Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management

A Story of Falsification and Authentication: Authenticity Control of Phytopharmaceuticals and Herbal Remedies

Natural remedies and phytopharmaceuticals are gaining popularity among both consumers and patients who seek a healthier and more environmentally friendly lifestyle. Healthcare professionals are also increasingly incorporating phytopharmaceuticals into therapeutic practices, recognizing their potential benefits for overall health and well-being. This trend is further driven by a growing distrust of synthetic compounds and dissatisfaction with conventional medical treatments, prompting many patients to explore natural alternatives. As a result, the demand for phytopharmaceuticals, essential oils, and nutraceuticals continues to rise steadily. However, falsified natural remedies can pose serious risks to human health, making quality assurance a critical priority. Sophisticated and reliable analytical strategies are essential to ensure product authenticity and to protect consumers and patients from potentially harmful adulteration. This review provides an overview of documented cases of adulteration and the corresponding countermeasures developed to detect such incidents. Commonly used analytical techniques for authenticity verification of phytopharmaceuticals, essential oils, and other natural remedies are presented. The review discusses the application of spectroscopic methods, mass spectrometry, chromatographic separation techniques, and DNA-based profiling, with reference to current scientific literature. In addition, selected chemometric tools are introduced to illustrate how meaningful information can be extracted from complex analytical data. The potential of multidimensional data analysis, combining complementary insights from various analytical platforms, is also explored. Finally, key considerations for generating and analysing reliable data are highlighted.

Metabolic insights into hypoxia adaptation in adolescent athletes at different altitudes: a cross-sectional study

Athletes use hypoxic training methods to enhance their performance under altitude conditions. Comparative studies involving populations from low (500-2,000 m) and moderate (2,000-3,000 m) altitudes offer an opportunity to understand the mechanisms behind adaptations to hypoxia. The present study combined data from metabolomics analysis based on gas- and liquid-chromatography mass spectrometry (GC-MS and LC-MS) to compare plasma profiles from 80 adolescent athletes at moderate- or low altitudes. 161 metabolites were identified, including 84 elevated and 77 decreased in moderate-altitude adolescents compared to their low-altitude counterparts. Pathway analysis revealed that metabolites related to carbohydrates, amino acids, and lipid metabolism differed between groups. Lipid metabolism was significantly altered in moderate-altitude athletes, including pathways such as linolenic and linoleic acid, sphingolipid, and arachidonic acid, as well as processes involving the transfer of acetyl groups into mitochondria and fatty acid biosynthesis. Biomarker analysis looking for signatures of chronic adaptation to moderate altitude identified glycerol and 5-oxoproline metabolites amongst the variables with the strongest sensitivity and specificity. This study demonstrates differences in metabolic profiles between moderate- and low-altitude populations and highlights the potential of these differential metabolites and associated metabolic pathways to provide new insights into the mechanisms of adaptation to moderate altitude.

Endogenous dysregulated energy and amino acid metabolism delay scaffold-guided large volume bone regeneration in a diabetic rat model with Leptin receptor deficiency

Scaffold-guided bone regeneration (SGBR) offers a promising solution for treating large-volume bone defects. However, its efficacy in compromised healing environments, such as those associated with metabolic conditions like Type 2 Diabetes (T2D), remains poorly understood. This study evaluates the potential of 3D-printed polycaprolactone (PCL) scaffolds for large-volume bone regeneration in preclinical models simulating T2D-induced metabolic challenges. Our results reveal that scaffolds alone are insufficient to overcome the metabolic barriers to effective bone regeneration. Metabolomic analysis of regenerating tissue identified significant disruptions in key metabolic pathways involved in energy production and amino acid synthesis in T2D rats compared to controls. Notably, aconitic acid, ornithine, and glycine levels were elevated in non-diabetic conditions, whereas phosphoenolpyruvate was markedly increased under T2D conditions. Secondary harmonic generation (SHG) imaging further demonstrated impaired collagen organization within T2D regenerating tissue, correlating with disrupted collagen synthesis critical for bone matrix formation. In vitro, the exogenous supplementation of alpha-ketoglutarate (α-KG)-a crucial citric acid cycle intermediate-enhanced mineralized tissue formation in human adipose-derived mesenchymal stem cells (hAdMSCs) from T2D donors, achieving levels superior to non-T2D cells. These findings underscore the metabolic underpinnings of impaired bone regeneration in T2D. Optimized 3D printed scaffolds alone do not counterbalance the impaired regeneration in T2D. Here we highlight a therapeutic potential of metabolic supplementation to optimize SGBR outcomes. This study provides a critical foundation for advancing translational research and developing regenerative therapies tailored to high-risk metabolic disease populations. STATEMENT OF SIGNIFICANCE: Scaffold-guided bone regeneration (SGBR) holds great promise for addressing large bone defects, but its efficacy in metabolically challenged conditions like Type 2 Diabetes (T2D) remains limited. This study uses a metabolomics-driven approach to reveal how metabolic dysregulation in T2D, including disruptions in energy and amino acid pathways, impairs collagen organization and extracellular matrix (ECM) formation-critical for successful bone healing. By identifying α-ketoglutarate (α-KG) as a potential supplement to restore metabolic balance, this work offers novel insights into enhancing scaffold performance under compromised conditions. These findings provide a foundation for integrating bioactive compounds into scaffold designs, advancing personalized strategies in regenerative medicine, and addressing a critical gap in bone defect treatment for diabetic patients.

Integrating metagenomics and metabolomics to study the gut microbiome and host relationships in sports across different energy systems

The gut microbiome plays a critical role in modulating host metabolism, influencing energy production, nutrient utilization, and overall physiological adaptation. In athletes, these microbial functions may be further specialized to meet the unique metabolic demands of different sports disciplines. This study explored the role of the gut microbiome in modulating host metabolism among Colombian athletes by comparing elite weightlifters (n = 16) and cyclists (n = 13) through integrative omics analysis. Fecal and plasma samples collected one month before an international event underwent metagenomic, metabolomic, and lipidomic profiling. Metagenomic analysis revealed significant microbial pathways, including L-arginine biosynthesis III and fatty acid biosynthesis initiation. Key metabolic pathways, such as phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and folate biosynthesis, were enriched in both athlete groups. Plasma metabolomics and lipidomics revealed distinct metabolic profiles and a separation between athlete types through multivariate models, with lipid-related pathways such as lipid droplet formation and glycolipid synthesis driving the differences. Notably, elevated carnitine, amino acid, and glycerolipid levels in weightlifters suggest energy system-specific metabolic adaptations. These findings underscore the complex relationship between the gut microbiota composition and metabolic responses tailored to athletic demands, laying the groundwork for personalized strategies to optimize performance. This research highlights the potential for targeted modulation of the gut microbiota as a basis for tailored interventions to support specific energy demands in athletic disciplines.

