A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer

An LC-MS/MS method for the simultaneous determination of 6-cyanodopamine, 6-nitrodopamine, 6-nitrodopa, 6-nitroadrenaline and 6-bromodopamine in human plasma and its clinical application in patients with chronic kidney disease

The aim of this study was to develop a high-performance liquid chromatography-tandem mass spectrometry method for the determination of 6-cyanodopamine, 6-nitrodopamine, 6-nitrodopa, 6-nitroadrenaline and 6-bromodopamine in human plasma samples. Strata-X 33 μm solid-phase extraction cartridges were used for the extraction of the catecholamines from human plasma samples. The catecholamines were separated in a 150 × 3 mm Shim-pack GIST C18-AQ column with 3 μm particle size, placed in an oven at 40°C and perfused with 82% mobile phase A (acetonitrile-H2O; 90:10, v/v) + 0.4% acetic acid and 18% mobile phase B (deionized H2O) + 0.2% formic acid at a flow rate of 340 μl/min in isocratic mode. The injected volume was 4 μl and the run lasted 4 min. The method was linear from 0.1 to 20 ng/ml and the lower limit of quantification was 0.1 ng/ml for all analytes. The method was applied to evaluate the plasma levels of catecholamines in plasma of patients with chronic kidney disease and allowed the detection for the first time of circulating levels of the novel catecholamines 6-bromodopamine and 6-cyanodopamine.

Integration of targeted metabolome and transcript profiling of Pseudomonas syringae-triggered changes in defence-related phytochemicals in oat plants

MAIN CONCLUSION

A gene-to-metabolite approach afforded new insights regarding defence mechanisms in oat plants that can be incorporated into plant breeding programmes for the selection of markers and genes related to disease resistance. Monitoring metabolite levels and changes therein can complement and corroborate transcriptome (mRNA) data on plant-pathogen interactions, thus revealing mechanisms involved in pathogen attack and host defence. A multi-omics approach thus adds new layers of information such as identifying metabolites with antimicrobial properties, elucidating metabolomic profiles of infected and non-infected plants, and reveals pathogenic requirements for infection and colonisation. In this study, two oat cultivars (Dunnart and SWK001) were inoculated with Pseudomonas syringae pathovars, pathogenic and non-pathogenic on oat. Following inoculation, metabolites were extracted with methanol from leaf tissues at 2, 4 and 6 days post-infection and analysed by multiple reaction monitoring (MRM) on a triple quadrupole mass spectrometer system. Relatedly, mRNA was isolated at the same time points, and the cDNA analysed by quantitative PCR (RT-qPCR) for expression levels of selected gene transcripts associated with avenanthramide (Avn) biosynthesis. The targeted amino acids, hydroxycinnamic acids and Avns were successfully quantified. Distinct cultivar-specific differences in the metabolite responses were observed in response to pathogenic and non-pathogenic strains. Trends in aromatic amino acids and hydroxycinnamic acids seem to indicate stronger activation and flux through these pathways in Dunnart as compared to SWK001. A positive correlation between hydroxycinnamoyl-CoA:hydroxyanthranilate N-hydroxycinnamoyl transferase (HHT) gene expression and the abundance of Avn A in both cultivars was documented. However, transcript profiling of selected genes involved in Avn synthesis did not reveal a clear pattern to distinguish between the tolerant and susceptible cultivars.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

Oxylipins, small polar molecules derived from the peroxidation of polyunsaturated fatty acids (PUFAs), serve as biomarkers for many diseases and play crucial roles in human physiology and inflammation. Despite their significance, many non-enzymatic oxygenated metabolites of PUFAs (NEO-PUFAs) remain poorly reported, resulting in a lack of public datasets of experimental data and limiting their dereplication in further studies. To overcome this limitation, we constructed a high-resolution tandem mass spectrometry (MS/MS) dataset comprising pure NEO-PUFAs (both commercial and self-synthesized) and in vitro free radical-induced oxidation of diverse PUFAs. By employing molecular networking techniques with this dataset and the existent ones in public repositories, we successfully mapped a wide range of NEO-PUFAs, expanding the strategies for annotating oxylipins, and NEO-PUFAs and offering a novel workflow for profiling these molecules in biological samples.

