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In untargeted metabolomics, the unambiguous identification of metabolites remains a major challenge. This requires high-quality spectral libraries for reliable metabolite identification, which is essential for translating metabolomics data into meaningful biological information. Several attempts have been made to generate reproducible product ion spectra (PIS) under a low collision energy ( E Lab ) regime and nonresonant collisional conditions but have not fully succeeded. We examined the ERMS (energy-resolved mass spectrometry) breakdown curves of two lipo-amino acids and showed the possibility to highlight "singular points", called descriptors hereafter (linked to respective E Lab depending on the instrument), for each of the monomodal product ion profiles. Using several instruments based on different technologies, the PIS recorded at these specific E at which PIS recorded in nonresonant conditions become highly similar and instrument-independent, thus comparable across platforms. This innovative but straightforward approach could help remove some of the obstacles to metabolite identification in nontargeted metabolomics, putting an end to a challenging chimera.Lab sites shows remarkable similarities. The descriptors appeared as being independent of the fragmentation mechanisms and can be used to overcome the main instrumental effects that limit the interoperability of spectral libraries. This proof-of-concept study, performed on two particular lipo-amino acids, demonstrates the high potential of ERMS-derived information to determine the instrument-specific E Lab at which PIS recorded in nonresonant conditions become highly similar and instrument-independent, thus comparable across platforms. This innovative but straightforward approach could help remove some of the obstacles to metabolite identification in nontargeted metabolomics, putting an end to a challenging chimera.

Introduction: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories., Objectives: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management., Methods: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles., Results: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases., Conclusion: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest ., (© 2022. The Author(s).)

There is an urgent need to implement holistic and untargeted doping control protocols with improved discriminatory power, compared to conventional methods that only target doping agents. Metabolomics, which aims to characterize all metabolites present in biological matrices, could fulfill this need. In this context, the aim of this study was to evaluate the impact of environmental factors on the ability to obtain a metabolic signature of stanozolol administration in horse doping situation. Urine samples from 16 horses breeded in two different places were collected over a one-year period, before, during and seven months after the administration of stanozolol, a horse doping agent. Metabolomic analysis was performed using ultra-high pressure reverse phase liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (MS). Results showed a major impact of the nutritional regimen, drug administration (for de-worming purpose) and breeding place on the metabolite profiles of horse urines, which hampered the detection of metabolic perturbations induced by stanozolol administration. After having used MS/MS experiments to characterize some MS features related to these environmental factors, we showed that highlighting and then removing the features impacted by these confounding factors before performing supervised multivariate statistical analyses could address this issue. In conclusion, adequate consideration should be given to environmental and physiological factors; otherwise, they can emerge as confounding factors and conceal doping administration.

Nowadays, high-resolution mass spectrometry is widely used for metabolomic studies. Thanks to its high sensitivity, it enables the detection of a large range of metabolites. In metabolomics, the continuous quest for a metabolite identification as complete and accurate as possible has led during the last decade to an ever increasing development of public MS databases (including LC-MS data) concomitantly with bioinformatic tool expansion. To facilitate the annotation process of MS profiles obtained from biological samples, but also to ease data sharing, exchange, and exploitation, the standardization and harmonization of the way to describe and annotate mass spectra seemed crucial to us. Indeed, under electrospray (ESI) conditions, a single metabolite does not produce a unique ion corresponding to its protonated or deprotonated form but could lead to a complex mixture of signals. These MS signals result from the existence of different natural isotopologues of the same compound and also to the potential formation of adduct ions, homomultimeric and heteromultimeric ions, fragment ions resulting from "prompt" in-source dissociations. As a joint reflection process within the French Infrastructure for Metabolomics and Fluxomics (MetaboHUB) and with the purpose of developing a robust and exchangeable annotated MS database made from pure reference compounds (chemical standards) analysis, it appeared to us that giving the metabolomics community some clues to standardize and unambiguously annotate each MS feature was a prerequisite to data entry and further efficient querying of the mass spectral database. The use of a harmonized notation is also mandatory for interlaboratory MS data exchange. Additionally, thorough description of the variety of MS signals arising from the analysis of a unique metabolite might provide greater confidence on its annotation., (© 2019 John Wiley & Sons, Ltd.)

