Spectral deconvolution for gas chromatography mass spectrometry-based metabolomics: current status and future perspectives

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.

Metabolic insights into HIV/TB co-infection: an untargeted urinary metabolomics approach

INTRODUCTION

Amid the global health crisis, HIV/TB co-infection presents significant challenges, amplifying the burden on patients and healthcare systems alike. Metabolomics offers an innovative window into the metabolic disruptions caused by co-infection, potentially improving diagnosis and treatment monitoring.

AIM

This study uses untargeted metabolomics to investigate the urinary metabolic signature of HIV/TB co-infection, enhancing understanding of the metabolic interplay between these infections.

METHODS

Urine samples from South African adults, categorised into four groups - healthy controls, TB-positive, HIV-positive, and HIV/TB co-infected - were analysed using GCxGC-TOFMS. Metabolites showing significant differences among groups were identified through Kruskal-Wallis and Wilcoxon rank sum tests.

RESULTS

Various metabolites (n = 23) were modulated across the spectrum of health and disease states represented in the cohorts. The metabolomic profiles reflect a pronounced disruption in biochemical pathways involved in energy production, amino acid metabolism, gut microbiome, and the immune response, suggesting a bidirectional exacerbation between HIV and TB. While both diseases independently perturb the host's metabolism, their co-infection leads to a unique metabolic phenotype, indicative of an intricate interplay rather than a simple additive effect.

CONCLUSION

Metabolic profiling revealed a unique metabolic landscape shaped by HIV/TB co-infection. The findings highlight the potential of urinary differential metabolites for co-infection, offering a non-invasive tool for enhancing diagnostic precision and tailoring therapeutic interventions. Future research should focus on expanding sample sizes and integrating longitudinal analyses to build upon these foundational insights, paving the way for metabolomic applications in combating these concurrent pandemics.

Deep learning framework for peak detection at the intact level of therapeutic proteins

While automated peak detection functionalities are available in commercially accessible software, achieving optimal true positive rates frequently necessitates visual inspection and manual adjustments. In the initial phase of this study, hetero-variants (glycoforms) of a monoclonal antibody were distinguished using liquid chromatography-mass spectrometry, revealing discernible peaks at the intact level. To comprehensively identify each peak (hetero-variant) in the intact-level analysis, a deep learning approach utilizing convolutional neural networks (CNNs) was employed in the subsequent phase of the study. In the current case study, utilizing conventional software for peak identification, five peaks were detected using a 0.5 threshold, whereas seven peaks were identified using the CNN model. The model exhibited strong performance with a probability area under the curve (AUC) of 0.9949, surpassing that of partial least squares discriminant analysis (PLS-DA) (probability AUC of 0.8041), and locally weighted regression (LWR) (probability AUC of 0.6885) on the data acquired during experimentation in real-time. The AUC of the receiver operating characteristic curve also illustrated the superior performance of the CNN over PLS-DA and LWR.

Untargeted metabolomics coupled with genomics in the study of sucrose and xylose metabolism in Pectobacterium betavasculorum

Introduction

Pectobacterium betavasculorum is a member of the Pectobacerium genus that inhabits a variety of niches and is found in all climates. Bacteria from the Pectobacterium genus can cause soft rot disease on various plants due to the secretion of plant cell wall degrading enzymes (PCWDEs). The species P. betavasculorum is responsible for the vascular necrosis of sugar beet and soft rot of many vegetables. It also infects sunflowers and artichokes. The main sugar present in sugar beet is sucrose while xylose is one of the main sugars in artichoke and sunflower.

Methods

In our work, we applied metabolomic studies coupled with genomics to investigate the metabolism of P. betavasculorum in the presence of xylose and sucrose as the only carbon source. The ability of the strains to use various sugars as the only carbon source were confirmed by the polypyridyl complex of Ru(II) method in 96-well plates.

Results

Our studies provided information on the metabolic pathways active during the degradation of those substrates. It was observed that different metabolic pathways are upregulated in the presence of xylose in comparison to sucrose.

Discussion

The presence of xylose enhances extracellular metabolism of sugars and glycerol as well as stimulates EPS and IPS synthesis. In contrast, in the presence of sucrose the intensive extracellular metabolism of amines and amino acids is promoted.

Screening Disinfection Byproducts in Arid-Coastal Wastewater: A Workflow Using GC×GC-TOFMS, Passive Sampling, and NMF Deconvolution Algorithm

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts-with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks-significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks.

