Liquid chromatography-mass spectrometry based global metabolite profiling: a review

MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments

Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are created as open-source software allowing the complete and exhaustive documentation of each step, ensuring the reproducibility of the analysis of extensive and often expensive experiments. In this paper, we will review the major steps which constitute such a data processing pipeline, discussing them in the context of an open-source software for untargeted MS-based metabolomics experiments recently developed at our institute. The software has been developed by integrating our metaMS R package with a user-friendly web-based application written in Grails. MetaMS takes care of data pre-processing and annotation, while the interface deals with the creation of the sample lists, the organization of the data storage, and the generation of survey plots for quality assessment. Experimental and biological metadata are stored in the ISA-Tab format making the proposed pipeline fully integrated with the Metabolights framework.

Transcription factor sensor system for parallel quantification of metabolites on-chip

Steadily growing demands for identification and quantification of cellular metabolites in higher throughput have brought a need for new analytical technologies. Here, we developed a synthetic biological sensor system for quantifying metabolites from biological cell samples. For this, bacterial transcription factors were exploited, which bind to or dissociate from regulatory DNA elements in response to physiological changes in the cellular metabolite concentration range. Representatively, the bacterial pyruvate dehydrogenase (PdhR), trehalose (TreR), and l-arginine (ArgR) repressor proteins were functionalized to detect pyruvate, trehalose-6-phosphate (T6P), and arginine concentration in solution. For each transcription factor the mutual binding behavior between metabolite and DNA, their working range, and othogonality were determined. High-throughput, parallel processing, and automation were achieved through integration of the metabolic sensor system on a microfluidic large-scale integration (mLSI) chip platform. To demonstrate the functionality of the integrated metabolic sensor system, we measured diurnal concentration changes of pyruvate and the plant signaling molecule T6P within cell etxracts of Arabidopsis thaliana rosettes. The transcription factor sensor system is of generic nature and extendable on the microfluidic chip.

High precision mass measurements for wine metabolomics

An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2). In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks

The rapid emergence of metabolomics has enabled system-wide measurements of metabolites in various organisms. However, advances in the mechanistic understanding of metabolic networks remain limited, as most metabolomics studies cannot routinely provide accurate metabolite identification, absolute quantification and flux measurement. Stable isotope labeling offers opportunities to overcome these limitations. Here we describe some current approaches to stable isotope-labeled metabolomics and provide examples of the significant impact that these studies have had on our understanding of cellular metabolism. Furthermore, we discuss recently developed software solutions for the analysis of stable isotope-labeled metabolomics data and propose the bioinformatics solutions that will pave the way for the broader application and optimal interpretation of system-scale labeling studies in metabolomics.

Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry

Liquid chromatography-mass spectrometry (LC-MS)-based lipidomics has been a subject of dramatic developments over the past decade. This review focuses on state of the art in LC-MS-based lipidomics, covering all the steps of global lipidomic profiling. On the basis of review of 185 original papers and application notes, we can conclude that typical LC-MS-based lipidomics methods involve: (1) extraction using chloroform/MeOH or MTBE/MeOH protocols, both with addition of internal standards covering each lipid class; (2) separation of lipids using short microbore columns with sub-2-μm or 2.6-2.8-μm (fused-core) particle size with C18 or C8 sorbent with analysis time <30 min; (3) electrospray ionization in positive- and negative-ion modes with full spectra acquisition using high-resolution MS with capability to MS/MS. Phospholipids (phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, phosphatidylserines, phosphatidylglycerols) followed by sphingomyelins, di- and tri-acylglycerols, and ceramides were the most frequently targeted lipid species.

Evaluation of analytical performance and reliability of direct nanoLC-nanoESI-high resolution mass spectrometry for profiling the (xeno)metabolome

Mass spectrometry (MS) profiling techniques are used for analysing metabolites and xenobiotics in biofluids; however, detection of low abundance compounds using conventional MS techniques is poor. To counter this, nanoflow ultra-high-pressure liquid chromatography-nanoelectrospray ionization-time-of-flight MS (nUHPLC-nESI-TOFMS), which has been used primarily for proteomics, offers an innovative prospect for profiling small molecules. Compared to conventional UHPLC-ESI-TOFMS, nUHPLC-nESI-TOFMS enhanced detection limits of a variety of (xeno)metabolites by between 2 and 2000-fold. In addition, this study demonstrates for the first time excellent repeatability and reproducibility for analysis of urine and plasma samples using nUHPLC-nESI-TOFMS, supporting implementation of this platform as a novel approach for high-throughput (xeno)metabolomics.

