bims-mebolo Biomed News
on Metabolomics
Issue of 2026–06–07
forty-five papers selected by
Daniel Méndez Rodríguez, Vbi-Ugent
  1. Neutral molecular networks: Polarity-independent tool for mass spectrometry data.
  2. Comprehensive optimization of chromatographic separation and iterative data acquisition for liquid chromatography-tandem mass spectrometry non-targeted plasma metabolomics: Enhancing feature-based molecular networking.
  3. Ordering molecular diversity in untargeted metabolomics via molecular community networking.
  4. MetaDilutionR: An R Package for Data-Driven Determination of Optimal Plasma Dilution in Untargeted Metabolomics to Achieve Maximum Metabolite Coverage with ESI Linearity.
  5. Targeted Livestock Metabolomics: A Review of Liquid chromatography-Mass Spectrometry-Based Approaches.
  6. Unveiling metabolic mechanism of aroma-active compounds during yogurt fermentation through volatilomics, sensomics, and untargeted metabolomics analysis.
  7. Evaluation of extraction solvents for untargeted metabolomics in characterizing Antarctic cryoconite hole dissolved organic matter.
  8. Exploring Acylcarnitine Metabolism Using Reverse Metabolomics.
  9. MS/MS-based molecular networking for exploring the chemical diversity of Chiliadenus montanus (Vahl.) brullo and Chiliadenus candicans (Delile) brullo and their anti-ulcer potential via modulation of Nrf2/ICAM-1 in rats.
  10. IMFrag: A Tool to Recognize In-Source Fragmentation in Ion Mobility-Enabled Data-Independent Acquisition Workflows.
  11. RNase 4 improves bottom-up modification mapping of E. coli total tRNAs using HILIC-MS/MS.
  12. MS-Net: Multi-Similarity-Based Network Annotation for Untargeted Metabolomics.
  13. Comprehensive investigation of matrix effect evaluation approaches for reliable quantification in bioanalysis using UHPLC-MS/MS.
  14. Pseudoenhanced Peak Efficiency in Liquid Chromatography-High Resolution Mass Spectrometry Using Multiple Second Derivatives.
  15. Trends in Computational Metabolomics: A Perspective on Five Years of Software Development, Challenges, and Opportunities (2021-2025).
  16. Identification of Potential Plasma Biomarkers of Abdominal Aortic Aneurysm Using a Metabolomics Approach.
  17. An LC-MS-guided AI enabled serum molecule-interpretable SERS platform for exploratory metabolomic analysis in Alzheimer's disease.
  18. Metabolomic alterations in marine mussels exposed to wastewater, stormwater, and environmental conditions.
  19. An integrated strategy of UPLC-Q/TOF MS-based untargeted metabolomics and targeted separation for identifying chemical markers of Fritillaria ussuriensis Maxim from different sources.
  20. Untargeted microLC-HRMS/MS for Measuring Endogenous Steroids in Mouse Plasma and Human Serum Samples Applied to Studying Polycystic Ovary Syndrome.
  21. Identification of QTLs linked to bioactive flavonoids and glycosides in the apricot fruit (Prunus armeniaca L.).
  22. Comprehensive Lipidomic Analysis of Streptomyces by NPLC-HRMS Reveals a Previously Unknown Phospholipid Class.
  23. A lipid compendium of a metabolically compromised bacterium provides insights into lipid acquisition, biosynthesis, and metabolism.
  24. A semi-automatization magnetic solid-phase extraction method of LC-MS/MS for the quantification of homocysteine and its related metabolites in serum.
  25. Detection of Artemisia mongolica floss adulteration in moxa floss: A strategy based on UPLC-Q/Orbitrap HRMS, chromatographic analysis, and machine learning.
  26. Comparative metabolomic profiling and potential bioactive marker discovery of three Clerodendrum species and their different plant parts.
  27. Metagenomic and metabolomic analyses of fecal samples from civet-digested coffee in Vietnam.
  28. Integrated volatile profiling and metabolomics reveal changes in volatile compounds  during low-temperature smoking of sea bass (Lateolabrax maculatus).
  29. Integrating liquid chromatography-high resolution mass spectrometry based suspect screening with ionization efficiency prediction to expand semi-quantitative coverage in wastewater-based epidemiology: application to environmental chemicals.
  30. Assessing current capabilities for incorporating lipidomics in multiomics data integration.
  31. A confirmation test for the presence of Quillaja saponin and adulteration with Madhuca saponin in poultry feed using high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry.
  32. HPLC-HRMS determination of anticancer drugs and their degradants identified by heterogeneous photocatalysis as potential river toxicants.
  33. High-Throughput Reactive Desorption Electrospray Ionization Mass Spectrometry for Targeted Derivatization and Analysis of Poorly Ionized Small Molecules.
  34. Untargeted and Targeted Liquid Chromatography-Mass Spectrometry-Based Lipidomic Profiling Revealed a Potential Biomarker Panel to Distinguish Clinical Isolates of M. tuberculosis From Nontuberculous M. kansasii.
  35. Streamlined LC-HRMS-based determination of sterols and triterpenic alcohols in edible oils for authentication purposes.
  36. Phytochemical Profile and Insecticidal Potential of Two Halophytes, Atriplex halimus and Suaeda fruticosa, Against Tribolium confusum: In Vitro and In Silico Approaches.
  37. Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning.
  38. Development and validation of a quantitative ultra-performance liquid chromatography quadrupole time-of-flight method for urine organic acid analysis.
  39. [Determination of 13 prohibited alkaloids in essential oil-based cosmetics by ultra performance liquid chromatography-tandem mass spectrometry combining QuEChERS purification].
  40. Development and Validation of a Rapid LC-MS/MS Method for Carfilzomib Quantification in Plasma from Multiple Myeloma Patients.
  41. Protein-protein interactions in Escherichia coli: Using cross-linking/mass spectrometry (XLMS) to reveal our current knowledge gaps in interactomes.
  42. Polyphenols of Zingiber roseum Leaf as a Potential Source of α‑Glucosidase Inhibitors for Type‑2 Diabetes Management: LC-MS/MS, Pharmacological and Computational Analysis.
  43. Mass spectrometry in laboratory medicine: advances in automation, multiplex biomarker quantification, and diagnostics.
  44. LipidCruncher: an open-source platform for processing, visualizing, and analyzing lipidomic data.
  45. A Comprehensive Strategy for Characterizing Metabolites and Metabolic Profile in Rat Urine and Feces Following Oral Administration of Huachansu Tablets Based on UPLC-ESI-QTOF/MSE.
  1. Anal Chim Acta. 2026 Sep 15. pii: S0003-2670(26)00658-6. [Epub ahead of print]1415 345708
    Neutral molecular networks: Polarity-independent tool for mass spectrometry data.
    Nicola Dalla Valle, Pietro Franceschi, Mar Garcia-Aloy, Peter Robatscher, Sabine Oettl, Michael Oberhuber.
      Untargeted mass spectrometry-based metabolomics generates large-scale fragmentation data, typically analyzed separately in positive and negative ionization modes. The fragmentation patterns of the same molecule usually capture distinct but complementary structural information across polarities. Evaluating them simultaneously, rather than in separate molecular networks, can enhance the overall informativeness and speed the analysis. In this paper, we introduce the Neutral Molecular Network (NMN) concept, a novel strategy that unifies complementary fragmentation patterns from both polarities into a single "neutral pseudo-spectra". NMNs outperformed polarity-specific networks on several large-scale publicly available MS/MS libraries in terms of chemical reliability of spectral matches and clustering capability. The improved performances were further confirmed through the analysis of a biological untargeted dataset of Alternaria fungal extracts, where this approach facilitated the identification of previously uncharacterized toxin derivatives. NMN offers a polarity-independent framework for the structural interpretation of untargeted MS/MS data. This method can improve the efficiency of metabolite annotation pipelines and can be applicable to diverse metabolomics workflows.
    Keywords:  Alternaria; Electrospray ionization; Mass spectrometry; Molecular network; Untargeted metabolomics
    DOI:  https://doi.org/10.1016/j.aca.2026.345708
  2. J Chromatogr A. 2026 May 27. pii: S0021-9673(26)00457-7. [Epub ahead of print]1783 467128
    Comprehensive optimization of chromatographic separation and iterative data acquisition for liquid chromatography-tandem mass spectrometry non-targeted plasma metabolomics: Enhancing feature-based molecular networking.
    Jiamin Yang, Xiaona Lai, Qing Liu, Yiwen Tao, Hongbo Huang, Bo Ding.
      Liquid chromatography-tandem mass spectrometry (LC-MS/MS) non-targeted plasma metabolomics is essential for biomarker discovery, yet the lack of standardized analytical protocols often compromises data reliability. In this study, we systematically optimized the entire metabolomics workflow, focusing on the integration of feature-based molecular networking (FBMN) to enhance metabolite annotation. Critical parameters, including protein precipitation chemistry, chromatographic selectivity, and data acquisition strategies, were comprehensively assessed. Optimal extraction was achieved using a methanol/ethanol (1:1, v/v) mixture at a 1:4(v/v) sample-to-solvent ratio. For chromatographic separation, an XBridge BEH C18 column outperformed alternatives. Superior peak capacity and ionization efficiency were obtained using 0.1% formic acid (or 10 mM ammonium acetate with 0.1% formic acid) in positive mode, and 10 mM ammonium formate with 0.1% acetic acid in negative mode. While the timing of internal standard addition did not significantly alter the metabolic profile, instrument signal stability became a critical factor for sequences exceeding one week. Notably, the transition to a 100 mm column and the implementation of iterative data-dependent acquisition (DDA) on quality control samples significantly expanded the FBMN size and the number of uniquely annotated compounds. This optimized, robust workflow provides a standardized framework for improving metabolite coverage and annotation confidence in large-scale clinical investigations.
    Keywords:  Chromatographic optimization; FBMN; Iterative acquisition; Non-targeted plasma metabolomics; Protein precipitation
    DOI:  https://doi.org/10.1016/j.chroma.2026.467128
  3. Cell Rep Methods. 2026 Jun 02. pii: S2667-2375(26)00168-2. [Epub ahead of print] 101468
    Ordering molecular diversity in untargeted metabolomics via molecular community networking.
    Elizabeth A Coler, Alexey Melnik, Ali Lotfi, Dana Moradi, Ben Ahiadu, Paulo Wender Portal Gomes, Abubaker Patan, Vincent Charron-Lamoureux, Pieter C Dorrestein, Stephen Barnes, Vladimir Boginski, Alexander Semenov, Alexander A Aksenov.
      Molecules in living systems are not random but are shaped by biological necessity. Mass spectrometry (MS) is a powerful tool for exploring these complex molecular landscapes. Molecular networking links metabolites by spectral similarity, but conventional methods leave many nodes disconnected. We introduce molecular community networking (MCN), which identifies natural molecular clusters and prunes them to keep the strongest links. The approach increases connectivity to about 95% of molecules and better captures structurally related compounds, including distinct ion forms and in-source fragmentation ions. MCN also improves the mapping of molecular space, helping distinguish true novel molecules from artifacts. Using MCN, we discovered dipeptide-conjugated bile acids associated with Bifidobacterium breve and proposed structures for previously unexplored N-acyl amides that interact with G protein-coupled receptors. We also built a global metabolome map from public GNPS/MassIVE data, covering about 8.4 million molecular features, creating a "roadmap" for molecular diversity.
    Keywords:  CP: computational biology; CP: metabolism; gas chromatography-mass spectrometry; liquid chromatography-mass spectrometry; metabolomics; molecular community networking; molecular networking
    DOI:  https://doi.org/10.1016/j.crmeth.2026.101468
  4. J Am Soc Mass Spectrom. 2026 Jun 03.
    MetaDilutionR: An R Package for Data-Driven Determination of Optimal Plasma Dilution in Untargeted Metabolomics to Achieve Maximum Metabolite Coverage with ESI Linearity.
    Keerthana Vinod Kumar, Aviral Singh, Sneha Rana, Rakesh Sahay, Pramod P Wangikar.
