Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput

Isotope dilution with isotopically labeled biomass: An effective alternative for quantitative metabolomics

BACKGROUND

State-of-the-art quantitative metabolomics relies on isotope dilution using internal standards (IS) derived from fully 13C labeled biomass. By spiking samples and external standards with known amounts of IS, the spike characterization demands are kept to a minimum. In fact, it is sufficient to experimentally assess the isotopic enrichment of the IS. This study develops the yeast derived IS toolbox further, (1) by characterizing the concentration levels of hydrophilic metabolites in a yeast fermentation batch and (2) by exploring the analytical figures of merit of one-point IS versus multipoint external calibration using IS, the established gold-standard for quantitative metabolomics.

RESULTS

Independent reverse isotope dilution experiments using different chromatographic methods over a period of several months, delivered a list of 83 13C-labeled metabolites with fully characterized concentration and their uncertainty, covering 5 orders of magnitude, from the nanomolar to the low millimolar range. The 13C-labeled yeast-derived IS showed excellent intermediate stability with 92 % of molecules showing inter-method RSDs ≤30 % (75 % of molecules showed RSDs ≤15 %) over a timeframe of five months. One-point internal standardization with the characterized labeled biomass achieved figures of merit equivalent to multipoint calibrations for the majority of metabolites.

SIGNIFICANCE

The proposed calibration workflow rationalizes time and standard expenditure and is particularly beneficial for laboratories dealing with wide-target assays and small analysis batches. The present assessment serves as a seminal study for further developments of the concept towards absolute quantification from archive high-resolution MS data of U13C-biomass-spiked samples and the implementation of quick biomass recalibration with each experiment, promising seamless transition between internal standards derived from different fermentation batches.

Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial

BACKGROUND

Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration.

AIM OF REVIEW

In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

First Proof of Concept of a Click Inverse Electron Demand Diels-Alder Reaction for Assigning the Regiochemistry of Carbon-Carbon Double Bonds in Untargeted Lipidomics

Lipidomics by high-resolution mass spectrometry (HRMS) has become a prominent tool in clinical chemistry due to the proven connections between lipid dysregulation and the insurgence of pathologies. However, it is difficult to achieve structural characterization beyond the fatty acid level by HRMS, especially when it comes to the regiochemistry of carbon-carbon double bonds, which play a major role in determining the properties of cell membranes. Several approaches have been proposed for elucidating the regiochemistry of double bonds, such as derivatization before MS analysis by photochemical reactions, which have shown great potential for their versatility but have the unavoidable drawback of splitting the MS signal. Among other possible approaches for derivatizing electron-rich double bonds, the emerging inverse-electron-demand Diels-Alder (IEDDA) reaction with tetrazines stands out for its unmatchable kinetics and has found several applications in basic biology and protein imaging. In this study, a catalyst-free click IEDDA reaction was employed for the first time to pinpoint carbon-carbon double bonds in free and conjugated fatty acids. Fatty acid and glycerophospholipid regioisomers were analyzed alone and in combination, demonstrating that the IEDDA reaction had click character and allowed the obtention of diagnostic product ions following MS/MS fragmentation as well as the possibility of performing relative quantitation of lipid regioisomers. The IEDDA protocol was later employed in an untargeted lipidomics study on plasma samples of patients suffering from prostate cancer and benign prostatic conditions, confirming the applicability of the proposed reaction to complex matrices of clinical interest.

MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization

Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional property of an ion that can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating the CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C-labeled lipids, derived from a U13C-labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R Markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C-labeled lipids per lipid class-adduct combination across 10 lipid classes without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C-labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary data set, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

Selected Ion Monitoring for Orbitrap-Based Metabolomics

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage.

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

Combining imaging mass spectrometry and immunohistochemistry to analyse the lipidome of spinal cord inflammation

Inflammation is a complex process that accompanies many pathologies. Actually, dysregulation of the inflammatory process is behind many autoimmune diseases. Thus, treatment of such pathologies may benefit from in-depth knowledge of the metabolic changes associated with inflammation. Here, we developed a strategy to characterize the lipid fingerprint of inflammation in a mouse model of spinal cord injury. Using lipid imaging mass spectrometry (LIMS), we scanned spinal cord sections from nine animals injected with lysophosphatidylcholine, a chemical model of demyelination. The lesions were demonstrated to be highly heterogeneous, and therefore, comparison with immunofluorescence experiments carried out in the same section scanned by LIMS was required to accurately identify the morphology of the lesion. Following this protocol, three main areas were defined: the lesion core, the peri-lesion, which is the front of the lesion and is rich in infiltrating cells, and the uninvolved tissue. Segmentation of the LIMS experiments allowed us to isolate the lipid fingerprint of each area in a precise way, as demonstrated by the analysis using classification models. A clear difference in lipid signature was observed between the lesion front and the epicentre, where the damage was maximized. This study is a first step to unravel the changes in the lipidome associated with inflammation in the context of diverse pathologies, such as multiple sclerosis.

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

Comparison of Amine-Modified Polymeric Stationary Phases for Polar Metabolomic Analysis Based on Unified-Hydrophilic Interaction/Anion Exchange Liquid Chromatography/High-Resolution Mass Spectrometry (Unified-HILIC/AEX/HRMS)

