Comparative Shotgun Proteomics Using Spectral Count Data and Quasi-Likelihood Modeling

Abstract

Shotgun proteomics provides the most powerful analytical platform for global inventory of complex proteomes using liquid chromatography−tandem mass spectrometry (LC−MS/MS) and allows a global analysis of protein changes. Nevertheless, sampling of complex proteomes by current shotgun proteomics platforms is incomplete, and this contributes to variability in assessment of peptide and protein inventories by spectral counting approaches. Thus, shotgun proteomics data pose challenges in comparing proteomes from different biological states. We developed an analysis strategy using quasi-likelihood Generalized Linear Modeling (GLM), included in a graphical interface software package (QuasiTel) that reads standard output from protein assemblies created by IDPicker, an HTML-based user interface to query shotgun proteomic data sets. This approach was compared to four other statistical analysis strategies: Student t test, Wilcoxon rank test, Fisher’s Exact test, and Poisson-based GLM. We analyzed the performance of these tests to identify differences in protein levels based on spectral counts in a shotgun data set in which equimolar amounts of 48 human proteins were spiked at different levels into whole yeast lysates. Both GLM approaches and the Fisher Exact test performed adequately, each with their unique limitations. We subsequently compared the proteomes of normal tonsil epithelium and HNSCC using this approach and identified 86 proteins with differential spectral counts between normal tonsil epithelium and HNSCC. We selected 18 proteins from this comparison for verification of protein levels between the individual normal and tumor tissues using liquid chromatography−multiple reaction monitoring mass spectrometry (LC−MRM-MS). This analysis confirmed the magnitude and direction of the protein expression differences in all 6 proteins for which reliable data could be obtained. Our analysis demonstrates that shotgun proteomic data sets from different tissue phenotypes are sufficiently rich in quantitative information and that statistically significant differences in proteins spectral counts reflect the underlying biology of the samples.

Protein Expression Signatures for Inhibition of Epidermal Growth Factor Receptor-mediated Signaling

“…Protein expression was compared on the basis of spectral counts using QuasiTel, a quasi-likelihood modeling software package (40). QuasiTel performed pair-wise comparison between two treatment/biological conditions based on protein spectral counts and variance across replicate analyses and computed p values and rate ratios (fold changes) for detected proteins.…”

A Secreted Bacterial Protease Tailors the Staphylococcus aureus Virulence Repertoire to Modulate Bone Remodeling during Osteomyelitis

“…These peptide spectral data were searched against a protein database using Sequest (Yates et al, 1995) and the resulting identifications collated and filtered using IDPicker (Ma et al, 2009) and Scaffold (Proteome Software, Portland, OR). Relative protein abundances were evaluated via spectral counting techniques using the QuasiTel program to calculate false discovery rate-corrected p -values (Li et al, 2010). …”

Analysis of Surface-Exposed Outer Membrane Proteins in Helicobacter pylori

“…All experiments were done in triplicate, and the resulting spectral data were merged prior to analysis (see Table S1 in the supplemental material). Proteins were identified as enriched in one set of preparations compared to another, based on both analysis of fold enrichment (comparing normalized ratios of numbers of assigned spectra, calculated using either QuasiTel model-generated rates or R sc values, which both account for abundance of spectral counts when analyzing normalized data) and the statistical significance of differences in abundance of spectral counts assigned to each protein (using Fisher's exact test with Bonferroni's correction) (33,34). Criteria for identifying proteins that were enriched in one set of samples compared to another were chosen based on comparative analysis of data for spectral proteins previously annotated as outer membrane proteins (likely to be surface exposed) and data for proteins annotated either as ribosomal proteins or inner membrane proteins or other non-outer membrane proteins based on annotation (not likely to be surface exposed) (see Fig.…”