Automated QA/QC reporting for non-targeted analysis: a demonstration of "INTERPRET NTA" with de facto water reuse data

The US Environmental Protection Agency (EPA) uses non-targeted analysis (NTA) to characterize potential risks associated with environmental pollutants and anthropogenic materials. NTA is used throughout EPA's Office of Research and Development (ORD) to support the needs of states, tribes, EPA regions, EPA program offices, and other outside partners. NTA methods are complex and conducted via myriad instrumental platforms and software products. Comprehensive standards do not yet exist to guide NTA quality assurance/quality control (QA/QC) procedures. Furthermore, no single software tool meets EPA's needs for QA/QC review and documentation. Considering these factors, ORD developed "INTERPRET NTA" (Interface for Processing, Reviewing, and Translating NTA data) to support liquid chromatography (LC) high-resolution mass spectrometry (HRMS) NTA experiments. For purposes of NTA QA/QC, INTERPRET NTA (1) calculates data quality statistics related to accuracy, precision, and reproducibility; (2) produces interactive visualizations to facilitate quality threshold optimization; and (3) outputs comprehensive documentation for inclusion in official reports and research publications. INTERPRET NTA has additional functionality to facilitate rapid chemical identification and risk-based prioritization. The current article describes only the QA/QC elements of INTERPRET NTA's MS1 workflow, which are demonstrated using published data from a de facto water reuse study. INTERPRET NTA, in its current form, exists primarily to meet the needs of EPA and its partners, but a public release is planned. Workflows, terminology, and outputs of INTERPRET NTA provide a focal point for necessary discussions on the harmonization of NTA QA/QC practices.

Community Needs and Proposed Solutions for a Broadly Applicable Standard/QC Mixture for High-Resolution Mass Spectrometry-Based Non-Targeted Analysis

Non-targeted analysis (NTA) using high-resolution mass spectrometry (HRMS) is a global chemical screening approach that generates information-rich data which can be used to detect and identify unknown chemicals. NTA is a powerful approach which is increasingly being used for a variety of sample types, research fields, and goals. However, there are challenges associated with accurate assessments of data quality and method performance, comparability across laboratories/instruments/methods, and communication of results/confidence. A standard mixture containing a sufficient number and diversity of chemicals would help address these needs, but is not yet commercially available. Thus, we conducted a survey of 146 NTA researchers to examine desired requirements for the broad fields, studies, and goals where NTA can be applied. We also compare this feedback to previously published in-house standard mixtures, which, we argue, are models for a standard that can be adjusted to fit the NTA community's needs and possibly commercialized. Reversed-phase liquid chromatography HRMS is one of the most common methods used for NTA; therefore, this survey is focused on characteristics necessary for these types of methods. We intend this information to communicate the need for an interdisciplinary NTA standard mixture, the importance of implementing standards, and to lower the barriers for chemical vendor standard mixture development and distribution.

Deeper insights into the stability of oxylipins in human plasma across multiple freeze-thaw cycles and storage conditions

Oxylipins are signaling lipids derived from the oxidation of polyunsaturated fatty acids (PUFAs). In lipidomic studies, human plasma may be subjected to various storage conditions and freeze-thaw cycles, which may impact the analysis of these compounds. In this study, we used liquid chromatography coupled with mass spectrometry (LC-MS) to examine the influence of up to five freeze-thaw cycles (FTCs) on free and total (mostly esterified) oxylipins in human plasma and the influence of temperature and storage duration (4 °C for up to 120 h and -20 °C and -80 °C for 1-98 days) in the presence or absence of butylated hydroxytoluene (BHT) on extracted oxylipins stored in LC-MS amber vials. In fresh plasma subjected to several FTCs, approximately 48 % of the detected free oxylipins were significantly altered by the third cycle, with increases in cytochrome P450 (CYP450) and lipoxygenase (LOX)-derived compounds and reductions in trihydroxylated oxylipins. In contrast, multiple FTCs did not significantly alter esterified oxylipins. At 4 °C, the extracted oxylipins did not change significantly for up to 120 h (5 days). Oxylipin levels remained stable for 98 days at -80 °C but decreased by 98 days at -20 °C. The antioxidant activity of butylated hydroxytoluene (BHT) did not influence oxylipin stability at 4 °C for 120 h or at -80 °C for 98 days, but it reduced oxylipin degradation at -20 °C at 98 days. Conversely, prostaglandin F2α (PGF2α) exhibited substantial increases at -20 °C and -80 °C, independent of BHT. This study demonstrates that (i) unlike free oxylipins, the esterified oxylipin pool remains stable following repeated FTCs, (ii) extracted oxylipins are stable at 4 °C for up to 120 h and at -80 °C for up to 98 days, but not at -20 °C for 98 days, and (iii) BHT may minimize oxylipin degradation of sample extracts stored at -20 °C. This study provides a framework for measuring oxylipins under various freeze-thaw and storage conditions.

Critical assessment of quenching and extraction/sample preparation methods for microorganisms in metabolomics

BACKGROUND

Advancements in the research of intracellular metabolome have the potential to affect our understanding of biological processes. The applications and findings of intracellular metabolome analysis are useful in understanding cellular pathways, microbial interactions, and the detection of secreted metabolites and their functions.

AIM OF REVIEW

This work focuses on the analysis of intracellular metabolomes in microorganisms. The techniques used for analyzing the intracellular metabolomes including metabolomics approaches such as mass spectrometry, nuclear magnetic resonance, liquid chromatography, and gas chromatography are discussed.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Challenges such as sample preparation, data analysis, metabolite extraction, sample storage and collection, and processing techniques were investigated, as they can highlight emerging technologies and advancements in metabolome analysis, future applications in drug discovery, personalized medicine, systems biology, and the limitations and challenges in studying the metabolome of microorganisms.

Human Untargeted Metabolomics in High-Throughput Gut Microbiome Research: Ethanol vs Methanol

Untargeted metabolomics is frequently performed on human fecal samples in conjunction with sequencing to unravel the gut microbiome functionality. As sample collection efforts are rapidly expanding, with individuals often collecting specimens at home, metabolomics experiments should adapt to accommodate the safety and needs of bulk off-site collections and improve high throughput. Here, we show that a 95% ethanol, safe to be shipped and handled, extraction part of the Matrix Method pipeline recovers comparable amounts of metabolites as a validated 50% methanol extraction, preserving metabolic profile differences between investigated subjects. Additionally, we show that the fecal metabolome remains relatively stable when stored in 95% ethanol for up to 1 week at room temperature. Finally, we suggest a metabolomics data analysis workflow based on robust centered log ratio transformation, which removes the variance introduced by possible different sample weights and concentrations, allowing for reliable and integration-ready untargeted metabolomics experiments in gut microbiome research.

A guide to reverse metabolomics-a framework for big data discovery strategy

Untargeted metabolomics is evolving into a field of big data science. There is a growing interest within the metabolomics community in mining tandem mass spectrometry (MS/MS)-based data from public repositories. In traditional untargeted metabolomics, samples to address a predefined question are collected and liquid chromatography with MS/MS data are generated. We then identify metabolites associated with a phenotype (for example, disease versus healthy) and elucidate or validate their structural details (for example, molecular formula, structural classification, substructure or complete structural annotation or identification). In reverse metabolomics, we start with MS/MS spectra for known or unknown molecules. These spectra are used as search terms to search public data repositories to discover phenotype-relevant information such as organ/biofluid distribution, disease condition, intervention status (for example, pre- and postintervention), organisms (for example, mammals versus others), geography and any other biologically relevant associations. Here we guide the reader through a four-part process: (1) obtaining the MS/MS spectra of interest (Universal Spectrum Identifier) and (2) Mass Spectrometry Search Tool searches to find the files associated with the MS/MS that are in available databases, (3) using the Reanalysis Data User Interface framework to link the files with their metadata and (4) validating the observations. Parts 1-3 could take from hours to days depending on the method used for collecting MS/MS spectra. For example, we use MS/MS spectra from three small molecules: phenylalanine-cholic acid (a microbially conjugated bile acid), phenylalanine-C4:0 and histidine-C4:0 (two N-acyl amides). We leverage the Global Natural Products Social Molecular Networking-based framework to explore the microbial producers of these molecules and their associations with health conditions and organ distributions in humans and rodents.