Changes in GC-MS metabolite profile, antioxidant capacity and anthocyanins content during fermentation of fine-flavor cacao beans from Ecuador

The fermentation of fine-flavor cacao beans is a key process contributing to the enhancement of organoleptic attributes and monetary benefits for cacao farmers. This work aimed to describe the dynamics of the gas chromatography-mass spectrometry (GC-MS) metabolite profile as well as the antioxidant capacity and anthocyanin contents during fermentation of fine-flavor cacao beans. Samples of Nacional x Trinitario cacao beans were obtained after 0, 24, 48, 72, 96, and 120 hours of spontaneous fermentation. Total phenolic content (TPC), ferric reducing antioxidant power (FRAP), and total anthocyanin content were measured by ultraviolet-visible (UV-Vis) spectrophotometry. Volatiles were adsorbed by headspace solid phase microextraction (HS-SPME) while other metabolites were assessed by an extraction-derivatization method followed by gas chromatography-mass spectrometry (GC-MS) detection and identification. Thirty-two aroma-active compounds were identified in the samples, including 17 fruity, and 9 floral-like volatiles as well as metabolites with caramel, chocolate, ethereal, nutty, sweet, and woody notes. Principal components analysis and Heatmap-cluster analysis of volatile metabolites grouped samples according to the fermentation time. Additionally, the total anthocyanin content declined during fermentation, and FRAP-TPC values showed a partial correlation. These results highlight the importance of fermentation for the improvement of the fine-flavor characteristics of cacao beans.

Human Metabolism and Excretion of Kawakawa (Piper excelsum) Leaf Chemicals

SCOPE

Piper excelsum (kawakawa) has a history of therapeutic use by Māori in Aotearoa New Zealand. It is currently widely consumed as a beverage and included as an ingredient in "functional" food product. Leaves contain compounds that are also found in a wide range of other spices, foods, and medicinal plants. This study investigates the human metabolism and excretion of kawakawa leaf chemicals.

METHODS AND RESULTS

Six healthy male volunteers in one study (Bioavailability of Kawakawa Tea metabolites in human volunteers [BOKA-T]) and 30 volunteers (15 male and 15 female) in a second study (Impact of acute Kawakawa Tea ingestion on postprandial glucose metabolism in healthy human volunteers [TOAST]) consume a hot water infusion of dried kawakawa leaves (kawakawa tea [KT]). Untargeted Liquid Chromatography-Tandem Mass spectrometry (LC-MS/MS) analyses of urine samples from BOKA-T identified 26 urinary metabolites that are significantly associated with KT consumption, confirmed by the analysis of samples from the independent TOAST study. Seven of the 26 metabolites are also detected in plasma. Thirteen of the 26 urinary compounds are provisionally identified as metabolites of specific compounds in KT, eight metabolites are identified as being derived from specific compounds in KT but without resolution of chemical structure, and five are of unknown origin.

CONCLUSIONS

Several kawakawa compounds that are also widely found in other plants are bioavailable and are modified by phase 1 and 2 metabolism.

Spatial metabolomics in head and neck tumors: a review

The joint analysis of single-cell transcriptomics, proteomics, lipidomics, metabolomics and spatial metabolomics is continually transforming our understanding of the mechanisms of metabolic reprogramming in tumor cells. Since head and neck tumor is the sixth most common tumor in the world, the study of the metabolic mechanism of its occurrence, development and prognosis is still undeveloped. In the past decade, this field has witnessed tremendous technological revolutions and considerable development that enables major breakthroughs to be made in the study of human tumor metabolism. In this review, a comprehensive comparison of traditional metabolomics and spatial metabolomics has been concluded, and the recent progress and challenges of the application of spatial metabolomics combined multi-omics in the research of metabolic reprogramming in tumors are reviewed. Furthermore, we also highlight the advances of spatial metabolomics in the study of metabolic mechanisms of head and neck tumors, and provide an outlook of its application prospects.

Quantitative 1H NMR with global spectral deconvolution approach for the determination of gamma-aminobutyric acid in Chinese yam (Dioscorea polystachya Turczaninow)

We developed a quantitative proton nuclear magnetic resonance (qNMR) with global spectral deconvolution (GSD) method to determine the gamma-aminobutyric acid content in Chinese yam with the proton signal at δ_H 2.30. Trimethylsilyl-2,2,3,3-tetradeuteropropionic acid sodium salt was set as the internal standard. The method was validated and showed admissible stability, repeatability, and precision. Compared to the traditional high-performance liquid chromatography method, this method did not involve tedious pre-treatment and expensive standard. Compared to ordinary qNMR, GSD algorithm could effectively remove the effect of noise, baseline distortions and signal overlapping. Overall, qNMR with GSD method is a rapid, simple and reliable method to quantitatively determine functional metabolites even overlapped with other compounds in herbs or foods.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Research on floral volatiles has grown substantially in the last 20 years, which has generated insights into their diversity and prevalence. These studies have paved the way for new research that explores the evolutionary origins and ecological consequences of different types of variation in floral scent, including community-level, functional, and environmentally induced variation. However, to address these types of questions, novel approaches are needed that can handle large sample sizes, provide quality control measures, and make volatile research more transparent and accessible, particularly for scientists without prior experience in this field. Drawing upon a literature review and our own experiences, we present a set of best practices for next-generation research in floral scent. We outline methods for data collection (experimental designs, methods for conducting field collections, analytical chemistry, compound identification) and data analysis (statistical analysis, database integration) that will facilitate the generation and interpretation of quality data. For the intermediate step of data processing, we created the R package bouquet, which provides a data analysis pipeline. The package contains functions that enable users to convert chromatographic peak integrations to a filtered data table that can be used in subsequent statistical analyses. This package includes default settings for filtering out non-floral compounds, including background contamination, based on our best-practice guidelines, but functions and workflows can be easily customized as necessary. Next-generation research into the ecology and evolution of floral scent has the potential to generate broadly relevant insights into how complex traits evolve, their genomic architecture, and their consequences for ecological interactions. In order to fulfill this potential, the methodology of floral scent studies needs to become more transparent and reproducible. By outlining best practices throughout the lifecycle of a project, from experimental design to statistical analysis, and providing an R package that standardizes the data processing pipeline, we provide a resource for new and seasoned researchers in this field and in adjacent fields, where high-throughput and multi-dimensional datasets are common.