Direct injection-mass spectrometry can be used to perform high-throughput metabolomic fingerprinting. This work aims to evaluate a global analytical workflow in terms of sample preparation (urine sample dilution), high-resolution detection (quality of generated data based on criteria such as mass measurement accuracy and detection sensitivity) and data analysis using dedicated bioinformatics tools. Investigation was performed on a large number of biological samples collected from sheep infected or not with scrapie. Direct injection-mass spectrometry approach is usually affected by matrix effects, eventually hampering detection of some relevant biomarkers. Reference compounds were spiked in biological samples to help evaluate the quality of direct injection-mass spectrometry data produced by Fourier Transform mass spectrometry. Despite the potential of high-resolution detection, some drawbacks still remain. The most critical is the presence of matrix effects, which could be minimized by optimizing the sample dilution factor. The data quality in terms of mass measurement accuracy and reproducible intensity was evaluated. Good repeatability was obtained for the chosen dilution factor (i.e., 2000). More than 150 analyses were performed in less than 16 hours using the optimized direct injection-mass spectrometry approach. Discrimination of different status of sheeps in relation to scrapie infection (i.e., scrapie-affected, preclinical scrapie or healthy) was obtained from the application of Shrinkage Discriminant Analysis to the direct injection-mass spectrometry data. The most relevant variables related to this discrimination were selected and annotated. This study demonstrated that the choice of appropriated dilution faction is indispensable for producing quality and informative direct injection-mass spectrometry data. Successful application of direct injection-mass spectrometry approach for high throughput analysis of a large number of biological samples constitutes the proof of the concept.

Annotation of signals of interest represents a key point in mass spectrometry-based metabolomics studies. The first level of investigation is the elemental composition, which can be deduced from accurately measured masses and isotope patterns. However, accuracy of these two parameters remains to be evaluated on last generation mass spectrometers to determine the level of confidence that can be used during the annotation process. In this context, we evaluated the performance of the Orbitrap Fusion mass spectrometer for the first time and demonstrated huge potential for metabolite annotation via elemental composition determination. This work was performed using a set of 50 standard compounds analyzed under LC/MS conditions in solvent and biological media. Accurate control of the number of trapped ions proved mandatory to avoid space charge effects, ensure sub-ppm mass accuracy (using external calibration), and reliable measurement of isotopic patterns at 500,000 resolution. On the basis of the results, we propose standard optimized experimental conditions for performing robust and accurate untargeted metabolomics on the Orbitrap Fusion at high mass measurement and mass spectral accuracy.

This work aims at evaluating the relevance and versatility of liquid chromatography coupled to high resolution mass spectrometry (LC/HRMS) for performing a qualitative and comprehensive study of the human serum metabolome. To this end, three different chromatographic systems based on a reversed phase (RP), hydrophilic interaction chromatography (HILIC) and a pentafluorophenylpropyl (PFPP) stationary phase were used, with detection in both positive and negative electrospray modes. LC/HRMS platforms were first assessed for their ability to detect, retain and separate 657 metabolite standards representative of the chemical families occurring in biological fluids. More than 75% were efficiently retained in either one LC-condition and less than 5% were exclusively retained by the RP column. These three LC/HRMS systems were then evaluated for their coverage of serum metabolome. The combination of RP, HILIC and PFPP based LC/HRMS methods resulted in the annotation of about 1328 features in the negative ionization mode, and 1358 in the positive ionization mode on the basis of their accurate mass and precise retention time in at least one chromatographic condition. Less than 12% of these annotations were shared by the three LC systems, which highlights their complementarity. HILIC column ensured the greatest metabolome coverage in the negative ionization mode, whereas PFPP column was the most effective in the positive ionization mode. Altogether, 192 annotations were confirmed using our spectral database and 74 others by performing MS/MS experiments. This resulted in the formal or putative identification of 266 metabolites, among which 59 are reported for the first time in human serum., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Background: Horse urine is the medium of choice for the implementation of metabolomic approaches aimed at improving horse doping control. However, drug analysis in this biofluid is a challenging task due to the presence of large amounts of interfering compounds. METHODOLOGY & RESULTS: A comparative study of sample preparation has been conducted to evaluate five sample-preparation methods, namely acetonitrile precipitation, proteinase K hydrolysis, membrane filtration and sample dilution with water by factors of five and 20, for metabolome analysis using liquid chromatography coupled to high resolution mass spectrometry. Assessment was performed at both global and targeted levels, by using a few thousand features obtained from peak detection software, and internal standards and 100 annotated or identified metabolites., Conclusion: By considering the number of detected signals, their intensity and their detection repeatability, acetonitrile precipitation was selected as the most efficient sample-preparation method for the analysis of horse urine metabolome in liquid chromatography coupled to high resolution mass spectrometry conditions.

