Untargeted Metabolomics

High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr.

Untargeted metabolomics is a powerful approach for identifying small molecules from highly complex mixtures, such as biological tissues or environmental samples. This technology enables the relatively fast and inexpensive identification of metabolites in situations where many or most of the chemical species are unknown before the experiment begins. This situation often arises in biomedical and environmental research, as well as in the case described here, the discovery of metabolites from plants. The objective of this paper is to provide practical and technical knowledge about untargeted metabolomics using mass spectrometry as the detection method. Specifically, we focus on liquid chromatography tandem mass spectrometry (LC‐MS/MS). We provide a consolidated protocol for new users, serving as a starting point for experimental design, data collection, and data analysis. We explain the terminology and technical details in the context of real experiments and samples. In addition to general background information, step‐by‐step protocols are provided for sample preparation, liquid chromatography‐tandem mass spectrometry data collection, and data analysis, utilizing readily available and widely used software. The chosen example data set is based on plant metabolites with varying chemical properties; however, the approach is applicable to essentially any complex biological sample. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Sample preparation for LC‐MS/MSSupport Protocol 1: Preparing a ‘master mix’ sample for assessment of liquid chromatography and sensitivityBasic Protocol 2: LC‐MS/MS data collectionBasic Protocol 3: Data analysis using the software MSConvert, MZMine, and SIRIUSSupport Protocol 2: Using the MZMine batch file

Combining untargeted and targeted metabolomics to reveal the mechanisms of herb pair Anemarrhena asphodeloides Bunge and Phellodendron chinense C. K. Schneid on benign prostatic hyperplasia

Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry

microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms’ role in ecology and human health.

Untargeted and spatial-resolved metabolomics characterize serum and tissue-specific metabolic reprogramming in acute kidney injury

Identification of non-invasive biomarkers of disease progression in multiple sclerosis (MS) is critically needed for monitoring the disease progression and for effective therapeutic interventions. Urine is an attractive source for non-invasive biomarkers because it is easily obtained in the clinic. In search of a urine metabolite signature of progression in chronic experimental autoimmune encephalomyelitis (EAE), we profiled urine at the chronic stage of the disease (day 45 post immunization) by global untargeted metabolomics. Using a combination of high-throughput liquid-and-gas chromatography with mass spectrometry, we found 105 metabolites (P < 0.05) significantly altered at the chronic stage, indicating a robust alteration in the urine metabolite profile during disease. Assessment of altered metabolites against the Kyoto Encyclopedia of Genes and Genomes revealed distinct non-overlapping metabolic pathways and revealed phenylalanine-tyrosine and associated metabolism being the most impacted. Combined with previously performed plasma profiling, eight common metabolites were significantly altered in both of the biofluids. Metaboanalyst analysis of these common metabolites revealed that phenylalanine metabolism and Valine, leucine, and isoleucine biosynthetic pathways are central metabolic pathways in both bio-fluids and could be analyzed further, either for the discovery of therapeutics or biomarker development. Overall, our study suggests that urine and plasma metabolomics may contribute to the identification of a distinct metabolic fingerprint of EAE disease discriminating from the healthy control which may aid in the development of an objective non-invasive monitoring method for progressive autoimmune diseases like MS. Graphical Abstract Untargeted urinary metabolomics of a chronic mouse model of multiple sclerosis identified Phenylalanine, tyrosine & tryptophan metabolism as the significantly altered metabolic pathway. Eight common metabolites were identified when we combined urinary and plasma metabolic signature, which revealed a perturbation of Phenylalanine metabolism and valine, leucine & isoleucine metabolic pathways, involved in CNS dysfunction during diseases. The identified eight metabolic signature of urine and plasma may be of clinical relevance as potential biomarkers and guide towards the identification of specific metabolic pathways as novel drug targets.

Dementia represents a significant global health challenge, with its prevalence steadily increasing; however, research on its metabolomic dimensions remains scarce, particularly within underrepresented populations. Gaining a deeper understanding of the metabolic changes underlying the disease is essential for advancing both diagnostic and therapeutic approaches. This exploratory untargeted Liquid Chromatography-Mass Spectrometry (LC-MS) metabolomic multi-center study seeks to elucidate the metabolic alterations linked to dementia within an Egyptian population, offering valuable insights into potential biomarkers and pathways that may be unique to this region. Serum samples were collected from 150 dementia patients and 100 age- and sex-matched controls from multiple centers spanning Egypt. These samples were processed and analyzed using LC-ESI-MS/MS with the ExionLC AC-SCIEX Triple Quad 5500+ MS/MS system (Sciex) employing both positive and negative ionization modes to capture a wider range of metabolites. Untargeted metabolomics experiments were performed using Data-Dependent Acquisition (DDA) mode. Comprehensive data acquisition, peak identification, and quality control procedures were applied. Statistical analyses were conducted to determine differentially expressed metabolites, and pathway enrichment analysis was performed to assess the biological relevance of the results. A total of 253 metabolites were identified and categorized into lipids, amino acids, nucleosides, and small molecules. Principal Component Analysis (PCA) revealed distinct clustering patterns in the metabolomic profiles of dementia patients when compared to controls. Regression analysis model demonstrated significant differential abundance of various metabolite groups between the two cohorts. Pathway enrichment analysis further highlighted disruptions in pathways related to metabolism, lipid homeostasis, signaling, protein synthesis, and inflammation, underscoring the complex interactions between metabolic processes and the pathogenesis of dementia. This exploratory untargeted LC-MS metabolomic study offers valuable insights into the metabolic landscape of dementia patients within the previously underexplored Egyptian population. The metabolites and pathways identified in this study contribute to the broader understanding of dementia and provide potential biomarkers and metabolic pathways associated with the disease. Further validation focusing on specific metabolites is necessary to confirm these preliminary findings and support the development of targeted interventions for dementia in this population.

