title: 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.
authors.affiliation: Department of Biostatistics, Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37232-8575, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Li
authors.forename: William
authors.initials: W
authors.lastname: Gray
authors.forename: Haixia
authors.initials: H
authors.lastname: Zhang
authors.forename: Christine H
authors.initials: CH
authors.lastname: Chung
authors.forename: Dean
authors.initials: D
authors.lastname: Billheimer
authors.forename: Wendell G
authors.initials: WG
authors.lastname: Yarbrough
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Liebler
authors.forename: Yu
authors.initials: Y
authors.lastname: Shyr
authors.forename: Robbert J C
authors.initials: RJ
authors.lastname: Slebos
mesh_terms: D002294:Carcinoma, Squamous Cell / Q000378:metabolism; D002853:Chromatography, Liquid; D006258:Head and Neck Neoplasms / Q000378:metabolism; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D014066:Palatine Tonsil / Q000378:metabolism; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D012984:Software; D053719:Tandem Mass Spectrometry
chemical_list: D011506:Proteins