Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study

Improved Accuracy and Reliability in Untargeted Analysis with LC-ESI-QTOF/MS1 by Ensemble Averaging

Untargeted liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is a powerful tool for comprehensive chemical analysis. Such techniques allow the detection and quantification of thousands of compounds in a sample. However, the complexity and variability in the data can introduce significant errors, impacting the reliability of the results. This study investigates ensemble averaging to mitigate these errors and improve signal-to-noise (S/N) ratios, feature detection, and data quality. In this work, 256 LC-qTOF/MS1 data sets from the analysis of Morning Glory seeds were averaged to generate merged data sets. The numbers of the pooled data sets in the merged files were varied, and the number of features, the S/N ratio, the accuracy and precision of the accurate masses, relative intensities, and migration time were examined. It was proved that ensemble averaging allows an increase in the S/N up to a factor of 10, and the relative standard deviation of the accurate masses and retention time decreased by a factor of 10. Moreover, the average number of features mined per data set increased from 1192 ± 129 with the original data set to 4408 when all data sets were averaged into one. Using known target compounds, ensemble averaging benefits on quantitative analysis were investigated. The measured and theoretical relative intensities between the [M+1]+H+, [M+2]+H+, and [M+3]+H+ and [M]+H+ isotopes of known alkaloids were used. The standard deviation decreased by up to a factor of 10, and the absolute error between theoretical and experimental relative intensities was below 3%, making the theoretical isotopic pattern a valid criterion for confirming a putative molecular formula. Using a targeted approach to recover quantitative data from the original data sets from information in the merged data sets provides an accurate quantitative means. Peak lists from the merged data sets and quantitative information from the original data sets were fused to obtain a robust clustering approach that allows recognizing features (adducts, isotopes, and fragments) generated by a common chemical in the ionization chamber. Two hundred and four clusters were obtained, characterized by two or more features with migration times that differ by less than 0.05 min and with similar response patterns.

Metabolic signature of renal cell carcinoma tumours and its correlation with the urinary metabolome

INTRODUCTION

Despite considerable advances in cancer research, the increasing prevalence and high mortality rate of clear cell renal cell carcinoma (ccRCC) remain a significant challenge. A more detailed comprehension of the distinctive metabolic characteristics of ccRCC is vital to enhance diagnostic, prognostic, and therapeutic strategies.

OBJECTIVES

This study aimed to investigate the metabolic signatures of ccRCC tumours and, for the first time, their correlation with the urinary metabolome of the same patients.

METHODS

We applied a gas chromatography-mass spectrometry (GC-MS)-based metabolomic approach to analyse matched tissue and urine samples from a cohort of 18 ccRCC patients and urine samples from 18 cancer-free controls. Multivariate and univariate statistical methods, as well as pathway and correlation analyses, were performed to assess metabolic dysregulations and correlations between tissue and urine.

RESULTS

The results showed a ccRCC metabolic signature characterized by reprogramming in amino acid, energy, and sugar and inositol phosphate metabolisms. Our study identified, for the first time, significantly decreased levels of asparagine, proline, gluconate, 3-aminoisobutanoate, 4-aminobutanoate and urea in ccRCC tumours, highlighting the involvement of arginine biosynthesis, β-alanine metabolism and purine and pyrimidine metabolism in ccRCC. The correlations between tissue and urine metabolomes provide evidence for the potential usefulness of urinary metabolites in understanding systemic metabolic changes driven by RCC tumours.

CONCLUSIONS

These findings significantly advance our understanding of metabolic reprogramming in ccRCC and the systemic metabolic changes associated with the disease. Future research is needed to validate these findings in larger cohorts and to determine their potential implications for diagnosis and targeted therapies.

Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS analysis on two different MS platforms

Quality control (QC) samples are commonly used in metabolomics approaches for three main reasons: (i) the initial conditioning of the column; (ii) the correction of analytical drift especially between batches; and (iii) the evaluation of measurement precision. In practice, there are several ways to prepare and conserve QC samples. The most common in untargeted metabolomics is to pool samples after or before extraction, in order to obtain pooled QC samples accounting, respectively, for analytical variance or for both analytical and sample preparation variances. In this study, focusing on untargeted analysis of tea (Camellia sinensis) leaves, we compared three ways of preparing pooled QC samples (two usual and one unusual QC sample preparations) and their efficiency to improve data quality in terms of inter-batch correction, measurement precision, and VIP candidates selection on datasets obtained using two mass spectrometry (MS) technologies (Orbitrap and time of flight (QToF)). We also investigated the effect of data processing modalities, based on the different QC preparations, on data loss and on the global structure of the datasets. Generally, our results show that usual QC sample preparation leads to comparable datasets quality in terms of precision and dispersion on both MS instruments. They also show that QC preparation is crucial for VIP selection; in fact, up to 54% of biomarkers candidates were specific of the QC preparation type used for data processing.

