Metabolite Coverage Research Articles

Solvent Selection in the Quality Analysis of White Tea by Nuclear Magnetic Resonance Spectroscopy

The study aimed to assess the influence of diverse solvent schemes on the qualitative analysis of white tea and to establish a more comprehensive quality evaluation. A systematic study of solvent optimization was carried out using the quality analysis of white tea as an exemplar, highlighting the critical role of selecting appropriate solvents in NMR-based sample preparation process. Six sample dissolution protocols, encompassing extraction and reconstitution solvents, were evaluated. The aqueous methanol solution proved effective as an extraction solvent, offering optimal metabolite coverage and spectral resolution. For reconstitution solvents, D2O facilitated identification of specific metabolites, such as amino acids, organic acids and carbohydrates, while DMSO-d6 exhibited better performance in detecting catechins, alkaloids and lipid resonances. Additionally, DMSO-d6 enabled better discrimination among various white tea varieties, whereas D2O system demonstrated better ability for estimating the storage duration of white tea. The characteristic metabolites were exactly those that each of the two solvents is suited to solubilize. Therefore, it can be concluded that solvents suitable for the detection of a class of metabolites are more likely to highlight differences in that type of metabolites and multi-solvent combination captures more comprehensive information, revealing the necessity of optimizing solvent protocols for different assay targets.

A Comprehensive LC-MS Metabolomics Assay for Quantitative Analysis of Serum and Plasma.

Background/Objectives: Targeted metabolomics is often criticized for the limited metabolite coverage that it offers. Indeed, most targeted assays developed or used by researchers measure fewer than 200 metabolites. In an effort to both expand the coverage and improve the accuracy of metabolite quantification in targeted metabolomics, we decided to develop a comprehensive liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay that could quantitatively measure more than 700 metabolites in serum or plasma. Methods: The developed assay makes use of chemical derivatization followed by reverse phase LC-MS/MS and/or direct flow injection MS (DFI-MS) in both positive and negative ionization modes to separate metabolites. Multiple reaction monitoring (MRM), in combination with isotopic standards and multi-point calibration curves, is used to detect and absolutely quantify the targeted metabolites. The assay has been adapted to a 96-well plate format to enable automated, high-throughput sample analysis. Results: The assay (called MEGA) is able to detect and quantify 721 metabolites in serum/plasma, covering 20 metabolite classes and many commonly used clinical biomarkers. The limits of detection were determined to range from 1.4 nM to 10 mM, recovery rates were from 80% to 120%, and quantitative precision was within 20%. LC-MS/MS metabolite concentrations of the NIST® SRM®1950 plasma standard were found to be within 15% of NMR quantified levels. The MEGA assay was further validated in a large dietary intervention study. Conclusions: The MEGA assay should make comprehensive quantitative metabolomics much more affordable, accessible, automatable, and applicable to large-scale clinical studies.

A systematic evaluation of quenching, extraction and analysis procedures for metabolomics study of the mechanism of QYSLD intervention in A549 cells.

The preparation of cellular metabolomics samples and how to achieve comprehensive coverage of different polar metabolites in cell samples in the analysis pose a challenge for cellular metabolomics. In this study, we optimized a metabolomics protocol based on ultra-high-performance liquid chromatography high-resolution mass spectrometry (UPLC/HRMS) for theextraction and detection of metabolites in A549 cells and exploration of the intervention effect of Qi-Yu-San-Long decoction (QYSLD) on A549 cells. The results indicate that the lowest level of ATP leakage was observed when A549 cells were quenched under liquid nitrogen. MeOH/chloroform/H2O (1:2:1) extraction yielded more chromatographic peaks and excellent reproducibility, and the relative extraction efficiency of most target metabolites was also high. And we optimized thechromatographic separation conditions in both HILIC and RPLC modes, enabling comprehensive detection and analysis of metabolites with varying polarities. Then, we applied the optimized method to UPLC-Q-TOF/MS-based metabolomics of A549 cells to study the mechanism of QYSLD intervention in non-small cell lung cancer (NSCLC). The CCK-8, EdU staining, and cell cycle assay showed that QYSLD inhibited the proliferation of A549 cells by interfering with the cell cycle and blocking them in theG1 phase. A total of 36 differential metabolites associated with the antitumor effects of QYSLD on NSCLC were identified, mainly involving nicotinate and nicotinamide metabolism, sphingolipid metabolism, and glycerophospholipid metabolism. And western blotting confirmed that thechange in 1-methylnicotinamide levels after QYSLD intervention was associated with theinhibition of nicotinamide N-methyltransferase expression in A549 cells.

