Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Total variance should drive data handling strategies in third generation proteomic studies

Quantitative proteomics is entering its "third generation" where intricate experimental designs aim to increase the spatial and temporal resolution of protein changes. This paper re-analyses multiple internally consistent proteomic datasets generated from whole cell homogenates and fractionated brain tissue samples providing a unique opportunity to explore the different factors influencing experimental outcomes. The results clearly indicate that improvements in data handling are required to compensate for the increased mean coefficient of variance associated with complex study design and intricate upstream tissue processing. Furthermore, applying arbitrary inclusion thresholds such as fold change in protein abundance between groups can lead to unnecessary exclusion of important and biologically relevant data. This article is protected by copyright. All rights reserved

Partitioning the Proteome: Phase Separation for Targeted Analysis of Membrane Proteins in Human Post-Mortem Brain

Neuroproteomics is a powerful platform for targeted and hypothesis driven research, providing comprehensive insights into cellular and sub-cellular disease states, Gene × Environmental effects, and cellular response to medication effects in human, animal, and cell culture models. Analysis of sub-proteomes is becoming increasingly important in clinical proteomics, enriching for otherwise undetectable proteins that are possible markers for disease. Membrane proteins are one such sub-proteome class that merit in-depth targeted analysis, particularly in psychiatric disorders. As membrane proteins are notoriously difficult to analyse using traditional proteomics methods, we evaluate a paradigm to enrich for and study membrane proteins from human post-mortem brain tissue. This is the first study to extensively characterise the integral trans-membrane spanning proteins present in human brain. Using Triton X-114 phase separation and LC-MS/MS analysis, we enriched for and identified 494 membrane proteins, with 194 trans-membrane helices present, ranging from 1 to 21 helices per protein. Isolated proteins included glutamate receptors, G proteins, voltage gated and calcium channels, synaptic proteins, and myelin proteins, all of which warrant quantitative proteomic investigation in psychiatric and neurological disorders. Overall, our sub-proteome analysis reduced sample complexity and enriched for integral membrane proteins by 2.3 fold, thus allowing for more manageable, reproducible, and targeted proteomics in case vs. control biomarker studies. This study provides a valuable reference for future neuroproteomic investigations of membrane proteins, and validates the use Triton X-114 detergent phase extraction on human post mortem brain

Adjusted Confidence Intervals for the Expression Change of Proteins observed in 2-Dimensional Difference Gel Electrophoresis

Differential proteome analyses focus on the detection and quantification of expression changes between samples from different biological groups. While the significance of an expression change is detected by some statistical test, the strength of an expression change is usually quantified by some ratio estimate, e.g. the ‘fold change’. Due to its quantitative character, the fold change is more intuitively for biologists than the decision of a statistical test. However, strong expression changes are often misleading if this change is not significant. For this reason, we propose the employment of confidence intervals, adjusted for multiple hypotheses testing, which naturally comprise both, test decision and quantification. The adjusted confidence intervals can be used for making test decisions under the control of error rates typically considered in multiple hypotheses testing (e.g. the familywise error rate or the false discovery rate). For biologists, test decisions based on adjusted confidence intervals offer a more intuitive method for selecting proteins with a significant expression change between two groups. The length of the intervals can be used for sample size planning of upcoming experiments. Our approach is primarily addressed to protein expression data recorded by two-dimensional Difference Gel Electrophoresis