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Blood metabolomics and impacted cellular mechanisms during transition into lactation in dairy cows that develop metritis

The objective of this study was to identify metabolites associated with metritis and use them for identification of cellular mechanisms affected during transition into lactation. Holstein cows (n = 104) had blood collected in the prepartum period (d -14 ± 6 relative to calving), at calving (d 0), and at the day of metritis diagnosis (d 7 ± 2 after calving). Cows with reddish or brownish, watery, and fetid discharge were diagnosed with metritis (n = 52). Cows with metritis were paired with herdmates without metritis (n = 52) based on days in milk. The metabolome of plasma samples was evaluated using untargeted gas chromatography time-of-flight mass spectrometry. Univariate analyses included t-tests and fold change analyses. Metabolites with false discovery rate adjusted P ≤ 0.10 on t-tests were used for partial least squares discriminant analysis coupled with permutational analysis using 2,000 permutations. Metabolites with false discovery rate adjusted P ≤ 0.10 on t-tests were also used for enriched pathway analyses and identification of cellular processes. Cows that developed metritis had affected cellular processes associated with lower amino acid metabolism in the prepartum period, greater lipolysis, cell death, and oxidative stress at calving and at metritis diagnosis, and greater leukocyte activation at calving, but lower immune cell activation at metritis diagnosis. In summary, cows that developed metritis had plasma metabolomic changes associated with greater lipolysis, oxidative stress, and a dysregulated immune response which may predispose cows to metritis development.

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

Inter-laboratory comparison of plant volatile analyses in the light of intra-specific chemodiversity

INTRODUCTION

Assessing intraspecific variation in plant volatile organic compounds (VOCs) involves pitfalls that may bias biological interpretation, particularly when several laboratories collaborate on joint projects. Comparative, inter-laboratory ring trials can inform on the reproducibility of such analyses.

OBJECTIVES

In a ring trial involving five laboratories, we investigated the reproducibility of VOC collections with polydimethylsiloxane (PDMS) and analyses by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). As model plant we used Tanacetum vulgare, which shows a remarkable diversity in terpenoids, forming so-called chemotypes. We performed our ring-trial with two chemotypes to examine the sources of technical variation in plant VOC measurements during pre-analytical, analytical, and post-analytical steps.

METHODS

Monoclonal root cuttings were generated in one laboratory and distributed to five laboratories, in which plants were grown under laboratory-specific conditions. VOCs were collected on PDMS tubes from all plants before and after a jasmonic acid (JA) treatment. Thereafter, each laboratory (donors) sent a subset of tubes to four of the other laboratories (recipients), which performed TD-GC-MS with their own established procedures.

RESULTS

Chemotype-specific differences in VOC profiles were detected but with an overall high variation both across donor and recipient laboratories. JA-induced changes in VOC profiles were not reproducible. Laboratory-specific growth conditions led to phenotypic variation that affected the resulting VOC profiles.

CONCLUSION

Our ring trial shows that despite large efforts to standardise each VOC measurement step, the outcomes differed both qualitatively and quantitatively. Our results reveal sources of variation in plant VOC research and may help to avoid systematic errors in similar experiments.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Application of Metabolomics in Childhood Leukemia Diagnostics

Metabolomics is a new field of science dealing with the study and analysis of metabolites formed in living cells. The biological fluids used in this test method are: blood, blood plasma, serum, cerebrospinal fluid, saliva and urine. The most popular methods of assessing the composition of metabolites include nuclear magnetic resonance spectroscopy and mass spectrometry (MS) in combination with gas chromatography–MS or liquid chromatography–MS. Metabolomics is used in many areas of medicine. The variability of biochemical processes in neoplastic cells in relation to healthy cells is the starting point for this type of research. The aim of the research currently being carried out is primarily to find biomarkers for quick diagnosis of the disease, assessment of its advancement and treatment effectiveness. The development of metabolomics may also contribute to the individualization of treatment of patients, adjusting drugs depending on the metabolic profile, and thus may improve the effectiveness of therapy, reduce side effects and help to improve the quality of life of patients. Here, we review the current and potential applications of metabolomics, focusing on its use as a biomarker method for childhood leukemia.

Graphic abstract

Combining iron affinity-based fractionation with non-targeted LC-ESI-TOFMS for the study of iron-binding molecules in dissolved organic matter

The low solubility of inorganic iron(III) in seawater leads to very limited availability of this important micronutrient for marine organisms. Estuarine or oceanic iron is almost entirely bound to organic ligands of mainly unknown chemical structure. In this context, riverine input of iron rich, land-derived dissolved organic matter (DOM) can play an important role in coastal areas and investigation of potential Fe-ligands in DOM is of high interest. Previous studies have suggested that iron is predominantly bound to the high molecular weight fraction of DOM, but distributed over the entire size range. Logically, structural elucidation needs to start from the smallest building blocks. A model study targeting low molecular weight iron-binding constituents in Suwannee River natural organic matter (NOM) using Fe-loaded Chelex or silica for immobilized-metal affinity (IMAC)-based fractionation was undertaken. The binding strengths of different compounds could be qualitatively assessed using a differential analysis workflow. IMAC-fractionated samples were acidified and analyzed via liquid chromatography high resolution mass spectrometry (LC-HRMS) and molecular formulas were assigned using state of the art software. A total of 144 Fe-binding constituents in Suwannee River NOM were found to be of interest with the largest number observed to interact with Chelex at pH 4 (55%), and the smallest with silica at neutral pH (24%). Most binding constituents were found in the lignin- and tannin-type region of the van Krevelen plot. Results from this study support the hypothesis that very low molecular weight constituents (below 300 Da) can play a role in the iron binding mechanism of DOM and demonstrate that the employed analytical workflow is suitable for their detection.

