bims-mascan 2026-08-16 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–08–16
eighteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

MetaboGraph: A Framework for Metabolomics and Lipidomics Annotation and Pathway Network Analysis.
A Derivatization-Free Parallel Reaction Monitoring-Based Proteomics Workflow for Quantitative, Site-Specific Analysis of Histone Post-Translational Modifications.
Systematic Workflow Optimization for Ultra-Sensitive Targeted Immunopeptidomics.
Chimeric structural isomer fragments as cost-efficient internal standards for amino acid quantification by mass spectrometry.
MSstatsResponse: Semi-parametric statistical model enhances detection of drug-protein interactions in chemoproteomics experiments.
Secondary Amine-Based Chemical Isotope Labeling Coupled with Mass Spectrometry for Simultaneous Quantification of Carboxylic Compounds during Hepatocellular Carcinoma Progression.
Simultaneous Quantification of Hundreds of Phosphorylated Metabolites in Multiple Biological Matrices with Ion-Pairing Reversed-Phase Ultrahigh-Performance Liquid Chromatography and Mass Spectrometry.
Proteomic Biomarker Discovery in Breast Cancer: Advances, Challenges, and Translational Prospects.
UbiSite Approach: Global Site-Specific Profiling of Canonical and Non-Canonical Ubiquitination in Cells and Tissues.
The Application of LC-MS in Pharmaceutical Analysis.
Protocol for proteomic profiling of hypusinated protein candidates using a clickable spermidine probe.
Profiling the aromatic amino acid metabolome in the human gut microbiota reveals Clostridioides difficile-specific N-acyl amino acids.
Aza-Paternò-Büchi derivatization enables annotation of triacylglycerols by negative-ion-mode mass spectrometry at the double bond level.
Protocol for untargeted LC-MS/MS metabolomics annotation and differential abundance analysis using the SIRIUS Python client.
Metabolic and Oxylipin Signature of Testicular Germ Cell Tumors in Seminal Plasma.
Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets.
Visible-Light-Activated Dual-Pathway Strategy for Comprehensive Lipid Isomer Analysis.
A Transformer-Based Framework for Quality Control of Peptide Tandem Mass Spectra.

Anal Chem. 2026 Aug 11. 98(31): 22755-22770

MetaboGraph: A Framework for Metabolomics and Lipidomics Annotation and Pathway Network Analysis.

Oluwatosin Daramola, Judith Nwaiwu, Odunayo Oluokun, Mojibola Fowowe, Yehia Mechref.

Untargeted metabolomics and lipidomics generate high-dimensional data sets whose biological interpretation remains challenging, particularly at the pathway and network levels. Here, we present MetaboGraph, a standalone Python-based workflow for end-to-end metabolomics and lipidomics analysis, enabling pathway-level interpretation from small-molecule data. MetaboGraph integrates automated data cleaning, comprehensive multidatabase metabolite and lipid annotation, pathway mapping, and direction-aware pathway inference. A central feature of the platform is its ability to predict pathway direction by integrating metabolite/lipid-level fold changes with pathway membership structure, supporting biologically interpretable pathway and network analyses beyond conventional enrichment approaches. MetaboGraph supports multiomics integration and comparative analysis, enabling consistent pathway-level interpretation across metabolomics, lipidomics, and multiple studies. We demonstrate the platform using untargeted LC-MS/MS metabolomics and lipidomics data comparing two breast cancer cell lines with distinct metastatic potential, MCF7 (HTB22; less metastatic) and MDA-MB-453 (HTB131; more metastatic). Relative to HTB22, the HTB131 cells exhibited coordinated metabolic remodeling, including altered amino acid and nitrogen metabolism, increased nucleotide biosynthetic demand, lipid remodeling, and changes in energy-associated pathways. These pathway-level alterations are consistent with established metabolic adaptations associated with increased cancer aggression. MetaboGraph expands the analytical toolbox for small-molecule biology and facilitates reproducible, biologically grounded insights from metabolomics and lipidomics data sets.