      High-resolution mass spectrometry (HRMS) instruments for untargeted metabolomics typically offer a linear dynamic range spanning approximately 4 orders of magnitude. However, biological samples contain metabolites spanning concentration ranges far exceeding this window, making dilution optimization critical for reliable quantification. Despite its importance, dilution selection in untargeted workflows is rarely standardized and is often determined empirically through manual inspection, leading to operation outside the linear dynamic range, ion suppression, and compromised reproducibility. To address this, we present MetaDilutionR, an open-source R package that standardizes dilution optimization by systematically evaluating electrospray ionization (ESI) linearity using plasma as a model. MetaDilutionR automates dilution assessments, applying user-adjustable slope and R2 thresholds to classify features as linear or nonlinear and executes the complete analysis via a single function call, ensuring algorithmic reproducibility across users and platforms. The package generates comprehensive outputs, including log2-transformed data, a summary of linear features with their optimal dilution ranges, nonlinear features highlighting potential ion suppression or detector saturation, detailed evaluations across dilution scenarios, and visual regression plot reports. Benchmarking against three established metabolomics workflows demonstrated that the R2-slope criterion of MetaDilutionR reduces false-positive linear assignments. Cross-platform applicability of the algorithm on an independent GC-MS data set confirmed consistent classification performance beyond LC-HRMS. By facilitating systematic identification of metabolite-specific optimal dilution conditions, MetaDilutionR enables metabolites to be quantified within their linear dynamic range─a prerequisite for reliable quantification─thereby enhancing reproducibility and consistency of downstream validation, making it readily integrable into existing metabolomics workflows.
    Keywords:  linearity; metabolite coverage; serial dilution; untargeted metabolomics
    DOI:  https://doi.org/10.1021/jasms.5c00419
  5. J Agric Food Chem. 2026 Jun 02.
    Targeted Livestock Metabolomics: A Review of Liquid chromatography-Mass Spectrometry-Based Approaches.
    Kangkang Xu, Franz Berthiller, Rudolf Krska, Heidi E Schwartz-Zimmermann.
      With the advancement of sophisticated analytical techniques, such as nuclear magnetic resonance spectroscopy and mass spectrometry (MS), metabolomics has become a powerful tool for analyzing and quantifying small molecules in cells, tissues, and biofluids. Among MS-based methods, liquid chromatography-mass spectrometry (LC-MS) is widely used due to its high analyte coverage, sensitivity, and selectivity. Targeted metabolomics quantifies predefined metabolites, in contrast to untargeted approaches that profile all detectable metabolites. This review provides an overview of targeted LC-MS methods and applications in livestock metabolomics, focusing on ruminants and swine, and covering research from the past decade. We discuss sample preparation, LC-MS instrumentation, and method validation, as well as emerging trends including combined LC techniques, integration of targeted and untargeted approaches, and multiomics studies. Current limitations, future directions, and a general workflow for method development are also addressed.
    Keywords:  LC-MS; analytical methods; animal metabolomics; derivatization; dilute and shoot; validation
    DOI:  https://doi.org/10.1021/acs.jafc.5c16870
  6. J Dairy Sci. 2026 May 29. pii: S0022-0302(26)02866-3. [Epub ahead of print]
    Unveiling metabolic mechanism of aroma-active compounds during yogurt fermentation through volatilomics, sensomics, and untargeted metabolomics analysis.
    Haoying Han, Yadong Wang, Ting Meng, Baochao Hou, Jian He, Baolei Li, Chunli Kong, Bei Wang.
      The flavor of yogurt is primarily determined by its aroma-active compounds. This study aimed to track the physicochemical, microbiological, flavor changes, and metabolomic profiles of yogurt during fermentation (0, 60, 120, 180, and 240 min). When the pH dropped to 4.54 and the acidity rose to 72.8 °T, the fermentation was terminated. Volatilomics identified 13 volatile compounds that distinguish different fermentation stages. Concurrent sensomics analysis detected 13 aroma-active compounds. Integrating both data sets yielded 7 highly variable aroma-active compounds (with OAV >1 and VIP >1). Furthermore, untargeted metabolomics coupled with multivariate statistics identified 81 highly significant differential metabolites during fermentation. KEGG enrichment analysis of these metabolites revealed 10 pathways (p  <  0.05). Correlation analysis revealed that the accumulation of aroma-active compounds, including acetic acid, hexanoic acid and 2,3-pentanedione, etc. is governed by hub metabolic pathways such as glyoxylate and dicarboxylate metabolism, the citrate (TCA) cycle, and alanine, aspartate and glutamate and pyruvate metabolism. Within this network, pivotal node metabolites, pyruvate, dihydrouracil, citrate, uracil, orotate, malate and isocitrate, were identified as hub metabolites potentially involved in volatile flavor formation. This work delineates the metabolic-network basis of fermentation flavor, providing defined engineering targets for the rational design of yogurt aroma.
    Keywords:  Sensomics; Untargeted metabolomics; Volatile components metabolic pathway; Volatilomics; Yogurt fermentation
    DOI:  https://doi.org/10.3168/jds.2026-28316
  7. Anal Chim Acta. 2026 Sep 01. pii: S0003-2670(26)00598-2. [Epub ahead of print]1413 345648
    Evaluation of extraction solvents for untargeted metabolomics in characterizing Antarctic cryoconite hole dissolved organic matter.
    Swapnil Mundhe, Saborni Maiti, Aritri Sanyal, Anirban Majumder, Narendra Y Kadoo, Dhiraj Dhotre, Vitthal T Barvkar, Shamim A Shaikh, Runa Antony, Dhiraj Paul.
       BACKGROUND: Cryoconite holes, a dynamic habitat in Antarctica and other glaciers, are glacial biological hotspots with high biogeochemical turnover rates, significantly influencing carbon and nitrogen cycling within climate-sensitive glacial ecosystems. They are found on glaciers worldwide, highlighting their global importance, and are likely to become more widespread as glaciers continue to retreat. Despite their global occurrence and sensitivity to climate warming, untargeted metabolomic studies of their dissolved organic matter (DOM) remain scarce, largely due to very low biomass, harsh sample matrices, and the absence of optimized extraction methods.
    RESULTS: We evaluated combinations of water, methanol (MeOH), and acetonitrile (ACN) to optimize the extraction of chemically diverse polar and non-polar metabolites from cryoconite holes. The individual extraction with organic-rich MeOH: Water (70:30 v/v) and in parallel two-solvent extractions with MeOH: Water (70:30 v/v) and ACN: MeOH: Water (40:40:20 v/v) yielded the greatest number and diversity of metabolites. Combining RP with HILIC chromatography provided the highest number of unique metabolites. This dual-LC and ionization-polarity combination increased the detection of metabolic features by 46.9% and 25.5% in single- and two-solvent combinations, respectively, compared to RP alone.
    SIGNIFICANCE: This study delivers the first comprehensive untargeted metabolomics workflow specifically optimized for the uniquely extreme environment of glacial cryoconite holes. By capturing both extracellular DOM and intracellular metabolites from cold-adapted microbial communities, the method reveals the complex metabolic landscape of these challenging cold-ecosystem niches. By characterizing these metabolic signatures, this research provides a foundation for future studies into metabolic interactions across polar and glacial environments.
    Keywords:  Antarctica; Cryoconite hole; Dissolved organic matter; Glacial; Hydrophilic interaction liquid chromatography; Reverse-phase liquid chromatography; Untargeted metabolomics
    DOI:  https://doi.org/10.1016/j.aca.2026.345648
  8. Anal Chem. 2026 Jun 04.
    Exploring Acylcarnitine Metabolism Using Reverse Metabolomics.
    Allison J Keys, Daniel J Wiener, Sara M Pacini, Chengze Sun, Lindsay E Sandusky, Huaiyang Zhong, Emily C Gentry.
      Untargeted mass spectrometry (MS) is a valuable tool for studying human metabolism and identifying small molecule disease biomarkers. However, annotation of chemical structures and validation of findings across numerous cohorts remains challenging. Reverse metabolomics employs a structure-driven approach to overcome these issues by searching spectra of known structures against an entire repository of untargeted LC-MS/MS data to see where metabolites of interest are found. This work uses reverse metabolomics to study acylcarnitine (AC) metabolism in humans and other animals. Here, a library of 76 ACs was chemically synthesized then searched against public metabolomics data to explore where metabolites of interest are detected. From this analysis, it was determined that acylcarnitines are most frequently observed in human and mouse samples, with about 90% of all searched AC structures present in both blood and fecal samples from these species. This work identified positive associations between certain AC structures and disease, indicating their capacity as health biomarkers. Machine learning was applied, determining that AC presence and absence data can accurately predict healthy versus unhealthy individuals with good precision and recall, albeit the models lack disease specificity. Overall, our findings suggest that AC profiles can serve as valuable biomarkers for disease detection throughout the entire lifespan and should be examined for their potential beyond current clinical screening protocols.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01418
  9. J Ethnopharmacol. 2026 Jun 01. pii: S0378-8741(26)00808-1. [Epub ahead of print]369 121954
    MS/MS-based molecular networking for exploring the chemical diversity of Chiliadenus montanus (Vahl.) brullo and Chiliadenus candicans (Delile) brullo and their anti-ulcer potential via modulation of Nrf2/ICAM-1 in rats.
    Alaa M Shaheen, Essam Abdel-Sattar, El-Sayeda A El-Kashoury, Wafaa A Tawfik, Tarik A Mohamed, Nesrine M Hegazi, Abeer A Salama, Mohamed-Elamir F Hegazy.
       ETHNOPHARMACOLOGICAL RELEVANCE: Chiliadenus montanus and Chiliadenus candicans are two closely related species that are traditionally used for treating gastric ailments and peptic ulcers; however, their therapeutic efficacy lacks scientific validation.
    AIM OF THE STUDY: This study aims to investigate the gastroprotective potential of both species through comprehensive metabolite profiling, in-vivo biological evaluation, and molecular docking studies.
    METHODS: Methylene chloride: methanol (1:1) and 70% methanolic extracts from the aerial parts of both C. montanus and C. candicans are subjected to ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) analysis. After this, the gastroprotective efficacy of the extracts is evaluated in an ethanol-induced gastric ulcer model in rats at 200 and 400 mg/kg. Histopathological examination of gastric tissue for all extracts and molecular docking study for the most active extract was established.
    RESULTS: UPLC-HRMS/MS allowed for the annotation of 217 metabolites, with only 30 metabolites previously reported in the studied species, and one potentially new compound annotated as dihydroxy costic acid O- caffeic acid hexoside, reported for the first time. Both plant extracts significantly attenuate ulcer indices and mitigate gastric inflammation through modulation of the Nrf2/ICAM-1 signaling pathway. Additionally, the extracts demonstrate potent antioxidant and anti-inflammatory activities.
    CONCLUSION: Both C. montanus and C. candicans show outstanding ulcer protective potential; these effects are attributed to high content of cinnamic acid derivatives, flavonoids, and terpenoid constituents as shown through LC/MS analysis. These findings provide scientific evidence for its traditional use as an herbal tea in the treatment of gastric ulcers by Bedouins in Saint Catherine.
    Keywords:  Gastric ulcer; IL-1β; Molecular docking; Nrf2/ICAM; UPLC–HRMS-MS; Untargeted metabolomics; VEGF
    DOI:  https://doi.org/10.1016/j.jep.2026.121954
  10. Anal Chem. 2026 Jun 06.
    IMFrag: A Tool to Recognize In-Source Fragmentation in Ion Mobility-Enabled Data-Independent Acquisition Workflows.
    Ryan Nguyen, Anvitaa Anandkumar, Griffin Rangel, Jennifer Liem, Yvonne S Lin, Libin Xu.
