Analytical strategies for LC-MS-based targeted metabolomics

Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry

Untargeted methods are typically used in the detection and discovery of small organic compounds in metabolomics research, and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is one of the most commonly used platforms for untargeted metabolomics. Although they are non-biased and have high coverage, untargeted approaches suffer from unsatisfying repeatability and a requirement for complex data processing. Targeted metabolomics based on triple-quadrupole mass spectrometry (TQMS) could be a complementary tool because of its high sensitivity, high specificity and excellent quantification ability. However, it is usually applicable to known compounds: compounds whose identities are known and/or are expected to be present in the analyzed samples. Pseudotargeted metabolomics merges the advantages of untargeted and targeted metabolomics and can act as an alternative to the untargeted method. Here, we describe a detailed protocol of pseudotargeted metabolomics using UHPLC-TQMS. An in-depth, untargeted metabolomics experiment involving multiple UHPLC-HRMS runs with MS at different collision energies (both positive and negative) is performed using a mixture obtained using small amounts of the analyzed samples. XCMS, CAMERA and Multiple Reaction Monitoring (MRM)-Ion Pair Finder are used to find and annotate peaks and choose transitions that will be used to analyze the real samples. A set of internal standards is used to correct for variations in retention time. High coverage and high-performance quantitative analysis can be realized. The entire protocol takes ~5 d to complete and enables the simultaneously semiquantitative analysis of 800-1,300 metabolites.

Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions

Mass spectrometry based metabolomics is a widely used approach in biomedical research. However, current methods coupling mass spectrometry with chromatography are time-consuming and not suitable for high-throughput analysis of thousands of samples. An alternative approach is flow-injection mass spectrometry (FI-MS) in which samples are directly injected to the ionization source. Here, we show that the sensitivity of Orbitrap FI-MS metabolomics methods is limited by ion competition effect. We describe an approach for overcoming this effect by analyzing the distribution of ion m/z values and computationally determining a series of optimal scan ranges. This enables reproducible detection of ~9,000 and ~10,000 m/z features in metabolomics and lipidomics analysis of serum samples, respectively, with a sample scan time of ~15 s and duty time of ~30 s; a ~50% increase versus current spectral-stitching FI-MS. This approach facilitates high-throughput metabolomics for a variety of applications, including biomarker discovery and functional genomics screens.

Chemical Evaluation of the Effects of Storage Conditions on the Botanical Goldenseal using Marker-based and Metabolomics Approaches

Hydrastis canadensis, commonly known as goldenseal, is a botanical native to the southeastern United States that has been used for the treatment of infection. The activity of goldenseal is often attributed to the presence of alkaloids (cyclic, nitrogen-containing compounds) present within its roots. Chemical components of botanical supplements like goldenseal may face degradation if not stored properly. The purpose of the research was to analyze the stability of known and unknown metabolites of H. canadensis during exposure to different storage conditions using mass spectrometry. Three abundant metabolites of H. canadensis, berberine, canadine, and hydrastine, were chosen for targeted analysis, and the stability of unknown metabolites was evaluated using untargeted metabolomics. The analysis and evaluation of H. canadensis samples were performed utilizing LC-MS and Principal Component Analysis (PCA). The research project focused on identifying the chemical changes in the metabolite content of H. canadensis under different temperature conditions (40°C ± 5°C, 20°C ± 5°C , and 4°C ± 5°C), different light:dark (hr:hr) cycles (16:8, 12:12, and 0:24), and different sample conditions (powdered roots versus whole roots) over a six month period. The results of this 6-month study revealed that the storage conditions evaluated had no significant effects on the chemical composition of H. canadensis roots. Hence, as long as H. canadensis roots are stored within the storage conditions tested in the study, no significant changes in chemical compositions of metabolites are expected.

A tps1Δ persister-like state in Saccharomyces cerevisiae is regulated by MKT1

Trehalose metabolism in yeast has been linked to a variety of phenotypes, including heat resistance, desiccation tolerance, carbon-source utilization, and sporulation. The relationships among the several phenotypes of mutants unable to synthesize trehalose are not understood, even though the pathway is highly conserved. One of these phenotypes is that tps1Δ strains cannot reportedly grow on media containing glucose or fructose, even when another carbon source they can use (e.g. galactose) is present. Here we corroborate the recent observation that a small fraction of yeast tps1Δ cells do grow on glucose, unlike the majority of the population. This is not due to a genetic alteration, but instead resembles the persister phenotype documented in many microorganisms and cancer cells undergoing lethal stress. We extend these observations to show that this phenomenon is glucose-specific, as it does not occur on another highly fermented carbon source, fructose. We further demonstrate that this phenomenon appears to be related to mitochondrial complex III function, but unrelated to inorganic phosphate levels in the cell, as had previously been suggested. Finally, we found that this phenomenon is specific to S288C-derived strains, and is the consequence of a variant in the MKT1 gene.

Development of ion pairing LC-MS/MS method for itaconate and cis-aconitate in cell extract and cell media

Accumulation of Immune Responsive Gene 1(IRG1) in macrophage induced by lipopolysaccharide (LPS) and interferon gamma (IFN-γ) leads to production of itaconate by decarboxylation of cis-aconitate. The biology associated with IRG1 and itaconate is not fully understood. A rapid and sensitive method for measurement of itaconate will benefit the study of IRG1 biology. Multiple HPLC and derivatization methods were tested. An ion pairing LC-MS/MS method using tributylamine/formic acid as ion pairing agents and a Hypercarb^TM guard column we proposed demonstrated better peak shape and better sensitivity for itaconate. The current protocol allows baseline separation of itaconate, citraconate, and cis-aconitate without derivatization and direct analysis of analytes in 80% methanol/water solution to avoid the dry-down step. It provides the limit of quantitation (LOQ) of 30 pg itaconate on column with a 4.5-minute run time. This method is validated for measurement of itaconate and cis-aconitate in RAW264.7 cell extract and cell media in a 96-well plate format. We applied this method to successfully measure the increase of itaconate and the decrease of cis-aconitate in RAW cell extract and cell media after LPS/IFN-γ treatment.

