bims-mascan 2026-03-15 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–03–15
eighteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Towards large-scale mass spectrometry-based omics for clinical applications.
Investigating Variability in Metabolomics: A Comparative Study of Analytical Platforms and Blood Matrices Using HPLC-HRMS.
Comprehensive lipidomic characterization of human blood by high-throughput UHPSFC/MS using bioinert column and modified MTBE extraction.
Polar microcystins or arginine methyl ester can serve as sensitive reference materials for system suitability tests in untargeted metabolomics using reversed-phase LC-MS: A case study.
Changes in Metabolism and Lipid Composition with Nitrogen Starvation and Recovery in a New Productive Strain of Neochlorella semenenkoi Using N15-Isotopic Labeling and HRMS.
Data-Independent Acquisition (DIA) Strategy for Measuring Protein Stability Using Stability of Proteins from Rates of Oxidation (SPROX).
Blood proteomics: insights from public data.
Spatial Proteomics Using S4P.
A label-free microLC-SWATH-MS methodology with immunoaffinity depletion of highly abundant serum proteins for quantitative proteomic comparison of fresh-frozen human normal breast tissue and tumor clinical specimens.
Resolving lipid isomers up to the double bond position level using reversed-phase chromatography, EAD fragmentation, and high-resolution mass spectrometry: Application to pancreatic cancer.
Advanced Phosphoproteomics Depth and Quantitative Performance Using an Integrated noFAIMS-FAIMS Approach.
Multiplexed LC-MS analysis reveals novel insights into grapevine defense mechanisms by expanding metabolome coverage.
Shared-autosampler parallel LC-MS/MS platform for high-throughput multi-analyte quantification in complex matrices.
Comprehensive top-down mass spectral repository enables pan-dataset analysis and top-down spectral prediction.
Biomolecular profiling for noninvasive health monitoring.
S'Wipe: user-friendly stool collection for high-throughput gut metabolomics and multi-omics.
Differentiation of cannabinoid isomers via Cu-mediated molecular ion formation and electrospray ionization-tandem mass spectrometry.
Proteomics and tracer metabolomics link GAPDH ISGylation to glycolytic control.

Expert Rev Proteomics. 2026 Mar 14.

Towards large-scale mass spectrometry-based omics for clinical applications.

Maor Arad, Leonard J Foster.

   INTRODUCTION: As healthcare advances toward personalized medicine, mass spectrometry-based research is advancing our understanding of cellular biology and disease states, and translating these findings into clinical applications. This review highlights recent advances in methodology and technology that demonstrate the capabilities of mass spectrometry-based proteomics, lipidomics, and metabolomics in clinical practice.
AREAS COVERED: The ability to directly analyze functional molecules with mass spectrometry uncovers crucial clinical information. Each data modality (proteins, lipids, and metabolites) provides essential insight into healthy and disease states. As technology advances, integrating data from different modalities unlocks new possibilities for clinical research. To gain the most from this multi-omic data, unsupervised integration methods can provide detailed insights into complex biological processes. As the field applies this knowledge, healthcare could experience significant leaps in the near future. This review examines recent advancements in mass spectrometry-based proteomics, lipidomics, and metabolomics, focusing on how improvements in sample preparation, automation, and multi-omics data integration are making large-scale clinical studies more accessible.
EXPERT OPINION: Recent technical and methodological advancements in mass spectrometry analysis have propelled healthcare toward a tipping point, shifting from traditional RNA- and DNA-based research to downstream analysis of protein, lipid, and metabolite effectors.

Keywords:  Automation; lipidomics; liquid chromatography; mass spectrometry; metabolomics; multi-omics; personalized medicine; proteomics; robotics; unsupervised data integration

DOI:  https://doi.org/10.1080/14789450.2026.2646657
Molecules. 2026 Feb 28. pii: 814. [Epub ahead of print]31(5):

Investigating Variability in Metabolomics: A Comparative Study of Analytical Platforms and Blood Matrices Using HPLC-HRMS.

Giulia Guerra, Alessio Polymeropoulos, Elisabetta Venturelli, Veronica Huber, Francesco Segrado, Daniele Morelli, Sabina Sieri.