Simultaneous metabolomics and lipidomics analysis based on 4in1 online analysis system reveal metabolic signatures in atherosclerotic mice

Developing efficient and comprehensive analysis methods for metabolomics and lipidomics in the biological tissues and body fluids is essential for understanding the disease mechanisms. Although various two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) methods have been proposed to expand metabolite coverage, achieving higher efficiency in integrated metabolomics and lipidomics studies remains a technical challenge. In this work, a novel 4in1 online analysis system with excellent reproducibility and mass accuracy was constructed for metabolomics and lipidomics study in various biological samples from atherosclerotic mice. This system enabled the simultaneous detection in both positive and negative ion modes with extensive polarity separation in a single analytical run. Using the 4in1 online analysis system, we identified distinct but complementary metabolic signatures associated with atherosclerosis in different biological samples. Specifically, a total of 230 and 170 differential metabolites or lipids were detected in mice plasma samples and aortic tissue samples, respectively, including glycerophospholipids, sphingolipids, fatty acyls, glycerolipids, carboxylic acids, and pyrimidine nucleosides. Additionally, atherosclerosis-related metabolic pathways involved in biosynthesis of unsaturated fatty acids, sphingolipid metabolism, cholesterol metabolism, glycerophospholipid metabolism, and choline metabolism further revealed. These findings demonstrate that the novel 4in1 online analysis system is a faithful, stable and powerful tool for comprehensive metabolomics and lipidomics studies in complex biological matrices.

Optimized high-throughput protocols for comprehensive metabolomic and lipidomic profiling of brain sample

Establishing direct causal and functional links between genotype and phenotype requires thoroughly analyzing metabolites and lipids in systems biology. Tissue samples, which provide localized and direct information and contain unique compounds, play a significant role in objectively classifying diseases, predicting prognosis, and deciding personalized therapeutic strategies. Comprehensive metabolomic and lipidomic analyses in tissue samples need efficient sample preparation steps, optimized analysis conditions, and the integration of orthogonal analytical platforms because of the physicochemical diversities of biomolecules. Here, we propose simple, rapid, and robust high-throughput analytical protocols based on the design of experiment (DoE) strategies, with the various parameters systematically tested for comprehensively analyzing the heterogeneous brain samples. The suggested protocols present a systematically DoE-based strategy for performing the most comprehensive analysis for integrated GC-MS and LC-qTOF-MS from brain samples. The five different DoE models, including D-optimal, full factorial, fractional, and Box-Behnken, were applied to increase extraction efficiency for metabolites and lipids and optimize instrumental parameters, including sample preparation and chromatographic parameters. The superior simultaneous extraction of metabolites and lipids from brain samples was achieved by the methanol-water-dichloromethane (2:1:3, v/v/v) mixture. For GC-MS based metabolomics analysis, incubation time, temperature, and methoxyamine concentration (10 mg/mL) affected metabolite coverage significantly. For LC-qTOF-MS based metabolomics analysis, the extraction solvent (methanol-water; 2:1, v/v) and the reconstitution solvent (%0.1 FA in acetonitrile) were superior on the metabolite coverage. On the other hand, the ionic strength and column temperature were critical and significant parameters for high throughput metabolomics and lipidomics studies using LC-qTOF-MS. In conclusion, DoE-based optimization strategies for a three-in-one single-step extraction enabled rapid, comprehensive, high-throughput, and simultaneous analysis of metabolites, lipids, and even proteins from a 10 mg brain sample. Under optimized conditions, 475 lipids and 158 metabolites were identified in brain samples.