Basics of mass spectrometry based metabolomics

The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS-based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18).

A new approach for plasma (xeno)metabolomics based on solid-phase extraction and nanoflow liquid chromatography-nanoelectrospray ionisation mass spectrometry

Current metabolite profiling methods based on liquid chromatography-mass spectrometry (LC-MS) platforms do not detect many of the components present at trace concentrations in extracts of plasma due to their low ionisation efficiency or to interference from highly abundant compounds. Nanoflow LC-nanospray MS platforms, which are commonly used in proteomics, could overcome these limitations and significantly increase analytical sensitivity and coverage of the plasma (xeno)metabolome (i.e., metabolites and xenobiotics), but require small injection volumes (<0.5μL). In this study, we developed sample preparation methods to remove ion suppressive phospholipids and concentrate remaining components of the plasma (xeno)metabolome in order to analyse sub-microliter volumes of plasma extracts for nanoflow ultra-high-performance liquid chromatography-nanoelectrospray ionisation-time-of-flight mass spectrometry (nUHPLC-nESI-TOFMS). These methods use phospholipid filtration plates in combination with polymeric or mixed mode exchange solid-phase extraction (SPE). The phospholipid filtration plates removed >94% of the predominant phospholipid/lysophospholipid species from plasma, whilst absolute recoveries of 63 selected (xeno)metabolites from spiked plasma were generally between 60 and 104%. After a further SPE step, recoveries of test compounds were between 50 and 81%. Studies revealed that both the sample preparation methodology and nUHPLC-nESI-TOFMS analyses gave acceptable repeatability. A qualitative comparison of SPE methods revealed that sample concentration by either polymer or mixed mode ion-exchange SPE gave comprehensive metabolite coverage of plasma extracts, but the use of cation exchange SPE significantly increased detection of many cationic compounds in the sample extracts. Method detection limits for steroid, eicosanoid and bile metabolites were <1.0ng/mL plasma and for pharmaceutical contaminants were between 0.01 and 30ng/mL plasma. Comparison of the phospholipid removal/cation exchange SPE and the classical protein precipitation (PPT) sample preparation methodologies revealed that both methods detected the same range of (xeno)metabolites. However, unlike PPT extracts, the SPE preparations allowed direct injection of more concentrated plasma extracts onto the nUHPLC-nESI-TOFMS platform without blockage of the nanocolumn or nanospray, thus resulting in a wider coverage of the (xeno)metabolome. This is the first work to demonstrate the significantly enhanced sensitivity arising from the use of concentrated SPE sample preparations and direct nUHPLC-nESI-TOFMS analysis for untargeted profiling of plasma samples and constitutes a step forward for identifying mixtures of chemical stressors accumulated in blood as well as the disruption of key metabolite pathways in the same sample.

Multidimensional gas chromatography methods for bioanalytical research

Multidimensional gas chromatography (MDGC) methods are high-resolution volatile chemical separation techniques, and comprise classical heart-cutting MDGC and its more recent incarnation, comprehensive 2D GC. Although available for a long period, MDGC approaches are still not widely practiced in the field of bioanalysis, possibly reflecting the general preference for regular GC versus MDGC approaches. With the recent introduction of ‘-omic’ techniques that emphasize global nontargeted profiling of metabolites within living systems, it is evident that MDGC is gaining momentum as a separation tool, since it offers very high resolution. By untangling metabolites within highly complex biological matrices, and expanding the metabolic coverage, MDGC plays a frontline role in ‘-omics’ based studies. This review highlights state-of-the-art MDGC approaches, and summarizes the recent developments in bioanalytics.

Metabolomics: a window into the adaptive physiology of Mycobacterium tuberculosis

Mycobacterium tuberculosis is the causative agent of tuberculosis (TB) and second leading cause of human mortality due to a single infectious agent. This is mostly because of M. tuberculosis' ability to adapt its metabolism to the host environment and regulate entry into and exit from cell cycle. Knowledge of the specific metabolic changes accompanying these transitions however is incomplete. Metabolomics has emerged as a new biochemical window into M. tuberculosis physiology. This review highlights recent insights from the application of such technologies to studies of the M. tuberculosis lifecycle.

