Table of Contents
International Journal of Proteomics
Volume 2013, Article ID 674282, 10 pages
http://dx.doi.org/10.1155/2013/674282
Research Article

Quantitative Proteomics via High Resolution MS Quantification: Capabilities and Limitations

1Global Discovery and Development Statistics, Lilly Research Laboratories, Indianapolis, IN 46285, USA
2Lilly Corporate Center, DC 0720, Indianapolis, IN 46285, USA
3Translational Sciences, Lilly Research Laboratories, Indianapolis, IN 46285, USA

Received 15 October 2012; Accepted 6 February 2013

Academic Editor: Valerie Wasinger

Copyright © 2013 Richard E. Higgs et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recent improvements in the mass accuracy and resolution of mass spectrometers have led to renewed interest in label-free quantification using data from the primary mass spectrum (MS1) acquired from data-dependent proteomics experiments. The capacity for higher specificity quantification of peptides from samples enriched for proteins of biological interest offers distinct advantages for hypothesis generating experiments relative to immunoassay detection methods or prespecified peptide ions measured by multiple reaction monitoring (MRM) approaches. Here we describe an evaluation of different methods to post-process peptide level quantification information to support protein level inference. We characterize the methods by examining their ability to recover a known dilution of a standard protein in background matrices of varying complexity. Additionally, the MS1 quantification results are compared to a standard, targeted, MRM approach on the same samples under equivalent instrument conditions. We show the existence of multiple peptides with MS1 quantification sensitivity similar to the best MRM peptides for each of the background matrices studied. Based on these results we provide recommendations on preferred approaches to leveraging quantitative measurements of multiple peptides to improve protein level inference.