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Using a new red blood cell (RBC) metabolite extraction protocol, we performed a metabolomic analysis on RBCs in rheumatoid arthritis (RA) patients treated or not with methotrexate (MTX), with the two following objectives: to compare the RBC metabolic profiles of MTX-naïve RA patients and healthy controls (HC), and to investigate whether RBC profiles before and after MTX treatment in RA differed between responders and non-responders. Plasma analysis was performed in parallel. Metabolites were extracted and identified in RBCs and plasma by liquid chromatography-mass spectrometry. We compared the metabolomic fingerprints of 31 DMARD-naïve RA patients and 39 HCs. We also compared the RBC and plasma metabolomes of 25 RA patients who responded or not to MTX therapy before (M0) and after a 3-month treatment period (M3). Significance was determined by Storey's false discovery rate (FDR) q-values to correct for multiple testing. RA patients and HCs differed in the metabolomic signature of RBCs. The signature mainly contained amino acids (AA). Eleven metabolites, including 4 metabolites belonging to the carbohydrate subclass and 2 amino acids (creatine and valine) showed accumulation in RBCs from RA patients. Conversely, citrulline (fold change = 0.83; q = 0.025), histidine (fold change = 0.86; q = 0.014) and ergothioneine (EGT) (fold change = 0.66; q = 0.024), were lower in RBC of RA patients. Five plasma metabolites, including succinic acid and hydroxyproline, were higher in RA patients, and 7 metabolites, including DHEA sulfate, alanine, threonine and ornithine, were lower. Among RA patients undergoing MTX treatment pre-treatment (M0), EGT values were significantly lower in non-responders. In conclusion, low RBC levels of EGT, a food-derived AA barely detectable in plasma, characterize DMARD naïve RA patients and lack of response to MTX treatment., (© 2024. The Author(s).)

The implementation of ion mobility spectrometry (IMS) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflows has become a valuable tool for improving compound annotation in metabolomics analyses by increasing peak capacity and by adding a new molecular descriptor, the collision cross section (CCS). Although some studies reported high repeatability and reproducibility of CCS determination and only few studies reported good interplatform agreement for small molecules, standardized protocols are still missing due to the lack of reference CCS values and reference materials. We present a comparison of CCS values of approximatively one hundred lipid species either commercially available or extracted from human plasma. We used three different commercial ion mobility technologies from different laboratories, drift tube IMS (DTIMS), travelling wave IMS (TWIMS) and trapped IMS (TIMS), to evaluate both instrument repeatability and interlaboratory reproducibility. We showed that CCS discrepancies of 0.3% (average) could occur depending on the data processing software tools. Moreover, eleven CCS calibrants were evaluated yielding mean RSD below 2% for eight calibrants, ESI Low concentration tuning mix (Tune Mix) showing the lowest RSD (< 0.5%) in both ion modes. Tune Mix calibrated CCS from the three different IMS instruments proved to be well correlated and highly reproducible (R 2  > 0.995 and mean RSD ≤ 1%). More than 90% of the lipid CCS had deviations of less than 1%, demonstrating high comparability between techniques, and the possibility to use the CCS as molecular descriptor. We highlighted the need of standardized procedures for calibration, data acquisition, and data processing. This work demonstrates that using harmonized analytical conditions are required for interplatform reproducibility for CCS determination of human plasma lipids., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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.

Collision cross sections (CCS) have been described as relevant molecular descriptors in metabolomics and lipidomics analyses for ascertaining compound identity. Ion mobility spectrometry (IMS) allows to determine CCS with different techniques, such as drift tube ion mobility spectrometry (DTIMS), traveling wave ion mobility spectrometry (TWIMS) or trapped ion mobility spectrometry (TIMS). In contrast with DTIMS where CCS can be obtained directly with measured drift times and mathematical relationship, TWIMS and TIMS techniques require an additional step of calibration to obtain CCS values. However, literature reports significantly disparate CCS values depending on the calibrant used (often more than 10%), as no consensus has been reached to define a universal CCS reference standard or harmonized calibration procedure. Therefore, publicly available CCS databases cannot be regarded as readily interoperable and exchangeable. Here, we performed a comprehensive evaluation of 11 distinct CCS calibrants in a traveling wave ion mobility spectrometry-mass spectrometry (TWIMS-MS) instrument. We showed that, using lipids from plasma as model compounds, CCS determination drastically fluctuates from one calibrant to the other with up to 25% differences, which precludes direct CCS comparison. Using the large panel of calibration curves generated, we showed that any CCS value can be efficiently re-calibrated relatively to the calibration curve made with the widely used Tune Mix solution whatever the calibration procedure originally used. The re-calibrated CCS values for each calibrant constitute a database which allows to correct any deviation on lipid CCS values whatever the calibrant originally used. Resulting corrected CCS values from plasma lipids were thus efficiently matched to those previously reported in the literature (with deviations<2%). Therefore, this work shows that unique and comparable CCS values can be obtained upon re-calibration relatively to Tune Mix CCS values, while also paving the way for the establishment of a universal CCS database of various metabolite or lipid classes., Competing Interests: Declaration of competing interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2022. Published by Elsevier B.V.)

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).)

Metabolomics refers to the study of biological components below 1000 Daltons (Da) involved in metabolic pathways as substrates, products or effectors. According to the interconnected metabolic disturbances that have been described in the pathophysiology of hepatic encephalopathy (HE), this technique appears to be well adapted to study and better delineate the disease. This review will focus on recent advances in metabolomics in the field of HE. Thus, after a brief overview of the general principles of metabolomics, we will discuss metabolomics as a potentially efficient tool for unraveling new HE pathophysiological insights, biomarkers identification, or as a predicting tool for treatment response or outcome prognosis. Finally, we will give our vision on the prospects offered by metabolomics for improving care of HE patients., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine., (© 2021. The Author(s).)

