High resolution mass spectrometry based techniques at the crossroads of metabolic pathways

Multi-omics in Allergic Rhinitis: Mechanism Dissection and Precision Medicine

Allergic rhinitis (AR) is a common chronic inflammatory airway disease caused by inhaled allergens, and its prevalence has increased in recent decades. AR not only causes nasal leakage, itchy nose, nasal congestion, sneezing, and allergic conjunctivitis but also induces asthma, as well as sleep disorders, anxiety, depression, memory loss, and other phenomena that seriously affect the patient's ability to study and work, lower their quality of life, and burden society. The current methods used to diagnose and treat AR are still far from ideal. Multi-omics technology can be used to comprehensively and systematically analyze the differentially expressed DNA, RNA, proteins, and metabolites and their biological functions in patients with AR. These capabilities allow for an in-depth understanding of the intrinsic pathogenic mechanism of AR, the ability to explore key cells and molecules that drive its progression, and to design personalized treatment for AR. This article summarizes the progress made in studying AR by use of genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics in order to illustrate the important role of multi-omics technologies in facilitating the precise diagnosis and treatment of AR.

Phytochemical fingerprinting of phytotoxins as a cutting-edge approach for unveiling nature's secrets in forensic science

The integration of phytochemistry into forensic science has emerged as a groundbreaking frontier, providing unprecedented insights into nature's secrets through the precise application of phytochemical fingerprinting of phytotoxins as a cutting-edge approach. This study explores the dynamic intersection of phytochemistry and forensic science, highlighting how the unique phytochemical profiles of toxic plants and their secondary metabolites, serve as distinctive markers for forensic investigations. By utilizing advanced techniques such as Ultra-High-Performance Liquid Chromatography (UHPLC) and High-Resolution Mass Spectrometry (HRMS), the detection and quantification of plant-derived are made more accurate in forensic contexts. Real-world case studies are presented to demonstrate the critical role of plant toxins in forensic outcomes and legal proceedings. The challenges, potential, and future prospects of integrating phytochemical fingerprinting of plant toxins into forensic science were discussed. This review aims to illuminate phytochemical fingerprinting of plant toxins as a promising tool to enhance the precision and depth of forensic analyses, offering new insights into the complex stories embedded in plant toxins.

Unravel the in-Source Fragmentation Patterns of Per- and Polyfluoroalkyl Substances during Analysis by LC-ESI-HRMS

In-source fragmentation (ISF) was inevitable during electrospray ionization (ESI) of per- and polyfluoroalkyl substances (PFAS) when analyzed by liquid chromatography coupled with mass spectrometry (LC-MS), resulting in reduced response of molecular ions and misannotation of MS features. Herein, we analyzed 82 PFAS across 12 classes to systematically identify the structures with ISF potentials and reveal the fragmentation pathways. We found up to 100% ISF for 38 PFAS in six classes, which all contain the carboxylate (CO2-) headgroup, including perfluoro(di)carboxylates (PF(di)CA), omega H/Cl substituted PFCA (ωH/Cl-PFCA), fluorotelomer carboxylates, and perfluoroalkyl ether carboxylates (PFECA). Seven ISF pathways were identified, including direct cleavage of C-CO2-, C-O, and C-C bonds and eliminations of HF/CO2HF through cyclic transition states by the mechanisms of β-elimination, McLafferty rearrangement, or H···F bridging. We found that the loss of CO2 is a prerequisite for most other pathways, explaining the absence of ISF for PFAS without a CO2- headgroup. The elevated bond dissociation energy of C-CO2- explained the reduced ISF for long-chain PFCA and ωH-PFCA. Raising the MS vaporizer and ion transfer tube temperatures significantly aggravated the ISF of most PFAS. These findings provide valuable references to inform the structural identification of PFAS and their degradation products.

IMSIS: An instrumented microphysiological system with integrated sensors for monitoring cellular metabolic activities

Well plates are widely used in biological experiments, particularly in pharmaceutical sciences and cell biology. Its popularity stems from its versatility to support a variety of fluorescent markers for high throughput monitoring of cellular activities. However, using fluorescent markers in traditional well plates has its own challenges, namely, they can be potentially toxic to cells, and thus, may perturb their biological functions; and it is difficult to monitor multiple analytes concurrently and in real-time inside each well. This paper presents a fully instrumented microphysiological system with integrated sensors (IMSIS) with a similar well format. Each well in the microphysiological system has a set of sensors for monitoring multiple metabolic analytes in real-time. The IMSIS platform is supported by integrated bioelectronic circuits and a graphical user interface for easy user configuration and monitoring. The system has integrated microfluidics to maintain its microphysiological environment within each well. The IMSIS platform currently incorporates O2, H2O2, and pH sensors inside each well, allowing up to six wells to perform concurrent measurements in real-time. Furthermore, the architecture is scalable to achieve an even higher level of throughput. The miniaturized design ensures portability, suitable for small offices and field applications. The IMSIS platform was successfully used to monitor in real-time the mitochondrial functions of live bovine embryos in O2 consumption, H2O2 release as an indication of ROS production, and extracellular acidity changes before and after the introduction of external substrates.

Machine learning methods for compound annotation in non-targeted mass spectrometry-A brief overview of fingerprinting, in silico fragmentation and de novo methods

Non-targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance-based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field. In this work, we illustrate which ML-based methods are available for compound annotation in non-targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies.

Application of a molecular networking approach using LC-HRMS combined with the MetWork webserver for clinical and forensic toxicology

Backgrounds and aims

In toxicology, LC-HRMS for untargeted screening yields a great deal of high quality spectral data. However, there we lack tools to visualize/organize the MS data. We applied molecular networking (MN) to untargeted screening interpretation. Our aims were to compare theoretical MS libraries obtained in silico with our experimental dataset in patients to broaden its application, and to use the MetWork web application for metabolite identification.

