Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics

Epimetabolites: discovering metabolism beyond building and burning

Enzymatic transformations of primary, canonical metabolites generate active biomolecules that regulate important cellular and physiological processes. Roles include regulation of histone demethylation in epigenetics, inflammation in tissue injury, insulin sensitivity, cancer cell invasion, stem cell pluripotency status, inhibition of nitric oxide signaling and others. Such modified compounds, defined as epimetabolites, have functions distinct from classic hormones as well as removed from generic anabolism and catabolism. Epimetabolites are discovered by untargeted metabolomics using liquid- or gas chromatography-high resolution mass spectrometry and structurally annotated by in-silico fragmentation prediction tools. Their specific biological functions are subsequently investigated by targeted metabolomics methods.

Recent advances in expanding the coverage of the lipidome

The lipidome comprises a large array of molecules with diverse physicochemical properties. Lipids are structural components of cells, act as a source of energy, and function as signaling mediators. Alterations in lipid metabolism are involved in the onset and progression of a variety of diseases, including metabolic syndrome and cancer. Because of this, interest in lipidomics, the comprehensive characterization of the lipidome by mass spectrometry, has intensified in recent years. However, obtaining a truly complete overview of all lipids in a sample has remained very challenging due to their enormous structural diversity. Here, we provide an overview of the collection of analytical approaches used to study various lipid classes, emphasizing innovations in sample preparation and liquid chromatography-mass spectrometry (LC-MS). Additionally, we provide practical suggestions for increasing the coverage of the lipidome.

Hyphenated MS-based targeted approaches in metabolomics

Review of targeted metabolomics, with a focus on the description of analytical methods.

LC-MS-Based Lipidomics and Automated Identification of Lipids Using the LipidBlast In-Silico MS/MS Library

This protocol describes the analysis, specifically the identification, of blood plasma lipids. Plasma lipids are extracted using methyl tert-butyl ether (MTBE), methanol, and water followed by separation and data acquisition of isolated lipids using reversed-phase liquid chromatography coupled to quadrupole/time-of-flight mass spectrometry (RPLC-QTOFMS) operated in MS/MS mode. For lipid identification, acquired MS/MS spectra are converted to the mascot generic format (MGF) followed by library search using the in-silico MS/MS library LipidBlast. Using this approach, lipid classes, carbon-chain lengths, and degree of unsaturation of fatty-acid components are annotated.

High-throughput lipidomics enables discovery of the mode of action of huaxian capsule impacting the metabolism of sepsis

Severe sepsis (SS) is a major cause of mortality and morbidity in the intensive care unit and requires rapid diagnosis and treatment.

A cross-platform metabolomics workflow for volume-restricted tissue samples: application to an animal model for polycystic kidney disease

Metabolic profiling provides an unbiased view of the physiological status of an organism as a “function” of the metabolic composition of a measured sample.

Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS

Gas Chromatography-Mass Spectrometry (GC-MS) has long been used for metabolite profiling of a wide range of biological samples. Many derivatisation protocols are already available and among these, trimethylsilyl (TMS) derivatisation is one of the most widely used in metabolomics. However, most TMS methods rely on off-line derivatisation prior to GC-MS analysis. In the case of manual off-line TMS derivatisation, the derivative created is unstable, so reduction in recoveries occurs over time. Thus, derivatisation is carried out in small batches. Here, we present a fully automated TMS derivatisation protocol using robotic autosamplers and we also evaluate a commercial software, Maestro available from Gerstel GmbH. Because of automation, there was no waiting time of derivatised samples on the autosamplers, thus reducing degradation of unstable metabolites. Moreover, this method allowed us to overlap samples and improved throughputs. We compared data obtained from both manual and automated TMS methods performed on three different matrices, including standard mix, wine, and plasma samples. The automated TMS method showed better reproducibility and higher peak intensity for most of the identified metabolites than the manual derivatisation method. We also validated the automated method using 114 quality control plasma samples. Additionally, we showed that this online method was highly reproducible for most of the metabolites detected and identified (RSD < 20) and specifically achieved excellent results for sugars, sugar alcohols, and some organic acids. To the very best of our knowledge, this is the first time that the automated TMS method has been applied to analyse a large number of complex plasma samples. Furthermore, we found that this method was highly applicable for routine metabolite profiling (both targeted and untargeted) in any metabolomics laboratory.

