Virtual Expert Mass Spectrometrist: iTRAQ tool for database-dependent search, quantitation and result storage

Computational Methods in Mass Spectrometry-Based Proteomics

This chapter introduces computational methods used in mass spectrometry-based proteomics, including those for addressing the critical problems such as peptide identification and protein inference, peptide and protein quantification, characterization of posttranslational modifications (PTMs), and data-independent acquisitions (DIA). The chapter concludes with emerging applications of proteomic techniques, such as metaproteomics, glycoproteomics, and proteogenomics.

Dissecting the iTRAQ Data Analysis

In the era of large-scale quantitative biology, mass spectrometry-based quantitative proteomics is progressively becoming indispensable for gaining insights into the biological systems at molecular level. Various quantitative study designs rely on chemical tagging approaches to study disease, stress, or drug response and temporal studies aiming at disease/developmental progression in a biological system. Isobaric tags for relative and absolute quantitation (iTRAQ) is one of the most popular chemical labeling techniques which allows four, six, or eight samples to be multiplexed in a single run. As the iTRAQ tag has a balancer group to equalize all states of a labeled peptide to same mass, the differentially labeled iTRAQ peptides are mixed before chromatography and elute as a single combined peak in MS. This enhances the peptide signal and quantitation is performed during MS/MS along with sequencing, where reporter ions of different masses are released to give relative quantitation. Known amount of a spiked-in protein can also help in absolute quantitation of the proteins in a sample.

From pathways to networks: connecting dots by establishing protein-protein interaction networks in signaling pathways using affinity purification and mass spectrometry

Signal transductions are the basis of biological activities in all living organisms. Studying the signaling pathways, especially under physiological conditions, has become one of the most important facets of modern biological research. During the last decade, MS has been used extensively in biological research and is proven to be effective in addressing important biological questions. Here, we review the current progress in the understanding of signaling networks using MS approaches. We will focus on studies of protein-protein interactions that use affinity purification followed by MS approach. We discuss obstacles to affinity purification, data processing, functional validation, and identification of transient interactions and provide potential solutions for pathway-specific proteomics analysis, which we hope one day will lead to a comprehensive understanding of signaling networks in humans.

General statistical framework for quantitative proteomics by stable isotope labeling

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 (18)O, 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.

Quantitative proteomic analysis of human lung tumor xenografts treated with the ectopic ATP synthase inhibitor citreoviridin

ATP synthase is present on the plasma membrane of several types of cancer cells. Citreoviridin, an ATP synthase inhibitor, selectively suppresses the proliferation and growth of lung cancer without affecting normal cells. However, the global effects of targeting ectopic ATP synthase in vivo have not been well defined. In this study, we performed quantitative proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) and provided a comprehensive insight into the complicated regulation by citreoviridin in a lung cancer xenograft model. With high reproducibility of the quantitation, we obtained quantitative proteomic profiling with 2,659 proteins identified. Bioinformatics analysis of the 141 differentially expressed proteins selected by their relative abundance revealed that citreoviridin induces alterations in the expression of glucose metabolism-related enzymes in lung cancer. The up-regulation of enzymes involved in gluconeogenesis and storage of glucose indicated that citreoviridin may reduce the glycolytic intermediates for macromolecule synthesis and inhibit cell proliferation. Using comprehensive proteomics, the results identify metabolic aspects that help explain the antitumorigenic effect of citreoviridin in lung cancer, which may lead to a better understanding of the links between metabolism and tumorigenesis in cancer therapy.

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.

LC-MS spectra processing

Peak extraction from raw data is the first step in LC-MS data analysis. The quality of this procedure is important since it affects the quality and accuracy of all subsequent analysis such as database searches and peak quantitation. The most important and most accurately measured physical entity provided by mass spectrometers is m/z values which need to be extracted by state of art algorithms and scrutinized thoroughly. The aim of this chapter is to provide a discussion of peak processing methods and furthermore discuss some of the yet unresolved or neglected issues. A few novel concepts are also proposed for analysis and visualization. The final section of this chapter provides a note on possible software for spectra processing.