In metabolomic analysis, one of the most commonly used techniques to support the detection sensitivity and quantitation of mass spectrometry is combining it with liquid chromatography. Recently, we developed a method that enables comprehensive single-run measurement of hydrophilic metabolites using unified-hydrophilic interaction/anion exchange liquid chromatography/high-resolution mass spectrometry (unified-HILIC/AEX/HRMS) with a polymer-based mixed amines column (Gelpack GL-HilicAex). However, the importance of stationary phase functional groups and mobile phase conditions for the separation mechanisms and sensitive detection in unified-HILIC/AEX/HRMS is not yet fully understood. This study aimed to understand the importance of the mobile and stationary phases in unified-HILIC/AEX/HRMS. Two different alkali-resistant polymer-based amines-modified columns (Gelpack GL-HilicAex, primary, secondary, tertiary, and quaternary amine-modified polyglycerol dimethacrylate gel; Asahipak NH2P-50 2D, secondary amine-modified polyvinyl alcohol gel) and two eluents (acetonitrile and ammonium bicarbonate solution, pH 9.8) were used for comparative validation. A comparison of mobile phase conditions using both columns confirmed that the two-step separation from HILIC to AEX characteristic of unified-HILIC/AEX requires a linear gradient condition from acetonitrile to nearly 50% water and AEX with up to 40 mM bicarbonate ions. We found that when alkali-resistant hydrophilic polymer packing materials are modified with amines, unified-HILIC/AEX separation can be reproduced if at least one secondary amine associated with the amine series is present in the stationary phase. Furthermore, the difference in sensitivity in the HILIC and AEX modes owing to the different columns indicates the need for further improvements in the mobile phase composition and stationary phase.

SpecLipIDA: a pseudotargeted lipidomics approach for polyunsaturated fatty acids in milk

Polyunsaturated fatty acids (PUFAs), such as arachidonic acid (ARA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), play an important role in the nutritional value of milk lipids. However, a comprehensive analysis of PUFAs and their esters in milk is still scarce. In this study, we developed a novel pseudotargeted lipidomics approach, named SpecLipIDA, for determining PUFA lipids in milk. Triglycerides (TGs) and phospholipids (PLs) were separated using NH2 cartridges, and mass spectrometry data in the information-dependent acquisition (IDA) mode were preprocessed by MS-DIAL, leading to improved identification in subsequent targeted analysis. The target matching algorithm, based on specific lipid cleavage patterns, demonstrated enhanced identification of PUFA lipids compared to the lipid annotations provided by MS-DIAL and GNPS. The approach was applied to identify PUFA lipids in various milk samples, resulting in the detection of a total of 115 PUFA lipids. The results revealed distinct differences in PUFA lipids among different samples, with 44 PUFA lipids significantly contributing to these differences. Our study indicated that SpecLipIDA is an efficient method for rapidly and specifically screening PUFA lipids.

Improved quantification of carbonyl sub-metabolome by liquid chromatography mass spectrometry using a fragment controlled multiplexed isotopic tag

BACKGROUND

Carbonyl-containing metabolites are a class of key intermediate in metabolism, which has potentials to be biomarkers. Since their poor ionization, derivatization reagents, such as dansylhydrazine, are usually used to improve the sensitivity and/or to facilitate quantification. However, most current carbonyl derivatization reagents only have two channels, one is isotopically labeled and the other one is non-labeled. To quantify more samples in a run and using data-independent acquisition (DIA) mode to get comprehensive and unbiased mass fragmentation, we proposed a fragment-controlled isotopic tag, called DiMe-FP-NHNH2 (FP) which has five channels: Δ0, Δ3, Δ6, Δ9, and Δ12, thus up to 5 samples can be analyzed in a run.

RESULTS

The most important improvement is that the FP tag can produce multiple characteristic signals in tandem mass, diagnostic ions and neutral losses, which helps to selectively detect aldehydes/ketones for targeted and untargeted analysis. To exhibit all capabilities of the FP tag, we mimicked an untargeted metabolomics experiment, which comprises two steps. First, discovery step, using Data-Independent Analysis (SWATH-MS) and the labeling of two channels (Δ0 and Δ3), we picked out aldehyde/ketone from the pooled urine samples based on three characteristic signals, including isotope patterns, diagnostic ions, and neutral losses. Second, five-plex quantification, relative and absolute quantification were achieved in a single LC-MS analysis. Notably, because of different nominal masses, the FP tag can be used on any low or high resolution mass spectrometers.

SIGNIFICANCE

The benefits and performance of the FP tag are demonstrated by the analysis of urine samples collected from patients from a prostate cancer study, in which more than a thousand features were found based on MS1 fingerprint, but only around 120 aldehyde/ketone candidates were confirmed with characteristic signals and nine of which were quantified showing significant differences from healthy and reference urine samples.

Comprehensive Single-Platform Lipidomic/Metabolomic Analysis Using Supercritical Fluid Chromatography-Mass Spectrometry

Supercritical fluid chromatography (SFC) is a rapidly expanding technique in the analysis of nonpolar to moderately polar substances and, more recently, also in the analysis of compounds with higher polarity. Herein, we demonstrate a proof of concept for the application of a commercial SFC instrument with electrospray ionization-mass spectrometry (MS) detection as a platform for the comprehensive analysis of metabolites with the full range of polarities, from nonpolar lipids up to highly polar metabolites. The developed single-platform SFC-MS lipidomic/metabolomic method is based on two consecutive injections of lipid and polar metabolite extracts from biphase methyl tert-butyl ether extraction using a diol column and two different gradient programs of methanol-water-ammonium formate modifier. Detailed development of the method focused mainly on the pressure limits of the system, the long-term repeatability of results, and the chromatographic performance, including optimization of the flow rate program, modifier composition and gradient, and injection solvent selection. The developed method enabled fast and comprehensive analysis of lipids and polar metabolites from plasma within a 24 min cycle with two injections using a simple analytical platform based on a single instrument, column, and mobile phase. Finally, the results from SFC-MS analysis of polar metabolites were compared with widely established liquid chromatography MS analysis in metabolomics. The comparison showed different separation selectivity of metabolites using both methods and overall lower sensitivity of the SFC-MS due to the higher flow rate and worse chromatographic performance.

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.