      High-resolution mass spectrometry is a powerful tool for untargeted analysis. However, in-source fragmentation (ISF) could lead to the misidentification of compounds in untargeted metabolomics or exposomics studies. To prevent misidentification and to strengthen compound identification through MS/MS spectral library matching, we developed IMFrag, a Jupyter notebook-based tool that utilizes structural information gained from ion mobility-mass spectrometry (IM-MS) as an orthogonal technique to differentiate independent precursor ions from fragments formed via ISF. We first examined l-tryptophan, which is an essential amino acid that undergoes extensive fragmentation during electrospray ionization (ESI). IM-enabled data-independent acquisition (IM-DIA) analysis revealed distinct mobility signatures for identical fragment ions formed at different instrument sites, enabling discrimination between ISFs generated prior to the IM drift tube and fragments produced via postmobility collision-induced dissociation. Similar patterns were observed for a structurally diverse collection of small molecules. Additional structural information could also be inferred from the IM-DIA workflow, such as unique dimers, protonation sites, and distinct ion types that were not apparent from LC-MS alone. These insights were shown to be useful when applied to healthy human plasma samples, which served as a more complex and biologically relevant matrix that contained ambiguities, such as coeluting, isobaric candidate structures. Thus, IMFrag was developed as an accessible framework for interrogating MS1 and post-IM MS2 chemical features in untargeted data sets and can be integrated into untargeted analysis pipelines or used to support the development of ISF-derived MS/MS spectral libraries.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01388
  11. bioRxiv. 2026 May 25. pii: 2026.05.23.727428. [Epub ahead of print]
    RNase 4 improves bottom-up modification mapping of E. coli total tRNAs using HILIC-MS/MS.
    Kaley M Simcox, Maddy Zamecnik, Robert T Kennedy, Kristin S Koutmou.
      The structural and functional diversity of RNAs is expanded by the post-transcriptional incorporation of nucleoside variants. Emblematic of this, tRNAs contain extensive modifications that ensure their function during protein synthesis. Mass spectrometry has long been the field standard for identifying specific sites of chemical modifications on RNA. Nonetheless, mass spectrometry-based mapping approaches are not widely implemented. This is partially due to technical challenges associated with current methodologies including the limited diversity of available RNases, complexity of RNA mixtures, and conventional use ion-pairing reagents that require dedicated instrumentation. Here, we present a bottom-up liquid chromatography-tandem mass spectrometry (LC-MS/MS) workflow employing hydrophilic interaction liquid chromatography (HILIC) without ion-pairing reagents to globally map E. coli tRNA modifications. We implement orthogonal digestions using RNase 4 and a folded digestion scheme with RNase T1 to generate uniquely mappable oligonucleotides compatible with HILIC-MS/MS analysis and achieve 75-100% sequence coverage for most tRNA isoacceptors. HILIC-MS/MS matches the performance of traditional ion-pairing reverse-phased LC-MS/MS. This level of coverage allowed us to discover a new site of methylation (Gm17) in tRNA Gly , and confirm the presence of an s 4 U8 modification predicted in tRNA Arg . Furthermore, by applying this method to E. coli lacking the m 5 U54 methyltransferase (trmA) we confirmed the established dependence of acp 3 U47 insertion on m 5 U54 in tRNA Phe . Our findings show that RNase 4 improves bottom-up tRNA sequencing, enabling high-quality E. coli tRNA analysis without ion-pairing reagents.
    Abstract Figure:
    DOI:  https://doi.org/10.64898/2026.05.23.727428
  12. Anal Chem. 2026 May 30.
    MS-Net: Multi-Similarity-Based Network Annotation for Untargeted Metabolomics.
    Victor Pereira Francisco, Simon Duthen, Elise Crossay, Amélie Perez, Solweig Hennechart, Marion Alignan, Romain Valentin, Franck Milone-Delacourt, Guillaume Marti.
      Confident metabolite annotation remains a critical bottleneck in untargeted LC-MS metabolomics, with experimental spectral libraries covering only 5-20% of detected features. While in silico tools generate extensive candidate lists per feature, top-ranked predictions frequently fail to reflect true molecular identities, leading to high false annotation rates. We present multi-similarity Network-based annotation (MS-Net), an accessible workflow that integrates mass spectral similarity networks, molecular structure similarity (Tanimoto metrics), and taxonomic knowledge to prioritize annotations within vast candidate spaces. High-confidence annotations from authentic standards, spectral libraries, and taxonomically filtered candidates seed iterative propagation throughout mass spectral similarity networks. The workflow employs a composite Link Score combining structural, spectral, and computational evidence to rescue correct annotations from lower-ranked positions. Applied to Cannabis sativa extracts (2595 features to 1297 after filtering), MS-Net assigned 1275 compounds from an initial candidate space of over 118,000 structures. Notably, 53% of final annotations were rescued from ranks 2-50, demonstrating correction of initial in silico ranking. The workflow successfully reconstructed known cannabinoid biosynthetic pathways, validating biological coherence. MS-Net is freely available as a KNIME workflow with complete documentation at https://forge.inrae.fr/metatoul/equipe-agromix/ms-net, enabling reproducible, offline annotation suitable for systems biology integration.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01026
  13. Anal Chim Acta. 2026 Sep 01. pii: S0003-2670(26)00592-1. [Epub ahead of print]1413 345642
    Comprehensive investigation of matrix effect evaluation approaches for reliable quantification in bioanalysis using UHPLC-MS/MS.
    Hana Kočová Vlčková, Kateřina Plachká, František Švec, Lucie Nováková.
       BACKGROUND: Matrix effect (ME) evaluation is an indispensable part of the method development and validation when using ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). The rotational and translational matrix effects are often observed. The rotational matrix effect depends on the analyte concentration and affects the slope of the calibration function, while the translational matrix effect is independent of the analyte concentration and affects the intercept of the calibration function. Two matrix effect evaluation strategies, the post-extraction addition and the comparison of calibration curve slopes, are the most widely used for the quantitative ME evaluation. The post-extraction addition approach recommended by the European Medicines Agency (EMA) guideline is considered the reference approach. However, the extent to which these approaches provide comparable results has not been systematically assessed.
    RESULTS: We evaluated the suitability of the calibration curve slope approach for quantifying ME. Two ME evaluation strategies were systematically compared using ESI-UHPLC-MS/MS in both negative and positive ionization modes for the analysis of 26 compounds in serum. Various calibration models, including 1/X0, 1/X, and 1/X2 weighting or logarithmic transformation, were assessed. None of the tested models provided overestimated results compared to the post-extraction addition approach. However, several models exhibited underestimated results. Thus, we developed a new approach for calculation of ME taking into account also the translational ME expressed by the intercept of the calibration curve. The accuracy of the new approach was subsequently determined using three different matrices and two different instrumental platforms.
    SIGNIFICANCE: It is not advisable to rely solely on the calibration curve slope approach for accurate estimation of ME unless translational ME derived from calibration curve intercept are also involved in calculation. Therefore, a novel intercept-based equation was proposed for the calculation of translational matrix effects. The total matrix effect, obtained as the sum of the slope- and intercept-derived contributions, closely corresponds to matrix effects determined by the post-extraction addition approach.
    Keywords:  Calibration curve slope; Calibration model; Mass spectrometry; Matrix effect; Post-extraction addition
    DOI:  https://doi.org/10.1016/j.aca.2026.345642
  14. J Am Soc Mass Spectrom. 2026 Jun 05.
    Pseudoenhanced Peak Efficiency in Liquid Chromatography-High Resolution Mass Spectrometry Using Multiple Second Derivatives.
    Guillaume Laurent Erny.
      A mathematical approach is introduced to achieve pseudoenhanced peak efficiency in liquid chromatography-high resolution mass spectrometry (LC-HRMS1), utilizing second-derivative transformations with respect to time and Gaussian smoothing cycles. The pipeline involved interpolating all MS scans, recorded as profile scans, onto a common m/z axis, allowing for second-derivative transformations for each m/z increment with respect to time across the entire data set. Validation using a public study (comprising 1100 compounds analyzed on a Thermo Q Exactive) demonstrates substantial improvements in feature detection and resolution, with qualitative and quantitative benefits. The transformation enables increased peak efficiency by up to 2 orders of magnitude in multicycle implementations without requiring physical hardware enhancements. The approach provides enhanced separation of complex mixtures, reduced background contributions, and increased reliability in untargeted metabolomics and related analyses.
    DOI:  https://doi.org/10.1021/jasms.5c00315
  15. Anal Chem. 2026 May 31.
    Trends in Computational Metabolomics: A Perspective on Five Years of Software Development, Challenges, and Opportunities (2021-2025).
    Daniel Domingo-Fernández, David Healey, Tobias Kind, August Allen, Viswa Colluru, Biswapriya Biswavas Misra.
      Metabolomics software development has accelerated rapidly, yet no recent systematic analysis has quantified how the landscape is evolving across computational methods, geographies, and the research community's technology adoption. There is a strong need within the metabolomics research community to keep pace with the rapid expansion of accessible and free computational tools and resources. Given the absence of such a treatise since 2021 and the surge in advances in ion mobility mass spectrometry (IM-MS), single-cell and spatial metabolomics, and multimodal omics-based discovery, we offer a curated database that aggregates 746 mass spectrometry- and spectroscopy-based tools across 37 categories from data preprocessing to metabolite annotation. We report four structural shifts that redefine the field's trajectory. First, machine learning (ML) adoption in tools increased by 2.4-fold from 10.9% (2021) to 26.6% (2025). Second, annotation as a category commands the most tools (16.8%) and the highest ML investment among any of the proposed tool categories. The dominant strategy has shifted from library matching (2021) to spectrum prediction (2024) and, more recently, to de novo structure generation (2025), thereby progressively reducing the reliance on accessible experimental spectral reference databases. Third, Python has displaced R as the dominant programming language, with a sharp inflection in 2023 coinciding with the ML surge, while web server-only tools have sharply declined. Fourth, transformer architectures grew significantly, and in 2025, the first few large language model (LLM)-based and other multimodal metabolomics tools emerged, signaling a transition from task-specific classifiers toward pretrained, transferable representations. Concurrently, adoption of preprints as a publishing venue also rose by 2.5-fold, and, notably, mentions of benchmarking and explainability each increased by 8-18-fold, indicating a growing community-wide need and maturation. This computational metabolomics database is now made available here: https://github.com/enveda/computational-metabolomics-review.
    DOI:  https://doi.org/10.1021/acs.analchem.6c00361
  16. Curr Mol Med. 2026 May 20.
    Identification of Potential Plasma Biomarkers of Abdominal Aortic Aneurysm Using a Metabolomics Approach.
    Wei Wang, Zhaoran Chen, Xiaoyue Tang, Xuebin Wang, Lianglin Wu, Wenxuan Xiang, Zhiwen Zhang, Hai Feng, Jianqiang Wu, Yuehong Zheng.
       OBJECTIVE: In the aging population, abdominal aortic aneurysm (AAA) is a widespread and serious disease. Currently, there are no available biomarkers for diagnosing or treating AAA with drugs in clinical use. Uncovering the metabolic signatures of AAA may provide new insights into its pathogenesis and facilitate the identification of novel metabolic biomarkers.
    METHODS: In the present study, we applied high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS)-based metabolomics to analyze plasma samples from 76 AAA patients and 72 controls.
    RESULTS: A total of 115 differentially abundant metabolites were identified, and these metabolites were mainly involved in retinol metabolism, glutathione metabolism, purine metabolism, D-amino acid metabolism, pantothenate and CoA biosynthesis, porphyrin metabolism, and sphingolipid metabolism. After biomarker screening in the discovery cohort and biomarker verification in the validation cohort, 28 potential metabolite biomarkers of AAA were identified. Further examination indicated that three metabolites, including 5-Delta-Hydroxybutyl Hydantoin, 2-Amino-4-[(2- hydroxy-1-oxopropyl) amino] butanoic acid, and 15(R),19(R)-hydroxy PGF2, showed remarkable sensitivity and specificity in diagnosing AAA, with AUC values of 0.997, 0.989, and 0.978, respectively.
    DISCUSSION: This metabolomics study revealed distinct plasma metabolic profiles in AAA patients compared to controls, identifying 115 differentially abundant metabolites. These metabolites were primarily enriched in pathways including retinol metabolism, glutathione metabolism, and purine metabolism. Three metabolites-5-delta-hydroxybutyl hydantoin, 2-amino-4-[(2-hydroxy-1- oxopropyl) amino] butanoic acid, and 15(R),19(R)-hydroxy-PGF2α-were identified as novel potential biomarkers and showed excellent diagnostic performance in combination. The findings suggest that dysregulation of retinol metabolism, implicated in vascular homeostasis, and glutathione metabolism, linked to oxidative stress and ferroptosis, may play roles in AAA pathogenesis. Alterations in purine metabolism further support the involvement of inflammatory and oxidative processes. While these biomarkers show promise, their functional roles in AAA and clinical utility require validation in larger, multicenter prospective studies.
    CONCLUSION: This study identified potential novel plasma biomarkers of AAA, which could contribute to the discovery of diagnostic biomarkers and therapeutic targets for this disease.
