The Multi-Q web server for multiplexed protein quantitation

Methods and Algorithms for Quantitative Proteomics by Mass Spectrometry

Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.

Methods and algorithms for quantitative proteomics by mass spectrometry

Protein quantitation by mass spectrometry (MS) is attractive since it is possible to obtain both identification and quantitative values of proteins and their posttranslational modifications in a single experiment. In contrast, protein arrays only provide quantitative values of targeted proteins and their modifications. There are an overwhelming number of quantitative MS methods for protein and peptide quantitation. The aim here is to provide an overview of the most common MS methods and algorithms used in quantitative proteomics and discuss the computational algorithms needed to reliably quantitate proteins, peptides, and their posttranslational modifications. One of the main challenges in data analysis of many experimental projects is to pipe together a number of software solutions that are either commercial or freely available. The aim of this chapter is to provide a good set of algorithms, ideas, and resources that can easily be implemented in scripting language like R, Python, or Perl. By understanding the algorithmic ideas presented here, data from any instrument or modified experimental protocol can be analyzed and is therefore in the authors' opinion more valuable than a black box concept.

The application of quantification techniques in proteomics for biomedical research

The systematic analysis of biological processes requires an understanding of the quantitative expression patterns of proteins, their interacting partners and their subcellular localization. This information was formerly difficult to accrue as the relative quantification of proteins relied on antibody-based methods and other approaches with low throughput. The advent of soft ionization techniques in mass spectrometry plus advances in separation technologies has aligned protein systems biology with messenger RNA, DNA, and microarray technologies to provide data on systems as opposed to singular protein entities. Another aspect of quantitative proteomics that increases its importance for the coming few years is the significant technical developments underway both for high pressure liquid chromatography and mass spectrum devices. Hence, robustness, reproducibility and mass accuracy are still improving with every new generation of instruments. Nonetheless, the methods employed require validation and comparison to design fit for purpose experiments in advanced protein analyses. This review considers the newly developed systematic protein investigation methods and their value from the standpoint that relative or absolute protein quantification is required de rigueur in biomedical research.

A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis

New methods for performing quantitative proteome analyses based on differential labeling protocols or label-free techniques are reported in the literature on an almost monthly basis. In parallel, a correspondingly vast number of software tools for the analysis of quantitative proteomics data has also been described in the literature and produced by private companies. In this article we focus on the review of some of the most popular techniques in the field and present a critical appraisal of several software packages available to process and analyze the data produced. We also describe the importance of community standards to support the wide range of software, which may assist researchers in the analysis of data using different platforms and protocols. It is intended that this review will serve bench scientists both as a useful reference and a guide to the selection and use of different pipelines to perform quantitative proteomics data analysis. We have produced a web-based tool ( http://www.proteosuite.org/?q=other_resources ) to help researchers find appropriate software for their local instrumentation, available file formats, and quantitative methodology.

PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios

Background

Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins.

Results

We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates.

Conclusions

Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.

Phosphoproteomics using iTRAQ

The identification of phosphorylation on proteins has become practicable for many laboratories in recent years, largely due to improvements in mass spectrometry (MS) and the development of methods to selectively enrich for phosphorylated peptides and proteins. However, phosphorylation is a dynamic and reversible modification which plays a central role in many biological processes including intracellular signalling. Therefore, the quantitative analysis of phosphorylated proteins and peptides is a subject of intense interest. We discuss three applications of isobaric tags for relative and absolute quantitation (iTRAQ) to the analysis of phosphopeptides from a variety of sample materials.

Strategies for quantitation of phosphoproteomic data

Recent developments in phosphoproteomic sample-preparation techniques and sensitive mass spectrometry instrumentation have led to large-scale identifications of phosphoproteins and phosphorylation sites from highly complex samples. This has facilitated the implementation of different quantitation strategies in order to study the biological role of protein phosphorylation during disease progression, differentiation or during external stimulation of a cellular system. In this article, a brief summary of the most popular strategies for phosphoproteomic studies is given; however, the main focus will be on different quantitation strategies. Methods for metabolic labeling, chemical modification and label-free quantitation and their applicability or inapplicability in phosphoproteomic studies are discussed.

Methods and algorithms for relative quantitative proteomics by mass spectrometry

Protein quantitation by mass spectrometry (MS) is attractive since it is possible to obtain both the identification and quantitative values of novel proteins and their posttranslational modifications in one experiment. In contrast, protein arrays only provide quantitative values of targeted proteins and their modifications. There are an overwhelming number of quantitative mass spectrometry (MS) methods for protein and peptide quantitation. The aim here is to provide an overview of the most common MS-based quantitative methods used in the proteomics field and discuss the computational algorithms needed for the robust quantitation of proteins, peptides, and their posttranslational modifications.

Overview of quantitative LC-MS techniques for proteomics and activitomics

LC-MS is a useful technique for protein and peptide quantification. In addition, as a powerful tool for systems biology research, LC-MS can also be used to quantify post-translational modifications and metabolites that reflect biochemical pathway activity. This review discusses the different analytical techniques that use LC-MS for the quantification of proteins, their modifications and activities in a multiplex manner.

iQuantitator: a tool for protein expression inference using iTRAQ

BACKGROUND

Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions.

RESULTS

This modeling approach provides a unified analysis of data from multiple iTRAQ experiments and links the observed quantity (reporter ion peak area) to the experiment design and the calculated quantity of interest (treatment-dependent protein and peptide fold change) through an additive model under log transformation. Others have demonstrated, through a case study, this modeling approach and noted the computational challenges of parameter inference in the unbalanced data set typical of multiple iTRAQ experiments. Here we present the development of an inference approach, based on hierarchical regression with batching of regression coefficients and Markov Chain Monte Carlo (MCMC) methods that overcomes some of these challenges. In addition to our discussion of the underlying method, we also present our implementation of the software, simulation results, experimental results, and sample output from the resulting analysis report.

CONCLUSION

iQuantitator's process-based modeling approach overcomes limitations in current methods and allows for application in a variety of experimental designs. Additionally, hypertext-linked documents produced by the tool aid in the interpretation and exploration of results.

Cross-talk between GlcNAcylation and phosphorylation: site-specific phosphorylation dynamics in response to globally elevated O-GlcNAc

Protein GlcNAcylation serves as a nutrient/stress sensor to modulate the functions of many nuclear and cytoplasmic proteins. O-GlcNAc cycles on serine or threonine residues like phosphorylation, is nearly as abundant, and functions, at least partially, via its interplay with phosphorylation. Here, we describe changes in site-specific phosphorylation dynamics in response to globally elevated GlcNAcylation. By combining sequential phospho-residue enrichment, iTRAQ labeling, and high throughput mass spectrometric analyses, phosphorylation dynamics on 711 phosphopeptides were quantified. Based upon their insensitivity to phosphatase inhibition, we conclude that approximately 48% of these phosphorylation sites were not actively cycling in the conditions of the study. However, increased GlcNAcylation influenced phosphate stoichiometry at most of the sites that did appear to be actively cycling. Elevated GlcNAcylation resulted in lower phosphorylation at 280 sites and caused increased phosphorylation at 148 sites. Thus, the cross-talk or interplay between these two abundant posttranslational modifications is extensive, and may arises both by steric competition for occupancy at the same or proximal sites and by each modification regulating the other's enzymatic machinery. The phosphoproteome dynamics presented by this large set of quantitative data not only delineates the complex interplay between phosphorylation and GlcNAcyation, but also provides insights for more focused investigations of specific roles of O-GlcNAc in regulating protein functions and signaling pathways.