Exact mass GC-MS analysis: Protocol, database, advantages and application to plant metabolic profiling

Plant metabolomics has been used widely in plant physiology, in particular to analyse metabolic responses to environmental parameters. Derivatization (via trimethylsilylation and methoximation) followed by GC-MS metabolic profiling is a major technique to quantify low molecular weight, common metabolites of primary carbon, sulphur and nitrogen metabolism. There are now excellent opportunities for new generation analyses, using high resolution, exact mass GC-MS spectrometers that are progressively becoming relatively cheap. However, exact mass GC-MS analyses for routine metabolic profiling are not common, since there is no dedicated available database. Also, exact mass GC-MS is usually dedicated to structural resolution of targeted secondary metabolites. Here, we present a curated database for exact mass metabolic profiling (made of 336 analytes, 1064 characteristic exact mass fragments) focused on molecules of primary metabolism. We show advantages of exact mass analyses, in particular to resolve isotopic patterns, localise S-containing metabolites, and avoid identification errors when analytes have common nominal mass peaks in their spectrum. We provide a practical example using leaves of different Arabidopsis ecotypes and show how exact mass GC-MS analysis can be applied to plant samples and identify metabolic profiles.

MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data

BACKGROUND

Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows.

RESULTS

Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study.

CONCLUSIONS

MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Metabolomic Studies for Metabolic Alterations Induced by Non-Steroidal Anti-Inflammatory Drugs: Mini Review

Non-steroidal anti-inflammatory drugs (NSAIDs) are Food and Drug Administration (FDA) approved antipyretic, anti-inflammatory, and analgesic drugs to mitigate pain, however it is associated with gastrointestinal injury and cardiovascular disease in some individuals. Metabolomics has the potential to understand the interaction of host and the drugs, such as NSAIDs administration. This discipline has been used by many researchers to understand the serious side effects of NSAIDs. We highlighted (1) the potential of metabolomics in understanding the pathogenesis of adverse events due to NSAIDs administration; (2) choice of metabolomics techniques, bio sample handling; (3) review of metabolomics studies in the front of NSAIDs in different biofluids and tissues; (4) pathway analysis of the data presented in the published literature. In our analysis we find tricarboxylic acid cycle (TCA), "glycine serine and threonine metabolism," "alanine, aspartate, and glutamate metabolism," and fatty acid metabolism to be altered by the NSAIDs like ibuprofen, indomethacin, naproxen, aspirin, and celecoxib. In conclusion, metabolomics allows the use of biological samples to identify useful pathways involved in disease progression, and subsequently inform a greater understanding of the disease pathogenesis. A further in-depth investigation of the associated pathways mentioned above holds the potential for drug targets for side effects mitigation.

Traffic-related microplastic particles, metals, and organic pollutants in an urban area under reconstruction

In urban environments, particularly areas under reconstruction, metals, organic pollutants (OP), and microplastics (MP), are released in large amounts due to heavy traffic. Road runoff, a major transport route for urban pollutants, contributes significantly to a deteriorated water quality in receiving waters. This study was conducted in Gothenburg, Sweden, and is unique because it simultaneously investigates the occurrence of OP, metals, and MP on roads and in stormwater from an urban area under reconstruction. Correlations between the various pollutants were also explored. The study was carried out by collecting washwater and sweepsand generated from street sweeping, road surface sampling, and flow-proportional stormwater sampling on several occasions. The liquid and solid samples were analyzed for metals, polycyclic aromatic hydrocarbons (PAH), oxy-PAH, aliphatics, aromatics, phthalates, and MP. The occurrence of OP was also analyzed with a non-target screening method of selected samples. Microplastics, i.e. plastic fragments/fibers, paint fragments, tire wear particles (TWP) and bitumen, were analyzed with a method based on density separation with sodium iodide and identification with a stereo microscope, melt-tests, and tactile identification. MP concentrations amounted to 1500 particles/L in stormwater, 51,000 particles/L in washwater, and 2.6 × 10⁶ particles/kg dw in sweepsand. In stormwater, washwater and sweepsand, MP ≥20 μm were found to be dominated by TWP (38%, 83% and 78%, respectively). The results confirm traffic as an important source to MP, OP, and metal emissions. Concentrations exceeding water and sediment quality guidelines for metals (e.g. Cu and Zn), PAH, phthalates, and aliphatic hydrocarbons in the C₁₆-C₃₅ fraction were found in most samples. The results show that the street sweeper collects large amounts of polluted materials and thereby prevents further spread of the pollutants to the receiving stormwater.

Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is common to detect thousands of features from a biological extract. Although it is impractical to collect non-chimeric MS/MS data for each in a single chromatographic run, this is generally unnecessary because most features do not correspond to unique metabolites of biological relevance. Here we show that relatively simple data-processing strategies that can be applied on the fly during acquisition of data with an Orbitrap ID-X, such as blank subtraction and well-established adduct or isotope calculations, decrease the number of features to target for MS/MS analysis by up to an order of magnitude for various types of biological matrices. We demonstrate that annotating these non-biological contaminants and redundancies in real time during data acquisition enables comprehensive MS/MS data to be acquired on each remaining feature at a single collision energy. To ensure that an appropriate collision energy is applied, we introduce a method using a series of hidden ion-trap scans in an Orbitrap ID-X to find an optimal value for each feature that can then be applied in a subsequent high-resolution Orbitrap scan. Data from 100 metabolite standards indicate that this real-time optimization of collision energies leads to more informative MS/MS patterns compared to using a single fixed collision energy alone. As a benchmark to evaluate the overall workflow, we manually annotated unique biological features by independently subjecting E. coli samples to a credentialing analysis. While credentialing led to a more rigorous reduction in feature number, on-the-fly annotation with blank subtraction on an Orbitrap ID-X did not inappropriately discard unique biological metabolites. Taken together, our results reveal that optimal fragmentation data can be obtained in a single LC/MS/MS run for >90% of the unique biological metabolites in a sample when features are annotated during acquisition and collision energies are selected by using parallel mass spectrometry detection.

Microalgal metabolites as anti-cancer/anti-oxidant agents reduce cytotoxicity of elevated silver nanoparticle levels against non-cancerous vero cells

Heavy metal pollution has become a major concern globally as it contaminates eco-system, water networks and as finely suspended particles in air. In this study, the effects of elevated silver nanoparticle (AgNPs) levels as a model system of heavy metals, in the presence of microalgal crude extracts (MCEs) at different ratios, were evaluated against the non-cancerous Vero cells, and the cancerous MCF-7 and 4T1 cells. The MCEs were developed from water (W) and ethanol (ETH) as green solvents. The AgNPs-MCEs-W at the 4:1 and 5:1 ratios (v/v) after 48 and 72 h treatment, respectively, showed the IC50 values of 83.17-95.49 and 70.79-91.20 μg/ml on Vero cells, 13.18-28.18 and 12.58-25.7 μg/ml on MCF-7; and 16.21-33.88 and 14.79-26.91 μg/ml on 4T1 cells. In comparison, the AgNPs-MCEs-ETH formulation achieved the IC50 values of 56.23-89.12 and 63.09-91.2 μg/ml on Vero cells, 10.47-19.95 and 13.48-26.61 μg/ml on MCF-7; 14.12-50.11 and 15.13-58.88 μg/ml on 4T1 cells, respectively. After 48 and 72 h treatment, the AgNPs-MCE-CHL at the 4:1 and 5:1 ratios exhibited the IC50 of 51.28-75.85 and 48.97-69.18 μg/ml on Vero cells, and higher cytotoxicity at 10.47-16.98 and 6.19-14.45 μg/ml against MCF-7 cells, and 15.84-31.62 and 12.58-24.54 μg/ml on 4T1 cells, respectively. The AgNPs-MCEs-W and ETH resulted in low apoptotic events in the Vero cells after 24 h, but very high early and late apoptotic events in the cancerous cells. The Liquid Chromatography-Mass Spectrometry-Electrospray Ionization (LC-MS-ESI) metabolite profiling of the MCEs exhibited 64 metabolites in negative ion and 56 metabolites in positive ion mode, belonging to different classes. The microalgal metabolites, principally the anti-oxidative components, could have reduced the toxicity of the AgNPs against Vero cells, whilst retaining the cytotoxicity against the cancerous cells.

