Increased Power for the Analysis of Label-Free LC-MS/MS Proteomic Data by Combining Spectral Counts and Peptide Peak Attributes

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics provides a wealth of information about proteins present in biological samples. In bottom-up LC-MS/MS-based proteomics, proteins are enzymatically digested into peptides prior to query by LC-MS/MS. Thus, the information directly available from the LC-MS/MS data is at the peptide level. If a protein-level analysis is desired, the peptide-level information must be tolled up into protein-level information. We propose a principal components analysis-based statistical method, ProPCA, for efficiently estimating relative protein abundance from bottom-up label-free LC-MS/MS data, which incorporates both spectral count information and LC-MS peptide ion peak attributes, such as peak area, volume or height. ProPCA may be used effectively with a variety of quantification platforms and is easily implemented. We show that ProPCA outperforms existing quantitative methods for peptide-protein roll up, including special counting methods and other methods for combining LC-MS peptide peak attributes. The performance of ProPCA is validated using a dataset derived from the LC-MS/MS analysis of a mixture of protein standards (the UPS2 proteomic dynamic range standard introduced by the ABRF Proteomics Standards Research Group, 2006). Finally, we apply ProPCA to a comparative LC-MS/MS analysis of digested total cell lysates prepared for LC-MS/MS analysis by alternative lysis methods and show that ProPCA identifies more differently abundant proteins than competing methods.