Integrated transcriptome and metabolome analyses reveal the differentially expressed metabolites and genes involved in lipid in olive fruits

Background

Olive (Olea europaea L.) oil is well-known commercial product worldwide for its nutritional and therapeutic properties. The molecular mechanisms underlying lipid variations in different olive cultivars remain unclear.

Methods

To investigate the molecular mechanism involved in lipid synthesis and metabolism, untargeted metabolome and RNA-Seq analyses were performed based on two varieties of olive fruits, i.e., Kalinjot (JZ) with low oil content and Coratina (KLD) with high oil content.

Results

Totally, 38 lipid compounds of 375 differentially accumulated metabolites (DAMs) were identified in JZ and KLD fruits, with 24 metabolites showing higher contents in KLD than those in JZ. Integrated transcriptome and metabolome analyses identified 48 differentially expressed genes (DEGs) associated with six lipid DAMs from JZ and KLD fruits. The contents of decanoic acid, sphinganine, and leukotriene D4 in KLD fruits were 2.33, 1.91, and 1.53 times greater than that of JZ fruits, respectively. In particular, two BCCP, one ACC, seven KAR, one EAR, one FATA and one SPT genes were observed involving to the content and quality of lipids in olive fruits. These DEGs were associated with the pathways of fatty acid biosynthesis, arachidonic acid metabolism, and limonene degradation. This study provides a strong theoretical and experimental foundation for further revealing the molecular mechanisms regulating lipid synthesis and metabolism in different olive cultivars.

Communicating with Stakeholders to Identify High-Impact Research Directions for Non-Targeted Analysis

Non-targeted analysis (NTA) using high-resolution mass spectrometry without defined chemical targets has the potential to expand and improve chemical monitoring in many fields. Despite rapid advancements within the research community, NTA methods and data remain underutilized by many potential beneficiaries. To better understand barriers toward widespread adoption, the Best Practices for Non-Targeted Analysis (BP4NTA) working group conducted focus group meetings and follow-up surveys with scientists (n = 61) from various sectors (e.g., drinking water utilities, epidemiologists, n = 9) where NTA is expected to provide future value. Meeting participants included producers and end-users of NTA data with a wide range of familiarity with NTA methods and outputs. Discussions focused on identifying specific barriers that limit adoption and on setting NTA product development priorities. Stated priorities fell into four major categories: 1) education and training materials; 2) QA/QC frameworks and study design guidance; 3) accessible compound databases and libraries; and 4) NTA data linkages with chemical fate and toxicity information. Based on participant feedback, this manuscript proposes research directions, such as standardization of training materials, that BP4NTA and other institutions can pursue to expand NTA use in various application scenarios and decision contexts.

Microbiota-Gut-Brain Axis: Mass-Spectrometry-Based Metabolomics in the Study of Microbiome Mediators-Stress Relationship

The microbiota-gut-brain axis is a complex bidirectional communication system that involves multiple interactions between intestinal functions and the emotional and cognitive centers of the brain. These interactions are mediated by molecules (metabolites) produced in both areas, which are considered mediators. To shed light on this complex mechanism, which is still largely unknown, a reliable characterization of the mediators is essential. Here, we review the most studied metabolites in the microbiota-gut-brain axis, the metabolic pathways in which they are involved, and their functions. This review focuses mainly on the use of mass spectrometry for their determination, reporting on the latest analytical methods, their limitations, and future perspectives. The analytical strategy for the qualitative-quantitative characterization of mediators must be reliable in order to elucidate the molecular mechanisms underlying the influence of the above-mentioned axis on stress resilience or vulnerability.

miR-142 deficit in T cells during blast crisis promotes chronic myeloid leukemia immune escape

We reported that an acquired miR-142 deficit transforms chronic phase (CP) chronic myeloid leukemia (CML) leukemic stem cells (LSCs) into blast crisis (BC) LSCs. Given the role of miR-142 in the development and activity of the immune system, we postulated that this deficit also promotes LSC immune escape. Herein, we report on IL-6-driven miR-142 deficit occurring in T cells during BC transformation. In CML murine models, miR-142 deficit impairs thymic differentiation of lymphoid-primed multipotent progenitors (LMPP) into T cells and prevents T cells' metabolic reprogramming, thereby leading to loss of T cells and leukemia immune escape. Correcting miR-142 deficit with a miR-142 mimic compound (M-miR-142), alone or in combination with immune checkpoint antibodies, restores T cell number and immune activity, leading to LSC elimination and prolonged survival of BC CML murine and patient-derived xenograft models. These observations may open new therapeutic opportunities for BC CML and other myeloid malignancies.

Forensic metabolomics: Tracing cyanide-induced metabolic changes in fatalities

Accurate detection of cyanide exposure is crucial, particularly in forensic science. However, cyanide's high volatility and potential biochemical conversions in biological samples pose challenges for direct detection, complicating the determination of cause of death. Identifying alternative cyanide metabolites as markers may mitigate false negatives and positives, extending the detection window in poisoning cases. This study aimed to evaluate metabolic changes induced by cyanide exposure in forensic cases using a multi-platform approach, including metabolomics and lipidomics analyses via liquid and gas chromatography coupled with high-resolution mass spectrometry. Results demonstrated clear discrimination between cyanide-exposed and control groups through OPLS-DA models. A total of 92 altered metabolites were identified in cyanide-exposed individuals compared to controls. Significant changes in metabolites primarily included glycerophospholipids (30.7 %), glycerolipids (14 %), fatty acyls (12.9 %), sphingolipids (8.0 %), amino acids and analogs (8.0 %), among others. Cyanide intoxication disrupted multiple metabolic pathways, including mitochondrial β-oxidation, acylcarnitine accumulation, a shift towards gluconeogenesis in amino acid metabolism, and ammonia homeostasis disturbance, affecting both ammonia recycling and the urea cycle. These pathways are essential for cellular energy production. The altered metabolic profiles provide insight into cyanide poisoning pathways, potentially aiding the development of new forensic diagnostic strategies. The area under the receiver operating characteristic curve was used to assess each model's predictive value. Findings suggest that metabolites such as phosphate and 3-hydroxybutyric acid could serve as diagnostic biomarkers in lethal cyanide poisoning cases. Future studies must evaluate these potential biomarkers' effectiveness in different fatal victim cohorts and validate the suggested panel through a targeted approach.