Green tea polyphenols boost gut-microbiota-dependent mitochondrial TCA and urea cycles in Sprague-Dawley rats

Green tea polyphenols (GTPs) were found to boost mammal energy conversion by modulating gut-microbial community structure, gene orthologs and metabolic pathways. Here we examined the metabolites present in the gut-microbiota-dependent mitochondrial tricarboxylic acid (TCA) cycle and urea cycle using hydrophilic interaction liquid chromatography (HILIC)-heated electrospray ionization (HESI)-tandem liquid chromatogram mass spectrometry (LC-MS). Six groups (n=12) of Sprague-Dawley rats (6-mo, ~250 g) were administered with water containing 0%, 0.5%, and 1.5% GTPs (wt/vol or g/dL). Gut-content samples were collected at 3- and 6-mo. Untargeted metabolomics detected 2177 features, with 91 features demonstrating significant dose- and time-dependencies on the GTPs treatment. Targeted metabolomics analysis revealed remarkable changes of 39 metabolites in the mitochondrial TCA cycle and urea cycle, including argininosuccunic acid (0.9-fold vs control), dihydrouracil (1.14-fold vs control), fumaric acid (1.19-fold vs control), malic acid (2.17-fold vs control), citrulline (1.86-fold vs control), and succinic acid (0.4-fold vs control). The untargeted metabolomics data were mined using bioinformatics approaches, such as analysis of variance-simultaneous component analysis (ASCA), enrichment pathway analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping analysis. The results of 16S rRNA survey, metagenomics analysis, and metabolomics analysis were extrapolated and integrated using databases of Integrated Microbial Genomes and Microbiomes (IMG/M) and KEGG. Our analysis demonstrates that GTPs enhance energy conversion by boosting mitochondrial TCA cycle and urea cycle of gut-microbiota in rats. This metabolic modulation is achieved by enriching many gene orthologs, following the increase of beneficial microbials in families C. Ruminococcaceae, C. Lachnospiraceae and B. Bacteroidaceae.

Assays for Intracellular Cyclic Adenosine Monophosphate (cAMP) and Lysosomal Acidification

Cyclic adenosine monophosphate (3',5'-cAMP) is a multifunctional second messenger which controls extremely diverse and physiologically important biochemical pathways. Among its myriad roles, 3',5'-cAMP functions as an intracellular regulator of lysosomal pH, which is essential for the activity of acidic lysosomal enzymes. Defects in lysosomal acidification are attributed to many diseases like macular degeneration, Parkinson's, Alzheimer's, and cystic fibrosis. Strategic re-acidification of defective lysosomes by pharmacological increase of intracellular cAMP offers exciting therapeutic potential in these diseases. Modular assays for accurate assessment of intracellular cAMP and lysosomal pH are a critical component of this research. We describe label-free targeted metabolomics for quantitating intracellular cAMP and integrated assays for measuring lysosomal pH. These hybrid assays offer fast, unbiased information on intracellular cAMP concentrations and lysosomal pH that can be applied to many cell types and putative drug screening strategies.

An Analytical System for Single-Cell Metabolomics of Typical Mammalian Cells Based on Highly Sensitive Nano-Liquid Chromatography Tandem Mass Spectrometry

The rapid development of next-generation sequencing techniques has enabled single-cell genomic and transcriptomic analyses, which have revealed the importance of heterogeneity in biological systems. However, analytical methods to accurately identify and quantify comprehensive metabolites from single mammalian cells with a typical diameter of 10-20 μm are still in the process of development. The aim of this study was to develop a single-cell metabolomic analytical system based on highly sensitive nano-liquid chromatography tandem mass spectrometry (nano-LC-MS/MS) with multiple reaction monitoring. A packed nano-LC column (3-μm particle-size pentafluorophenylpropyl Discovery HSF5 of dimensions 100 μm i.d.×180 mm) was prepared using a slurry technique. The optimized nano-LC-MS/MS method showed 3-132-fold (average value, 26-fold) greater sensitivity than semimicro-LC-MS/MS, and the detection limits for several hydrophilic metabolites, including amino acids and nucleic acid related metabolites were in the sub-fmol range. By combining live single-cell sampling and nano-LC-MS/MS, we successfully detected 18 relatively abundant hydrophilic metabolites (16 amino acids and 2 nucleic acid related metabolites) from single HeLa cells (n=22). Based on single-cell metabolic profiles, the 22 HeLa cells were classified into three distinct subclasses, suggesting differences in metabolic function in cultured HeLa cell populations. Our single-cell metabolomic analytical system represents a potentially useful tool for in-depth studies focused on cell metabolism and heterogeneity.

Metabolic profiling by reversed-phase/ion-exchange mass spectrometry

Metabolic profiling is commonly achieved by mass spectrometry (MS) following reversed-phase (RP) and hydrophilic interaction chromatography (HILIC) either performed independently, leading to overlapping datasets, or in a coupled configuration, requiring multiple liquid chromatography (LC) systems. To overcome these limitations, we developed a single, 20-minute chromatographic method using an in-line RP-ion-exchange (IEX) column arrangement and a single LC system. This configuration separates clinically significant polar and non-polar compounds without derivatization or ion-pairing reagents, allowing ionization in both polarities. An in-house library was created with 397 authentic standards, including acylcarnitines, amino acids, bile acids, nucleosides, organic acids, steroid hormones, and vitamins. Analysis of pooled plasma and urine samples revealed 5445 and 4111 ion features, leading to 88 and 82 confirmed metabolite identifications, respectively. Metabolites were detected at clinically relevant concentrations with good precision, and good chromatographic separation was demonstrated for clinically significant isomers including methylmalonic acid and succinic acid, as well as alloisoleucine and isoleucine/leucine. Evaluation of the samples by unsupervised principal component analysis showed excellent analytical quality.

Large-Area Triboelectric Nanogenerator Mass Spectrometry: Expanded Coverage, Double-Bond Pinpointing, and Supercharging

Efficient ionization is a necessary condition for mass spectrometric analysis, but many compounds fail to ionize well enough to yield sufficient detection limits. Triboelectric nanogenerators (TENG) coupled to nanoelectrospray ionization (nanoESI) mass spectrometry (MS) are a highly effective approach to high sensitivity MS analysis. Here, we report on new, large-area TENG that constructively leverage the relationship between electrode size, created charges, and open-circuit voltage, leading to wider chemical coverage. Large-area TENG were found to also promote electrospray gas-phase oxidation reactions that enabled double bond position pinpointing for unsaturated lipids and species-specific lipid quantitation. Furthermore, large-area TENG MS of proteins was observed to yield higher charge state distributions (i.e., supercharging) without the need for high surface tension additives.

