Precision mapping of the metabolome

Metabolite and reaction inference based on enzyme specificities

Motivation: Many enzymes are not absolutely specific, or even promiscuous: they can catalyze transformations of more compounds than the traditional ones as listed in, e.g. KEGG. This information is currently only available in databases, such as the BRENDA enzyme activity database. In this article, we propose to model enzyme aspecificity by predicting whether an input compound is likely to be transformed by a certain enzyme. Such a predictor has many applications, for example, to complete reconstructed metabolic networks, to aid in metabolic engineering or to help identify unknown peaks in mass spectra.

Results: We have developed a system for metabolite and reaction inference based on enzyme specificities (MaRIboES). It employs structural and stereochemistry similarity measures and molecular fingerprints to generalize enzymatic reactions based on data available in BRENDA. Leave-one-out cross-validation shows that 80% of known reactions are predicted well. Application to the yeast glycolytic and pentose phosphate pathways predicts a large number of known and new reactions, often leading to the formation of novel compounds, as well as a number of interesting bypasses and cross-links.

Availability: Matlab and C++ code is freely available at https://gforge.nbic.nl/projects/mariboes/

Contact:d.deridder@tudelft.nl

Supplementary information:Supplementary data are available at Bioinformatics online.

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

Background

Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI).

Results

Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50%) of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data.

Conclusion

We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data.

A signal filtering method for improved quantification and noise discrimination in fourier transform ion cyclotron resonance mass spectrometry-based metabolomics data

Direct-infusion electrospray-ionization Fourier transform ion cyclotron resonance mass spectrometry (DI ESI FT-ICR MS) is increasingly being utilized in metabolomics, including the high sensitivity selected ion monitoring (SIM)-stitching approach. Accurate signal quantification and the discrimination of real signals from noise remain major challenges for this approach, with both adversely affected by factors including ion suppression during electrospray, ion-ion interactions in the detector cell, and thermally-induced white noise. This is particularly problematic for complex mixture analysis where hundreds of metabolites are present near the noise level. Here we address relative signal quantification and noise discrimination issues in SIM-stitched DI ESI FT-ICR MS-based metabolomics. Using liver tissue, we first optimized the number of scans (n) acquired per SIM window to address the balance between quantification accuracy versus acquisition time (and thus sample throughput); a minimum of n = 5 is recommended. Secondly, we characterized and computationally-corrected an effect whereby an ion's intensity is dependent upon its location within a SIM window, exhibiting a 3-fold higher intensity at the high m/z end. This resulted in significantly improved quantification accuracy. Finally, we thoroughly characterized a three-stage filter to discriminate noise from real signals, which comprised a signal-to-noise-ratio (SNR) hard threshold, then a "replicate" filter (retaining only peaks in r-out-of-3 replicate analyses), and then a "sample" filter (retaining only peaks in >s% of biological samples). We document the benefits of three-stage filtering versus one- and two-stage filters, and show the importance of selecting filter parameters that balance the confidence that a signal is real versus the total number of peaks detected.

High-resolution extracted ion chromatography, a new tool for metabolomics and lipidomics using a second-generation orbitrap mass spectrometer

Most analytical methods in metabolomics are based on one of two strategies. The first strategy is aimed at specifically analysing a limited number of known metabolites or compound classes. Alternatively, an unbiased approach can be used for profiling as many features as possible in a given metabolome without prior knowledge of the identity of these features. Using high-resolution mass spectrometry with instruments capable of measuring m/z ratios with sufficiently low mass measurement uncertainties and simultaneous high scan speeds, it is possible to combine these two strategies, allowing unbiased profiling of biological samples and targeted analysis of specific compounds at the same time without compromises. Such high mass accuracy and mass resolving power reduces the number of candidate metabolites occupying the same retention time and m/z ratio space to a minimum. In this study, we demonstrate how targeted analysis of phospholipids as well as unbiased profiling is achievable using a benchtop orbitrap instrument after high-speed reversed-phase chromatography. The ability to apply both strategies in one experiment is an important step forward in comprehensive analysis of the metabolome.