Application of metabolomics in diagnostics and differentiation of meningitis: A narrative review with a critical approach to the literature

Due to its high mortality rate associated with various life-threatening sequelae, meningitis poses a vital problem in contemporary medicine. Numerous algorithms, many of which were derived with the aid of artificial intelligence, were brought up in a strive for perfection in predicting the status of sepsis-related survival or exacerbation. This review aims to provide key insights on the contextual utilization of metabolomics. The aim of this the metabolomic approach set of methods can be used to investigate both bacterial and host metabolite sets from both the host and its microbes in several types of specimens - even in one's breath, mainly with use of two methods - Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR). Metabolomics, and has been used to elucidate the mechanisms underlying disease development and metabolic identification changes in a wide range of metabolite contents, leading to improved methods of diagnosis, treatment, and prognosis of meningitis. Mass spectrometry (MS) and Nuclear Magnetic Resonance (NMR) are the main analytical platforms used in metabolomics. Its high sensitivity accounts for the usefulness of metabolomics in studies into meningitis, its sequelae, and concomitant comorbidities. Metabolomics approaches are a double-edged sword, due to not only their flexibility, but also - high complexity, as even minor changes in the multi-step methods can have a massive impact on the results. Information on the differential diagnosis of meningitis act as a background in presenting the merits and drawbacks of the use of metabolomics in context of meningeal infections.

An overview of food lipids toward food lipidomics

Increasing evidence regarding lipids' beneficial effects on human health has changed the common perception of consumers and dietary officials about the role(s) of food lipids in a healthy diet. However, lipids are a wide group of molecules with specific nutritional and bioactive properties. To understand their true nutritional and functional value, robust methods are needed for accurate identification and quantification. Specific analytical strategies are crucial to target specific classes, especially the ones present in trace amounts. Finding a unique and comprehensive methodology to cover the full lipidome of each foodstuff is still a challenge. This review presents an overview of the lipids nutritionally relevant in foods and new trends in food lipid analysis for each type/class of lipids. Food lipid classes are described following the LipidMaps classification, fatty acids, endocannabinoids, waxes, C8 compounds, glycerophospholipids, glycerolipids (i.e., glycolipids, betaine lipids, and triglycerides), sphingolipids, sterols, sercosterols (vitamin D), isoprenoids (i.e., carotenoids and retinoids (vitamin A)), quinones (i.e., coenzyme Q, vitamin K, and vitamin E), terpenes, oxidized lipids, and oxylipin are highlighted. The uniqueness of each food group: oil-, protein-, and starch-rich, as well as marine foods, fruits, and vegetables (water-rich) regarding its lipid composition, is included. The effect of cooking, food processing, and storage, in addition to the importance of lipidomics in food quality and authenticity, are also discussed. A critical review of challenges and future trends of the analytical approaches and computational methods in global food lipidomics as the basis to increase consumer awareness of the significant role of lipids in food quality and food security worldwide is presented.

Application and prospect of metabolomics in the early diagnosis of osteoporosis: a narrative review

This paper reviews the application of metabolomics in the early diagnosis of osteoporosis in recent years. The authors searched electronic databases for the keywords "metabolomics", "osteoporosis" and "biomarkers", then analyzed the relationship between functional markers and osteoporosis using categorical summarization. Lipid metabolism, amino acid metabolism and energy metabolism are closely related to osteoporosis development and can become early diagnostic markers of the condition. However, the existing studies in metabolomics suffer from varying application methods, difficulty in identifying isomers, small study cohorts and insufficient research on metabolic mechanisms. Consequently, it is important for future research to focus on broadening and standardizing the scope of the application of metabolomics. High-quality studies on a large scale should also be conducted while promoting the early diagnosis of osteoporosis in a more precise, comprehensive and sensitive manner.

Program for Integration and Rapid Analysis of Mass Isotopomer Distributions (PIRAMID)

SUMMARY

The analysis of stable isotope labeling experiments requires accurate, efficient, and reproducible quantification of mass isotopomer distributions (MIDs), which is not a core feature of general-purpose metabolomics software tools that are optimized to quantify metabolite abundance. Here, we present PIRAMID (Program for Integration and Rapid Analysis of Mass Isotopomer Distributions), a MATLAB-based tool that addresses this need by offering a user-friendly, graphical user interface-driven program to automate the extraction of isotopic information from mass spectrometry (MS) datasets. This tool can simultaneously extract ion chromatograms for various metabolites from multiple data files in common vendor-agnostic file formats, locate chromatographic peaks based on a targeted list of characteristic ions and retention times, and integrate MIDs for each target ion. These MIDs can be corrected for natural isotopic background based on the user-defined molecular formula of each ion. PIRAMID offers support for datasets acquired from low- or high-resolution MS, and single (MS) or tandem (MS/MS) instruments. It also enables the analysis of single or dual labeling experiments using a variety of isotopes (i.e. 2H, 13C, 15N, 18O, 34S).

DATA AVAILABILITY AND IMPLEMENTATION

MATLAB p-code files are freely available for non-commercial use and can be downloaded from https://mfa.vueinnovations.com/. Commercial licenses are also available. All the data presented in this publication are available under the "Help_menu" folder of the PIRAMID software.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Exploring the Impact of Organic Solvent Quality and Unusual Adduct Formation during LC-MS-Based Lipidomic Profiling

Liquid chromatography-mass spectrometry (LC-MS) is the key technique for analyzing complex lipids in biological samples. Various LC-MS modes are used for lipid separation, including different stationary phases, mobile-phase solvents, and modifiers. Quality control in lipidomics analysis is crucial to ensuring the generated data's reliability, reproducibility, and accuracy. While several quality control measures are commonly discussed, the impact of organic solvent quality during LC-MS analysis is often overlooked. Additionally, the annotation of complex lipids remains prone to biases, leading to potential misidentifications and incomplete characterization of lipid species. In this study, we investigate how LC-MS-grade isopropanol from different vendors may influence the quality of the mobile phase used in LC-MS-based untargeted lipidomic profiling of biological samples. Furthermore, we report the occurrence of an unusual, yet highly abundant, ethylamine adduct [M+46.0651]+ that may form for specific lipid subclasses during LC-MS analysis in positive electrospray ionization mode when acetonitrile is part of the mobile phase, potentially leading to lipid misidentification. These findings emphasize the importance of considering solvent quality in LC-MS analysis and highlight challenges in lipid annotation.