Large-scale, comprehensive plasma metabolomic analyses reveal potential biomarkers for the diagnosis of early-stage coronary atherosclerosis

BackgroundCoronary atherosclerosis (CAS) is a prevalent and chronic life-threatening disease. However, the detection of CAS at an early stage is difficult because of the lack of effective noninvasive diagnostic methods. The present study aimed to characterize the plasma metabolome of early-stage CAS patients to discover metabolomic biomarkers, develop a novel metabolite-based model for accurate noninvasive diagnosis of early-stage CAS, and explore the underlying metabolic mechanisms involved. MethodsA total of 100 patients with early-stage CAS and 120 age- and sex-matched control subjects were recruited from the Chinese Han population and further randomly divided into training (n = 120) and test sets (n = 100). The metabolomic profiles of the plasma samples were analyzed by an integrated untargeted liquid chromatography–mass spectrometry approach, including two separation modes and two ionization modes. Univariate and multivariate statistical analyses were employed to identify potential biomarkers and construct an early-stage CAS diagnostic model. ResultsThe integrated analytical method established herein improved metabolite coverage compared with single chromatographic separation and MS ionization mode. A total of 80 metabolites were identified as potential biomarkers of early-stage CAS, and these metabolites were mainly involved in glycerophospholipid, fatty acid, sphingolipid, and amino acid metabolism. An effective diagnostic model for early-stage CAS was established, incorporating 11 metabolites and achieving areas under the receiver operating characteristic curve (AUCs) of 0.984 and 0.908 in the training and test sets, respectively. ConclusionsOur study not only successfully developed an effective noninvasive diagnostic model for identifying early-stage CAS but also provided novel insights into the pathogenesis of CAS.

In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level

Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

Metabolite profiling of chemically modified oligonucleotides in rat liver homogenates using hydrophilic interaction liquid chromatography-high resolution mass spectrometry

Chemically modified oligonucleotide is a rapidly emerging field of biotherapeutics. The evaluation of the metabolic stability of therapeutic oligonucleotides is critical. The current methods are limited to the ion-pairing reversed phase chromatographic techniques. Due to complex chemical modifications of therapeutic oligonucleotides and diverse structural changes in their metabolites, comprehensive metabolite profiling is still challenging. Therefore, an alternative analytical method for the metabolism study of oligonucleotides is urgently needed. In this study, a novel hydrophilic interaction liquid chromatography-high resolution mass spectrometry (HILIC-HRMS) method was developed and applied to the metabolism study of two chemically modified oligonucleotides, i.e., N11-GalNAc (phosphorothioate, 2′-F, 2′-OMe, and 3′-GalNAc) and N9 (phosphorothioate, 2′-F), in rat liver homogenates. Highly broad retention of metabolites on the HILIC column was achieved, with truncated metabolite having a single nucleotide being detected. A stepped normalized collisional energy (NCE) in HRMS led to characteristic c/y, d/y, and b/y fragment ion series, and diagnostic fragment ions for linker and GalNAc, which covered the whole sequence of oligonucleotides. A total number of twenty-seven and twenty-nine metabolites were identified in rat liver homogenates for N11-GalNAc and N9, respectively, by using the isotopic envelope of the precursor ion and stepped NCE-based sequence mapping data. The time-dependent production of metabolites was observed. The UCU sequence with the uniform 2′-F modification on ribose was identified as the major metabolic site for N11-GalNAc, and the phosphodiester bonds between nucleosides involving unmodified uracil were identified as the metabolic soft sites for N9. The developed HILIC-HRMS method provided a high coverage of various oligonucleotide metabolites. Characteristic fragment ions enable sequence mapping and therefore unambiguous metabolite identifications. This approach can be broadly applicable to the metabolism study of chemically modified oligonucleotides and the identification of their metabolic soft sites.

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Comparison of Three Widely Employed Extraction Methods for Metabolomic Analysis of Trypanosoma brucei.