Multiomics Evaluation of Human Fat-Derived Mesenchymal Stem Cells on an Osteobiologic Nanocomposite

Effective graft technologies for bone repair have been a primary focus in the field of bone tissue engineering. We have previously fabricated and examined a nanocomposite composed of polyurethane, nano-hydroxyapatite, and decellularized bone particles, which demonstrated osteobiologic characteristics. To evaluate the underlying mechanisms of this biomaterial, human adipose-derived mesenchymal stem cell seeded scaffolds were assessed using a combinatorial approach of transcriptomic and metabolomic analyses. Data from osteogenic and signal transduction polymerase chain reaction arrays and small molecule abundances, measured through liquid chromatography-mass spectrometry, were cross-examined using Integrated Molecular Pathway Level Analysis, Database for Annotation, Visualization, and Integrated Discovery, and ConsensusPathDB online tools to generate a fundamental collection of scaffold-influenced pathways. Results demonstrated upregulation of key osteogenic, cellular adhesion cell signaling markers and indicated that Hedgehog and Wnt signaling pathways were primary candidates for the osteobiologic mechanisms of the scaffold design. The detection of complimentary metabolites, such as ascorbate, further indicates that scaffolds generate intricate cellular environments, promoting cell attachment and subsequent osteodifferentiation.

Improved workflow for mass spectrometry-based metabolomics analysis of the heart

MS-based metabolomics methods are powerful techniques to map the complex and interconnected metabolic pathways of the heart; however, normalization of metabolite abundance to sample input in heart tissues remains a technical challenge. Herein, we describe an improved GC-MS-based metabolomics workflow that uses insoluble protein-derived glutamate for the normalization of metabolites within each sample and includes normalization to protein-derived amino acids to reduce biological variation and detect small metabolic changes. Moreover, glycogen is measured within the metabolomics workflow. We applied this workflow to study heart metabolism by first comparing two different methods of heart removal: the Langendorff heart method (reverse aortic perfusion) and in situ freezing of mouse heart with a modified tissue freeze-clamp approach. We then used the in situ freezing method to study the effects of acute β-adrenergic receptor stimulation (through isoproterenol (ISO) treatment) on heart metabolism. Using our workflow and within minutes, ISO reduced the levels of metabolites involved in glycogen metabolism, glycolysis, and the Krebs cycle, but the levels of pentose phosphate pathway metabolites and of many free amino acids remained unchanged. This observation was coupled to a 6-fold increase in phosphorylated adenosine nucleotide abundance. These results support the notion that ISO acutely accelerates oxidative metabolism of glucose to meet the ATP demand required to support increased heart rate and cardiac output. In summary, our MS-based metabolomics workflow enables improved quantification of cardiac metabolites and may also be compatible with other methods such as LC or capillary electrophoresis.

Metabolomics study of the anti-inflammatory effects of endogenous omega-3 polyunsaturated fatty acids

Low-grade inflammation is usually defined as the chronic production and a low-grade state of inflammatory factors, it often does not have symptoms, and has been associated with neurodegenerative disease, obesity, and diabetes. Omega-3 polyunsaturated fatty acids (n-3 PUFAs) are the precursors of many anti-inflammatory metabolites, such as resolvins and neuroprotectins. It is of interest to study the metabolic profile of endogenous n-3 PUFAs in low-grade inflammatory conditions. To evaluate the protective effects of endogenous n-3 PUFAs on low-grade inflammation with the metabolomics approach, we fed fat-1 mice with an n-6 PUFAs rich diet for a long time to induce a low-grade inflammatory condition. Multi-analysis techniques, including structural analysis using quadrupole time-of-flight mass spectrometry with MS^E mode, were applied in untargeted metabolomics to search for meaningful metabolites with significant variance in fat-1 mice under low-grade inflammation. Following the untargeted metabolomics screening, several meaningful metabolites were selected which were associated with anti-inflammatory effects generated from endogenous n-3 PUFAs for further analysis. The results revealed that the purine metabolism, fatty acid metabolism and oxidative stress response pathways through insulin resistance were involved in anti-inflammatory mechanisms of n-3 PUFA in low-grade inflammatory conditions. For the first time, this study explored the highlighted pathways as contributors to the anti-inflammatory effects of endogenous n-3 PUFAs in low-grade inflammatory conditions.

Low-grade inflammation is usually defined as the chronic production and a low-grade state of inflammatory factors, it often does not have symptoms, and has been associated with neurodegenerative disease, obesity, and diabetes.

How do cancer-sniffing dogs sort biological samples? Exploring case-control samples with non-targeted LC-Orbitrap, GC-MS, and immunochemistry methods

Early identification of disease onset is regarded as an important factor for successful medical intervention. However, cancer and other long-term latency diseases are rare and may take years to manifest clinically. As such, there are no gold standards with which to immediately validate proposed preclinical screening methodologies. There is evidence that dogs can sort samples reproducibly into yes/no categories based on case-control training, but the basis of their decisions is unknown. Because dogs are sniffing air, the distinguishing chemicals must be either in the gas-phase or attached to aerosols and/or airborne particles. Recent biomonitoring research has shown how to extract and analyze semi- and non-volatile compounds from human breath in exhaled condensates and aerosols. Further research has shown that exhaled aerosols can be directly collected on standard hospital-style olefin polypropylene masks and that these masks can be used as a simple sampling scheme for canine screening. In this article, detailed liquid chromatography-high resolution mass spectrometry (LC-HR-MS) with Orbitrap instrumentation and gas chromatography-mass spectrometry (GC-MS) analyses were performed on two sets of masks sorted by consensus of a four-dog cohort as either cancer or control. Specifically, after sorting by the dogs, sample masks were cut into multiple sections and extracted for LC-MS and GC-MS non-targeted analyses. Extracts were also analyzed for human cytokines, confirming the presence of human aerosol content above levels in blank masks. In preliminary evaluations, 345 and 44 high quality chemical features were detected by LC-MS and GC-MS analyses, respectively. These features were used to develop provisional orthogonal projection to latent structures-discriminant analysis (OPLS-DA) models to determine if the samples classified as cancer (case) or non-cancer (control) by the dogs could be separated into the same groups using analytical instrumentation. While the OPLS-DA model for the LC-HR-MS data was able to separate the two groups with statistical significance, although weak explanatory power, the GC-MS model was not found to be significant. These results suggest that the dogs may rely on the less volatile compounds from breath aerosol that were analyzed by LC-HR-MS than the more volatile compounds observed by GC-MS to sort mask samples into groups. These results provide justification for more expansive studies in the future that aim to characterize specific chemical features, and the role(s) of these features in maintaining homeostatic biological processes.