DOI: https://doi.org/10.1021/acs.analchem.6c01446
ACS Omega. 2026 Jul 21. 11(28): 42597-42609

A Derivatization-Free Parallel Reaction Monitoring-Based Proteomics Workflow for Quantitative, Site-Specific Analysis of Histone Post-Translational Modifications.

Hyoungjoo Lee, Ashley K Wiseman, Bailey M Tibben, Ibukunoluwa Sodiya, Scott B Rothbart, Molly T Soper-Hopper, Ryan D Sheldon.

Histone post-translational modifications (PTMs) are key regulators of chromatin architecture and gene expression. Although mass spectrometry (MS)-based data-independent acquisition (DIA) pipelines for histone PTM quantification are available, multiplexed targeted assays remain underdeveloped. Here, we present a derivatization-free parallel reaction monitoring (PRM) workflow for robust, quantitative, and site-specific analysis of major histone H3 and H4 PTMs. We employed highly efficient ArgC digestion to generate peptides of optimal length for liquid chromatography-tandem mass spectrometry (LC-MS/MS) while preserving endogenous PTMs, and we optimized chromatographic conditions to achieve isobaric separation and stable retention times. Co-eluting isobaric PTM species were confidently distinguished using site-specific fragment ions. The resulting PRM method enabled sensitive and reproducible detection of histone PTM isoforms across diverse biological systems. To illustrate its utility, we analyzed PTM dynamics in cells expressing histone H3.3 lysine-to-methionine substitutions and in cells treated with the histone deacetylase inhibitor entinostat, yielding results that correlated strongly with antibody-based readouts. This PRM platform provides a complementary, targeted approach to current chemical derivatization-based methods, enabling reliable validation of selected histone PTMs.

DOI: https://doi.org/10.1021/acsomega.6c02411
Mol Cell Proteomics. 2026 Aug 04. pii: S1535-9476(26)00130-1. [Epub ahead of print] 101634

Systematic Workflow Optimization for Ultra-Sensitive Targeted Immunopeptidomics.

Jonas D Förster, Jonas P Becker, Nika Vučković, Angelika B Riemer.

  Epitope detection sensitivity remains a primary bottleneck in mass spectrometry (MS)-based immunopeptidomics, as conventional discovery-based workflows such as data-dependent (DDA) and data-independent acquisition (DIA) frequently lack the sensitivity required to detect ultra-low abundant targets. While these untargeted methods are powerful for mapping the general immunopeptidome, the stochastic nature of precursor selection and the presence of complex, chimeric spectra mean that rare species, such as viral or mutation-derived neoepitopes, often remain undetected. In this study, we present optiPRM+, an ultra-sensitive targeted-first workflow for the Orbitrap Exploris 480 platform that integrates systematically optimized targeted acquisition with untargeted DIA contextualization to bridge this sensitivity gap. Our approach centers on the empirical characterization of target peptides using direct infusion-MS to determine optimal fragmentation conditions and inclusion list-driven data-dependent acquisition (iDDA). To maximize signal-to-noise ratios for these trace-level targets, we employed ultra-high MS2 resolutions (up to 480,000), ion injection times up to 1000 ms, and narrow precursor isolation windows. Additionally, we discovered that precursors with a charge state exceeding their basic amino acid count require unusually low energies for optimal fragmentation, which is especially relevant for the non-tryptic peptides characteristic of the immunopeptidome. We applied the optiPRM+ workflow to the challenging biological case of Human Papillomavirus type 16 (HPV16), a virus known to suppress antigen presentation pathways. This optimized strategy enabled the confident identification and validation of the human leukocyte antigen (HLA)-A*02:01-restricted epitope TIHDIILECV and, to our knowledge, the first MS-based detection of two novel viral targets: ISEYRHYCY (HLA-A*01:01) and CVYCKQQLLR (HLA-A*11:01). Subsequent global immunopeptidome analysis via DIA confirmed that these ultra-low abundance peptides were not detectable through untargeted methods despite being clearly validated by our targeted approach. By successfully detecting these viral peptides, we demonstrate that a systematically optimized targeted-first approach can uncover biologically relevant epitopes that remain invisible to conventional discovery-based workflows.