    Keywords:  Abdominal aortic aneurysm; Biomarkers; Diagnosis.; LC-MS/MS; Metabolites; Metabolomics
    DOI:  https://doi.org/10.2174/0115665240435737260306084312
  17. Anal Chim Acta. 2026 Aug 15. pii: S0003-2670(26)00587-8. [Epub ahead of print]1411 345637
    An LC-MS-guided AI enabled serum molecule-interpretable SERS platform for exploratory metabolomic analysis in Alzheimer's disease.
    Yanling Wang, Chi Zhang, Tong Liu, Qingsong Li, Xue Guan, Yutian Hu, Bo Cao, Xiaohong Wu, Qin Zhou, Feiyun Cui.
      Label-free surface-enhanced Raman spectroscopy (SERS) offers a promising avenue for rapid metabolic phenotyping in complex biofluids, yet its translational potential is hindered by the challenge of deconvoluting overlapping spectral contributions and identifying specific metabolite signatures. Herein, we report a liquid chromatography-mass spectrometry (LC-MS)-guided AI enabled serum molecule-interpretable SERS Platform, termed AMI-SERS, which integrates SERS, untargeted metabolomics and machine learning (ML) for Alzheimer's disease (AD) patient serum exploratory metabolic profiling. ML has achieved excellent classification performance with an accuracy of 96.67% under leave-one-out cross-validation. A novel multi-source matching algorithm was devised to trace dominant spectral features directly to specific metabolite changes, uncovering hypoxanthine as a key pre-analytical confounder and nominating adenine as a potential AD biomarker. However, due to the limited sample size, these results are only applicable to exploratory analysis. In conclusion, the AMI-SERS platform establishes a molecular-resolvable, LC-MS-interpretable AI methodological framework for exploratory serum metabolomics and its clinical applicability requires large-scale independent validation.
    Keywords:  Alzheimer's disease (AD); Machine learning (ML); Metabolism analysis; Surface-enhanced Raman spectroscopy (SERS)
    DOI:  https://doi.org/10.1016/j.aca.2026.345637
  18. Mar Pollut Bull. 2026 Jun 03. pii: S0025-326X(26)00723-X. [Epub ahead of print]231 119936
    Metabolomic alterations in marine mussels exposed to wastewater, stormwater, and environmental conditions.
    Jessica Clemens, Maya Faber, Hayley M Mathews, C Andrew James.
      Organisms in nearshore environments can be affected by anthropogenic contaminants, potentially leading to poor outcomes. This study applies metabolomic methods to evaluate the influence of stormwater, wastewater, and field exposures on the biological responses of bay mussels (Mytilus trossulus). Metabolomics provides a sensitive measure of interactions between an organism and its environment and can show indications of contaminant exposure well before other symptoms appear. Mussels were exposed under laboratory conditions to multiple dilutions of stormwater or wastewater. Additionally, mussels from the same source were deployed to select field sites throughout Puget Sound, Washington that were expected to be influenced by stormwater and/or wastewater. Hemolymph was extracted, and samples were analyzed for metabolite abundance using Liquid Chromatography-High Resolution Mass Spectrometry instrumentation. Analysis showed exposure-related metabolic alterations, with 0.5% stormwater exposure resulting in 5 significantly altered metabolites. Wastewater exposure resulted in a greater number of significant alterations and provided insight into the impacts of different contaminant doses. Exposure to a 0.1% wastewater solution resulted in 3 significantly altered metabolites, a number that increased to 34 significantly altered metabolites in the 1% wastewater exposure group. Field deployment resulted in 28 significantly altered metabolites across locations, with one site showing significant metabolic alteration consistent with wastewater exposure when compared to the reference site. Results from the wastewater exposure indicate alterations in bioenergetic pathways associated with key outcomes, including growth and reproductive success.
    Keywords:  Metabolomics; Mytilus trossulus; Reproduction; Stormwater; Wastewater
    DOI:  https://doi.org/10.1016/j.marpolbul.2026.119936
  19. J Pharm Biomed Anal. 2026 May 28. pii: S0731-7085(26)00256-6. [Epub ahead of print]280 117588
    An integrated strategy of UPLC-Q/TOF MS-based untargeted metabolomics and targeted separation for identifying chemical markers of Fritillaria ussuriensis Maxim from different sources.
    Shikui Bi, Zejun Liu, Haibo Wu, Deqiang Yang, Lihong Wu, Mingzhuo Wei, Zhenyue Wang, Haixue Kuang, Zhibin Wang.
      Quality control of natural products is an important prerequisite for their formulation or clinical application. Defining the material basis and chemical markers of natural products is a core requirement for quality control. Fritillaria ussuriensis Maxim (FU), as a natural product, is widely used as food and health supplements. However, the chemical composition and content of FU from different sources are still unclear, which poses a challenge to the quality control of FU. This study proposes a comprehensive analytical strategy for HPLC-ELSD to distinguish FU from different sources. First, untargeted metabolomics was combined with chemical profiling to identify biomarkers that distinguish FU from different sources. A total of 164 steroidal alkaloids were identified, of which 21 are potential chemical markers. Subsequently, three steroidal alkaloids were obtained as chemical markers using targeted separation technology. The contents of three steroidal alkaloids in FU from different sources were determined by HPLC-ELSD. The results showed that FU from different sources could be distinguished by detecting the content of three chemical markers using HPLC-ELSD. This study established a reliable method for distinguishing FU from different sources using HPLC-ELSD technology. This strategy can be extended to distinguish the origins of other natural products.
    Keywords:  Chemical markers; Fritillaria ussuriensis Maxim; Metabolomics; Steroidal alkaloids; UPLC-Q/TOF MS
    DOI:  https://doi.org/10.1016/j.jpba.2026.117588
  20. J Am Soc Mass Spectrom. 2026 Jun 05.
    Untargeted microLC-HRMS/MS for Measuring Endogenous Steroids in Mouse Plasma and Human Serum Samples Applied to Studying Polycystic Ovary Syndrome.
    Nathan Ghafari, Danko Brukner, Sherin A Nawaito, Danielle E Berbrier, Charlotte W Usselman, Nicolas Pilon, Lekha Sleno.
      Steroids play essential roles in regulating metabolism, response to stress, electrolyte balance, and reproductive function; however, their analysis in complex biological matrices remains challenging. Low endogenous concentrations, structural similarities, and poor ionization efficiencies can limit their detection, and conventional workflows frequently require large sample volumes and/or chemical derivatization, often restricting quantitative applications to targeted analyses. A workflow was developed for the untargeted analysis of endogenous steroids in serum or plasma samples using microflow liquid chromatography coupled to high-resolution tandem mass spectrometry. The Evosep One LC platform, applied widely for bottom-up proteomics, was coupled to a Sciex ZenoTOF 7600 quadrupole-time-of-flight system to enable the separation and detection of unconjugated and sulfated steroids in low-volume serum and plasma samples. The method was tested with a range of analytical standards, demonstrating efficient chromatographic separation and detection with high-accuracy MS/MS for structural confirmation. A total of 13 unconjugated steroids were confirmed and detected from female mouse plasma and/or human serum in positive mode, whereas three sulfated steroids were detected exclusively in human serum using negative mode. The sulfated steroids were confirmed using in vitro incubations of the parent steroids. Considerable interspecies differences were observed, consistent with the known literature on steroid metabolism. In a transgenic mouse model developed to mimic a metabolic subtype of polycystic ovary syndrome (PCOS), significant alterations in corticosteroids were detected. In human serum samples, dehydroepiandrosterone and androstenedione were significantly elevated in PCOS patients compared with healthy volunteers. The observed relative changes in both species showed similarities to the steroid perturbation patterns previously reported in PCOS.
    Keywords:  endogenous steroids; high-resolution tandem mass spectrometry; microflow liquid chromatography; plasma; polycystic ovary syndrome; serum
    DOI:  https://doi.org/10.1021/jasms.6c00096
  21. BMC Genomics. 2026 May 30.
    Identification of QTLs linked to bioactive flavonoids and glycosides in the apricot fruit (Prunus armeniaca L.).
    Germán Ortuño-Hernández, Jesús Guillamón Guillamón, Raquel Sánchez-Pérez, Álvaro Delgado, José Enrique Yuste, David Ruiz, Pedro Martínez-Gómez, Juan Alfonso Salazar.
      Apricots are highly valued for their nutritional and functional properties, largely due to their rich content of bioactive compounds, particularly flavonoids, phenolic acids, and glycosides. These secondary metabolites are associated with antioxidant, anti-inflammatory, and antimicrobial activities, as well as contributing to fruit flavor, aroma, and overall quality. This study investigated the genetic basis of flavonoid and glycoside accumulation by integrating untargeted metabolomics (UPLC-QToF-MS/MS) with QTL mapping in two segregating apricot populations: 'Bergeron' × 'Currot' and 'Goldrich' × 'Currot'. Five flavonoids (catechin, epicatechin, myricitrin, quercetin, and rutin), three phenolic acids (coumaric, caffeic, and ferulic acids), and five glycosides (kiwiionoside, neryl arabinofuranosyl-glucoside, vanilloyl glucose, zizybeoside I, and 3-hydroxy-beta-ionol 3-[glucosyl-(1- > 6)-glucoside]) were tentatively identified from metabolite features showing significant differences between parental genotypes (ANOVA, P < 0.05) and subsequently detected across parents and seedlings. QTL mapping identified loci associated with epicatechin on linkage group 1 (LG1), myricitrin and rutin on LG7, and vanilloyl glucose on LG4. In contrast, zizybeoside I exhibited polygenic genomic architecture, with associations distributed across several chromosomes, predominantly on LG2, LG3, and LG5. These findings highlight the genetic loci associated with flavonoid and glycoside biosynthesis, providing valuable insights into marker-assisted selection (MAS) strategies in apricot breeding programs aimed at enhancing the nutritional and functional value of this species.
    Keywords:   Prunus armeniaca L.; Apricot; Flavan-3-ols; Flavonols; Glycosides; Phenolic compounds; QTLs; UPLC-QToF-MS/MS
    DOI:  https://doi.org/10.1186/s12864-026-12989-0
  22. J Nat Prod. 2026 Jun 03.
    Comprehensive Lipidomic Analysis of Streptomyces by NPLC-HRMS Reveals a Previously Unknown Phospholipid Class.
    Sonia Abreu, Clara Lejeune, Colin Grunberger, Enora Jaffrezic, Marie-Joelle Virolle, Pierre Chaminade.
      Studies on lipids in Streptomyces bacteria are limited and primarily rely on thin-layer chromatography (TLC) and off-line mass spectrometry. In this article, we present an in-depth analysis of Streptomyces lipids, performed using normal-phase liquid chromatography coupled with high-resolution mass spectrometry (NPLC-HRMS), applied to various Streptomyces strains in recent collaborations. All identified lipid classes detected in the extracts are described. Numerous mass spectra are provided, offering a detailed view of the distribution of molecular species and their structural characteristics. Above all, HRMS analyses allowed us to elucidate the structure of a new class of phospholipids that had never been described before. This new phospholipid, called PIPA, was shown to result from the condensation of phosphatidylinositol (PI) and phosphatidic acid (PA) and is detected at a specific time in the Streptomyces cell cycle.
    DOI:  https://doi.org/10.1021/acs.jnatprod.5c01603
  23. bioRxiv. 2026 May 23. pii: 2026.05.22.727245. [Epub ahead of print]
    A lipid compendium of a metabolically compromised bacterium provides insights into lipid acquisition, biosynthesis, and metabolism.
    Poulami Chatterjee, Hyejin Esther Shin, Miles I Tuncel, Isaac A Paddy, Alysha K Lee, Joshua McCausland, Paula V Welander, Christine Jacobs-Wagner, Laura M K Dassama.
