Abstract
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.
Footnotes
↵* This work was supported by National Institutes of Health Grants PL1 AG032118 (to B. W. G.), R24 DK085610 (to E. V.), T32AG000266 (to M. J. R.), R01 RR032708 (to M. J. M.), and P41 RR011823 (to M. J. M.) and by National Cancer Institute Grant U24 CA126477 (to B. W. G., subcontract to UCSF), which is part of the National Cancer Institute Clinical Proteomic Technologies for Cancer initiative. This work was also supported in part by NCRR Shared Instrumentation Grant S10 RR024615 (to B. W. G.) and by funds from AB SCIEX for evaluation of the TripleTOF 5600 at the Buck Institute. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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This article contains supplemental material.
- Received February 3, 2012.
- Revision received March 5, 2012.
- © 2012 by The American Society for Biochemistry and Molecular Biology, Inc.
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