Tear Metabolomics in Dry Eye Disease: A Review

Dry eye disease (DED) is a multifactorial syndrome that can be caused by alteration in the quality or quantity of the precorneal tear film. It is considered one of the most common ocular conditions leading patients to seek eye care. The current method for diagnostic evaluations and follow-up examinations of DED is a combination of clinical signs and symptoms determined by clinical tests and questionnaires, respectively. The application of powerful omics technologies has opened new avenues toward analysis of subjects in health and disease. Metabolomics is a new emerging and complementary research discipline to all modern omics in the comprehensive analysis of biological systems. The identification of distinct metabolites and integrated metabolic profiles in patients can potentially inform clinicians at an early stage or during monitoring of disease progression, enhancing diagnosis, prognosis, and the choice of therapy. In ophthalmology, metabolomics has gained considerable attention over the past decade but very limited such studies have been reported on DED. This paper aims to review the application of tear metabolomics in DED.

Tailored therapeutics based on 1,2,3-1H-triazoles: a mini review

Contemporary drug discovery approaches rely on library synthesis coupled with combinatorial methods and high-throughput screening to identify leads. However, due to the multitude of components involved, a majority of optimization techniques face persistent challenges related to the efficiency of synthetic processes and the purity of compound libraries. These methods have recently found an upgradation as fragment-based approaches for target-guided synthesis of lead molecules with active involvement of their biological target. The click chemistry approach serves as a promising tool for tailoring the therapeutically relevant biomolecules of interest, improving their bioavailability and bioactivity and redirecting them as efficacious drugs. 1,2,3-1H-Triazole nucleus, being a planar and biologically acceptable scaffold, plays a crucial role in the design of biomolecular mimetics and tailor-made molecules with therapeutic relevance. This versatile scaffold also forms an integral part of the current fragment-based approaches for drug design, kinetic target guided synthesis and bioorthogonal methodologies.

Systemic and Local Metabolic Alterations in Sleep-Deprivation-Induced Stress: A Multiplatform Mass-Spectrometry-Based Lipidomics and Metabolomics Approach

Sleep deprivation (SD) is known to be associated with metabolic disorders and chronic diseases. Complex metabolic alterations induced by SD at omics scale and the associated biomarker candidates have been proposed. However, in vivo systemic and local metabolic shift patterns of the metabolome and lipidome in acute and chronic partial SD models remain to be elucidated. In the present study, the serum, hypothalamus, and hippocampus CA1 of sleep-deprived rats (SD rats) from acute and chronic sleep restriction models were analyzed using three different omics platforms for the discovery and mechanistic assessment of systemic and local SD-induced dysregulated metabolites. We found a similar pattern of systemic metabolome alterations between two models, for which the area under the curve (AUC) of receiver operating characteristic curves was AUC = 0.847 and 0.930 with the pseudotargeted and untargeted metabolomics approach, respectively. However, SD-induced systemic lipidome alterations were significantly different and appeared to be model-dependent (AUC = 0.374). Comprehensive pathway analysis of the altered lipidome and metabolome in the hypothalamus indicated the abnormal behavior of eight metabolic and lipid metabolic pathways. The metabolic alterations of the hippocampus CA1 was subtle in two SD models. Collectively, these results extend our understanding of the quality of sleep and suggest metabolic targets in developing diagnostic biomarkers for better SD control.

Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics

The diverse characteristics and large number of entities make metabolite separation challenging in metabolomics. To date, there is not a singular instrument capable of analyzing all types of metabolites. In order to achieve a better separation for higher peak capacity and accurate metabolite identification and quantification, we integrated GC × GC-MS and parallel 2DLC-MS for analysis of polar metabolites. To test the performance of the developed system, 13 rats were fed different diets to form two animal groups. Polar metabolites extracted from rat livers were analyzed by GC × GC-MS, parallel 2DLC-MS (-) and parallel 2DLC-MS (+), respectively. By integrating all data together, 58 metabolites were detected with significant change in their abundance levels between groups (p≤ 0.05). Of the 58 metabolites, three metabolites were detected in two platforms and two in all three platforms. Manual examination showed that discrepancy of metabolite regulation measured by different platforms was mainly caused by the poor shape of chromatographic peaks resulting from low instrument response. Pathway analysis demonstrated that integrating the results from multiple platforms increased the confidence of metabolic pathway assignment.

Metabolome of Exosomes: Focus on Vesicles Released by Cancer Cells and Present in Human Body Fluids

Exosomes and other classes of extracellular vesicles (EVs) have gained interest due to their role in cell-to-cell communication. Knowledge of the molecular content of EVs may provide important information on features of parental cells and mechanisms of cross-talk between cells. To study functions of EVs it is essential to know their composition, that includes proteins, nucleic acids, and other classes biomolecules. The metabolome, set of molecules the most directly related to the cell phenotype, is the least researched component of EVs. However, the metabolome of EVs circulating in human blood and other bio-fluids is of particular interest because of its potential diagnostic value in cancer and other health conditions. On the other hand, the metabolome of EVs released to culture media in controlled conditions in vitro could shed light on important aspects of communication between cells in model systems. This paper summarizes the most common approaches implemented in EV metabolomics and integrates currently available data on the composition of the metabolome of EVs obtained in different models with particular focus on human body fluids and cancer cells.

A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics

INTRODUCTION

We previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways.

OBJECTIVES

We aim to analyze a large-scale heterogeneous data compendium generated from our LC-MS/MS platform with both RPLC and HILIC methods to systematically assess measurement quality in biological replicate groups and to investigate metabolite abundance changes and patterns across different biological conditions.

METHODS

Our metabolomics framework was applied in a wide range of experimental systems including cancer cell lines, tumors, extracellular media, primary cells, immune cells, organoids, organs (e.g. pancreata), tissues, and sera from human and mice. We also developed computational and statistical analysis pipelines, which include hierarchical clustering, replicate-group CV analysis, correlation analysis, and case-control paired analysis.