A system suitability testing platform for untargeted, high-resolution mass spectrometry

The broad coverage of untargeted metabolomics poses fundamental challenges for the harmonization of measurements along time, even if they originate from the very same instrument. Internal isotopic standards can hardly cover the chemical complexity of study samples. Therefore, they are insufficient for normalizing data a posteriori as done for targeted metabolomics. Instead, it is crucial to verify instrument’s performance a priori, that is, before samples are injected. Here, we propose a system suitability testing platform for time-of-flight mass spectrometers independent of liquid chromatography. It includes a chemically defined quality control mixture, a fast acquisition method, software for extracting ca. 3,000 numerical features from profile data, and a simple web service for monitoring. We ran a pilot for 21 months and present illustrative results for anomaly detection or learning causal relationships between the spectral features and machine settings. Beyond mere detection of anomalies, our results highlight several future applications such as 1) recommending instrument retuning strategies to achieve desired values of quality indicators, 2) driving preventive maintenance, and 3) using the obtained, detailed spectral features for posterior data harmonization.

Instrument-type effects on chemical isotope labeling LC-MS metabolome analysis: Quadrupole time-of-flight MS vs. Orbitrap MS

Chemical isotope labeling (CIL) LC-MS is a powerful tool for metabolome analysis with markedly improved metabolomic coverage and quantification accuracy over the conventional LC-MS technique. In addition, with differential isotope labeling, each labeled metabolite is detected as a peak pair in the mass spectra, offering the possibility of differentiating true metabolite peaks from the singlet noise or background peaks. In this study, we examined the effects of instrument type on the detectability of true metabolites with a focus on the comparison of quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometers. Using the same ultra-high-performance liquid chromatography setup and optimized running conditions for QTOF and Orbitrap, we compared the total number of peak pairs detected and identified from the two instruments using human urine and serum as the test samples. Many common peak pairs were detected from the two instruments; however, there were a significant number of unique peak pairs detected in each type of instrument. By combining the datasets obtained using QTOF and Orbitrap, the total number of peak pairs detected could be significantly increased. We also examined the effect of mass resolving power on peak pair detection in Orbitrap (60,000 vs. 120,000 resolution). The observed differences in peak pair detectability were much less than those of QTOF vs. Orbitrap. However, the type of peak pairs detected using different resolutions could be somewhat different, offering the possibility of increasing the overall number of peak pairs by combining the two datasets obtained at two different resolutions. The results from this study clearly indicate that instrument type can have a profound effect on metabolite detection in CIL LC-MS. Therefore, comparison of metabolome data generated using different instruments needs to be carefully done. Moreover, future research (e.g., hardware modifications) is warranted to minimize the differences in order to generate more reproducible metabolome data from different types of instruments.

Fish metabolome from sub-urban lakes of the Paris area (France) and potential influence of noxious metabolites produced by cyanobacteria

The recent democratization of high-throughput molecular phenotyping allows the rapid expansion of promising untargeted multi-dimensional approaches (e.g. epigenomics, transcriptomics, proteomics, and/or metabolomics). Indeed, these emerging omics tools, processed for ecologically relevant species, may present innovative perspectives for environmental assessments, that could provide early warning of eco(toxico)logical impairments. In a previous pilot study (Sotton et al., Chemosphere 2019), we explore by ¹H NMR the bio-indicative potential of metabolomics analyses on the liver of 2 sentinel fish species (Perca fluviatilis and Lepomis gibbosus) collected in 8 water bodies of the peri-urban Paris' area (France). In the present study, we further investigate on the same samples the high potential of high-throughput UHPLC-HRMS/MS analyses. We show that the LC-MS metabolome investigation allows a clear separation of individuals according to the species, but also according to their respective sampling lakes. Interestingly, similar variations of Perca and Lepomis metabolomes occur locally indicating that site-specific environmental constraints drive the metabolome variations which seem to be influenced by the production of noxious molecules by cyanobacterial blooms in certain lakes. Thus, the development of such reliable environmental metabolomics approaches appears to constitute an innovative bio-indicative tool for the assessment of ecological stress, such as toxigenic cyanobacterial blooms, and aim at being further follow up.

