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The use of stable isotopically labeled tracers is a long-proven way of specifically detecting and tracking derived metabolites through a metabolic network of interest. While the recently developed stable isotope-assisted methods and associated, supporting data analysis tools have greatly improved untargeted metabolomics approaches, no software tool is currently available that allows us to automatically and flexibly search liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) chromatograms for user-definable isotopolog patterns expected for the metabolism of labeled tracer substances. Here, we present Custom Pattern Extract (CPExtract), a versatile software tool that allows for the first time the high-throughput search for user-defined isotopolog patterns in LC-HRMS data. The patterns can be specified via a set of rules including the presence or absence of certain isotopologs, their relative intensity ratios as well as chromatographic coelution. Each isotopolog pattern satisfying the respective rules is verified on an MS scan level and also in the chromatographic domain. The CPExtract algorithm allows the use of both labeled tracer compounds in nonlabeled biological samples as well as a reversed tracer approach, employing nonlabeled tracer compounds along with globally labeled biological samples. In a proof-of-concept study, we searched for metabolites specifically arising from the malonate pathway of the filamentous fungi Fusarium graminearum and Trichoderma reesei . 1,2,3- 13 C 3 -malonic acid diethyl ester and native malonic acid monomethyl ester were used as tracers. We were able to reliably detect expected fatty acids and known polyketides. In addition, up to 46 and 270 further, unknown metabolites presumably including novel polyketides were detected in the F. graminearum and T. reesei culture samples, respectively, all of which exhibited the user-predicted isotopolog patterns originating from the malonate tracer incorporation. The software can be used for every conceivable tracer approach. Furthermore, the rule sets can be easily adapted or extended if necessary. CPExtract is available free of charge for noncommercial use at https://metabolomics-ifa.boku.ac.at/CPExtract.

A method based on reversed phase high-performance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (RP-HPLC-ESI-HRMS) for the comprehensive and reliable detection of secondary metabolites of Trichoderma reesei cultured in synthetic minimal liquid medium is presented. A stable isotope-assisted (SIA) workflow is used, which allows the automated, comprehensive extraction of truly fungal metabolite-derived LC-MS signals from the acquired chromatographic data. The subsequent statistical data analysis and a typical outcome of such a metabolomics data evaluation are shown by way of example in a previously published study on the influence of the pleiotropic regulator transcription factor Xylanase promoter binding protein 1 (Xpp1) in T. reesei on secondary metabolism.

Xenobiotics are ubiquitous in the environment and modified in the human body by phase I and II metabolism. Liquid chromatography coupled to high resolution mass spectrometry is a powerful tool to investigate these biotransformation products. We present a workflow based on stable isotope-assisted metabolomics and the bioinformatics tool MetExtract II for deciphering xenobiotic metabolites produced by human cells. Its potential was demonstrated by the investigation of the metabolism of deoxynivalenol (DON), an abundant food contaminant, in a liver carcinoma cell line (HepG2) and a model for colon carcinoma (HT29). Detected known metabolites included DON-3-sulfate, DON-10-sulfonate 2, and DON-10-glutathione as well as DON-cysteine. Conjugation with amino acids and an antibiotic was confirmed for the first time. The approach allows the untargeted elucidation of human xenobiotic products in tissue culture. It may be applied to other fields of research including drug metabolism, personalized medicine, exposome research, and systems biology to better understand the relevance of in vitro experiments.