Scrutinizing different ionization responses of polar lipids in a reversed-phase gradient by implementing a counter-gradient

Lipidomics studies strive for a comprehensive identification and quantification of lipids. While reversed phase (RP) liquid chromatography (LC) coupled to high resolution mass spectrometry (MS) offers unrivalled selectivity and thus is the preferred method for lipid identification, accurate lipid quantification remains challenging. The widely adopted one-point lipid class specific quantification (one internal standard per lipid class) suffers from the fact that ionization of internal standard and target lipid occurs under different solvent composition as a consequence of chromatographic separation. To address this issue, we established a dual flow injection and chromatography setup that allows to control solvent conditions during ionization enabling isocratic ionization while running a RP gradient through the use of a counter-gradient. Using this dual LC pump platform, we investigated the impact of solvent conditions within a RP gradient on ionization response and arising quantification biases. Our results confirmed that changing solvent composition significantly influences ionization response. Quantification of human plasma (SRM 1950) lipids under gradient and isocratic ionization conditions further confirmed these findings as significant differences between the two conditions were found for the majority of lipids. While the quantity of sphingomyelins with >40 C atoms was consistently overestimated under gradient ionization, isocratic ionization improved their recovery compared to consensus values. However, the limitation of consensus values was demonstrated as overall only small changes in z-score were observed because of high uncertainties of the consensus values. Furthermore, we observed a trueness bias between gradient and isocratic ionization when quantifying a panel of lipid species standards which is highly dependent on lipid class and ionization mode. Uncertainty calculations under consideration of the trueness bias as RP gradient uncertainty revealed that especially ceramides with >40 C atoms had a high bias leading to total combined uncertainties of up to 54%. The assumption of isocratic ionization significantly decreases total measurement uncertainty and highlights the importance of studying the trueness bias introduced by a RP gradient to reduce quantification uncertainty.

Implication of metabolism in the polarization of tumor-associated-macrophages: the mass spectrometry-based point of view

Tumor-associated macrophages (TAMs) represent one of the main tumor-infiltrating immune cell types and are generally categorized into either of two functionally contrasting subtypes, namely classical activated M1 macrophages and alternatively activated M2 macrophages. TAMs showed different activation states that can be represent by the two extremes of the complex profile of macrophages biology, the M1-like phenotype (pro-inflammatory activity) and the M2-like phenotype (anti-inflammatory activity). Based on the tumor type, and grades, TAMs can acquire different functions and properties; usually, the M1-like phenotype is typical of early tumor stages and is associated to an anti-tumor activity, while M2-like phenotype has a pro-inflammatory activity and is related to a poor patients' prognosis. The classification of macrophages into M1/M2 groups based on well-defined stimuli does not model the infinitely more complex tissue milieu where macrophages (potentially of different origin) would be exposed to multiple signals in different sequential order. This review aims to summarize the recent mass spectrometry-based (MS-based) metabolomics findings about the modifications of metabolism in TAMs polarization in different tumors. The published data shows that MS-based metabolomics is a promising tool to help better understanding TAMs metabolic phenotypes, although it is still poorly applied for TAMs metabolism. The knowledge of key metabolic alterations in TAMs is an essential step for discovering TAMs polarization novel biomarkers and developing novel therapeutic approaches targeting TAM metabolism to repolarize TAMs towards their anti-tumor phenotype.

Hydrogen/deuterium exchange mass spectrometry in the world of small molecules

The combined use of hydrogen/deuterium exchange (HDX) and mass spectrometry (MS), referred to as HDX-MS, is a powerful tool for exploring molecular edifices and has been used for over 60 years. Initially for structural and mechanistic investigation of low-molecular weight organic compounds, then to study protein structure and dynamics, then, the craze to study small molecules by HDX-MS accelerated and has not stopped yet. The purpose of this review is to present its different facets with particular emphasis on recent developments and applications. Reversible H/D exchanges of mobilizable protons as well as stable exchanges of non-labile hydrogen are considered whether they are taking place in solution or in the gas phase, or enzymatically in a biological media. Some fundamental principles are restated, especially for gas-phase processes, and an overview of recent applications, ranging from identification to quantification through the study of metabolic pathways, is given.

Integration of Vitreous Lipidomics and Metabolomics for Comprehensive Understanding of the Pathogenesis of Proliferative Diabetic Retinopathy

As a vision-threatening complication of diabetes mellitus (DM), proliferative diabetic retinopathy (PDR) is associated with sustained metabolic disorders. Herein, we collected the vitreous cavity fluid of 49 patients with PDR and 23 control subjects without DM for metabolomics and lipidomics analyses. Multivariate statistical methods were performed to explore relationships between samples. For each group of metabolites, gene set variation analysis scores were generated, and we constructed a lipid network by using weighted gene co-expression network analysis. The association between lipid co-expression modules and metabolite set scores was investigated using the two-way orthogonal partial least squares (O2PLS) model. A total of 390 lipids and 314 metabolites were identified. Multivariate statistical analysis revealed significant vitreous metabolic and lipid differences between PDR and controls. Pathway analysis showed that 8 metabolic processes might be associated with the development of PDR, and 14 lipid species were found to be altered in PDR patients. Combining metabolomics and lipidomics, we identified fatty acid desaturase 2 (FADS2) as an important potential contributor to the pathogenesis of PDR. Collectively, this study integrates vitreous metabolomics and lipidomics to comprehensively unravel metabolic dysregulation and identifies genetic variants associated with altered lipid species in the mechanistic pathways for PDR.

Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning

Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organization estimated that population aging will cause global PD prevalence to double in the coming 30 years. Optimal management of PD shall start at diagnosis and requires both a timely and accurate method. Conventional PD diagnosis needs observations and clinical signs assessment, which are time-consuming and low-throughput. A lack of body fluid diagnostic biomarkers for PD has been a significant challenge, although substantial progress has been made in genetic and imaging marker development. Herein, a platform that noninvasively collects saliva metabolic fingerprinting (SMF) by nanoparticle-enhanced laser desorption-ionization mass spectrometry with high-reproducibility and high-throughput, using ultra-small sample volume (down to 10 nL), is developed. Further, excellent diagnostic performance is achieved with an area-under-the-curve of 0.8496 (95% CI: 0.7393-0.8625) by constructing deep learning model from 312 participants. In conclusion, an alternative solution is provided for the molecular diagnostics of PD with SMF and metabolic biomarker screening for therapeutic intervention.

Robust quantitation of gangliosides and sulfatides in human brain using UHPLC-MRM-MS: Method development and application in Alzheimer's disease

Gangliosides (GAs) and sulfatides (STs) are acidic glycosphingolipids that are particularly abundant in the nervous system and are closely related to aging and neurodegenerative disorders. To explore their roles in brain diseases, in-depth molecular profiling, including structural variations of sphingoid backbone, fatty acyl group, and sugar chain of GAs and STs was performed. A total of 210 GAs and 38 STs were characterized in the inferior frontal gyrus (IFG) of human brain, with 90 GAs discovered in brain tissues for the first time. Influential MS parameters for detecting GAs and STs in multiple reaction monitoring (MRM) mode were systematically examined and optimized to minimize in-source fragmentation, resulting in remarkable signal intensity enhancement for GAs and STs, especially for polysialylated species. To eliminate analytical variations, isotopic interference-free internal standards were prepared by simple and fast reduction reaction. The final established method facilitated the simultaneous quantitation of 184 GAs and 30 STs from 25 subtypes, which represents the highest number of GAs quantitated among all quantitation methods recorded in literature so far. The method was further validated and applied to reveal the aberrant change of GAs and STs in the IFG of 12 Alzheimer's disease (AD) patients. Four GAs exhibited high classification capacity for AD (AUC ≥0.80) and were thereby considered the most promising signatures for AD. These findings suggested the close correlation between GAs and the pathogenesis of AD, highlighting the achievements of our robust method for investigating the roles of GAs and STs in various physiological states and diseases.

Comparison of chromatographic conditions for the targeted tandem mass spectrometric determination of 354 mammalian metabolites

Metabolomics is becoming increasingly popular in livestock research, but no single analytical method can cover the entire metabolome. As such, we compared similar and complementary chromatographic methods with respect to analyte coverage and chromatographic properties of mammalian metabolites. We investigated 354 biologically relevant primary metabolites from 19 compound classes including amino acids, bile acids, biogenic amines, carboxylic acids, lipids, nucleotides and sugars. A total of 2063 selected reaction monitoring transitions were optimized on a triple quadrupole mass spectrometer. We then determined the retention profiles and peak parameters of our compounds using an anion exchange chromatography (AIC), three reversed-phase (RP) and three hydrophilic interaction liquid chromatography (HILIC) methods. On average, HILIC methods covered 54% of all metabolites with retention factors >1, while average RP coverage was 41%. In contrast to RP, HILIC methods could also retain polar metabolites such as amino acids and biogenic amines. Carboxylic acids, nucleotides, and sugar related compounds were best separated by AIC or zwitterionic pHILIC with alkaline eluents. Combining two complementary HILIC and RP methods increased the library coverage to 92%. By further including important short chain fatty acids, a combination of HILIC, RP and AIC methods achieved a coverage of 97%. The resulting dataset of LC and MS/MS parameters will facilitate the development of tailor-made quantitative targeted LC-MS/MS methods to investigate the mammalian metabolome.

Diagnostic, Prognostic and Mechanistic Biomarkers of COVID-19 Identified by Mass Spectrometric Metabolomics

A number of studies have assessed the impact of SARS-CoV-2 infection and COVID-19 severity on the metabolome of exhaled air, saliva, plasma, and urine to identify diagnostic and prognostic biomarkers. In spite of the richness of the literature, there is no consensus about the utility of metabolomic analyses for the management of COVID-19, calling for a critical assessment of the literature. We identified mass spectrometric metabolomic studies on specimens from SARS-CoV2-infected patients and subjected them to a cross-study comparison. We compared the clinical design, technical aspects, and statistical analyses of published studies with the purpose to identify the most relevant biomarkers. Several among the metabolites that are under- or overrepresented in the plasma from patients with COVID-19 may directly contribute to excessive inflammatory reactions and deficient immune control of SARS-CoV2, hence unraveling important mechanistic connections between whole-body metabolism and the course of the disease. Altogether, it appears that mass spectrometric approaches have a high potential for biomarker discovery, especially if they are subjected to methodological standardization.

Evaluation of Sustainable Recycled Products to Increase the Production of Nutraceutical and Antibacterial Molecules in Basil Plants by a Combined Metabolomic Approach

BACKGROUND

An important goal of modern medicine is the development of products deriving from natural sources to improve environmental sustainability. In this study, humic substances (HS) and compost teas (CTs) extracted from artichoke (ART) and coffee grounds (COF) as recycled biomasses were employed on Ocimum basilicum plants to optimize the yield of specific metabolites with nutraceutical and antibacterial features by applying sustainable strategies.

METHODS

The molecular characteristics of compost derivates were elucidated by Nuclear Magnetic Resonance spectroscopy to investigate the structure-activity relationship between organic extracts and their bioactive potential. Additionally, combined untargeted and targeted metabolomics workflows were applied to plants treated with different concentrations of compost extracts.