LC-MS-Based Global Metabolic Profiles of Alternative Blood Specimens Collected by Microsampling

Blood microsampling (BμS) has recently emerged as an interesting approach in the analysis of endogenous metabolites but also in metabolomics applications. Their non-invasive way of use and the simplified logistics that they offer renders these technologies highly attractive in large-scale studies, especially the novel quantitative microsampling approaches such as VAMs or qDBS. Objectives: Herein, we investigate the potential of BµS devices compared to the conventional plasma samples used in global untargeted mass spectrometry-based metabolomics of blood. Methods: Two novel quantitative devices, namely, Mitra, Capitainer, and the widely used Whatman cards, were selected for comparison with plasma. Venous blood was collected from 10 healthy, overnight-fasted individuals and loaded on the devices; plasma was also collected from the same venous blood. An extraction solvent optimization study was first performed on the three devices before the main study, which compared the global metabolic profiles of the four extracts (three BµS devices and plasma). Analysis was conducted using reverse phase LC-TOF MS in positive mode. Results: BµS devices, especially Mitra and Capitainer, provided equal or even superior information on the metabolic profiling of human blood based on the number and intensity of features and the precision and stability of some annotated metabolites compared to plasma. Despite their rich metabolic profiles, BµS did not capture metabolites associated with biological differentiation of sexes. Conclusions: Overall, our results suggest that a more in-depth investigation of the acquired information is needed for each specific application, as a metabolite-dependent trend was obvious.

Bioinert UHPLC system improves sensitivity and peak shapes for ionic metabolites

The analysis of ionic compounds by liquid chromatography is challenging due to the interaction of analytes with the metal surface of the instrument and the column, leading to poor peak shape and decreased sensitivity. The use of bioinert materials in the chromatographic system minimizes these unrequired interactions. In this work, the ultrahigh-performance liquid chromatography (UHPLC) with bioinert components was connected to a high-resolution mass spectrometer to develop a method for untargeted metabolomic analysis. 81 standards of metabolites were used for the development and optimization of the method. In comparison to the conventional chromatographic system, the application of bioinert technology resulted in significantly improved peak shapes and increased sensitivity, especially for metabolites containing phosphate groups. The calibration curves were constructed for the evaluation of the method performance, showing a wide dynamic range, low limit of detection, and linear regression coefficients higher than 0.99 for all standards. The optimized method was applied to the analysis of NIST SRM 1950 human plasma, which allowed the detection of 156 metabolites and polar lipids based on the combination of mass accuracy in the full-scan mass spectra in both polarity modes, characteristic fragment ions in MS/MS, and logical chromatographic behavior leading to the high confidence level of annotation/identification. We have demonstrated an improvement in the peak shapes and sensitivity of ionic metabolites using bioinert technology, which indicates the potential for the analysis of other ionic compounds, e.g., molecules containing phosphate groups.

Comparative analysis of anticoagulant influence on PMI estimation based on porcine blood metabolomics profile measured using GC-MS

Introduction

Accurate post-mortem interval (PMI) estimation is essential in forensic investigations. Although various methods for PMI determination have been developed, only an approximate estimation is still achievable, and an accurate PMI indication is still challenging. Therefore, in this study, we employed gas chromatography-mass spectrometry (GC-MS)-based metabolomics to assess post-mortem changes in porcine blood samples collected with and without the addition of anticoagulant (EDTA). Our study aimed to identify metabolites dependent on the EDTA addition and time (taking into account the biodiversity of the studied organism) and those that are time-dependent but resistant to the addition of an anticoagulant.

Methods

The experiment was performed on blood samples collected from 16 animals (domestic pig, breed: Polish Large White), 8 with and 8 without EDTA addition. The moment of death (time 0) and 15 additional time points (from 3 to 168 h after death) were selected to examine changes in metabolites' levels in specific time intervals. We employed linear mixed models to study the relationship between metabolite intensities, time and presence of EDTA while accounting for the effect of individual pigs.

Results and Discussion

We confirmed that the intensity of 16 metabolites (mainly amino acids) significantly depends on PMI and the presence of EDTA. However, the intensity of the ideal biomarker(s) for PMI estimation should be determined only by the time after death and not by external factors such as the presence of the anticoagulant agent. Thus, we identified 41 metabolites with time-dependent intensities that were not susceptible to EDTA presence. Finally, we assessed the performance of these metabolites in a PMI predictive model. Citraconic acid yielded one of the lowest errors in general PMI estimation (32.82 h). Moreover, similar errors were observed for samples with and without EDTA (33.32 h and 32.34 h, respectively). Although the small sample size and information leak in predictive modelling prevent drawing definite conclusions, citraconic acid shows potential as a robust PMI estimator.

An optimization protocol of the volatile organic compounds analysis in earwax samples for untargeted volatilomics

Recent studies have highlighted the potential of earwax or cerumen, a non-conventional biomatrix, in volatilomics research as a valuable matrix for disease biomarker discovery. Despite that, there are still gaps in using non-conventional biomatrices in metabolomics research. In this sense, this study aimed to elucidate the main analytical factors involved in the extraction and analysis of volatile organic compounds (VOCs) in cerumen by headspace/gas chromatography-mass spectrometry (HS/GC-MS) using Design of Experiments (DoE) approaches. Furthermore, we present a repeatability study for the proposed method as a quality control process for cerumenomic assays. By applying factorial designs, it was possible to determine that the sample mass, splitless injector sampling time, headspace extraction time, headspace extraction temperature, injection volume, and vial volume were significant factors for the cerumen VOC analysis by HS/GC-MS. Throughout univariate and multivariate statistical approaches, we demonstrate that different analytical conditions lead to distinct chemical profiling of a sample. The most suitable analytical condition was determined after the optimization steps, and the proposed method's repeatability was evaluated by the metabolites coefficient variation (CV) calculation. Seventy-one earwax VOCs reached a CV considered adequate for untargeted metabolomics studies via GC-MS. In summary, this study describes a protocol for analysis optimization of a non-conventional biomatrix and also reports a quality control process in untargeted volatilomics assays using earwax. Our findings shed light on the potential of using earwax in volatolomic studies and establish analytical criteria to ensure quality in cerumenomic assays.

Quality Assurance in Metabolomics and Metabolic Profiling

Metabolic profiling (untargeted metabolomics) aims for a global unbiased analysis of metabolites in a cell or biological system. It remains a highly useful research tool used across various analytical platforms. Incremental improvements across multiple steps in the analytical process may have large consequences for the end quality of the data. Thus, this chapter concentrates on which aspects of quality assurance can be implemented by a lab in the (pre-)analytical stages of the analysis to improve the overall end quality of their data. The scope of this chapter is limited to liquid-chromatography-mass spectrometry (LC-MS)-based profiling, which is one of the most widely utilized platforms, although the general principles are applicable to all metabolomics experiments.

Characterization of differences in volatile compounds and metabolites of six varieties of potato with different processing properties

Potato is the fourth-most important food crop around the world, and most of the potatoes are used for foodstuffs and starch products. The aim of this paper is to identify the volatile compounds and metabolites in potatoes with different processing properties. The results showed large differences of volatile and metabolite compounds such as 2,4-Heptadienal and rhoifolin in potatoes and indicated the potential regulations between volatile compounds and metabolites. Moreover, the differences in volatile and metabolite compounds were compared between fresh eating and processing type potatoes. Compared to process type potatoes, fresh eating potatoes contained a higher proportion of aldehyde and alcohol compounds, but being lower in hydrocarbon, furan, and ketone compounds. Moreover, the different expressed metabolites were involved in the metabolism of amino acids, flavone and flavanol biosynthesis, and tryptophan metabolism. The Random forest showed that the fresh eating and processing type potatoes could be distinguished by the content of amino acids and phenols.

Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS analysis on two different MS platforms

Quality control (QC) samples are commonly used in metabolomics approaches for three main reasons: (i) the initial conditioning of the column; (ii) the correction of analytical drift especially between batches; and (iii) the evaluation of measurement precision. In practice, there are several ways to prepare and conserve QC samples. The most common in untargeted metabolomics is to pool samples after or before extraction, in order to obtain pooled QC samples accounting, respectively, for analytical variance or for both analytical and sample preparation variances. In this study, focusing on untargeted analysis of tea (Camellia sinensis) leaves, we compared three ways of preparing pooled QC samples (two usual and one unusual QC sample preparations) and their efficiency to improve data quality in terms of inter-batch correction, measurement precision, and VIP candidates selection on datasets obtained using two mass spectrometry (MS) technologies (Orbitrap and time of flight (QToF)). We also investigated the effect of data processing modalities, based on the different QC preparations, on data loss and on the global structure of the datasets. Generally, our results show that usual QC sample preparation leads to comparable datasets quality in terms of precision and dispersion on both MS instruments. They also show that QC preparation is crucial for VIP selection; in fact, up to 54% of biomarkers candidates were specific of the QC preparation type used for data processing.

Quality Control and Validation Issues in LC-MS-Based Metabolomics

Metabolic profiling performed using untargeted metabolomics of different, complex biological samples aims to apply agnostic/holistic, hypothesis-free, analysis of the small molecules that are present in the analyzed sample. This approach has been the center of major investments and dedicated efforts from the research community for many years. However, limitations and challenges remain, particularly with regard to the validation and the quality of the obtained results. This has led to increasing community engagement, with the formation of think tanks, the establishment of working groups, and the many seminars on quality control (QC) in metabolomics. Here we describe a quality control (QC) protocol used to monitor LC-MS-based metabolomics analysis. A key target is the monitoring of analytical precision. This methodology is described for the analysis of urine but can be applied to different biological matrices, such as various biofluids, cell, and tissue extracts.

CE-MS-Based Clinical Metabolomics of Human Plasma

Capillary electrophoresis coupled to mass spectrometry (CE-MS) has emerged as a powerful analytical technique with significant implications for clinical research and diagnostics. The integration of information from CE and MS strengthens confidence in the identification of compounds present in clinical samples. The ability of CE to separate molecules based on their electrophoretic mobility coupled to MS enables the accurate identification and quantification of analytes, even in complex biological matrices such as human plasma.Here, we present a detailed protocol for an untargeted metabolomics study using CE-MS and its application in a study on human plasma from patients suffering Long COVID syndrome. The protocol ranges from sample preparation to biological interpretation, detailing a workflow enabling the analysis of cationic and anionic compounds, metabolite identification, and data processing.

Fuel for thought: targeting metabolism in lung cancer

For over a century, we have appreciated that the biochemical processes through which micro- and macronutrients are anabolized and catabolized-collectively referred to as "cellular metabolism"-are reprogrammed in malignancies. Cancer cells in lung tumors rewire pathways of nutrient acquisition and metabolism to meet the bioenergetic demands for unchecked proliferation. Advances in precision medicine have ushered in routine genotyping of patient lung tumors, enabling a deeper understanding of the contribution of altered metabolism to tumor biology and patient outcomes. This paradigm shift in thoracic oncology has spawned a new enthusiasm for dissecting oncogenotype-specific metabolic phenotypes and creates opportunity for selective targeting of essential tumor metabolic pathways. In this review, we discuss metabolic states across histologic and molecular subtypes of lung cancers and the additional changes in tumor metabolic pathways that occur during acquired therapeutic resistance. We summarize the clinical investigation of metabolism-specific therapies, addressing successes and limitations to guide the evaluation of these novel strategies in the clinic. Beyond changes in tumor metabolism, we also highlight how non-cellular autonomous processes merit particular consideration when manipulating metabolic processes systemically, such as efforts to disentangle how lung tumor cells influence immunometabolism. As the future of metabolic therapeutics hinges on use of models that faithfully recapitulate metabolic rewiring in lung cancer, we also discuss best practices for harmonizing workflows to capture patient specimens for translational metabolic analyses.

Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study

AIMS/HYPOTHESIS

Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes.

METHODS

As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively.

RESULTS

In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes.

CONCLUSIONS/INTERPRETATION

Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions.

Interlaboratory comparison of standardised metabolomics and lipidomics analyses in human and rodent blood using the MxP® Quant 500 kit

Metabolomics and lipidomics are pivotal in understanding phenotypic variations beyond genomics. However, quantification and comparability of mass spectrometry (MS)-derived data are challenging. Standardised assays can enhance data comparability, enabling applications in multi-center epidemiological and clinical studies. Here we evaluated the performance and reproducibility of the MxP® Quant 500 kit across 14 laboratories. The kit allows quantification of 634 different metabolites from 26 compound classes using triple quadrupole MS. Each laboratory analysed twelve samples, including human plasma and serum, lipaemic plasma, NIST SRM 1950, and mouse and rat plasma, in triplicates. 505 out of the 634 metabolites were measurable above the limit of detection in all laboratories, while eight metabolites were undetectable in our study. Out of the 505 metabolites, 412 were observed in both human and rodent samples. Overall, the kit exhibited high reproducibility with a median coefficient of variation (CV) of 14.3 %. CVs in NIST SRM 1950 reference plasma were below 25 % and 10 % for 494 and 138 metabolites, respectively. To facilitate further inspection of reproducibility for any compound, we provide detailed results from the in-depth evaluation of reproducibility across concentration ranges using Deming regression. Interlaboratory reproducibility was similar across sample types, with some species-, matrix-, and phenotype-specific differences due to variations in concentration ranges. Comparisons with previous studies on the performance of MS-based kits (including the AbsoluteIDQ p180 and the Lipidyzer) revealed good concordance of reproducibility results and measured absolute concentrations in NIST SRM 1950 for most metabolites, making the MxP® Quant 500 kit a relevant tool to apply metabolomics and lipidomics in multi-center studies.

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors

INTRODUCTION

Human metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications.

OBJECTIVES

This study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements.

METHODS

An interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR).

RESULTS

Different laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques.

CONCLUSION

This collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.

Evaluating protocols for reproducible targeted metabolomics by NMR

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes.

Comparison analysis of bioactive metabolites in soybean, pea, mung bean, and common beans: reveal the potential variations of their antioxidant property

This study showed the significantly differences of basic nutrients and metabolite compounds in nine types of beans involved in soybean, mung bean, pea, and common beans. The metabolomics results showed that serval metabolites such as histidine, proline, 3-alanine, and myricetin which could be used to identify different beans. The random forest model showed that amino acid and fatty acid could be used as special indexes to distinguish different types of beans in practice. The different expressed metabolites among different types of beans were involved in various pathways including alanine, aspartate and glutamate metabolism, arginine and proline metabolism, and purine metabolism. The antioxidant activity was significantly different among different types of beans, and the contents of amino acid, coumarin, and polyphenol contributed the antioxidant activities of beans. Together, these results will provide a comprehensive understanding of metabolites in different types of beans and theoretical guideline for the future application of beans.

