Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results.     

A partial set covering model for protein mixture identification using mass spectrometry data

Protein identification is a key and essential step in mass spectrometry (MS) based proteome research. To date, there are many protein identification strategies that employ either MS data or MS/MS data for database searching. While MS-based methods provide wider coverage than MS/MS-based methods, their identification accuracy is lower since MS data have less information than MS/MS data. Thus, it is desired to design more sophisticated algorithms that achieve higher identification accuracy using MS data. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from MS data for many years. In this paper, we extend this technology to protein mixture identification. First, we formulate the problem of protein mixture identification as a Partial Set Covering (PSC) problem. Then, we present several algorithms that can solve the PSC problem efficiently. Finally, we extend the partial set covering model to both MS/MS data and the combination of MS data and MS/MS data. The experimental results on simulated data and real data demonstrate the advantages of our method: 1) it outperforms previous MS-based approaches significantly; 2) it is useful in the MS/MS-based protein inference; and 3) it combines MS data and MS/MS data in a unified model such that the identification performance is further improved.     

Toward better annotation in plant metabolomics: isolation and structure elucidation of 36 specialized metabolites from Oryza sativa (rice) by using MS/MS and NMR analyses

Metabolomics plays an important role in phytochemical genomics and crop breeding; however, metabolite annotation is a significant bottleneck in metabolomic studies. In particular, in liquid chromatography–mass spectrometry (MS)-based metabolomics, which has become a routine technology for the profiling of plant-specialized metabolites, a substantial number of metabolites detected as MS peaks are still not assigned properly to a single metabolite. Oryza sativa (rice) is one of the most important staple crops in the world. In the present study, we isolated and elucidated the structures of specialized metabolites from rice by using MS/MS and NMR. Thirty-six compounds, including five new flavonoids and eight rare flavonolignan isomers, were isolated from the rice leaves. The MS/MS spectral data of the isolated compounds, with a detailed interpretation of MS fragmentation data, will facilitate metabolite annotation of the related phytochemicals by enriching the public mass spectral data depositories, including the plant-specific MS/MS-based database, ReSpect.     

RIBAR and xRIBAR: Methods for reproducible relative MS/MS-based label-free protein quantification

Mass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new methods (called RIBAR and xRIBAR) that use the available MS/MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones.     

Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides

Shotgun proteomics via mass spectrometry (MS) is a powerful technology for biomarker discovery that has the potential to lead to noninvasive disease screening mechanisms. Successful application of MS-based proteomics technologies for biomarker discovery requires accurate expectations of bias, reproducibility, variance, and the true detectable differences in platforms chosen for analyses. Characterization of the variability inherent in MS assays is vital and should affect interpretation of measurements of observed differences in biological samples. Here we describe observed biases, variance structure, and the ability to detect known differences in spike-in data sets for which true relative abundance among defined samples were known and were subsequently measured with the iTRAQ technology on two MS platforms. Global biases were observed within these data sets. Measured variability was a function of mean abundance. Fold changes were biased toward the null and variance of a fold change was a function of protein mass and abundance. The information presented herein will be valuable for experimental design and analysis of the resulting data.     

Ubiquitinomics: History,methods, and applications in basic research and drug discovery

The ubiquitin-proteasome system (UPS) was discovered about 40 years ago and is known to regulate a multitude of cellular processes including protein homeostasis. Ubiquitylated proteins are recognized by downstream effectors, resulting in alterations of protein abundance, activity, or localization. Not surprisingly, the ubiquitylation machinery is dysregulated in numerous diseases, including cancers and neurodegeneration. Mass spectrometry (MS)-based proteomics has emerged as a transformative technology for characterizing protein ubiquitylation in an unbiased fashion. Here, we provide an overview of the different MS-based approaches for studying protein ubiquitylation. We review various methods for enriching and quantifying ubiquitin modifications at the peptide or protein level, outline MS acquisition, and data processing approaches and discuss key challenges. Finally, we examine how MS-based ubiquitinomics can aid both basic biology and drug discovery research.     

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.     

Recent progress in mass spectrometry proteomics for biomedical research

Proteins are the key players in many cellular processes. Their composition, trafficking, and interactions underlie the dynamic processes of life. Furthermore, diseases are frequently accompanied by malfunction of proteins at multiple levels. Understanding how biological processes are regulated at the protein level is critically important to understanding the molecular basis for diseases and often shed light on disease prevention, diagnosis, and treatment. With rapid advances in mass spectrometry (MS) instruments and experimental methodologies, MS-based proteomics has become a reliable and essential tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed great expansion of knowledge of human diseases with the application of MS-based proteomic technologies, which has led to many exciting discoveries. Herein we review the recent progress in MS-based proteomics in biomedical research, including that in establishing disease-related proteomes and interactomes. We also discuss how this progress will benefit biomedical research and clinical diagnosis and treatment of disease.     