RESULTS

The substances HS-ART and CT-COF improved both antioxidant activity (TEAC values between 39 and 55 μmol g^-1) and the antimicrobial efficacy (MIC value between 3.7 and 1.3 μg mL^-1) of basil metabolites. The metabolomic approach identified about 149 metabolites related to the applied treatments. Targeted metabolite quantification further highlighted the eliciting effect of HS-ART and CT-COF on the synthesis of aromatic amino acids and phenolic compounds for nutraceutical application.

CONCLUSIONS

The combination of molecular characterization, biological assays, and an advanced metabolomic approach, provided innovative insight into the valorization of recycled biomass to increase the availability of natural compounds employed in the medical field.

Optimization of Mobile Phase Modifiers for Fast LC-MS-Based Untargeted Metabolomics and Lipidomics

Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for the untargeted profiling of biological samples. A multiplatform LC-MS-based approach is needed to screen polar metabolites and lipids comprehensively. Different mobile phase modifiers were tested to improve the electrospray ionization process during metabolomic and lipidomic profiling. For polar metabolites, hydrophilic interaction LC using a mobile phase with 10 mM ammonium formate/0.125% formic acid provided the best performance for amino acids, biogenic amines, sugars, nucleotides, acylcarnitines, and sugar phosphate, while reversed-phase LC (RPLC) with 0.1% formic acid outperformed for organic acids. For lipids, RPLC using a mobile phase with 10 mM ammonium formate or 10 mM ammonium formate with 0.1% formic acid permitted the high signal intensity of various lipid classes ionized in ESI(+) and robust retention times. For ESI(-), the mobile phase with 10 mM ammonium acetate with 0.1% acetic acid represented a reasonable compromise regarding the signal intensity of the detected lipids and the stability of retention times compared to 10 mM ammonium acetate alone or 0.02% acetic acid. Collectively, we show that untargeted methods should be evaluated not only on the total number of features but also based on common metabolites detected by a specific platform along with the long-term stability of retention times.

Two-dimensional liquid chromatography-mass spectrometry for lipidomics using off-line coupling of hydrophilic interaction liquid chromatography with 50 cm long reversed phase capillary columns

Comprehensive characterization of the lipidome remains a challenge requiring development of new analytical approaches to expand lipid coverage in complex samples. In this work, offline two-dimensional liquid chromatography-mass spectrometry was investigated for lipidomics from human plasma. Hydrophilic interaction liquid chromatography was implemented in the first dimension to fractionate lipid classes. Nine fractions were collected and subjected to a second-dimension separation utilizing 50 cm capillary columns packed with 1.7 µm C18 particles operated on custom-built instrumentation at 35 kpsi. Online coupling with time-of-flight mass spectrometry allowed putative lipid identification from precursor-mass based library searching. The method had good orthogonality (fractional coverage of ∼40%), achieved a peak capacity of approximately 1900 in 600 min, and detected over 1000 lipids from a 5 µL injection of a human plasma extract while consuming less than 3 mL of solvent. The results demonstrate the expected gains in peak capacity when employing long columns and two-dimensional separations and illustrate practical approaches for improving lipidome coverage from complex biological samples.

Integration of metabolomics and proteomics analysis to explore the mechanism of neurotoxicity induced by receipt of isoniazid and rifampicin in mice

Isoniazid (INH) and rifampicin (RIF) are co-administered in tuberculosis treatment but can cause neurotoxicity, and the mechanism is not known. To explore this mechanism, we employed an integrated approach using metabolomics analysis (MA) and proteomics analysis (PA). Male mice were divided into three groups and administered vehicle (control group), or co-administered INH (120 mg/kg) and RIF (240 mg/kg), for 7 or 14 days. Mice brains were collected for mass spectrometry-based PA and MA plus lipidomics analysis. Measurement of brain levels of malondialdehyde and superoxide dismutase revealed time-dependent brain injury after exposure to INH+RIF for 7 and 14 days. Also, 422 proteins, 35 metabolites, and 21 lipids were dysregulated and identified. MA demonstrated "purine metabolism," "phenylalanine, tyrosine and tryptophan biosynthesis," "biosynthesis of unsaturated fatty acids," "phenylalanine metabolism," and "arginine biosynthesis" to be disturbed significantly. PA demonstrated pathways such as "lipids," "amino acids," and "energy metabolism" to be disrupted. Peroxisome proliferator-activated receptor (PPAR) pathways were changed in energy metabolism, which led to the neurotoxicity induced by INH+RIF. Immunohistochemical analyses of PPARs in mice brains verified that PPAR-α and -γ expression was downregulated. PPAR-α and -γ activation might be a key target for alleviating INH+RIF-induced neurotoxicity.

Exploration of the underlying mechanisms of isoniazid/rifampicin-induced liver injury in mice using an integrated proteomics and metabolomics approach

The hepatotoxic mechanism resulting from coadministration of isoniazid (INH) and rifampicin (RIF) are complex and studies remain inconclusive. To systematically explore the underlying mechanisms, an integrated mass-based untargeted metabolomics and label-free quantitative proteomics approach was used to clarify the mechanism of INH/RIF-induced liver injury. Thirty male mice were randomly divided into three groups: control (receiving orally administered vehicle solution), INH (150 mg/kg) + RIF (300 mg/kg) orally administered for either 7 or 14 days, respectively. Serum was collected for the analysis of biochemical parameters and liver samples were obtained for mass spectrum-based proteomics, metabolomics, and lipidomics analysis. Overall, 511 proteins, 31 metabolites, and 23 lipids were dysregulated and identified, and disordered biological pathways were identified. The network of integrated multiomics showed that glucose, lipid, and amino acid metabolism as well as energy metabolism were mainly dysregulated and led to oxidative stress, inflammation, liver steatosis, and cell death induced by INH and RIF. Coadministration of INH and RIF can induce liver injury by oxidative stress, inflammation, liver steatosis, and cell death, and the reduction in glutathione levels may play a critical role in these systematic changes and warrants further study.