RESULTS

We generated a compendium of 42 heterogeneous deidentified datasets with 635 samples using both RPLC and HILIC methods. There exist metabolite signatures that correspond to various phenotypes of the heterogeneous datasets, involved in several metabolic pathways. The RPLC method shows overall better reproducibility than the HILIC method for most metabolites including polar amino acids. Correlation analysis reveals high confidence metabolites irrespective of experimental systems such as methionine, phenylalanine, and taurine. We also identify homocystine, reduced glutathione, and phosphoenolpyruvic acid as highly dynamic metabolites across all case-control paired samples.

CONCLUSIONS

Our study is expected to serve as a resource and a reference point for a systematic analysis of label-free LC-MS/MS targeted metabolomics data in both RPLC and HILIC methods with dMRM.

Epigenetic Regulation at the Interplay Between Gut Microbiota and Host Metabolism

Gut microbiota communities have coevolved for millions of years in a symbiotic relationship with their mammalian hosts. Elucidating and understanding the molecular mechanisms by which microbiota interacts with its host and how this contributes to the homeostasis of the host is crucial. One of these molecular relationships is the so-called chemical crosstalk between microbiota and host metabolisms, including the poorly explored epigenetic regulation of host tissues by the metabolic activity of gut microbiota in response to changes in diet. DNA methylation and histone modifications are epigenetic marks partly regulated by enzymes such as methylases and acetylases, whose activity depend on host and microbiota metabolites that act as substrates and cofactors for these reactions. However, providing a complete mechanistic description of the regulatory interactions between both metabolisms and the impact on the expression of host genes through an epigenetic modulation, remains elusive. This article presents our perspective on how metabolomic, metagenomic, transcriptomic, and epigenomic data can be used to investigate the "microbiota-nutrient metabolism-epigenetics axis." We also discuss the implications and opportunities this knowledge may have for basic and applied science, such as the impact on the way we structure future research, understand, and prevent diseases like type 2 diabetes or obesity.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Metabolic Remodeling during Biofilm Development of Bacillus subtilis

Bacterial biofilms are ubiquitous in natural environments and play an important role in many clinical, industrial, and ecological settings. Although much is known about the transcriptional regulatory networks that control biofilm formation in model bacteria such as Bacillus subtilis, very little is known about the role of metabolism in this complex developmental process. To address this important knowledge gap, we performed a time-resolved analysis of the metabolic changes associated with bacterial biofilm development in B. subtilis by combining metabolomic, transcriptomic, and proteomic analyses. Here, we report a widespread and dynamic remodeling of metabolism affecting central carbon metabolism, primary biosynthetic pathways, fermentation pathways, and secondary metabolism. This report serves as a unique hypothesis-generating resource for future studies on bacterial biofilm physiology. Outside the biofilm research area, this work should also prove relevant to any investigators interested in microbial physiology and metabolism.

Biofilms are structured communities of tightly associated cells that constitute the predominant state of bacterial growth in natural and human-made environments. Although the core genetic circuitry that controls biofilm formation in model bacteria such as Bacillus subtilis has been well characterized, little is known about the role that metabolism plays in this complex developmental process. Here, we performed a time-resolved analysis of the metabolic changes associated with pellicle biofilm formation and development in B. subtilis by combining metabolomic, transcriptomic, and proteomic analyses. We report surprisingly widespread and dynamic remodeling of metabolism affecting central carbon metabolism, primary biosynthetic pathways, fermentation pathways, and secondary metabolism. Most of these metabolic alterations were hitherto unrecognized as biofilm associated. For example, we observed increased activity of the tricarboxylic acid (TCA) cycle during early biofilm growth, a shift from fatty acid biosynthesis to fatty acid degradation, reorganization of iron metabolism and transport, and a switch from acetate to acetoin fermentation. Close agreement between metabolomic, transcriptomic, and proteomic measurements indicated that remodeling of metabolism during biofilm development was largely controlled at the transcriptional level. Our results also provide insights into the transcription factors and regulatory networks involved in this complex metabolic remodeling. Following upon these results, we demonstrated that acetoin production via acetolactate synthase is essential for robust biofilm growth and has the dual role of conserving redox balance and maintaining extracellular pH. This report represents a comprehensive systems-level investigation of the metabolic remodeling occurring during B. subtilis biofilm development that will serve as a useful road map for future studies on biofilm physiology.

Development of a new quantification method for organic acids in urine as potential biomarkers for respiratory illness

Asthma and chronic obstructive pulmonary disease (COPD) are common respiratory disorders that have similar clinical presentation and misdiagnosis may lead to improper treatment. There is a need for a better, non-invasive test for the differentiation of asthma and COPD. In this study, we developed a new validated LC-MS/MS method for 17 urinary organic acids that could serve as potential biomarkers. Human urine samples were collected from adults with asthma or COPD. LC-MS/MS was performed using the differential isotope labeling approach. 4-(Dimethylamino) phenacyl bromide (DmPA) was used for derivatization using two different carbon isotopes, allowing for the formation of internal standard for each metabolite. Gradient elution was employed on a C₁₈ column while the LC-MS/MS operated in the multiple reaction monitoring mode (MRM). Regulatory guidelines were used for method validation. Partial Least Squares Discriminative Analysis (PLS-DA) was applied to the log-transformed values of metabolites in each group of asthma and COPD subjects. Full validation in targeted metabolomics is scarce with usually limited number of metabolites, unlike fit-for-purpose approach. Due to the endogenous nature of the metabolites, numerous challenges were encountered during method development and validation, such as the lactic acid interference from the surrounding environment. The required specificity, accuracy and precision was successfully achieved. The method was fully validated, ensuring robustness and reproducibility when analyzing patient samples. The method was applied to analyze human urine samples and PLS-DA analysis showed differentiation of asthma and COPD subjects (R2 0.89, Q2 0.68). As targeted metabolomics is expanding to the clinical sphere, more validated methods/strategies are needed. Our work will expand the current knowledge-base regarding targeted metabolomics.

Application of Metabolomics to Osteoarthritis: from Basic Science to the Clinical Approach

PURPOSE OF THE REVIEW

Osteoarthritis (OA) is a multifactorial and progressive disease affecting whole synovial joint. The extract pathogenic mechanisms and diagnostic biomarkers of OA remain unclear. In this article, we review the studies related to metabolomics of OA, discuss the biomarkers as a tool for early OA diagnosis. Furthermore, we examine the major studies on the application of metabolomics methodology in the complex context of OA and create a bridge from findings in basic science to their clinical utility.