Trypanosoma brucei (Tb) is the causative agent of human African trypanosomiasis (HAT), also known as sleeping sickness, which can be fatal if left untreated. An understanding of the parasite's cellular metabolism is vital for the discovery of new antitrypanosomal drugs and for disease eradication. Metabolomics can be used to analyze numerous metabolic pathways described as essential to Tb. brucei but has some limitations linked to the metabolites' physicochemical properties and the extraction process. To develop an optimized method for extracting and analyzing Tb. brucei metabolites, we tested the three most commonly used extraction methods, analyzed the extracts by hydrophilic interaction liquid chromatography high-resolution mass spectrometry (HILIC LC-HRMS), and further evaluated the results using quantitative criteria including the number, intensity, reproducibility, and variability of features, as well as qualitative criteria such as the specific coverage of relevant metabolites. Here, we present the resulting protocols for untargeted metabolomic analysis of Tb. brucei using (HILIC LC-HRMS). © 2024 Wiley Periodicals LLC. Basic Protocol 1: Culture of Trypanosoma brucei brucei parasites Basic Protocol 2: Preparation of samples for metabolomic analysis of Trypanosoma brucei brucei Basic Protocol 3: LC-HRMS-based metabolomic data analysis of Trypanosoma brucei brucei.

Computational methods for processing and interpreting mass spectrometry-based metabolomics.

Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.

Development of metabolome extraction strategy for metabolite profiling of skin tissue.

Changes in skin phenotypic characteristics are based on skin tissue. The study of the metabolic changes in skin tissue can help understand the causes of skin diseases and identify effective therapeutic interventions. We aimed to establish and optimize a non-targeted skin metabolome extraction system for skin tissue metabolomics with high metabolite coverage, recovery, and reproducibility using gas chromatography/mass spectrometry. The metabolites in skin tissues were extracted using eleven different extraction systems, which were designed using reagents with different polarities based on sequential solid-liquid extraction employing a two-step strategy and analyzed using gas chromatograph/mass spectrometry. The extraction efficiency of diverse solvents was evaluated by coefficient of variation (CV), multivariate analysis, metabolites coverage, and relative peak area analysis. We identified 119 metabolites and the metabolite profiles differed significantly between the eleven extraction systems. Metabolites with high abundances in the organic extraction systems, followed by aqueous extraction, were involved in the biosynthesis of unsaturated fatty acids, while metabolites with high abundances in the aqueous extraction systems, followed by organic extraction, were involved in amino sugar and nucleotide sugar metabolism, and glycerolipid metabolism. MeOH/chloroform-H2O and MeOH/H2O-chloroform were the extraction systems that yielded the highest number of metabolites, while MeOH/acetonitrile (ACN)-H2O and ACN/H2O-IPA exhibited superior metabolite recoveries. Our results demonstrated that our research facilitates the selection of an appropriate metabolite extraction approach based on the experimental purpose for the metabolomics study of skin tissue.

An Integrated Hepatocyte Stability Assay for Simultaneous Metabolic Stability Assessment and Metabolite Profiling.

The determination of metabolic stability is critical for drug discovery programs, allowing for the optimization of chemical entities and compound prioritization. As such, it is common to perform high-volume in vitro metabolic stability experiments early in the lead optimization process to understand metabolic liabilities. Additional metabolite identification experiments are subsequently performed for a more comprehensive understanding of the metabolic clearance routes to aid medicinal chemists in the structural design of compounds. Collectively, these experiments require extensive sample preparation and a substantial amount of time and resources. To overcome the challenges, a high-throughput integrated assay for simultaneous hepatocyte metabolic stability assessment and metabolite profiling was developed. This assay platform consists of four parts: 1) an automated liquid-handling system for sample preparation and incubation, 2) a liquid chromatography and high-resolution mass spectrometry-based system to simultaneously monitor the parent compound depletion and metabolite formation, 3) an automated data analysis and report system for hepatic clearance assessment; and 4) streamlined autobatch processing for software-based metabolite profiling. The assay platform was evaluated using eight control compounds with various metabolic rates and biotransformation routes in hepatocytes across three species. Multiple sample preparation and data analysis steps were evaluated and validated for accuracy, repeatability, and metabolite coverage. The combined utility of an automated liquid-handling instrument, a high-resolution mass spectrometer, and multiple streamlined data processing software improves the process of these highly demanding screening assays and allows for simultaneous determination of metabolic stability and metabolite profiles for more efficient lead optimization during early drug discovery. SIGNIFICANCE STATEMENT: Metabolic stability assessment and metabolite profiling are pivotal in drug discovery to fully comprehend metabolic liabilities for chemical entity optimization and lead selection. Process of these assays can be repetitive and resource demanding. Here, we developed an integrated hepatocyte stability assay that combines automation, high-resolution mass spectrometers, and batch-processing software to improve and combine the workflow of these assays. The integrated approach allows simultaneous metabolic stability assessment and metabolite profiling, significantly accelerating screening and lead optimization in a resource-effective manner.

