Innovative direct introduction-ion mobility-mass spectrometry (DI-IM-MS) approach for fast and robust isomer-specific quantification in a complex matrix: Application to 2'-fucosyllactose (2'-FL) in breast milk.

Identification and specific quantification of isomers in a complex biological matrix by mass spectrometry alone is not an easy task due to their identical chemical formula and therefore their same mass-to-charge ratio (m/z). Here, the potential of direct introduction combined with ion mobility-mass spectrometry (DI-IM-MS) for rapid quantification of isomers as human milk oligosaccharides (HMOs) was investigated. Differences in HMO profiles between various analyzed breast milk samples were highlighted using the single ion mobility monitoring (SIM2) acquisition for high ion mobility resolution detection. Furthermore, the Se+ (secretor) or Se- (non-secretor) phenotype could be assigned to breast milk samples studied based on their HMO contents, especially on the response of 2'-fucosyllactose (2'-FL) and lacto-N-fucopentaose I (LNFP I). The possibility of quantifying a specific isomer in breast milk by DI-IM-MS was also investigated. The standard addition method allowed the determination of the 2'-FL despite the presence of other oligosaccharides, including 3-fucosyllactose (3-FL) isomer in breast milk. This proof-of-concept study demonstrated the high potential of such an approach for the rapid and convenient quantification of isomers in complex mixtures.

Chemical Exposure Highlighted without Any A Priori Information in an Epidemiological Study by Metabolomic FT-ICR-MS Fingerprinting at High Throughput and High Resolution.

Epidemiological studies aim to assess associations between diseases and risk factors. Such investigations involve a large sample size and require powerful analytical methods to measure the effects of risk factors, resulting in a long analysis time. In this study, chemical exposure markers were detected as the main variables strongly affecting two components coming from a principal component analysis (PCA) exploration of the metabolomic data generated from urinary samples collected on a cohort of about 500 individuals using direct introduction coupled with a Fourier-transform ion cyclotron resonance instrument. The assignment of their chemical identity was first achieved based on their isotopic fine structures detected at very high resolution (Rp > 900,000). Their identification as dimethylbiguanide and sotalol was obtained at level 1, thanks to the available authentic chemical standards, tandem mass spectrometry (MS/MS) experiments, and collision cross section measurements. Epidemiological data confirmed that the subjects discriminated by PCA had declared to be prescribed these drugs for either type II diabetes or cardiac arrhythmia. Concentrations of these drugs in urine samples of interest were also estimated by rapid quantification using an external standard calibration method, direct introduction, and MS/MS experiments. Regression analyses showed a good correlation between the estimated drug concentrations and the scores of individuals distributed on these specific PCs. The detection of these chemical exposure markers proved the potential of the proposed high-throughput approach without any prior drug exposure knowledge as a powerful emerging tool for rapid and large-scale phenotyping of subjects enrolled in epidemiological studies to rapidly characterize the chemical exposome and adherence to medical prescriptions.

Rapid structural characterization of human milk oligosaccharides and distinction of their isomers using trapped ion mobility spectrometry time-of-flight mass spectrometry.

Oligosaccharides have multiple functions essential for health. Derived from the condensation of two to several monosaccharides, they are structurally diverse with many co-occurring structural isomer families, which make their characterization difficult. Thanks to its ability to separate small molecules based on their mass, size, shape, and charge, ion mobility-mass spectrometry (IM-MS) has emerged as a powerful tool for separating glycan isomers. Here, the potential of such a technique for the rapid characterization of main human milk oligosaccharides (HMOs) was investigated. Our study focused on 18 HMO standards. The IM-MS analysis enabled to distinguish almost all the HMOs studied, in particular thanks to the single ion mobility monitoring acquisition using the trapped ion mobility spectrometry device, providing high ion mobility resolution and enhanced ion mobility separation. Alternatively, the combination of IM-MS separation with MS/MS experiments has proven to increase performance in identifying HMOs and especially isomers poorly separated by ion mobility alone. Finally, collision cross-section values are provided for each species generated from the 18 HMOs standards, which can serve as an additional identifier to characterize HMOs.

PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management.

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 .