The inclusion of stable isotope-labeled reference standards in the sample is an established method for the detection and relative quantification of metabolic features in untargeted metabolomics. In order to quantify as many metabolites as possible, the reference should ideally include the same metabolites in their stable isotope-labeled form as the sample under investigation. We present here an attempt to use partially 13 C-labeled mouse material as internal standard for relative metabolite quantification of mouse and human samples in untargeted metabolomics. We fed mice for 14 days with a 13 C-labeled Ralstonia eutropha based diet. Tissue and blood amino acids from these mice showed 13 C enrichment levels that ranged from 6% to 75%. We used MetExtract II software to automatically detect native and labeled peak pairs in an untargeted manner. In a dilution series and with the implementation of a correction factor, partially 13 C-labeled mouse plasma resulted in accurate relative quantification of human plasma amino acids using liquid chromatography coupled to mass spectrometry, The coefficient of variation for the relative quantification is reduced from 27% without internal standard to 10% with inclusion of partially 13 C-labeled internal standard. We anticipate the method to be of general use for the relative metabolite quantification of human specimens., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The aim of this manuscript is to present a protocol describing the metabolomic analysis of Bornean 'exploding ants' belonging to the Colobopsis cylindrica (COCY) group. For this purpose, the model species C. explodens is used. Ants belonging to the minor worker caste possess distinctive hypertrophied mandibular glands (MGs). In territorial combat, they use the viscous contents of their enlarged mandibular gland reservoirs (MGRs) to kill rival arthropods in characteristic suicidal 'explosions' by voluntary rupture of the gastral integument (autothysis). We show the dissection of worker ants of this species for the isolation of the gastral portion of the wax-like MGR contents as well as listing the necessary steps required for solvent-extraction of the therein contained volatile compounds with subsequent gas chromatography-mass spectrometry (GC-MS) analysis and putative identification of metabolites contained in the extract. The dissection procedure is performed under cooled conditions and without the use of any dissection buffer solution to minimize the changes in the chemical composition of the MGR contents. After solvent-based extraction of volatile metabolites contained therein, the necessary steps for analyzing the samples via liquid-injection-GC-MS are presented. Lastly, data processing and putative metabolite identification with the use of the open-source software MetaboliteDetector is shown. With this approach, the profiling and identification of volatile metabolites in MGRs of ants belonging to the COCY group via GC-MS and the MetaboliteDetector software become possible.

Liquid chromatography (LC) coupled with mass spectrometry (MS) is widely used for the determination of mycotoxins in cereals and cereal-based products. In addition to the regulated mycotoxins, for which official control is required, LC-MS is often used for the screening of a large range of mycotoxins and/or for the identification and characterization of novel metabolites. This review provides insight into the LC-MS methods used for the determination of co-occurring mycotoxins with special emphasis on multiple-analyte applications. The first part of the review is focused on targeted LC-MS approaches using cleanup methods such as solid-phase extraction and immunoaffinity chromatography, as well as on methods based on minimum cleanup (quick, easy, cheap, effective, rugged, and safe; QuEChERS) and dilute and shoot. The second part of the review deals with the untargeted determination of mycotoxins by LC coupled with high-resolution MS, which includes also metabolomics techniques to study the fate of mycotoxins in plants.

Stable isotope labeling (SIL) techniques have the potential to enhance different aspects of liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics methods including metabolite detection, annotation of unknown metabolites, and comparative quantification. In this work, we present MetExtract II, a software toolbox for detection of biologically derived compounds. It exploits SIL-specific isotope patterns and elution profiles in LC-HRMS(/MS) data. The toolbox consists of three complementary modules: M1 (AllExtract) uses mixtures of uniformly highly isotope-enriched and native biological samples for selective detection of the entire accessible metabolome. M2 (TracExtract) is particularly suited to probe the metabolism of endogenous or exogenous secondary metabolites and facilitates the untargeted screening of tracer derivatives from concurrently metabolized native and uniformly labeled tracer substances. With M3 (FragExtract), tandem mass spectrometry (MS/MS) fragments of corresponding native and uniformly labeled ions are evaluated and automatically assigned with putative sum formulas. Generated results can be graphically illustrated and exported as a comprehensive data matrix that contains all detected pairs of native and labeled metabolite ions that can be used for database queries, metabolome-wide internal standardization, and statistical analysis. The software, associated documentation, and sample data sets are freely available for noncommercial use at http://metabolomics-ifa.boku.ac.at/metextractII .