A systems biology-based approach to uncovering the molecular mechanisms underlying the effects of dragon's blood tablet in colitis, involving the integration of chemical analysis, ADME prediction, and network pharmacology

Traditional Chinese medicine (TCM) is one of the oldest East Asian medical systems. The present study adopted a systems biology-based approach to provide new insights relating to the active constituents and molecular mechanisms underlying the effects of dragon's blood (DB) tablets for the treatment of colitis. This study integrated chemical analysis, prediction of absorption, distribution, metabolism, and excretion (ADME), and network pharmacology. Firstly, a rapid, reliable, and accurate ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry method was employed to identify 48 components of DB tablets. In silico prediction of the passive absorption of these compounds, based on Caco-2 cell permeability, and their P450 metabolism enabled the identification of 22 potentially absorbed components and 8 metabolites. Finally, networks were constructed to analyze interactions between these DB components/metabolites absorbed and their putative targets, and between the putative DB targets and known therapeutic targets for colitis. This study provided a great opportunity to deepen the understanding of the complex pharmacological mechanisms underlying the effects of DB in colitis treatment.

Using gas modifiers to significantly improve sensitivity and selectivity in a cylindrical FAIMS device

Recent reports describing enhanced performance when using gas additives in a DMS device (planar electrodes) have indicated that comparable benefits are not attainable using FAIMS (cylindrical electrodes), owing to the non-homogeneous electric fields within the analyzer region. In this study, a FAIMS system (having cylindrical electrodes) was modified to allow for controlled delivery of gas additives. An experiment was carried out that illustrates the important distinction between gas modifiers present as unregulated contaminants and modifiers added in a controlled manner. The effect of contamination was simulated by adjusting the ESI needle position to promote incomplete desolvation, thereby permitting ESI solvent vapor into the FAIMS analyzer region, causing signal instability and irreproducible CV values. However, by actively controlling the delivery of the gas modifier, reproducible CV spectra were obtained. The effects of adding different gas modifiers were examined using 15 positive ions having mass-to-charge (m/z) values between 90 and 734. Significant improvements in peak capacity and increases in ion transmission were readily attained by adding acetonitrile vapor, even at trace levels (≤0.1%). Increases in signal intensity were greatest for the low m/z ions; for the six lowest molecular weight species, signal intensities increased by ∼10- to over 100-fold compared with using nitrogen without gas additives, resulting in equivalent or better signal intensities compared with ESI without FAIMS. These results confirm that analytical benefits derived from the addition of gas modifiers reported with a uniform electric field (DMS) also are observed using a non-homogenous electric field (FAIMS) in the analyser region.

Chemometric evaluation of Saccharomyces cerevisiae metabolic profiles using LC-MS

A new liquid chromatography mass spectrometry (LC-MS) metabolomics strategy coupled to chemometric evaluation, including variable and biomarker selection, has been assessed as a tool to discriminate between control and stressed Saccharomyces cerevisiae yeast samples. Metabolic changes occurring during yeast culture at different temperatures (30 and 42 °C) were analysed and the complex data generated in profiling experiments were evaluated by different chemometric multivariate approaches. Multivariate curve resolution alternating least squares (MCR-ALS) was applied to full spectral scan LC-MS preprocessed data multisets arranged in augmented column-wise data matrices. The results showed that sectioning the MS-chromatograms in different windows and analysing them by MCR-ALS enabled the proper resolution of very complex coeluted chromatographic peaks. The investigation of possible relationships between MCR-ALS resolved chromatographic peak areas and culture temperature was then investigated by partial least squares discriminant analysis (PLS-DA). Selection of most relevant resolved chromatographic peaks associated to yeast culture temperature changes was achieved according to PLS-DA-Variable Importance in Projection scores. A metabolite identification workflow was developed utilizing MCR-ALS resolved pure MS spectra and high-resolution accurate mass measurements to confirm assigned structures based on entries in metabolite databases. A total of 65 metabolites were identified. A preliminary interpretation of these results indicates that the strategy described in this study can be proposed as a general tool to facilitate biomarker identification and modelling in similar untargeted metabolomic studies.