TICL--a web tool for network-based interpretation of compound lists inferred by high-throughput metabolomics

High-throughput metabolomics is a dynamically developing technology that enables the mass separation of complex mixtures at very high resolution. Metabolic profiling has begun to be widely used in clinical research to study the molecular mechanisms of complex cell disorders. Similar to transcriptomics, which is capable of detecting genes at differential states, metabolomics is able to deliver a list of compounds differentially present between explored cell physiological conditions. The bioinformatics challenge lies in a statistically valid interpretation of the functional context for identified sets of metabolites. Here, we present TICL, a web tool for the automatic interpretation of lists of compounds. The major advance of TICL is that it not only provides a model of possible compound transformations related to the input list, but also implements a robust statistical framework to estimate the significance of the inferred model. The TICL web tool is freely accessible at http://mips.helmholtz-muenchen.de/proj/cmp.

Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics

The chemical identification of mass spectrometric signals in metabolomic applications is important to provide conversion of analytical data to biological knowledge about metabolic pathways. The complexity of electrospray mass spectrometric data acquired from a range of samples (serum, urine, yeast intracellular extracts, yeast metabolic footprints, placental tissue metabolic footprints) has been investigated and has defined the frequency of different ion types routinely detected. Although some ion types were expected (protonated and deprotonated peaks, isotope peaks, multiply charged peaks) others were not expected (sodium formate adduct ions). In parallel, the Manchester Metabolomics Database (MMD) has been constructed with data from genome scale metabolic reconstructions, HMDB, KEGG, Lipid Maps, BioCyc and DrugBank to provide knowledge on 42,687 endogenous and exogenous metabolite species. The combination of accurate mass data for a large collection of metabolites, theoretical isotope abundance data and knowledge of the different ion types detected provided a greater number of electrospray mass spectrometric signals which were putatively identified and with greater confidence in the samples studied. To provide definitive identification metabolite-specific mass spectral libraries for UPLC-MS and GC-MS have been constructed for 1,065 commercially available authentic standards. The MMD data are available at http://dbkgroup.org/MMD/.

[Recent development of metabonomics and its applications in clinical research]

In the post-genomic era, systems biology is central to the biological sciences. Functional genomics such as transcriptomics and proteomics can simultaneous determine massive gene or protein expression changes following drug treatment or other intervention. However, these changes can't be coupled directly to changes in biological function. As a result, metabonomics and its many pseudonyms (metabolomics, metabolic profiling, etc.) have exploded onto the scientific scene in the past several years. Metabonomics is a rapidly growing research area and a system approach for comprehensive and quantitative analysis of the global metabolites in a biological matrix. Analytical chemistry approach is necessary for the development of comprehensive metabonomics investigations. Fundamentally, there are two types of metabonomics approaches: mass-spectrometry (MS) based and nuclear magnetic resonance (NMR) methodologies. Metabonomics measurements provide a wealth of data information and interpretation of these data relies mainly on chemometrics approaches to perform large-scale data analysis and data visualization, such as principal and independent component analysis, multidimensional scaling, a variety of clustering techniques, and discriminant function analysis, among many others. In this review, the recent development of analytical and statistical techniques used in metabonomics is summarized. Major applications of metabonomics relevant to clinical and preclinical study are then reviewed. The applications of metabonomics in study of liver diseases, cancers and other diseases have proved useful both as an experimental tool for pathogenesis mechanism re-search and ultimately a tool for diagnosis and monitoring treatment response of these diseases. Next, the applications of metabonomics in preclinical toxicology are discussed and the role that metabonomics might do in pharmaceutical research and development is explained with special reference to the aims and achievements of the Consortium for Metabonomic Toxicology (COMET), and the concept of pharmacometabonomics as a way of predicting an individual's response to treatment is highlighted. Finally, the role of metabonomics in elucidating the function of the unknown or novel enzyme is mentioned.