The evaluation of extraction protocols for untargeted metabolomics approaches is still difficult. We have applied a novel stable isotope-assisted workflow for untargeted LC-HRMS-based plant metabolomics , which allows for the first time every detected feature to be considered for method evaluation. The efficiency and complementarity of commonly used extraction solvents, namely 1 + 3 (v/v) mixtures of water and selected organic solvents (methanol, acetonitrile or methanol/acetonitrile 1 + 1 (v/v)), with and without the addition of 0.1% (v/v) formic acid were compared. Four different wheat organs were sampled, extracted and analysed by LC-HRMS. Data evaluation was performed with the in-house-developed MetExtract II software and R. With all tested solvents a total of 871 metabolites were extracted in ear, 785 in stem, 733 in leaf and 517 in root samples, respectively. Between 48% (stem) and 57% (ear) of the metabolites detected in a particular organ were found with all extraction mixtures, and 127 of 996 metabolites were consistently shared between all extraction agent/organ combinations. In aqueous methanol, acidification with formic acid led to pronounced pH dependency regarding the precision of metabolite abundance and the number of detectable metabolites, whereas extracts of acetonitrile-containing mixtures were less affected. Moreover, methanol and acetonitrile have been found to be complementary with respect to extraction efficiency. Interestingly, the beneficial properties of both solvents can be combined by the use of a water-methanol-acetonitrile mixture for global metabolite extraction instead of aqueous methanol or aqueous acetonitrile alone.

Background: Metabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process., Results: The software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection., Conclusions: QCScreen is an open source software tool which provides a useful basis for assessing the suitability of LC-HRMS data prior to time consuming, detailed data processing and subsequent statistical analysis. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple quality control sample types as well as experimental samples in one or more measurement sequences.

Anthocyanin degradation has been proposed as one of the primary causes for reduced colour and quality in red wine grapes grown in a warm climate. To study anthocyanin degradation we infused berries with L-phenyl-(13)C₆-alanine and then tracked the fate of the anthocyanins comparing normal (25 °C) and warm (45 °C) temperature conditions. An untargeted metabolomics approach was aided by filtering the MS data using software algorithms to extract all M and M+6 isotopic peak pairs, allowing the analysis to focus solely on the metabolites of phenylalanine. A paired-comparison t-test was performed over the 8 biological replicates revealing 13 metabolites that were statistically different between 25 °C and 45 °C treatments. Most of these features had lower abundances in 45 °C samples, confirming that 45 °C treatment caused anthocyanin degradation. In addition, resveratrol was significantly reduced following heat treatment. However, 5 metabolites increased following the 45 °C treatment. These unidentified metabolites are therefore suspects for anthocyanin degradation products., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Structure elucidation of biological compounds is still a major bottleneck of untargeted LC-HRMS approaches in metabolomics research. The aim of the present study was to combine stable isotope labeling and tandem mass spectrometry for the automated interpretation of the elemental composition of fragment ions and thereby facilitate the structural characterization of metabolites. The software tool FragExtract was developed and evaluated with LC-HRMS/MS spectra of both native (12)C- and uniformly (13)C (U-(13)C)-labeled analytical standards of 10 fungal substances in pure solvent and spiked into fungal culture filtrate of Fusarium graminearum respectively. Furthermore, the developed approach is exemplified with nine unknown biochemical compounds contained in F. graminearum samples derived from an untargeted metabolomics experiment. The mass difference between the corresponding fragment ions present in the MS/MS spectra of the native and U-(13)C-labeled compound enabled the assignment of the number of carbon atoms to each fragment signal and allowed the generation of meaningful putative molecular formulas for each fragment ion, which in turn also helped determine the elemental composition of the precursor ion. Compared to laborious manual analysis of the MS/MS spectra, the presented algorithm marks an important step toward efficient fragment signal elucidation and structure annotation of metabolites in future untargeted metabolomics studies. Moreover, as demonstrated for a fungal culture sample, FragExtract also assists the characterization of unknown metabolites, which are not contained in databases, and thus exhibits a significant contribution to untargeted metabolomics research.