  Untargeted metabolomics faces significant challenges in standardization due to variability introduced by sample preparation and analytical workflows. We systematically evaluated the impact of biological matrices, extraction protocols, and chromatographic configurations to establish a mechanism-informed framework aimed at improving reproducibility in large-scale clinical and epidemiological studies. Three extraction protocols were compared using an in-house pooled heparin plasma: monophasic protein precipitation with isopropanol (IPA), methanol:acetonitrile (MeOH:ACN), and a modified Matyash biphasic method. The most reproducible protocol was then applied to four blood matrices. Samples were analysed using untargeted metabolomics on hydrophilic interaction liquid chromatography (HILIC) and reversed-phase (RP) HPLC columns, with mass spectrometry data processed using Compound Discoverer. Both IPA and MeOH:ACN extractions achieved over 80% of features with coefficient of variation (CV%) ≤ 30% for both RP and HILIC, whereas the Matyash method showed higher variability, with a larger proportion of metabolites exhibiting CV% > 30%. Across matrices, RP chromatography detected over 80% of metabolites with CV% < 30%, while HILIC showed higher variability, with at least 20% of metabolites above this threshold. Among matrices, serum and heparin plasma outperformed EDTA and citrate in reproducibility. We propose a standardized workflow in which monophasic extractions combined with RP chromatography maximize reproducibility and metabolite coverage, minimizing methodological artefacts and providing a reliable framework for robust biological discovery in large-scale untargeted metabolomics studies.

Keywords:  blood matrix; data quality; human plasma; metabolomics; validation

DOI:  https://doi.org/10.3390/molecules31050814
Anal Chim Acta. 2026 May 01. pii: S0003-2670(26)00213-8. [Epub ahead of print]1397 345263

Comprehensive lipidomic characterization of human blood by high-throughput UHPSFC/MS using bioinert column and modified MTBE extraction.

Petra Peroutková, Robert Jirásko, Anna Taylor, Michal Holčapek, Ondřej Peterka.

   BACKGROUND: Supercritical fluid chromatography is traditionally employed for nonpolar and moderately polar analytes, while the analysis of ionic compounds remains a recognized limitation of the technique. Moreover, some polar lipids may contain a chromatographically challenging ionic group, which can interact with the metal surfaces of the instrument and column, resulting in poor peak shape and loss of sensitivity. Here, we introduce a novel ultrahigh-performance supercritical fluid chromatography-mass spectrometry (UHPSFC/MS) method using a bioinert column, enabling the separation of lipids with a broad polarity range from nonpolar to ionic species.
RESULTS: The UHPSFC/MS method was optimized using 79 lipid species across 41 lipid subclasses, achieving a total run time of 7.5 min, including the column equilibration. The comparison of the separation with conventional and bioinert columns revealed a substantial improvement in peak shapes for ionic lipid classes, such as PS, LPS, PA, LPA, CerP, and SPBP. Additionally, we introduce a combination of the modified chloroform-free extraction followed by a hexane elimination step. The optimized methodology was applied for the untargeted analysis of human plasma and erythrocyte-rich fraction to achieve highly confident identification of 657 lipid species across 37 lipid subclasses in human blood. The method follows the recommendations for validation of (bio)analytical methods, and its accuracy was confirmed by quantitative analysis of the reference material NIST SRM 1950, with the determined concentrations in agreement with the consensus values from ring trials.
SIGNIFICANCE: The current methodology represents a novel high-throughput and comprehensive quantitative lipidomic method for biological samples. The modified MTBE extraction enhances workflow efficiency by reducing concentrations of nonpolar lipids, which enables injection of more concentrated lipid extracts while minimizes ion source contamination. Moreover, the findings highlight the potential for the development of bioinert components specifically designed for SFC platforms, enabling broader applicability of the technique.

Keywords:  Bioinert column; Human blood; Lipid extraction; Lipidomics; Mass spectrometry; Supercritical fluid chromatography

DOI:  https://doi.org/10.1016/j.aca.2026.345263
Metabolomics. 2026 Mar 07. pii: 38. [Epub ahead of print]22(2):

Polar microcystins or arginine methyl ester can serve as sensitive reference materials for system suitability tests in untargeted metabolomics using reversed-phase LC-MS: A case study.

Tommy Melzer, Georg Pohnert, Nico Ueberschaar.