Exploring metallodrug-protein interactions by mass spectrometry: comparisons between platinum coordination complexes and an organometallic ruthenium compound

Electrospray ionisation mass spectrometry was used to analyse the reactions of metal compounds with mixtures of selected proteins. Three representative medicinally relevant compounds, cisplatin, transplatin and the organometallic ruthenium compound RAPTA-C, were reacted with a pool of three proteins, ubiquitin, cytochrome c and superoxide dismutase, and the reaction products were analysed using high-resolution mass spectrometry. Highly informative electrospray ionisation mass spectra were acquired following careful optimisation of the experimental conditions. The formation of metal-protein adducts was clearly observed for the three proteins. In addition, valuable information was obtained on the nature of the protein-bound metallofragments, on their distribution among the three different proteins and on the binding kinetics. The platinum compounds were less reactive and considerably less selective in protein binding than RAPTA-C, which showed a high affinity towards ubiquitin and cytochrome c, but not superoxide dismutase. In addition, competition studies between cisplatin and RAPTA-C showed that the two metallodrugs have affinities for the same amino acid residues on protein binding.

Structural characterization of in vitro rat liver microsomal metabolites of antihistamine desloratadine using LTQ-Orbitrap hybrid mass spectrometer in combination with online hydrogen/deuterium exchange HR-LC/MS

In vitro drug metabolism study is an integral part of drug discovery process. In this report, we have described the application of LTQ-Orbitrap hybrid mass spectrometer in conjunction with online hydrogen (H)/deuterium (D) exchange high resolution (HR)-LC/MS for structural characterization of in vitro rat liver microsomal metabolites of antihistamine desloratadine. Five metabolites M1--M5 have been identified, including three hydroxylated metabolites M1--M3, one N-oxide M4 and one uncommon aromatized N-oxide M5. Accurate mass data have been obtained in both full scan and MSn mode support assignments of metabolite structures with reported mass errors less than 3 ppm. Online H/D exchange HR-LC/MS experiments provide additional evidence in differentiating hydroxylated metabolites from N-oxides. This study demonstrates the effectiveness of this approach in structural characterization of drug metabolites.

Live single-cell video-mass spectrometry for cellular and subcellular molecular detection and cell classification

The molecular content from the cytoplasm of a live, single mammalian cell and its organelle were trapped with a nano-electrospray ionization (ESI) tip acting as a micropipette under a video microscope, and hundreds of small molecular peaks were detected by direct nano-ESI mass spectrometry (MS). Granule- or cytoplasm-specific peaks in a mast cell (RBL 2H3) model were extracted by paired t-test to demonstrate their specific localization. Some of the typical and specific molecules were successfully identified by MS/MS analysis. This method was also applied to the cell classification of seven types of cell lines at the single-cellular level by principal component analysis (PCA), revealing seven clusters in the multivariate score plot.

Orbitrap mass spectrometry: instrumentation, ion motion and applications

Since its introduction, the orbitrap has proven to be a robust mass analyzer that can routinely deliver high resolving power and mass accuracy. Unlike conventional ion traps such as the Paul and Penning traps, the orbitrap uses only electrostatic fields to confine and to analyze injected ion populations. In addition, its relatively low cost, simple design and high space-charge capacity make it suitable for tackling complex scientific problems in which high performance is required. This review begins with a brief account of the set of inventions that led to the orbitrap, followed by a qualitative description of ion capture, ion motion in the trap and modes of detection. Various orbitrap instruments, including the commercially available linear ion trap-orbitrap hybrid mass spectrometers, are also discussed with emphasis on the different methods used to inject ions into the trap. Figures of merit such as resolving power, mass accuracy, dynamic range and sensitivity of each type of instrument are compared. In addition, experimental techniques that allow mass-selective manipulation of the motion of confined ions and their potential application in tandem mass spectrometry in the orbitrap are described. Finally, some specific applications are reviewed to illustrate the performance and versatility of the orbitrap mass spectrometers.