Analysis of chemical deposits on tooth enamel exposed to total particulate matter from cigarette smoke and tobacco heating system 2.2 aerosol by novel GC-MS deconvolution procedures

Tobacco smoking contributes to tooth discoloration. Pigmented compounds in the smoke generated by combustion of tobacco can cause discoloration of dental hard tissues. However, aerosols from heated tobacco products cause less discoloration than cigarette smoke (CS) in vitro. The objective of the present study was to optimize a method for extracting the colored chemical compounds deposited on tooth enamel following exposure to total particulate matter (TPM) from CS or a heated tobacco product (Tobacco Heating System [THS] 2.2), analyze the extracts by gas chromatography coupled to time-of-flight mass spectrometry, and identify the key chemicals associated with tooth discoloration. Sixty bovine enamel blocks were exposed for 2 weeks to TPM from CS or THS 2.2 aerosol or to artificial saliva as a control. Brushing without toothpaste and color measurements were performed each week. Noticeable discoloration of enamel was observed following exposure to CS TPM. The discoloration following exposure to THS 2.2 aerosol TPM or artificial saliva was not distinguishable to the eye (ΔE < 3.3). Carbon disulfide was used to extract surface-deposited chemicals. Untargeted analyses were followed by partial least squares correlation against discoloration scores (R2 = 0.96). Eleven compounds had variable importance in projection scores greater than 2. Discriminant autocorrelation matrix calculation of their mass spectral information identified eight of the eleven compounds as terpenoids. None of the compounds were related to nicotine. Several of these compounds were also detected in THS 2.2 aerosol TPM-exposed enamel, but at lower levels, in line with our findings showing less discoloration. Compared with CS TPM exposure, THS 2.2 aerosol TPM exposure resulted in lower deposition of color-related compounds on enamel surface, consistent with minimal discoloration of dental enamel.

Alternative sorption filter materials effectively remove non-particulate organic pollutants from stormwater

Urban runoff contains a mixture of both particulate and non-particulate organic pollutants (OPs). Hydrophobic OPs such as higher petroleum hydrocarbons, phthalates, and polycyclic organic hydrocarbons (PAHs) are not exclusively bound to particles, but also present in runoff in colloidal and truly dissolved forms. These hydrophobic compounds can also form nano- and microsized emulsions that may carry pollutants in stormwater. Hence, it is of great importance to develop treatment technologies such as sorption filters that can remove non-particulate OPs from contaminated stormwater. A pilot plant using column bed-filters of sand as a pre-filter, in combination with granulated activated carbon, Sphagnum peat or Pinus sylvestris bark, was used to investigate the removal of non-particulate OPs from urban stormwater. Samples from the filter effluents were collected weekly; during or after rain events; and during stress tests when incoming water was spiked with contaminated sediment and petrol or diesel. All sorption filters showed efficient reduction of aliphatic diesel hydrocarbons C₁₆-C₃₅, benzene, and the PAHs phenanthrene, fluoranthene, and pyrene during most of the operation time, which was 18 months. During the stress test events, all sorption filters showed 100% reduction of PAH-16, petrol and diesel aliphatics C₅-C₃₅. All sorption filters released DOC and nanoparticles, which may explain some of the transportation of OPs through the filter beds. The recommendation is to use a combination of sand pre-filtration and all the studied sorption materials in stormwater filters in series, to achieve effective removal of different types of OPs. It is also important to improve the hydraulic conditions to obtain sufficient water flows through the filters.

Metabolomics for Investigating Physiological and Pathophysiological Processes

Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics is leading to a renewed interest in metabolism and the role that small molecule metabolites play in many biological processes. As a result, traditional views of metabolites as being simply the "bricks and mortar" of cells or just the fuel for cellular energetics are being upended. Indeed, metabolites appear to have much more varied and far more important roles as signaling molecules, immune modulators, endogenous toxins, and environmental sensors. This review explores how metabolomics is yielding important new insights into a number of important biological and physiological processes. In particular, a major focus is on illustrating how metabolomics and discoveries made through metabolomics are improving our understanding of both normal physiology and the pathophysiology of many diseases. These discoveries are yielding new insights into how metabolites influence organ function, immune function, nutrient sensing, and gut physiology. Collectively, this work is leading to a much more unified and system-wide perspective of biology wherein metabolites, proteins, and genes are understood to interact synergistically to modify the actions and functions of organelles, organs, and organisms.

The effect of anaerobic digestate derived composts on the metabolite composition and thermal behaviour of rosemary

The study reports on the effect of anaerobic digestate derived composts on the metabolite composition and thermal behaviour of rosemary (Rosmarinus officinalis L.). Plants were cultivated in semiarid soil under four different fertiliser treatments (composts of anaerobic digested cattle (C) or pig slurry (P) at 30t/ha and 60 t/ha, and two control treatments (inorganic fertiliser and no fertiliser application). Samples of leaves and stems were analysed to investigate the effect of treatment on chemical composition and thermochemical properties. Three orthogonal analytical approaches were used, namely: Fourier transform mid infrared spectroscopy (FTIR), gas chromatography/mass spectrometry (GC/MS) and thermochemical gravimetric analysis (TGA). FTIR and GC/MS showed fertiliser treatment resulted in tissue specific changes in sample metabolite composition. Fertiliser treatment was detected to change the thermogravimetric properties of the leaf samples and from inorganic and composted pig slurry digestate treatments had greater ash content and lower proportions of fixed carbon compared with samples from the unfertilised control treatment. This study provides information on how the composition of rosemary might be altered by fertiliser application in regions of poor soil, and has implications for biomass quality when rosemary is grown on semi-wild sites for the purpose of soil improvement.