Methods

Samples were analyzed using an LC-HRMS system. For MN, data was generated using MZmine, and analyzed and visualized using MetGem. MetWork annotations were filtered and this file was used for annotation of the previously obtained MN.

Results

155 compounds including drugs found in patients were recorded. Using this dataset, we confirmed in 60 patients intake of tramadol, amitriptyline bromazepam, and cocaine. The results obtained by the reference methods were confirmed by MN approaches. Eighty percent of the compounds were common to both conventional and MN approaches. Using MetWork, metabolites and parent drugs such as amitriptyline, its metabolite nortriptyline and amitriptyline glucuronide phase 2 metabolites were anticipated and proposed as putative annotations.

Conclusion

The workflow increases confidence in toxicological screening by highlighting putative structures in biological matrices in combination with CFM-ID (Competitive Fragmentation Modeling for Metabolite Identification) and MetWork to extend the annotation of potential drugs even without a reference standard.

Chemodiversity of organic nitrogen emissions from light-duty gasoline vehicles is governed by engine displacements and driving speed

Organic nitrogen emissions from light-duty gasoline vehicles (LDGVs) is believed to play a pivotal role in atmospheric particulate matter (PM) in urban environments. Here, the characterization of organic nitrogen emitted by LDGVs with varying engine displacements at different speed phases was analyzed using a Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) at molecular level. For the LDGV with small engine displacements, the nitrogen-containing organic (CHON) compounds exhibit higher abundance, molecular weight, oxygen content and aromaticity in the extra-high-speed phase. Conversely, for the LDGV with big engine displacements, more CHON compounds with elevated abundance, molecular weight, oxygen content and aromaticity were observed in the low-speed phase. Our study assumed that the formation of CHON compounds emitted from LDGVs is mainly the oxidation reaction during fuel combustion, so the potential precursor-product pairs related to oxidation process were used to study the degree of combustion reaction. The results show that the highest proportion of oxidation occurs during extra-high-speed phase for LDGV with small engine displacements, and during low-speed phase for LDGV with big engine displacements. These results offer a novel perspective for comprehending the mechanism behind vehicle emissions formation and contribute valuable insights for crafting effective air pollution regulations.

OMICs approaches and technologies for understanding low-high feed efficiency traits in chicken: implication to breeding

In poultry production, there has been a trend of continuous increase in cost of feed ingredients which represents the major proportion of the production costs. Feed costs can be reduced by improving feed efficiency traits which increase the possibility of using various indigestible feed sources and decrease the environmental impact of the enhanced poultry production. Therefore, feed efficiency has been used as one of the most important economic traits of selection in the breeding program of chickens. Recently, many OMICs experimental studies have been designed to characterize biological differences between the high and low feed efficiency chicken phenotypes. Biological complexity cannot be fully captured by main individual OMICs such as genomics, transcriptomics, proteomics and metabolomics. Therefore, researchers have combined multiple assays from the same set of samples to create multi-OMICs datasets. OMICs findings are crucial in improving existing approaches to poultry breeding. The current review aimed to highlight the components of feed efficiency and general OMICs approaches and technologies. Besides, individual and multi-OMICs based understanding of chicken feed efficiency traits and the application of the acquired knowledge in the chicken breeding program were addressed.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Metabolomic profiling of cardiac allografts after controlled circulatory death

BACKGROUND

Assessment of myocardial viability during ex situ heart perfusion (ESHP) is based on the measurement of lactate concentrations. As this provides with limited information, we sought to investigate the metabolic signature associated with donation after circulatory death (DCD) and the impact of ESHP on the myocardial metabolome.

METHODS

Porcine hearts were retrieved either after warm ischemia (DCD group, N = 6); after brain-stem death (BSD group, N = 6); or without DCD nor BSD (Control group, N = 6). Hearts were perfused using normothermic oxygenated blood for 240 minutes. Plasma and myocardial samples were collected respectively every 30 and 60 minutes, and analyzed by an untargeted metabolomic approach using liquid chromatography coupled to high-resolution mass spectrometry.

RESULTS

Median duration of warm ischemia was 23 minutes [19-29] in DCD animals. Lactate level within myocardial biopsies was not significantly different between groups at T0 (p = 0.281), and remained stable over the 4-hour period of ESHP. More than 300 metabolites were detected in plasma and heart biopsy samples. Compared to BSD animals, metabolomics changes involving energy and nucleotide metabolisms were observed in plasma samples of DCD animals before initiation of ESHP, whereas 2 metabolites (inosine monophosphate and methylbutyrate) exhibited concentration changes in biopsy samples. Normalization of DCD metabolic profile was remarkable after 4 hours of ESHP.

CONCLUSION

A specific metabolic profile was observed in DCD hearts, mainly characterized by an increased nucleotide catabolism. DCD and BSD metabolomes proved normalized during ESHP. Complementary investigations are needed to correlate these findings to cardiac performances.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

- Invited Review - Postmortem skeletal muscle metabolism of farm animals approached with metabolomics

Skeletal muscle metabolism regulates homeostatic balance in animals. The metabolic impact persists even after farm animal skeletal muscle is converted to edible meat through postmortem rigor mortis and aging. Muscle metabolites resulting from animal growth and postmortem storage have a significant impact on meat quality, including flavor and color. Metabolomics studies of postmortem muscle aging have identified metabolisms that contain signatures inherent to muscle properties and the altered metabolites by physiological adaptation, with glycolysis as the pivotal metabolism in postmortem aging. Metabolomics has also played a role in mining relevant postmortem metabolisms and pathways, such as the citrate cycle and mitochondrial metabolism. This leads to a deeper understanding of the mechanisms underlying the generation of key compounds that are associated with meat quality. Genetic background, feeding strategy, and muscle type primarily determine skeletal muscle properties in live animals and affect post-mortem muscle metabolism. With comprehensive metabolite detection, metabolomics is also beneficial for exploring biomarker candidates that could be useful to monitor meat production and predict the quality traits. The present review focuses on advances in farm animal muscle metabolomics, especially postmortem muscle metabolism associated with genetic factors and muscle type.