How subtle is the "terroir" effect? Chemistry-related signatures of two "climats de Bourgogne"

The chemical composition of grape berries is influenced by various environmental conditions often considered to be representative of a “terroir”. If grapes from a given terroir are assumed to reflect this origin in their chemical compositions, the corresponding wine should also reflect it. The aim of this work was therefore to reveal the “terroir” expression within the chemodiversity of grapes and related wines, using ultrahigh-resolution mass spectrometry. Grapes and corresponding wines, from two distinct – though very proximate – terroirs of Burgundy were analyzed over three vintages (2010, 2011 and 2012). Ultrahigh-resolution mass spectrometry and ultra-high performance liquid chromatography were used as untargeted and targeted approaches to discriminate complex chemical fingerprints for vintages, classes (wines, skins or musts), and terroirs. Statistical analyses revealed that even if vintages have the most significant impact on fingerprints, the most significant terroir differences are seen in the grapes of a given vintage.

Amino acid metabolic dysfunction revealed in the prefrontal cortex of a rat model of depression

Major depressive disorder (MDD) is a debilitating mood disorder. However, the molecular mechanism(s) underlying depression remain largely unknown. Here, we applied a GC-MS-based metabonomic approach in the chronic unpredictable mild stress (CUMS) model, a well-established rodent model of depression, to investigate significant metabolic changes in the rat prefrontal cortex (PFC). Multivariate statistical analysis - including principal component analysis, partial least squares-discriminate analysis, and pair-wise orthogonal projections to latent structures discriminant - was applied to identify differential PFC metabolites between CUMS rats and healthy controls. As compared to healthy control rats, CUMS rats were characterized by lower levels of isoleucine and glycerol in combination with higher levels of N-acetylaspartate and β-alanine. These findings should provide insight into the pathophysiological mechanism(s) underlying MDD and preliminary leads relevant to diagnostic biomarker discovery for depression.

Quantitative metabolite profiling utilizing parallel column analysis for simultaneous reversed-phase and hydrophilic interaction liquid chromatography separations combined with tandem mass spectrometry

In this work, a fully automated parallel LC column method was established in order to perform orthogonal hydrophilic interaction chromatography (HILIC) and reversed-phase (RPLC) chromatography within one analytical run for targeted quantitative mass spectrometric determination of metabolites from central carbon metabolism. In this way, the analytical throughput could be significantly improved compared to previously established dual separation work flows involving two separate analytical runs. Two sample aliquots were simultaneously injected onto a dual column setup columns using a ten-port valve, and parallel separations were carried out. Sub 2 μm particle size stationary phases were employed for both separation methods. HILIC and RPLC eluents were combined post column followed by ESI-MS/MS detection. The orthogonal separations were optimized, aiming at an overall separation with 2 retention time segments, while reversed-phase separation was accomplished within 5.5 min; metabolites on the HILIC phase were retained for a minimum time of 6 min. The overall run time was 15 min. The setup was applied to the quantification of 30 primary intercellular metabolites, including amino acids, organic acids, and nucleotides employing internal standardization by a fully (13)C-labeled yeast extract. The comparison with HILIC-MS/MS and RPLC-MS/MS in separate analytical runs revealed that an excellent analytical performance was achieved by the parallel LC column method. The experimental repeatability (N = 5) was on average <5% (only for 2 compounds >5%). Moreover, limits of detection for the new approach ranging from 0.002-15 μM were in a good agreement with ones obtained in separate HILIC-MS/MS and RPLC-MS/MS runs (ranging from 0.01-44 μM).

Recent developments in liquid-phase separation techniques for metabolomics

Metabolomics is the comprehensive analysis of low molecular weight compounds in biological samples such as cells, body fluids and tissues. Comprehensive profiling of metabolites in complex sample matrices with the current analytical toolbox remains a huge challenge. Over the past few years, liquid chromatography–mass spectrometry (LC–MS) and capillary electrophoresis–mass spectrometry (CE–MS) have emerged as powerful complementary analytical techniques in the field of metabolomics. This Review provides an update of the most recent developments in LC–MS and CE–MS for metabolomics. Concerning LC–MS, attention is paid to developments in column technology and miniaturized systems, while strategies are discussed to improve the reproducibility and the concentration sensitivity of CE–MS for metabolomics studies. Novel interfacing techniques for coupling CE to MS are also considered. Representative examples illustrate the potential of the recent developments in LC–MS and CE–MS for metabolomics. Finally, some conclusions and perspectives are provided.

