Semi‐quantitative and structural metabolic phenotyping by direct infusion ion trap mass spectrometry and its application in genetical metabolomics

The identification of quantitative trait loci (QTL) for plant metabolites requires the quantitation of these metabolites across a large range of progeny. We developed a rapid metabolic profiling method using both untargeted and targeted direct infusion tandem mass spectrometry (DIMSMS) with a linear ion trap mass spectrometer yielding sufficient precision and accuracy for the quantification of a large number of metabolites in a high‐throughput environment. The untargeted DIMSMS method uses top‐down data‐dependent fragmentation yielding MS² and MS³ spectra. We have developed software tools to assess the structural homogeneity of the MS² and MS³ spectra hence their utility for phenotyping and genetical metabolomics. In addition we used a targeted DIMS(MS) method for rapid quantitation of specific compounds. This method was compared with targeted LC/MS/MS methods for these compounds. The DIMSMS methods showed sufficient precision and accuracy for QTL discovery. We phenotyped 200 individual Lolium perenne genotypes from a mapping population harvested in two consecutive years. Computational and statistical analyses identified 246 nominal m/z bins with sufficient precision and homogeneity for QTL discovery. Comparison of the data for specific metabolites obtained by DIMSMS with the results from targeted LC/MS/MS analysis showed that quantitation by this metabolic profiling method is reasonably accurate. Of the top 100 MS¹ bins, 22 ions gave one or more reproducible QTL across the 2 years. Copyright © 2009 John Wiley & Sons, Ltd.     

Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures–Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p?=?0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products.     

New insights into the conversion of electropherograms to the effective electrophoretic mobility scale

CE–MS is increasingly gaining momentum as an analytical tool in metabolomics, due to its ability to obtain information about the most polar elements in biological samples. This has been helped by improvements of robustness in peak identification by means of mobility-scale representations of the electropherograms (mobilograms). As a necessary step toward facilitating the use of CE–MS for untargeted metabolomics data, the authors previously developed and introduced ROMANCE, a software automating mobilogram generation for large untargeted datasets through a simple and self-contained user interface. Herein, we introduce a new version of ROMANCE including new features such as compatibility with other types of data (targeted MS data and 2D UV-Vis absorption-like electropherograms), and the much needed additional flexibility in the transformation parameters (including field ramping and the use of secondary markers), more measurement conditions (depending on detection and integration modes), and most importantly tackling the issue of quantitative peak conversion. First, we present a review of the current theoretical framework with regard to peak characterization, and we develop new formulas for multiple marker peak area corrections, for anticipating peak position precision, and for assessing peak shape distortion. Then, the new version of the software is presented and validated experimentally. We contrast the multiple marker mobility transformations with previous results, finding increased peak position precision, and finally we showcase an application to actual untargeted metabolomics data.     

Fast quantitative 2D NMR for metabolomics and lipidomics: A tutorial

Nuclear magnetic resonance (NMR) is a well-known analytical technique for the analysis of complex mixtures. Its quantitative capability makes it ideally suited to metabolomics or lipidomics studies involving large sample collections of complex biological samples. To overcome the ubiquitous limitation of spectral overcrowding when recording 1D NMR spectra on such samples, the acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. Moreover, the experiment duration can be considerably reduced by applying fast acquisition methods. Here, we describe the general workflow to acquire fast quantitative 2D NMR spectra in the “omics” context. It is illustrated on three representative and complementary experiments: UF COSY, ZF-TOCSY with nonuniform sampling, and HSQC with nonuniform sampling. After giving some details and recommendations on how to apply this protocol, its implementation in the case of targeted and untargeted metabolomics/lipidomics studies is described.     

Metabolomics‐based parallel discovery of xenobiotics and induced endogenous metabolic dysregulation in clinical toxicology

Intoxication by xenobiotics triggers the perturbation of metabolic fingerprints in biofluids, including the accumulation of xenobiotic compounds and the dysregulation of endogenous metabolites. In this work, an untargeted metabolomics workflow was developed to simultaneously profile both xenobiotic and endogenous metabolites for the identification of the xenobiotic origin and an in‐depth understanding of the intoxication mechanism. This workflow was demonstrated in a real‐world clinical case. Plasma samples were collected from four intoxicated children and another three healthy children. Untargeted metabolomics analysis was performed using ultraperformance liquid chromatography (UPLC) coupled to a high‐resolution mass spectrometer (HRMS) with data‐independent MS^E acquisition. LC–MS^E data was processed using an untargeted metabolomics data interpretation workflow, in which the identities of xenobiotics and altered endogenous metabolic features were determined via database searching. Five xenobiotic chemicals and 19 endogenous metabolites were found to be dysregulated. Combined with the clinical evidence, penfluridol was confirmed as the xenobiotic toxin. Furthermore, a mechanistic hypothesis was developed to explain the dysregulation of the four endogenous acyl‐carnitines. This workflow can be readily applied to a wide range of clinical toxicology cases, offering a powerful and convenient means of simultaneous discovery of intoxication source and the understanding of intoxication mechanisms.     