A Reproducibility Crisis for Clinical Metabolomics Studies

Cancer is a leading cause of world-wide death and a major subject of clinical studies focused on the identification of new diagnostic tools. An in-depth meta-analysis of 244 clinical metabolomics studies of human serum samples highlights a reproducibility crisis. A total of 2,206 unique metabolites were reported as statistically significant across the 244 studies, but 72% (1,582) of these metabolites were identified by only one study. Further analysis shows a random disparate disagreement in reported directions of metabolite concentration changes when detected by multiple studies. Statistical models revealed that 1,867 of the 2,206 metabolites (85%) are simply statistical noise. Only 3 to 12% of these metabolites reach the threshold of statistical significance for a specific cancer type. Our findings demonstrate the absence of a detectable metabolic response to cancer and provide evidence of a serious need by the metabolomics community to establish widely accepted best practices to improve future outcomes.

Detailed lipid investigation of edible seaweeds by photochemical derivatization and untargeted lipidomics

Seaweeds are macrophytic algae that have been gaining interest as alternative healthy foods, renewable drug sources, and climate change mitigation agents. In terms of their nutritional value, seaweeds are renowned for their high content of biologically active polyunsaturated fatty acids. However, little is known about the regiochemistry-the geometry and position of carbon-carbon double bonds-of free and conjugated fatty acids in seaweeds. In the present work, a detailed characterization of the seaweed lipidome was achieved based on untargeted HRMS-based analysis and lipid derivatization with a photochemical aza-Paternò-Büchi reaction. A triple-data processing strategy was carried out to achieve high structural detail on the seaweed lipidome, i.e., (i) a first data processing workflow with all samples for aligning peak and statistical analysis that led to the definition of lipid sum compositions (e.g., phosphatidylglycerol (PG) 34:1), (ii) a second data processing workflow in which the samples of each seaweed were processed separately to annotate molecular lipids with known fatty acyl isomerism (e.g., PG 16:0_18:1), and (iii) the annotation of lipid regioisomers following MS/MS annotation of the lipid derivatives obtained following the aza-Paternò-Büchi reaction (e.g., PG 16:0_18:1 ω-9). Once the platform was set up, the lipid extracts from 8 seaweed species from different seaweed families were characterized, describing over 900 different lipid species, and information on the regiochemistry of carbon-carbon double bonds uncovered unknown peculiarities of seaweeds belonging to different families. The overall analytical approach helped to fill a gap in the knowledge of the nutritional composition of seaweeds.

Highly reliable LC-MS lipidomics database for efficient human plasma profiling based on NIST SRM 1950

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS)-based methods have become the gold standard methodology for the comprehensive profiling of the human plasma lipidome. However, both the complexity of lipid chemistry and LC-HRMS-associated data pose challenges to the characterization of this biological matrix. In accordance with the current consensus of quality requirements for LC-HRMS lipidomics data, we aimed to characterize the NIST® Standard Reference Material for Human Plasma (SRM 1950) using an LC-ESI(+/-)-MS method compatible with high-throughput lipidome profiling. We generated a highly curated lipid database with increased coverage, quality, and consistency, including additional quality assurance procedures involving adduct formation, within-method m/z evaluation, retention behavior of species within lipid chain isomers, and expert-driven resolution of isomeric and isobaric interferences. As a proof-of-concept, we showed the utility of our in-house LC-MS lipidomic database -consisting of 592 lipid entries- for the fast, comprehensive, and reliable lipidomic profiling of the human plasma from healthy human volunteers. We are confident that the implementation of this robust resource and methodology will have a significant impact by reducing data redundancy and the current delays and bottlenecks in untargeted plasma lipidomic studies.

Differential serum metabolites in patients with pregnancy-associated venous thromboembolism analyzed using GC-MS/LC-MS untargeted metabolomics

Untargeted metabolomics can be used for the comprehensive analysis of metabolite profiles in biological samples without preset targets, making them particularly suitable for exploring metabolic characteristics and potential mechanisms in complex diseases. Therefore, in this study, we employed gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) techniques to analyze the serum metabolic characteristics of patients with pregnancy-associated venous thromboembolism (PA-VTE). In this study, 11 pregnant women with VTE and 11 healthy pregnant women were included in the experimental and control groups, respectively. Using GC-MS, we identified 325 metabolites, with the highest proportion being organic oxygen compounds. Using LC-MS, we identified 3104 metabolites, with the highest proportion being acylcarnitine. The results revealed significant differences in the levels of lipids, organic compounds, and other metabolites between patients compared to healthy pregnant women. Pathways such as pyrimidine metabolism, linoleic acid metabolism, and mineral absorption differed between patients with PA-VTE and controls. Furthermore, we identified biomarkers associated with metabolic processes, such as fatty acids and amino acids (2-hydroxyhexanedioic acid, hexadecenal, palmitoylethanolamide, glycerol-1-phosphate, and N-acetyl-beta-D-glucosamine). These findings revealed the metabolic characteristics of PA-VTE and provided important clues for further research on its pathophysiological mechanisms. Our findings may contribute to the development of new diagnostic markers and support early diagnosis and treatment of PA-VTE.

Alterations of Ceramides, Acylcarnitines, GlyceroLPLs, and Amines in NSCLC Tissues

Abnormal lipid metabolism plays an important role in cancer development. In this study, nontargeted lipidomic study on 230 tissue specimens from 79 nonsmall cell lung cancer (NSCLC) patients was conducted using ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). Downregulation of sphingosine and medium-long-chain ceramides and short-medium-chain acylcarnitine, upregulation of long-chain acylcarnitine C20:0, and enhanced histamine methylation were revealed in NSCLC tissues. Compared with paired noncancerous tissues, adenocarcinoma (AC) tissues had significantly decreased levels of sphingosine, medium-long-chain ceramides (Cer d18:1/12:0 and Cer d16:1/14:0, Cer d18:0/16:0, Cer d18:1/16:0, Cer d18:2/16:0, Cer d18:2/18:0), short-medium-chain (C2-C16) acylcarnitines, LPC 20:0 and LPC 22:1, and significantly increased levels of the long-chain acylcarnitine C20:0, LPC 16:0, LPC P-16:0, LPC 20:1, LPC 20:2, glyceroPC, LPE 16:0, and LPE 18:2. In squamous cell carcinoma (SCC) tissues, sphingosine, Cer d18:2/16:0 and Cer d18:2/18:0, and short-medium-chain acylcarnitines had significantly lower levels, while long-chain acylcarnitines (C20:0, and C22:0 or C22:0 M), LPC 20:1, LPC 20:2, and N1,N12-diacetylspermine had significantly higher levels compared to controls. In AC and SCC tissues, the levels of LPG 18:0, LPG 18:1, and LPS 18:1 were significantly decreased, while the levels of ceramide-1-phosphate (C1P) d18:0/3:0 or LPE P-16:0, N1-acetylspermidine, and 1-methylhistamine were significantly increased than controls. Furthermore, an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model based on a 4-lipid panel was established, showing good discrimination ability between cancerous and noncancerous tissues.