Different ion mobility-mass spectrometry coupling techniques to promote metabolomics.

Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.

Trapped ion mobility spectrometry time-of-flight mass spectrometry for high throughput and high resolution characterization of human milk oligosaccharide isomers.

The microbiome and immune system of infants are shaped by various bioactive components of human breastmilk, notably human milk oligosaccharides (HMOs). HMOs represent the third component of breastmilk and exhibit extremely high structural diversity with many isomers. Here, we propose a high throughput and high resolution approach to characterize main oligosaccharides present in breastmilk with high identification level thanks to ion mobility spectrometry. Four pairs of standard HMO isomers, that are (LNT/LNnT), (LNFP I/LNFP V), (3'-SL/6'-SL) and (2'-FL/3-FL), were first investigated under both positive and negative ionization mode using direct introduction-trapped ion mobility spectrometry-time-of-flight mass spectrometry (TIMS-TOF). By examining all the ionic species formed (i.e. protonated and deprotonated ions as well as adduct species), every isomer pair could be distinguished through the separation of at least one species, even with a small difference in collision cross section values (as small as 1.5%) thanks to the flexible resolution capacity of the TIMS instrument. Although multiple mobility peaks resulting from different glycan anomeric conformers, open-ring and/or different ionic isomer structures (i.e. various charge site locations), could be observed for some HMO species. The reduction at the reducing-end of HMOs did not significantly facilitate the isomer distinction. Finally, the unambiguous identification of the studied HMOs in a breastmilk sample showed the potential of the approach combining ion mobility separation and MS/MS experiments for high throughput distinction of HMO isomers in complex breastmilk samples without laborious sample preparation.

Mass spectrometry-based metabolomics for an in-depth questioning of human health.

Today, metabolomics is becoming an indispensable tool to get a more comprehensive analysis of complex living systems, providing insights on multiple aspects of physiology. Although its application in large scale population-based studies is very challenging due to the processing of large sample sets as well as the complexity of data information, its potential to characterize human health is well recognized. Technological advances in metabolomics pave the way for the efficient biomarker discovery of disease etiology, diagnosis and prognosis. Here, different steps of the metabolomics workflow, particularly mass spectrometry-based approaches, are discussed to demonstrate the potential of metabolomics to address biological questioning in human health. First an overview of metabolomics is provided with its interest in human health studies. Analytical development and advances in mass spectrometry instrumentation and computational tools are discussed regarding their application limits. Advancing metabolomics for applicability in human health and large-scale studies is presented and discussed in conclusion.

An emerging powerful technique for distinguishing isomers: Trapped ion mobility spectrometry time-of-flight mass spectrometry for rapid characterization of estrogen isomers.

RATIONALE: Isomer metabolites are involved in metabolic pathways, and their characterization is essential but remains challenging even using high-performance analytical platforms. The addition of ion mobility prior to mass analysis can help to separate isomers. Here, the ability of a recently developed trapped ion mobility spectrometry system to separate metabolite isomers was examined. METHODS: Three pairs of estrogen isomers were studied as a model of isomeric metabolites under both negative and positive electrospray ionization (ESI) modes using a commercial trapped ion mobility spectrometry-TOF mass spectrometer. The standard metabolites were also spiked into human urine to evaluate the efficiency of trapped ion mobility spectrometry to separate isomers in complex mixtures. RESULTS: The estradiol glucuronide isomers (E2 ß-3G and E2 ß-17G) could be distinguished as deprotonated species, while the estradiol epimers (E2 ß and E2 α) and the methoxyestradiol isomers (2-MeO-E2 ß and 4-MeO-E2 ß) were separated as lithiated adducts in positive ionization mode. When performing analyses in the urine matrix, no alteration in the ion mobility resolving power was observed and the measured collision cross section (CCS) values varied by less than 1.0%. CONCLUSIONS: The trapped ion mobility spectrometry-TOF mass spectrometer enabled the separation of the metabolite isomers with very small differences in CCS values (ΔCCS% = 2%). It is shown to be an effective tool for the rapid characterization of isomers in complex matrices.

Evaluation and validation of an analytical approach for high-throughput metabolomic fingerprinting using direct introduction-high-resolution mass spectrometry: Applicability to classification of urine of scrapie-infected ewes.