Metabolomic profiling of prostate cancer by matrix assisted laser desorption/ionization-Fourier transform ion cyclotron resonance mass spectrometry imaging using Matrix Coating Assisted by an Electric Field (MCAEF)

In this work, we combined the use of two MALDI matrices (quercetin and 9-aminoacridine), a recently developed new matrix coating technique - matrix coating assisted by an electric field (MCAEF), and matrix-assisted laser desorption/ionization - Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FTICRMS) to detect and image endogenous compounds in the cancerous and non-cancerous regions of three human prostate cancer (stage II) tissue specimens. After three rounds of imaging data acquisitions (i.e., quercetin for positive and negative ion detection and 9-aminoacridine for negative ion detection), and metabolite identification, a total of 1091 metabolites including 1032 lipids and 59 other metabolites were routinely detected and successfully localized. Of these compounds, 250 and 217 were only detected in either the cancerous or the non-cancerous regions respectively, although we cannot rule out the presence of these metabolites at concentrations below the detection limit. In addition, 152 of the other 624 metabolites showed differential distributions (p<0.05, t-test) between the two regions of the tissues. Further studies on a larger number of clinical specimens will need to be carried out to confirm this large number of apparently cancer-related metabolites. The successful determination of the spatial locations and abundances of these endogenous biomolecules indicated significant metabolism abnormalities - e.g., increased energy charge and under-expression of neutral acyl glycerides, in the prostate cancer samples. To our knowledge, this work has resulted in MALDI-MS imaging of the largest group of metabolites in prostate cancer thus far and demonstrated the importance of using complementary matrices for comprehensive metabolomic imaging by MALDI-MS. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Systematic Assessment of Seven Solvent and Solid-Phase Extraction Methods for Metabolomics Analysis of Human Plasma by LC-MS

The comparison of extraction methods for global metabolomics is usually executed in biofluids only and focuses on metabolite coverage and method repeatability. This limits our detailed understanding of extraction parameters such as recovery and matrix effects and prevents side-by-side comparison of different sample preparation strategies. To address this gap in knowledge, seven solvent-based and solid-phase extraction methods were systematically evaluated using standard analytes spiked into both buffer and human plasma. We compared recovery, coverage, repeatability, matrix effects, selectivity and orthogonality of all methods tested for non-lipid metabolome in combination with reversed-phased and mixed-mode liquid chromatography mass spectrometry analysis (LC-MS). Our results confirmed wide selectivity and excellent precision of solvent precipitations, but revealed their high susceptibility to matrix effects. The use of all seven methods showed high overlap and redundancy which resulted in metabolite coverage increases of 34-80% depending on LC-MS method employed as compared to the best single extraction protocol (methanol/ethanol precipitation) despite 7x increase in MS analysis time and sample consumption. The most orthogonal methods to methanol-based precipitation were ion-exchange solid-phase extraction and liquid-liquid extraction using methyl-tertbutyl ether. Our results help facilitate rational design and selection of sample preparation methods and internal standards for global metabolomics.

Untargeted Metabolomics Strategies-Challenges and Emerging Directions

Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies-specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described. Graphical Abstract ᅟ.

Atmospheric pressure MALDI mass spectrometry imaging of tissues and cells at 1.4-μm lateral resolution

We report an atmospheric pressure (AP) matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) setup with a lateral resolution of 1.4 μm, a mass resolution greater than 100,000, and accuracy below ±2 p.p.m. We achieved this by coupling a focusing objective with a numerical aperture (NA) of 0.9 at 337 nm and a free working distance of 18 mm in coaxial geometry to an orbitrap mass spectrometer and optimizing the matrix application. We demonstrate improvement in image contrast, lateral resolution, and ion yield per unit area compared with a state-of-the-art commercial MSI source. We show that our setup can be used to detect metabolites, lipids, and small peptides, as well as to perform tandem MS experiments with 1.5-μm² sampling areas. To showcase these capabilities, we identified subcellular lipid, metabolite, and peptide distributions that differentiate, for example, cilia and oral groove in Paramecium caudatum.

Ion-Mobility-Derived Collision Cross Section as an Additional Identification Point for Multiresidue Screening of Pesticides in Fish Feed

Ion mobility spectrometry allows for the measurement of the collision cross section (CCS), which provides information about the shape of an ionic molecule in the gas phase. Although the hyphenation of traveling-wave ion mobility spectrometry (TWIMS) with high-resolution quadrupole time-of-flight mass spectrometry (QTOFMS) has been mainly used for structural elucidation purposes, its potential for fast screening of small molecules in complex samples has not yet been thoroughly evaluated. The current work explores the capabilities of ultrahigh-performance liquid chromatography (UHPLC) coupled to a new design TWIMS-QTOFMS for the screening and identification of a large set of pesticides in complex salmon feed matrices. A database containing TWIMS-derived CCS values for more than 200 pesticides is hereby presented. CCS measurements showed high intra- and interday repeatability (RSD < 1%), and they were not affected by the complexity of the investigated matrices (ΔCCS ≤ 1.8%). The use of TWIMS in combination with QTOFMS was demonstrated to provide an extra-dimension, which resulted in increased peak capacity and selectivity in real samples. Thus, many false-positive detections could be straightforwardly discarded just by applying a maximum ΔCCS tolerance of ±2%. CCS was proposed as a valuable additional identification point in the pesticides screening workflow. Several commercial fish feed samples were finally analyzed to demonstrate the applicability of the proposed approach. Ethoxyquin and pirimiphos-methyl were identified in most of the analyzed samples, whereas tebuconazole and piperonil butoxide were identified for the first time in fish feed samples.