Recently, high-resolution mass spectrometry has been largely employed for compound identification, thanks to accurate mass measurements. As additional information, relative isotope abundance (RIA) is often needed to reduce the number of candidates prior to tandem MS(n). Here, we report on the evaluation of the LTQ-Orbitrap, in terms of accurate mass and RIA measurements for building further metabolomics spectral databases. Accurate mass measurements were achieved in the ppm range, using external calibration within 24 h, and remained at <5 ppm over a one-week period. The experimental relative abundances of (M+1) isotopic ions were evaluated in different data sets. First of all, 137 solutions of commercial compounds were analyzed by flow injection analysis in both the positive and negative ion modes. It was found that the ion abundance was the main factor impacting the accuracy of RIA measurements. It was possible to define some intensity thresholds above which errors were systematically <20% of their theoretical values. The same type of results were obtained with analyses from two biological media. Otherwise, no significant effect of ion transmission between the LTQ ion trap and the Orbitrap analyzer on RIA measurement errors was found, whereas the reliability of RIA measurements was dramatically improved by reducing the mass detection window. It was also observed that the signal integration method had a significant impact on RIA measurement errors, with the most-reliable results being obtained with peak height integrations. Finally, automatic integrations using the data preprocessing software XCMS and MZmine gave results similar to those obtained by manual integration, suggesting that it is relevant to use the RIA information in automatic elemental composition determination software from metabolomic peak tables.

Reproducibility among different types of excitation modes is a major bottleneck in the field of tandem mass spectrometry library development in metabolomics. In this study, we specifically evaluated the influence of collision voltage and activation time parameters on tandem mass spectrometry spectra for various excitation modes [collision-induced dissociation (CID), pulsed Q dissociation (PQD) and higher-energy collision dissociation (HCD)] of Orbitrap-based instruments. For this purpose, internal energy deposition was probed using an approach based on Rice-Rampserger-Kassel-Marcus modeling with three thermometer compounds of different degree of freedom (69, 228 and 420) and a thermal model. This model treats consecutively the activation and decomposition steps, and the survival precursor ion populations are characterized by truncated Maxwell-Boltzmann internal energy distributions. This study demonstrates that the activation time has a significant impact on MS/MS spectra using the CID and PQD modes. The proposed model seems suitable to describe the multiple collision regime in the PQD and HCD modes. Linear relationships between mean internal energy and collision voltage are shown for the latter modes and the three thermometer molecules. These results suggest that a calibration based on the collision voltage should provide reproducible for PQD, HCD to be compared with CID in tandem in space instruments. However, an important signal loss is observed in PQD excitation mode whatever the mass of the studied compounds, which may affect not only parent ions but also fragment ions depending on the fragmentation parameters. A calibration approach for the CID mode based on the variation of activation time parameter is more appropriate than one based on collision voltage. In fact, the activation time parameter in CID induces a modification of the collisional regime and thus helps control the orientation of the fragmentation pathways (competitive or consecutive dissociations). Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