Cross-sample validation provides enhanced proteome coverage in rat vocal fold mucosa

The vocal fold mucosa is a biomechanically unique tissue comprised of a densely cellular epithelium, superficial to an extracellular matrix (ECM)-rich lamina propria. Such ECM-rich tissues are challenging to analyze using proteomic assays, primarily due to extensive crosslinking and glycosylation of the majority of high M(r) ECM proteins. In this study, we implemented an LC-MS/MS-based strategy to characterize the rat vocal fold mucosa proteome. Our sample preparation protocol successfully solubilized both proteins and certain high M(r) glycoconjugates and resulted in the identification of hundreds of mucosal proteins. A straightforward approach to the treatment of protein identifications attributed to single peptide hits allowed the retention of potentially important low abundance identifications (validated by a cross-sample match and de novo interpretation of relevant spectra) while still eliminating potentially spurious identifications (global single peptide hits with no cross-sample match). The resulting vocal fold mucosa proteome was characterized by a wide range of cellular and extracellular proteins spanning 12 functional categories.     

Going forward: Increasing the accessibility of imaging mass spectrometry

The driving force behind the high and increasing popularity of imaging mass spectrometry is its demonstrated potential for the determination of new diagnostic/prognostic biomarkers and its ability to simultaneously trace the distributions of pharmaceuticals and their metabolites in tissues without the need to develop expensive radioactively-labeled analogues. Both of these applications would benefit from standardized methods, for the development of novel MS-based molecular histology tests and governmental-approved MS-based assays for pharmaceutical development. In addition, the broader scientific community would benefit from the increased accessibility of the technique. Currently imaging MS studies are individual endeavors, utilizing the individual expertise and infrastructure of a single laboratory and their immediate collaborators. A wide array of tissue preparation, data acquisition and data analysis techniques has been developed but lacks an international collaborative structure and data sharing capabilities. Such a collaborative framework would enable methodological exchange and detailed comparisons of analytical capabilities, to explore synergies between the different methods and result in the development of robust standardized methods. Here we describe the activities of a new European imaging MS network that will explicitly compare and contrast existing methods to provide best practice guidelines for the entire healthcare research community.     

Web Resources for Mass Spectrometry-based Proteomics

With the development of high-resolution and high-throughput mass spectrometry(MS)technology, a large quantum of proteomic data is continually being generated. Collecting and sharing these data are a challenge that requires immense and sustained human effort. In this report, we provide a classification of important web resources for MS-based proteomics and present rating of these web resources, based on whether raw data are stored, whether data submission is supported,and whether data analysis pipelines are provided. These web resources are important for biologists involved in proteomics research.     

Mass spectrometric analysis of formalin-fixed paraffin-embedded tissue: unlocking the proteome within

The predominance of tissues stored worldwide in hospitals and clinical laboratories exist in formalin-fixed paraffin-embedded (FFPE) blocks that are generated by simple and well-established protocols. Although generation of FFPE tissues has facilitated their characterization by such techniques as histopathology, they have proven refractory to biomarker discovery investigations using state-of-the-art MS-based proteomic methodologies. Very recently new methods have been developed that enable proteins extracted from FFPE tissues to be analyzed by MS. This review will highlight and discuss those efforts that have led to this exciting recent progress. Although these developments are quite new, the ability to conduct MS-based proteomic analyses of FFPE tissues opens heretofore intractable clinical samples for discovery-based biomarker research.     

Science, marketing and wishful thinking in quantitative proteomics

In a recent editorial (J. Proteome Res. 2007, 6, 1633) and elsewhere questions have been raised regarding the lack of attention paid to good analytical practice with respect to the reporting of quantitative results in proteomics. Using those comments as a starting point, several issues are discussed that relate to the challenges involved in achieving adequate sampling with MS-based methods in order to generate valid data for large-scale studies. The discussion touches on the relationships that connect sampling depth and the power to detect protein abundance change, conflict of interest, and strategies to overcome bureaucratic obstacles that impede the use of peer-to-peer technologies for transfer and storage of large data files generated in such experiments.     

Recent advances in mass spectrometry analysis of histone post-translational modifications: potential clinical impact of the PAT-H-MS approach

Histone post-translational modifications (hPTMs) contribute to the regulation of gene expression and increasing evidence links them to the development of various pathologies, highlighting their potential as biomarkers for prognostic, diagnostic and therapeutic applications. Mass spectrometry (MS) has emerged as a powerful analytical tool for hPTM analysis, which has also been applied to the analysis of epigenetic aberrations in diseases. However, the potential offered by the MS-based hPTM analysis of clinical samples for epigenetic biomarker discovery has been left largely unexploited. This article summarizes the contribution of MS-based approaches to clinical epigenetics, with a special focus on the PAThology tissue analysis of Histones by Mass Spectrometry (PAT-H-MS) approach – which represents the first application of MS-based hPTM analysis to formalin-fixed paraffin-embedded clinical samples – discussing its strengths and limitations, as well as possible implementations.     