Nutritional, functional, and bioactive properties of african underutilized legumes

Globally, legumes are vital constituents of diet and perform critical roles in maintaining well-being owing to the dense nutritional contents and functional properties of their seeds. While much emphasis has been placed on the major grain legumes over the years, the neglected and underutilized legumes (NULs) are gaining significant recognition as probable crops to alleviate malnutrition and give a boost to food security in Africa. Consumption of these underutilized legumes has been associated with several health-promoting benefits and can be utilized as functional foods due to their rich dietary fibers, vitamins, polyunsaturated fatty acids (PUFAs), proteins/essential amino acids, micro-nutrients, and bioactive compounds. Despite the plethora of nutritional benefits, the underutilized legumes have not received much research attention compared to common mainstream grain legumes, thus hindering their adoption and utilization. Consequently, research efforts geared toward improvement, utilization, and incorporation into mainstream agriculture in Africa are more convincing than ever. This work reviews some selected NULs of Africa (Adzuki beans (Vigna angularis), African yam bean (Sphenostylis stenocarpa), Bambara groundnut (Vigna subterranea), Jack bean (Canavalia ensiformis), Kidney bean (Phaseolus vulgaris), Lima bean (Phaseolus lunatus), Marama bean (Tylosema esculentum), Mung bean, (Vigna radiata), Rice bean (Vigna Umbellata), and Winged bean (Psophocarpus tetragonolobus)), and their nutritional, and functional properties. Furthermore, we highlight the prospects and current challenges associated with the utilization of the NULs and discusses the strategies to facilitate their exploitation as not only sources of vital nutrients, but also their integration for the development of cheap and accessible functional foods.

Metabolomic exploration of the effects of habituation to livestock trailer and extended transportation in goats

Goats raised for meat production are often transported long distances. Twelve-month-old male Spanish goats were used to determine the effects of habituation to trailers on plasma metabolomic profiles when transported for extended periods. In a split-plot design, 168 goats were separated into two treatment (TRT; whole plot) groups and maintained on two different paddocks. Concentrate supplement was fed to one group inside two livestock trailers (habituated group, H), while the other group received the same quantity of concentrate, but not inside the trailers (non-habituated, NH). Goats were subjected to a 10-h transportation stress in 4 replicates (n = 21 goats/replicate/TRT) after 4 weeks of habituation period. Blood samples were collected prior to loading, 20 min after loading (0 h), and at 2, 4, 6, 8, and 10 h of transportation (Time; subplot). A targeted quantitative metabolomics approach was employed to analyze the samples. The data were analyzed using R software and MIXED procedures in SAS. Several amino acids (alanine, serine, glycine, histidine, glutamate, trans-hydroxyproline, asparagine, threonine, methylhistidine, ornithine, proline, leucine, tryptophan) were higher (p < 0.05) in the H group compared to the NH group. Six long-chain acylcarnitines were higher (p < 0.05), while free (C0) and short-chain (C3, C5) carnitines were lower (p < 0.05) in the NH goats compared to the H goats. In general, amino acid concentrations decreased and long-chain acylcarnitine (>C10) levels increased with transportation time (p < 0.05). Butyric acid, α-ketoglutaric acid, and α-aminoadipic acid concentrations were lower (p < 0.05) and β-hydroxybutyric acid concentrations were higher in the NH goats compared to the H goats. Plasma glucose, non-esterified fatty acid (NEFA) and urea nitrogen concentrations were significantly influenced by Time (p < 0.01). Plasma NEFA concentrations were significantly lower (p < 0.01) in the H group than the NH group. Habituation to trailers can be beneficial in enhancing stress coping abilities in goats due to higher concentrations of metabolites such as butyrate and certain amino acids that support antioxidant activities and immune function. Plasma long-chain acylcarnitines may be good indicators of stress during long-distance transportation in goats.

Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes

Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.

Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra

High-resolution mass spectrometry is widely used in many research fields allowing for accurate mass determinations. In this context, it is pretty standard that high-resolution profile mode mass spectra are reduced to centroided data, which many data processing routines rely on for further evaluation. Yet information on the peak profile quality is not conserved in those approaches; i.e., describing results reliability is almost impossible. Therefore, we overcome this limitation by developing a new statistical parameter called data quality score (DQS). For the DQS calculations, we performed a very fast and robust regression analysis of the individual high-resolution peak profiles and considered error propagation to estimate the uncertainties of the regression coefficients. We successfully validated the new algorithm with the vendor-specific algorithm implemented in Proteowizard’s msConvert. Moreover, we show that the DQS is a sum parameter associated with centroid accuracy and precision. We also demonstrate the benefit of the new algorithm in nontarget screenings as the DQS prioritizes signals that are not influenced by non-resolved isobaric ions or isotopic fine structures. The algorithm is implemented in Python, R, and Julia programming languages and supports multi- and cross-platform downstream data handling.

MAVEN2: An Updated Open-Source Mass Spectrometry Exploration Platform

MAVEN, an open-source software program for analysis of LC-MS metabolomics data, was originally released in 2010. As mass spectrometry has advanced in the intervening years, MAVEN has been periodically updated to reflect this advancement. This manuscript describes a major update to the program, MAVEN2, which supports LC-MS/MS analysis of metabolomics and lipidomics samples. We have developed algorithms to support MS/MS spectral matching and efficient search of large-scale fragmentation libraries. We explore the ability of our approach to separate authentic from spurious metabolite identifications using a set of standards spiked into water and yeast backgrounds. To support our improved lipid identification workflow, we introduce a novel in-silico lipidomics library covering major lipid classes and compare searches using our novel library to searches with existing in-silico lipidomics libraries. MAVEN2 source code and cross-platform application installers are freely available for download from GitHub under a GNU permissive license [ver 3], as are the in silico lipidomics libraries and corresponding code repository.