Mass spectrometry as a quantitative tool in plant metabolomics

Metabolomics is a research field used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include the analysis of a wide range of chemical species with very diverse physico-chemical properties, and therefore powerful analytical tools are required for the separation, characterization and quantification of this vast compound diversity present in plant matrices. In this review, challenges in the use of mass spectrometry (MS) as a quantitative tool in plant metabolomics experiments are discussed, and important criteria for the development and validation of MS-based analytical methods provided.This article is part of the themed issue 'Quantitative mass spectrometry'.

Biodiesel and poly-unsaturated fatty acids production from algae and crop plants - a rapid and comprehensive workflow for lipid analysis

Fatty acid methyl ester analysis (FAME) by gas chromatography coupled to mass spectrometry (GC-MS) is a widely used technique in biodiesel/bioproduct (e.g. poly-unsaturated fatty acids, PUFA) research but typically does not allow distinguishing between bound and free fatty acids. To understand and optimize biosynthetic pathways, however, the origin of the fatty acid is an important information. Furthermore the annotation of PUFAs is compromised in classical GC-EI-MS because the precursor molecular ion is missing. In the present protocol an alkaline methyl esterification step with TMS derivatization enabling the simultaneous analysis of bound and free fatty acids but also further lipids such as sterols in one GC-MS chromatogram is combined. This protocol is applied to different lipid extracts from single cell algae to higher plants: Chlorella vulgaris, Chlamydomonas reinhardtii, Coffea arabica, Pisum sativum and Cuscuta japonica. Further, field ionization (GC-FI-MS) is introduced for a better annotation of fatty acids and exact determination of the number of double bonds in PUFAs. The proposed workflow provides a convenient strategy to analyze algae and other plant crop systems with respect to their capacity for third generation biodiesel and high-quality bioproducts for nutrition such as PUFAs.

Metabolic variations in different citrus rootstock cultivars associated with different responses to Huanglongbing

Huanglongbing (HLB) is one of the most destructive bacterial diseases of citrus. No resistant cultivars have been identified, although tolerance has been observed in the genus Poncirus and some of its hybrids with Citrus that are commonly used as rootstocks. In this study we exploited this tolerance by comparing five different tolerant hybrids with a cultivar that shows pronounced HLB sensitivity to discern potential contributing metabolic factors. Whole leaves of infected and non-infected greenhouse-grown seedlings were extracted and subjected to untargeted GC-TOF MS based metabolomics. After BinBase data filtering, 342 (experiment 1) and 650 (experiment 2) unique metabolites were quantified, of which 122 and 195, respectively, were assigned by chemical structures. The number of metabolites found to be differently regulated in the infected state compared with the non-infected state varied between the cultivars and was largest (166) in the susceptible cultivar Cleopatra mandarin (Citrus reticulata) and lowest (3) in the tolerant cultivars US-897 (C. reticulata 'Cleopatra' × Poncirus trifoliata) and US-942 (C. reticulata 'Sunki' × P. trifoliata) from experiment 2. Tolerance to HLB did not appear to be associated with accumulation of higher amounts of protective metabolites in response to infection. Many metabolites were found in higher concentrations in the tolerant cultivars compared with susceptible Cleopatra mandarin and may play important roles in conferring tolerance to HLB. Lower availability of specific sugars necessary for survival of the pathogen may also be a contributing factor in the decreased disease severity observed for these cultivars.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

SWATH acquisition mode for drug metabolism and metabolomics investigations

Aim: Sequential window acquisition of all theoretical fragment-ion spectra (SWATH) has recently emerged as a powerful high resolution mass spectrometric data independent acquisition technique. In the present work, the potential and challenges of an integrated strategy based on LC-SWATH/MS for simultaneous drug metabolism and metabolomics studies was investigated. Methodology: The richness of SWATH data allows numerous data analysis approaches, including: detection of metabolites by prediction; metabolite detection by mass defect filtering; quantification from high-resolution MS precursor chromatograms or fragment chromatograms. Multivariate analysis can be applied to the data from the full scan or SWATH windows and allows changes in endogenous metabolites as well as xenobiotic metabolites, to be detected. Principal component variable grouping detects intersample variable correlation and groups variables with similar profiles which simplifies interpretation and highlights related ions and fragments. Principal component variable grouping can extract product ion spectra from the data collected by fragmenting a wide precursor ion window. Conclusion: It was possible to characterize 28 vinpocetine metabolites in urine, mostly mono- and di-hydroxylated forms, and detect endogenous metabolite expression changes in urine after the administration of a single dose of a model drug (vinpocetine) to rats.