Algorithms for database-dependent search of MS/MS data

The frequent used bottom-up strategy for identification of proteins and their associated modifications generate nowadays typically thousands of MS/MS spectra that normally are matched automatically against a protein sequence database. Search engines that take as input MS/MS spectra and a protein sequence database are referred as database-dependent search engines. Many programs both commercial and freely available exist for database-dependent search of MS/MS spectra and most of the programs have excellent user documentation. The aim here is therefore to outline the algorithm strategy behind different search engines rather than providing software user manuals. The process of database-dependent search can be divided into search strategy, peptide scoring, protein scoring, and finally protein inference. Most efforts in the literature have been put in to comparing results from different software rather than discussing the underlining algorithms. Such practical comparisons can be cluttered by suboptimal implementation and the observed differences are frequently caused by software parameters settings which have not been set proper to allow even comparison. In other words an algorithmic idea can still be worth considering even if the software implementation has been demonstrated to be suboptimal. The aim in this chapter is therefore to split the algorithms for database-dependent searching of MS/MS data into the above steps so that the different algorithmic ideas become more transparent and comparable. Most search engines provide good implementations of the first three data analysis steps mentioned above, whereas the final step of protein inference are much less developed for most search engines and is in many cases performed by an external software. The final part of this chapter illustrates how protein inference is built into the VEMS search engine and discusses a stand-alone program SIR for protein inference that can import a Mascot search result.

Find pairs: the module for protein quantification of the PeakQuant software suite

Accurate quantification of proteins is one of the major tasks in current proteomics research. To address this issue, a wide range of stable isotope labeling techniques have been developed, allowing one to quantitatively study thousands of proteins by means of mass spectrometry. In this article, the FindPairs module of the PeakQuant software suite is detailed. It facilitates the automatic determination of protein abundance ratios based on the automated analysis of stable isotope-coded mass spectrometric data. Furthermore, it implements statistical methods to determine outliers due to biological as well as technical variance of proteome data obtained in replicate experiments. This provides an important means to evaluate the significance in obtained protein expression data. For demonstrating the high applicability of FindPairs, we focused on the quantitative analysis of proteome data acquired in (14)N/(15)N labeling experiments. We further provide a comprehensive overview of the features of the FindPairs software, and compare these with existing quantification packages. The software presented here supports a wide range of proteomics applications, allowing one to quantitatively assess data derived from different stable isotope labeling approaches, such as (14)N/(15)N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.

Diffprot - software for non-parametric statistical analysis of differential proteomics data

Mass spectrometry-based global proteomics experiments generate large sets of data that can be converted into useful information only with an appropriate statistical approach. We present Diffprot - a software tool for statistical analysis of MS-derived quantitative data. With implemented resampling-based statistical test and local variance estimate, Diffprot allows to draw significant results from small scale experiments and effectively eliminates false positive results. To demonstrate the advantages of this software, we performed two spike-in tests with complex biological matrices, one label-free and one based on iTRAQ quantification; in addition, we performed an iTRAQ experiment on bacterial samples. In the spike-in tests, protein ratios were estimated and were in good agreement with theoretical values; statistical significance was assigned to spiked proteins and single or no false positive results were obtained with Diffprot. We compared the performance of Diffprot with other statistical tests - widely used t-test and non-parametric Wilcoxon test. In contrast to Diffprot, both generated many false positive hits in the spike-in experiment. This proved the superiority of the resampling-based method in terms of specificity, making Diffprot a rational choice for small scale high-throughput experiments, when the need to control the false positive rate is particularly pressing.

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.

Software tools for MS-based quantitative proteomics: a brief overview

Proteomics is turning more and more towards quantitative measurements of biological systems. This in turn has spurred the development of numerous experimental methods that enable such measurements. Vast quantities of mostly mass spectrometric data are often generated as a result which requires the use of software tools that turns raw data into useful quantitative information from which knowledge about the biological system can eventually be derived. This chapter gives a brief overview of available software tools for mass spectrometry based quantitative proteomics.