   INTRODUCTION: LC-MS system suitability test (SST) is crucial for reliable data acquisition especially in untargeted metabolomics.
OBJECTIVES: Identification of best reference materials (RMs) to improve best quality assurance (QA) and quality control (QC) practices.
METHODS: Investigations were performed using a C18 reversed-phase (RP) column LC-MS approach.
RESULTS: Targeted cyanotoxin analysis revealed a performance loss of the used C18 RP column although the SST confirmed a fit for purpose instrument which prompted to test several additional RMs.
CONCLUSION: QA procedures for LC-MS can be improved by incorporating polar microcystins or arginine methyl ester as RMs for SST.

Keywords:  LC-MS; Quality assurance; Reference materials; System suitability test; Untargeted metabolomics

DOI:  https://doi.org/10.1007/s11306-026-02413-9
Int J Mol Sci. 2026 Feb 25. pii: 2128. [Epub ahead of print]27(5):

Changes in Metabolism and Lipid Composition with Nitrogen Starvation and Recovery in a New Productive Strain of Neochlorella semenenkoi Using N15-Isotopic Labeling and HRMS.

Anna Vishnevskaya, Anton Bashilov, Dmitry Senko, Sergey Osipenko, Maria Sinetova, Nikita Malyshev, Philipp Khaitovich, Eugene Nikolaev, Yury Kostyukevich.

  Microscopic green algae are active producers of beneficial compounds, particularly those containing nitrogen. However, the metabolism of nitrogen-containing compounds is diverse and depends on the conditions of the nitrogen source. As a result, the approach to studying the metabolism of nitrogen-containing compounds becomes more complicated. This work demonstrates the metabolic changes in the high-productive green algae Neochlorella semenenkoi IPPAS C-1210 under conditions of nitrogen starvation and subsequent reintake, using high-performance liquid chromatography-mass spectrometry (HPLC-MS) with 15N isotopic labeling. The presented results include semi-quantitative chromatography-mass spectrometric analysis for 17 amino acids, a metabolomic profile of over 40 isotopically labeled compounds, an assessment of metabolic flux via isotopic incorporation, and an analysis of cellular lipid composition under varying growth conditions. The findings indicate that this strain can utilize ammonium acetate as a nitrogen source, consuming nitrogen in the ammonium form. The degree of isotopic labeling in compounds often diverged significantly from their quantitative changes (concentrations and chromatographic peak areas), suggesting that isotopic analysis may offer advantages over purely quantitative analysis for biological systems. Furthermore, in vivo biological isotopic labeling is shown to assist in identifying compounds absent from standard mass spectrometric databases.

Keywords:  HPLC-MS; lipidomics; metabolomics; microalgae; nitrogen starvation; stable isotope labeling

DOI:  https://doi.org/10.3390/ijms27052128
J Am Soc Mass Spectrom. 2026 Mar 11.

Data-Independent Acquisition (DIA) Strategy for Measuring Protein Stability Using Stability of Proteins from Rates of Oxidation (SPROX).

Natalie M Labbe, Meghan K Virani, Michael C Fitzgerald.

The stability of proteins from the rates of oxidation (SPROX) technique is a mass spectrometry-based approach for making protein folding stability measurements on the proteomic scale. The development and application of SPROX, to date, have primarily relied on the use of quantitative bottom-up proteomics and data-dependent acquisition (DDA) strategies using isobaric mass tags. Use of isobaric mass tags is attractive, as it enables the mass spectrometry readout in SPROX to be highly multiplexed. However, the use of such isobaric mass tags is restricted to DDA strategies, which can be limited in their proteomic coverage compared with data-independent acquisition (DIA) strategies. Reported here is a new "one-pot" SPROX workflow that employs a DIA readout and a label-free quantification strategy. Analysis of the proteins in an E. coli cell lysate using the DIA-SPROX strategy allowed for the calculation of transition midpoints with reasonable accuracy. The proteins from a S. cerevisiae cell lysate were also assessed for ligand-induced changes in their transition midpoints upon the introduction of cyclosporine A (CsA) to identify the protein targets of this well-studied ligand. The DIA-SPROX strategy developed here successfully identified known protein targets of CsA with a low false positive rate using a combination of two different software, Spectronaut and DIA-NN, for DIA data processing. We also find that the proteomic coverage obtained using DIA-SPROX is comparable to the coverage obtained in conventional DDA-SPROX experiments. Significantly, this comparable coverage can be achieved without a fractionation strategy (e.g., methionine-containing peptide enrichment) in DIA-SPROX.

