in 15N did not result in adverse health consequences. The average 15N amino acid enrichments reflected the relative turnover rates in the different tissues and ranged from 74.3 mpe in brain to 92.2 mpe in plasma. Using the 15N-enriched liver as a quantitative internal standard, changes in individual protein levels in response to cycloheximide treatment were measured for 310 proteins. These measurements revealed 127 proteins with altered protein level (p < 0.05). Most proteins with altered level have previously reported functions involving xenobiotic metabolism and protein-folding machinery of the endoplasmic reticulum. This approach is a powerful tool for the global quantitation of proteins, is capable of measuring proteome-wide changes in response to a drug, and will be useful for studying animal models of disease.

Database-searching algorithms compatible with shotgun proteomics match a peptide tandem mass spectrum to a predicted mass spectrum for an amino acid sequence within a database. SEQUEST is one of the most common software algorithms used for the analysis of peptide tandem mass spectra by using a cross-correlation (XCorr) scoring routine to match tandem mass spectra to model spectra derived from peptide sequences. To assess a match, SEQUEST uses the difference between the first- and second-ranked sequences (ACn). This value is dependent on the database size, search parameters, and sequence homologies. In this report, we demonstrate the use of a scoring routine (SEQUEST-NORM) that normalizes XCorr values to be independent of peptide size and the database used to perform the search. This new scoring routine is used to objectively calculate the percent confidence of protein identifications and posttranslational modifications based solely on the XCorr value.