Alternative electrolyte solutions for untargeted breath metabolomics using secondary-electrospray ionization high-resolution mass spectrometry.

Secondary-electrospray ionization (SESI) coupled with high-resolution mass spectrometry is a powerful tool for the discovery of biomarkers in exhaled breath. A primary electrospray consisting of aqueous formic acid (FA) is currently used to charge the volatile organic compounds in breath. To investigate whether alternate electrospray compositions could enable different metabolite coverage and sensitivities, the electrospray dopants NaI and AgNO3 were tested. In a proof-of-principle manner, the exhaled breath of one subject was analyzed repeatedly with different electrospray solutions and with the help of a spectral stitching technique. Capillary diameter and position were optimized to achieve proper detection of exhaled breath. The detected features were then compared using formula annotation. Using an evaporation-based gas standard system, the signal response of the different solutions was probed. Principal component analysis revealed a substantial difference in features detected with AgNO3 . With silver, more sulfur-containing features and more unsaturated hydrocarbon compounds were detected. Furthermore, more primary amines were potentially ionized, as indicated by van Krewelen diagrams. In total, twice as many features were unique to AgNO3 than for other electrospray dopants. Using gas standards at known concentrations, the high sensitivity of FA as a dopant was demonstrated but also indicated alternate sensitivities of the other electrospray solutions. This work demonstrated the potential of AgNO3 as a complementary dopant for further biomarker discovery in SESI-based breath analysis.

Wide metabolite coverage LC-MS/MS assay for the diagnosis of inherited metabolic disorders in urine

ObjectiveThe laboratory diagnosis of inherited metabolic disorders (IMD) has undergone significant development in recent decades, mainly due to the use of mass spectrometry, which allows rapid multicomponent analysis of a wide range of metabolites. Combined with advanced software tools, the diagnosis becomes more efficient as a benefit for both physicians and patients. MethodsA hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry assay for determination of urinary purines, pyrimidines, N-acylglycines, N-acetylated amino acids, sugars, sugar alcohols and other diagnostically important biomarkers was developed and validated. Evaluation of the results consisting of utilisation of robust scaling and advanced visualization tools is simple and even suitable for urgent requirements. ResultsThe developed method, covering 65 biomarkers, provides a comprehensive diagnostic platform for 51 IMD. For most analytes, linearity with R2 > 0.99, intra and inter-day accuracy between 80 and 120 % and precision lower than 20 % were achieved. Diagnostic workflow was evaluated on 47 patients and External Quality Assurance samples involving a total of 24 different IMD. Over seven years, more than 2300 urine samples from patients suspected for IMD have been routinely analysed. ConclusionsThis method offers the advantage of a broad coverage of intermediate metabolites of interest and therefore may be a potential alternative and simplification for clinical laboratories that use multiple methods for screening these markers.

IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.

High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers.

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

High-Resolution Mass Spectrometry-Based Metabolomics for Increased Grape Juice Metabolite Coverage.