DOI: https://doi.org/10.1021/jasms.5c00316
Genome Biol. 2026 Mar 12. pii: 81. [Epub ahead of print]27(1):

Blood proteomics: insights from public data.

Asier Larrea-Sebal, Chengxin Dai, Alejandro J Brenes, Kathrin Korff, Benjamin A Neely, Philipp E Geyer, Laura F Dagley, Richard D Unwin, Alexandra Naba, Michael J MacCoss, Tiannan Guo, Eric W Deutsch, Cesar Martin, Jochen M Schwenk, Yasset Perez-Riverol.

The circulating blood proteome comprises soluble and cellular components that reflect physiological and pathological states across tissues. Advances in mass spectrometry and affinity-based proteomics have improved sensitivity and throughput, enabling the generation of public blood proteomics resources. However, comprehensive assessments of these datasets remain limited. This work reviews the cellular and molecular complexity of publicly available blood proteomics data, recent methodological developments, and the complementarity of diverse data sources across the abundance range, while outlining remaining challenges for translating blood proteomics into personalized medicine.

DOI: https://doi.org/10.1186/s13059-026-04027-9
Bio Protoc. 2026 Mar 05. 16(5): e5620

Spatial Proteomics Using S4P.

Ritian Qin, Fuchu He, Weijie Qin.

  Spatial proteomics enables the mapping of protein distribution within tissues, which is crucial for understanding cellular functions in their native context. While spatial transcriptomics has seen rapid advancement, spatial proteomics faces challenges due to protein non-amplifiability and mass spectrometry sensitivity limitations. This protocol describes a sparse sampling strategy for spatial proteomics (S4P) that combines multi-angle tissue strip microdissection with deep learning-based image reconstruction. The method achieves whole-tissue slice coverage with significantly reduced sampling requirements, enabling mapping of over 9,000 proteins in mouse brain tissue at 525 μm resolution within 200 h of mass spectrometry time. Key advantages include reduced sample processing time, deep proteome coverage, and applicability to centimeter-sized tissue samples. Key features • Achieves whole-tissue slice coverage for spatial proteomics mapping. • Enables reconstruction of spatial protein distribution using sparse sampling with multi-angle strip projections. • Combines mass spectrometry-based proteomics with deep learning-based image reconstruction. • Reduces required mass spectrometry time by 50%-90% compared to gridding-based approaches.

Keywords:  Deep learning; Image reconstruction; Mass spectrometry; Sparse sampling; Spatial proteomics

DOI:  https://doi.org/10.21769/BioProtoc.5620
Front Mol Biosci. 2026 ;13 1739472

A label-free microLC-SWATH-MS methodology with immunoaffinity depletion of highly abundant serum proteins for quantitative proteomic comparison of fresh-frozen human normal breast tissue and tumor clinical specimens.

Katarzyna Macur, Aleksandra E Bogucka, Anna Fel-Tukalska, Jarosław Skokowski, Stanisław Ołdziej, Paulina Czaplewska.