Metabolomics in Diabetic Kidney Disease: Unraveling the Biochemistry of a Silent Killer

The development of new therapies for chronic diseases, such as diabetic kidney disease (DKD), will continue to be hampered by lack of sufficient biomarkers that will provide insights and will be responsive to treatment interventions. The recent application of metabolomic technologies, such as nuclear magnetic resonance and mass spectroscopy, has allowed large-scale analysis of small molecules to be interrogated in a targeted or untargeted manner. Recent advances from both human and animal studies that have arisen from metabolomic analysis have recognized that mitochondrial function and fatty acid oxidation play key roles in the development and progression of DKD. Although many challenges in the technology for clinical chronic kidney disease (CKD) are yet to be validated, there will very likely be ongoing major contributions of metabolomics to develop new biochemical understanding for diabetic and CKD. The clinical application of metabolomics and accompanying bioinformatic tools will likely be a cornerstone of personalized medicine triumphs for CKD.

Analytical pitfalls and challenges in clinical metabolomics

Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.

Global Metabolic Regulation of the Snow Alga Chlamydomonas nivalis in Response to Nitrate or Phosphate Deprivation by a Metabolome Profile Analysis

In the present work, Chlamydomonas nivalis, a model species of snow algae, was used to illustrate the metabolic regulation mechanism of microalgae under nutrient deprivation stress. The seed culture was inoculated into the medium without nitrate or phosphate to reveal the cell responses by a metabolome profile analysis using gas chromatography time-of-flight mass spectrometry (GC/TOF-MS). One hundred and seventy-one of the identified metabolites clustered into five groups by the orthogonal partial least squares discriminant analysis (OPLS-DA) model. Among them, thirty of the metabolites in the nitrate-deprived group and thirty-nine of the metabolites in the phosphate-deprived group were selected and identified as “responding biomarkers” by this metabolomic approach. A significant change in the abundance of biomarkers indicated that the enhanced biosynthesis of carbohydrates and fatty acids coupled with the decreased biosynthesis of amino acids, N-compounds and organic acids in all the stress groups. The up- or down-regulation of these biomarkers in the metabolic network provides new insights into the global metabolic regulation and internal relationships within amino acid and fatty acid synthesis, glycolysis, the tricarboxylic acid cycle (TCA) and the Calvin cycle in the snow alga under nitrate or phosphate deprivation stress.

Development and application of a comprehensive lipidomic analysis to investigate Tripterygium wilfordii-induced liver injury

Lipid metabolic pathways play pivotal roles in liver function, and disturbances of these pathways are associated with various diseases. Thus, comprehensive characterization and measurement of lipid metabolites are essential to deciphering the contributions of lipid network metabolism to diseases or its responses to drug intervention. Here, we report an integrated lipidomic analysis for the comprehensive detection of lipid metabolites. To facilitate the characterization of untargeted lipids through fragmentation analysis, nine formulas were proposed to identify the fatty acid composition of lipids from complex MS (n) spectrum information. By these formulas, the co-eluted isomeric compounds could be distinguished. In total, 250 lipids were detected and characterized, including diacylglycerols, triacylglycerols, glycerophosphoethanolamines, glycerophosphocholines, glycerophosphoserines, glycerophosphoglycerols, glycerophosphoinositols, cardiolipins, ceramides, and sphingomyelins. Integrated with the targeted lipidomics, a total of 27 inflammatory oxylipins were also measured. To evaluate the aberrant lipid metabolism involved in liver injury induced by Tripterygium wilfordii, lipid network metabolism was further investigated. Results indicated that energy lipid modification, membrane remodeling, potential signaling lipid alterations, and abnormal inflammation response were associated with injury. Because of the important roles of lipids in liver metabolism, this new method is expected to be useful in analyzing other lipid metabolism diseases.

Discovering lipid phenotypic changes of sepsis-induced lung injury using high-throughput lipidomic analysis

The aim of this study was to use lipidomics to identify lipid molecules that could predict patients with sepsis-induced lung injury.

Halofuginone reduces the inflammatory responses of DSS-induced colitis through metabolic reprogramming

Halofuginone inhibits both HIF-1alpha and incomplete FAO to reduce the inflammatory response in DSS-induced colitis.