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.

An untargeted evaluation of food contact materials by flow injection analysis-mass spectrometry (FIA-MS) combined with independent components analysis (ICA).

Food contact materials (FCMs), especially plastics, are known to be a potential source of contaminants in food. In fact, various groups of additives are used to protect the integrity of the material during processing and life time. However, these intentionally added substances (IAS) could also lead to degradation products called non-intentionally added substances (NIAS), due to reactions occurring in the polymeric material. Complex mixtures of components may therefore be generated within the material, creating a source of potential migrating substances towards food in contact. In this context, an innovative analytical approach is proposed in order to assess IAS and NIAS in plastic FCMs for a fast screening of their composition. For this purpose, solvent extracts of polyethylene (PE) pellets, containers and films were analyzed by flow injection analysis-mass spectrometry (FIA-MS). This direct approach offers the ability to perform a large number of analyses in a short time. Mass spectral fingerprints were then treated by a multivariate data analysis technique called independent components analysis (ICA) in order to overcome the complexity of such data and to highlight hidden information related to IAS and NIAS molecules. ICA applied on mass spectral fingerprints of PE extracts highlighted group discriminations related to different m/z values which were putatively assigned to IAS and also to NIAS. In order to confirm these putative annotations, a hybrid LTQ-Orbitrap was used for high resolution mass spectrometry analysis. Moreover, MS/MS experiments were performed on some discriminant ions to improve their putative identification. The proposed methodology combining FIA-MS fingerprints and ICA proved its efficiency in identifying IAS and NIAS in plastic FCMs and its capability to discriminate different PE samples, in a relatively fast approach compared to classical analytical techniques. This approach may help the FCMs classification for compounders in the selection of the starting substances in plastic formulation and for plastic converters in the control of manufacturing processes as well as for the monitoring of final products.

Potential of dynamically harmonized Fourier transform ion cyclotron resonance cell for high-throughput metabolomics fingerprinting: control of data quality.

Due to the presence of pollutants in the environment and food, the assessment of human exposure is required. This necessitates high-throughput approaches enabling large-scale analysis and, as a consequence, the use of high-performance analytical instruments to obtain highly informative metabolomic profiles. In this study, direct introduction mass spectrometry (DIMS) was performed using a Fourier transform ion cyclotron resonance (FT-ICR) instrument equipped with a dynamically harmonized cell. Data quality was evaluated based on mass resolving power (RP), mass measurement accuracy, and ion intensity drifts from the repeated injections of quality control sample (QC) along the analytical process. The large DIMS data size entails the use of bioinformatic tools for the automatic selection of common ions found in all QC injections and for robustness assessment and correction of eventual technical drifts. RP values greater than 106 and mass measurement accuracy of lower than 1 ppm were obtained using broadband mode resulting in the detection of isotopic fine structure. Hence, a very accurate relative isotopic mass defect (RΔm) value was calculated. This reduces significantly the number of elemental composition (EC) candidates and greatly improves compound annotation. A very satisfactory estimate of repeatability of both peak intensity and mass measurement was demonstrated. Although, a non negligible ion intensity drift was observed for negative ion mode data, a normalization procedure was easily applied to correct this phenomenon. This study illustrates the performance and robustness of the dynamically harmonized FT-ICR cell to perform large-scale high-throughput metabolomic analyses in routine conditions. Graphical abstract Analytical performance of FT-ICR instrument equipped with a dynamically harmonized cell.

An efficient data-filtering strategy for easy metabolite detection from the direct analysis of a biological fluid using Fourier transform mass spectrometry.

