General Statistical Framework for Quantitative Proteomics by Stable Isotope Labeling

Abstract

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.

A Novel Systems-Biology Algorithm for the Analysis of Coordinated Protein Responses Using Quantitative Proteomics

“…the elements at the spectrum level) are not defined; these weights must be calculated in advance using other algorithms. In this work, we estimated the variances of the elements at the spectrum level using the method described in a previous work (21).…”

“…Sample 18 O or iTRAQ and the resulting data integrated as described (21). Trypsin digestion of all protein extracts was performed using a robust protocol described previously (31).…”

“…Quantitative information was extracted from MS spectra, for 18 O samples, or MS/MS spectra, for iTRAQ samples, using an in-house developed program (QuiXoT), as described (21), and protein abundance changes were analyzed using the Generic Integration Algorithm, as described above. Calculation of statistical weights of each quantitation at the spectrum level was performed according to the WSPP model (21). The validity of the null hypothesis at each one of the levels (spectrum, peptide, protein, and functional category) was carefully checked by plotting the cumulative distributions, as described (21).…”

“…Calculation of statistical weights of each quantitation at the spectrum level was performed according to the WSPP model (21). The validity of the null hypothesis at each one of the levels (spectrum, peptide, protein, and functional category) was carefully checked by plotting the cumulative distributions, as described (21). iBAQ parameter for all quantified proteins was calculated as in (36).…”

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues

“…Quantitative proteomic technologies have shown great potential in delineating dysregulated proteomes in diseases such as cancer (1)(2)(3)(4). Quantitative schemes via either stable isotope labeling or label-free quantitation (LFQ) 1 are used widely to assist MS for quantitative assessments of the changes in protein expression, post-translational modifications (5), and protein-protein interactions (6) in many biological systems, including tumor samples (7)(8)(9)(10)(11). However, the integration of accuracy, sensitivity, and totality in the analysis of tumor-specific proteoforms from individual patients still remains challenging with the current quantitative platforms.…”