Single cell metabolomics

Recent discoveries suggest that cells of a clonal population often display multiple metabolic phenotypes at the same time. Motivated by the success of mass spectrometry (MS) in the investigation of population-level metabolomics, the analytical community has initiated efforts towards MS-based single cell metabolomics to investigate metabolic phenomena that are buried under the population average. Here, we review the current approaches and illustrate their advantages and disadvantages. Because of significant advances in the field, different technologies are now at the verge of generating data that are useful for exploring and investigating metabolic heterogeneity.     

Metabolomics- and proteomics-assisted genome annotation and analysis of the draft metabolic network of Chlamydomonas reinhardtii

We present an integrated analysis of the molecular repertoire of Chlamydomonas reinhardtii under reference conditions. Bioinformatics annotation methods combined with GCxGC/MS-based metabolomics and LC/MS-based shotgun proteomics profiling technologies have been applied to characterize abundant proteins and metabolites, resulting in the detection of 1069 proteins and 159 metabolites. Of the measured proteins, 204 currently do not have EST sequence support; thus a significant portion of the proteomics-detected proteins provide evidence for the validity of in silico gene models. Furthermore, the generated peptide data lend support to the validity of a number of proteins currently in the proposed model stage. By integrating genomic annotation information with experimentally identified metabolites and proteins, we constructed a draft metabolic network for Chlamydomonas. Computational metabolic modeling allowed an identification of missing enzymatic links. Some experimentally detected metabolites are not producible by the currently known and annotated enzyme set, thus suggesting entry points for further targeted gene discovery or biochemical pathway research. All data sets are made available as supplementary material as well as web-accessible databases and within the functional context via the Chlamydomonas-adapted MapMan annotation platform. Information of identified peptides is also available directly via the JGI-Chlamydomonas genomic resource database (http://genome.jgi-psf.org/Chlre3/Chlre3.home.html).     

Unbiased identification of cysteine S-nitrosylation sites on proteins

Covalent addition of nitric oxide (NO) to Cys-sulfur in proteins, or S-nitrosylation, plays pervasive roles in the physiological and pathophysiological modulation of mammalian protein functions. Knowledge of the specific protein Cys residues that undergo NO addition in different biological settings is fundamental to understanding NO-mediated signal transduction. Here, we describe in detail an MS-based proteomic protocol for facile, high-throughput and unbiased discovery of SNO-Cys residues in proteins from complex biological samples. The approach, termed SNOSID (SNO-Cys site identification), can be used to identify endogenous and chemically induced S-nitrosylation sites in proteins from tissues or cells. Identified SNO-Cys sites may provide insights into novel mechanisms and proteins that mediate NO bioactivities in health and disease. SNOSID builds on the biotin-switch method for covalent addition of disulfide-linked biotin at S-nitrosylation sites on proteins. Biotinylated proteins are then subjected to trypsinolysis and the resulting biotin-tagged peptides are affinity-captured on streptavidin-agarose. After selective elution with beta-mercaptoethanol, the peptides are sequenced using nanoflow liquid chromatography tandem mass spectrometry (nLC-MS/MS). Validation that identified peptide ions as originating from authentic NO-Cys-containing precursor proteins can be provided by establishing that these peptide ions are absent from control samples where S-NO bonds were subjected to prior photolysis, using a UV transilluminator. The protocol requires approximately 2 days for sample processing, including the incubation time for proteolysis. An additional 1-2 days is needed for sample analysis by nLC-MS/MS and data analysis/interpretation.     

Global mapping of post-translational modifications on histone H3 variants in mouse testes

Mass spectrometry (MS)-based characterization is important in proteomic research for verification of structural features and functional understanding of gene expression. Post-translational modifications (PTMs) such as methylation and acetylation have been reported to be associated with chromatin remodeling during spermatogenesis. Although antibody- and MS-based approaches have been applied for characterization of PTMs on H3 variants during spermatogenesis, variant-specific PTMs are still underexplored. We identified several lysine modifications in H3 variants, including testis-specific histone H3 (H3t), through their successful separation with MS-based strategy, based on differences in masses, retention times, and presence of immonium ions. Besides methylation and acetylation, we detected formylation as a novel PTM on H3 variants in mouse testes. These patterns were also observed in H3t. Our data provide high-throughput structural information about PTMs on H3 variants in mouse testes and show possible applications of this strategy in future proteomic studies on histone PTMs.     

MetaQuant: a tool for the automatic quantification of GC/MS-based metabolome data

MetaQuant is a Java-based program for the automatic and accurate quantification of GC/MS-based metabolome data. In contrast to other programs MetaQuant is able to quantify hundreds of substances simultaneously with minimal manual intervention. The integration of a self-acting calibration function allows the parallel and fast calibration for several metabolites simultaneously. Finally, MetaQuant is able to import GC/MS data in the common NetCDF format and to export the results of the quantification into Systems Biology Markup Language (SBML), Comma Separated Values (CSV) or Microsoft Excel (XLS) format. AVAILABILITY: MetaQuant is written in Java and is available under an open source license. Precompiled packages for the installation on Windows or Linux operating systems are freely available for download. The source code as well as the installation packages are available at http://bioinformatics.org/metaquant