High resolution mass spectrometry (HRMS) is increasingly used to produce metabolomics data. Thanks to its high mass resolution and mass measurement accuracy, it is also very useful for metabolite identification. Nevertheless, a rigorous methodology is required. This manuscript describes different steps involved in the structural elucidation of metabolites and demonstrates the utility of HRMS for such purpose. After a brief overview of HRMS performances in terms of mass measurement accuracy, peak resolution, isotopic clusters/patterns and the instrumentation used, the first section is devoted to the data processing generally performed to reduce the data set size. Based on the mass accuracy measurements, different post-acquisition data processing procedures have been developed for complex mixture analysis and can be used in metabolomics. The second section describes protocols used to process putative metabolite annotations or identifications with HRMS data, based on elemental composition determined from accurately measured m/z value and mass spectral databases. Non-classical approaches are also proposed for tentative structure elucidation of unknown metabolites. Finally, limitations of the proposed workflow for metabolite structure elucidation are discussed and possible improvements are proposed. [ABSTRACT FROM AUTHOR]

Abstract: Metabolomics investigations have driven extensive developments in analytical platforms. Among analytical technologies, mass spectrometry plays a central role into the wide range of metabolomics applications as global metabolite profiling, absolute quantification and also metabolite structural elucidation. This chapter presents an overview of Fourier transform mass spectrometer (FT-MS) advantageous features for the large-scale measurements of metabolites. Principles of FT-MS devices are described with some details on the performance characteristics such as the highest available mass resolving power, mass measurement ability with sub-ppm errors, the most powerful dynamic range and various activation modes. Particularly, we focused onto the usefulness of atmospheric pressure ionization sources coupled to FT analyzers for very high-throughput direct injection mass spectrometry approach. However, different analytical issues should be considered when employing Direct Injection High Resolution Mass Spectrometry approach, including matrix effects and metabolite identification complexity. The collection of large HRMS data sets constitutes only the preliminary step of the metabolomics workflow. Indeed, metabolite identification and ultimately the wished better biological understanding require traditional bioinformatics tools such as the metabolite database query but also new data processing specifically developed on the basis of HRMS data characteristics, that is, accurate mass, mass defect or highly resolved isotopic peaks. [Copyright &y& Elsevier]

Abstract: The metabolome is characterized by a large number of molecules exhibiting a high diversity of chemical structures and abundances, requiring complementary analytical platforms to reach its extensive coverage. Among them, atmospheric pressure ionization mass spectrometry (API-MS)-based technologies, and especially those using electrospray ionization are now very popular. In this context, this review deals with strengths, limitations and future trends in the identification of signals highlighted by API-MS-based metabolomics. It covers the identification process from the determination of the molecular mass and/or its elemental composition to the confirmation of structural hypotheses. Furthermore, some tools that were developed in order to address the MS signal redundancy and some approaches that could facilitate identification by improving the visualization and organization of complex data sets are also reported and discussed. [Copyright &y& Elsevier]

Abstract: Understanding the metabolic answer and the adaptation of plants towards heavy metal exposure opens the way to future phytoremediation of polluted sites. For this reason, we studied the impact of cadmium, a toxic heavy metal, on the metabolome of the model plant Arabidopsis thaliana. The analytical methodology (liquid chromatography coupled with mass spectrometry) that was used within the framework of a designed experiment conducted on A. thaliana cells exposed to cadmium generated an important volume of data. Multivariate statistical analyses appeared relevant to compare the metabolic fingerprints in order to isolate and identify some discriminating metabolites. Three types of data pretreatment, i.e., reduction of dimensionality, bucketing and automatic processing by the MetAlign™ software were compared for efficiency in extracting the information. The pretreated data were then subjected to multivariate statistical analysis by principal component analysis (PCA) and partial least square regression (PLS). Finally, an OSC (Orthogonal Signal Correction)-PLS2 approach performed on kinetic and dose ranging studies allowed to visualize time- and cadmium dose-induced changes on the metabolism of A. thaliana cells. [Copyright &y& Elsevier]