Investigation into variation of endogenous metabolites in bone marrow cells and plasma in C3H/He mice exposed to benzene

Benzene is identified as a carcinogen. Continued exposure of benzene may eventually lead to damage to the bone marrow, accompanied by pancytopenia, aplastic anemia or leukemia. This paper explores the variations of endogenous metabolites to provide possible clues for the molecular mechanism of benzene-induced hematotoxicity. Liquid chromatography coupled with time of flight-mass spectrometry (LC-TOF-MS) and principal component analysis (PCA) was applied to investigate the variation of endogenous metabolites in bone marrow cells and plasma of male C3H/He mice. The mice were injected subcutaneously with benzene (0, 300, 600 mg/day) once daily for seven days. The body weights, relative organ weights, blood parameters and bone marrow smears were also analyzed. The results indicated that benzene caused disturbances in the metabolism of oxidation of fatty acids and essential amino acids (lysine, phenylalanine and tyrosine) in bone marrow cells. Moreover, fatty acid oxidation was also disturbed in plasma and thus might be a common disturbed metabolic pathway induced by benzene in multiple organs. This study aims to investigate the underlying molecular mechanisms involved in benzene hematotoxicity, especially in bone marrow cells.

Metabolite induction via microorganism co-culture: a potential way to enhance chemical diversity for drug discovery

Microorganisms have a long track record as important sources of novel bioactive natural products, particularly in the field of drug discovery. While microbes have been shown to biosynthesize a wide array of molecules, recent advances in genome sequencing have revealed that such organisms have the potential to yield even more structurally diverse secondary metabolites. Thus, many microbial gene clusters may be silent under standard laboratory growth conditions. In the last ten years, several methods have been developed to aid in the activation of these cryptic biosynthetic pathways. In addition to the techniques that demand prior knowledge of the genome sequences of the studied microorganisms, several genome sequence-independent tools have been developed. One of these approaches is microorganism co-culture, involving the cultivation of two or more microorganisms in the same confined environment. Microorganism co-culture is inspired by the natural microbe communities that are omnipresent in nature. Within these communities, microbes interact through signaling or defense molecules. Such compounds, produced dynamically, are of potential interest as new leads for drug discovery. Microorganism co-culture can be achieved in either solid or liquid media and has recently been used increasingly extensively to study natural interactions and discover new bioactive metabolites. Because of the complexity of microbial extracts, advanced analytical methods (e.g., mass spectrometry methods and metabolomics) are key for the successful detection and identification of co-culture-induced metabolites. This review focuses on co-culture studies that aim to increase the diversity of metabolites obtained from microbes. The various strategies are summarized with a special emphasis on the multiple methods of performing co-culture experiments. The analytical approaches for studying these interaction phenomena are discussed, and the chemical diversity and biological activity observed among the induced metabolites are described.

An R package to analyse LC/MS metabolomic data: MAIT (Metabolite Automatic Identification Toolkit)

Summary: Current tools for liquid chromatography and mass spectrometry for metabolomic data cover a limited number of processing steps, whereas online tools are hard to use in a programmable fashion. This article introduces the Metabolite Automatic Identification Toolkit (MAIT) package, which makes it possible for users to perform metabolomic end-to-end liquid chromatography and mass spectrometry data analysis. MAIT is focused on improving the peak annotation stage and provides essential tools to validate statistical analysis results. MAIT generates output files with the statistical results, peak annotation and metabolite identification.

Availability and implementation:http://b2slab.upc.edu/software-and-downloads/metabolite-automatic-identification-toolkit/.

Contact:francesc.fernandez.albert@upc.edu

Supplementary information:Supplementary data are available at Bioinformatics online

LC-MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives

The present review aims to critically discuss some of the major problems and limitations of LC-MS based metabolomics as experienced from an analytical chemistry standpoint. Metabolomics offers distinct advantages to a variety of life sciences. Continuous development of the field has been realised due to intensive efforts from a great many scientists from widely divergent backgrounds and research interests as demonstrated by the contents of this special issue. The aim of this commentary is to describe current hindrances to field's progress, (some unique to metabolomics, some common with other omics fields or with conventional targeted bioanalysis) to propose some potential solutions to overcome these constraints and to provide a future perspective for likely developments in the field.

