Strategy for choosing extraction procedures for NMR-based metabolomic analysis of mammalian cells

Metabolomic analysis of mammalian cells can be applied across multiple fields including medicine and toxicology. It requires the acquisition of reproducible, robust, reliable, and homogeneous biological data sets. Particular attention must be paid to the efficiency and reliability of the extraction procedure. Even though a number of recent studies have dealt with optimizing a particular protocol for specific matrices and analytical techniques, there is no universal method to allow the detection of the entire cellular metabolome. Here, we present a strategy for choosing extraction procedures from adherent mammalian cells for the global NMR analysis of the metabolome. After the quenching of cells, intracellular metabolites are extracted from the cells using one of the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol, methanol/water, and methanol/chloroform/water. The hydrophilic metabolite profiles are analysed using ¹H nuclear magnetic resonance (NMR) spectroscopy. We propose an original geometric representation of metabolites reflecting the efficiency of extraction methods. In the case of NMR-based analysis of mammalian cells, this methodology demonstrates that a higher portion of intracellular metabolites are extracted by using methanol or methanol/chloroform/water. The preferred method is evaluated in terms of biological variability for studying metabolic changes caused by the phenotype of four different human breast cancer cell lines, showing that the selected extraction procedure is a promising tool for metabolomic and metabonomic studies of mammalian cells. The strategy proposed in this paper to compare extraction procedures is applicable to NMR-based metabolomic studies of various systems.     

(Xeno)metabolomics for the evaluation of aquatic organism’s exposure to field contaminated water

Environmental (xeno)metabolomics offers a major advantage compared to other approaches for the evaluation of aquatic organism’s exposure to contaminated water because its allows the simultaneous profiling of the xenometabolome (chemical xenobiotics and their metabolites accumulated in an organism exposed to environmental contaminants) and the metabolome (endogenous metabolites whose levels are altered due to an external stressor). This approach has been widely explored in lab exposure experiments, however in field studies environmental (xeno)metabolomics has only started in the last years. In this review, the papers published so far that have performed different (xeno)metabolomics approaches for the evaluation of aquatic organisms exposed to contaminated water are presented, together with their main achievements, current limitations, and future perspectives. The different analytical methods applied including sample pre-treatment (considering matrix type), platforms used (Nuclear Magnetic Resonance (NMR) and low- or high-resolution Mass Spectrometry (MS or HRMS)), and the analytical strategy (target vs non-target analysis) are discussed. The application of (xeno)metabolomics to provide information of xenobiotics mixtures accumulated in exposed organisms, either in lab or field studies, as well as biomarkers of exposure and biomarkers of effect are debated, and finally, the most commonly metabolic pathways disrupted by chemical contamination are highlighted.     

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. Copyright © 2009 John Wiley & Sons, Ltd.     

Recent and potential developments in the analysis of urine: a review

Analysis of urine is a widely used diagnostic tool that traditionally measured one or, at most, a few metabolites. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to urine for disease diagnostics. This review looks at various aspects of urinalysis including sampling and traditional approaches before reviewing recent developments using metabolomics. Spectrometric approaches are covered briefly since there are already a number of very good reviews on NMR spectroscopy and mass spectrometry and other spectrometries are not as highly developed in their applications to metabolomics. On the other hand, there has been a recent surge in chromatographic applications dedicated to characterising the human urinary metabolome. While developments in the analysis of urine encompassing both classical approaches of urinalysis and metabolomics are covered, it must be emphasized that these approaches are not orthogonal - they both have their uses and are complementary. Regardless, the need to normalise analytical data remains an important impediment.     

Ultra-high performance liquid chromatography tandem mass spectrometry for comprehensive analysis of urinary acylcarnitines

We report an enabling mass spectrometric method for the analysis of lipid metabolites in order to define better the lipid metabolome in terms of chemical diversity and generate fragment ion spectra of these metabolites as a potential resource for unknown metabolite identification. This work focuses on the analysis of one important class of lipid metabolites, the acylcarnitines. Current analytical methods have only detected and identified a limited number of these metabolites. The method described herein provides the most comprehensive acylcarnitine profile in urine of healthy individuals up to date. It involves an optimized solid phase extraction technique for selective analyte extraction using cartridges containing both lipophilic and cation-exchange properties. The captured analytes are then subjected to ultra-high performance liquid chromatography (UPLC) separation, followed by tandem mass spectrometry (MS/MS) analysis using information-dependent acquisitions and selected reaction monitoring (SRM). The urine of six healthy individuals was analyzed using this method. A total of 355 acylcarnitines were detected; only 43 of them have been previously reported in the urine of healthy individuals. Detection of this large number of acylcarnitines illustrates the great diversity of the lipid metabolome as well as the usefulness of the method for profiling acylcarnitines. Furthermore, the MS/MS spectra of the 355 acylcarnitines will be uploaded to a public human metabolome database as a mass spectrometric resource for unknown metabolite identification.     

Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal

The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms – gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) – to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1–86% for GC-MS and 0.9–42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2–97% for the positive and 4–69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a multi-platform metabolomics approach followed by multivariate and univariate data analysis for a broad-scale screen of the individual metabolome, particularly for studies using repeated measures to determine dietary response phenotype.     

Optimization of Sample Preparation for Metabolomics Exploration of Urine,Feces, Blood and Saliva in Humans Using Combined NMR and UHPLC-HRMS Platforms

Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (¹H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.     

Knowledge discovery in metabolomics: An overview of MS data handling

While metabolomics attempts to comprehensively analyse the small molecules characterising a biological system, MS has been promoted as the gold standard to study the wide chemical diversity and range of concentrations of the metabolome. On the other hand, extracting the relevant information from the overwhelming amount of data generated by modern analytical platforms has become an important issue for knowledge discovery in this research field. The appropriate treatment of such data is therefore of crucial importance in order, for the data, to provide valuable information. The aim of this review is to provide a broad overview of the methodologies developed to handle and process MS metabolomic data, compare the samples and highlight the relevant metabolites, starting from the raw data to the biomarker discovery. As data handling can be further separated into data processing, data pre‐treatment and data analysis, recent advances in each of these steps are detailed separately.     

Identification of metabolites in coriander seeds (Coriandrum Sativum L.) aided by ultrahigh resolution total correlation NMR spectroscopy

NMR is a fast method for obtaining a holistic snapshot of the metabolome and also offers quantitative information without separating the compounds present in a complex mixture. Identification of the metabolites present in a plant extract sample is a crucial step for all plant metabolomics studies. In the present work, we used various two dimensional (2D) NMR methods such as J-resolved NMR, total correlation spectroscopy (TOCSY), and heteronuclear single quantum coherence sensitivity enhanced NMR spectroscopy for the identification of 36 common metabolites present in Coriandrum sativum L. seed extract. The identified metabolites belong to the following classes: organic acids, amino acids, and carbohydrates. ¹H NMR spectra of such complex mixtures in general display tremendous signal overlap due to the presence of a large number of metabolites with closely resonating multiplet signals. This signal overlapping leads to ambiguity in an assignment, and hence, identification of metabolites becomes tedious or impossible in many cases. Therefore, the utility of pure-shift proton spectrum along the indirect (F₁) dimension of the F₁-PSYCHE-TOCSY spectrum is demonstrated for overcoming ambiguity in assignment of metabolites in crowded spectral regions from Coriandrum sativum L. seed extract sample. Because pure-shift NMR methods yield ultrahigh resolution spectrum (i.e., a singlet peak per chemical site) along one or more dimensions, such spectra provide better identification of metabolites compared with regular 2D TOCSY where signal overlap and peak distortions lead to ambiguity in the assignment. Nine metabolites were unambiguously assigned by pure-shift F₁-PSYCHE-TOCSY spectrum, which was unresolved in regular TOCSY spectrum.     

Untargeted Tumor Metabolomics with Liquid Chromatography–Surface-Enhanced Raman Spectroscopy

Metabolomics is a powerful systems biology approach that monitors changes in biomolecule concentrations to diagnose and monitor health and disease. However, leading metabolomics technologies, such as NMR and mass spectrometry (MS), access only a small portion of the metabolome. Now an approach is presented that uses the high sensitivity and chemical specificity of surface-enhanced Raman scattering (SERS) for online detection of metabolites from tumor lysates following liquid chromatography (LC). The results demonstrate that this LC-SERS approach has metabolite detection capabilities comparable to the state-of-art LC-MS but suggest a selectivity for the detection of a different subset of metabolites. Analysis of replicate LC-SERS experiments exhibit reproducible metabolite patterns that can be converted into barcodes, which can differentiate different tumor models. Our work demonstrates the potential of LC-SERS technology for metabolomics-based diagnosis and treatment of cancer.     

Untargeted Tumor Metabolomics with Liquid Chromatography–Surface‐Enhanced Raman Spectroscopy

Metabolomics is a powerful systems biology approach that monitors changes in biomolecule concentrations to diagnose and monitor health and disease. However, leading metabolomics technologies, such as NMR and mass spectrometry (MS), access only a small portion of the metabolome. Now an approach is presented that uses the high sensitivity and chemical specificity of surface‐enhanced Raman scattering (SERS) for online detection of metabolites from tumor lysates following liquid chromatography (LC). The results demonstrate that this LC‐SERS approach has metabolite detection capabilities comparable to the state‐of‐art LC‐MS but suggest a selectivity for the detection of a different subset of metabolites. Analysis of replicate LC‐SERS experiments exhibit reproducible metabolite patterns that can be converted into barcodes, which can differentiate different tumor models. Our work demonstrates the potential of LC‐SERS technology for metabolomics‐based diagnosis and treatment of cancer.     

NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).     

Advances in mass spectrometry applied to pharmaceutical metabolomics

Metabolomics, also referred to in the literature as metabonomics, is a relatively new systems biology tool for drug discovery and development and is increasingly being used to obtain a detailed picture of a drug’s effect on the body. Metabolomics is the qualitative assessment and relative or absolute quantitative measurement of the endogenous metabolome, defined as the complement of all native small molecules (metabolites less than 1,500 Da). A metabolomics study frequently involves the comparative analysis of sample sets from a normal state and a perturbed state, where the perturbation can be of any nature, such as genetic knockout, administration of a drug, or change in diet or lifestyle. Advances in mass spectrometry (MS) technologies including direct introduction or in-line chromatographic separation modes, ionization techniques, mass analyzers, and detection methods have provided powerful tools to assess the molecular changes in the metabolome. This review focuses on advances in MS pertaining to the analytical data generation for the main metabolomics methods, namely, fingerprinting, nontargeted, and targeted approaches, as they are applied to pharmaceutical drug discovery and development.     

Modern analytical techniques in metabolomics analysis

Metabolomics is the comprehensive assessment of endogenous metabolites and attempts to systematically identify and quantify metabolites from a biological sample. Small-molecule metabolites have an important role in biological systems and represent attractive candidates to understand disease phenotypes. Metabolites represent a diverse group of low-molecular-weight structures including lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols and carbohydrates, which makes global analysis a difficult challenge. The recent rapid development of a range of analytical platforms, including GC, HPLC, UPLC, CE coupled to MS and NMR spectroscopy, could enable separation, detection, characterization and quantification of such metabolites and related metabolic pathways. Owing to the complexity of the metabolome and the diverse properties of metabolites, no single analytical platform can be applied to detect all metabolites in a biological sample. The combined use of modern instrumental analytical approaches has unravelled the ideal outcomes in metabolomics, and is beneficial to increase the coverage of detected metabolites that can not be achieved by single-analysis techniques. Integrated platforms have been frequently used to provide sensitive and reliable detection of thousands of metabolites in a biofluid sample. Continued development of these analytical platforms will accelerate widespread use and integration of metabolomics into systems biology. Here, the application of each hyphenated technique is discussed and its strengths and limitations are discussed with selected illustrative examples; furthermore, this review comprehensively highlights the role of integrated tools in metabolomic research.     

Metabolic profiling of Escherichia coli by ion mobility-mass spectrometry with MALDI ion source

Comprehensive metabolome analysis using mass spectrometry (MS) often results in a complex mass spectrum and difficult data analysis resulting from the signals of numerous small molecules in the metabolome. In addition, MS alone has difficulty measuring isobars and chiral, conformational and structural isomers. When a matrix-assisted laser desorption ionization (MALDI) source is added, the difficulty and complexity are further increased. Signal interference between analyte signals and matrix ion signals produced by MALDI in the low mass region (<1500 Da) cause detection and/or identification of metabolites difficult by MS alone. However, ion mobility spectrometry (IMS) coupled with MS (IM-MS) provides a rapid analytical tool for measuring subtle structural differences in chemicals. IMS separates gas-phase ions based on their size-to-charge ratio. This study, for the first time, reports the application of MALDI to the measurement of small molecules in a biological matrix by ion mobility-time of flight mass spectrometry (IM-TOFMS) and demonstrates the advantage of ion-signal dispersion in the second dimension. Qualitative comparisons between metabolic profiling of the Escherichia coli metabolome by MALDI-TOFMS, MALDI-IM-TOFMS and electrospray ionization (ESI)-IM-TOFMS are reported. Results demonstrate that mobility separation prior to mass analysis increases peak-capacity through added dimensionality in measurement. Mobility separation also allows detection of metabolites in the matrix-ion dominated low-mass range (m/z < 1500 Da) by separating matrix signals from non-matrix signals in mobility space.     

Quantitative detection of metabolites using matrix-assisted laser desorption/ionization mass spectrometry with 9-aminoacridine as the matrix

Quantitative detection of metabolites is a highly desirable feature in metabolome analyses. Recently, the successful detection of multiple metabolites using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in the negative ion mode employing 9-aminoacridine as the organic matrix was reported (Edwards JL, Kennedy RT. Anal. Chem. 2005; 77: 2201-2209). However, there is little information available on quantitative detection of multiple metabolites using MALDI-MS and in particular the influence changes in metabolite levels have on such detections. We investigated this aspect by spiking a synthetic metabolite cocktail (consisting of 39 metabolites including amino acids, organic acids and phospho-metabolites) with five representative metabolites at increasing concentrations, one metabolite at a time, and assessed the signals from replicate determinations. It was possible to detect quantitative changes in the spiked metabolites. Although analyte suppression was observed, it was possible to observe scenarios where the spiked metabolite had little or no influence on the quantitative detection of some metabolites. It appears that the mass spectral response of the metabolite is suppressed only when the spiked chemical species are relatively similar in chemical terms. This suggests that quantitation is possible in scenarios where changes in a specific metabolite or a class of metabolites are monitored following appropriate analyte separation strategies, and that careful interpretations must be made when using the technique for quantitative analysis in unbiased metabolomic approaches.     