Harmonized Quality Assurance/Quality Control Provisions for Nontargeted Measurement of Urinary Pesticide Biomarkers in the HBM4EU Multisite SPECIMEn Study

A set of quality assurance/quality control (QA/QC) criteria for nontargeted measurement of pesticide exposure markers in a large-scale study of human urine has been proposed and applied across five laboratories within the HBM4EU project. Quality control material, including reference standards and fortified pooled urine samples (QC urine) were prepared in a centralized way and distributed across participants to monitor analytical performance and consistency of the liquid chromatography coupled to high-resolution mass spectrometry data generated with a harmonized workflow. Signal intensities, mass accuracy, and retention times of selected QA/QC markers covering a broad range of physicochemical properties were monitored across QC solvent standards, QC urine samples, study urine samples, and procedural blanks, setting acceptance thresholds for repeatability and accuracy. Overall, results showed high repeatability of the collected data. The RSDs of the signal intensities were typically below 20-30% in QC and study samples, with good stability of the chromatographic separation (retention time drift within 2-4 s intrabatch and 5 s interbatch) and excellent mass accuracy (average error < 2 ppm). The use of the proposed criteria allowed for the identification of handling errors, instrumental issues, and potential batch effects. This is the first elaboration of harmonized QA/QC criteria applied across multiple laboratories to assess the quality of data generated by nontargeted analysis of human samples.

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

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

Combining Isotopologue Workflows and Simultaneous Multidimensional Separations to Detect, Identify, and Validate Metabolites in Untargeted Analyses

While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-¹³C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their ¹²C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

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.

Metabolomics in the understanding and management of hepatic encephalopathy

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.

Non-targeted LC-MS and CE-MS for biomarker discovery in bioreactors: Influence of separation, mass spectrometry and data processing tools

Liquid separation coupled to mass spectrometry is often used for non-targeted analyses in various fields, such as metabolomics. However, the combination of non-standardized methods, various mass spectrometers (MS) and processing tools for data evaluation affect biomarker discovery potentially. Here, we present a comprehensive study of these factors based on non-targeted liquid chromatography coupled to time-of-flight (TOF) and Orbitrap MS and capillary zone electrophoresis to Orbitrap analyses of the same bioreactor samples, describing the correlation of its gas yield with changing feature signal intensity. The three datasets were processed with MZmine 2 and XCMS online and subsequential Partial Least Square Regression (PLSR) with Variable Importance in Projection (VIP) ranking for feature prioritization. The six feature tables were compared to evaluate their overlap of shared features and the influence of the processing software and MS instrument on the VIP values and fold changes. The overlaps, defined as a fraction of one feature table found in the comparative table, were from 27% to 57% for the comparison of MZmine and XCMS and from 15% to 50% between Orbitrap and TOF data sets, respectively. Considering the most relevant features only (VIP >1.5), the overlaps were increased significantly in all cases from 26% to 95%. For the same data set, both VIP values and fold changes were well correlated, however, varied significantly between TOF and Orbitrap. CE-MS showed higher total feature numbers compared to LC-MS, most likely due to its more appropriate selectivity, different sample preparation, and/or the sensitive nano-ESI interface. Since only less than 10% of MS/MS data overlapped, CE-MS provided complementary information to LC-MS. Overall, our systematic study proves the benefits of using different separation techniques and processing tools but also indicates a significant influence of mass spectrometry on comprehensive biomarker discovery.

Integrative Modeling of Plasma Metabolic and Lipoprotein Biomarkers of SARS-CoV-2 Infection in Spanish and Australian COVID-19 Patient Cohorts

Quantitative plasma lipoprotein and metabolite profiles were measured on an autonomous community of the Basque Country (Spain) cohort consisting of hospitalized COVID-19 patients (n = 72) and a matched control group (n = 75) and a Western Australian (WA) cohort consisting of (n = 17) SARS-CoV-2 positives and (n = 20) healthy controls using 600 MHz ¹H nuclear magnetic resonance (NMR) spectroscopy. Spanish samples were measured in two laboratories using one-dimensional (1D) solvent-suppressed and T₂-filtered methods with in vitro diagnostic quantification of lipoproteins and metabolites. SARS-CoV-2 positive patients and healthy controls from both populations were modeled and cross-projected to estimate the biological similarities and validate biomarkers. Using the top 15 most discriminatory variables enabled construction of a cross-predictive model with 100% sensitivity and specificity (within populations) and 100% sensitivity and 82% specificity (between populations). Minor differences were observed between the control metabolic variables in the two cohorts, but the lipoproteins were virtually indistinguishable. We observed highly significant infection-related reductions in high-density lipoprotein (HDL) subfraction 4 phospholipids, apolipoproteins A1 and A2,that have previously been associated with negative regulation of blood coagulation and fibrinolysis. The Spanish and Australian diagnostic SARS-CoV-2 biomarkers were mathematically and biologically equivalent, demonstrating that NMR-based technologies are suitable for the study of the comparative pathology of COVID-19 via plasma phenotyping.

Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles

The development of machine learning provides solutions for predicting the complicated immune responses and pharmacokinetics of nanoparticles (NPs) in vivo. However, highly heterogeneous data in NP studies remain challenging because of the low interpretability of machine learning. Here, we propose a tree-based random forest feature importance and feature interaction network analysis framework (TBRFA) and accurately predict the pulmonary immune responses and lung burden of NPs, with the correlation coefficient of all training sets >0.9 and half of the test sets >0.75. This framework overcomes the feature importance bias brought by small datasets through a multiway importance analysis. TBRFA also builds feature interaction networks, boosts model interpretability, and reveals hidden interactional factors (e.g., various NP properties and exposure conditions). TBRFA provides guidance for the design and application of ideal NPs and discovers the feature interaction networks that contribute to complex systems with small-size data in various fields.

Breaking Through the Barrier: Regulatory Considerations Relevant to Ambient Mass Spectrometry at the Bedside

Rapid characterization of tissue disorder using ambient mass spectrometry (MS) techniques, requiring little to no preanalytical preparations of sampled tissues, has been shown using a variety of ion sources and with many disease classes. A brief overview of ambient MS in clinical applications, the state of the art in regulatory affairs, and recommendations to facilitate adoption for use at the bedside are presented. Unique challenges in the validation of untargeted MS methods and additional safety and compliance requirements for deployment within a clinical setting are further discussed. Development of a harmonized validation strategy for ambient MS methods is emphasized.

metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.

Interlaboratory Comparison of Untargeted Mass Spectrometry Data Uncovers Underlying Causes for Variability

Despite the value of mass spectrometry in modern natural products discovery workflows, it remains very difficult to compare data sets between laboratories. In this study we compared mass spectrometry data for the same sample set from two different laboratories (quadrupole time-of-flight and quadrupole-Orbitrap) and evaluated the similarity between these two data sets in terms of both mass spectrometry features and their ability to describe the chemical composition of the sample set. Somewhat surprisingly, the two data sets, collected with appropriate controls and replication, had very low feature overlap (25.7% of Laboratory A features overlapping 21.8% of Laboratory B features). Our data clearly demonstrate that differences in fragmentation, charge state, and adduct formation in the ionization source are a major underlying cause for these differences. Consistent with other recent literature, these findings challenge the conventional wisdom that electrospray ionization mass spectrometry (ESI-MS) yields a simple one-to-one correspondence between analytes in solution and features in the data set. Importantly, despite low overlap in feature lists, principal component analysis (PCA) generated qualitatively similar PCA plots. Overall, our findings demonstrate that comparing untargeted metabolomics data between laboratories is challenging, but that data sets with low feature overlap can yield the same qualitative description of a sample set using PCA.

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

Coffee quality is determined by several factors and, in the chemometric domain, the multi-block data analysis methods are valuable to study multiple information describing the same samples. In this industrial study, the Common Dimension (ComDim) multi-block method was applied to evaluate metabolite fingerprints, near-infrared spectra, sensory properties, and quality parameters of coffee blends of different cup and roasting profiles and to search relationships between these multiple data blocks. Data fusion-based Principal Component Analysis was not effective in exploiting multiple data blocks like ComDim. However, when a multi-block was applied to explore the data sets, it was possible to demonstrate relationships between the methods and techniques investigated and the importance of each block or criterion involved in the industrial quality control of coffee. Coffee blends were distinguished based on their qualities and metabolite composition. Blends with high cup quality and lower roasting degrees were generally differentiated from those with opposite characteristics.

Mass-Spectrometry-Based Food Metabolomics in Routine Applications: A Basic Standardization Approach Using Housekeeping Metabolites for the Authentication of Asparagus

The low reproducibility of non-targeted liquid chromatography-mass spectrometry-based metabolomics approaches represents a major challenge for their implementation in routine analyses, because it is impossible to compare individual measurements directly with each other, if they were not analyzed in the same batch. This study describes a normalization process based on housekeeping metabolites in plant-based raw materials, which are present in comparatively constant concentrations and are subject to no or only minor deviations as a result of exogenous influences. As a model, an authenticity study was selected to determine the origin of white asparagus (Asparagus officinalis). Using three model data sets and one test data set, we were able to show that samples that have been measured independently of one another can be correctly assigned in terms of origin after the normalization with housekeeping metabolites. The procedure does not require internal standards or the measurements of further reference samples and can also be applied to other matrices and scientific issues.