Comprehensive Analysis of LC/MS Data Using Pseudocolor Plots

We have developed new applications of the pseudocolor plot for the analysis of LC/MS data. These applications include spectral averaging, analysis of variance, differential comparison of spectra, and qualitative filtering by compound class. These applications have been motivated by the need to better understand LC/MS data generated from analysis of human biofluids. The examples presented use data generated to profile steroid hormones in urine extracts from a Cushing’s disease patient relative to a healthy control, but are general to any discovery-based scanning mass spectrometry technique. In addition to new visualization techniques, we introduce a new metric of variance: the relative maximum difference from the mean. We also introduce the concept of substructure-dependent analysis of steroid hormones using precursor ion scans. These new analytical techniques provide an alternative approach to traditional untargeted metabolomics workflow. We present an approach to discovery using MS that essentially eliminates alignment or preprocessing of spectra. Moreover, we demonstrate the concept that untargeted metabolomics can be achieved using low mass resolution instrumentation.      

Metabolite profiling and beyond: approaches for the rapid processing and annotation of human blood serum mass spectrometry data

In this paper, we describe data processing and metabolite identification approaches which lead to a rapid and semi-automated interpretation of metabolomics experiments. Data from metabolite fingerprinting using LC-ESI-Q-TOF/MS were processed with several open-source software packages, including XCMS and CAMERA to detect features and group features into compound spectra. Next, we describe the automatic scheduling of tandem mass spectrometry (MS) acquisitions to acquire a large number of MS/MS spectra, and the subsequent processing and computer-assisted annotation towards identification using the R packages MetShot, Rdisop, and the MetFusion application. We also implement a simple retention time prediction model using predicted lipophilicity logD, which predicts retention times within 42 s (6 min gradient) for most compounds in our setup. We putatively identified 44 common metabolites including several amino acids and phospholipids at metabolomics standards initiative (MSI) levels two and three and confirmed the majority of them by comparison with authentic standards at MSI level one. To aid both data integration within and data sharing between laboratories, we integrated data from two labs and mapped retention times between the chromatographic systems. Despite the different MS instrumentation and different chromatographic gradient programs, the mapped retention times agree within 26 s (20 min gradient) for 90 % of the mapped features.

Geographic impact evaluation of the quality of Alismatis Rhizoma by untargeted metabolomics and quantitative assay

The geographic impact on the quality of Alismatis Rhizoma (derived from the tuber of Alisma orientale), a reputable diuretic traditional Chinese medicine, has seldom been evaluated. Here a metabolomics‐driven approach targeting the bioactive protostane triterpenes was developed, by incorporating UHPLC with quadrupole time‐of‐flight mass spectrometry‐based untargeted metabolite profiling and multiple reaction monitoring quantitative assay, to probe the triterpene differences between Alismatis Rhizoma samples collected from Sichuan, Fujian, and Jiangxi Provinces. Following the metabolomics workflows, the samples from Sichuan and Jiangxi displayed distinct differences in their triterpene profiles, whereas those from Fujian showed remarkable intra‐class variation. Twenty‐three triterpenes were identified to contribute most to the differentiated clustering. A sensitive, precise, repeatable, and accurate quantitative assay method was established on a hybrid triple quadrupole‐linear ion trap mass spectrometer to quantify the contents of eight triterpene compounds. Taking into account the metabolomics and quantitation results, alisol B 23‐acetate and alisol A are significantly different in Alismatis Rhizoma from Sichuan and Jiangxi Provinces, and they may have the potential for geographic discrimination. These results illustrate how geographic difference impacts the triterpene chemistry of Alismatis Rhizoma. Metabolomics‐driven chemical comparison is suitable for the quality evaluation of traditional Chinese medicine.     

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. Copyright © 2014 John Wiley & Sons, Ltd.     

Separation strategies for untargeted metabolomics

Metabolomics has rapidly become a profiling technique of choice for biomarker elucidation and molecular diagnostics in addition to studies focused on understanding disease pathogenesis. Key to the success of metabolomics in these areas has been the techniques to separate and analyze the chemically diverse group of compounds comprising the metabolome by using global and untargeted approaches. Untargeted metabolomic efforts have the goal of examining as many metabolites as possible simultaneously and most frequently use an LC/MS-based approach. Here, the importance of LC in an untargeted metabolomic workflow is outlined and separation strategies for optimization are reviewed within the context of these criteria.     