Total flavonoids of Inula japonica alleviated the inflammatory response and oxidative stress in LPS-induced acute lung injury via inhibiting the sEH activity: Insights from lipid metabolomics

BACKGROUND

Acute lung injury (ALI) is a severe respiratory disease characterized by diffuse lung interstitial and respiratory distress and pulmonary edema with a mortality rate of 35%-40%. Inula japonica Thunb., known as "Xuan Fu Hua" in Chinese, is a traditional Chinese medicine Inulae Flos to use for relieving cough, eliminating expectorant, and preventing bacterial infections in the clinic, and possesses an anti-pulmonary fibrosis effect. However, the effect and action mechanism of I. japonica on ALI is still unclear.

PURPOSE

This study aimed to investigate the protective effect and underlying mechanism of total flavonoids of I. japonica (TFIJ) in the treatment of ALI.

STUDY DESIGN AND METHODS

A mouse ALI model was established through administration of LPS by the intratracheal instillation. Protective effects of TFIJ in the inflammation and oxidative stress were studied in LPS-induced ALI mice based on inflammatory and oxidative stress factors, including MDA, MPO, SOD, and TNF-α. Lipid metabolomics, bioinformatics, Western blot, quantitative real-time PCR, and immunohistochemistry were performed to reveal the potential mechanism of TFIJ in the treatment of ALI.

RESULTS

TFIJ significantly alleviated the interstitial infiltration of inflammatory cells and the collapse of the alveoli in LPS-induced ALI mice. Lipid metabolomics demonstrated that TFIJ could significantly affect the CYP2J/sEH-mediated arachidonic acid metabolism, such as 11,12-EET, 14,15-EET, 8,9-DHET, 11,12-DHET, and 14,15-DHET, revealing that sEH was the potential target of TFIJ, which was further supported by the recombinant sEH-mediated the substrate hydrolysis in vitro (IC₅₀ = 1.18 μg/ml). Inhibition of sEH by TFIJ alleviated the inflammatory response and oxidative stress via the MAPK, NF-κB, and Nrf2 signaling pathways.

CONCLUSION

These results demonstrated that TFIJ could suppress the sEH activity to stabilize the level of EETs, allowing the alleviation of the pathological course of lung injury in LPS-treated mice, which suggested that TFIJ could serve as the potential agents in the treatment of ALI.

Integrated Exposomics/Metabolomics for Rapid Exposure and Effect Analyses

The totality of environmental exposures and lifestyle factors, commonly referred to as the exposome, is poorly understood. Measuring the myriad of chemicals that humans are exposed to is immensely challenging, and identifying disrupted metabolic pathways is even more complex. Here, we present a novel technological approach for the comprehensive, rapid, and integrated analysis of the endogenous human metabolome and the chemical exposome. By combining reverse-phase and hydrophilic interaction liquid chromatography (HILIC) and fast polarity-switching, molecules with highly diverse chemical structures can be analyzed in 15 min with a single analytical run as both column's effluents are combined before analysis. Standard reference materials and authentic standards were evaluated to critically benchmark performance. Highly sensitive median limits of detection (LODs) with 0.04 μM for >140 quantitatively assessed endogenous metabolites and 0.08 ng/mL for the >100 model xenobiotics and human estrogens in solvent were obtained. In matrix, the median LOD values were higher with 0.7 ng/mL (urine) and 0.5 ng/mL (plasma) for exogenous chemicals. To prove the dual-column approach's applicability, real-life urine samples from sub-Saharan Africa (high-exposure scenario) and Europe (low-exposure scenario) were assessed in a targeted and nontargeted manner. Our liquid chromatography high-resolution mass spectrometry (LC-HRMS) approach demonstrates the feasibility of quantitatively and simultaneously assessing the endogenous metabolome and the chemical exposome for the high-throughput measurement of environmental drivers of diseases.

Decoding Distinct Ganglioside Patterns of Native and Differentiated Mesenchymal Stem Cells by a Novel Glycolipidomics Profiling Strategy

Gangliosides are an indispensable glycolipid class concentrated on cell surfaces with a critical role in stem cell differentiation. Nonetheless, owing to the lack of suitable methods for scalable analysis covering the full scope of ganglioside molecular diversity, their mechanistic properties in signaling and differentiation remain undiscovered to a large extent. This work introduces a sensitive and comprehensive ganglioside assay based on liquid chromatography, high-resolution mass spectrometry, and multistage fragmentation. Complemented by an open-source data evaluation workflow, we provide automated in-depth lipid species-level and molecular species-level annotation based on decision rule sets for all major ganglioside classes. Compared to conventional state-of-the-art methods, the presented ganglioside assay offers (1) increased sensitivity, (2) superior structural elucidation, and (3) the possibility to detect novel ganglioside species. A major reason for the highly improved sensitivity is the optimized spectral readout based on the unique capability of two parallelizable mass analyzers for multistage fragmentation. We demonstrated the high-throughput universal capability of our novel analytical strategy by identifying 254 ganglioside species. As a proof of concept, 137 unique gangliosides were annotated in native and differentiated human mesenchymal stem cells including 78 potential cell-state-specific markers and 38 previously unreported gangliosides. A general increase of the ganglioside numbers upon differentiation was observed as well as cell-state-specific clustering based on the ganglioside species patterns. The combination of the developed glycolipidomics assay with the extended automated annotation tool enables comprehensive in-depth ganglioside characterization as shown on biological samples of interest. Our results suggest ganglioside patterns as a promising quality control tool for stem cells and their differentiation products. Additionally, we believe that our analytical workflow paves the way for probing glycolipid-based biochemical processes shedding light on the enigmatic processes of gangliosides and glycolipids in general.