RECENT FINDINGS

Recently, the tissue metabolomics signature permits a view into transitional phases between the healthy and OA joint. Both nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry-based metabolomics approaches have been used to interrogate the metabolic alterations that may indicate the complex progression of OA. Specifically, studies on alterations pertaining to lipids, glucose, and amino acid metabolism have aided in the understanding of the complex pathogenesis of OA. The discovery of identified metabolites could be important for diagnosis and staging of OA, as well as for the assessment of efficacy of new drugs.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

Co-Culture Systems for the Production of Secondary Metabolites: Current and Future Prospects

Microorganisms are the great sources of Natural Products (NPs); these are imperative to their survival apart from conferring competitiveness amongst each other within their environmental niches. Primary and secondary metabolites are the two major classes of NPs that help in cell development, where antimicrobial activity is closely linked with secondary metabolites. To capitalize on the effects of secondary metabolites, co-culture methods have been often used to develop an artificial microbial community that promotes the action of these metabolites. Different analytical techniques will subsequently be employed based on the metabolite specificity and sensitivity to further enhance the metabolite induction. Liquid Chromatography-Mass Spectrometry (LC-MS) and Gas Chromatography (GC)-MS are commonly used for metabolite separation while Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) have been used as tools to elucidate the structure of compounds. This review intends to discuss current systems in use for co-culture in addition to its advantages, with discourse into the investigation of specific techniques in use for the detailed study of secondary metabolites. Further advancements and focus on co-culture technologies are required to fully realize the massive potential in synthetic biological systems.

Untargeted LC/MS-based metabolic phenotyping (metabonomics/metabolomics): The state of the art

Liquid chromatography (LC) hyphenated to mass spectrometry is currently the most widely used means of determining metabolic phenotypes via both untargeted and targeted analysis. At present a range of analytical separations, including reversed-phase, hydrophilic interaction and ion-pair LC are employed to maximise metabolome coverage with ultra (high) performance liquid chromatography (UHPLC) increasingly displacing conventional high performance liquid chromatography because of the need for short analysis times and high peak capacity in such applications. However, it is widely recognized that these methodologies do not entirely solve the problems facing researchers trying to perform comprehensive metabolic phenotyping and in addition to these "routine" approaches there are continuing investigations of alternative separation methods including 2-dimensional/multi column approaches. These involve either new stationary phases or multidimensional combinations of the more conventional materials currently used, as well as application of miniaturization or "new" approaches such as supercritical HP and UHP- chromatographic separations. There is also a considerable amount of interest in the combination of chromatographic and ion mobility separations, with the latter providing both an increase in resolution and the potential to provide additional structural information via the determination of molecular collision cross section data. However, key problems remain to be solved including ensuring quality, comparability across different laboratories and the ever present difficulty of identifying unknowns.

Intralaboratory comparison of analytical methods for quantification of major phytocannabinoids

This study compares alternative approaches for analyzing phytocannabinoids in different plant materials. Three chromatographic analytical methods (ultra-high-performance liquid chromatography with tandem mass spectrometric detection and gas chromatography with mass spectrometric and flame ionization detection) were evaluated regarding selectivity, sensitivity, analytical accuracy, and precision. The performance of the methods was compared and all three methods were demonstrated to be appropriate tools for analyzing phytocannabinoids in cannabis. Gas chromatography coupled with mass spectrometric detection showed slightly better accuracy in determining phytocannabinoid acids, which are often difficult to quantify owing to their limited stability. Aspects of sample preparation, such as material homogenization and extraction, were also considered. A single ultrasonic-assisted ethanolic extraction of dried and powdered plant samples of cannabis was shown to be exhaustive for extracting the samples prior to analysis.

Metabolomic Insights into Human Arboviral Infections: Dengue, Chikungunya, and Zika Viruses

The global burden of arboviral diseases and the limited success in controlling them calls for innovative methods to understand arbovirus infections. Metabolomics has been applied to detect alterations in host physiology during infection. This approach relies on mass spectrometry or nuclear magnetic resonance spectroscopy to evaluate how perturbations in biological systems alter metabolic pathways, allowing for differentiation of closely related conditions. Because viruses heavily depend on host resources and pathways, they present unique challenges for characterizing metabolic changes. Here, we review the literature on metabolomics of arboviruses and focus on the interpretation of identified molecular features. Metabolomics has revealed biomarkers that differentiate disease states and outcomes, and has shown similarities in metabolic alterations caused by different viruses (e.g., lipid metabolism). Researchers investigating such metabolomic alterations aim to better understand host⁻virus dynamics, identify diagnostically useful molecular features, discern perturbed pathways for therapeutics, and guide further biochemical research. This review focuses on lessons derived from metabolomics studies on samples from arbovirus-infected humans.

Minor Isozymes Tailor Yeast Metabolism to Carbon Availability

Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time, suggesting that they contribute to fitness. We developed an unsupervised computational method for identifying environmental conditions under which isozymes are likely to make fitness contributions. This method analyzes published gene expression data to find specific experimental perturbations that induce differential isozyme expression. In yeast, we found that isozymes are strongly enriched in the pathways of central carbon metabolism and that many isozyme pairs show anticorrelated expression during the respirofermentative shift. Building on these observations, we assigned function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2’s deletion can be rescued by expressing the major pyruvate kinase only if that enzyme carries mutations mirroring PYK2’s allosteric regulation. Thus, central carbon isozymes help to optimize allosteric metabolic regulation under a broad range of potential nutrient conditions while requiring only a small number of transcriptional states.

IMPORTANCE Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Evaluation of Sample Preparation Methods for the Analysis of Reef-Building Corals Using ¹H-NMR-Based Metabolomics

The field of metabolomics generally lacks standardized methods for the preparation of samples prior to analysis. This is especially true for metabolomics of reef-building corals, where the handful of studies that were published employ a range of sample preparation protocols. The utilization of metabolomics may prove essential in understanding coral biology in the face of increasing environmental threats, and an optimized method for preparing coral samples for metabolomics analysis would aid this cause. The current study evaluates three important steps during sample processing of stony corals: (i) metabolite extraction, (ii) metabolism preservation, and (iii) subsampling. Results indicate that a modified Bligh and Dyer extraction is more reproducible across multiple coral species compared to methyl tert-butyl ether and methanol extractions, while a methanol extraction is superior for feature detection. Additionally, few differences were detected between spectra from frozen or lyophilized coral samples. Finally, extraction of entire coral nubbins increased feature detection, but decreased throughput and was more susceptible to subsampling error compared to a novel tissue powder subsampling method. Overall, we recommend the use of a modified Bligh and Dyer extraction, lyophilized samples, and the analysis of brushed tissue powder for the preparation of reef-building coral samples for ¹H NMR metabolomics.