Metabolome Profiling: A Breeding Prediction Tool for Legume Performance under Biotic Stress Conditions

Legume crops such as common bean, pea, alfalfa, cowpea, peanut, soybean and others contribute significantly to the diet of both humans and animals. They are also important in the improvement of cropping systems that employ rotation and fix atmospheric nitrogen. Biotic stresses hinder the production of leguminous crops, significantly limiting their yield potential. There is a need to understand the molecular and biochemical mechanisms involved in the response of these crops to biotic stressors. Simultaneous expressions of a number of genes responsible for specific traits of interest in legumes under biotic stress conditions have been reported, often with the functions of the identified genes unknown. Metabolomics can, therefore, be a complementary tool to understand the pathways involved in biotic stress response in legumes. Reports on legume metabolomic studies in response to biotic stress have paved the way in understanding stress-signalling pathways. This review provides a progress update on metabolomic studies of legumes in response to different biotic stresses. Metabolome annotation and data analysis platforms are discussed together with future prospects. The integration of metabolomics with other “omics” tools in breeding programmes can aid greatly in ensuring food security through the production of stress tolerant cultivars.

Moving beyond descriptive studies: harnessing metabolomics to elucidate the molecular mechanisms underpinning host-microbiome phenotypes

Advances in technology and software have radically expanded the scope of metabolomics studies and allow us to monitor a broad transect of central carbon metabolism in routine studies. These increasingly sophisticated tools have shown that many human diseases are modulated by microbial metabolism. Despite this, it remains surprisingly difficult to move beyond these statistical associations and identify the specific molecular mechanisms that link dysbiosis to the progression of human disease. This difficulty stems from both the biological intricacies of host-microbiome dynamics as well as the analytical complexities inherent to microbiome metabolism research. The primary objective of this review is to examine the experimental and computational tools that can provide insights into the molecular mechanisms at work in host-microbiome interactions and to highlight the undeveloped frontiers that are currently holding back microbiome research from fully leveraging the benefits of modern metabolomics.

Developments in high-resolution mass spectrometric analyses of new psychoactive substances

The proliferation of new psychoactive substances (NPS) has necessitated the development and improvement of current practices for the detection and identification of known NPS and newly emerging derivatives. High-resolution mass spectrometry (HRMS) is quickly becoming the industry standard for these analyses due to its ability to be operated in data-independent acquisition (DIA) modes, allowing for the collection of large amounts of data and enabling retrospective data interrogation as new information becomes available. The increasing popularity of HRMS has also prompted the exploration of new ways to screen for NPS, including broad-spectrum wastewater analysis to identify usage trends in the community and metabolomic-based approaches to examine the effects of drugs of abuse on endogenous compounds. In this paper, the novel applications of HRMS techniques to the analysis of NPS is reviewed. In particular, the development of innovative data analysis and interpretation approaches is discussed, including the application of machine learning and molecular networking to toxicological analyses.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Metabolomics in the understanding and management of hepatic encephalopathy

Metabolomics refers to the study of biological components below 1000 Daltons (Da) involved in metabolic pathways as substrates, products or effectors. According to the interconnected metabolic disturbances that have been described in the pathophysiology of hepatic encephalopathy (HE), this technique appears to be well adapted to study and better delineate the disease. This review will focus on recent advances in metabolomics in the field of HE. Thus, after a brief overview of the general principles of metabolomics, we will discuss metabolomics as a potentially efficient tool for unraveling new HE pathophysiological insights, biomarkers identification, or as a predicting tool for treatment response or outcome prognosis. Finally, we will give our vision on the prospects offered by metabolomics for improving care of HE patients.

High-coverage metabolomics uncovers microbiota-driven biochemical landscape of interorgan transport and gut-brain communication in mice

The mammalian gut harbors a complex and dynamic microbial ecosystem: the microbiota. While emerging studies support that microbiota regulates brain function with a few molecular cues suggested, the overall biochemical landscape of the “microbiota-gut-brain axis” remains largely unclear. Here we use high-coverage metabolomics to comparatively profile feces, blood sera, and cerebral cortical brain tissues of germ-free C57BL/6 mice and their age-matched conventionally raised counterparts. Results revealed for all three matrices metabolomic signatures owing to microbiota, yielding hundreds of identified metabolites including 533 altered for feces, 231 for sera, and 58 for brain with numerous significantly enriched pathways involving aromatic amino acids and neurotransmitters. Multicompartmental comparative analyses single out microbiota-derived metabolites potentially implicated in interorgan transport and the gut-brain axis, as exemplified by indoxyl sulfate and trimethylamine-N-oxide. Gender-specific characteristics of these landscapes are discussed. Our findings may be valuable for future research probing microbial influences on host metabolism and gut-brain communication.

The gut microbiota harbours neuroactive potential with links to neurological disorders. Here, the authors apply global metabolomics with an integrated annotation strategy to comparatively profile fecal, blood serum and cerebral cortical brain tissues of eight-week-old germ-free mice vs. age-matched specific-pathogen-free mice, providing a snapshot of the metabolome status linked to the gut-brain axis.