NMR‐based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so‐called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice. In this review, we will present several special examples of a successful application of NMR for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and discuss possible future improvements. Copyright © 2013 John Wiley & Sons, Ltd.     

Software-assisted serum metabolite quantification using NMR

The goal of metabolomics is to analyze a whole metabolome under a given set of conditions, and accurate and reliable quantitation of metabolites is crucial. Absolute concentration is more valuable than relative concentration; however, the most commonly used method in NMR-based serum metabolic profiling, bin-based and full data point peak quantification, provides relative concentration levels of metabolites and are not reliable when metabolite peaks overlap in a spectrum. In this study, we present the software-assisted serum metabolite quantification (SASMeQ) method, which allows us to identify and quantify metabolites in NMR spectra using Chenomx software. This software uses the ERETIC2 utility from TopSpin to add a digitally synthesized peak to a spectrum. The SASMeQ method will advance NMR-based serum metabolic profiling by providing an accurate and reliable method for absolute quantification that is superior to bin-based quantification.     

Development of chemical isotope labeling liquid chromatography mass spectrometry for silkworm hemolymph metabolomics

Silkworm (Bombyx mori) is a very useful target insect for evaluation of endocrine disruptor chemicals (EDCs) due to mature breeding techniques, complete endocrine system and broad basic knowledge on developmental biology. Comparative metabolomics of silkworms with and without EDC exposure offers another dimension of studying EDCs. In this work, we report a workflow on metabolomic profiling of silkworm hemolymph based on high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) and demonstrate its application in studying the metabolic changes associated with the pesticide dichlorodiphenyltrichloroethane (DDT) exposure in silkworm. Hemolymph samples were taken from mature silkworms after growing on diet that contained DDT at four different concentrations (1, 0.1, 0.01, 0.001 ppm) as well as on diet without DDT as controls. They were subjected to differential ¹²C-/¹³C-dansyl labeling of the amine/phenol submetabolome, LC-UV quantification of the total amount of labeled metabolites for sample normalization, and LC-MS detection and relative quantification of individual metabolites in comparative samples. The total concentration of labeled metabolites did not show any significant change between four DDT-treatment groups and one control group. Multivariate statistical analysis of the metabolome data set showed that there was a distinct metabolomic separation between the five groups. Out of the 2044 detected peak pairs, 338 and 1471 metabolites have been putatively identified against the HMDB database and the EML library, respectively. 65 metabolites were identified by the dansyl library searching based on the accurate mass and retention time. Among the 65 identified metabolites, 33 positive metabolites had changes of greater than 1.20-fold or less than 0.83-fold in one or more groups with p-value of smaller than 0.05. Several useful biomarkers including serine, methionine, tryptophan, asymmetric dimethylarginine, N-Methyl-D-aspartic and tyrosine were identified. The changes of these biomarkers were likely due to the disruption of the endocrine system of silkworm by DDT. This work illustrates that the method of CIL LC-MS is useful to generate quantitative submetabolome profiles from a small volume of silkworm hemolymph with much higher coverage than conventional LC-MS methods, thereby facilitating the discovery of potential metabolite biomarkers related to EDC or other chemical exposure.     

Plasma lipid analysis by hydrophilic interaction liquid chromatography coupled with electrospray ionization tandem mass spectrometry

A novel method for the analysis of endogenous lipids and related compounds was developed employing hydrophilic interaction liquid chromatography with electrospray ionization tandem mass spectrometry. A hydrophilic interaction liquid chromatography with carbamoyl stationary phase achieved clear separation of phosphatidylcholine, lysophosphatidylcholine, sphingomyelin, ceramide, and mono‐hexsosyl ceramide groups with good peak area repeatability (RSD% < 10) and linearity (R² > 0.99). The established method was applied to human plasma assays and a total of 117 endogenous lipids were successfully detected and reproducibly identified. In addition, we investigated the simultaneous detection of small polar metabolites such as amino and organic acids co‐existing in the same biological samples processed in a single analytical run with lipids. Our results show that hydrophilic interaction liquid chromatography is a useful tool for human plasma lipidome analysis and offers more comprehensive metabolome coverage.