   Background: Mass spectrometry (MS)-based proteomics can provide deep insights into protein-driven molecular processes and signaling pathways in breast cancer, thereby contributing to improvements in disease diagnosis, treatment, and prevention. This study focuses on the development of a label-free quantitative proteomic profiling approach for the analysis of fresh-frozen human normal breast tissue (BTIS) and breast tumor (BTUM) samples.
Methods: A pilot set of BTIS and BTUM samples obtained from eight patients diagnosed with luminal B (Lum B) or triple-negative breast cancer (TNBC) was analyzed using micro-liquid chromatography coupled to tandem mass spectrometry (microLC-MS/MS) in a data-independent acquisition sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mode. To expand proteome coverage during SWATH data extraction, an experimental spectral ion library was generated from the MS/MS spectra of a pooled sample comprising aliquots from all analyzed BTIS and BTUM samples. To expand the spectral library, the pooled sample was immunodepleted of the 14 most abundant serum proteins, enabling deeper proteome coverage.
Results: A total of 562 proteins were identified at a false discovery rate (FDR) of <1%, of which 299 were successfully quantified across all samples. Among these, 158 proteins showed statistically significant differences (p < 0.05) between breast tumor and normal breast tissue samples, including 59 proteins that were upregulated and 23 that were downregulated by at least 1.5-fold. Functional enrichment analysis revealed that the quantified proteins were associated with cellular structures and compartments relevant to breast cancer biology, such as the extracellular matrix (ECM), extracellular exosomes, and nucleosomes. These proteins were also involved in biological processes implicated in disease development and progression, including ECM organization, focal adhesion, mRNA splicing via the spliceosome, interleukin-12-mediated signaling, platelet activation, and metabolic pathways related to amino acid metabolism and gluconeogenesis/glycolysis.
Conclusion: This proof-of-concept study demonstrates that the developed microLC-SWATH-MS approach, combined with a custom spectral library generated from pooled breast tissue and tumor samples immunoaffinity-depleted of 14 high-abundance serum proteins, enables robust and high-throughput proteomic profiling of breast tissue and tumors. Further expansion of high-quality spectral libraries may enhance proteome coverage and improve the clinical applicability of this approach. While the methodology supports the discovery of candidate biomarkers and therapeutic targets relevant to translational research and precision oncology, the biological conclusions drawn from this study should be interpreted with caution due to the limited sample size. Validation in larger patient cohorts using orthogonal methods will be required to confirm the potential clinical utility of the identified proteins.

Keywords:  SWATH-MS; breast cancer; breast tissue; breast tumor; extracellular matrix proteins; immunoaffinity depletion of highly abundant proteins; microLC; quantitative proteomics

DOI:  https://doi.org/10.3389/fmolb.2026.1739472
Anal Chim Acta. 2026 May 08. pii: S0003-2670(26)00175-3. [Epub ahead of print]1398 345225

Resolving lipid isomers up to the double bond position level using reversed-phase chromatography, EAD fragmentation, and high-resolution mass spectrometry: Application to pancreatic cancer.

Marlene Pühringer, Leonida M Lamp, Denisa Kolářová, Milan Vošmik, Lisa Panzenboeck, Gunda Köllensperger, Michal Holčapek, Jürgen Hartler, Evelyn Rampler.

   BACKGROUND: Electron-activated dissociation (EAD) is a radical-based fragmentation technique that provides detailed structural information on lipids in a single spectrum, including double-bond positions, sn-1/sn-2 assignment, and molecular species. Its broader application, however, remains limited by the low intensity of diagnostic fragments. An alternative computational approach, called LC=CL, which is an extension of the software Lipid Data Analyzer (LDA), leverages retention time (RT) information obtained by reversed-phase liquid chromatography (RPLC) to identify ω-positions in intact lipid species.
RESULTS: Here, we present an integrated RPLC-EAD-TOF-MS/MS workflow that combines LC=CL's RT-based with EAD's fragmentation-based annotation for deep structural characterization using a ZenoTOF 7600. This strategy was validated using both unlabeled and uniformly 13C-labeled yeast extracts. By combining the benefits of retention-time identification and radical-induced fragmentation, our method enables the reliable identification of double-bond locations and sn-positional isomers across 15 lipid classes.
SIGNIFICANCE: In the analysis of plasma samples from pancreatic ductal adenocarcinoma (PDAC) patients and healthy controls, we demonstrate the power of our strategy for unambiguously resolving lipid isomers, revealing structure-specific patterns of dysregulated lipids. A total of 353 lipids were identified across 15 classes composed of glycerolipids, glycerophospholipids, and sphingolipids. Our findings not only confirmed known biomarkers, but also revealed additional chain isomers (e.g., SM 18:1; O2/20:0 and SM 16:1; O2/22:0), where only one of them was differentially regulated. Moreover, we discovered novel double-bond location-specific regulations, such as differential regulation of PC 16:0/18:3(n-6) in PDAC patients, whereas the isomer PC 16:0/18:3(n-3) did not exhibit any significant changes. Such an observation would have remained concealed using conventional methods. Accordingly, the presented RPLC-EAD-TOF-MS/MS platform facilitates detailed structural lipidomics in biologically and clinically relevant samples.