Determination of Free Soluble Phenolic Compounds in Grains of Ancient Wheat Varieties ( Triticum sp. pl.) by Liquid Chromatography-Tandem Mass Spectrometry

A method of liquid chromatography coupled with tandem mass spectrometry was developed and validated for the determination of free soluble phenolic compounds in eight ancient varieties of wheat ( Triticum sp. pl.): Autonomia, Gentil rosso, Inallettabile, Leone aristato, Mentana, Poulard di Ciano, Risciola, and Terminillo. Trace compounds such as two conjugated flavones, vitexin (17.13-34.32 μg/kg) and isovitexin (9.76-30.01 μg/kg), were also determined. Poulard di Ciano, presumably an autochthonous wheat of the Reggio Emilia province (northern Italy), showed a peculiar quali/quantitative phenolic profile (7097.03 μg/kg sum of total phenolic compounds and 1.97 sum of hydroxycinnamic acids to sum of hydroxybenzoic acids ratio) along with a tetraploid genome. Terminillo, Risciola, Gentil rosso, Mentana, and Leone aristato showed hexaploid genomes and high concentrations of phenolic compounds (ranging from 6796.12 to 7605.78 μg/kg), also in comparison with two modern varieties of bread wheat, Bolero and Blasco. The targeted metabolomic approach proved to be effective to determine some secondary metabolites of wheat. The richness in phenolic compounds combined with high rusticity and adaptability to marginal soils showed by ancient wheat varieties make them suitable for sustainable agricultural and organic cultivation.

El-MAVEN: A Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine for Metabolomics

Analysis of large metabolomic datasets is becoming commonplace with the increased realization of the role that metabolites play in biology and pathophysiology. While there are many open-source analysis tools to extract peaks from liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and tandem mass spectrometry (LC-MS/MS) data, these tools are not very interactive and are suboptimal when a large number of samples are to be analyzed. El-MAVEN is an open-source analysis platform that extends MAVEN and provides fast, powerful, and interactive analysis capabilities especially for datasets containing over 100 samples. The El-MAVEN workflow is easy to use with just four steps from loading data to exporting of the results. Advanced analysis and software techniques such as multiprocessing, machine learning, and reduction of memory leaks are implemented so as to provide a seamless and interactive user experience. Results from El-MAVEN can be exported in a range of formats allowing continued analysis on other platforms. Additionally, El-MAVEN is also fully integrated with Polly™, a cloud-based analysis platform that provides a range of tools for flux analysis and integrative-omics analysis. El-MAVEN is a powerful tool that enables fast and efficient analysis of large metabolomic datasets to accelerate the process of gaining insight from raw data.

Untargeted and Semi-targeted Lipid Analysis of Biological Samples Using Mass Spectrometry-Based Metabolomics

Liquid chromatography coupled to mass spectrometry (LC-MS)-based metabolomics and lipidomics offers invaluable tools to qualitatively and quantitatively study biological systems. Historically, unbiased (or discovery) analysis has been performed independently of targeted, quantitative analysis such as multiple reaction monitoring (MRM). These practices have been aptly carried out based on technical limitations of each assay. The wide mass scanning ranges typical of discovery approaches limit assay sensitivity, while targeted methods that improve analyte detection do not acquire data on ions not included in the targeted assay design. Recent improvements to quadrupole-Orbitrap technology have improved both scan speed as well as sensitivity, thus making these instruments more robust. By combining the improved robustness and coverage with stable isotope dilution (SID) techniques, advantages of the separate assays can now be realized in a single run, thereby improving the throughput of this type of analysis.

A High-Throughput Targeted Metabolomics Workflow for the Detection of 200 Polar Metabolites in Central Carbon Metabolism

Targeted metabolomics aims to analyze a set of pre-selected metabolites from biologically relevant metabolic pathways. The triple quadrupole mass spectrometry (QqQ-MS) based multiple reaction monitoring (MRM) technique is the most widely approach used for targeted metabolomics, and features high selectivity and sensitivity, good reproducibility and wide dynamic range in quantitative analysis. Here, we describe an MRM based targeted metabolomics workflow for the quantitative analysis of 200 polar metabolites in central carbon metabolic pathways, including the data acquisition method and the automated data processing procedures using our in-house R package MRMAnalyzer. The workflow described in this chapter combines a hydrophilic interaction liquid chromatography (HILIC) separation and positive/negative ion polarity switching based MS detection, and is able to acquire data from multiple types of biological samples such as bacteria, cultured mammalian cells, animal tissues and biofluids (e.g., serum and urine). Finally, the MRMAnalyzer software can automatically process the generated large-scale data set with high efficiency. We hope it is a valuable and efficient workflow for researchers to facilitate the respective biological studies using targeted metabolomics.

LC-MS/MS-MRM-Based Targeted Metabolomics for Quantitative Analysis of Polyunsaturated Fatty Acids and Oxylipins

LC-MS/MS with multiple reaction monitoring (MRM) is a powerful tool for targeted metabolomics analysis including screening and quantification of known metabolites. Given the complexity of biological samples, the difference in ionization efficiency, and signal intensity of each metabolite, isotopically labeled internal standards are often used for accurate quantification. In this chapter, we describe a detailed protocol for the quantitative analysis of polyunsaturated fatty acids (PUFAs) and their oxidized products (oxylipins) by LC-MS/MS-MRM with isotope dilution. PUFAs are very susceptible to oxidation by both enzymatic and nonenzymatic pathways. Free PUFAs and corresponding oxylipins, known as bioactive lipids, are involved in many processes with varying biological functions depending on their chemical structure and concentration. Accurate quantification is thus becoming crucial to understanding the role of these bioactive lipids in health, disease(s), and other settings.