An untargeted screening strategy for the detection of biotransformation products of xenobiotics using stable isotopic labelling (SIL) and liquid chromatography-high resolution mass spectrometry (LC-HRMS) is reported. The organism of interest is treated with a mixture of labelled and non-labelled precursor and samples are analysed by LC-HRMS. Raw data are processed with the recently developed MetExtract software for the automated extraction of corresponding peak pairs. The SIL-assisted approach is exemplified by the metabolisation of the Fusarium mycotoxin deoxynivalenol (DON) in planta. Flowering ears were inoculated with 100 μg of a 1 + 1 (v/v) mixture of non-labelled and fully labelled DON. Subsequent sample preparation, LC-HRMS measurements and data processing revealed a total of 57 corresponding peak pairs, which originated from ten metabolites. Besides the known DON and DON-3-glucoside, which were confirmed by measurement of authentic standards, eight further DON-biotransformation products were found by the untargeted screening approach. Based on a mass deviation of less than ±5 ppm and MS/MS measurements, one of these products was annotated as DON-glutathione (GSH) conjugate, which is described here for the first time for wheat. Our data further suggest that two DON-GSH-related metabolites, the processing products DON-S-cysteine and DON-S-cysteinyl-glycine and five unknown DON conjugates were formed in planta. Future MS/MS measurements shall reveal the molecular structures of the detected conjugates in more detail.

Metabolomics has emerged as the latest of the so-called "omics" disciplines and has great potential to provide deeper understanding of fundamental biochemical processes at the biological system level. Among recent technological developments, LC-HRMS enables determination of hundreds to thousands of metabolites over a wide range of concentrations and has developed into one of the most powerful techniques in non-targeted metabolomics. The analysis of mixtures of in-vivo-stable isotopic-labeled samples or reference substances with un-labeled samples leads to specific LC-MS data patterns which can be systematically exploited in practically all data-processing steps. This includes recognition of true metabolite-derived analytical features in highly complex LC-MS data and characterization of the global biochemical composition of biological samples. In addition, stable-isotopic labeling can be used for more accurate quantification (via internal standardization) and identification of compounds in different organisms.

Introduction: Volatile organic compounds (VOCs) occurring in leaves of plants carry information about the physiological state of the plant. Monitoring of VOCs assists in detecting plant stress before visible signs are present., Objective: To establish and apply a simple workflow for the automated extraction, measurement and annotation/identification of Vitis vinifera cv. Pinot Noir leaf metabolites., Methodology: Leaf samples were harvested, cooled with liquid nitrogen and homogenised under cooled conditions. VOCs were extracted and enriched by solid phase microextraction (SPME) and analysed by GC-MS. Samples were measured on two columns with different polarity of stationary phases. Mass spectral deconvolution and identification was done by AMDIS software. Strict identification criteria were applied: match factor ≥ 90; relative retention index deviation ≤ 2% from reference value on both columns. Data of two sampling dates were analysed with multivariate statistics., Results: We found ~600 components in a single chromatogram. Applying the mentioned criteria resulted in annotation of 63 metabolites of which 47 were confirmed with authentic standards. For the majority of the compounds technical variability was < < 40% (RSD), biological variability among plants was 7-119%. Principal component analysis (PCA) scores plot of leaf samples from two different sampling dates showed two clearly separated clusters. The presented workflow enabled for the first time the detection and identification of 19 metabolites that have so far not been described for Vitis spp., Conclusion: The developed workflow enabled the identification of grapevine leaf metabolites, which allowed the separation of leaves from two sampling dates by PCA., (Copyright © 2011 John Wiley & Sons, Ltd.)

Motivation: Liquid chromatography-mass spectrometry (LC/MS) is a key technique in metabolomics. Since the efficient assignment of MS signals to true biological metabolites becomes feasible in combination with in vivo stable isotopic labelling, our aim was to provide a new software tool for this purpose., Results: An algorithm and a program (MetExtract) have been developed to search for metabolites in in vivo labelled biological samples. The algorithm makes use of the chromatographic characteristics of the LC/MS data and detects MS peaks fulfilling the criteria of stable isotopic labelling. As a result of all calculations, the algorithm specifies a list of m/z values, the corresponding number of atoms of the labelling element (e.g. carbon) together with retention time and extracted adduct-, fragment- and polymer ions. Its function was evaluated using native (12)C- and uniformly (13)C-labelled standard substances., Availability: MetExtract is available free of charge and warranty at http://code.google.com/p/metextract/. Precompiled executables are available for Windows operating systems., Supplementary Information: Supplementary data are available at Bioinformatics online.