Recent developments in environmental metabolomics

Metabolomics is increasingly being used to characterise the interactions of organisms with their natural environment. This article describes the latest developments in this vibrant field. The first section highlights environmental studies that are exploiting recent technological advances in metabolomics, including developments in NMR spectroscopy and mass spectrometry, with a particular focus on toxicity testing in ecological risk assessment. Subsequently, recent laboratory studies of organism function and metabolic responses to stress are reviewed, including investigations of cold, heat and anoxic stress. The importance of model organisms and systems biology within environmental metabolomics is then highlighted. Finally, the first applications of metabolomics to actual field investigations are discussed, with a particular focus on environmental monitoring. During the past year, environmental metabolomics research has been conducted on more than 20 model and non-model species, including eight freshwater and marine fish, nine species of aquatic and terrestrial invertebrates, as well as plants and microbes, demonstrating the rapid growth of this field.

MassTRIX: mass translator into pathways

Recent technical advances in mass spectrometry (MS) have brought the field of metabolomics to a point where large numbers of metabolites from numerous prokaryotic and eukaryotic organisms can now be easily and precisely detected. The challenge today lies in the correct annotation of these metabolites on the basis of their accurate measured masses. Assignment of bulk chemical formula is generally possible, but without consideration of the biological and genomic context, concrete metabolite annotations remain difficult and uncertain. MassTRIX responds to this challenge by providing a hypothesis-driven approach to high precision MS data annotation. It presents the identified chemical compounds in their genomic context as differentially colored objects on KEGG pathway maps. Information on gene transcription or differences in the gene complement (e.g. samples from different bacterial strains) can be easily added. The user can thus interpret the metabolic state of the organism in the context of its potential and, in the case of submitted transcriptomics data, real enzymatic capacities. The MassTRIX web server is freely accessible at http://masstrix.org

Metabolic profiling of serum using Ultra Performance Liquid Chromatography and the LTQ-Orbitrap mass spectrometry system

Advances in analytical instrumentation can provide significant advantages to the volume and quality of biological knowledge acquired in metabolomic investigations. The interfacing of sub-2 microm liquid chromatography (UPLC ACQUITY) and LTQ-Orbitrap mass spectrometry systems provides many theoretical advantages. The applicability of the interfaced systems was investigated using a simple 11-component metabolite mix and a complex mammalian biofluid, serum. Metabolites were detected in the metabolite mix with signals that were linear with their concentration over 2.5-3.5 orders of magnitude, with correlation coefficients greater than 0.993 and limits of detection less than 1 micromol L(-1). Reproducibility of retention time (RSD<3%) and chromatographic peak area (RSD<15%) and a high mass accuracy (<2 ppm) were observed for 14 QC serum samples interdispersed with other serum samples, analysed over a period of 40 h. The evaluation of a single deconvolution software package (XCMS) was performed and showed that two parameters (snthresh and bw) provided significant changes to the number of peaks detected and the peak area reproducibility for the dataset used. The data were used to indicate possible biomarkers of pre-eclampsia and showed both the instruments and XCMS to be applicable to the reproducible and valid detection of disease biomarkers present in serum.

Biosynthetic origin of natural products isolated from marine microorganism-invertebrate assemblages

In all probability, natural selection began as ancient marine microorganisms were required to compete for limited resources. These pressures resulted in the evolution of diverse genetically encoded small molecules with a variety of ecological and metabolic roles. Remarkably, many of these same biologically active molecules have potential utility in modern medicine and biomedical research. The most promising of these natural products often derive from organisms richly populated by associated microorganisms (e.g., marine sponges and ascidians), and often there is great uncertainty about which organism in these assemblages is making these intriguing metabolites. To use the molecular machinery responsible for the biosynthesis of potential drug-lead natural products, new tools must be applied to delineate their genetic and enzymatic origins. The aim of this perspective is to highlight both traditional and emerging techniques for the localization of metabolic pathways within complex marine environments. Examples are given from the literature as well as recent proof-of-concept experiments from the authors' laboratories.