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration., (© 2021. The Author(s).)

Ultra-high-resolution mass spectrometry, in the absence of chromatography, is finding its place for direct analyses of highly complex mixtures, such as those encountered during untargeted metabolomics screening. Advances, however, have been tempered by difficulties such as uneven signal suppression experienced during electrospray ionization. Moreover, ultra-high-resolution mass spectrometers that use Orbitrap and ICR analyzers both suffer from limited ion trapping capacities, owing principally to space-charge effects. This study has evaluated and contrasted the above two types of Fourier transform mass spectrometers for their abilities to detect and identify by accurate mass measurement, small molecule metabolites present in complex mixtures. For these direct introduction studies, the Orbitrap Fusion showed a major advantage in terms of speed of analysis, enabling detection of 218 of 440 molecules (<2 ppm error, 500 000 resolution at m/z 200) present in a complex mixture in 5 min. This approach is the most viable for high-throughput workflows, such as those used in investigations involving very large cohorts of metabolomics samples. From the same mixture, 183 unique molecules were observed by FT-ICR in the broadband mode, but this number was raised to 235 when "selected ion monitoring-stitching" (SIM-stitching) was employed (<0.1 ppm error, 7 T magnet with dynamic harmonization cell, 1.8 million resolution at m/z 200, both cases). SIM-stitching FT-ICR thus offered the most complete detection, which may be of paramount importance in situations where it is essential to obtain the most complete metabolic profile possible. This added completeness, however, came at the cost of a more lengthy analysis time (120 min including manual treatment). Compared to the data presented here, future automation of processing, plus the use of absorption mode detection, segmented ion detection (stepwise detection of smaller width m/z sections), and higher magnetic field strengths, can substantially reduce FT-ICR acquisition times., (© 2020 John Wiley & Sons, Ltd.)

Background & Aims: Acute-on-chronic liver failure (ACLF), which develops in patients with cirrhosis, is characterized by intense systemic inflammation and organ failure(s). Because systemic inflammation is energetically expensive, its metabolic costs may result in organ dysfunction/failure. Therefore, we aimed to analyze the blood metabolome in patients with cirrhosis, with and without ACLF., Methods: We performed untargeted metabolomics using liquid chromatography coupled to high-resolution mass spectrometry in serum from 650 patients with AD (acute decompensation of cirrhosis, without ACLF), 181 with ACLF, 43 with compensated cirrhosis, and 29 healthy individuals., Results: Of the 137 annotated metabolites identified, 100 were increased in patients with ACLF of any grade, relative to those with AD, and 38 comprised a distinctive blood metabolite fingerprint for ACLF. Among patients with ACLF, the intensity of the fingerprint increased across ACLF grades, and was similar in patients with kidney failure and in those without, indicating that the fingerprint reflected not only decreased kidney excretion but also altered cell metabolism. The higher the ACLF-associated fingerprint intensity, the higher the plasma levels of inflammatory markers, tumor necrosis factor α, soluble CD206, and soluble CD163. ACLF was characterized by intense proteolysis and lipolysis; amino acid catabolism; extra-mitochondrial glucose metabolism through glycolysis, pentose phosphate, and D-glucuronate pathways; depressed mitochondrial ATP-producing fatty acid β-oxidation; and extra-mitochondrial amino acid metabolism giving rise to metabotoxins., Conclusions: In ACLF, intense systemic inflammation is associated with blood metabolite accumulation and profound alterations in major metabolic pathways, in particular inhibition of mitochondrial energy production, which may contribute to the development of organ failures., Lay Summary: Acute-on-chronic liver failure (ACLF), which develops in patients with cirrhosis, is characterized by intense systemic inflammation and organ failure(s). Because systemic inflammation is energetically expensive, its metabolic costs may result in organ dysfunction/failure. We identified a 38-metabolite blood fingerprint specific for ACLF that revealed mitochondrial dysfunction in peripheral organs. This may contribute to organ failures., (Copyright © 2019 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

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.)

Staphylococcus aureus is a major human pathogen that can readily acquire antibiotic resistance. For instance, methicillin-resistant S. aureus represents a major cause of hospital- and community-acquired bacterial infections. In this chapter, we first provide a detailed protocol for obtaining unbiased and reproducible S. aureus metabolic profiles. The resulting intracellular metabolome is then analyzed in an untargeted manner by using both hydrophilic interaction liquid chromatography and pentafluorophenyl-propyl columns coupled to high-resolution mass spectrometry. Such analyses are done in conjunction with our in-house spectral database to identify with high confidence as many meaningful S. aureus metabolites as possible. Under these conditions, we can routinely monitor more than 200 annotated S. aureus metabolites. We also indicate how this protocol can be used to investigate the metabolic differences between methicillin-resistant and susceptible strains.

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.

Typical mass spectrometry (MS) based untargeted metabolomics protocols are tedious as well as time- and sample-consuming. In particular, they often rely on "full-scan-only" analyses using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) from which metabolites of interest are first highlighted, and then tentatively identified by using targeted MS/MS experiments. However, this situation is evolving with the emergence of integrated HRMS based-data acquisition protocols able to perform multi-event acquisitions. Most of these protocols, referring to as data dependent and data independent acquisition (DDA and DIA, respectively), have been initially developed for proteomic applications and have recently demonstrated their applicability to biomedical studies. In this context, the aim of this article is to take stock of the progress made in the field of DDA- and DIA-based protocols, and evaluate their ability to change conventional metabolomic and lipidomic data acquisition workflows, through a review of HRMS instrumentation, DDA and DIA workflows, and also associated informatics tools., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics., (© 2013 Wiley Periodicals, Inc.)

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.)