      The Lyme disease agent Borrelia burgdorferi belongs to a class of metabolically compromised bacteria that cannot survive without host-derived lipids. Survival of the agent in tick and vertebrate hosts requires substantial nutrient acquisition and potential cell envelope remodeling. While prior studies identified cholesterol, cholesterol glycolipids, and phosphatidylcholines as membrane lipids in B. burgdorferi , the identity of many other membrane lipids, their origin, and their physiological relevance remain unknown. Here, we used a suite of untargeted and targeted high-resolution mass spectrometry methods to reveal a complex lipid profile of the pathogen and to identify the origin of its lipids. The analysis detected more than 500 lipids in B. burgdorferi , the majority of which are sourced from the environment. However, the bacterium selectively accumulates certain lipids while excluding others, suggesting discriminatory uptake. These include cholesteryl esters and triglycerides that are organized in foci within the pathogen. Intriguingly, the pathogen also synthesizes predominantly eukaryotic lipids such as the lysosomal bis(monoacylglycerol)phosphate and the plant glycolipid sulfoquinovosyl diacylglycerol (SQDG). The biosynthesis of the latter is carried out by enzymes that exhibit structural homology to plant oxidoreductases and galactosyltransferases, yet their closest orthologs are found in bacteria. This hints that the capability of SQDG synthesis is more widespread in spirochaetes and other bacteria. Together, the comprehensive lipid profiling we report here uncovers novel aspects of the physiology of the metabolically challenged B. burgdorferi and highlights lipid acquisition and synthesis pathways as potentially critical for pathogen survival.
    DOI:  https://doi.org/10.64898/2026.05.22.727245
  24. Pract Lab Med. 2026 Jul;50 e00535
    A semi-automatization magnetic solid-phase extraction method of LC-MS/MS for the quantification of homocysteine and its related metabolites in serum.
    Zhicheng Ye, Xueding Han, Guanfeng Lin, Jianwei Zhou, Yingsong Wu.
       Background: Clinical mass spectrometry is recognized for its high specificity and sensitivity in quantifying small-molecule biomarkers in serum. However, its broad adoption in clinical settings has been limited by challenges such as low automation and time-consuming workflows.
    Methods: This study aimed to develop a rapid, sensitive, and automated magnetic solid-phase extraction(MSPE) method using Hydrophilic-Lipophilic Balance (HLB) magnetic bead-based sample preparation coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the simultaneous quantification of homocysteine(Hcy) and its related nine metabolites. The method was comprehensively validated for specificity, linearity, sensitivity, accuracy, precision, matrix effects, carry-over; and compared it with solid-phase extraction (SPE) methods.
    Results: Results showed excellent linearity (r2 > 0.995) for all nine biomarkers associated with the homocysteine metabolic cycle. Both the limits of detection and quantification met the clinical requirements. Recoveries at low, medium, and high spiking levels ranged from 85.46% to 114.48%. Intra-day precision (CV) was between 0.82% and 7.63%, and inter-day precision (CV) ranged from 1.62% to 11.43%. Matrix effects were acceptable, with internal standard-normalized matrix factors ranging from 0.83 to 1.19. Carry-over rates were between -7.43% and 5.21%. Method comparison with protein precipitation sample preparation showed correlation coefficients from 0.9462 to 0.9957, indicating no systematic bias.
    Conclusions: In conclusion, the developed semi-automatization method is rapid, highly sensitive, and reproducible, making it suitable for quantitative analysis of these nine homocysteine cycle-related biomarkers in clinical serum samples. It provides a reliable analytical tool for the diagnosis, treatment, and prevention of associated diseases.
    Keywords:  Diagnosis; Homocysteine metabolism cycle; LC-MS/MS; Magnetic solid-phase extraction; Semi-automatization
    DOI:  https://doi.org/10.1016/j.plabm.2026.e00535
  25. J Pharm Biomed Anal. 2026 May 29. pii: S0731-7085(26)00255-4. [Epub ahead of print]280 117587
    Detection of Artemisia mongolica floss adulteration in moxa floss: A strategy based on UPLC-Q/Orbitrap HRMS, chromatographic analysis, and machine learning.
    Lin Chen, Han Huang, Zhonge Li.
      Detecting adulteration in closely related herbal medicines is a challenge for quality control and market standardization. This study aimed to identify whether Artemisia mongolica floss was adulterated into moxa floss. First, ultra-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry was employed to analyze the chemical compositions of moxa floss and Artemisia mongolica floss. Combined with partial least squares discriminant analysis, four key differential biomarkers were identified. Subsequently, high-performance liquid chromatography (HPLC) methods were established for these biomarkers, providing the data foundation for subsequent adulteration discrimination model development. Moreover, four machine learning classification models were constructed: logistic regression, decision tree, random forest, and extreme gradient boosting to compare their ability to identify adulterated samples. The results indicated that the decision tree model exhibited strong generalization capability and stability. Based on the established HPLC method and decision tree model, the ratio range of peak areas of the biomarker 5-hydroxy-3',4',6,7-tetramethoxyflavone to eupatilin or jaceosidin can be used as an indicator to effectively identify adulterated samples. In summary, the study established an analytical strategy for identifying specific markers, thereby facilitating practical discrimination in cases of moxa floss adulteration.
    Keywords:  Adulteration detection; Artemisia mongolica floss; Biomarkers; Machine learning; Moxa floss
    DOI:  https://doi.org/10.1016/j.jpba.2026.117587
  26. Fitoterapia. 2026 Jun 02. pii: S0367-326X(26)00241-8. [Epub ahead of print] 107322
    Comparative metabolomic profiling and potential bioactive marker discovery of three Clerodendrum species and their different plant parts.
    Ti-Yu Xia, Xiao-Kang Liu, Hui-Ling Jiang, Yan-Bo Xu, Jia-Wei Wang, Lin Feng, Jie-Ting Chen, Zhen-Wei Li, Dai-di Zhang, Li-Hua Peng, Wei-Lin Qiao, Minling Lai, De-An Guo.
      This study established a multi-dimensional analytical strategy combining UHPLC-Q-TOF/MS, untargeted metabolomics, network pharmacology and molecular docking to systematically explore potential bioactive markers and related metabolic pathways of Clerodendrum plants. Three dominant Clerodendrum species, namely Clerodendrum philippinum var. simplex, Clerodendrum philippinum and Clerodendrum lindleyi, were analyzed using their roots and stems. This work aimed to clarify the scientific evidence supporting their rational medicinal use and efficient resource exploitation. A total of 95 metabolites were annotated via UHPLC-Q-TOF/MS, mainly consisting of phenylethanoid glycosides, diterpenoids and flavonoids. Multivariate statistical analysis screened out 18 differential characteristic compounds capable of differentiating plant species and parts. Notably, such variations were mainly reflected in content levels, while their core chemical skeletons kept highly consistent. Eleven candidate bioactive ingredients were excavated by network pharmacology, and molecular docking verification confirmed that these ingredients exhibited favorable binding activities against core targets PIK3CA and AKT1. The highly consistent chemical profiles and predicted therapeutic pathways solidly support the feasibility of developing and applying the three Clerodendrum species as homologous medicinal materials. Collectively, this study provides fundamental experimental evidence for quality standard formulation and rational development of multi-origin Clerodendrum medicinal herbs, especially for their clinical application in prescriptions including Shiqi Waigan Granules.
    Keywords:  Antipyretic and anti-inflammatory; Clerodendrum; Different species; Network pharmacology; Quality control
    DOI:  https://doi.org/10.1016/j.fitote.2026.107322
  27. PeerJ. 2026 ;14 e21262
    Metagenomic and metabolomic analyses of fecal samples from civet-digested coffee in Vietnam.
    Tam Thi Thanh Tran, Oanh Thi Kieu Nguyen, Phuong Hanh Hoang, Nhung Phuong Nguyen, Huong Thi Mai To, Hoa Quynh Nguyen.
       Background: Civet-digested coffee originates from the feces of civets that consume coffee cherries, where microbial fermentation in the gastrointestinal tract imparts distinctive flavor attributes, thereby enhancing its global reputation and market value. Gut microbiota is considered important drivers of coffee-bean fermentation, potentially shaping the unique and region-specific flavor characteristics of civet-digested coffee. To address this context, the present study integrated metagenomic and metabolomic analyses to compare the gut microbiota and secondary metabolites involved in coffee-bean fermentation inside Vietnamese civets.
    Methods: Fecal samples were collected under two dietary conditions: a standardized one containing 20% protein, 6% fiber, and 0.4-1.5% lysine, and the same diet supplemented with coffee cherries. Metagenomic 16S rRNA sequencing and untargeted ultra-performance liquid chromatography quadrupole time-of-flight (UPLC-QTOF) revealed clear differences between the two groups.
    Results: Integrated metagenomic and metabolomic analyses revealed clear distinctions between the two groups. Civets on the coffee-cherry diet exhibited higher microbial diversity at the family and genus levels. Specifically, among 31 classified bacterial genera showing a trend toward significant differences in abundance, Enterococcus and Escherichia/Shigella decreased, whereas Gluconobacter, and Pseudomonas increased following the diet shift. Metabolomic profiling identified 46 metabolites across both ionization modes, and strong correlations were observed between microbial genera and metabolite profiles. Specifically, 6-hydroxyangolensic acid methyl ester, 4-aminobenzoic acid and caffeine were more abundant in civets on a coffee-cherry diet, meanwhile the other nine metabolites were more prevalent in the normal diet. Overall, the findings demonstrate that civet gut microbiota and metabolic output were highly responsive to dietary inputs, and that coffee cherries promoted a unique fermentation environment. This represents the first integrative metagenomic and metabolomic study of civets consuming coffee in Vietnam, providing valuable insights into microbial contributions to coffee fermentation.
    Keywords:  16S rRNA metagenomics; Civet coffee; Gut microbiota; UPLC-QToF HRMS; Untargeted metabolomics
    DOI:  https://doi.org/10.7717/peerj.21262
  28. J Sci Food Agric. 2026 Jun 01.
    Integrated volatile profiling and metabolomics reveal changes in volatile compounds  during low-temperature smoking of sea bass (Lateolabrax maculatus).
    Hua Feng, Minqiang Guo, Jianing Guan, Shiqiang Yin, Xinyu Xu, Zhiyu Liu, Hao Dong, Hao Wang, Ishfaq Ahmed, Hong Lin, Zhenxing Li.
       BACKGROUND: Low-temperature smoking is increasingly used as a mild processing approach to develop the characteristic aromas of fish products; however, the origins and stage-dependent changes in volatile compounds during processing remain insufficiently understood. This study investigated volatile changes in sea bass (Lateolabrax maculatus) during low-temperature smoking using an integrated strategy combining gas chromatography-ion mobility spectrometry (GC-IMS), gas chromatography-mass spectrometry (GC-MS), multivariate analysis, and untargeted metabolomics.
    RESULTS: Gas chromatography-ion mobility spectrometry detected 31 volatile compounds and GC-MS detected 39, showing pronounced stage-dependent shifts in volatile profiles. Multivariate analysis, specifically orthogonal partial least squares discriminant analysis (OPLS-DA), clearly discriminated early stage samples (S1-S3) from smoked and matured samples (S4-S5) and identified stage-discriminant volatiles. Metabolomics-based pathway enrichment indicated that representative lipid-related volatiles, including hexanal, (E)-2-octenal, and 1-octen-3-ol, were associated with linoleic and α-linolenic acid oxidation pathways. After smoking, phenolic and furanic compounds commonly associated with wood thermal decomposition (e.g., guaiacol and 5-methylfurfural) increased markedly and were retained after maturation, supporting substantial smoke-derived contributions to the final volatile profile. In addition, aldehyde-alcohol associations, together with odor-threshold and relative odor activity value results, suggested progressive softening of sharp green oxidized notes during low-temperature smoking and maturation.
    CONCLUSION: The results support a process-dependent aroma development pattern in which endogenous lipid-related reactions and smoke-derived constituents jointly shape volatile profiles during low-temperature smoking. This work provides a practical basis for volatile-marker selection, process optimization, and flavor quality control for smoked fish products. © 2026 Society of Chemical Industry.
    Keywords:  GC–IMS; GC–MS; low‐temperature smoking; metabolomics; sea bass (Lateolabrax maculatus); volatile profiling
    DOI:  https://doi.org/10.1002/jsfa.70747
  29. Environ Int. 2026 May 30. pii: S0160-4120(26)00294-1. [Epub ahead of print]213 110336
    Integrating liquid chromatography-high resolution mass spectrometry based suspect screening with ionization efficiency prediction to expand semi-quantitative coverage in wastewater-based epidemiology: application to environmental chemicals.