Reference Materials for Improving Reliability of Multiomics Profiling

High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications, offering a more comprehensive understanding of biological processes and diseases. Omics reference materials play a pivotal role in ensuring the accuracy, reliability, and comparability of laboratory measurements and analyses. However, the current application of omics reference materials has revealed several issues, including inappropriate selection and underutilization, leading to inconsistencies across laboratories. This review aims to address these concerns by emphasizing the importance of well-characterized reference materials at each level of omics, encompassing (epi-)genomics, transcriptomics, proteomics, and metabolomics. By summarizing their characteristics, advantages, and limitations along with appropriate performance metrics pertinent to study purposes, we provide an overview of how omics reference materials can enhance data quality and data integration, thus fostering robust scientific investigations with omics technologies.

Plant Metabolomics: The Future of Anticancer Drug Discovery

Drug development from medicinal plants constitutes an important strategy for finding natural anticancer therapies. While several plant secondary metabolites with potential antitumor activities have been identified, well-defined mechanisms of action remained uncovered. In fact, studies of medicinal plants have often focused on the genome, transcriptome, and proteome, dismissing the relevance of the metabolome for discovering effective plant-based drugs. Metabolomics has gained huge interest in cancer research as it facilitates the identification of potential anticancer metabolites and uncovers the metabolomic alterations that occur in cancer cells in response to treatment. This holds great promise for investigating the mode of action of target metabolites. Although metabolomics has made significant contributions to drug discovery, research in this area is still ongoing. In this review, we emphasize the significance of plant metabolomics in anticancer research, which continues to be a potential technique for the development of anticancer drugs in spite of all the challenges encountered. As well, we provide insights into the essential elements required for performing effective metabolomics analyses.

Validation of two LCHRMS methods for large-scale untargeted metabolomics of serum samples: Strategy to establish method fitness-for-purpose

Untargeted metabolomics by LCHRMS is a powerful tool to enhance our knowledge of pathophysiological processes. Whereas validation of a bioanalytical method is customary in most analytical chemistry fields, it is rarely performed for untargeted metabolomics. This study aimed to establish and validate an analytical platform for a long-term, clinical metabolomics study. Sample preparation was performed with an automated liquid handler and four analytical methods were developed and evaluated. The validation study spanned three batches with twelve runs using individual serum samples and various quality control samples. Data was acquired with untargeted acquisition and only metabolites identified at level 1 were evaluated. Validation parameters were set to evaluate key performance metrics relevant for the intended application: reproducibility, repeatability, stability, and identification selectivity, emphasizing dataset intrinsic variance. Concordance of semi-quantitative results between methods was evaluated to identify potential bias. Spearman rank correlation coefficients (rs) were calculated from individual serum samples. Of the four methods tested, two were selected for validation. A total of 47 and 55 metabolites (RPLC-ESI+- and HILIC-ESI--HRMS, respectively) met specified validation criteria. Quality assurance involved system suitability testing, sample release, run release, and batch release. The median repeatability and within-run reproducibility as coefficient of variation% for metabolites that passed validation on RPLC-ESI+- and HILIC-ESI--HRMS were 4.5 and 4.6, and 1.5 and 3.8, respectively. Metabolites that passed validation on RPLC-ESI+-HRMS had a median D-ratio of 1.91, and 89 % showed good signal intensity after ten-fold dilution. The corresponding numbers for metabolites with the HILIC-ESI--HRMS method was 1.45 and 45 %, respectively. The rs median ({range}) for metabolites that passed validation on RPLC-ESI+- was 0.93 (N = 9 {0.69-0.98}) and on HILIC-ESI--HRMS was 0.93 (N = 22 {0.55-1.00}). The validated methods proved fit-for-purpose and the laboratory thus demonstrated its capability to produce reliable results for a large-scale, untargeted metabolomics study. This validation not only bolsters the reliability of the assays but also significantly enhances the impact and credibility of the hypotheses generated from the studies. Therefore, this validation study serves as a benchmark in the documentation of untargeted metabolomics, potentially guiding future endeavors in the field.

Metabolomics and cellular altered pathways in cancer biology: A review

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Communicating Mass Spectrometry Quality Information in mzQC with Python, R, and Java

Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intralaboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organization's Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionalities, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries in a workflow for extracting, analyzing, and visualizing QC metrics from different sources. Additionally, we show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).

Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics

Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105-106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.

Tidy-Direct-to-MS: An Open-Source Data-Processing Pipeline for Direct Mass Spectrometry-Based Metabolomics Experiments

Direct-to-Mass Spectrometry and ambient ionization techniques can be used for biochemical fingerprinting in a fast way. Data processing is typically accomplished with vendor-provided software tools. Here, a novel, open-source functionality, entitled Tidy-Direct-to-MS, was developed for data processing of direct-to-MS data sets. It allows for fast and user-friendly processing using different modules for optional sample position detection and separation, mass-to-charge ratio drift detection and correction, consensus spectra calculation, and bracketing across sample positions as well as feature abundance calculation. The tool also provides functionality for the automated comparison of different sets of parameters, thereby assisting the user in the complex task of finding an optimal combination to maximize the total number of detected features while also checking for the detection of user-provided reference features. In addition, Tidy-Direct-to-MS has the capability for data quality review and subsequent data analysis, thereby simplifying the workflow of untargeted ambient MS-based metabolomics studies. Tidy-Direct-to-MS is implemented in the Python programming language as part of the TidyMS library and can thus be easily extended. Capabilities of Tidy-Direct-to-MS are showcased in a data set acquired in a marine metabolomics study reported in MetaboLights (MTBLS1198) using a transmission mode Direct Analysis in Real Time-Mass Spectrometry (TM-DART-MS)-based method.

Reinforcing the Evidence of Mitochondrial Dysfunction in Long COVID Patients Using a Multiplatform Mass Spectrometry-Based Metabolomics Approach

Despite the recent and increasing knowledge surrounding COVID-19 infection, the underlying mechanisms of the persistence of symptoms for a long time after the acute infection are still not completely understood. Here, a multiplatform mass spectrometry-based approach was used for metabolomic and lipidomic profiling of human plasma samples from Long COVID patients (n = 40) to reveal mitochondrial dysfunction when compared with individuals fully recovered from acute mild COVID-19 (n = 40). Untargeted metabolomic analysis using CE-ESI(+/-)-TOF-MS and GC-Q-MS was performed. Additionally, a lipidomic analysis using LC-ESI(+/-)-QTOF-MS based on an in-house library revealed 447 lipid species identified with a high confidence annotation level. The integration of complementary analytical platforms has allowed a comprehensive metabolic and lipidomic characterization of plasma alterations in Long COVID disease that found 46 relevant metabolites which allowed to discriminate between Long COVID and fully recovered patients. We report specific metabolites altered in Long COVID, mainly related to a decrease in the amino acid metabolism and ceramide plasma levels and an increase in the tricarboxylic acid (TCA) cycle, reinforcing the evidence of an impaired mitochondrial function. The most relevant alterations shown in this study will help to better understand the insights of Long COVID syndrome by providing a deeper knowledge of the metabolomic basis of the pathology.