A Probiotic Mixture Induces Anxiolytic- and Antidepressive-Like Effects in Fischer and Maternally Deprived Long Evans Rats

A role of the gut microbiota in psychiatric disorders is supported by a growing body of literature. The effects of a probiotic mixture of four bacterial strains were studied in two models of anxiety and depression, naturally stress-sensitive Fischer rats and Long Evans rats subjected to maternal deprivation. Rats chronically received either the probiotic mixture (1.10⁹ CFU/day) or the vehicle. Anxiety- and depressive-like behaviors were evaluated in several tests. Brain monoamine levels and gut RNA expression of tight junction proteins (Tjp) and inflammatory markers were quantified. The gut microbiota was analyzed in feces by 16S rRNA gene sequencing. Untargeted metabolite analysis reflecting primary metabolism was performed in the cecal content and in serum. Fischer rats treated with the probiotic mixture manifested a decrease in anxiety-like behaviors, in the immobility time in the forced swimming test, as well as in levels of dopamine and its major metabolites, and those of serotonin metabolites in the hippocampus and striatum. In maternally deprived Long Evans rats treated with the probiotic mixture, the number of entries into the central area in the open-field test was increased, reflecting an anxiolytic effect. The probiotic mixture increased Tjp1 and decreased Ifnγ mRNA levels in the ileum of maternally deprived rats. In both models, probiotic supplementation changed the proportions of several Operational Taxonomic Units (OTU) in the gut microbiota, and the levels of certain cecal and serum metabolites were correlated with behavioral changes. Chronic administration of the tested probiotic mixture can therefore beneficially affect anxiety- and depressive-like behaviors in rats, possibly owing to changes in the levels of certain metabolites, such as 21-deoxycortisol, and changes in brain monoamines.

An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines

INTRODUCTION

It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites.

OBJECTIVES

Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines.

METHODS

We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the 'exometabolome' or metabolic footprint).

RESULTS

Our method measures some 4000-5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other.

CONCLUSION

Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.

Five Easy Metrics of Data Quality for LC-MS-Based Global Metabolomics

Data quality in global metabolomics is of great importance for biomarker discovery and system biology studies. However, comprehensive metrics and methods to evaluate and compare the data quality of global metabolomics data sets are lacking. In this work, we combine newly developed metrics, along with well-known measures, to comprehensively and quantitatively characterize the data quality across two similar liquid chromatography coupled with mass spectrometry (LC-MS) platforms, with the goal of providing an efficient and improved ability to evaluate the data quality in global metabolite profiling experiments. A pooled human serum sample was run 50 times on two high-resolution LC-QTOF-MS platforms to provide profile and centroid MS data. These data were processed using Progenesis QI software and then analyzed using five important data quality measures, including retention time drift, the number of compounds detected, missing values, and MS reproducibility (2 measures). The detected compounds were fit to a γ distribution versus compound abundance, which was normalized to allow comparison of different platforms. To evaluate missing values, characteristic curves were obtained by plotting the compound detection percentage versus extraction frequency. To characterize reproducibility, the accumulative coefficient of variation (CV) versus the percentage of total compounds detected and intraclass correlation coefficient (ICC) versus compound abundance were investigated. Key findings include significantly better performance using profile mode data compared to centroid mode as well quantitatively better performance from the newer, higher resolution instrument. A summary table of results gives a snapshot of the experimental results and provides a template to evaluate the global metabolite profiling workflow. In total, these measures give a good overall view of data quality in global profiling and allow comparisons of data acquisition strategies and platforms as well as optimization of parameters.

A Non-Targeted High-Resolution Mass Spectrometry Study for Extra Virgin Olive Oil Adulteration with Soft Refined Oils: Preliminary Findings from Two Different Laboratories

This work presents a non-targeted high-resolution mass spectrometry inter-laboratory study for the detection of new chemical markers responsible of soft refined oils addition to extra virgin olive oils. Refined oils (soft deodorized and soft deacidified) were prepared on a laboratory scale starting from low-quality olive oils and analyzed together with a set of pure extra virgin olive oil (EVOO) samples and with mixtures of adulterated and pure EVOO at different percentages. The same analytical workflow was applied in two different laboratories equipped with two types of instrumentation (Q-Orbitrap and Q-TOF); a group of discriminant molecules was selected, and a tentative identification of compounds was also proposed. In summary, 12 molecules were identified as markers of this specific adulteration, and seven of them were selected as discriminative in both the laboratories, with a similar trend throughout the samples (i.e., propylene glycol 1 stearate). The results obtained in the two laboratories are comparable, concretely demonstrating the inter-laboratory repeatability of non-targeted studies. As a confirmation, the same markers were detected also in "in-house" mixtures and in suspect commercial deodorized mixtures, reinforcing the robustness of the results obtained and proving that, thanks to these molecules, mixtures containing at least 40% of adulterated oils can be detected.

Assessment of substrate biodegradability improvement in anaerobic Co-digestion using a chemometrics-based metabolomic approach

Anaerobic co-digestion (AcoD) can increase methane production of anaerobic digesters in plants treating wastewater sludge by improving the nutrient balance needed for the microorganisms to grow in the digesters, resulting in a faster process stabilization. Substrate mixture proportions are usually optimized in terms of biogas production, while the metabolic biodegradability of the whole mixture is neglected in this optimisation. In this aim, we developed a strategy to assess AcoD using metabolomics data. This strategy was explored in two different systems. Specifically, we investigated the co-digestion of wastewater sludge with different proportions of either grass or fish waste using untargeted High Performance Liquid Chromatography coupled to Mass Spectrometry (HPLC-MS) metabolomics and chemometrics methods. The analysis of these data revealed that adding grass waste did not improve the metabolic biodegradability of wastewater sludge. Conversely, a synergistic effect in the metabolic biodegradability was observed when fish waste was used, this effect being the highest for 25% of fish waste. In conclusion, metabolomics can be regarded as a promising tool both for characterizing the biochemical processes occurring during anaerobic digestion, and for providing a better understanding of the anaerobic digestion processes.