Keywords:  Data-independent acquisition (DIA); Human Papillomavirus (HPV); Immunopeptidomics; Method optimization; Parallel reaction monitoring (PRM); Targeted mass spectrometry; Tumor antigens

DOI:  https://doi.org/10.1016/j.mcpro.2026.101634
Anal Methods. 2026 Aug 12.

Chimeric structural isomer fragments as cost-efficient internal standards for amino acid quantification by mass spectrometry.

Luis Alberto De León-Jiménez, Robert Winkler.

Amino acid (AA) profiles from body fluids such as blood and urine are clinical indicators for diagnosing metabolic and hepatic diseases. Current quantitative methods, such as liquid chromatography-mass spectrometry (LC-MS) with isotopically labelled internal standards (ISs), are costly and technically demanding. This study proposes a cost-efficient alternative using structural isomers as ISs in a direct liquid infusion (DLI) tandem mass spectrometry (MS/MS) approach. The method leverages chimeric spectra and fragment intensity ratios to quantify AAs, demonstrating high linearity and precision even with a 3D ion trap mass analyser. This approach offers a viable strategy for AA quantification in preventive medicine, particularly for screening metabolic diseases such as phenylketonuria, diabetes, and liver dysfunction.

DOI: https://doi.org/10.1039/d6ay01194b
Mol Cell Proteomics. 2026 Aug 12. pii: S1535-9476(26)00133-7. [Epub ahead of print] 101637

MSstatsResponse: Semi-parametric statistical model enhances detection of drug-protein interactions in chemoproteomics experiments.

Sarah Szvetecz, Devon Kohler, Joel D Federspiel, S Denise Field, Pierre Jean-Beltran, Robert J Seward, Hyunsuk Suh, Liang Xue, Olga Vitek.

  Chemoproteomics is a popular approach for the identification of small molecule-protein interactions in biological systems. Several chemoproteomics workflows leverage functionalized chemical probes and mass spectrometry to measure protein engagement through direct protein enrichment or competition using a range of small molecule concentrations. Statistical methods for analysis of such dose-response chemoproteomics datasets are limited. For example, existing methods rely on fixed curve shapes and are sensitive to experimental variation, particularly when the number of doses or replicates is limited. Here, we present MSstatsResponse, a semi-parametric statistical framework for analyzing chemoproteomic dose-response experiments that uses isotonic regression that does not require a fixed curve shape. This approach improves the accuracy and robustness of curve fitting, target identification, and half-response estimation across diverse experimental designs. We evaluate MSstatsResponse by generating a benchmark chemoproteomic dataset that profiled the competition between the kinase-binding probe XO44 and the drug Dasatinib using three mass spectrometry acquisition strategies: data-independent acquisition, tandem mass tag-based data-dependent acquisition, and selected reaction monitoring. We further evaluate the method on simulated datasets that vary the number of doses, number of replicates, and levels of noise, and demonstrate that MSstatsResponse consistently improves sensitivity, specificity, and reproducibility compared to existing methods, particularly in low-replicate and low-dose settings. MSstatsResponse is implemented as an open-source R/Bioconductor package that integrates with the MSstats ecosystem for quantitative proteomics. It provides a unified workflow for preprocessing, curve fitting, target identification, and experimental design, enabling researchers to select the number of doses and replicates appropriate to their experimental goals. The software and documentation are freely available at https://bioconductor.org/packages/MSstatsResponse.

Keywords:  Chemoproteomics; Dose-response analysis; Drug discovery; Experimental design; Mass spectrometry; Quantitative proteomics; Statistical modeling

DOI:  https://doi.org/10.1016/j.mcpro.2026.101637
Anal Chem. 2026 Aug 11. 98(31): 23032-23040

Secondary Amine-Based Chemical Isotope Labeling Coupled with Mass Spectrometry for Simultaneous Quantification of Carboxylic Compounds during Hepatocellular Carcinoma Progression.

Jun Zhang, Yu-Han Dai, Yue Zhu, Xue-Yi Zhao, Guo-Jia Song, Chengjie Ma, Fei-Long Liu.