Keywords:  Chromatography; Double bond localization; EAD; Fragmentation; LC=CL; Lipidomics; Mass spectrometry; Pancreatic cancer; RPLC-MS; Structural lipidomics

DOI:  https://doi.org/10.1016/j.aca.2026.345225
Rapid Commun Mass Spectrom. 2026 Jun 15. 40(11): e70062

Advanced Phosphoproteomics Depth and Quantitative Performance Using an Integrated noFAIMS-FAIMS Approach.

Byoung-Kyu Cho, Antonia Zamacona Calderon, Young Ah Goo.

RATIONALE: Protein phosphorylation plays a central role in regulating cellular signaling, and its dysregulation is closely linked to diseases such as cancer and neurodegeneration. Mass spectrometry-based phosphoproteomics allows comprehensive mapping of phosphorylation; however, detecting low-abundance and poor ionization phosphopeptides remains a challenge. High-field asymmetric waveform ion mobility spectrometry (FAIMS) offers orthogonal gas-phase fractionation, enhancing phosphopeptide detection. However, FAIMS and conventional non-FAIMS (noFAIMS) analyses often identify partially overlapping yet complementary subsets of phosphopeptides. This suggests that integrating both approaches could significantly increase the depth of phosphoproteomic analysis.
METHODS: Phosphopeptide samples were analyzed using a Vanquish Neo UHPLC system coupled to an Orbitrap Eclipse Tribrid mass spectrometer. Using parallel reaction monitoring (PRM) on a panel of 200 synthetic phosphopeptides, we assessed ionization efficiencies under noFAIMS conditions and at six different FAIMS compensation voltages (CVs). The integrated noFAIMS and FAIMS approach was further evaluated using enriched phosphopeptide samples from HEK293 and HeLa cells, analyzed by data-dependent acquisition (DDA).
RESULTS: The integrated noFAIMS-FAIMS approach resulted in a substantial increase in phosphopeptide identifications (14.9%-46.5%) compared with either the noFAIMS or FAIMS method alone. This integrated approach also exhibited high reproducibility across technical and biological replicates. Importantly, the integrated approach expanded the coverage of key signaling pathways such as EGF/EGFR, VEGFA-VEGFR2, and PI3K-AKT, by capturing phosphoproteins identified exclusively in either dataset.
CONCLUSIONS: This study demonstrates that an integrated noFAIMS-FAIMS approach significantly enhances phosphoproteomic depth and quantification by leveraging the complementary advantages of each method. By capturing unique phosphopeptides from each analysis, this strategy can be a practical and efficient phosphoproteomic approach, providing deeper insights into cellular signaling pathways.

DOI: https://doi.org/10.1002/rcm.70062
Metabolomics. 2026 Mar 07. pii: 35. [Epub ahead of print]22(2):

Multiplexed LC-MS analysis reveals novel insights into grapevine defense mechanisms by expanding metabolome coverage.

Pedro G Vásquez-Ocmín, Amelie Pérez, Ana Romeo-Oliván, Virginie Puech-Pages, Sylvie Fournier, Bernard Dumas, Alban Jacques, Guillaume Marti.

   INTRODUCTION: Grapevine trunk diseases (GTDs), such as esca, pose a major threat to viticulture worldwide and are associated with complex biochemical responses in woody tissues. Comprehensive metabolome coverage remains a challenge, as conventional methods often overlook non-polar metabolites critical to plant defense mechanisms.
OBJECTIVES: This study aimed to expand metabolome and lipidome coverage of grapevine wood by integrating complementary LC-MS approaches, in order to identify metabolic signatures linked to pathogenic fungi and to a biocontrol agent.
METHODS: Woody tissues of Vitis vinifera cv. Cabernet-Sauvignon were inoculated with Phaeomoniella chlamydospora, Phaeoacremonium minimum, and/or the biocontrol fungus Trichoderma atroviride (Vintec®). A biphasic extraction was coupled with three orthogonal LC-MS methods-reverse-phase (RP), hydrophilic interaction chromatography (HILIC), and lipidomics-focused RP. Data were processed through the MSCleanR workflow and integrated using the DIABLO multi-block statistical framework. Compound classification was performed with NPClassifier.
RESULTS: The multiplexed strategy enabled the annotation of 1,425 unique features, representing an 83% increase compared to previous studies. Distinct metabolomic and lipidomic signatures were associated with fungal infection and biocontrol treatments. Lipidomic analysis highlighted oxidized fatty acids (oxylipins) -specifically hydroxy-eicosatetraenoic acids (13-HETE, 16(R)-HETE, and 11(R)-HETE)-as potential signaling molecules in defense responses. NPClassifier revealed diverse biosynthetic classes, including phenylpropanoids, terpenoids, and sphingolipids, underscoring the chemical heterogeneity of grapevine responses.
CONCLUSION: This multiplexed LC-MS workflow provides a versatile analytical pipeline for untargeted metabolomics and lipidomics in plants. By integrating complementary methods, the study uncovered novel biomarkers of grapevine defense, particularly oxylipins, emphasizing the critical role of lipidomics in deciphering plant-pathogen interactions.