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.

Unravelling the brain metabolome: A review of liquid chromatography - mass spectrometry strategies for extracellular brain metabolomics

The analysis of the brain extracellular metabolome is of interest for numerous subdomains within neuroscience. Not only does it provide information about normal physiological functions, it is even more of interest for biomarker discovery and target discovery in disease. The extracellular analysis of the brain is particularly interesting as it provides information about the release of mediators in the brain extracellular fluid to look at cellular signaling and metabolic pathways through the release, diffusion and re-uptake of neurochemicals. In vivo samples are obtained through microdialysis, cerebral open-flow microperfusion or solid-phase microextraction. The analytes of potential interest are typically low in concentration and can have a wide range of physicochemical properties. Liquid chromatography coupled to mass spectrometry has proven its usefulness in brain metabolomics. It allows sensitive and specific analysis of low sample volumes, obtained through different approaches. Several strategies for the analysis of the extracellular fluid have been proposed. The most widely used approaches apply sample derivatization, specific stationary phases and/or hydrophilic interaction liquid chromatography. Miniaturization of these methods allows an even higher sensitivity. The development of chiral metabolomics is indispensable, as it allows to compare the enantiomeric ratio of compounds and provides even more challenges. Some limitations continue to exist for the previously developed methods and the development of new, more sensitive methods remains needed. This review provides an overview of the methods developed for sampling and liquid chromatography-mass spectrometry analysis of the extracellular metabolome.

CIL-ExPMRM: An Ultrasensitive Chemical Isotope Labeling Assisted Pseudo-MRM Platform to Accelerate Exposomic Suspect Screening

Exposome is the future of next-generation environmental health to establish the association between environmental exposure and diseases. However, due to low concentrations of exposure chemicals, exposome has been hampered by lacking an effective analytical platform to characterize its composition. In this study, by combining the benefit of chemical isotope labeling and pseudo-multiple reaction monitoring (CIL-pseudo-MRM), we have developed one highly sensitive and high-throughput platform (CIL-ExPMRM) by isotope labeling urinary exposure biomarkers. Dansyl chloride (DnsCl), N-methylphenylethylamine (MPEA), and their isotope-labeled forms were used to derivatize polar hydroxyl and carboxyl compounds, respectively. We have programmed a series of scripts to optimize MRM transition parameters, curate the MRM database (>70,000 compounds), predict accurate retention time (RT), and automize dynamic MRMs. This was followed by an automated MRM peak assignment, peak alignment, and statistical analysis. A computational pipeline was eventually incorporated into a user-friendly website interface, named CIL-ExPMRM (http://www.exposomemrm.com/). The performance of this platform has been validated with a relatively low false positive rate (10.7%) across instrumental platforms. CIL-ExPMRM has systematically overcome key bottlenecks of exposome studies to some extent and outperforms previous methods due to its independence of MS/MS availability, accurate RT prediction, and collision energy optimization, as well as the ultrasensitivity and automated robust intensity-based quantification. Overall, CIL-ExPMRM has great potential to advance the exposomic studies based on urinary biomarkers.

Optimization of Mass Spectrometric Parameters in Data Dependent Acquisition for Untargeted Metabolomics on the Basis of Putative Assignments