Carboxylic metabolites and drugs play essential roles in biological regulation and disease progression. Their simultaneous quantification remains analytically challenging due to their low ionization efficiency and poor chromatographic retention. Here, we synthesized a pair of secondary amine-based chemical isotope labeling reagents, N-(piperidin-4-ylmethyl)benzamide (PMBA) and d5-N-(piperidin-4-ylmethyl)benzamide (d5-PMBA), for the simultaneous labeling of carboxylic metabolites and drugs. The secondary amine moiety on PMBA/d5-PMBA can efficiently react with carboxyl groups on carboxylic compounds, substantially improving the reversed-phase (RP) chromatographic retention and separation, along with the enhancement of the MS sensitivity. Upon this strategy, the limits of detection (LODs) of 32 carboxylic metabolites and drugs were determined to range from 0.001 to 0.13 ng mL-1. The chemical isotope labeling approach with liquid chromatography-mass spectrometry (LC-MS) analysis enabled reliable quantification of diverse carboxylic compounds in serum samples and revealed significant alterations in their abundances during hepatocellular carcinoma progression. This method provides a sensitive and robust analytical platform for the simultaneous quantification of carboxylic metabolites and drugs and facilitates investigations of their biological and pharmacological functions in diseases.

DOI: https://doi.org/10.1021/acs.analchem.6c02580
Anal Chem. 2026 Aug 11. 98(31): 22666-22673

Simultaneous Quantification of Hundreds of Phosphorylated Metabolites in Multiple Biological Matrices with Ion-Pairing Reversed-Phase Ultrahigh-Performance Liquid Chromatography and Mass Spectrometry.

Huan Wang, Dandan Yang, Qi Wang, Qingyu Hu, Qinsheng Chen, Huiru Tang.

Many phosphorylated analytes are present in complex mixtures and have diverse essential functions involving biology, chemistry, food, and environmental sciences; their simultaneous quantification is vital for efficiently revealing their crucial functions but remains challenging. Here, we developed a high-coverage method for quantifying many phosphorylated analytes in one run using ion-pairing reversed-phase ultrahigh-performance liquid chromatography and tandem mass spectrometry. Good sensitivity (limit-of-detection <0.95 pmol), linearity (R2 > 0.99), recoveries (80%-120%), precision (CV < 20%), and intertechnician consistency (CV < 20%) were demonstrated for simultaneously quantifying 125 such analytes including nucleotides, nucleotide sugars, phosphorylated amino acids and sugars, and the enzyme-cofactors derived from vitamin B1 (TMP, TPP, TTP), B2 (FMN, FAD), B3 (NAD, NADP), B5 (acyl-CoAs), and B6 (PLP, PMP). The method applicability was also confirmed by quantifying 43-78 phosphorylated metabolites in five typical biological matrices including human urine, plasma, cells, feces, and rabbit liver tissue, illustrating their significant molecular phenotypic differences in the phosphometabolome. By quantifying the phosphometabolomic differences using the method, we further revealed some metabolic characteristics associated with the drug resistances for human non-small cell lung cancer cells. This offers a reliable method for the quantitative investigation of phosphorylated metabolites and their functions.

DOI: https://doi.org/10.1021/acs.analchem.5c08214
J Biochem Mol Toxicol. 2026 Aug;40(8): e71049

Proteomic Biomarker Discovery in Breast Cancer: Advances, Challenges, and Translational Prospects.

Mahan Hassani, Iman Morshedi, K J Matmurotov, Isanova Shoira Tulkinovna, Abdullaev Makhmudjon Mukhamedovich, Mahdieh Khorsandi, Abdiyev Rasul Ernazar Ugli, Tianyao Yang, Xi Lin.