Characterization of Cabernet Sauvignon Wines by Untargeted HS-SPME GC-QTOF-MS

Untargeted metabolomics approaches are emerging as powerful tools for the quality evaluation and authenticity of food and beverages and have been applied to wine science. However, most fail to report the method validation, quality assurance and/or quality control applied, as well as the assessment through the metabolomics-methodology pipeline. Knowledge of Mexican viticulture, enology and wine science remains scarce, thus untargeted metabolomics approaches arise as a suitable tool. The aim of this study is to validate an untargeted HS-SPME-GC-qTOF/MS method, with attention to data processing to characterize Cabernet Sauvignon wines from two vineyards and two vintages. Validation parameters for targeted methods are applied in conjunction with the development of a recursive analysis of data. The combination of some parameters for targeted studies (repeatability and reproducibility < 20% RSD; linearity > 0.99; retention-time reproducibility < 0.5% RSD; match-identification factor < 2.0% RSD) with recursive analysis of data (101 entities detected) warrants that both chromatographic and spectrometry-processing data were under control and provided high-quality results, which in turn differentiate wine samples according to site and vintage. It also shows potential biomarkers that can be identified. This is a step forward in the pursuit of Mexican wine characterization that could be used as an authentication tool.     

Mass spectrometric analysis of nanoscale sample volumes extracted from open microchannels after sample preconcentration applied on amyloid beta peptides

A new instrumental concept for extraction of nanovolumes from open microchannels (dimensions 150 μm?×?50 μm, length 10 mm) manufactured on silicon microchips has been used in combination with a previously developed method for preconcentrating proteins and peptides in the open channels through electromigration. The extracted nanovolumes were further analyzed using nanoelectrospray ionization (nESI) or matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) directly or with subsequent enzymatic protein digestion in a nanodroplet prior to the MS analysis. Preconcentration of the samples resulted in a 15-fold sensitivity increase in nESI for a neurotensin solution, and using MALDI-MS, amyloid beta (Aβ) peptides could be detected in concentrations down to 1 nM. The method was also successfully applied for detection of cell culture Aβ.     

Untargeted Metabolomics Strategies—Challenges and Emerging Directions

Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies—specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described.

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Simple Determination of 1-Deoxynojirimycin in a New Dietary Supplement by Liquid Chromatography

A simple and fast chromatographic method using ultraviolet diode-array detector (UV-DAD) was developed for the high performance liquid chromatography determination of the content of 1-deoxynojirimycin (DNJ) in a new dietary supplement in the form of granules for oral solution preparation. The derivatization reaction was carried out at room temperature for 15 min at pH 7. The reaction reached completeness at a reagent to analyte molar ratio of about 60. The chromatographic separations were performed on a C18 Phenomenex Synergi Fusion stainless steel column (250 mm?×?4.6 mm; 4 µm) with detection at λ?=?254 nm. The mobile phase consisted of triethylammonium (TEA) phosphate buffer (0.05 M; pH 3) and acetonitrile under gradient conditions at a flow-rate ramping from 1 to 1.2 mL/min. The validation parameters (linearity, sensitivity, accuracy, precision, specificity and stability) were satisfactory. Intra-day precision (relative standard deviation, RSD) was ≤?2.23% for peak area and retention time without significant differences between intra- and inter-day data. Recovery studies gave good results (93.59%; n?=?15) with a RSD of 2.64%. The developed method is suitable for the quality control of DNJ in raw material and industrial products. The method can be applied in any analytical laboratory and does not require sophisticated instrumentation.     

Validation of UV Spectrophotometric Method for Fexofenadine Hydrochloride in Pharmaceutical Formulations and Comparison with HPLC

Abstract

A simple, reproducible, accurate, and effective spectrophotometric method was developed and validated for the quantitation of the antihistamine fexofenadine in capsules and coated tablets. Ethanol was used as solvent and the absorbance at the wavelength of 220 nm was employed to the quantitation of the drug. The method validation was fulfilled through the evaluation of the analytical parameters of linearity, precision, accuracy, limits of detection, and quantitation and specificity. The method was linear (r=0.9999) at concentrations ranging from 8.0 to 20.0 µg ml^?1, precise (RSD intra‐day=0.29; 0.18; 0.39; RSD inter‐day=0.12 for capsules and RSD intra‐day=0.13; 0.16; 0.13; RSD inter‐day=0.13 for coated tablets), accurate (percentage recovery=99.97% for capsules and 100.51% for tablets), sensitive (limits of detection and quantitation of 0.10 and 0.29 µg ml^?1, respectively) and specific. The method was compared to a high performance liquid chromatography (HPLC) method, which was previously developed to the same drug. The results showed no significant difference between the methods in fexofenadine hydrochloride quantitation.     