Optimization of mass spectrometric parameters for a data dependent acquisition (DDA) experiment is essential to increase the MS/MS coverage and hence increase metabolite identifications in untargeted metabolomics. We explored the influence of mass spectrometric parameters including mass resolution, radio frequency (RF) level, signal intensity threshold, number of MS/MS events, cycle time, collision energy, maximum ion injection time (MIT), dynamic exclusion, and automatic gain control (AGC) target value on metabolite annotations on an Exploris 480-Orbitrap mass spectrometer. Optimal annotation results were obtained by performing ten data dependent MS/MS scans with a mass isolation window of 2.0 m/z and a minimum signal intensity threshold of 1 × 10⁴ at a mass resolution of 180,000 for MS and 30,000 for MS/MS, while maintaining the RF level at 70%. Furthermore, combining an AGC target value of 5 × 10⁶ and MIT of 100 ms for MS and an AGC target value of 1 × 10⁵ and an MIT of 50 ms for MS/MS scans provided an improved number of annotated metabolites. A 10 s exclusion duration and a two stepped collision energy were optimal for higher spectral quality. These findings confirm that MS parameters do influence metabolomics results, and propose strategies for increasing metabolite coverage in untargeted metabolomics. A limitation of this work is that our parameters were only optimized for one RPLC method on single matrix and may be different for other protocols. Additionally, no metabolites were identified at level 1 confidence. The results presented here are based on metabolite annotations and need to be validated with authentic standards.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies

The success of precision medicine relies upon collecting data from many individuals at the population level. Although advancing technologies have made such large-scale studies increasingly feasible in some disciplines such as genomics, the standard workflows currently implemented in untargeted metabolomics were developed for small sample numbers and are limited by the processing of liquid chromatography/mass spectrometry data. Here we present an untargeted metabolomics workflow that is designed to support large-scale projects with thousands of biospecimens. Our strategy is to first evaluate a reference sample created by pooling aliquots of biospecimens from the cohort. The reference sample captures the chemical complexity of the biological matrix in a small number of analytical runs, which can subsequently be processed with conventional software such as XCMS. Although this generates thousands of so-called features, most do not correspond to unique compounds from the samples and can be filtered with established informatics tools. The features remaining represent a comprehensive set of biologically relevant reference chemicals that can then be extracted from the entire cohort's raw data on the basis of m/z values and retention times by using Skyline. To demonstrate applicability to large cohorts, we evaluated >2000 human plasma samples with our workflow. We focused our analysis on 360 identified compounds, but we also profiled >3000 unknowns from the plasma samples. As part of our workflow, we tested 14 different computational approaches for batch correction and found that a random forest-based approach outperformed the others. The corrected data revealed distinct profiles that were associated with the geographic location of participants.

A multidimensional metabolomics workflow to image biodistribution and evaluate pharmacodynamics in adult zebrafish

An integrated evaluation of the tissue distribution and pharmacodynamic properties of a therapeutic is essential for successful translation to the clinic. To date, however, cost-effective methods to measure these parameters at the systems level in model organisms are lacking. Here, we introduce a multidimensional workflow to evaluate drug activity that combines mass spectrometry-based imaging, absolute drug quantitation across different biological matrices, in vivo isotope tracing and global metabolome analysis in the adult zebrafish. As a proof of concept, we quantitatively determined the whole-body distribution of the anti-rheumatic agent hydroxychloroquine sulfate (HCQ) and measured the systemic metabolic impacts of drug treatment. We found that HCQ distributed to most organs in the adult zebrafish 24 h after addition of the drug to water, with the highest accumulation of both the drug and its metabolites being in the liver, intestine and kidney. Interestingly, HCQ treatment induced organ-specific alterations in metabolism. In the brain, for example, HCQ uniquely elevated pyruvate carboxylase activity to support increased synthesis of the neuronal metabolite, N-acetylaspartate. Taken together, this work validates a multidimensional metabolomics platform for evaluating the mode of action of a drug and its potential off-target effects in the adult zebrafish. This article has an associated First Person interview with the first author of the paper.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Bacterial Metabolomics: Sample Preparation Methods