  Breast cancer remains a leading cause of cancer-related morbidity and mortality among women worldwide. Despite major progress in genomic classification, molecular pathology, and targeted therapy, the identification of clinically reliable proteomics-derived biomarkers for early detection, prognostic stratification, treatment response prediction, and longitudinal monitoring remains an unresolved translational challenge. Recent studies have applied proteomic profiling to breast cancer tissues and clinically accessible biofluids, including serum, plasma, saliva, nipple aspirate fluid, urine, and extracellular vesicle-enriched fractions, to identify protein signatures with potential diagnostic and therapeutic relevance. Contemporary platforms, including LC-MS/MS, data-independent acquisition mass spectrometry, multiplexed quantitative proteomics, targeted proteomics using multiple reaction monitoring and parallel reaction monitoring, spatial proteomics, and affinity-based high-throughput assays such as Olink and SomaScan, have expanded the analytical depth and clinical scalability of breast cancer biomarker research. However, many candidate biomarkers remain confined to discovery or early verification stages because of limited analytical sensitivity, insufficient specificity, inter-platform variability, small and heterogeneous cohorts, incomplete external validation, and uncertainty regarding clinical utility beyond established pathological markers. This review critically evaluates current proteomic technologies and proteomics-derived biomarker candidates in breast cancer, distinguishes discovery-level findings from clinically validated evidence, and discusses the major methodological, regulatory, and implementation barriers that continue to limit translation into routine oncology practice. Greater emphasis on standardized workflows, targeted verification, multi-center validation, and clinically actionable biomarker panels will be essential for integrating proteomics into precision breast cancer care.

Keywords:  biomarker validation; breast cancer; clinical translation; mass spectrometry; protein biomarkers; proteomics

DOI:  https://doi.org/10.1002/jbt.71049
Methods Mol Biol. 2026 ;3069 53-72

UbiSite Approach: Global Site-Specific Profiling of Canonical and Non-Canonical Ubiquitination in Cells and Tissues.

Nicoline Kingo Pedersen, Vyacheslav Akimov, Blagoy Blagoev.

  Ubiquitination is a complex post-translational modification that regulates a wide range of cellular processes through the covalent attachment of ubiquitin to substrate proteins. While canonical ubiquitination occurs on lysine residues, non-canonical modifications on serine, threonine, and the protein N-terminus are increasingly recognized. This chapter describes the UbiSite approach, a refined strategy for site-specific mapping of global ubiquitination using mass spectrometry-based proteomics. The method relies on digestion of the proteome of interest with LysC endopeptidase. LysC digestion leaves a 13 amino acid remnant on the ubiquitinated site of modified peptides, and this remnant is recognized by the monoclonal UbiSite antibody allowing selective enrichment of ubiquitinated peptides. Tens of thousands modification sites are readily identified from cells or tissue extracts in a single UbiSite experiment with the workflow described here. The 13 amino acid remnant is unique to ubiquitin and the UbiSite antibody thereby discriminates ubiquitin from other ubiquitin-like modifiers and enables detection of both canonical and non-canonical ubiquitination. The UbiSite procedure can be automated and combined with label-free quantification as well. UbiSite offers a robust and versatile platform for comprehensive ubiquitinome profiling with enhanced specificity and depth, enabling new insights into the regulatory roles of ubiquitin across diverse biological contexts.

Keywords:  Affinity purification; Canonical ubiquitination; Mass spectrometry; N-terminal ubiquitination; Non-lysine ubiquitination; UbiSite; Ubiquitin; Ubiquitylation

DOI:  https://doi.org/10.1007/978-1-0716-5508-5_4
Molecules. 2026 Jul 28. pii: 2624. [Epub ahead of print]31(15):

The Application of LC-MS in Pharmaceutical Analysis.

Wing Lam, Wei Cai.

Liquid chromatography-mass spectrometry (LC-MS) has evolved from a specialized analytical tool into an indispensable cornerstone of pharmaceutical analysis over the past three decades [...].

DOI: https://doi.org/10.3390/molecules31152624
STAR Protoc. 2026 Aug 10. pii: S2666-1667(26)00427-2. [Epub ahead of print]7(3): 104774

Protocol for proteomic profiling of hypusinated protein candidates using a clickable spermidine probe.

Tian Zhang, Jianlong Li, Xinbo Hu, Hua Zhu, Xin Zhou, Zheng-Jiang Zhu, Li Tan, Zhaoyin Wang, Yaoyang Zhang, Junying Yuan, Bing Shan.