Combining Chemical Knowledge and Quantum Calculation for Interpreting Low-Energy Product Ion Spectra of Metabolite Adduct Ions: Sodiated Diterpene Diester Species as a Case Study

We investigated the product ion spectra of [M + Na]⁺ from diterpene diester species and low molecular mass metabolites analyzed by electrospray ionization (ESI). Mainly, the formation of protonated salt structures was proposed to explain the observed neutral losses of carboxylic acids. It also facilitates understanding sodium retention on product ions or on neutral losses. In addition, the occurrence of consecutive carboxylic acid losses is rather unexpected under resonant excitation conditions. Quantum calculation demonstrated that the exothermic character of such neutral losses can represent a relevant explanation. There is no doubt that the formation and role of the protonated salt structures will be helpful for a better understanding and software-assisted interpretation of tandem mass spectra from small molecules, especially in the ever-growing metabolomics field.

FT-ICR-MS-based metabolomics: A deep dive into plant metabolism

Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.

Lipidomics in Biomarker Research

Lipids are natural substances found in all living organisms and involved in many biological functions. Imbalances in the lipid metabolism are linked to various diseases such as obesity, diabetes, or cardiovascular disease. Lipids comprise thousands of chemically distinct species making them a challenge to analyze because of their great structural diversity.Thanks to the technological improvements in the fields of chromatography, high-resolution mass spectrometry, and bioinformatics over the last years, it is now possible to perform global lipidomics analyses, allowing the concomitant detection, identification, and relative quantification of hundreds of lipid species. This review shall provide an insight into a general lipidomics workflow and its application in metabolic biomarker research.

Current Challenges and Recent Developments in Mass Spectrometry-Based Metabolomics

High-resolution mass spectrometry (MS) has advanced the study of metabolism in living systems by allowing many metabolites to be measured in a single experiment. Although improvements in mass detector sensitivity have facilitated the detection of greater numbers of analytes, compound identification strategies, feature reduction software, and data sharing have not kept up with the influx of MS data. Here, we discuss the ongoing challenges with MS-based metabolomics, including de novo metabolite identification from mass spectra, differentiation of metabolites from environmental contamination, chromatographic separation of isomers, and incomplete MS databases. Because of their popularity and sensitive detection of small molecules, this review focuses on the challenges of liquid chromatography-mass spectrometry-based methods. We then highlight important instrumentational, experimental, and computational tools that have been created to address these challenges and how they have enabled the advancement of metabolomics research.

Right Ventricle Remodeling Metabolic Signature in Experimental Pulmonary Hypertension Models of Chronic Hypoxia and Monocrotaline Exposure

INTRODUCTION

Over time and despite optimal medical management of patients with pulmonary hypertension (PH), the right ventricle (RV) function deteriorates from an adaptive to maladaptive phenotype, leading to RV failure (RVF). Although RV function is well recognized as a prognostic factor of PH, no predictive factor of RVF episodes has been elucidated so far. We hypothesized that determining RV metabolic alterations could help to understand the mechanism link to the deterioration of RV function as well as help to identify new biomarkers of RV failure.

METHODS

In the current study, we aimed to characterize the metabolic reprogramming associated with the RV remodeling phenotype during experimental PH induced by chronic-hypoxia-(CH) exposure or monocrotaline-(MCT) exposure in rats. Three weeks after PH initiation, we hemodynamically characterized PH (echocardiography and RV catheterization), and then we used an untargeted metabolomics approach based on liquid chromatography coupled to high-resolution mass spectrometry to analyze RV and LV tissues in addition to plasma samples from MCT-PH and CH-PH rat models.

RESULTS

CH exposure induced adaptive RV phenotype as opposed to MCT exposure which induced maladaptive RV phenotype. We found that predominant alterations of arginine, pyrimidine, purine, and tryptophan metabolic pathways were detected on the heart (LV+RV) and plasma samples regardless of the PH model. Acetylspermidine, putrescine, guanidinoacetate RV biopsy levels, and cytosine, deoxycytidine, deoxyuridine, and plasmatic thymidine levels were correlated to RV function in the CH-PH model. It was less likely correlated in the MCT model. These pathways are well described to regulate cell proliferation, cell hypertrophy, and cardioprotection. These findings open novel research perspectives to find biomarkers for early detection of RV failure in PH.

High Molecular Diversity of Organic Nitrogen in Urban Snow in North China

Snow serves as a vital scavenging mechanism to gas-phase and particle-phase organic nitrogen substances in the atmosphere, providing a significant link between land-atmosphere flux of nitrogen in the surface-earth system. Here, we used optical instruments (UV-vis and excitation-emission matrix fluorescence) and a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) to elucidate the molecular composition and potential precursors of snow samples collected simultaneously at four megacities in North China. The elemental O/N ratio (≥3), together with the preference in the negative ionization mode, indicates that the one and two nitrogen atom-containing organics (CHON₁ and CHON₂) in snow were largely in the oxidized form (as organic nitrates, -ONO₂). This study assumed that scavenging of particle-phase and gas-phase organic nitrates might be significant sources of CHON in precipitation. A gas-phase oxidation process and a particle-phase hydrolysis process, at a molecular level, were used to trace the potential precursors of CHON. Results show that more than half of the snow CHON molecules may be related to the oxidized and hydrolyzed processes of atmospheric organics. Potential formation processes of atmospheric organics on a molecular level provide a new concept to better understand the sources and scavenging mechanisms of organic nitrogen species in the atmosphere.

Identifying metabolites of diphenidol by liquid chromatography-quadrupole/orbitrap mass spectrometry using rat liver microsomes, human blood, and urine samples

In recent years, diphenidol [1,1-diphenyl-4-piperidino-1-butanol] has been one of the drugs that appears in suicide cases, but there are few research data on its metabolic pathways and main metabolites. Metabolite identification plays a key role in drug safety assessment and clinical application. In this study, in vivo and in vitro samples were analyzed with ultra-high-performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry. Structural elucidation of the metabolites was performed by comparing their molecular weights and product ions with those of the parent drug. As a result, 10 Phase I metabolites and 5 glucuronated Phase II metabolites were found in a blood sample and a urine sample from authentic cases. Three other Phase I metabolites were identified in the rat liver microsomes incubation solution. The results showed that the main metabolic pathways of diphenidol in the human body include hydroxylation, oxidation, dehydration, N-dealkylation, methylation, and conjugation with glucuronic acid. This study preliminarily clarified the metabolic pathways and main metabolites of diphenidol. For the development of new methods for the identification of diphenidol consumption, we recommend using M2-2 as a marker of diphenidol entering the body. The results of this study provide a theoretical basis for the pharmacokinetics and forensic scientific research of diphenidol.

