Herein, we aim to establish a straightforward and versatile reversed-phase liquid chromatography/mass spectrometry (RP-LC/MS) methodology for analyzing a wide range of polar and mid-polar metabolites utilizing a single instrument, column, and mobile phase. We present a comprehensive evaluation of three C18 columns compatible with aqueous solutions using 19 mobile phases in terms of the number of detected metabolites, chromatographic performance, and MS response. The RP-LC/MS platform utilizes the HSS T3 column with a mobile phase consisting of 0.2 % formic acid, acetonitrile, and propan-2-ol, effectively separating polar and mid-polar metabolites through various mobile phase gradients. Our developed method outperforms the conventional hydrophilic interaction liquid chromatography metabolomic method, yielding a higher number of detected metabolites and better chromatographic performance. The RP-LC/MS platform demonstrates excellent intrabatch and interbatch retention time repeatability (<0.8 %). Furthermore, the determined concentrations of metabolites show strong agreement with certified and published concentrations of metabolites in the SRM 1950 plasma sample. We successfully annotate 71 polar metabolites, 36 acylcarnitines, 23 endocannabinoids, 42 oxylipins, and 16 fatty acids in plasma, placenta, and brain samples. The developed RP-LC/MS approach represents a robust and adaptable technique for the targeted or untargeted analysis of polar and mid-polar metabolites employing a single chromatographic column and mobile phase. This is achieved through the simple modification of the gradient program and MS conditions. Consequently, this methodology offers a highly valuable tool for conducting comprehensive, large-scale metabolomic investigations on a variety of biological samples.
- Klíčová slova
- Liquid chromatography, Mass spectrometry, Metabolomics, Reversed-phase, Targeted analysis, Untargeted analysis,
- MeSH
- chromatografie s reverzní fází * metody MeSH
- hmotnostní spektrometrie * metody MeSH
- lidé MeSH
- metabolomika * metody MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.
- MeSH
- data mining * metody MeSH
- hmotnostní spektrometrie * metody MeSH
- lidé MeSH
- metabolomika * metody MeSH
- programovací jazyk * MeSH
- software * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
OBJECTIVE: Insulin-sensitizing drugs, despite their broad use against type 2 diabetes, can adversely affect bone health, and the mechanisms underlying these side effects remain largely unclear. Here, we investigated the different metabolic effects of a series of thiazolidinediones, including rosiglitazone, pioglitazone, and the second-generation compound MSDC-0602K, on human mesenchymal stem cells (MSCs). METHODS: We developed 13C subcellular metabolomic tracer analysis measuring separate mitochondrial and cytosolic metabolite pools, lipidomic network-based isotopologue models, and bioorthogonal click chemistry, to demonstrate that MSDC-0602K differentially affected bone marrow-derived MSCs (BM-MSCs) and adipose tissue-derived MSCs (AT-MSCs). In BM-MSCs, MSDC-0602K promoted osteoblastic differentiation and suppressed adipogenesis. This effect was clearly distinct from that of the earlier drugs and that on AT-MSCs. RESULTS: Fluxomic data reveal unexpected differences between this drug's effect on MSCs and provide mechanistic insight into the pharmacologic inhibition of mitochondrial pyruvate carrier 1 (MPC). Our study demonstrates that MSDC-0602K retains the capacity to inhibit MPC, akin to rosiglitazone but unlike pioglitazone, enabling the utilization of alternative metabolic pathways. Notably, MSDC-0602K exhibits a limited lipogenic potential compared to both rosiglitazone and pioglitazone, each of which employs a distinct lipogenic strategy. CONCLUSIONS: These findings indicate that the new-generation drugs do not compromise bone structure, offering a safer alternative for treating insulin resistance. Moreover, these results highlight the ability of cell compartment-specific metabolite labeling by click reactions and tracer metabolomics analysis of complex lipids to discover molecular mechanisms within the intersection of carbohydrate and lipid metabolism.
- Klíčová slova
- Adipocyte, Bone marrow, Lipid flux analysis, Mitochondrial pyruvate carrier, Tracer metabolomics,
- MeSH
- adipogeneze * účinky léků MeSH
- buněčná diferenciace účinky léků MeSH
- hypoglykemika * farmakologie MeSH
- kultivované buňky MeSH
- lidé MeSH
- metabolomika metody MeSH
- mezenchymální kmenové buňky účinky léků metabolismus MeSH
- osteogeneze * účinky léků MeSH
- pioglitazon MeSH
- rosiglitazon farmakologie MeSH
- thiazolidindiony * farmakologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- hypoglykemika * MeSH
- pioglitazon MeSH
- rosiglitazon MeSH
- thiazolidindiony * MeSH
BACKGROUND AND OBJECTIVE: Metabolomic interaction networks provide critical insights into the dynamic relationships between metabolites and their regulatory mechanisms. This study introduces MInfer, a novel computational framework that integrates outputs from MetaboAnalyst, a widely used metabolomic analysis tool, with Jacobian analysis to enhance the derivation and interpretation of these networks. METHODS: MInfer combines the comprehensive data processing capabilities of MetaboAnalyst with the mathematical modeling power of Jacobian analysis. This framework was applied to various metabolomic datasets, employing advanced statistical tests to construct interaction networks and identify key metabolic pathways. RESULTS: The application of MInfer revealed significant metabolic pathways and potential regulatory mechanisms across multiple datasets. The framework demonstrated high precision, sensitivity, and specificity in identifying interactions, enabling robust network interpretations. CONCLUSIONS: MInfer enhances the interpretation of metabolomic data by providing detailed interaction networks and uncovering key regulatory insights. This tool holds significant potential for advancing the study of complex biological systems.