A metabolic biosignature of early response to anti-tuberculosis treatment

Background

The successful treatment of tuberculosis (TB) requires long-term multidrug chemotherapy. Clinical trials to evaluate new drugs and regimens for TB treatment are protracted due to the slow clearance of Mycobacterium tuberculosis (Mtb) infection and the lack of early biomarkers to predict treatment outcome. Advancements in the field of metabolomics make it possible to identify metabolic profiles that correlate with disease states or successful chemotherapy. However, proof-of-concept of this approach has not been provided for a TB-early treatment response biosignature (TB-ETRB).

Methods

Urine samples collected at baseline and during treatment from 48 Ugandan and 39 South African HIV-seronegative adults with pulmonary TB were divided into discovery and qualification sets, normalized to creatinine concentration, and analyzed by liquid chromatography-mass spectrometry to identify small molecule molecular features (MFs) in individual patient samples. A biosignature that distinguished baseline and 1 month treatment samples was selected by pairwise t-test using data from two discovery sample sets. Hierarchical clustering and repeated measures analysis were applied to additional sample data to down select molecular features that behaved consistently between the two clinical sites and these were evaluated by logistic regression analysis.

Results

Analysis of discovery samples identified 45 MFs that significantly changed in abundance at one month of treatment. Down selection using an extended set of discovery samples and qualification samples confirmed 23 MFs that consistently changed in abundance between baseline and 1, 2 and 6 months of therapy, with 12 MFs achieving statistical significance (p < 0.05). Six MFs classified the baseline and 1 month samples with an error rate of 11.8%.

Conclusions

These results define a urine based TB-early treatment response biosignature (TB-ETRB) applicable to different parts of Africa, and provide proof-of-concept for further evaluation of this technology in monitoring clinical responses to TB therapy.

Alkaloids analysis using off-line two-dimensional supercritical fluid chromatography × ultra-high performance liquid chromatography

2-D SFC × UHPLC method with high orthogonality for analysis of the amide alkaloids fraction of P. longum L.

Potential input from metabolomics for exploring and understanding the links between environment and health

Humans may be exposed via their environment to multiple chemicals as a consequence of human activities and use of synthetic products. Little knowledge is routinely generated on the hazards of these chemical mixtures. The metabolomic approach is widely used to identify metabolic pathways modified by diseases, drugs, or exposures to toxicants. This review, based on the state of the art of the current applications of metabolomics in environmental health, attempts to determine whether metabolomics might constitute an original approach to the study of associations between multiple, low-dose environmental exposures in humans. Studying the biochemical consequences of complex environmental exposures is a challenge demanding the development of careful experimental and epidemiological designs, in order to take into account possible confounders associated with the high level of interindividual variability induced by different lifestyles. The choices of populations studied, sampling and storage procedures, statistical tools used, and system biology need to be considered. Suggestions for improved experimental and epidemiological designs are described. Evidence indicates that metabolomics may be a powerful tool in environmental health in the identification of both complex exposure biomarkers directly in human populations and modified metabolic pathways, in an attempt to improve understanding the underlying environmental causes of diseases. Nevertheless, the validity of biomarkers and relevancy of animal-to-human extrapolation remain key challenges that need to be properly explored.

A novel stable isotope labelling assisted workflow for improved untargeted LC-HRMS based metabolomics research

Many untargeted LC-ESI-HRMS based metabolomics studies are still hampered by the large proportion of non-biological sample derived signals included in the generated raw data. Here, a novel, powerful stable isotope labelling (SIL)-based metabolomics workflow is presented, which facilitates global metabolome extraction, improved metabolite annotation and metabolome wide internal standardisation (IS). The general concept is exemplified with two different cultivation variants, (1) co-cultivation of the plant pathogenic fungi Fusarium graminearum on non-labelled and highly ¹³C enriched culture medium and (2) experimental cultivation under native conditions and use of globally U-¹³C labelled biological reference samples as exemplified with maize and wheat. Subsequent to LC-HRMS analysis of mixtures of labelled and non-labelled samples, two-dimensional data filtering of SIL specific isotopic patterns is performed to better extract truly biological derived signals together with the corresponding number of carbon atoms of each metabolite ion. Finally, feature pairs are convoluted to feature groups each representing a single metabolite. Moreover, the correction of unequal matrix effects in different sample types and the improvement of relative metabolite quantification with metabolome wide IS are demonstrated for the F. graminearum experiment. Data processing employing the presented workflow revealed about 300 SIL derived feature pairs corresponding to 87-135 metabolites in F. graminearum samples and around 800 feature pairs corresponding to roughly 350 metabolites in wheat samples. SIL assisted IS, by the use of globally U-¹³C labelled biological samples, reduced the median CV value from 7.1 to 3.6 % for technical replicates and from 15.1 to 10.8 % for biological replicates in the respective F. graminearum samples.