Analytical Strategies for Fingerprinting of Antioxidants, Nutritional Substances, and Bioactive Compounds in Foodstuffs Based on High Performance Liquid Chromatography-Mass Spectrometry: An Overview

New technology development and globalisation have led to extreme changes in the agri-food sector in recent years that need an important food supply chain characterisation from plant materials to commercial productions. Many analytical strategies are commonly utilised in the agri-food industry, often using complementary technologies with different purposes. Chromatography on-line coupled to mass spectrometry (MS) is one of the most selective and sensitive analytical methodologies. The purpose of this overview is to present the most recent MS-based techniques applied to food analysis. An entire section is dedicated to the recent applications of high-resolution MS. Covered topics include liquid (LC)– and gas chromatography (GC)–MS analysis of natural bioactive substances, including carbohydrates, flavonoids and related compounds, lipids, phenolic compounds, vitamins, and other different molecules in foodstuffs from the perspectives of food composition, food authenticity and food adulteration. The results represent an important contribution to the utilisation of GC–MS and LC–MS in the field of natural bioactive compound identification and quantification.

High-coverage lipidomics for functional lipid and pathway analyses

Rapid advances in front-end separation approaches and analytical technologies have accelerated the development of lipidomics, particularly in terms of increasing analytical coverage to encompass an expanding repertoire of lipids within a single analytical approach. Developments in lipid pathway analysis, however, have somewhat lingered behind, primarily due to (1) the lack of coherent alignment between lipid identifiers in common databases versus that generated from experiments, owing to the differing structural resolution of lipids at molecular level that is specific to the analytical approaches adopted by various laboratories; (2) the immense complexity of lipid metabolic relationships that may entail head group changes, fatty acyls modifications of various forms (e.g. elongation, desaturation, oxidation), as well as active remodeling that demands a multidimensional, panoramic view to take into account all possibilities in lipid pathway analyses. Herein, we discuss current efforts undertaken to address these challenges, as well as alternative form of "pathway analyses" that may be particularly useful for uncovering functional lipid interactions under different biological contexts. Consolidating lipid pathway analyses will be indispensable in facilitating the transition of lipidomics from its prior role of phenotype validation to a hypothesis-generating tool that uncovers novel molecular targets to drive downstream mechanistic pursuits under biomedical settings.

Lipidomic analyses reveal enhanced lipolysis in planthoppers feeding on resistant host plants

The brown planthopper (BPH) (Nilaparvata lugens Stål) is a highly destructive pest that seriously damages rice (Oryza sativa L.) and causes severe yield losses. To better understand the physiological and metabolic mechanisms through which BPHs respond to resistant rice, we combined mass-spectrometry-based lipidomics with transcriptomic analysis and gene knockdown techniques to compare the lipidomes of BPHs feeding on either of the two resistant (NIL-Bph6 and NIL-Bph9) plants or a wild-type, BPH susceptible (9311) plant. Insects that were fed on resistant rice transformed triglyceride (TG) to phosphatidylcholine (PC) and digalactosyldiacylglycerol (DGDG), with these lipid classes showing significant alterations in fatty acid composition. Moreover, the insects that were fed on resistant rice were characterized by prominent expression changes in genes involved in lipid metabolism processes. Knockdown of the NlBmm gene, which encodes a lipase that regulates the mobilization of lipid reserves, significantly increased TG content and feeding performance of BPHs on resistant plants relative to dsGFP-injected BPHs. Our study provides the first detailed description of lipid changes in BPHs fed on resistant and susceptible rice genotypes. Results from BPHs fed on resistant rice plants reveal that these insects can accelerate TG mobilization to provide energy for cell proliferation, body maintenance, growth and oviposition.

Insights into glucocorticoid responses derived from omics studies

Glucocorticoid drugs are commonly used in the treatment of several conditions, including autoimmune diseases, asthma and cancer. Despite their widespread use and knowledge of biological pathways via which they act, much remains to be learned about the cell type-specific mechanisms of glucocorticoid action and the reasons why patients respond differently to them. In recent years, human and in vitro studies have addressed these questions with genomics, transcriptomics and other omics approaches. Here, we summarize key insights derived from omics studies of glucocorticoid response, and we identify existing knowledge gaps related to mechanisms of glucocorticoid action that future studies can address.

High-resolution mass spectrometry for bioanalytical applications: Is this the new gold standard?

Liquid chromatography coupled to quadrupole-based tandem mass spectrometry (QqQ) is termed the "gold standard" for bioanalytical applications because of its unpreceded selectivity, sensitivity, and the ruggedness of the technology. More recently, however, high-resolution mass spectrometry (HRMS) has become increasingly popular for bioanalytical applications. Nonetheless, this technique is still viewed, either as a screening technology or as a research tool. Although HRMS is actively discussed during scientific conferences, it is yet to be widely utilised in routine laboratory settings and there remains a reluctance to use HRMS for quantitative measurements in regulated environments. This paper does not aim to comprehensively describe the potential of the latest HRMS technology, but rather, it focuses on what results can be obtained and outlines the author's experiences over a period of many years of the routine application of various forms of HRMS instrumentation. Fifteen years ago, some nine different QqQ methods were used in the author's laboratory to analyse a variety of different veterinary drug resides. Today, many more analytes are quantified by seven HRMS methods and just three QqQ methods remain in use for the analysis of a small set of compounds yet to be upgraded to HRMS analysis. This continual upgrading and migration of analytical methods were accompanied by regularly participating in laboratory proficiency tests (PTs). The PT reports (covering a range of analytes and analytical methods) were used to compare the accuracy of HRMS- versus QqQ-based measurements. In the second part of this paper, the particular strengths and limitations of HRMS for both method development and routine measurements are critically discussed. This also includes some anecdotal experiences encountered when replacing QqQ assays with HRMS methods.