  Hypusination is a unique posttranslational modification in which deoxyhypusine synthase (DHPS) transfers an aminobutyl moiety from spermidine to specific lysine residues, followed by deoxyhypusine hydroxylase (DOHH)-mediated hydroxylation. Here, we present a protocol that enables proteome-wide identification of candidate hypusinated proteins. We describe steps for the synthesis of a clickable alkynyl-spermidine probe, DHPS-dependent metabolic labeling in cells, click chemistry-mediated biotinylation, streptavidin-based enrichment, and subsequent mass spectrometry analysis of probe-labeled proteins. We also describe the procedures for data processing and statistical analysis. For complete details on the use and execution of this protocol, please refer to Zhang et al.1.

Keywords:  Cell Biology; Mass Spectrometry; Molecular/Chemical Probes

DOI:  https://doi.org/10.1016/j.xpro.2026.104774
mSystems. 2026 Aug 10. e0077926

Profiling the aromatic amino acid metabolome in the human gut microbiota reveals Clostridioides difficile-specific N-acyl amino acids.

David Chun-Cheng Hsieh, Linhai Jiang, Mengzhao Xue, Niv Antonovsky, Sean F Brady.

  Owing to its compositional and chemical complexity, much of the gut microbiota metabolome remains poorly characterized. Aromatic amino acids (AAAs) have a history of being privileged substrates for the biosynthesis of diverse bioactive metabolites and thus represent a potentially rich source of bioactive molecules within the microbiota metabolome. In this study, we leveraged 13C- and 2H-labeled aromatic amino acids and untargeted liquid chromatography-mass spectrometry (LC-MS) to profile AAA-derived metabolites produced by 80 phylogenetically diverse human gut bacterial isolates. Collectively, we found 93 unique LC-MS features, majority of which, predominantly produced by Clostridioides difficile, were identified as N-acyl amino acids. C. difficile produced the highest levels of the AAA-derived phenylacetic acid and phenylpropionic acid, exceeding all Bacteroidetes and Proteobacteria strains in our panel. C. difficile's uniquely diverse N-acyl amino acids have the potential to serve as biomarkers for C. difficile colonization and mediators of C. difficile-specific host interaction.IMPORTANCEThe bacterial metabolome is a key component of the microbiota's effect on host physiology, but identifying small molecules that potentially drive this interaction has remained a challenge. This study uses high-throughput and quantitative mass spectrometry metabolomics to show that Clostridioides difficile uniquely converts amino acids into at least 28 N-acyl amino acids, a metabolite family historically linked to diverse bioactivities. Additionally, in C. difficile cultures, high levels of phenylacetic acid, the precursor of 6 N-acyl amino acids, are of interest because previous studies have mechanistically linked microbially produced phenylacetic acid to cardiovascular disease via β2-adrenergic receptor (β2AR) signaling. The identification of species-specific metabolites produced by commensal bacteria provides not only compounds that could serve as sensitive biomarkers of colonization but also helps support the formulation of mechanistic hypotheses regarding how individual species influence their host.

Keywords:  Clostridioides difficile; gut microbiome; isotope tracing; mass spectrometry; metabolism; metabolomics

DOI:  https://doi.org/10.1128/msystems.00779-26
Talanta. 2026 Aug 07. pii: S0039-9140(26)01052-0. [Epub ahead of print]312(Pt A): 130396

Aza-Paternò-Büchi derivatization enables annotation of triacylglycerols by negative-ion-mode mass spectrometry at the double bond level.

Andrea Cerrato, Aldo Laganà, Carmela Maria Montone, Enrico Taglioni, Daniel Weissman, Anna Laura Capriotti.

  Triacylglycerols (TGs) represent one of the most abundant lipid classes in biological and food matrices; however, their structural characterization by mass spectrometry remains challenging because conventional workflows often provide only sum composition information. In the present study, an aza-Paternò-Büchi (aPB) derivatization strategy based on 6-azauracil was optimized and applied for negative-ion-mode, isomer-resolved analysis of TGs by high-resolution mass spectrometry. Reaction conditions were systematically investigated through solvent optimization, mixture design modeling, and kinetic studies. Moreover, the ionization and fragmentation behavior of aPB-derivatized TGs was comprehensively investigated, revealing distinct fragmentation pathways associated with charge localization effects in neutral lipids. Application to complex matrices demonstrated the feasibility of the approach, providing direct access to TG regioisomer distributions in human plasma and vegetable seed oils. Overall, the proposed strategy enables untargeted and double-bond-resolved annotation of TGs in complex matrices while maintaining straightforward sample preparation and compatibility with routine lipidomics workflows. For the first time, negative-ion mode was employed for TG annotation, expanding the molecular information accessible in TG lipidomics and food analysis.