The composition of the juice from grape berries is at the basis of the definition of technological ripeness before harvest, historically evaluated from global sugar and acid contents. If many studies have contributed to the identification of other primary and secondary metabolites in whole berries, deepening knowledge about the chemical composition of the sole flesh of grape berries (i.e., without considering skins and seeds) at harvest is of primary interest when studying the enological potential of widespread grape varieties producing high-added-value wines. Here, we used non-targeted DI-FT-ICR-MS and RP-UHPLC-Q-ToF-MS analyses to explore the extent of metabolite coverage of up to 290 grape juices from four Vitis vinifera grape varieties, namely Chardonnay, Pinot noir, Meunier, and Aligoté, sampled at harvest from 91 vineyards in Europe and Argentina, over three successive vintages. SPE pretreatment of samples led to the identification of more than 4500 detected C,H,O,N,S-containing elemental compositions, likely associated with tens of thousands of distinct metabolites. We further revealed that a major part of this chemical diversity appears to be common to the different juices, as exemplified by Pinot noir and Chardonnay samples. However, it was possible to build significant models for the discrimination of Chardonnay from Pinot noir grape juices, and of Chardonnay from Aligoté grape juices, regardless of the geographical origin or the vintage. Therefore, this metabolomic approach opens access to a remarkable holistic molecular description of the instantaneous composition of such a biological matrix, which is the result of complex interplays among environmental, biochemical, and vine growing practices.

LC-MS investigated as a tool to study the metabolomic characteristics of cereal fermentation

Fermentation of cereals to obtain desired food products is an age-old technique. It is often used to enhance the acceptability and shelf-life of the product. Fermentation can be viewed as a process where microbes bring about metabolic changes in the overall metabolic constitution of the product. In recent years, food metabolomics is emerging as a field to decipher these metabolomic changes to develop a more sustainable and robust food industry. With the advent of advanced analytical platforms, it is now possible to analyse cereal fermentation in-depth in a high-throughput manner. One such tool is LC-MS which has now become an indispensable tool for such food-based studies. The dynamic coverage of metabolites along with high qualitative and quantitative reproducibility makes it a method of choice in many metabolomic studies. The present study undertakes an exhaustive survey of the scholarly literature from 2005 to 2023 focusing on key studies that employ the LC-MS platform to study fermentation, particularly in cereals. The findings strongly advocate for the versatility and utility of LC-MS especially when complemented with chemometric approach in unravelling the intricacies of cereal fermentation. By leveraging these insights, the food industry can further enhance the development of functional foods and foster innovation.

The dentate gyrus differentially metabolizes glucose and alternative fuels during rest and stimulation.

The metabolic demands of neuronal activity are both temporally and spatially dynamic, and neurons are particularly sensitive to disruptions in fuel and oxygen supply. Glucose is considered an obligate fuel for supporting brain metabolism. Although alternative fuels are often available, the extent of their contribution to central carbon metabolism remains debated. Differential fuel metabolism likely depends on cell type, location, and activity state, complicating its study. While biosensors provide excellent spatial and temporal information, they are limited to observations of only a few metabolites. On the other hand, mass spectrometry is rich in chemical information, but traditionally relies on cell culture or homogenized tissue samples. Here, we use mass spectrometry imaging (MALDI-MSI) to focus on the fuel metabolism of the dentate granule cell (DGC) layer in murine hippocampal slices. Using stable isotopes, we explore labeling dynamics at baseline, as well as in response to brief stimulation or fuel competition. We find that at rest, glucose is the predominant fuel metabolized through glycolysis, with little to no measurable contribution from glycerol or fructose. However, lactate/pyruvate, β-hydroxybutyrate (βHB), octanoate, and glutamine can contribute to TCA metabolism to varying degrees. In response to brief depolarization with 50 mM KCl, glucose metabolism was preferentially increased relative to the metabolism of alternative fuels. With an increased supply of alternative fuels, both lactate/pyruvate and βHB can outcompete glucose for TCA cycle entry. While lactate/pyruvate modestly reduced glucose contribution to glycolysis, βHB caused little change in glycolysis. This approach achieves broad metabolite coverage from a spatially defined region of physiological tissue, in which metabolic states are rapidly preserved following experimental manipulation. Using this powerful methodology, we investigated metabolism within the dentate gyrus not only at rest, but also in response to the energetic demand of activation, and in states of fuel competition.