    Yuan-Chih Chen, I-Ha Lao, Jen-Yi Hsu, Yung-Chieh Lin, Chiau-Jun Chu, Yen-Ping Lin, Yun-Ju Tsai, Pao-Chi Liao.
      Wastewater-based epidemiology (WBE) is increasingly used to assess population-level chemical exposure, yet its analytical scope is often limited by target-based methods and the availability of reference standards. In this study, we developed an integrated analytical workflow that expanded the coverage of environmental chemicals and enabled standard-free semi-quantitative analysis under a realistic post hoc WBE scenario. Influent wastewater samples from nine wastewater treatment plants in Taiwan were re-examined using a single additional liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis in full-scan and data-dependent acquisition modes. A total of 189 environmental compound signals were structurally identified, representing 79 unique chemical structures. Semi-quantitative concentration estimation was achieved using a single-point calibration strategy based on isotope-labeled internal standards originally included for illicit drug analysis, combined with ionization efficiency (IE) prediction tools. Internal cross-validation yielded median prediction errors of 2.3-fold in positive ionization mode and 5.0-fold in negative ionization mode. Furthermore, cross-method validation against historical targeted datasets for six illicit drugs within the same sample cohort revealed a median underestimation of 6.7-fold. To evaluate accuracy under controlled conditions, an independent spiking experiment using authentic standards in groundwater demonstrated an overall median error of 4.3-fold, with 81.3 % of the quantified analytes falling within one order of magnitude of their nominal concentrations. Population-normalized mass load analysis revealed regional differences in chemical exposure profiles, with elevated mass loads of smoking-related biomarkers, including cotinine and nornicotine, observed in wastewater from southern Taiwan.
    Keywords:  Ionization efficiency prediction; Liquid chromatography–high-resolution mass spectrometry; Semi-quantification; Suspect screening; Wastewater-based epidemiology
    DOI:  https://doi.org/10.1016/j.envint.2026.110336
  30. Brief Bioinform. 2026 May 04. pii: bbag276. [Epub ahead of print]27(3):
    Assessing current capabilities for incorporating lipidomics in multiomics data integration.
    Dylan H Ross, Raghav Jain, Hyeyoon Kim, Javier E Flores, Soumaydeep Sarkar, Chaevien S Clendinen, Jennifer E Kyle, Tao Liu, Sara J C Gosline.
      Comprehensive analyses of multiple biological components including nucleic acids, proteins, metabolites, and lipids (i.e. "multiomics") provide unique insights into complex biological processes. Combining insights from these components through multiomics data integration enhances the depth and nuance of biological understanding available from these measurements. Among methods that integrate data across different technologies (e.g. mass spectrometry, sequencing), those that link components based on biological prior knowledge-pathway analyses-represent the most direct way of translating molecular-level observations into meaningful biological insights. However, significant barriers exist that prevent full utilization of metabolomics and especially lipidomics data in pathway integration. Challenges include the fast turnover and complex interactions of small molecules compared to biological macromolecules, low metabolite annotation rates, isomerism among lipids, and a lack of lipid representation in existing pathway knowledge bases. While these issues stem from a variety of causes, improvements to multiomics pathway integration including better incorporation of lipids into pathway knowledge bases and increased adoption of artificial intelligence approaches can greatly enhance the utility of small molecule -omics data, particularly for lipidomics. Here, we describe the current landscape of multiomics integration tools with an emphasis on support for metabolomics and lipidomics data, we highlight their capabilities and gaps with tangible examples using real multiomics data, and provide our perspective on how these approaches can be improved to better support generation of useful biological insights from complex multiomics data.
    Keywords:  artificial intelligence; lipidomics; multi-omics integration; pathway analysis
    DOI:  https://doi.org/10.1093/bib/bbag276
  31. Poult Sci. 2026 May 25. pii: S0032-5791(26)00808-4. [Epub ahead of print]105(9): 107177
    A confirmation test for the presence of Quillaja saponin and adulteration with Madhuca saponin in poultry feed using high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry.
    Bernhard Thalhamer, Markus Himmelsbach, Christian Klampfl, Wolfgang Buchberger.
      To date, there is a lack of a confirmation test for the presence of Quillaja saponin in poultry feed. This gap persists despite the fact that thousands of tonnes of feed with added saponin are produced and used annually. As a result, even basic tasks such as the analytical confirmation of feed formulations have remained unaddressed. Our work intentionally targets this gap by introducing a presence confirmation. Additionally, poultry feed was examined for the presence of Madhuca longifolia L., as previous studies had demonstrated that commercially available Quillaja saponin extracts are often adulterated with cost-effective Madhuca saponin. Pelleted poultry feed samples were analyzed via high-performance liquid chromatography (HPLC) hyphenated with quadrupole time-of-flight mass spectrometry (QToF-MS) in the negative ionization mode, targeting the main saponin compounds serving as markers for presence confirmation of Quillaja and Madhuca saponin in complex feed matrices. The unequivocal confirmation of Quillaja in feed samples containing additives classified as flavoring substances or sensory additives was enabled for the first time by this newly developed test. Unexpectedly, Madhuca was also found in some samples, clearly revealing adulteration for the first time. This confirmation test may support industry and authorities to confirm the presence of Quillaja saponin in feed and to check poultry feed for adulteration with Madhuca saponin. Furthermore it provides a practical tool for feed manufacturers by offering clarity as often the composition of saponin-containing additives is not fully disclosed by additive suppliers.
    Keywords:  Adulteration; Confirmation test; Madhuca longifolia L; Poultry feed; Quillaja saponaria L
    DOI:  https://doi.org/10.1016/j.psj.2026.107177
  32. Eur J Mass Spectrom (Chichester). 2026 Jun 01. 14690667261455594
    HPLC-HRMS determination of anticancer drugs and their degradants identified by heterogeneous photocatalysis as potential river toxicants.
    Serena Arpaia, Andrea Magnani, Alberto Asteggiano, Debora Fabbri, Paola Calza, Claudio Medana, Federica Dal Bello.
      Anticancer drugs are considered pseudo-persistent pollutants for the water environmental compartments due to their continuous emission into water bodies. Many sources contribute to the bioaccumulation of the drugs, and the investigation of their residues and transformation products (TPs) fate is mandatory, in particular for compounds displaying a high degree of toxicity. Here, we present a High-Performance Liquid Chromatography - High-Resolution Mass Spectrometry (HPLC-HRMS) method developed to identify and characterize the TPs of cyclophosphamide (CYC), 5-fluorouracil (5FU) and oxaliplatin (OXA), subjected to heterogenous photocatalysis mediated by titanium dioxide. The irradiation experiments were performed at different times, and the HPLC separation was achieved using a reverse-phase column. A high-resolution Orbitrap mass analyzer was used in the positive and negative ionization modes with a resolution of 30k. Tandem mass spectrometry experiments were acquired in the CID activation mode. With the developed method, we recognized and tentatively assigned the structures of six TPs for CYC, four TPs for 5FU and one TPs for OXA. The developed analytical method was then applied to real environmental water samples coming from nine sampling sites of Po River (Italy). We found the presence of CYC in four site points at level of ppb. The developed HPLC-HRMS method was a satisfactory tool to identify anticancer drugs and their TPs and to quantify them in real water environmental samples.
    Keywords:  Anticancer drugs; HPLC-HRMS; photocatalysis; titanium dioxide; transformation products; water environmental samples
    DOI:  https://doi.org/10.1177/14690667261455594
  33. Anal Chem. 2026 Jun 01.
    High-Throughput Reactive Desorption Electrospray Ionization Mass Spectrometry for Targeted Derivatization and Analysis of Poorly Ionized Small Molecules.
    Yunfei Feng, Kitmin Chen, Kai-Hung Huang, Joseph V Caruso, Christopher J Welch, R Graham Cooks, Christina R Ferreira, Nicolás M Morato.
      Mass spectrometry (MS) analysis of some biologically relevant compounds is limited because of their inherently poor ionization efficiency. This challenge can often be overcome by incorporating easily ionized moieties via functional-group-targeted chemical derivatization. However, the wide variety of derivatization agents and conditions can make method development cumbersome. In this study, we employ an automated high-throughput (HT) platform based on desorption electrospray ionization (DESI) MS to rapidly screen (1 sample per second) and select appropriate derivatization strategies for poorly ionized analytes leveraging accelerated on-the-fly microdroplet reactions. This approach allowed the rapid identification of efficient derivatization strategies that were then readily applied to the qualitative and quantitative analyses of molecules of biological importance. Specifically, hydroxysteroids (e.g., cholesterol, testosterone, and cholecalciferol) were imaged in tissue sections using 4-formyl-1-methylpyridinium benzenesulfonate without the loss of the intrinsic spatial resolution of DESI, while 4-borono-N,N,N-trimethylbenzenaminium allowed sensitive and HT quantitation of urinary 3-methoxy-4-hydroxyphenylglycol, a metabolite whose urine levels have been correlated with neurological disorders.
    DOI:  https://doi.org/10.1021/acs.analchem.6c00291
  34. Pulm Med. 2026 ;2026(1): e5535650
    Untargeted and Targeted Liquid Chromatography-Mass Spectrometry-Based Lipidomic Profiling Revealed a Potential Biomarker Panel to Distinguish Clinical Isolates of M. tuberculosis From Nontuberculous M. kansasii.
    Meenakshi Chugh, Saif Hameed, Jitendra Singh, Zeeshan Fatima.
      The rising incidence of nontuberculous mycobacteria (NTM) infections, particularly Mycobacterium kansasii (M. kansasii), and their overlapping clinical features with Mycobacterium tuberculosis (MTB) are leading to diagnostic ambiguity in tuberculosis-endemic regions. Accurate differentiation remains limited by conventional diagnostic methods, emphasizing the need for molecular- and lipid-based biomarkers. In the present era of "OMICS" sciences, herein we attempted a comprehensive analysis of MTB and M. kansasii lipid profiles to elucidate species-specific lipidomic features as potential biomarkers. Three clinical isolates each of MTB and M. kansasii were obtained from treatment-naïve patients with microbiologically confirmed pulmonary infections. Lipid profiling was performed by integrating untargeted and targeted profiling through ultraperformance liquid chromatography coupled with tandem mass spectrometry from bacilli total lipid and mycobacterial cell wall lipid extracts. Untargeted profiling exhibited a higher abundance of fatty acyls, GLs, selected GPLs (PE, PG, PI, and PA), and saccharolipids (SLs) specifically Ac2SGL in MTB, whereas M. kansasii isolates were characterized by elevated levels of GPLs like PC and LPC, polyketides, and specific SLs such as diacylated trehalose species. Targeted quantification confirmed differential expression of GL (TG and DG) and GPL (PC, PE, LPC, PI, and PS) species, supporting their diagnostic relevance. Additionally, biomarker analysis further identified five lipid species with strong discriminative potential. Collectively, these findings support the development of a robust lipidomic biomarker panel for the accurate differentiation of MTB and M. kansasii, with potential implications for improved diagnostics and targeted therapeutic strategies after further confirmation.
    Keywords:  Mycobacterium tuberculosis; biomarker panel; lipidomics; mass spectrometry; nontuberculous mycobacteria; tuberculosis
    DOI:  https://doi.org/10.1155/pm/5535650
  35. J Chromatogr A. 2026 Jun 01. pii: S0021-9673(26)00482-6. [Epub ahead of print]1783 467153
    Streamlined LC-HRMS-based determination of sterols and triterpenic alcohols in edible oils for authentication purposes.
    Irene Caño-Carrillo, Beatrice Schiavone, Andrés J Rascón, Bienvenida Gilbert-López, Juan F García-Reyes.
      Sterol analysis is a key parameter for assessing the authenticity and quality of olive oil, as defined by official methods. Nevertheless, these protocols are labor-intensive and require multiple preparation steps, including sample fractionation and derivatization. In the present work, a simplified analytical methodology is proposed for the determination of free sterols and triterpenic alcohols, eliminating these time-consuming procedures. The method is based on the direct analysis of saponified extracts, prepared according to the official protocol, followed by a single dilution step and analysis by liquid chromatography coupled to high-resolution mass spectrometry with electrospray ionization (LC-ESI-HRMS). To enhance the ionization efficiency of free sterols, which exhibit low response under conventional ESI conditions due to their non-polar nature, ammonium formate was added to the mobile phase. The analytical performance of the method was evaluated in terms of linearity, reproducibility, and limits of quantification to support its suitability for comparative fingerprinting analysis. Based on the obtained sterol profiles, several compounds were identified as potential markers for differentiating edible oils, including extra virgin olive oil, pomace olive oil, rapeseed oil, avocado oil, almond oil, and sunflower oil. The application of principal component analysis (PCA) to these data further enabled the distinction of extra virgin olive oil from the remaining vegetable oils, demonstrating the discriminatory potential of the proposed approach for oil characterization and classification.