RATIONALE: High-throughput analyses require an overall analytical workflow including not only a robust and high-speed technical platform, but also dedicated data-processing tools able to extract the relevant information. This work aimed at evaluating post-acquisition data-mining tools for selective extraction of metabolite species from direct introduction high-resolution mass spectrometry data. METHODS: Investigations were performed on spectral data in which seven metabolites of vinclozolin, a dicarboximide fungicide containing two chloride atoms, were previously manually identified. The spectral data obtained from direct introduction (DI) and high-resolution mass spectrometry (HRMS) detection were post-processed by plotting the mass defect profiles and applying various data-filtering methods based on accurate mass values. RESULTS: Exploration of mass defect profiles highlighted, in a specific plotting region, the presence of compounds containing common chemical elements and pairs of conjugated and non-conjugated metabolites resulting from classical metabolic pathways. Additionally, the judicious application of mass defect and/or isotope pattern filters removed many interfering ions from DI-HRMS data, greatly facilitating the detection of vinclozolin metabolites. Compared with previous results obtained by manual data treatment, three additional metabolites of vinclozolin were detected and putatively annotated. CONCLUSIONS: Tracking simultaneously several specific species could be efficiently performed using data-mining tools based on accurate mass values. The selectivity of the data extraction was improved when the isotope filter was used for halogenated compounds, facilitating metabolite ion detection even for low-abundance species. Copyright © 2016 John Wiley & Sons, Ltd.

Rumen microbial communities influence metabolic phenotypes in lambs.

The rumen microbiota is an essential part of ruminants shaping their nutrition and health. Despite its importance, it is not fully understood how various groups of rumen microbes affect host-microbe relationships and functions. The aim of the study was to simultaneously explore the rumen microbiota and the metabolic phenotype of lambs for identifying host-microbe associations and potential biomarkers of digestive functions. Twin lambs, separated in two groups after birth were exposed to practices (isolation and gavage with rumen fluid with protozoa or protozoa-depleted) that differentially restricted the acquisition of microbes. Rumen microbiota, fermentation parameters, digestibility and growth were monitored for up to 31 weeks of age. Microbiota assembled in isolation from other ruminants lacked protozoa and had low bacterial and archaeal diversity whereas digestibility was not affected. Exposure to adult sheep microbiota increased bacterial and archaeal diversity independently of protozoa presence. For archaea, Methanomassiliicoccales displaced Methanosphaera. Notwithstanding, protozoa induced differences in functional traits such as digestibility and significantly shaped bacterial community structure, notably Ruminococcaceae and Lachnospiraceae lower up to 6 folds, Prevotellaceae lower by ~40%, and Clostridiaceae and Veillonellaceae higher up to 10 folds compared to microbiota without protozoa. An orthogonal partial least squares-discriminant analysis of urinary metabolome matched differences in microbiota structure. Discriminant metabolites were mainly involved in amino acids and protein metabolic pathways while a negative interaction was observed between methylotrophic methanogens Methanomassiliicoccales and trimethylamine N-oxide. These results stress the influence of gut microbes on animal phenotype and show the potential of metabolomics for monitoring rumen microbial functions.

Identification of xenobiotic metabolites from biological fluids using flow injection analysis high-resolution mass spectrometry and post-acquisition data filtering.

RATIONALE: Concern for public health entails the need to evaluate the degree of exposure of population to toxicants. To do this, robust high-throughput approaches are required to be able to perform a large number of analyses in cohort studies. In this study, a data-filtering procedure was applied to mass spectral data acquired by direct analysis of biological fluids leading to rapid detection of metabolites in a model xenobiotic system. METHODS: Flow injection analysis (FIA) coupled to negative electrospray ionization (ESI)-LTQ Orbitrap Fourier transform mass spectrometry was used to directly analyze urine of rats treated with vinclozolin. Tandem mass spectrometry (MS/MS) experiments were subsequently performed for confirmation of a new metabolite structure. The isotope filtering based on the difference between accurate masses of (35)Cl and (37)Cl was applied to the raw data for the specific detection of ions containing at least one chlorine atom. RESULTS: Seven metabolites of vinclozolin were manually identified thanks to the characteristic isotope pattern of dichlorinated compounds. A new metabolite of vinclozolin was detected for the first time and identified as a sulfate conjugate. The application of an isotope-filtering procedure allowed the selective extraction of pertinent signals from the data. The processed mass spectrum was greatly simplified, significantly facilitating the detection of the seven metabolites previously identified. CONCLUSIONS: The use of FIA-HRMS in combination with dedicated bio-informatics data processing is shown to be an efficient approach for the rapid detection of metabolites in biological fluids. This is a very promising high-throughput approach for rapid characterization of the exposure status to xenobiotics.