Urinary Metabolomics Study of Patients with Gout Using Gas Chromatography-Mass Spectrometry

Objectives

Gout is a common type of inflammatory arthritis. The aim of this study was to detect urinary metabolic changes in gout patients which may contribute to understanding the pathological mechanism of gout and discovering potential metabolite markers.

Methods

Urine samples from 35 gout patients and 29 healthy volunteers were analyzed by gas chromatography-mass spectrometry (GC-MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed to screen differential metabolites between two groups, and the variable importance for projection (VIP) values and Student's t-test results were combined to define the significant metabolic changes caused by gout. Further, binary logistic regression analysis was performed to establish a model to distinguish gout patients from healthy people, and receiver operating characteristic (ROC) curve was made to evaluate the potential for diagnosis of gout.

Result

A total of 30 characteristic metabolites were significantly different between gout patients and controls, mainly including amino acids, carbohydrates, organic acids, and their derivatives, associated with perturbations in purine nucleotide synthesis, amino acid metabolism, purine metabolism, lipid metabolism, carbohydrate metabolism, and tricarboxylic acid cycle. Binary logistic regression and ROC curve analysis showed the combination of urate and isoxanthopterin can effectively discriminate the gout patients from controls with the area under the curve (AUC) of 0.879.

Conclusion

Thus, the urinary metabolomics study is an efficient tool for a better understanding of the metabolic changes of gout, which may support the clinical diagnosis and pathological mechanism study of gout.

An optimized band-target entropy minimization for mass spectral reconstruction of severely co-eluting and trace-level components

Gas chromatography-mass spectrometry (GC-MS) is a versatile analytical method but its data is usually complicated by the presence of severely co-eluting and trace-level components. In this work, we introduce an optimized band-target entropy minimization approach for the analysis of complex mass spectral data. This new approach enables an automated mass spectral analysis which does not require any user-dependent inputs. Moreover, the approach provides improved sensitivity and accuracy for mass spectral reconstruction of severely co-eluting and trace-level components. The accuracy of our approach is compared to the automatic mass spectral deconvolution and identification system (AMDIS) with two controlled mixtures and a sample of Eucalyptus essential oil. Our approach was able to putatively identify 130 compounds in Eucalyptus essential oil, which was 46% in excess of that identified by AMDIS. This new approach is expected to benefit GC-MS analysis of complex mixtures such as biological samples and essential oils, in which the data are often complicated by co-eluting and trace-level components. Graphical abstract ᅟ.

Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics

Mass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

Recommendations for Improving Identification and Quantification in Non-Targeted, GC-MS-Based Metabolomic Profiling of Human Plasma

The field of metabolomics as applied to human disease and health is rapidly expanding. In recent efforts of metabolomics research, greater emphasis has been placed on quality control and method validation. In this study, we report an experience with quality control and a practical application of method validation. Specifically, we sought to identify and modify steps in gas chromatography-mass spectrometry (GC-MS)-based, non-targeted metabolomic profiling of human plasma that could influence metabolite identification and quantification. Our experimental design included two studies: (1) a limiting-dilution study, which investigated the effects of dilution on analyte identification and quantification; and (2) a concentration-specific study, which compared the optimal plasma extract volume established in the first study with the volume used in the current institutional protocol. We confirmed that contaminants, concentration, repeatability and intermediate precision are major factors influencing metabolite identification and quantification. In addition, we established methods for improved metabolite identification and quantification, which were summarized to provide recommendations for experimental design of GC-MS-based non-targeted profiling of human plasma.

Practical application of in silico fragmentation based residue screening with ion mobility high-resolution mass spectrometry

RATIONALE

A screening concept for residues in complex matrices based on liquid chromatography coupled to ion mobility high-resolution mass spectrometry LC/IMS-HRMS is presented. The comprehensive four-dimensional data (chromatographic retention time, drift time, mass-to-charge and ion abundance) obtained in data-independent acquisition (DIA) mode was used for data mining. An in silico fragmenter utilizing a molecular structure database was used for suspect screening, instead of targeted screening with reference substances.

METHODS

The utilized data-independent acquisition mode relies on the MS^E concept; where two constantly alternating HRMS scans (low and high fragmentation energy) are acquired. Peak deconvolution and drift time alignment of ions from the low (precursor ion) and high (product ion) energy scan result in relatively clean product ion spectra. A bond dissociation in silico fragmenter (MassFragment) supplied with mol files of compounds of interest was used to explain the observed product ions of each extracted candidate component (chromatographic peak).