Microbiota independent effects of oligosaccharides on Caco-2 cells -A semi-targeted metabolomics approach using DI-FT-ICR-MS coupled with pathway enrichment analysis

Milk oligosaccharides (MOS) and galactooligosaccharides (GOS) are associated with many benefits, including anti-microbial effects and immune-modulating properties. However, the cellular mechanisms of these are largely unknown. In this study, the effects of enriched GOS and MOS mixtures from caprine and bovine milk consisting mainly 6'-galactosyllactose, 3'-sialyllactose, and 6'-sialyllactose on Caco-2 cells were investigated, and the treatment-specific metabolomes were described. In the control, the cells were treated with a sugar mix consisting of one-third each of glucose, galactose and lactose. A local metabolomics workflow with pathway enrichment was established, which specifically addresses DI-FT-ICR-MS analyses and includes adaptations in terms of measurement technology and sample matrices. By including quality parameters, especially the isotope pattern, we increased the precision of annotation. The independence from online tools, the fast adaptability to changes in databases, and the specific adjustment to the measurement technology and biomaterial used, proved to be a great advantage. For the first time it was possible to find 71 active pathways in a Caco-2 cell experiment. These pathways were assigned to 12 main categories, with amino acid metabolism and carbohydrate metabolism being the most dominant categories in terms of the number of metabolites and metabolic pathways. Treatment of Caco-2 cells with high GOS and glucose contents resulted in significant effects on several metabolic pathways, whereas the MOS containing treatments resulted only for individual metabolites in significant changes. An effect based on bovine or caprine origin alone could not be observed. Thus, it was shown that MOS and GOS containing treatments can exert microbiome-independent effects on the metabolome of Caco-2 cells.

Tapping into Plant-Microbiome Interactions through the Lens of Multi-Omics Techniques

This review highlights the pivotal role of root exudates in the rhizosphere, especially the interactions between plants and microbes and between plants and plants. Root exudates determine soil nutrient mobilization, plant nutritional status, and the communication of plant roots with microbes. Root exudates contain diverse specialized signaling metabolites (primary and secondary). The spatial behavior of these metabolites around the root zone strongly influences rhizosphere microorganisms through an intimate compatible interaction, thereby regulating complex biological and ecological mechanisms. In this context, we reviewed the current understanding of the biological phenomenon of allelopathy, which is mediated by phytotoxic compounds (called allelochemicals) released by plants into the soil that affect the growth, survival, development, ecological infestation, and intensification of other plant species and microbes in natural communities or agricultural systems. Advances in next-generation sequencing (NGS), such as metagenomics and metatranscriptomics, have opened the possibility of better understanding the effects of secreted metabolites on the composition and activity of root-associated microbial communities. Nevertheless, understanding the role of secretory metabolites in microbiome manipulation can assist in designing next-generation microbial inoculants for targeted disease mitigation and improved plant growth using the synthetic microbial communities (SynComs) tool. Besides a discussion on different approaches, we highlighted the advantages of conjugation of metabolomic approaches with genetic design (metabolite-based genome-wide association studies) in dissecting metabolome diversity and understanding the genetic components of metabolite accumulation. Recent advances in the field of metabolomics have expedited comprehensive and rapid profiling and discovery of novel bioactive compounds in root exudates. In this context, we discussed the expanding array of metabolomics platforms for metabolome profiling and their integration with multivariate data analysis, which is crucial to explore the biosynthesis pathway, as well as the regulation of associated pathways at the gene, transcript, and protein levels, and finally their role in determining and shaping the rhizomicrobiome.

Novel Aza-Paternò-Büchi Reaction Allows Pinpointing Carbon-Carbon Double Bonds in Unsaturated Lipids by Higher Collisional Dissociation

The evaluation of double bond positions in fatty acyl chains has always been of great concern given their significance in the chemical and biochemical role of lipids. Despite being the foremost technique for lipidomics, it is difficult in practice to obtain identification beyond the fatty acyl level by the sole high-resolution mass spectrometry. Paternò–Büchi reactions of fatty acids (FAs) with ketones have been successfully proposed for pinpointing double bonds in FAs in combination with the collision-induced fragmentation technique. In the present paper, an aza-Paternò–Büchi (aPB) reaction of lipids with 6-azauracil (6-AU) was proposed for the first time for the determination of carbon–carbon double bonds in fatty acyl chains using higher collisional dissociation in the negative ion mode. The method was optimized using free FA and phospholipid analytical standards and compared to the standard Paternò–Büchi reaction with acetone. The introduction of the 6-AU moiety allowed enhancing the ionization efficiency of the FA precursor and diagnostic product ions, thanks to the presence of ionizable sites on the derivatizing agent. Moreover, the aPB derivatization allowed the obtention of deprotonated ions of phosphatidylcholines, thanks to an intramolecular methyl transfer from the phosphocholine polar heads during ionization. The workflow was finally applied for pinpointing carbon–carbon double bonds in 77 polar lipids from an yeast (Saccharomyces cerevisiae) extract.

Recent advances in proteomics and metabolomics in plants

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

AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions

Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline ( https://skyline.ms/skyline.url ) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline's recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.

Development and Application of Feature-Based Molecular Networking for Phospholipidomics Analysis

Phospholipids are small but critical lipids in milk. Conventional lipidomics is a powerful method for the analysis of lipids in milk. Although the number of lipidomics software has drastically increased over the past five years, reducing false positives and obtaining structurally accurate annotations of phospholipids remain a significant challenge. In this study, we developed a rapid and accurate method for measuring a wide spectrum of phospholipids in milk. The developed approach that employed information-dependent acquisition (IDA) mode and feature-based molecular networking has exhibited better performance on data processing and lipid annotation when compared with sequential window acquisition of all theoretical mass spectra (SWATH) and MS-DIAL. This validated method was further evaluated using three kinds of sheep milk. A total of 150 phospholipids were identified, including rarely reported phospholipids containing ethers or vinyl ethers. The result indicated that phospholipids could be used as potential markers to distinguish sheep milk from different varieties and origins. The experimental and computational methods provide a rapid and reliable method for the investigation of phospholipids in milk.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography−mass spectrometry, ion mobility or MS imaging approaches on MS¹ and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.