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.

Figure 1

Outline of proteomic procedures for shotgun proteomic analysis of HNSCC and tonsil epithelium protein lysates and for protein quantitation analysis by MRM.

Figure 2

Comparison of MRM results obtained for actin and annexin-A1 on the 20 individual normal tonsil epithelia (normal) and 20 individual HNSCCs (tumor). (A) Results obtained for a single actin-specific peptide without normalizing the MRM measurements. (B) Results obtained with the same actin-specific peptide, normalized against an isotopically labeled version of the same sequence. (C) Measurements for annexin-A1 without normalization. (D) Measurements for annexin-A1, normalized against a labeled version of the identical annexin-A1 peptide. The actin example illustrates how an apparent difference in mean MRM measurement disappears after normalization to the identical labeled actin peptide, which indicates successful normalization for instrument variation. The annexin-A1 example shows that in contrast to actin, an apparent lower annexin-A1 level in HNSCCs remains after normalizing for instrument variation.

Figure 3

Examples of LC−MRM-MS analyses of a selected set of 6 proteins with large spectral count differences between HNSCC and normal tonsil epithelium. Unfractionated lysates were tested for 4 proteins with higher spectral counts in tumor versus normal tonsil epithelium in the shotgun data set (KRT1, KRT17, FABP5, and FTL) and 2 proteins with lower spectral counts in tumor versus normal tonsil epithelium (GBP6 and SPRR3). Results from these proteins confirmed the original findings in the shotgun proteomic data obtained from pooled samples. Indicated p-values were calculated using Student’s t test.