RESULTS

Two complex matrices (fish and bovine liver extract) were fortified with 98 veterinary drugs. Out of 98 screened compounds 94 could be detected with the in silico based screening approach. The high correlation among drift time and m/z value of equally charged ions was utilized for an orthogonal filtration (ranking). Such an orthogonal ion mobility based filter removes multiply charged ions (e.g. peptides and proteins from the matrix) as well as noise and artefacts. Most significantly, this filtration dramatically reduces false positive findings but hardly increases false negative findings.

CONCLUSIONS

The proposed screening approach may offer new possibilities for applications where reference compounds are hardly or not at all commercially available. Such areas may be the analysis of metabolites of drugs, pyrrolizidine alkaloids, marine toxins, derivatives of sildenafil or novel designer drugs (new psychoactive substances). Copyright © 2017 John Wiley & Sons, Ltd.

Optimizing targeted/untargeted metabolomics by automating gas chromatography/mass spectrometry workflows

New database building and MS subtraction algorithms have been developed for automated, sequential two-dimensional gas chromatography/mass spectrometry (GC-GC/MS). This paper reports the first use of a database building tool, with full mass spectrum subtraction, that does not rely on high resolution MS data. The software was used to automatically inspect GC-GC/MS data of high elevation tea from Yunnan, China, to build a database of 350 target compounds. The database was then used with spectral deconvolution to identify 285 compounds by GC/MS of the same tea. Targeted analysis of low elevation tea by GC/MS resulted in the detection of 275 compounds. Non-targeted analysis, using MS subtraction, yielded an additional eight metabolites, unique to low elevation tea.

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.

Optimizing 2D gas chromatography mass spectrometry for robust tissue, serum and urine metabolite profiling

Two-dimensional gas chromatography mass spectrometry (GCxGC-MS) is utilized to an increasing extent in biomedical metabolomics. Here, we established and adapted metabolite extraction and derivatization protocols for cell/tissue biopsy, serum and urine samples according to their individual properties. GCxGC-MS analysis revealed detection of ~600 molecular features from which 165 were characterized representing different classes such as amino acids, fatty acids, lipids, carbohydrates, nucleotides and small polar components of glycolysis and the Krebs cycle using electron impact (EI) spectrum matching and validation using external standard compounds. Advantages of two-dimensional gas chromatography based resolution were demonstrated by optimizing gradient length and separation through modulation between the first and second column, leading to a marked increase in metabolite identification due to improved separation as exemplified for lactate versus pyruvate, talopyranose versus methyl palmitate and inosine versus docosahexaenoic acid. Our results demonstrate that GCxGC-MS represents a robust metabolomics platform for discovery and targeted studies that can be used with samples derived from the clinic.

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GCxGC-MS detected ~600 features;165 represented metabolites of different classes.

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Optimizing gradient length and separation through modulation improved metabolite ID.

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improved separation of lactate/pyruvate, talopyranose/palmitate and inosine/docosahexaenoate.

Dereplication of Natural Products Using GC-TOF Mass Spectrometry: Improved Metabolite Identification by Spectral Deconvolution Ratio Analysis

Dereplication based on hyphenated techniques has been extensively applied in plant metabolomics, thereby avoiding re-isolation of known natural products. However, due to the complex nature of biological samples and their large concentration range, dereplication requires the use of chemometric tools to comprehensively extract information from the acquired data. In this work we developed a reliable GC-MS-based method for the identification of non-targeted plant metabolites by combining the Ratio Analysis of Mass Spectrometry deconvolution tool (RAMSY) with Automated Mass Spectral Deconvolution and Identification System software (AMDIS). Plants species from Solanaceae, Chrysobalanaceae and Euphorbiaceae were selected as model systems due to their molecular diversity, ethnopharmacological potential, and economical value. The samples were analyzed by GC-MS after methoximation and silylation reactions. Dereplication was initiated with the use of a factorial design of experiments to determine the best AMDIS configuration for each sample, considering linear retention indices and mass spectral data. A heuristic factor (CDF, compound detection factor) was developed and applied to the AMDIS results in order to decrease the false-positive rates. Despite the enhancement in deconvolution and peak identification, the empirical AMDIS method was not able to fully deconvolute all GC-peaks, leading to low MF values and/or missing metabolites. RAMSY was applied as a complementary deconvolution method to AMDIS to peaks exhibiting substantial overlap, resulting in recovery of low-intensity co-eluted ions. The results from this combination of optimized AMDIS with RAMSY attested to the ability of this approach as an improved dereplication method for complex biological samples such as plant extracts.

eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics

Gas chromatography coupled to mass spectrometry (GC/MS) has been a long-standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex data sets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information on metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC/MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-time-of-flight (TOF) MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign, and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available at http://CRAN.R-project.org/package=erah .

ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies

ADAP-GC is an automated computational pipeline for untargeted, GC/MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of coeluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information on compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection. In this paper, we report how these goals were achieved and compare ADAP-GC 3.0 against three other software tools including ChromaTOF, AnalyzerPro, and AMDIS that are widely used in the metabolomics community.

Metabolite profiling of Dioscorea (yam) species reveals underutilised biodiversity and renewable sources for high-value compounds

Yams (Dioscorea spp.) are a multispecies crop with production in over 50 countries generating ~50 MT of edible tubers annually. The long-term storage potential of these tubers is vital for food security in developing countries. Furthermore, many species are important sources of pharmaceutical precursors. Despite these attributes as staple food crops and sources of high-value chemicals, Dioscorea spp. remain largely neglected in comparison to other staple tuber crops of tropical agricultural systems such as cassava (Manihot esculenta) and sweet potato (Ipomoea batatas). To date, studies have focussed on the tubers or rhizomes of Dioscorea, neglecting the foliage as waste. In the present study metabolite profiling procedures, using GC-MS approaches, have been established to assess biochemical diversity across species. The robustness of the procedures was shown using material from the phylogenetic clades. The resultant data allowed separation of the genotypes into clades, species and morphological traits with a putative geographical origin. Additionally, we show the potential of foliage material as a renewable source of high-value compounds.

Metabolomics and Its Application to Acute Lung Diseases

Metabolomics is a rapidly expanding field of systems biology that is gaining significant attention in many areas of biomedical research. Also known as metabonomics, it comprises the analysis of all small molecules or metabolites that are present within an organism or a specific compartment of the body. Metabolite detection and quantification provide a valuable addition to genomics and proteomics and give unique insights into metabolic changes that occur in tangent to alterations in gene and protein activity that are associated with disease. As a novel approach to understanding disease, metabolomics provides a "snapshot" in time of all metabolites present in a biological sample such as whole blood, plasma, serum, urine, and many other specimens that may be obtained from either patients or experimental models. In this article, we review the burgeoning field of metabolomics in its application to acute lung diseases, specifically pneumonia and acute respiratory disease syndrome (ARDS). We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized environmental toxins. Recent reports have suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe disease, such as sepsis, which kills many patients each year. In addition, metabolomics may provide more detailed phenotyping of patient heterogeneity, which is needed to achieve the goal of precision medicine. However, although several experimental and clinical metabolomics studies have been conducted assessing the application of the science to acute lung diseases, only incremental progress has been made. Specifically, little is known about the metabolic phenotypes of these illnesses. These data are needed to substantiate metabolomics biomarker credentials so that clinicians can employ them for clinical decision-making and investigators can use them to design clinical trials.

From sample treatment to biomarker discovery: A tutorial for untargeted metabolomics based on GC-(EI)-Q-MS

This tutorial provides a comprehensive description of the GC-MS-based untargeted metabolomics workflow including: ethical approval requirement, sample collection and storage, equipment maintenance and setup, sample treatment, monitoring of analytical variability, data pre-processing including deconvolution by free software such as AMDIS, data processing, statistical analysis and validation, detection of outliers and biological interpretation of the results. For each stage tricks will be suggested, pitfalls will be highlighted and advice will be provided on how to get the best from this methodology and technique. In addition, a step-by-step procedure and an example of our in-house library have been included in the supplementary material to lead the user through the concepts described herein. As a case study, an interesting example from one of our experiments at CEMBIO Research Centre is described, presenting an example of the use of this ready-to use protocol for identification of a metabolite that was not previously included in Fiehn commercial target library.

A peaklet-based generic strategy for the untargeted analysis of comprehensive two-dimensional gas chromatography mass spectrometry data sets

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a well-established key technology in analytical chemistry and increasingly used in the field of untargeted metabolomics. However, automated processing of large GC×GC-MS data sets is still a major bottleneck in untargeted, large-scale metabolomics. For this reason we introduce a novel peaklet-based alignment strategy. The algorithm is capable of an untargeted deterministic alignment exploiting a density based clustering procedure within a time constrained similarity matrix. Exploiting minimal (1)D and (2)D retention time shifts between peak modulations, the alignment is done without the need for peak merging which also eliminates the need for linear or nonlinear retention time correction procedures. The approach is validated in detail using data of urine samples from a large human metabolomics study. The data was acquired by a Shimadzu GCMS-QP2010 Ultra GC×GC-qMS system and consists of 512 runs, including 312 study samples and 178 quality control sample injections, measured within a time period of 22 days. The final result table consisted of 313 analytes, each of these being detectable in at least 75% of the study samples. In summary, we present an automated, reliable and fully transparent workflow for the analysis of large GC×GC-qMS metabolomics data sets.

Analytical methods in untargeted metabolomics: state of the art in 2015

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.