Decomposing the molecular complexity of brewing

The compositional space of a set of 120 diverse beer samples was profiled by rapid flow-injection analysis (FIA) Fourier transform ion cyclotron mass spectrometry (FTICR-MS). By the unrivaled mass resolution, it was possible to uncover and assign compositional information to thousands of yet unknown metabolites in the beer matrix. The application of several statistical models enabled the assignment of different molecular pattern to certain beer attributes such as the beer type, the way of adding hops and the grain used. The dedicated van Krevelen diagrams and mass difference networks displayed the structural connectivity of the annotated sum formulae. Thereby it was possible to provide a base of knowledge of the beer metabolome far above database-dependent annotations. Typical metabolic signatures for beer types, which reflect differences in ingredients and ways of brewing, could be extracted. Besides, the complexity of isomeric compounds, initially profiled as single mass values in fast FIA-FTICR-MS, was resolved by selective UHPLC-ToF-MS² analysis. Thereby structural hypotheses based on FTICR’s sum formulae could be confirmed. Benzoxazinoid hexosides deriving from the wheat’s secondary metabolism were uncovered as suitable marker substances for the use of whole wheat grains, in contrast to merely wheat starch or barley. Furthermore, it was possible to describe Hydroxymethoxybenzoxazinone(HMBOA)-hexosesulfate as a hitherto unknown phytoanticipin derivative in wheat containing beers. These findings raise the potential of ultrahigh resolution mass spectrometry for rapid quality control and inspection purposes as well as deep metabolic profiling, profound search for distinct hidden metabolites and classification of archeological beer samples.

Links between Nutrition, Infectious Diseases, and Microbiota: Emerging Technologies and Opportunities for Human-Focused Research

The interaction between nutrition and human infectious diseases has always been recognized. With the emergence of molecular tools and post-genomics, high-resolution sequencing technologies, the gut microbiota has been emerging as a key moderator in the complex interplay between nutrients, human body, and infections. Much of the host-microbial and nutrition research is currently based on animals or simplistic in vitro models. Although traditional in vivo and in vitro models have helped to develop mechanistic hypotheses and assess the causality of the host-microbiota interactions, they often fail to faithfully recapitulate the complexity of the human nutrient-microbiome axis in gastrointestinal homeostasis and infections. Over the last decade, remarkable progress in tissue engineering, stem cell biology, microfluidics, sequencing technologies, and computing power has taken place, which has produced a new generation of human-focused, relevant, and predictive tools. These tools, which include patient-derived organoids, organs-on-a-chip, computational analyses, and models, together with multi-omics readouts, represent novel and exciting equipment to advance the research into microbiota, infectious diseases, and nutrition from a human-biology-based perspective. After considering some limitations of the conventional in vivo and in vitro approaches, in this review, we present the main novel available and emerging tools that are suitable for designing human-oriented research.

MEATabolomics: Muscle and Meat Metabolomics in Domestic Animals

In the past decades, metabolomics has been used to comprehensively understand a variety of food materials for improvement and assessment of food quality. Farm animal skeletal muscles and meat are one of the major targets of metabolomics for the characterization of meat and the exploration of biomarkers in the production system. For identification of potential biomarkers to control meat quality, studies of animal muscles and meat with metabolomics (MEATabolomics) has been conducted in combination with analyses of meat quality traits, focusing on specific factors associated with animal genetic background and sensory scores, or conditions in feeding system and treatments of meat in the processes such as postmortem storage, processing, and hygiene control. Currently, most of MEATabolomics approaches combine separation techniques (gas or liquid chromatography, and capillary electrophoresis)-mass spectrometry (MS) or nuclear magnetic resonance (NMR) approaches with the downstream multivariate analyses, depending on the polarity and/or hydrophobicity of the targeted metabolites. Studies employing these approaches provide useful information to monitor meat quality traits efficiently and to understand the genetic background and production system of animals behind the meat quality. MEATabolomics is expected to improve the knowledge and methodologies in animal breeding and feeding, meat storage and processing, and prediction of meat quality.

Comparative Evaluation of Data Dependent and Data Independent Acquisition Workflows Implemented on an Orbitrap Fusion for Untargeted Metabolomics

Constant improvements to the Orbitrap mass analyzer, such as acquisition speed, resolution, dynamic range and sensitivity have strengthened its value for the large-scale identification and quantification of metabolites in complex biological matrices. Here, we report the development and optimization of Data Dependent Acquisition (DDA) and Sequential Window Acquisition of all THeoretical fragment ions (SWATH-type) Data Independent Acquisition (DIA) workflows on a high-field Orbitrap Fusion^TM Tribrid^TM instrument for the robust identification and quantification of metabolites in human plasma. By using a set of 47 exogenous and 72 endogenous molecules, we compared the efficiency and complementarity of both approaches. We exploited the versatility of this mass spectrometer to collect meaningful MS/MS spectra at both high- and low-mass resolution and various low-energy collision-induced dissociation conditions under optimized DDA conditions. We also observed that complex and composite DIA-MS/MS spectra can be efficiently exploited to identify metabolites in plasma thanks to a reference tandem spectral library made from authentic standards while also providing a valuable data resource for further identification of unknown metabolites. Finally, we found that adding multi-event MS/MS acquisition did not degrade the ability to use survey MS scans from DDA and DIA workflows for the reliable absolute quantification of metabolites down to 0.05 ng/mL in human plasma.