    Keywords:  Authentication; Edible oils; LC-MS; Mass spectrometry; Olive oil; Sterols
    DOI:  https://doi.org/10.1016/j.chroma.2026.467153
  36. Chem Biodivers. 2026 Jun;23(6): e71394
    Phytochemical Profile and Insecticidal Potential of Two Halophytes, Atriplex halimus and Suaeda fruticosa, Against Tribolium confusum: In Vitro and In Silico Approaches.
    Mounira Tabouri, Mustapha Djellouli, Khadidja Belkheir, Kamel Benotmane, Meriem Dahoun, Abdesselem Si Mohammed, Saïd Nemmiche, Hamdi Bendif, Sulaiman A Alsalamah, Walid Elfalleh, Fehmi Boufahja, Inmaculada Moscoso-Ruiz, Inés Cea-Pavez, Vito Verardo, Stefania Garzoli.
      This study aimed to investigate the phytochemical profile and evaluate the in vitro and in silico insecticidal activities of methanolic extracts from two halophytes, Atriplex halimus (A. halimus) and Suaeda fruticosa (S. fruticosa). The investigations included phytochemical screening, determination of total phenolic, flavonoid, and tannin contents, as well as compound identification by high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (HPLC-ESI-QTOF-MS). The insecticidal activity was assessed through direct-contact bioassays against adults of Tribolium confusum. In addition, a molecular docking study was performed targeting the acetylcholinesterase enzyme (AChE). Phytochemical screening revealed the presence of several bioactive compounds in both plant extracts. A. halimus showed the highest flavonoid content, whereas S. fruticosa exhibited the highest levels of total phenolics and tannins, corresponding to superior insecticidal effect, as evidenced by its lower lethal concentration 50% (LC50) value after 96 h of treatment. HPLC-ESI-QTOF-MS analysis in negative ion mode led to the identification of 24 bioactive constituents in A. halimus and 29 in S. fruticosa. The in silico study confirmed that most of the identified polyphenols exhibited binding affinity toward insect acetylcholinesterase, including kaempferol 3-O-rutinoside-7-O-glucoside, naringin, feruloyltyramine, and rutin.
    Keywords:  Atriplex halimus; Suaeda fruticosa; Tribolium confusum; insecticidal activity; molecular docking; phytochemical profile
    DOI:  https://doi.org/10.1002/cbdv.71394
  37. Sci Rep. 2026 Jun 02.
    Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning.
    Diane Duroux, Yiqi Yang, Janko Sattler, Michael Krauthammer, Adrian Egli.
      Antimicrobial resistance (AMR) is a significant global health threat. Recent studies have shown that combining MALDI-TOF mass spectrometry with machine learning algorithms can accelerate AMR determination. However, these efforts have predominantly focused on bacterial pathogens. The significant morbidity, mortality, and healthcare costs associated with fungal infections highlight the need for accurate and early detection of antifungal resistance. We developed a machine learning pipeline integrating MALDI-TOF mass spectrometry data and drug features to predict antifungal resistance and identify spectral biomarkers. By leveraging the DRIAMS dataset, we included 658 pathogen spectra linked to 3,046 phenotypic antifungal resistance results across three drug classes and seven yeast species. Models were trained using categorical phenotypic antifungal susceptibility testing results as ground truth. We systematically investigated how different dimensionality reduction methods, antifungal encodings, and model types affected predictive performance using nested cross-validation. We identified that applying principal component analysis to MALDI-TOF mass spectra, and training a multi-layer perceptron yielded the highest and most stable performance for the prediction of antifungal resistance. Our method achieved an AUPRC of 0.77 across the 10 highest-performing species-drug pairs. The model demonstrated the best performance for the species-drug combinations of Candida albicans with micafungin, Candida parapsilosis with fluconazole, and Saccharomyces cerevisiae with itraconazole and fluconazole. By comparing established species-based guidelines, susceptibility test results, and machine learning predictions, we estimated that integrating our algorithm into antifungal selection could help avoid prescriptions to likely resistant pathogens in approximately 3 out of 10 patients for whom standard guidelines recommend such treatments.
    DOI:  https://doi.org/10.1038/s41598-026-53519-y
  38. Am J Clin Pathol. 2026 Jun 04. pii: aqag061. [Epub ahead of print]165(6):
    Development and validation of a quantitative ultra-performance liquid chromatography quadrupole time-of-flight method for urine organic acid analysis.
    Yi Xiao, Michael Wakefield, Mari Ishak Gabra, Lidong Zhai, Edward Ki Yun Leung.
       OBJECTIVES: Urine organic acid analysis is essential for diagnosing inborn errors of metabolism and is conventionally performed using multistep, labor-intensive sample preparation and gas chromatography-mass spectrometry (GC-MS). We sought to develop and validate a quantitative ultra-performance liquid chromatography quadrupole time-of-flight (UPLC-QTOF) method with a "dilute-and-shoot" approach.
    METHODS: 20 µL of calibrator, quality control material, or urine specimen, normalized by creatinine concentration, was mixed with mobile phase A (0.05% formic acid in water) and internal standards to a final volume of 440 µL. The supernatant was injected onto a Waters ACUITY Premier HSS T3 UPLC column, with data acquired in MSE mode on a Waters Xevo G3 QTOF mass spectrometer and quantification achieved using both linear and quadratic regressions.
    RESULTS: The method quantifies 27 analytes and separates diagnostically important isomers in 20 minutes. Repeatability and reproducibility were 12% or less coefficient of variation, with no carryover observed. Spike-recovery studies demonstrated recoveries between 85% and 115%, and concordant results were obtained from 51 urine specimens vs the conventional GC-MS method. No matrix effect was identified except for 3-hydroxyglutaric acid. Compared with other UPLC-QTOF methods, improved chromatographic performance was achieved with the Premier HSS T3 column, while MSE high-resolution MS data provided fragmentation information to support higher-confidence compound identification. Compared with conventional GC-MS methods, this method requires substantially lower specimen volume and simplified sample preparation.
    CONCLUSIONS: This UPLC-QTOF dilute-and-shoot urine organic acid method demonstrated acceptable analytical and clinical performance. Continued optimization will be pursued to expand the panel and support the diagnosis of a broader range of inborn errors of metabolism.
    Keywords:  UPLC-QTOF; clinical mass spectrometry; high-resolution mass spectrometry; inborn errors of metabolism; mass spectrometry validation; urine organic acid
    DOI:  https://doi.org/10.1093/ajcp/aqag061
  39. Se Pu. 2026 Jun;44(6): 667-674
    [Determination of 13 prohibited alkaloids in essential oil-based cosmetics by ultra performance liquid chromatography-tandem mass spectrometry combining QuEChERS purification].
    Zhen Liu, Yu-Mei Wang, Jian-Bin Pan, Ling-Li Zong, Yu-Han Song, Xiao-Jie Sun, Hong-Yuan Chen.
      Alkaloids represent a class of naturally-occurring nitrogen-containing compounds widely distributed across diverse plant species. Owing to their well-documented potential to induce adverse effects on human health， certain alkaloids are explicitly prohibited from being used in cosmetic formulations. The escalating global popularity of essential oil-based cosmetics， which commonly incorporate complex botanical extracts， presents a potential avenue for the inadvertent introduction of these prohibited substances. Consequently， the development of robust， sensitive， and efficient analytical methods for their monitoring is of utmost significance for consumer safety and regulatory compliance. This study devises a reliable， high-throughput approach for the simultaneous determination of 13 prohibited alkaloids in essential oil-based cosmetics. It integrates optimized QuEChERS sample preparation with ultra performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS）. The sample preparation procedure was meticulously designed to maximize efficiency and minimize analyte loss. Chromatographic separation was accomplished on a Waters ACQUITY UPLC HSS T3 column （100 mm × 2.1 mm， 1.8 μm） maintained at a constant temperature of 30 ℃. The mobile phase was composed of （A） acetonitrile and （B） 0.1% （volume fraction） formic acid aqueous solution. A gradient elution program was implemented at a stable flow rate of 0.3 mL/min according to the following profile： an initial 5， a linear increase to 15 （0-2 min）， a rapid rise to 70 （2-4.5 min）， followed by an immediate reversion to the initial 5 （4.5-5.5 min）， which was maintained for re-equilibration until 7.0 min. The injection volume was 5 µL. Detection and quantification were conducted using a triple quadrupole mass spectrometer equipped with an electrospray ionization （ESI） source operating in positive ion mode （ESI+）. Data acquisition was carried out in the multiple reaction monitoring （MRM） mode to ensure superior specificity and sensitivity. For each of the 13 alkaloids， two specific ion transitions were monitored： one for quantitative analysis and the other for confirmatory identification. The method was rigorously validated in accordance with accepted analytical guidelines. All 13 target alkaloids displayed excellent linearity within a mass concentration range of 0.2 to 50 ng/mL， with correlation coefficients （R2） consistently exceeding 0.99. The limits of detection （LODs） and limits of quantification （LOQs）， determined with acceptable accuracy and precision， ranged from 1 µg/kg to 4 µg/kg and 2 µg/kg to 10 µg/kg， respectively. Method accuracy and precision were assessed through recovery tests at three spiking levels， with six replicates at each level. The mean recoveries for all analytes ranged from 83.9% to 119.1%， with associated relative standard deviations （RSDs） all being ≤7.3%， validating the method's high reliability and repeatability. Systematic evaluation indicated that the matrix effects for the 13 analytes were negligible； hence， the solvent calibration curve was adopted for quantitative analysis. The practical applicability of the validated method was demonstrated through the analysis of 50 batches of commercially available essential oil-based cosmetics. This market survey encompassed 15 products specifically marketed for infants or children and 35 products intended for adult use. As a result， none of the 13 target prohibited alkaloids were detected in any of the tested samples above their respective LOQs. A particularly notable accomplishment of this work is the successful development of a sensitive and reliable quantification strategy for oleandrin， a potent cardiotoxic alkaloid for which standardized detection methods in complex cosmetic matrices such as essential oils have been conspicuously absent. In conclusion， this study successfully establishes a simple， rapid， sensitive， and robust QuEChERS-UPLC-MS/MS method. It is comprehensively validated and clearly well-suited for the routine screening， risk monitoring， and quality control of 13 prohibited alkaloids in a wide array of essential oil-based cosmetics. The method offers reliable technical support for regulatory bodies to enforce safety standards and for manufacturers to ensure the safety and compliance of their products， thereby effectively contributing to the protection of consumer health.
    Keywords:  QuEChERS purification; essential oil-based cosmetics; prohibited alkaloids; ultra performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS）
    DOI:  https://doi.org/10.3724/SP.J.1123.2025.10009
  40. J Vis Exp. 2026 May 12.
    Development and Validation of a Rapid LC-MS/MS Method for Carfilzomib Quantification in Plasma from Multiple Myeloma Patients.
    Zhengyan Liang, Jing Qian, Mao Tang, Liya Ye, Zhenhui Jiang, Huanxing Han, Xinran Wang, Xia Tao, Xue Qi, Shouhong Gao.
      Carfilzomib, a second-generation proteasome inhibitor, requires therapeutic drug monitoring (TDM). This study aimed to develop and validate a sensitive and robust liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the quantification of carfilzomib in human plasma. Plasma samples were processed using protein precipitation with acetonitrile (ACN). Chromatographic separation was achieved on a C18 column (2.1 mm × 100 mm, 3.5 µm) maintained at 40 °C, with a mobile phase composed of ACN and 10 mmol/L ammonium acetate in water (80:20, v/v) delivered at a flow rate of 0.35 mL/min. The method demonstrated excellent linearity over a concentration range of 2.00-1000.00 ng/mL. In addition, high intra-day and inter-day precision and accuracy were observed, with recovery rates for carfilzomib ranging from 84.1%-93.0%. This validated LC-MS/MS method enables accurate, efficient, and sensitive determination of carfilzomib concentrations in plasma from patients with multiple myeloma, thereby supporting the optimization of carfilzomib-based therapy.