Keywords:  Carbon-carbon double bonds; High-resolution mass spectrometry; Lipidomics; Plasma; Regioisomer; Seed oil

DOI:  https://doi.org/10.1016/j.talanta.2026.130396
STAR Protoc. 2026 Aug 07. pii: S2666-1667(26)00424-7. [Epub ahead of print]7(3): 104771

Protocol for untargeted LC-MS/MS metabolomics annotation and differential abundance analysis using the SIRIUS Python client.

Jonas A Emmert, Sebastian Böcker, Markus Fleischauer.

  We present a protocol for programmatic untargeted LC-MS/MS metabolomics using PySirius, the Python client for SIRIUS. We describe steps for importing data with LC-MS feature alignment, and annotating features with molecular formulas, structures, and compound classes. We then detail procedures for analyzing differential abundance by fold change between groups and visualizing results. As proof of concept, we replicate the finding that rosmarinic acid is more abundant in old than young rosemary (Rosmarinus officinalis) leaves.

Keywords:  Bioinformatics; Metabolomics; Protocols in Metabolomics and Lipidomics; Special Issue

DOI:  https://doi.org/10.1016/j.xpro.2026.104771
Andrology. 2026 Aug 09.

Metabolic and Oxylipin Signature of Testicular Germ Cell Tumors in Seminal Plasma.

Luz Alonso-Dasques, Maricruz Mamani-Huanca, Sandra M Camunas-Alberca, Nina Mørup, Sofia B Winge, Constanza Fernández-Hernández, Ángeles López-Gonzálvez, Francisco J Rupérez, Antonia García, Serge Rudaz, Serge Nef, Ameer Y Taha, Kristian Alsmtrup, Coral Barbas, Víctor González-Ruiz.

   BACKGROUND: Testicular germ cell tumors (TGCTs) are the most common malignancy in young adult men, and the development of reliable non-invasive diagnostic tools remains a clinical priority. Seminal plasma is a complex yet accessible biofluid that reflects the function of the male reproductive system and may capture metabolic alterations associated with testicular pathology.
OBJECTIVES: This study aimed to characterize the seminal plasma metabolome of TGCT patients using a multi-platform metabolomics strategy that covers polar metabolites and oxylipins.
MATERIALS AND METHODS: Seminal plasma from 37 TGCT patients and 11 non-TGCT clinical controls was analyzed by capillary electrophoresis-mass spectrometry (CE-MS) and oxylipin profiling using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Immunohistochemical analysis of testicular tumor tissue was used to confirm enzymatic expression degree.
RESULTS: CE-MS revealed coordinated alterations in polar metabolites, particularly many involved in energy metabolism, suggesting a disruption of testicular metabolic homeostasis in TGCTs. Reduced levels of key energy-related amino acids in plasma suggested increased metabolic demand in tumor cells, consistent with altered biosynthetic and redox pathways. Additional changes in hexosamine pathway metabolites indicated potential alterations in protein glycosylation and secretory activity. Oxylipin profiling using targeted LC-MS/MS identified several lipid mediators with significant differences between groups. Four compounds (9,10-DiHOME, 6-trans-LTB4, TXB2, and 14,15-DiHETrE) were elevated in TGCT samples and demonstrated discriminatory potential in univariate analyses. A composite oxylipin ratio of these compounds achieved excellent diagnostic performance, indicating that oxylipin-based signatures may support TGCT detection. Immunohistochemical staining confirmed arachidonate 12-lipoxygenase (ALOX12) expression in tumor tissue, confirming biological plausibility for the observed oxylipin alterations.
DISCUSSION AND CONCLUSION: Seminal plasma carries detectable metabolic signatures of TGCT, encompassing alterations in energy metabolism and inflammation-related lipid mediators. These findings establish a foundation for developing non-invasive metabolomic biomarkers for TGCT and warrant validation in larger and independent cohorts.