Keywords:  Esca; Lipidomics; Metabolomics; Oxylipins

DOI:  https://doi.org/10.1007/s11306-026-02410-y
J Chromatogr A. 2026 Mar 07. pii: S0021-9673(26)00215-3. [Epub ahead of print]1774 466885

Shared-autosampler parallel LC-MS/MS platform for high-throughput multi-analyte quantification in complex matrices.

Shanshan Cai, Qisheng Zhong, Jiaqi Liu, Lisong Chen, Lingling Shen, Hongyuan Hao, Ting Zhou.

  Modern analytical tasks increasingly require the simultaneous quantification of large numbers of chemically diverse analytes in complex matrices, which remains challenging for a single LC-MS method. Here, we developed a shared-autosampler parallel LC-MS/MS strategy that enables two fully independent chromatographic methods to be executed sequentially within a single analytical cycle while generating one unified chromatogram-mass spectrum. The system employed two LC pump modules and two columns to form two independent LC pathways, which shared one autosampler and one MS detector for selective MRM acquisition and subsequent signal integration. Integration was performed exclusively at the MS detection and data-processing level, without inter-dimensional coupling or analyte transfer between columns. This avoided the mobile-phase compatibility and dead-volume limitations associated with 2D-LC, enabled modular recombination of chromatographic and MS conditions, and improved analytical throughput without additional MS detector or autosampler hardware. The performance of the strategy was demonstrated in two applications. An acidic/basic dual-method configuration enabled quantitative analysis of 394 emerging contaminants from 14 chemical classes in human serum within 28 min, exhibiting good linearity, limits of quantification not exceeding 0.20 ng/mL for >90 % of analytes, and peak-area RSDs generally below 10 %. A HILIC/RP parallel-column combination achieved simultaneous determination of 27 hypoglycemic agents spanning a log P range of -6.8 to 5.9 in a single 20 min run, reducing analysis time by >50 % compared with standard methods, with good precision and recoveries in spiked food samples. Overall, the shared-autosampler parallel LC-MS/MS strategy provided a flexible and efficient platform for high-throughput multi-analyte quantification in complex matrices.

Keywords:  Complex matrices; High-throughput analysis; Multi-analyte quantification; Parallel chromatography; Shared-autosampler LC–MS/MS

DOI:  https://doi.org/10.1016/j.chroma.2026.466885
bioRxiv. 2026 Feb 23. pii: 2026.02.20.707032. [Epub ahead of print]

Comprehensive top-down mass spectral repository enables pan-dataset analysis and top-down spectral prediction.

Kun Li, Kaiyuan Liu, James M Fulcher, Haixu Tang, Xiaowen Liu.

Mass spectral libraries have become essential resources for training deep learning (DL) models for spectral prediction and de novo sequencing in bottom-up mass spectrometry (BU-MS). Compared with BU-MS, top-down MS (TD-MS) offers unique advantages for characterizing intact proteoforms by analyzing proteoforms without enzymatic digestion. Despite these advantages, large-scale spectral libraries for TD-MS are currently lacking. Here we present TopRepo, the first comprehensive repository of TD-MS spectra, comprising more than 18 million spectra acquired from 12 species across eight types of mass spectrometers. Using TopRepo, we constructed a large-scale top-down spectral library containing over 5 million spectra with curated proteoform and fragment-ion annotations. We demonstrate that TopRepo enables pan-dataset analyses of N-terminal processing, mass shifts, and other proteoform characteristics identified by TD-MS. Furthermore, we show that the TopRepo spectral library substantially improves proteoform identification through spectral library searching and supports the training of DL models for high-accuracy top-down spectral prediction.