Compound identification and annotation in (untargeted) metabolomics experiments based on accurate mass require the highest possible accuracy of the mass determination. Experimental LC/TOF-MS platforms equipped with a time-to-digital converter (TDC) give the best mass estimate for those mass signals with an intensity similar to that of the lock-mass used for internal calibration. However, they systematically underestimate the mass obtained at higher signal intensity and overestimate it at low signal intensities compared to that of the lock-mass. To compensate for these effects, specific tools are required for correction and automation of accurate mass calculations from LC/MS signals. Here, we present a computational procedure for the derivation of an intensity-dependent mass correction function. The chromatographic mass signals for a set of known compounds present in a large number of samples were reconstructed over consecutive scans for each sample. It was found that the mass error is a linear function of the logarithm of the signal intensity adjusted to the associated lock-mass intensity. When applied to all mass data points, the correction function reduced the mass error for the majority of the tested compounds to ≤1 ppm over a wide range of signal intensities. The mass correction function has been implemented in a Python 2.4 script, which accepts raw data in NetCDF format as input, corrects the detected masses and returns the corrected NetCDF files for subsequent (automated) processing, such as mass signal alignment and database searching.

How different is the global proteomic and metabolic profile of mouse blastocysts generated by IVF, cultured in optimal (5% O2) or stressful (20% O2) conditions, compared to in vivo generated blastocysts? We found that in IVF-generated embryos: (i) the proteome was more sensitive to high oxygen levels than the global metabolomic profile; (ii) enzymes involved in splicing and the spliceosome are altered; (iii) numerous metabolic pathways, particularly amino acids metabolism, are altered (iv) there is activation of the integrated stress response (ISR) and downregulation of mTOR pathways. IVF culture conditions are known to affect the gene expression of embryos. However, comprehensive data on the global metabolic and proteomic changes that occur in IVF-generated embryos are unknown. Mouse embryos were generated by natural mating (in vivo control or flushed blastocyst-FB-group) or by IVF using KSOM medium and two distinct oxygen concentrations: 5% O2 (optimal) and 20% O2 (stressful). Proteomic and metabolomic analyses were performed using state-of-the-art mass spectrometry techniques in triplicate (n = 100 blastocysts per replicate), allowing for detailed profiling of protein and metabolite alterations in each group. Mouse blastocysts were collected from CD-1 and B6D2F1 strains as specified above. High-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used for proteomics, while high-performance liquid chromatography coupled with mass spectrometry (HILIC-MS) was used for metabolomics. In addition, Immunofluorescence was used to assess the activation of stress response pathways, including the ISR. Proteomic analysis revealed significant changes in protein expression in embryos cultured under 20% O2 compared to 5% O2 and in vivo embryos. Compared to in vivo embryos, IVF embryos cultured under 20% O2 exhibited 599 differentially expressed proteins, with an increase in proteins involved in oxidative stress responses, aminoacyl-tRNA synthesis, and spliceosome pathways. In contrast, IVF embryos cultured under 5% O2 showed fewer changes, with 426 differentially expressed proteins, though still reflecting significant alterations compared to in vivo embryos. These results indicate that embryos in stressful conditions (20% O2) exhibit a stronger stress response and alterations in critical pathways for protein synthesis and DNA repair. Metabolomic analysis revealed that embryos cultured under 20% O2 showed changes in branch-chained amino acid levels, and decreased levels of key metabolites of the TCA cycle and pentose phosphate pathway. Embryos cultured under 5% O2 had increased pyruvate levels, suggesting altered glycolysis. Immunofluorescence confirmed that oxidative stress markers such as GCN2, EIF2α, and ATF4 were upregulated in IVF embryos, indicating ISR activation. Overall, IVF and embryo culture have a direct impact on embryo proteomes and metabolomes affecting amino acid metabolism and stress-related pathways. N/A. Results in a murine model should be extrapolated with caution to human embryos. These findings offer valuable insights into how different IVF culture conditions, specifically oxygen levels, impact the global metabolic and proteomic profiles of embryos. These findings provide critical insights into the profound impact of IVF culture conditions, particularly oxygen levels, on the global metabolic and proteomic landscapes of embryos. By identifying key metabolic pathways disrupted by oxidative stress, we highlight the potential clinical importance of proteomic and metabolomic analyses in understanding embryo quality, improving ART, and ultimately enhancing pregnancy outcomes. The integration of metabolomic and proteomic data offers a comprehensive understanding of how oxidative stress influences cellular function. These insights have direct clinical relevance, providing a foundation for optimizing ART protocols to mitigate oxidative stress. This work was supported by grant R01 HD108166-01A1 from the National Institute of Child Health and Human Development (NICHD) to P.F.R. The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Background: Chronic inflammation contributes to functional decline in older adults, yet interventions targeting inflammatory pathways have shown inconsistent results. Metabolomics offers a promising approach to identify biological heterogeneity and uncover molecular signatures underlying differential functional trajectories. Objective: Our objective was to examine whether untargeted serum metabolomics can identify metabolic signatures associated with baseline physical function, functional trajectories, and treatment response in older adults with chronic inflammation participating in the ENRGISE trial. Methods: We performed untargeted metabolomic profiling on serum samples (n = 731) collected at baseline, 6, and 12 months from participants (mean age ≥ 70) enrolled in the ENRGISE pilot randomized trial. Participants were randomized to losartan, omega-3 supplementation, both, or placebo. Functional measures included grip strength and 400 m gait speed. Group-based trajectory modeling classified participants into functional trajectories over 12 months. Partial least squares-discriminant analysis (PLS-DA) and pathway enrichment (mummichog algorithm) were used to identify differentially abundant metabolites and perturbed pathways. Results: Baseline metabolomic profiles differed by physical function status. Participants with low grip strength showed enrichment in vitamin A metabolism pathways, while slower gait speed was associated with higher levels of prostaglandin and eicosanoid metabolites. Baseline metabolic profiles distinguished individuals who later declined versus improved in functional performance. Omega-3 supplementation, but not losartan, induced distinct changes in lipid-related pathways, including fatty acid activation, omega-3 metabolism, and prostaglandin biosynthesis, indicating that individuals responded to these interventions metabolically despite null clinical outcomes. Conclusions: Serum metabolomic signatures were associated with baseline physical function, predicted functional trajectories, and revealed pharmacologic activity of omega-3 supplementation. These findings support the use of metabolomics to uncover biological heterogeneity and inform precision geroscience strategies in aging populations.