New surveyor tools for charting microbial metabolic maps

The computational reconstruction and analysis of cellular models of microbial metabolism is one of the great success stories of systems biology. The extent and quality of metabolic network reconstructions is, however, limited by the current state of biochemical knowledge. Can experimental high-throughput data be used to improve and expand network reconstructions to include unexplored areas of metabolism? Recent advances in experimental technology and analytical methods bring this aim an important step closer to realization. Data integration will play a particularly important part in exploiting the new experimental opportunities.

Plant metabolomics and its potential application for human nutrition

With the growing interest in the use of metabolomic technologies for a wide range of biological targets, food applications related to nutrition and quality are rapidly emerging. Metabolomics offers us the opportunity to gain deeper insights into, and have better control of, the fundamental biochemical basis of the things we eat. So doing will help us to design modified breeding programmes aimed at better quality produce; optimised food processing strategies and ultimately, improved (micro)nutrient bioavailability and bioefficacy. A better understanding of the pathways responsible for the biosynthesis of nutritionally relevant metabolites is key to gaining more effective control of the absence/level of presence of such components in our food. Applications of metabolomic technologies in both applied and fundamental science strategies are therefore growing rapidly in popularity. Currently, the world has two highly contrasting nutrition-related problems--over-consumption and under-nourishment. Dramatic increases in the occurrence of overweight individuals and obesity in developed countries are in staggering contrast to the still-familiar images of extreme malnutrition in many parts of the developing world. Both problems require a modified food supply, achieved through highly contrasting routes. For each, metabolomics has a future role to play and this review shall deal with this key dichotomy and illustrate where metabolomics may have a future part to play. In this short overview, attention is given to how the various technologies have already been exploited in a plant-based food context related to key issues such as biofortification, bioprotectants and the general link between food composition and human health. Research on key crops such as rice and tomato are used as illustration of potentially broader application across crop species. Although the focus is clearly on food supply, some attention is given to the complementary field of research, nutrigenomics, where similar technologies are being applied to understand nutrition better from the human side.

Getting to grips with the plant metabolic network

Research into plant metabolism has a long history, and analytical approaches of ever-increasing breadth and sophistication have been brought to bear. We now have access to vast repositories of data concerning enzymology and regulatory features of enzymes, as well as large-scale datasets containing profiling information of transcripts, protein and metabolite levels. Nevertheless, despite this wealth of data, we remain some way off from being able to rationally engineer plant metabolism or even to predict metabolic responses. Within the past 18 months, rapid progress has been made, with several highly informative plant network interrogations being discussed in the literature. In the present review we will appraise the current state of the art regarding plant metabolic network analysis and attempt to outline what the necessary steps are in order to further our understanding of network regulation.

Liquid chromatography-mass spectrometry in metabolite profiling

Recent advances in the use of liquid chromatography-mass spectrometry for the study of metabolomics are reviewed. Sample preparations of biofluids and practical aspects of ultra-high pressure liquid chromatography are discussed. Applicability of different kinds of mass spectrometers for metabolite profiling is described. New tools-ion mobility spectroscopy and automated chip-based nanoelectrospray system with potentials to be applied in the metabolomics analysis are described.

The Potential of Metabolomics Tools in Bioremediation Studies

As a post-genomics tool, metabolomics is a young and vibrant field of science in its exponential growth phase. Metabolome analysis has become very popular recently, and novel techniques for acquiring and analyzing metabolomics data continue to emerge that are useful for a variety of biological studies. The bioremediation field has a lot to gain from the advances in this emerging area. Thus, this review article focuses on the potential of various experimental and conceptual approaches developed for metabolomics to be applied in bioremediation research, such as strategies for elucidation of biodegradation pathways using isotope distribution analysis and molecular connectivity analysis, the assessment of mineralization process using metabolic footprinting analysis, and the improvement of the biodegradation process via metabolic engineering. We demonstrate how the use of metabolomics tools can significantly extend and enhance the power of existing bioremediation approaches by providing a better overview of the biodegradation process.