General statistical modeling of data from protein relative expression isobaric tags

Quantitative comparison of the protein content of biological samples is a fundamental tool of research. The TMT and iTRAQ isobaric labeling technologies allow the comparison of 2, 4, 6, or 8 samples in one mass spectrometric analysis. Sound statistical models that scale with the most advanced mass spectrometry (MS) instruments are essential for their efficient use. Through the application of robust statistical methods, we developed models that capture variability from individual spectra to biological samples. Classical experimental designs with a distinct sample in each channel as well as the use of replicates in multiple channels are integrated into a single statistical framework. We have prepared complex test samples including controlled ratios ranging from 100:1 to 1:100 to characterize the performance of our method. We demonstrate its application to actual biological data sets originating from three different laboratories and MS platforms. Finally, test data and an R package, named isobar, which can read Mascot, Phenyx, and mzIdentML files, are made available. The isobar package can also be used as an independent software that requires very little or no R programming skills.

COMPASS: a suite of pre- and post-search proteomics software tools for OMSSA

Here we present the Coon OMSSA Proteomic Analysis Software Suite (COMPASS): a free and open-source software pipeline for high-throughput analysis of proteomics data, designed around the Open Mass Spectrometry Search Algorithm. We detail a synergistic set of tools for protein database generation, spectral reduction, peptide false discovery rate analysis, peptide quantitation via isobaric labeling, protein parsimony and protein false discovery rate analysis, and protein quantitation. We strive for maximum ease of use, utilizing graphical user interfaces and working with data files in the original instrument vendor format. Results are stored in plain text comma-separated value files, which are easy to view and manipulate with a text editor or spreadsheet program. We illustrate the operation and efficacy of COMPASS through the use of two LC-MS/MS data sets. The first is a data set of a highly annotated mixture of standard proteins and manually validated contaminants that exhibits the identification workflow. The second is a data set of yeast peptides, labeled with isobaric stable isotope tags and mixed in known ratios, to demonstrate the quantitative workflow. For these two data sets, COMPASS performs equivalently or better than the current de facto standard, the Trans-Proteomic Pipeline.

Discussion on common data analysis strategies used in MS-based proteomics

Current proteomics technology is limited in resolving the proteome complexity of biological systems. The main issue at stake is to increase throughput and spectra quality so that spatiotemporal dimensions, population parameters and the complexity of protein modifications on a quantitative scale can be considered. MS-based proteomics and protein arrays are the main players in large-scale proteome analysis and an integration of these two methodologies is powerful but presently not sufficient for detailed quantitative and spatiotemporal proteome characterization. Improvements of instrumentation for MS-based proteomics have been achieved recently resulting in data sets of approximately one million spectra which is a large step in the right direction. The corresponding raw data range from 50 to 100 Gb and are frequently made available. Multidimensional LC-MS data sets have been demonstrated to identify and quantitate 2000-8000 proteins from whole cell extracts. The analysis of the resulting data sets requires several steps from raw data processing, to database-dependent search, statistical evaluation of the search result, quantitative algorithms and statistical analysis of quantitative data. A large number of software tools have been proposed for the above-mentioned tasks. However, it is not the aim of this review to cover all software tools, but rather discuss common data analysis strategies used by various algorithms for each of the above-mentioned steps in a non-redundant approach and to argue that there are still some areas which need improvements.

IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT

Isobaric peptide labeling plays an important role in relative quantitative comparisons of proteomes. Isobaric labeling techniques utilize MS/MS spectra for relative quantification, which can be either based on the relative intensities of reporter ions in the low mass region (iTRAQ and TMT) or on the relative intensities of quantification signatures throughout the spectrum due to isobaric peptide termini labeling (IPTL). Due to the increased quantitative information found in MS/MS fragment spectra generated by the recently developed IPTL approach, new software was required to extract the quantitative information. IsobariQ was specifically developed for this purpose; however, support for the reporter ion techniques iTRAQ and TMT is also included. In addition, to address recently emphasized issues about heterogeneity of variance in proteomics data sets, IsobariQ employs the statistical software package R and variance stabilizing normalization (VSN) algorithms available therein. Finally, the functionality of IsobariQ is validated with data sets of experiments using 6-plex TMT and IPTL. Notably, protein substrates resulting from cleavage by proteases can be identified as shown for caspase targets in apoptosis.