Comparison of Commonly Used Methods for Protein Relative Quantification in Complex Samples

摘要

One major application of proteomics is to identify proteins with changed expression levels under different conditions (control Vs treatment, wild type Vs. mutant, etc.) in a complex proteome. Over the last decade, dozens of tools, both chemically and computationally, have been developed to help with this kind of analysis. In this presentation, we are attempting to benchmark several commercial tools available at our facility on their effectiveness in identification of differentially expressed proteins in a model system using a shotgun approach. When looking for differentiated proteins, the underlying assumption is that expression of the majority of proteins (house-keeping proteins) remains unchanged. Based on this assumption, we developed a model system with whole cell lysate as a “base” that does not change, and 11 commercially available proteins spiked in at different levels and ratios as “targets”. Protein mixtures were digested with trypsin and tryptic peptides were analyzed in triplicate with a 4-hour gradient by nano LCMSMS using LTQ Orbitrap XL. The last version of ipi human protein database (V3.75) was modified to include the 11 proteins that we spiked in for both search engines (Proteome Discoverer and Mascot) prior to data processing. Methods are categorized into 3 areas and data analyzed with different software tools available to us and evaluated for number of proteins identified and accuracy in protein relative abundance. While the focus is on the 11 “target” proteins, we will evaluate number of “base” proteins that are identified to have altered levels (false discovery). The three areas are: 1. Amine reacting chemical labeling (iTraq, and TMT), 2. Label free spectra counting, and 3. Chromatography based label-free analysis.