Contemporary network proteomics and its requirements

Spectra-first feature analysis in clinical proteomics — A case study in renal cancer

In proteomics, useful signal may be unobserved or lost due to the lack of confident peptide-spectral matches. Selection of differential spectra, followed by associative peptide/protein mapping may be a complementary strategy for improving sensitivity and comprehensiveness of analysis (spectra-first paradigm). This approach is complementary to the standard approach where functional analysis is performed only on the finalized protein list assembled from identified peptides from the spectra (protein-first paradigm). Based on a case study of renal cancer, we introduce a simple spectra-binning approach, MZ-bin. We demonstrate that differential spectra feature selection using MZ-bin is class-discriminative and can trace relevant proteins via spectra associative mapping. Moreover, proteins identified in this manner are more biologically coherent than those selected directly from the finalized protein list. Analysis of constituent peptides per protein reveals high expression inconsistency, suggesting that the measured protein expressions are in fact, poor approximations of true protein levels. Moreover, analysis at the level of constituent peptides may provide higher resolution insight into the underlying biology: Via MZ-bin, we identified for the first time differential splice forms for the known renal cancer marker MAPT. We conclude that the spectra-first analysis paradigm is a complementary strategy to the traditional protein-first paradigm and can provide deeper level insight.

Evaluating feature-selection stability in next-generation proteomics

Identifying reproducible yet relevant features is a major challenge in biological research. This is well documented in genomics data. Using a proposed set of three reliability benchmarks, we find that this issue exists also in proteomics for commonly used feature-selection methods, e.g. [Formula: see text]-test and recursive feature elimination. Moreover, due to high test variability, selecting the top proteins based on [Formula: see text]-value ranks - even when restricted to high-abundance proteins - does not improve reproducibility. Statistical testing based on networks are believed to be more robust, but this does not always hold true: The commonly used hypergeometric enrichment that tests for enrichment of protein subnets performs abysmally due to its dependence on unstable protein pre-selection steps. We demonstrate here for the first time the utility of a novel suite of network-based algorithms called ranked-based network algorithms (RBNAs) on proteomics. These have originally been introduced and tested extensively on genomics data. We show here that they are highly stable, reproducible and select relevant features when applied to proteomics data. It is also evident from these results that use of statistical feature testing on protein expression data should be executed with due caution. Careless use of networks does not resolve poor-performance issues, and can even mislead. We recommend augmenting statistical feature-selection methods with concurrent analysis on stability and reproducibility to improve the quality of the selected features prior to experimental validation.

Quantitative proteomics signature profiling based on network contextualization

Background

We present a network-based method, namely quantitative proteomic signature profiling (qPSP) that improves the biological content of proteomic data by converting protein expressions into hit-rates in protein complexes.

Results

We demonstrate, using two clinical proteomics datasets, that qPSP produces robust discrimination between phenotype classes (e.g. normal vs. disease) and uncovers phenotype-relevant protein complexes. Regardless of acquisition paradigm, comparisons of qPSP against conventional methods (e.g. t-test or hypergeometric test) demonstrate that it produces more stable and consistent predictions, even at small sample size. We show that qPSP is theoretically robust to noise, and that this robustness to noise is also observable in practice. Comparative analysis of hit-rates and protein expressions in significant complexes reveals that hit-rates are a useful means of summarizing differential behavior in a complex-specific manner.

Conclusions

Given qPSP’s ability to discriminate phenotype classes even at small sample sizes, high robustness to noise, and better summary statistics, it can be deployed towards analysis of highly heterogeneous clinical proteomics data.

Reviewers

This article was reviewed by Frank Eisenhaber and Sebastian Maurer-Stroh.

Open peer review

Reviewed by Frank Eisenhaber and Sebastian Maurer-Stroh.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0098-x) contains supplementary material, which is available to authorized users.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.