Metabolic bioactivation of oestradiol-17beta (E2beta) in mouse colon epithelial cells bearing ApcMin mutation.

Epidemiological studies have revealed a protective role of oestrogens against the promotion of colorectal cancer (CRC). Therefore, the oestrogen metabolism status of colonic cells is studied to explain it. Loss of function of adenomatous polyposis coli (Apc) gene product is an early and frequent event in human colorectal carcinogenesis. Normal (Apc(+/+)) and premalignant (Apc(multiple intestinal neoplasia (Min)/+)) mouse colonic epithelial cells were used to compare their respective metabolic capabilities towards oestradiol-17beta (E(2)beta), with or without an inducer of the CYP1 family, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). In both cell types, the major metabolite was oestradiol-17beta-3-glucuronide. The formation of catechol (CE) metabolites by cytochromes P450 of the CYP1 family and their derivatives was shown. Among these metabolites, several O-methyl-ether derivatives were detected, as unconjugated metabolites in Apc(+/+) cells and as glucuroconjugates in Apc(Min/+) cells, after TCDD treatment. Apc(Min/+) cells are metabolically more competent than Apc(+/+) cells to produce different hydroxylated metabolites as well as glucuroconjugates. Quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR) experiments corroborate these results. Indeed, induction by TCDD has prevailing effects in gene expression of CYP1A1, CYP1A2 and CYP1B1 in Apc(Min/+) cells, compared with Apc(+/+) ones. Apc(Min/+) cells displayed higher rates of oestrogen metabolic biotransformation than Apc(+/+) ones, but exhibited two opposite tendencies. Apc(Min/+) cells were able to detoxify E(2)beta mainly by the formation of glucuronides and displayed at the same time a striking potential to bioactivate E(2)beta by producing only the electrophilic 2-CE derivatives, not the 4-CE ones, even though a significant CYP1B1 mRNA induction was noticed. These specific electrophilic metabolites may form DNA adducts but are not prone to generate new mutations. Interestingly, the ultimate 2-O-methyl-ether metabolite of E(2)beta may be an endogenous protective factor against CRC promotion given its recognised anti-angiogenic and pro-apoptotic properties.

Loss of hepatocyte nuclear factor 1alpha function in human hepatocellular adenomas leads to aberrant activation of signaling pathways involved in tumorigenesis.

UNLABELLED: Hepatocellular adenomas (HCAs) are benign liver tumors that usually develop in women who are taking oral contraceptives. Among these tumors, biallelic inactivating mutations of the hepatocyte nuclear factor 1alpha (HNF1A) transcription factor have been frequently identified and in rare cases of hepatocellular carcinomas developed in noncirrhotic liver. Because HNF1A meets the genetic criteria of a tumor suppressor gene, we aimed to elucidate the tumorigenic mechanisms related to HNF1alpha inactivation in hepatocytes. We searched for signaling pathways aberrantly activated in human HNF1A-mutated HCA (H-HCA) using a genome-wide transcriptome analysis comparing five H-HCA with four normal livers. We validated the main pathways by quantitative reverse transcription polymerase chain reaction (RT-PCR) and western blotting in a large series of samples. Then, we assessed the role of HNF1alpha in the observed deregulations in hepatocellular cell models (HepG2 and Hep3B) by silencing its endogenous expression using small interfering RNA. Along with the previously described induction of glycolysis and lipogenesis, H-HCA also displayed overexpression of several genes encoding growth factor receptors, components of the translation machinery, cell cycle, and angiogenesis regulators, with, in particular, activation of the mammalian target of rapamycin (mTOR) pathway. Moreover, estradiol detoxification activities were shut down, suggesting a hypersensitivity of H-HCA to estrogenic stimulation. In the cell model, inhibition of HNF1alpha recapitulated most of these identified transcriptional deregulations, demonstrating that they were related to HNF1alpha inhibition. CONCLUSION: H-HCA showed a combination of alterations related to HNF1alpha inactivation that may cooperate to promote tumor development. Interestingly, mTOR appears as a potential new attractive therapeutic target for treatment of this group of HCAs.