Keywords:  TGCT; multi‐platform metabolomics; oxylipins; testicular germ cell tumors

DOI:  https://doi.org/10.1111/andr.70347
Anal Chem. 2026 Aug 11. 98(31): 22586-22594

Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets.

Andrea Junior Carnoli, Federico Padilla-Gonzalez, Leonieke M van den Bulk, Daan Korporaal, Martin Alewijn, Marco H Blokland, Bas H M van der Velden.

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a widely used analytical technique for characterizing the chemical composition of organic samples. Due to its high sensitivity and ability to detect thousands of chemical features in a single run, untargeted LC-HRMS experiments generate highly complex and data-rich datasets that typically require advanced computational methods, including machine learning, for meaningful interpretation. While traditional machine learning approaches have been applied to LC-HRMS data, their performance remains limited for complex tasks. Deep learning has demonstrated improved performance, but both machine and deep learning are often constrained by the complexity and scarcity of labeled LC-HRMS data. Foundation models present a promising new horizon for LC-HRMS data analysis, given their ability to learn transferable representations from large-scale unlabeled data and adapt efficiently to downstream tasks with limited labeled samples. Recent studies have shown that foundation models can outperform conventional machine learning approaches in chemical annotation and molecular property prediction. We envision that foundation models for LC-HRMS data will benefit from the expansion of curated sample repositories and spectral libraries, developing privacy-preserving training strategies, enabling simultaneous modeling of multiple LC-HRMS data types, and improving model explainability.

DOI: https://doi.org/10.1021/acs.analchem.6c01825
Anal Chem. 2026 Aug 11. 98(31): 23289-23300

Visible-Light-Activated Dual-Pathway Strategy for Comprehensive Lipid Isomer Analysis.

Rongrong Fu, Yanan Xiao, Yashu Xiao, Bailin Wu, Rong Chen, Suming Chen.

Phospholipids are essential components of biological membranes. Their structural diversity, due to the presence of sn-positions, C = C locations and geometric configurations, significantly influences their biological functions. The isomerization of unsaturated lipid isomers plays a crucial role in bacterial responses to environmental stimuli. However, in-depth mechanistic studies have been lacking effective analytical tools. Here, we developed a visible light-activated cycloaddition and isomerization reaction between methyl benzoylformate and unsaturated lipids, combined with liquid chromatography-mass spectrometry, to establish a comprehensive method for resolving lipid isomers. Subsequently applying this method to investigate bacterial stress response mechanisms, we identified unique sn-position, C = C location and cis/trans isomers conversion patterns in Pseudomonas under high-temperature stress. This reveals the critical role of lipid isomers in bacterial responses to environmental stress. This study not only enables comprehensive identification of lipid isomers but also provides an effective tool and key insights for investigating bacterial stress response mechanisms.

DOI: https://doi.org/10.1021/acs.analchem.6c03367
ACS Omega. 2026 Aug 04. 11(30): 45167-45175

A Transformer-Based Framework for Quality Control of Peptide Tandem Mass Spectra.

Mengxiao Cao, Penghuan Liu.

In tandem mass spectrometry (MS/MS)-based proteomics, a significant portion of acquired spectra remains unidentified due to poor quality, which consumes excessive computational resources and increases false-positive rates during database searches. Traditional quality assessment methods rely on handcrafted features or classical machine learning that often generalize poorly across different instruments. While recent deep-learning approaches like SPEQ (spectrum quality) have introduced automation, their reliance on convolutional architectures and supervised learning limits their ability to capture global spectral dependencies and transfer across heterogeneous data sets. To address these limitations, we present a pretrained transformer framework that utilizes self-attention for automated MS/MS quality assessment. By leveraging self-supervised pretraining to learn robust, contextualized spectral representations, our model can capture global fragment relationships and ensure superior cross-instrument transferability. Our results demonstrate that it consistently outperforms existing models like SPEQ, offering higher accuracy and enhanced generalization across diverse data sets.

DOI: https://doi.org/10.1021/acsomega.6c03661