Inter-laboratory reproducibility of an untargeted metabolomics GC-MS assay for analysis of human plasma

There is a long-standing concern for the lack of reproducibility of the untargeted metabolomic approaches used in pharmaceutical research. Two types of human plasma samples were split into two batches and analyzed in two individual labs for untargeted GC–MS metabolomic profiling. The two labs used the same silylation sample preparation protocols but different instrumentation, data processing software, and database. There were 55 metabolites annotated reproducibly, independent of the labs. The median coefficient variations (CV%) of absolute spectra ion intensities in both labs were less than 30%. However, the comparison of normalized ion intensity among biological groups, were inconsistent across labs. Predicted power based on annotated metabolites was evaluated post various normalization, data transformation and scaling. For the first time our study reveals the numerical details about the variations in metabolomic annotation and relative quantification using plain inter-laboratory GC–MS untargeted metabolomic approaches. Especially we compare several commonly used post-acquisition strategies and found normalization could not strengthen the annotation accuracy or relative quantification precision of untargeted approach, instead it will impact future experimental design. Standardization of untargeted metabolomics protocols, including sample preparation, instrumentation, data processing, etc., is critical for comparison of untargeted data across labs.

Investigation of Early Death-Induced Changes in Rat Brain by Solid Phase Microextraction via Untargeted High Resolution Mass Spectrometry: In Vivo versus Postmortem Comparative Study

Analysis of brain samples obtained postmortem remains a standard approach in neuroscience, despite often being suboptimal for inferring roles of small molecules in the pathophysiology of brain diseases. Sample collection and preservation further hinders conclusive interpretation of biomarker analysis in autopsy samples. We investigate purely death-induced changes affecting rat hippocampus in the first hour of postmortem interval (PMI) by means of untargeted liquid chromatography-mass spectrometry-based metabolomics. The unique possibility of sampling the same brain area of each animal both in vivo and postmortem was enabled by employing solid phase microextraction (SPME) probes. Four millimeter probes coated with mixed mode extraction phase were used to sample awake, freely roaming animals, with 2 more sampling events performed after death. Significant changes in brain neurochemistry were found to occur as soon as 30 min after death, further progressing with increasing PMI, evidenced by relative changes in levels of metabolites and lipids. These included species from several distinct groups, which can be classified as engaged in energy metabolism-related processes, signal transduction, neurotransmission, or inflammatory response. Additionally, we perform thorough analysis of interindividual variability in response to death, which provides insights into how this aspect can obscure conclusions drawn from an untargeted study at single metabolite and pathway level. The results suggest high demand for systematic studies examining the PMI time course with in vivo sampling as a starting point to eliminate artifacts in the form of neurochemical changes assumed to occur in vivo.

Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective

The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.

Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology

Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.

Metabolomics Benefits from Orbitrap GC-MS-Comparison of Low- and High-Resolution GC-MS

The development of improved mass spectrometers and supporting computational tools is expected to enable the rapid annotation of whole metabolomes. Essential for the progress is the identification of strengths and weaknesses of novel instrumentation in direct comparison to previous instruments. Orbitrap liquid chromatography (LC)–mass spectrometry (MS) technology is now widely in use, while Orbitrap gas chromatography (GC)–MS introduced in 2015 has remained fairly unexplored in its potential for metabolomics research. This study aims to evaluate the additional knowledge gained in a metabolomics experiment when using the high-resolution Orbitrap GC–MS in comparison to a commonly used unit-mass resolution single-quadrupole GC–MS. Samples from an osmotic stress treatment of a non-model organism, the microalga Skeletonema costatum, were investigated using comparative metabolomics with low- and high-resolution methods. Resulting datasets were compared on a statistical level and on the level of individual compound annotation. Both MS approaches resulted in successful classification of stressed vs. non-stressed microalgae but did so using different sets of significantly dysregulated metabolites. High-resolution data only slightly improved conventional library matching but enabled the correct annotation of an unknown. While computational support that utilizes high-resolution GC–MS data is still underdeveloped, clear benefits in terms of sensitivity, metabolic coverage, and support in structure elucidation of the Orbitrap GC–MS technology for metabolomics studies are shown here.

The ABRF Metabolomics Research Group 2016 Exploratory Study: Investigation of Data Analysis Methods for Untargeted Metabolomics

Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.