Polar metabolomics using trichloroacetic acid extraction and porous graphitic carbon stationary phase

INTRODUCTION

Accurately identifying and quantifying polar metabolites using untargeted metabolomics has proven challenging in comparison to mid to non-polar metabolites. Hydrophilic interaction chromatography and gas chromatography-mass spectrometry are predominantly used to target polar metabolites.

OBJECTIVES

This study aims to demonstrate a simple one-step extraction combined with liquid chromatography-mass spectrometry (LC-MS) that reliably retains polar metabolites.

METHODS

The method involves a MilliQ + 10% trichloroacetic acid extraction from 6 healthy individuals serum, combined with porous graphitic carbon liquid chromatography-mass spectrometry (LC-MS). The coefficient of variation (CV) assessed retention reliability of polar metabolites with logP as low as - 9. QreSS (Quantification, Retention, and System Suitability) internal standards determined the method's consistency and recovery efficiency.

RESULTS

The method demonstrated reliable retention (CV < 0.30) of polar metabolites within a logP range of - 9.1 to 5.6. QreSS internal standards confirmed consistent performance (CV < 0.16) and effective recovery (70-130%) of polar to mid-polar metabolites. Quality control dilution series demonstrated that ~ 80% of annotated metabolites could be accurately quantified (Pearson's correlation coefficient > 0.80) within their concentration range. Repeatability was demonstrated through clustering of repeated extractions from a single sample.

CONCLUSION

This LC-MS method is better suited to covering the polar segment of the metabolome than current methods, offering a reliable and efficient approach for accurate quantification of polar metabolites in untargeted metabolomics.

Mass spectrometry-based metabolomics for the investigation of antibiotic-bacterial interactions

With the development of analytical technologies especially mass spectrometry, metabolomics is becoming increasingly hot in the field of studying antibiotic-bacterial interactions. On the one hand, metabolomics can reveal metabolic perturbations in bacteria in the presence of antibiotics and expose metabolic mechanisms. On the other hand, through in-depth analysis of bacterial metabolic profiles, biomarkers and bioactive secondary metabolites with great potential as drug precursors can be discovered. This review focuses on the experimental workflow of bacterial metabolomics and its application to study the interaction between bacteria and antibiotics. Metabolomics improves the understanding of antibiotic lethality, reveals metabolic perturbations in antibiotic-resistant bacteria, guides the diagnosis and antibiotic treatment of infectious diseases, and aids in the exploration of antibacterial metabolites in nature. Furthermore, current limitations and directions for future developments in this area are discussed.

Recent Advancement in NMR Based Plant Metabolomics: Techniques, Tools, and Analytical Approaches

Plant metabolomics, a rapidly advancing field within plant biology, is dedicated to comprehensively exploring the intricate array of small molecules in plant systems. This entails precisely gathering comprehensive chemical data, detecting numerous metabolites, and ensuring accurate molecular identification. Nuclear magnetic resonance (NMR) spectroscopy, with its detailed chemical insights, is crucial in obtaining metabolite profiles. Its widespread application spans various research disciplines, aiding in comprehending chemical reactions, kinetics, and molecule characterization. Biotechnological advancements have further expanded NMR's utility in metabolomics, particularly in identifying disease biomarkers across diverse fields such as agriculture, medicine, and pharmacology. This review covers the stages of NMR-based metabolomics, including historical aspects and limitations, with sample preparation, data acquisition, spectral processing, analysis, and their application parts.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Soil metabolomics: Deciphering underground metabolic webs in terrestrial ecosystems

Soil metabolomics is an emerging approach for profiling diverse small molecule metabolites, i.e., metabolomes, in the soil. Soil metabolites, including fatty acids, amino acids, lipids, organic acids, sugars, and volatile organic compounds, often contain essential nutrients such as nitrogen, phosphorus, and sulfur and are directly linked to soil biogeochemical cycles driven by soil microorganisms. This paper presents an overview of methods for analyzing soil metabolites and the state-of-the-art of soil metabolomics in relation to soil nutrient cycling. We describe important applications of metabolomics in studying soil carbon cycling and sequestration, and the response of soil organic pools to changing environmental conditions. This includes using metabolomics to provide new insights into the close relationships between soil microbiome and metabolome, as well as responses of soil metabolome to plant and environmental stresses such as soil contamination. We also highlight the advantage of using soil metabolomics to study the biogeochemical cycles of elements and suggest that future research needs to better understand factors driving soil function and health.

Production of Polyphenolic Natural Products by Bract-Derived Tissue Cultures of Three Medicinal Tilia spp.: A Comparative Untargeted Metabolomics Study

Medicinal plant tissue cultures are potential sources of bioactive compounds. In this study, we report the chemical characterization of the callus cultures of three medicinal Tilia spp. (Tilia cordata, Tilia vulgaris and Tilia tomentosa), along with the comparison to bracts and flowers of the same species. Our aim was to show that calli of Tilia spp. are good alternatives to the calli of T. americana for the production of polyphenols and are better sources of a subset of polyphenolic metabolites, compared to the original organs. Calli were initiated from young bracts and grown on woody plant medium containing 1 mg L-1 2,4-D and 0.1 mg L-1 BAP. For chemical characterization, a quality-controlled untargeted metabolomics approach and the quantification of several bioactive compounds was performed with the use of LC-ESI-MS/MS. While bracts and flowers contained flavonoid glycosides (astragalin, isoquercitrin) as major polyphenols, calli of all species contained catechins, coumarins (fraxin, esculin and scopoletin) and flavane aglyca. T. tomentosa calli contained 5397 µg g DW-1 catechin, 201 µg g DW-1 esculin, 218 µg g DW-1 taxifolin and 273 µg g DW-1 eriodictyol, while calli from other species contained lower amounts. T. cordata and T. tomentosa flowers were rich in isoquercitrin, containing 8134 and 6385 µg g DW-1, respectively. The currently tested species contained many of the bioactive metabolites described from T. americana. The production of catechin was shown to be comparable to the most efficient tissue cultures reported. Flowers and bracts contained flavonoid glycosides, including tiliroside, resembling bioactive fractions of T. americana. In addition, untargeted metabolomics has shown fingerprint-like differences among species, highlighting possible chemotaxonomic and quality control applications, especially for bracts.

Comparative characterization of the infant gut microbiome and their maternal lineage by a multi-omics approach

The human gut microbiome establishes and matures during infancy, and dysregulation at this stage may lead to pathologies later in life. We conducted a multi-omics study comprising three generations of family members to investigate the early development of the gut microbiota. Fecal samples from 200 individuals, including infants (0-12 months old; 55% females, 45% males) and their respective mothers and grandmothers, were analyzed using two independent metabolomics platforms and metagenomics. For metabolomics, gas chromatography and capillary electrophoresis coupled to mass spectrometry were applied. For metagenomics, both 16S rRNA gene and shotgun sequencing were performed. Here we show that infants greatly vary from their elders in fecal microbiota populations, function, and metabolome. Infants have a less diverse microbiota than adults and present differences in several metabolite classes, such as short- and branched-chain fatty acids, which are associated with shifts in bacterial populations. These findings provide innovative biochemical insights into the shaping of the gut microbiome within the same generational line that could be beneficial in improving childhood health outcomes.