- Klíčová slova
- Dynamic relationships, Metabolite interactions, Metabolomic data analysis, Systems biology,
- MeSH
- algoritmy MeSH
- lidé MeSH
- metabolické sítě a dráhy * MeSH
- metabolomika * metody MeSH
- software * MeSH
- výpočetní biologie metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Pollen is a cornerstone of life for plants. Its durability, adaptability, and complex design are the key factors to successful plant reproduction, genetic diversity, and the maintenance of ecosystems. A detailed study of its chemical composition is important to understand the mechanism of pollen-pollinator interactions, pollination processes, and allergic reactions. In this study, a multimodal approach involving Fourier transform infrared spectrometry (FTIR), direct mass spectrometry with an atmospheric solids analysis probe (ASAP), matrix-assisted laser desorption/ionization (MALDI) and ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) was applied for metabolite profiling. ATR-FTIR provided an initial overview of the present metabolite classes. Phenylpropanoid, lipidic, and carbohydrate structures were revealed. The hydrophobic outer layer of pollen was characterized in detail by ASAP-MS profiling, and esters, phytosterols, and terpenoids were observed. Diacyl- and triacylglycerols and carbohydrate structures were identified in MALDI-MS spectra. The MALDI-MS imaging of lipids proved to be helpful during the microscopic characterization of pollen species in their mixture. Polyphenol profiling and the quantification of important secondary metabolites were performed by UHPLC-MS in context with pollen coloration and their antioxidant and antimicrobial properties. The obtained results revealed significant chemical differences among Magnoliophyta and Pinophyta pollen. Additionally, some variations within Magnoliophyta species were observed. The obtained metabolomics data were utilized for pollen differentiation at the taxonomic scale and provided valuable information in relation to pollen interactions during reproduction and its related applications.
- Klíčová slova
- Magnoliophyta, Pinophyta, atmospheric solids analysis probe, infrared spectrometry, mass spectrometry, matrix-assisted laser desorption/ionization, metabolite profiling, metabolomics, pollen, secondary metabolite, taxonomic differentiation, ultra-high-performance liquid chromatography,
- MeSH
- metabolom * MeSH
- metabolomika * metody MeSH
- pyl * metabolismus chemie MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice MeSH
- spektroskopie infračervená s Fourierovou transformací MeSH
- vysokoúčinná kapalinová chromatografie MeSH
- Publikační typ
- časopisecké články MeSH
Autism spectrum disorder (ASD) has been associated with disruptions in tryptophan (TRP) metabolism, affecting the production of key neuroactive metabolites. Investigating these metabolic pathways could yield valuable biomarkers for ASD severity and progression. We included 44 children with ASD and 44 healthy children, members of the same family. The average age in the ASD group was 10.7 years, while the average age in the control group was 9.4 years. Urinary tryptophan metabolites were quantified via liquid chromatography-mass spectrometry operating multiple reaction monitoring (MRM). Urinary creatinine was analyzed on an Advia 2400 analyzer using the Jaffe reaction. Statistical comparisons were made between ASD subgroups based on CARS scores. Our findings indicate that children with ASD have higher TRP concentrations (19.94 vs. 16.91; p = 0.04) than their siblings. Kynurenine (KYN) was found at higher levels in children with ASD compared to children in the control group (82.34 vs. 71.20; p = 0.86), although this difference was not statistically significant. The ASD group showed trends of higher KYN/TRP ratios and altered TRP/ indole-3-acetic acid (IAA) and TRP/5-hydroxyindoleacetic acid (5-HIAA) ratios, correlating with symptom severity. Although the numbers of the two groups were different, our findings suggest that mild and severe illnesses involve separate mechanisms. However, further comprehensive studies are needed to validate these ratios as diagnostic tools for ASD.