Metabolomics is a comprehensive analysis of metabolites existing in biological systems. As one of the important "omics" tools, the approach has been widely employed in various fields in helping to better understand the complex cellular metabolic states and changes. Bacterial metabolomics has gained a significant interest as bacteria serve to provide a better subject or model at systems level. The approach in metabolomics is categorized into untargeted and targeted which serves different paradigms of interest. Nevertheless, the bottleneck in metabolomics has been the sample or metabolite preparation method. A custom-made method and design for a particular species or strain of bacteria might be necessary as most studies generally refer to other bacteria or even yeast and fungi that may lead to unreliable analysis. The paramount aspect of metabolomics design comprises sample harvesting, quenching, and metabolite extraction procedures. Depending on the type of samples and research objective, each step must be at optimal conditions which are significantly important in determining the final output. To date, there are no standardized nor single designated protocols that have been established for a specific bacteria strain for untargeted and targeted approaches. In this paper, the existing and current developments of sample preparation methods of bacterial metabolomics used in both approaches are reviewed. The review also highlights previous literature of optimized conditions used to propose the most ideal methods for metabolite preparation, particularly for bacterial cells. Advantages and limitations of methods are discussed for future improvement of bacterial metabolomics.

Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners

Background

Responses of the metabolome to acute aerobic exercise may predict maximum oxygen consumption (VO₂max) and longer-term outcomes, including the development of diabetes and its complications.

Methods

Serum samples were collected from overweight/obese trained (OWT) and normal-weight trained (NWT) runners prior to and immediately after a supervised 90-minute treadmill run at 60% VO₂max (NWT = 14, OWT = 11) in a cross-sectional study. We applied a liquid chromatography high-resolution–mass spectrometry–based untargeted metabolomics platform to evaluate the effect of acute aerobic exercise on the serum metabolome.

Results

NWT and OWT metabolic profiles shared increased circulating acylcarnitines and free fatty acids (FFAs) with exercise, while intermediates of adenine metabolism, inosine, and hypoxanthine were strongly correlated with body fat percentage and VO₂max. Untargeted metabolomics-guided follow-up quantitative lipidomic analysis revealed that baseline levels of fatty acid esters of hydroxy fatty acids (FAHFAs) were generally diminished in the OWT group. FAHFAs negatively correlated with visceral fat mass and HOMA-IR. Strikingly, a 4-fold decrease in FAHFAs was provoked by acute aerobic running in NWT participants, an effect that negatively correlated with circulating IL-6; these effects were not observed in the OWT group. Machine learning models based on a preexercise metabolite profile that included FAHFAs, FFAs, and adenine intermediates predicted VO₂max.

Conclusion

These findings in overweight human participants and healthy controls indicate that exercise-provoked changes in FAHFAs distinguish normal-weight from overweight participants and could predict VO₂max. These results support the notion that FAHFAs could modulate the inflammatory response, fuel utilization, and insulin resistance.

Trial registration

ClinicalTrials.gov, NCT02150889.

Funding

NIH DK091538, AG069781, DK098203, TR000114, UL1TR002494.

Combined Plasma and Urinary Metabolomics Uncover Metabolic Perturbations Associated with Severe Respiratory Syncytial Viral Infection and Future Development of Asthma in Infant Patients

A large percentage of infants develop viral bronchiolitis needing medical intervention and often develop further airway disease such as asthma. To characterize metabolic perturbations in acute respiratory syncytial viral (RSV) bronchiolitis, we compared metabolomic profiles of moderate and severe RSV patients versus sedation controls. RSV patients were classified as moderate or severe based on the need for invasive mechanical ventilation. Whole blood and urine samples were collected at two time points (baseline and 72 h). Plasma and urinary metabolites were extracted in cold methanol and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), and data from the two biofluids were combined for multivariate data analysis. Metabolite profiles were clustered according to severity, characterized by unique metabolic changes in both plasma and urine. Plasma metabolites that correlated with severity included intermediates in the sialic acid biosynthesis, while urinary metabolites included citrate as well as multiple nucleotides. Furthermore, metabolomic profiles were predictive of future development of asthma, with urinary metabolites exhibiting higher predictive power than plasma. These metabolites may offer unique insights into the pathology of RSV bronchiolitis and may be useful in identifying patients at risk for developing asthma.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.