Sampling and analysis of metabolomes in biological fluids

Metabolome analysis involves the study of small molecules that are involved in the metabolic responses that occur through patho-physiological changes caused by genetic stimuli or chemical agents.

Global metabolic profiling using ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry

Currently, liquid chromatography-mass spectrometry (LC-MS) is one of the most important analytical technologies for detecting hundreds of metabolites in the field of metabolomics. A recent advance in LC that has impacted metabolomics is the development of UPLC (ultra-performance liquid chromatography). In this chapter, we describe the analytical methodologies for the global metabolic profiling of serum, urine, and tissue samples using UPLC-Q-TOF (quadrupole-time-of-flight)-MS. Aqueous metabolites are extracted after adding methanol/acetonitrile/acetone and then analyzed by UPLC-MS under positive and/or negative ionization mode. With the aid of multivariate statistical analysis, separation between various groups can be observed in the score plots, and biomarkers are screened in the loading/weight/VIP (variable importance in the projection) scatterplots. Furthermore, putative markers can be identified through comparison with the authentic standards based on tandem mass spectrometry (MS/MS) fragmentation pattern and LC retention. We expect that our protocol, with modifications if necessary, can be useful in many metabolomics studies and a wide range of research areas related to small molecules and LC-MS.

Metabolomic profiling of tumor-bearing mice

Metabolomics is one of the newcomers among the "omics" techniques, perhaps also constituting the most relevant for the study of pathophysiological conditions. Metabolomics may indeed yield not only disease-specific biomarkers but also profound insights into the etiology and progression of a variety of human disorders. Various metabolomic approaches are currently available to study oncogenesis and tumor progression in vivo, in murine tumor models. Many of these models rely on the xenograft of human cancer cells into immunocompromised mice. Understanding how the metabolism of these cells evolves in vivo is critical to evaluate the actual pertinence of xenograft models to human pathology. Here, we discuss various tumor xenograft models and methods for their metabolomic profiling to provide a short guide to investigators interested in this field of research.

Multiplexed analysis of cage and cage free chicken egg fatty acids using stable isotope labeling and mass spectrometry

Binary stable isotope labeling couple with LC-ESI-MS has been used as a powerful non-targeted approach for the relative quantification of lipids, amino acids, and many other important metabolite classes. A multiplexed approach using three or more isotopic labeling reagents greatly reduces analytical run-time while maintaining excellent sensitivity and reproducibility. Three isotopic cholamine labeling reagents have been developed to take advantage of the pre-ionized character of cholamine, for ESI, and the ease by which stable isotopes can be incorporated into the cholamine structure. These three cholamine labeling reagents have been used to relatively quantify three fatty acid samples simultaneously. The quantification resulted in the observation of 12 fatty acids that had an average absolute error of 0.9% and an average coefficient of variation of 6.1%. Caged versus cage-free isotope labeling experiments showed that cage-free eggs have an increased level of omega-3 fatty acids as compared to caged eggs. This multiplexed fatty acid analysis provides an inexpensive and expedited tool for broad-based lipid profiling that will further aid discoveries in the mechanisms of fatty acid action in cells.