    DOI:  https://doi.org/10.3791/70167
  41. J Proteomics. 2026 Jun 04. pii: S1874-3919(26)00085-0. [Epub ahead of print] 105682
    Protein-protein interactions in Escherichia coli: Using cross-linking/mass spectrometry (XLMS) to reveal our current knowledge gaps in interactomes.
    Giovanna Lopes de Araújo, Guilherme Reis-de-Oliveira, Fabio Gozzo.
      Protein-protein interactions (PPIs) are fundamental to cellular processes and often define phenotypes more accurately than protein abundance alone. Despite their importance, confidently identifying direct physical interactions remains challenging. Even in the benchmark organism Escherichia coli, our survey of the IntAct database reveals that only 8% of reported interactions are annotated as direct physical associations. Of these, 44% rely on gold-standard structural methods, while XLMS accounts for only 11%, highlighting a lack of high-confidence, scalable data for primary interactors. In this study, we employed an XLMS workflow using the MS-cleavable cross-linker DSSO and SCX-based enrichment to investigate the E. coli interactome. This targeted approach yielded 21,599 cross-linked spectrum matches (CSMs), corresponding to 2334 residue pairs. These data mapped 663 intra-protein cross-linking and 137 PPIs, 47 of which were previously unreported. Notably, XLMS identified interactions with confidence scores below 0.7 in the STRING database, demonstrating its capability to detect low-confidence or uncharacterized associations. We further illustrate the power of this technique by analyzing the ElaB-YqjD complex, where our experimental distance constraints revealed a mismatch with AlphaFold 3 predictions. These results demonstrate how XLMS can effectively bridge the gap in current PPI datasets, providing high-confidence, mechanistically informative data even in well-studied biological systems. SIGNIFICANCE: Protein-protein interactions (PPIs) are essential to cellular organization and function, and even the extensively studied Escherichia coli, the majority of interactions remain either uncharacterized or poorly supported by high-confidence experimental data. As part of this study, we conducted a systematic analysis of the IntAct database, which showed that only 8% of annotated interactions correspond to direct physical associations, with 44% supported by gold-standard structural methods and only 11% derived from XL-MS. This analysis highlights a clear gap between interaction annotations and experimentally validated physical interactions, providing the rationale for the experimental strategy adopted here. To address this gap, we applied a state-of-the-art cross-linking mass spectrometry (XL-MS) approach using the cleavable cross-linker DSSO, coupled with SCX enrichment and advanced data analysis tools, to generate a high-confidence, experimentally derived E. coli interactome. This effort uncovered 47 novel PPIs and revealed substantial discrepancies between database annotations and physical interaction evidence. Our integrative workflow not only provides direct evidence of protein interactions within native cellular environments but also yields spatial constraints that challenge and refine current structural models, as illustrated by the elaB-yqjD complex. By bridging the gap between proteomic data and structural validation, this work demonstrates how XL-MS can meaningfully expand and validate biological interaction networks.
    Keywords:  Alphafold; Cross-linking; Interactome; Structural proteomics
    DOI:  https://doi.org/10.1016/j.jprot.2026.105682
  42. ACS Omega. 2026 May 26. 11(20): 29360-29373
    Polyphenols of Zingiber roseum Leaf as a Potential Source of α‑Glucosidase Inhibitors for Type‑2 Diabetes Management: LC-MS/MS, Pharmacological and Computational Analysis.
    Md Shadman Sakib, Nuraniye Eruygur, Shakhawat Ahmed, Mustafa Abdullah Yilmaz, Syed Mumtahin Mannan Siam, Md Moniruzzaman, S M Naim Uddin, Abbas Tarhan, Md Naiemur Rahman, A F M Shahid Ud Daula.
      Zingiber roseum Roscoe, a plant belonging to the Zingiberaceae family, has been extensively utilized in traditional medicine for its efficacy in treating diabetes, inflammation, and cancer. Earlier studies focused on the rhizomes of Z. roseum, which are rich in secondary plant metabolites such as phenolic acids, flavonoids, terpenoids, gingerols, and shogaols. However, fresh research suggests that the leaves may have similar or perhaps greater pharmacological potential. Therefore, the objective of this study was to identify and characterize the bioactive polyphenolic compounds responsible for antidiabetic action as well as to assess the antidiabetic properties of the methanolic extract of Z. roseum leaves (ZrlME). First, 16 phenolic compounds were identified and quantified in ZrlME by LC-MS/MS analysis. In addition, the in vitro results demonstrated that ZrlME had substantial α-glucosidase inhibitory activity, as evidenced by the low IC50 value of 570 μg/mL which was more pronounced than the IC50 value of acarbose (585 μg/mL). Furthermore, for the 20 day treatment period, both the 400 and 200 mg/kg BW dosages of ZrlME exhibited a substantial (P < 0.001) decrease in blood glucose levels in diabetic mice. Among the polyphenols, miquelianin, nicotiflorin, astragalin, kaempferol, and acacetin demonstrated higher binding affinity (-7.5 to -6.8 kcal/mol) to α-glucosidase enzyme (PDB ID: 5NN8) than the reference standard, acarbose (-6.6 kcal/mol) and a significant number of hydrogen bonds in the enzyme's active site under molecular docking studies. Molecular dynamics simulations (MDS) also affirmed the stability of binding of miquelianin and nicotiflorin against α-glucosidase. Moreover, the pharmacokinetic parameters prediction, bioactivity and toxicity assessment ensured the safety and efficacy of those key compounds, suggesting the potential of Z. roseum leaves as a valuable natural source for the development of antidiabetic drugs.
    DOI:  https://doi.org/10.1021/acsomega.5c04507
  43. Clin Chim Acta. 2026 Jun 01. pii: S0009-8981(26)00310-4. [Epub ahead of print] 121128
    Mass spectrometry in laboratory medicine: advances in automation, multiplex biomarker quantification, and diagnostics.
    Farida Khamidova, Ugilzhan Tatykayeva, Gulsara Reymnazarova, Shakhnoza Davronova, Ikrom Tilyabov, Shakhboz Khasanov.
      Mass spectrometry (MS) has evolved from a specialized research tool into an increasingly indispensable platform in laboratory medicine. Its clinical value is driven by molecular specificity, multiplex quantification, and the ability to measure small molecules, peptides, proteins, drugs, metabolites, and microbial spectral fingerprints in workflows that are becoming more automated and standardized. The field is nevertheless heterogeneous: liquid chromatography-tandem mass spectrometry (LC-MS/MS), matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), gas chromatography-mass spectrometry (GC-MS), and emerging high-resolution or miniature instruments address different clinical questions and require different validation strategies. The review focuses on the realistic transition of MS from specialist reference laboratories to routine clinical practice. It summarizes fundamental principles of MS measurement, current clinical platforms, microbial identification by MALDI-TOF MS, automated monoclonal protein analysis, LC-MS/MS steroid and therapeutic drug monitoring, GC-MS applications, MassARRAY genotyping, pre-analytical vulnerabilities, and barriers to implementation. Fully automated MALDI-TOF MS platforms (e.g., EXENT) now achieve 92.6-95.7% concordance with conventional electrophoresis for monoclonal immunoglobulin detection while showing superior sensitivity for low-concentration M-proteins. Multiplex LC-MS/MS panels enable simultaneous quantification of 23 plasma steroids and 15 protein-bound uremic toxins with excellent linearity and precision, whereas MassARRAY-based genotyping has achieved 99.75% detection success in newborn screening for primary carnitine deficiency. Critical remaining limitations include incomplete inter-laboratory harmonization, high capital and maintenance costs, the need for trained personnel, matrix effects, pre-analytical instability, limited evidence for some biomarker panels, and uncertain regulatory pathways for laboratory-developed tests. The strongest near-term applicability is expected in high-volume, well-validated use cases - newborn screening, therapeutic drug monitoring, steroid profiling, toxicology, microbial identification, monoclonal protein testing, and selected multiplex biomarker panels - rather than unrestricted replacement of immunoassays. The future relevance of clinical MS is likely to remain strong, provided that automation is coupled with rigorous analytical validation, external quality assessment, and clear demonstration of clinical utility.
    Keywords:  Automation; Clinical chemistry; Gas chromatography-mass spectrometry; Liquid chromatography-tandem mass spectrometry; Mass spectrometry; Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry; Metabolomics; Newborn screening; Pre-analytical factors; Steroid profiling; Therapeutic drug monitoring
    DOI:  https://doi.org/10.1016/j.cca.2026.121128
  44. BMC Bioinformatics. 2026 Jun 04.
    LipidCruncher: an open-source platform for processing, visualizing, and analyzing lipidomic data.
    Hamed Abdi, Yohannes A Ambaw, Zon Weng Lai, Ritchie Ly, Chandramohan Chitraju, Shubham Singh, Robert V Farese, Tobias C Walther.
       BACKGROUND: Advances in mass spectrometry (MS)-based lipidomics have led to a significant surge in data volume, underscoring a need for robust tools to efficiently evaluate and visualize these expansive datasets. While numerous software tools have been developed, current workflows are hindered by manual spreadsheet handling and insufficient data quality assessment prior to analysis. Here, we introduce LipidCruncher, an open-source, web-based platform designed to easily process, visualize, and analyze lipidomic data with high efficiency and rigor.
    RESULTS: LipidCruncher consolidates key steps of the lipidomics analysis workflow, including data standardization, normalization, and stringent quality controls. The platform also provides advanced visualization and analysis tools that are tailored to interrogate lipidomic data and enable detailed and holistic data exploration. To illustrate LipidCruncher's utility, we analyzed lipidomic data from adipose tissue of mice lacking the triacylglycerol synthesis enzymes DGAT1 and DGAT2.
    CONCLUSIONS: LipidCruncher fills a specific gap in the lipidomics analysis ecosystem by providing an integrated, quality-focused platform that accepts data from multiple sources and complements existing specialized tools. By bridging the critical divide between data generation and biological interpretation, LipidCruncher facilitates rigorous lipidomics analyses to accelerate the translation of complex lipid profiles into biological insights.
    Keywords:  Bioinformatics; Computational biology; Lipidomics; Lipids; Mass spectrometry; Open-source software; Phospholipids; Scientific software; Sphingolipids; Sterols
    DOI:  https://doi.org/10.1186/s12859-026-06483-3
  45. Biomed Chromatogr. 2026 Jul;40(7): e70514
    A Comprehensive Strategy for Characterizing Metabolites and Metabolic Profile in Rat Urine and Feces Following Oral Administration of Huachansu Tablets Based on UPLC-ESI-QTOF/MSE.
    Yongzhi Zhu, Ruijuan Li, Hui Cheng, Meng Wang, An Zhou, Rong Wang, Chuan Luo, Qinglin Li, Huan Wu.
      Huachansu (HCS) tablets, prepared from dried toad skin through water decoction and ethanol extraction, possess detoxifying and analgesic effects and are clinically used in the treatment of advanced tumors. However, the in vivo metabolism of HCS tablets has not been systematically investigated. In this study, a comprehensive analysis strategy based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOF/MSE) was established for screening and identifying the prototype components and metabolites of HCS tablets in rat urine and feces. A total of 28 prototype components (16 bufadienolides, six alkaloids, four amino acids and peptides, two nucleosides) and 64 metabolites (44 bufadienolide-related metabolites and 20 alkaloid-related metabolites) were identified in the urine and feces of rats treated with HCS tablets. Based on the analysis of 64 metabolites, the metabolic processes of HCS tablets in rats mainly include desaturation, reduction, dehydroxylation, hydroxylation, hydration, dehydration, glucuronidation, methylation, acetylcysteine conjugation, acetylcysteine-S conjugation, and other related conjugation reactions. This approach enabled the rapid identification of HCS-related exogenous components in rat urine and feces, helping to elucidate the active substances and facilitate mechanistic research.
    Keywords:  Huachansu tablets; UPLC–ESI–QTOF/MSE; metabolic profile
    DOI:  https://doi.org/10.1002/bmc.70514
This page is being maintained by Thomas Krichel. It was last updated on 2026‒06‒08 at 7:06.