Metabolomics: current state and evolving methodologies and tools

In recent years, metabolomics developed to an accepted and valuable tool in life sciences. Substantial improvements of analytical hardware allow metabolomics to run routinely now. Data are successfully used to investigate genotype-phenotype relations of strains and mutants. Metabolomics facilitates metabolic engineering to optimise mircoorganisms for white biotechnology and spreads to the investigation of biotransformations and cell culture. Metabolomics serves not only as a source of qualitative but also quantitative data of intra-cellular metabolites essential for the model-based description of the metabolic network operating under in vivo conditions. To collect reliable metabolome data sets, culture and sampling conditions, as well as the cells' metabolic state, are crucial. Hence, application of biochemical engineering principles and method standardisation efforts become important. Together with the other more established omics technologies, metabolomics will strengthen its claim to contribute to the detailed understanding of the in vivo function of gene products, biochemical and regulatory networks and, even more ambitious, the mathematical description and simulation of the whole cell in the systems biology approach. This knowledge will allow the construction of designer organisms for process application using biotransformation and fermentative approaches making effective use of single enzymes, whole microbial and even higher cells.

Parts-per-billion mass measurement accuracy achieved through the combination of multiple linear regression and automatic gain control in a Fourier transform ion cyclotron resonance mass spectrometer

Fourier transform ion cyclotron resonance mass spectrometry has the ability to achieve unprecedented mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ion cyclotron resonance (ICR) cell for a particular measurement. Even through the use of automatic gain control (AGC), the total ion population is not constant between spectra. Multiple linear regression calibration in conjunction with AGC is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. This ability allows for the extension of dynamic range of the instrument and for the mean MMA values to remain less than 1 part-per-million (ppm). In addition, multiple linear regression calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion (ppb) level.

Chemoselective probes for metabolite enrichment and profiling

Chemical probes that target classes of proteins based on shared functional properties have emerged as powerful tools for proteomics. The metabolome rivals, if not surpasses, the proteome in terms of size and complexity, suggesting that efforts to profile metabolites would also benefit from targeted technologies. Here we apply the principle of chemoselective probes to the metabolome, creating a general strategy to tag, enrich and profile large classes of small molecules from biological systems. Key to success was incorporation of a protease-cleavage step to release captured metabolites in a format compatible with liquid chromatography-mass spectrometry (LC-MS) analysis. This technology, termed metabolite enrichment by tagging and proteolytic release (METPR), is applicable to small molecules of any physicochemical class, including polar, labile and low-mass (<100 Da) compounds. We applied METPR to profile changes in the thiol metabolome of human cancer cells treated with the antioxidant N-acetyl-L-cysteine.

Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry

Untargeted metabolomics aims to gather information on as many metabolites as possible in biological systems by taking into account all information present in the data sets. Here we describe a detailed protocol for large-scale untargeted metabolomics of plant tissues, based on reversed phase liquid chromatography coupled to high-resolution mass spectrometry (LC-QTOF MS) of aqueous methanol extracts. Dedicated software, MetAlign, is used for automated baseline correction and alignment of all extracted mass peaks across all samples, producing detailed information on the relative abundance of thousands of mass signals representing hundreds of metabolites. Subsequent statistics and bioinformatics tools can be used to provide a detailed view on the differences and similarities between (groups of) samples or to link metabolomics data to other systems biology information, genetic markers and/or specific quality parameters. The complete procedure from metabolite extraction to assembly of a data matrix with aligned mass signal intensities takes about 6 days for 50 samples.