Comparison of Widely Targeted Metabolomics and Untargeted Metabolomics of Wild Ophiocordyceps sinensis

The authors of this paper conducted a comparative metabolomic analysis of Ophiocordyceps sinensis (OS), providing the metabolic profiles of the stroma (OSBSz) and sclerotia (OSBSh) of OS by widely targeted metabolomics and untargeted metabolomics. The results showed that 778 and 1449 metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. The metabolites in OSBSz and OSBSh are significantly differentiated; 71 and 96 differentially expressed metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. This suggests that these 71 metabolites (riboflavine, tripdiolide, bromocriptine, lumichrome, tetrahymanol, citrostadienol, etc.) and 96 metabolites (sancycline, vignatic acid B, pirbuterol, rubrophen, epalrestat, etc.) are potential biomarkers. 4-Hydroxybenzaldehyde, arginine, and lumichrome were common differentially expressed metabolites. Using the widely targeted metabolomics approach, the key pathways identified that are involved in creating the differentiation between OSBSz and OSBSh may be nicotinate and nicotinamide metabolism, thiamine metabolism, riboflavin metabolism, glycine, serine, and threonine metabolism, and arginine biosynthesis. The differentially expressed metabolites identified using the untargeted metabolomics approach were mainly involved in arginine biosynthesis, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, and cysteine and methionine metabolism. The purpose of this research was to provide support for the assessment of the differences between the stroma and sclerotia, to furnish a material basis for the evaluation of the physical effects of OS, and to provide a reference for the selection of detection methods for the metabolomics of OS.     

Development of a read-across workflow for skin irritation and corrosion predictions

We developed a read-across workflow using the OECD QSAR Toolbox for the prediction of skin irritation and corrosion. In the workflow, we gathered analogues using an improved profiler for skin irritation and corrosion to define valid categories. In addition, we refined categories by removing chemicals based on melting points and structural features. Finally, prediction results were obtained using our self-determined rule for read-across. In this rule, we decided the number of analogues from which the read-across is performed, analogue selection criteria (i.e. high similarity vs. near log P_ow) and prediction rule (i.e. majority vs. unanimity). We created a program for the optimization of read-across workflows. We applied this program to 313 chemicals in the training set and sought the optimized workflows among >1000 possible choices of profilers and ways of subcategorization and data gap filling. Use of the optimized workflows provided highly accurate, unbiased, user-independent and reproducible read-across predictions. The prediction results obtained from read-across workflows can be used for the selection of in vitro test methods or as part of the weight-of-evidence approaches in the Integrated Approach on Testing and Assessment for skin irritation and corrosion. Moreover, these results can be used for screening purposes and/or preliminary hazard assessment.     

GC Determination of Prilocaine HCl in Human Plasma: Analytical Application to Real Samples

A gas chromatographic (GC) method with a rapid and simple sample preparation was developed and validated for determination of prilocaine in human plasma. The validation parameters of linearity, precision, accuracy, recovery, specificity, limit of detection and limit of quantification were studied. The range of quantification for the GC method was 50–300 ng mL^?1 in plasma. Intra- and inter-day precision, expressed as the relative standard deviation (RSD) were less than 4.5%, and accuracy (relative error) was better than 8.0% (n = 6). The analytical recovery of prilocaine HCl from plasma has averaged 96.5% and the recovery of internal standard (lidocaine HCl) reached 96.8%. The limit of quantification (LOQ) and the limit of detection (LOD) of the method for plasma were 50 and 40 ng mL^?1, respectively. Also the developed and validated method was applied to three healthy volunteers to whom a local anaesthesia with citanest was administered.     

Determination of Organotin Compounds in Wine by Microwave-Assisted Extraction and High Performance Liquid Chromatography–Inductively Coupled Plasma Mass Spectrometry

A new analytical procedure for the determination of five organotin compounds in several matrix wine samples is reported. The organotin compounds were extracted by microwave-assisted extraction with n-hexane. Extraction conditions, such as volume of n-hexane required, extraction temperature, and extraction time, were investigated and optimized by an orthogonal array experimental design. The determination of organotin compounds in the final extracts was carried out by liquid chromatography–inductively coupled plasma mass spectrometry. The procedure showed limits of detection between 0.029–0.049 µg · L^?1. The linearity was in the range of 0.5 to 100 µg · L^?1. The precision expressed as relative standard deviation (RSD) was below 9.43%. The developed method was successfully employed to analyze different matrix wine samples, and some analytes were detected at the level of 0.053 to 1.14 µg · L^?1.