Application of fluorescence two-dimensional difference in-gel electrophoresis as a proteomic biomarker discovery tool in muscular dystrophy research

In this article, we illustrate the application of difference in-gel electrophoresis for the proteomic analysis of dystrophic skeletal muscle. The mdx diaphragm was used as a tissue model of dystrophinopathy. Two-dimensional gel electrophoresis is a widely employed protein separation method in proteomic investigations. Although two-dimensional gels usually underestimate the cellular presence of very high molecular mass proteins, integral membrane proteins and low copy number proteins, this method is extremely powerful in the comprehensive analysis of contractile proteins, metabolic enzymes, structural proteins and molecular chaperones. This gives rise to two-dimensional gel electrophoretic separation as the method of choice for studying contractile tissues in health and disease. For comparative studies, fluorescence difference in-gel electrophoresis has been shown to provide an excellent biomarker discovery tool. Since aged diaphragm fibres from the mdx mouse model of Duchenne muscular dystrophy closely resemble the human pathology, we have carried out a mass spectrometry-based comparison of the naturally aged diaphragm versus the senescent dystrophic diaphragm. The proteomic comparison of wild type versus mdx diaphragm resulted in the identification of 84 altered protein species. Novel molecular insights into dystrophic changes suggest increased cellular stress, impaired calcium buffering, cytostructural alterations and disturbances of mitochondrial metabolism in dystrophin-deficient muscle tissue.

Two birds with one stone: doing metabolomics with your proteomics kit

Proteomic research facilities and laboratories are facing increasing demands for the integration of biological data from multiple ‘-OMICS’ approaches. The aim to fully understand biological processes requires the integrated study of genomes, proteomes and metabolomes. While genomic and proteomic workflows are different, the study of the metabolome overlaps significantly with the latter, both in instrumentation and methodology. However, chemical diversity complicates an easy and direct access to the metabolome by mass spectrometry (MS). The present review provides an introduction into metabolomics workflows from the viewpoint of proteomic researchers. We compare the physicochemical properties of proteins and peptides with metabolites/small molecules to establish principle differences between these analyte classes based on human data. We highlight the implications this may have on sample preparation, separation, ionisation, detection and data analysis. We argue that a typical proteomic workflow (nLC-MS) can be exploited for the detection of a number of aliphatic and aromatic metabolites, including fatty acids, lipids, prostaglandins, di/tripeptides, steroids and vitamins, thereby providing a straightforward entry point for metabolomics-based studies. Limitations and requirements are discussed as well as extensions to the LC-MS workflow to expand the range of detectable molecular classes without investing in dedicated instrumentation such as GC-MS, CE-MS or NMR.

Metabolomic profiling of urine: response to a randomised, controlled feeding study of select fruits and vegetables, and application to an observational study

Metabolomic profiles were used to characterise the effects of consuming a high-phytochemical diet compared with a diet devoid of fruits and vegetables (F&V) in a randomised trial and cross-sectional study. In the trial, 8 h fasting urine from healthy men (n 5) and women (n 5) was collected after a 2-week randomised, controlled trial of two diet periods: a diet rich in cruciferous vegetables, citrus and soya (F&V), and a fruit- and vegetable-free (basal) diet. Among the ions found to differentiate the diets, 176 were putatively annotated with compound identifications, with forty-six supported by MS/MS fragment evidence. Metabolites more abundant in the F&V diet included markers of the dietary intervention (e.g. crucifers, citrus and soya), fatty acids and niacin metabolites. Ions more abundant in the basal diet included riboflavin, several acylcarnitines and amino acid metabolites. In the cross-sectional study, we compared the participants based on the tertiles of crucifers, citrus and soya from 3 d food records (n 36) and FFQ (n 57); intake was separately divided into the tertiles of total fruit and vegetable intake for FFQ. As a group, ions individually differential between the experimental diets differentiated the observational study participants. However, only four ions were significant individually, differentiating the third v. first tertile of crucifer, citrus and soya intake based on 3 d food records. One of these ions was putatively annotated: proline betaine, a marker of citrus consumption. There were no ions significantly distinguishing tertiles by FFQ. The metabolomic assessment of controlled dietary interventions provides a more accurate and stronger characterisation of the diet than observational data.

Selection of Analytical Methodology for Metabolomics

The main challenge in metabolomics analysis is the separation, identification and quantification of a large number of known or unknown metabolites in complex samples. The correct selection of sample collection and preparation method, type of separation methodology and detection are all crucial steps in the analysis of metabolomics. This chapter provides an overview of and general guidelines for strategies involved in the analysis of metabolomics, and different chromatographic techniques used in metabolomics studies are briefly presented. The methods are compared and the main features of the separation methods are listed. Finally, general conclusions and future perspectives are provided.