Nanopillar-Assisted SERS Chromatography

Practical implementation of surfaced enhanced Raman spectroscopy (SERS) sensing is hindered by complexity of real-life samples, which often requires long and costly pretreatment and purification. Here, we present a novel nanopillar-assisted SERS chromatography (NPC-SERS) method for simultaneous quantitation of target molecules and analysis of complex, multicomponent fluids, e.g., human urine spiked with a model drug paracetamol (PAR). Gold-coated silicon nanopillar (AuNP) SERS substrates and a centrifugal microfluidic platform are tactfully combined, which allows (i) a precise and fully automated sample manipulation and (ii) spatial separation of different molecular species on the AuNP substrate. The NPC-SERS technique provides a novel approach for wetting the stationary phase (AuNP) using the "wicking effect", and thus minimizes dilution of analytes. Separation of PAR and the main human urine components (urea, uric acid, and creatinine) has been demonstrated. Quantitative detection of PAR with ultrawide linear dynamic range (0-500 ppm) is achieved by analyzing the spreading profiles of PAR on the AuNP surface. NPC-SERS transforms SERS into a sensing technique with general applicability, facilitating rapid and quantitative detection of analytes in complex biofluids, such as saliva, blood, and urine.

Untargeted Molecular Discovery in Primary Metabolism: Collision Cross Section as a Molecular Descriptor in Ion Mobility-Mass Spectrometry

In this work, we established a collision cross section (CCS) library of primary metabolites based on analytical standards in the Mass Spectrometry Metabolite Library of Standards (MSMLS) using a commercially available ion mobility-mass spectrometer (IM-MS). From the 554 unique compounds in the MSMLS plate library, we obtained a total of 1246 CCS measurements over a wide range of biochemical classes and adduct types. Resulting data analysis demonstrated that the curated CCS library provides broad molecular coverage of metabolic pathways and highlights intrinsic mass-mobility relationships for specific metabolite superclasses. The separation and characterization of isomeric metabolites were assessed, and all molecular species contained within the plate library, including isomers, were critically evaluated to determine the analytical separation efficiency in both the mass ( m/ z) and mobility (CCS/ΔCCS) dimension required for untargeted metabolomic analyses. To further demonstrate the analytical utility of CCS as an additional molecular descriptor, a well-characterized biological sample of human plasma serum (NIST SRM 1950) was examined by LC-IM-MS and used to provide a detailed isomeric analysis of carbohydrate constituents by ion mobility.

Discovering biomarkers in bladder cancer by metabolomics

It has become increasingly clear that the development of cancer, a multifactorial disease, cannot be explained by a single molecule or gene mutation. As a new discipline, metabolomics focuses on the body's metabolite changes, and attempts to find differences to explain the development of cancer; it has proven to be effective and credible. Metabolic studies of bladder cancer (BCa) lag behind those of other tumors. This review systematically outlines the specific process of metabolomics and the use of metabolomics in BCa studies in recent years. We have reviewed the in vitro cell line, bladder tumor tissue and biofluid (urine, plasma and serum) studies used in metabolomics analyses of BCa. The advantages and drawbacks of the use of different samples were compared. Based on the available studies, we have further described the aberrant metabolic pathways of BCa and have suggested some metabolites that may be potential biomarkers for BCa detection.

Improving liquid chromatography-tandem mass spectrometry determination of polycarboxylic acids in human urine by chemical derivatization. Comparison of o-benzyl hydroxylamine and 2-picolyl amine

Due to its high sensitivity and specificity, liquid chromatography-electrospray tandem mass spectrometry (LC-MS/MS) could be considered as the gold-standard in targeted metabolomics. Although LC-MS/MS allows for the direct detection of a large number of molecules, the proper quantification of highly polar compounds such as poly-carboxylic acids in complex matrices like urine is still a challenge. Chemical derivatization offers a suitable way to improve chromatographic behavior and sensitivity for these compounds. Several derivatizing agents have been proposed for the LC-MS/MS determination of carboxylic acids but studies dealing with their comparison in challenging scenarios are scarce. Here we present the evaluation of two different derivatization agents; o-benzylhydroxyl amine (oBHA) and 2-picolyl amine (2-PA); for the quantification of the (poly)-carboxylic acids belonging to the tricarboxylic acid cycle in urine. The suitability of both derivatizating agents was compared by validation of the two approaches. Derivatization with oBHA showed important advantages against 2-PA derivatization such as (i) providing better sensitivity, (ii) more stable derivatives and (iii) allowing for the proper validation of a larger number of analytes. Moreover, while 2-PA derivatization failed in the determination of the target analytes in some stored urine samples, oBHA derivatization successfully allowed for their appropriate determination in the same samples. A comparison between the concentrations obtained using oBHA derivatization and those provided by an external laboratory using UV and GC-MS detection revealed a satisfactory agreement between both results. Additionally, the concentrations obtained by the oBHA method for a set of 38 urines are in agreement with those previously reported in the literature. As a conclusion, our results show that the use of oBHA is preferred against 2-PA for the detection and quantification of (poly)-carboxylic acids in urine.

Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry

Advances in liquid chromatography-mass spectrometry (LC-MS) instruments and analytical strategies have brought about great progress in targeted metabolomics analysis. This methodology is now capable of performing precise targeted measurement of dozens or hundreds of metabolites in complex biological samples. Classic targeted quantification assay using the multiple reaction monitoring (MRM) mode has been the foundation of high-quality metabolite quantitation. However, utilization of this strategy in biological studies has been limited by its relatively low metabolite coverage and throughput capacity. A number of methods for large-scale targeted metabolomics assay which have been developed overcome these limitations. These strategies have enabled extended metabolite coverage which is defined as targeting of large numbers of metabolites, while maintaining reliable quantification performance. These recently developed techniques thus bridge the gap between traditional targeted metabolite quantification and untargeted metabolomics profiling, and have proven to be powerful tools for metabolomics study. Although the LC-MRM-MS strategy has been used widely in large-scale metabolomics quantification analysis due to its fast scan speed and ideal analytic stability, there are still drawbacks which are due to the low resolution of the triple quadrupole instruments used for MRM assays. New approaches have been developed to expand the options for large-scale targeted metabolomics study, using high-resolution instruments such as parallel reaction monitoring (PRM). MRM and PRM-based techniques are now attractive strategies for quantitative metabolomics analysis and high-throughput biomarker discovery. Here we provide an overview of the major developments in LC-MS-based strategies for large-scale targeted metabolomics quantification in biological samples. The advantages of LC-MRM/PRM-MS based analytical strategies which may be used in multiplexed and high throughput quantitation for a wide range of metabolites are highlighted. In particular, PRM and MRM strategies are compared, and we summarize the work flow commonly used for large-scale targeted metabolomics analysis including sample preparation, LC separation and data analysis, as well as recent applications in biological studies.