Parallel Detection of Fundamental and Sixth Harmonic Signals Using an ICR Cell with Dipole and Sixth Harmonic Detectors

Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) is a powerful instrument for high-resolution analysis of biomolecules. However, relatively long signal acquisition periods are needed to achieve mass spectra with high resolution. The use of multiple detector electrodes for detection of harmonic frequencies has been introduced as one approach to increase scan rate for a given resolving power or to obtain increased resolving power for a given detection period. The achieved resolving power and scan rate increase linearly with the order of detected harmonic signals. In recent years, ICR cell geometries have been investigated to increase the order of the harmonic frequencies and enhance harmonic signal intensities. In this study, we demonstrated PCB-based ICR cell designs with dipole and sixth harmonic detectors for parallel detection of fundamental and harmonic (6f) signals. The sixth harmonic signals from the sixth harmonic detector showed an expected 6 times higher resolving power with (M + 3H)3+ charge state insulin ions as compared with that from fundamental signals from the dipole detector. Moreover, the insulin isotopic peaks with sixth harmonic frequency signals acquired with the sixth harmonic detector were resolved for a 40 ms data acquisition period but unresolved with the same duration dipole detector signals, corresponding to a 6-fold improvement in achievable spectral acquisition rates for a given resolving power.

Impact of storage conditions on the human stool metabolome and lipidome: Preserving the most accurate fingerprint

Faecal metabolomics markedly emerged in clinical as well as analytical chemistry through the unveiling of aberrations in metabolic signatures as reflection of variance in gut (patho)physiology and beyond. Logistic hurdles, however, hinder the analysis of stool samples immediately following collection, inferring the need of biobanking. Yet, the optimum way of storing stool material remains to be determined, in order to conserve an accurate snapshot of the metabolome and circumvent artifacts regarding the disease and parameter(s) under observation. To address this problem, this study scrutinised the impact of freeze-thaw cycling, storage duration, temperature and aerobicity, thereby using ultra-high performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS)-based polar metabolomics and lipidomics methodologies for faecal metabolomics. Both targeted (n > 400) and untargeted approaches were implemented to assess storage effects on individual chemical classes of metabolites as well as the faecal fingerprint. In general, recommendations are that intact stool samples should be divided into aliquots, lyophilised and stored at -80 °C for a period no longer than 18 weeks, and avoiding any freeze-thawing. The first preservation week exerted the most decisive impact regarding storage temperature, i.e. 12.1% and 6.4% of the polar metabolome experienced a shift at -20 °C and at -80 °C, respectively, whereas 8.6% and 7.9% was observed to be changed significantly for the lipidome. In addition, aside from the negligible impact of aerobicity, the polar metabolome appeared to be more dependent on the storage conditions applied compared to the lipidome, which emerged as the more stable fraction when assessing the storage duration for 25 weeks. If the interest would greatly align with particular chemical classes, such as branched-chain amino acids or short-chain fatty acids, specific storage duration recommendations are reported. The provided insights on the stability of the faecal metabolome may contribute to a more reasoned design of experiments in biomarker detection or pathway elucidation within the field of faecal metabolomics.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

High-resolution mass spectrometry-based metabolomics reveal the disruption of jasmonic pathway in Arabidopsis thaliana upon copper oxide nanoparticle exposure

Mass-spectrometry based metabolomics has recently emerged as a valuable technique in understanding the ecotoxicity and mode of action of a wide range of xenobiotics in the environment, including engineered nanomaterials (ENMs). However, the applications of metabolomics in elucidating the biochemical pathways affected by xenobiotics have been mostly performed using targeted analysis. In this study, the effects of copper oxide nanoparticles (CuO NPs) on Arabidopsis thaliana, a model plant, was investigated using untargeted metabolite profiling based on two platforms of high-resolution mass spectrometry (MS): (1) liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF-MS) and (2) LC Q Exactive™ Hybrid Quadrupole-Orbitrap™-MS (LC-Orbitrap-MS). This approach was performed to identify specific features (mass-to-charge ratios, m/z's) that are significantly changed in a reproducible manner regardless of the MS platform used in metabolomics. In addition, the total copper concentrations taken up in plant tissues were quantified using inductively coupled plasma mass spectrometry (ICP-MS), which provided evidence of translocation of CuO NPs from roots to leaves and flowering shoots. Results from untargeted metabolomics showed that there were 65 plant metabolites that were altered commonly in both LC/MS platforms resulting from CuO NPs exposure of Arabidopsis thaliana. These metabolites belong to the jasmonic acid and glucosinolates pathways, suggesting the stress response induced by CuO NPs in Arabidopsis. This study demonstrated the effectiveness of high-resolution LC/MS in providing insight on the mechanism of nanotoxicity of CuO NPs in plants.

Quantification of Drug Molecules in Live Single Cells Using the Single-Probe Mass Spectrometry Technique

Analyzing cellular constituents on the single-cell level through mass spectrometry (MS) allows for a wide range of compounds to be studied simultaneously. However, there is a need for quantitative single-cell mass spectrometry (qSCMS) methods to fully characterize drug efficacy from individual cells within cell populations. In this study, qSCMS experiments were carried out using the Single-probe MS technique. The method was successfully used to perform rapid absolute quantifications of the anticancer drug irinotecan in individual mammalian cancer cells under ambient conditions in real time. Traditional liquid chromatography/mass spectrometry (LC/MS) quantifications of irinotecan in cell lysate samples were used to compare the results from Single-probe qSCMS. This technique showcases heterogeneity of drug efficacy on the single-cell level.