Metabolomics evaluation of repeated administration of potassium iodide on adult male rats

The long-lasting consequence of a new iodine thyroid blocking strategy (ITB) to be used in case of nuclear accident is evaluated in male Wistar rats using a metabolomics approach applied 30 days after ITB completion. The design used 1 mg/kg/day of KI over 8 days. Thyroid hormones remained unchanged, but there was a metabolic shift measured mainly in thyroid then in plasma and urine. In the thyroid, tyrosine metabolism associated to catecholamine metabolism was more clearly impacted than thyroid hormones pathway. It was accompanied by a peripheral metabolic shift including metabolic regulators, branched-chain amino acids, oxidant stress and inflammation-associated response. Our results suggested that iodide intake can impact gut microbiota metabolism, which was related to host metabolic regulations including in the thyroid. As there were no clear clinical signs of dysfunction or toxicity, we concluded that the measured metabolomics response to the new ITB strategy, especially in thyroid, is unlikely to reveal a pathological condition but a shift towards a new adaptive homeostatic state, called 'allostatic regulation'. The question now is whether or not the shift is permanent and if so at what cost for long-term health. We anticipate our data as a start point for further regulatory toxicity studies.

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite

There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material^®s (SRM^®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data.

Resveratrol-mediated glycemic regulation is blunted by curcumin and is associated to modulation of gut microbiota

The polyphenols resveratrol (RSV) and curcumin (Cur) are phytoalexines and natural antibiotics with numerous pharmacological functions and metabolic impacts. Recent evidences show a broad control of gut microbiota by polyphenols which could influence glycemic regulation. The aim of this work is to estimate the respective effect of RSV and Cur alone or in association on the control of glycemia and on gut microbiota. A 5-week chronic treatment of hyperglycemic mice with RSV and/or Cur resulted in a differential effect on glucose tolerance test and modified gut microbiome. We precisely identified groups of bacteria representing a specific signature of the glycemic effect of RSV. Inferred metagenomic analysis and metabolic pathway prediction showed that the sulfur and branched-chain amino-acid (BCAA) metabolic activities are tightly correlated with the efficacy of RSV for the control of glycaemia. The impact on BCAA metabolism was further validated by serum metabolomics analysis. Altogether, we show that polyphenols specifically impact gut microbiota and corresponding metabolic functions which could be responsible for their therapeutic role.

Assessment of the effects of repeated doses of potassium iodide intake during pregnancy on male and female rat offspring using metabolomics and lipidomics

Preparedness for nuclear accident responsiveness includes interventions to protect pregnancies against prolonged exposure to radioactive iodine. The aim of this study was to investigate a new design consisting of repeated administration of potassium iodide (KI, 1 mg/kg) for 8 days in late pregnancy gestational day 9-16 (GD9-GD16) in rats. The later-life effects of this early-life iodine thyroid blocking (ITB) strategy were assessed in offspring two months afterbirth. Functional behavioral tests including forced swimming test (FST) and rotarod test (RRT) in rats of both genders showed lower FST performance in KI-treated females and lower RRT performance in KI-treated male pups. This performance decline was associated with metabolic disruptions in cortex involving amino acid metabolism, tyrosine metabolism, as well as docosahexaenoic acid (DHA) lipids and signaling lipids in males and females. Beyond these behavior-associated metabolic changes, a portion of the captured metabolome (17-25%) and lipidome (3.7-7.35%) remained sensitive to in utero KI prophylactic treatment in both cortex and plasma of post-weaning rats, with some gender-related variance. Only part of these disruptions was attributed to lower levels of TSH and T4 (males only). The KI-induced metabolic shifts involved a broad spectrum of functions encompassing metabolic and cell homeostasis and cell signaling functions. Irrespective Regardless of gender and tissues, the predominant effects of KI affected neurotransmitters, amino acid metabolism, and omega-3 DHA metabolism. Taken together, data demonstrated that repeated daily KI administration at 1 mg/kg/day for 8 days during late pregnancy failed to protect the mother-fetus against nuclear accident radiation. Abbreviations: CV-ANOVA: Cross-validation analysis of variance; DHA: Docosahexaenoic acid; FST: Forced swimming test; FT3: plasma free triiodothyronine; FT4: plasma free thyroxine; GD: Gestational day; ITB: Iodine thyroid blocking; KI: potassium iodide; LC/MS: Liquid chromatography coupled with mass spectrometry; MTBE: Methyl tert-butyl ether; m/z: mass-to-charge ratio; PLS-DA: Partial least squares-discriminant analysis; PRIODAC: Repeated stable iodide prophylaxis in accidental radioactive releases; RRT: Rotarod test; TSH: Thyroid-stimulating hormone; VIP: Variable importance in projection.

Ontology-based metabolomics data integration with quality control

Aim: The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. Results & methodology: This paper presents an integrated system of quality control (QC) methods to assess metabolomics results by evaluating the data acquisition strategies and metabolite identification process when integrating datasets for meta-analysis. An ontology knowledge base and a rule-based system representing the experiment and chemical background information direct the processes involved in data integration and QC verification. A diabetes meta-analysis study using these QC methods finds putative biomarkers that differ between cohorts. Conclusion: The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.