- Klíčová slova
- CARS, autism spectrum disorder, kynurenine, tryptophan,
- MeSH
- biologické markery moč MeSH
- dítě MeSH
- kynurenin moč MeSH
- kyselina hydroxyindoloctová moč MeSH
- lidé MeSH
- metabolom * MeSH
- metabolomika * metody MeSH
- mladiství MeSH
- poruchy autistického spektra * moč metabolismus MeSH
- studie případů a kontrol MeSH
- tryptofan * moč metabolismus MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mladiství MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- biologické markery MeSH
- kynurenin MeSH
- kyselina hydroxyindoloctová MeSH
- tryptofan * MeSH
Plant specialized metabolites play key roles in diverse physiological processes and ecological interactions. Identifying structurally novel metabolites, as well as discovering known compounds in new species, is often crucial for answering broader biological questions. The Piper genus (Piperaceae family) is known for its special phytochemistry and has been extensively studied over the past decades. Here, we investigated the alkaloid diversity of Piper fimbriulatum, a myrmecophytic plant native to Central America, using a metabolomics workflow that combines untargeted LC-MS/MS analysis with a range of recently developed computational tools. Specifically, we leverage open MS/MS spectral libraries and metabolomics data repositories for metabolite annotation, guiding isolation efforts toward structurally new compounds (i.e., dereplication). As a result, we identified several alkaloids belonging to five different classes and isolated one novel seco-benzylisoquinoline alkaloid featuring a linear quaternary amine moiety which we named fimbriulatumine. Notably, many of the identified compounds were never reported in Piperaceae plants. Our findings expand the known alkaloid diversity of this family and demonstrate the value of revisiting well-studied plant families using state-of-the-art computational metabolomics workflows to uncover previously overlooked chemodiversity. To contextualize our findings within a broader biological context, we employed a workflow for automated mining of literature reports of the identified alkaloid scaffolds and mapped the results onto the angiosperm tree of life. By doing so, we highlight the remarkable alkaloid diversity within the Piper genus and provide a framework for generating hypotheses on the biosynthetic evolution of these specialized metabolites. Many of the computational tools and data resources used in this study remain underutilized within the plant science community. This manuscript demonstrates their potential through a practical application and aims to promote broader accessibility to untargeted metabolomics approaches.
- Klíčová slova
- Piper fimbriulatum, Piperaceae, Wikidata, alkaloids, angiosperms, computational metabolomics, mass spectrometry, technical advance,
- MeSH
- alkaloidy * metabolismus chemie MeSH
- chromatografie kapalinová MeSH
- metabolomika * metody MeSH
- myrmekofyty MeSH
- Piper * metabolismus chemie MeSH
- tandemová hmotnostní spektrometrie MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- alkaloidy * MeSH
Various spectrometric methods can be used to conduct metabolomics studies. Nuclear magnetic resonance (NMR) or mass spectrometry (MS) coupled with separation methods, such as liquid or gas chromatography (LC and GC, respectively), are the most commonly used techniques. Once the raw data have been obtained, the real challenge lies in the bioinformatics required to conduct: (i) data processing (including preprocessing, normalization, and quality control); (ii) statistical analysis for comparative studies (such as univariate and multivariate analyses, including PCA or PLS-DA/OPLS-DA); (iii) annotation of the metabolites of interest; and (iv) interpretation of the relationships between key metabolites and the relevant phenotypes or scientific questions to be addressed. Here, we will introduce and detail a stepwise protocol for use of the Workflow4Metabolomics platform (W4M), which provides user-friendly access to workflows for processing of LC-MS, GC-MS, and NMR data. Those modular and extensible workflows are composed of existing standalone components (e.g., XCMS and CAMERA packages) as well as a suite of complementary W4M-implemented modules. This tool suite is accessible worldwide through a web interface and is hosted on UseGalaxy France. The extensible Virtual Research Environment (VRE) provided offers pre-configured workflows for metabolomics communities (platforms, end users, etc.), as well as possibilities for sharing among users. By providing a consistent ecosystem of tools and workflows through Galaxy, W4M makes it possible to process MS and NMR data from hundreds of samples using an ordinary personal computer, after step-by-step workflow optimization. © 2025 Wiley Periodicals LLC. Basic Protocol 1: W4M account creation, working history preparation, and data upload Support Protocol 1: How to prepare an NMR zip file Support Protocol 2: How to convert MS data from proprietary format to open format Support Protocol 3: How to get help with W4M (IFB forum) and how to report a problem on the GitHub repository Basic Protocol 2: LC-MS data processing Alternate Protocol 1: GC-MS data processing Alternate Protocol 2: NMR data processing Basic Protocol 3: Statistical analysis Basic Protocol 4: Annotation of metabolites from LC-MS data Alternate Protocol 3: Annotation of metabolites from NMR data.
- Klíčová slova
- GC–MS, LC–MS, NMR, annotation, chemometrics, statistics, untargeted metabolomics,
- MeSH
- hmotnostní spektrometrie * metody MeSH
- lidé MeSH
- magnetická rezonanční spektroskopie metody MeSH
- metabolomika * metody MeSH
- plynová chromatografie s hmotnostně spektrometrickou detekcí metody MeSH
- průběh práce MeSH
- software * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
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.
- Klíčová slova
- Bioinert system, Human plasma, Mass spectrometry, Metabolomics, Ultrahigh-performance liquid chromatography,
- MeSH
- ionty chemie MeSH
- lidé MeSH
- limita detekce MeSH
- lineární modely MeSH
- metabolomika * metody MeSH
- tandemová hmotnostní spektrometrie metody MeSH
- vysokoúčinná kapalinová chromatografie metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- ionty MeSH
Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.