Gut microbiota and systemic metabolites are critical for breast cancer progression and therapeutic response. This study investigated gut microbiota and metabolic profiles of breast cancer patients before and after adjuvant endocrine therapy (AET). Using 16S rRNA sequencing and untargeted metabolomics, we identified significant disruptions in microbial diversity and metabolic pathways. Alpha diversity was reduced in pre-AET patients, with partial restoration observed post-AET. Key genera, such as Bifidobacterium and Coprococcus, were enriched in pre-AET patients, whereas Proteus and Methylobacterium were enriched in post-AET patients. Metabolomic analysis revealed significant reductions in the levels of vitamin B6 metabolites in both the pre-AET and post-AET groups compared to those in the healthy control (HC) group, indicating potential nutrient deficiencies or metabolic stress. Elevated cholesterol and estrogen metabolite levels in pre-AET patients reflect dysregulated lipid and hormone metabolism, with post-AET decreases in estrogen metabolites, confirming therapeutic efficacy. Correlation analysis revealed that Klebsiella_724518 was positively correlated with estrogen and vitamin B6 metabolites, whereas Proteus, Methylobacterium, Treponema_D, and Holdemanella were negatively correlated with cholesterol. Receiver operating characteristic (ROC) curve analysis identified estriol (area under the curve [AUC] = 1.000) as a strong diagnostic biomarker for distinguishing HCs from pre-AET patients, whereas cholesterol (AUC = 0.880) and estradiol-17β (AUC = 0.870) were highly effective in monitoring the therapeutic response to AET. This study highlights the role of gut microbiota and its metabolic byproducts in breast cancer development and treatment outcomes. It also reveals promising microbial and metabolite signatures for non-invasive cancer detection, tracking progression, and monitoring treatment response.IMPORTANCEBreast cancer progression and treatment response remain challenging to predict and monitor effectively. Our findings demonstrate the dual role of the gut microbiota and its metabolic products in influencing both. Strong correlations between specific microbial taxa and key metabolites provide new mechanistic insights into the influence of gut microbes on therapeutic outcomes during endocrine therapy. Importantly, we identified high-performance biomarkers, with estriol showing perfect diagnostic accuracy (AUC = 1.000) and cholesterol effectively monitoring treatment response (AUC = 0.880), highlighting their potential for non-invasive clinical applications. This study provides a foundation for applying gut microbiome research to develop clinical tools that could improve breast cancer management.

A liquid chromatography-mass spectrometry-based metabolomics strategy to explore plant metabolic diversity.

The community ecology of microbial molecules.

WITHDRAWN: Tracking development of the superficial scald disorder and effects of treatments with diphenylamine and 1-MCP using an untargeted metabolomic approach in apple fruit