DOI: https://doi.org/10.64898/2026.02.20.707032
Nat Biotechnol. 2026 Mar 12.

Biomolecular profiling for noninvasive health monitoring.

Moon-Ju Kim, José A Lasalde-Ramírez, Wenzheng Heng, Wei Gao.

Biomolecular profiling offers a powerful lens into human physiology, yet current diagnostics often rely on invasive sampling and delayed, centralized analysis. Advances in mass spectrometry (MS), particularly untargeted metabolomics and proteomics, have expanded molecular access to noninvasive biofluids such as sweat, saliva, tears and interstitial fluid, revealing dynamic biomarkers linked to both chronic and acute conditions. In parallel, wearable biosensors enable real-time, on-body chemical sensing, but remain limited to a narrow panel of predefined analytes. This Review highlights how MS-based molecular discovery and wearable sensing serve as complementary approaches-MS enabling high-dimensional untargeted profiling and wearables delivering longitudinal real-time data-and also discusses how their bidirectional integration and co-evolution open new possibilities for personalized noninvasive health monitoring. We discuss advances in sampling strategies, sensing modalities and system integration, and outline criteria for identifying biomarkers amenable to sensor translation. By uniting untargeted discovery with real-world deployment, this convergence shifts personalized noninvasive healthcare from episodic diagnostics to continuous, context-aware monitoring.

DOI: https://doi.org/10.1038/s41587-026-03050-2
mSystems. 2026 Mar 12. e0145925

S'Wipe: user-friendly stool collection for high-throughput gut metabolomics and multi-omics.

Dana Moradi, Ali Lotfi, Alexey V Melnik, Aleksandr Smirnov, Konstantin Pobozhev, Hannah Monahan, Evguenia Kopylova, Yanjiao Zhou, Alexander A Aksenov.

  The microbiome is increasingly recognized as a key factor in health. Intestinal microbiota modulates gut homeostasis via a range of diverse metabolites. In particular, molecules such as short-chain fatty acids (SCFAs), the microbial fermentation products of dietary fiber, have been established to be reflective of microbiome and/or dietary shifts, and SCFAs alterations have been linked to multiple gastrointestinal disorders, from cancer to colitis. Despite their potential as biomarkers, technical challenges in stool collection have limited clinical translation. Here, we present Stool Wipe (S'Wipe), an ultra-low-cost fecal collection method using lint-free, mass spectrometry (MS)-compatible cellulose wipes as toilet paper. Specimens are preserved in ethanol without refrigeration and can be shipped via regular mail. Mass spectrometry analysis demonstrated that S'Wipe captures both volatile and non-volatile metabolites with reproducibility and stability validated for diagnostically relevant molecules. We show that S'Wipe performs equivalently to direct stool collection, enabling interchangeable use and comparison with existing studies. This methodology is ideally suited for large-scale population studies, longitudinal tracking, and personalized medicine applications.
IMPORTANCE: Gut microbiome and intestinal metabolome present invaluable diagnostic and therapeutic targets. However, conventional stool testing has several barriers, limiting bioassessment from populations. Routine, high-temporal-resolution monitoring of stool metabolome, including extensively validated and broadly informative biomarkers such as short chain fatty acids (SCFAs), is not implemented due to relatively high cost and inconvenience of sampling, possible need for clinical setting for sample collection, difficulty in collecting samples reproducibly-especially due to potential for user errors-requirement for freezer storage and maintenance of the cold chain during shipment. We present a sampling strategy specifically designed to overcome these obstacles. We demonstrate how this method can enable capturing accurate molecular snapshots at massive scales, at ultra-low cost. The approach collapses complex medical-grade collection into easy self-administration. Individuals can thereby self-monitor therapeutic responses through routine metabolome tracking, including the volatilome, otherwise hindered by infrastructure restrictions. Ultimately, this sampling approach is intended to enable participatory wellness transformation through practical high-frequency self-sampling.

Keywords:  disease biomarkers; economical; fecal metabolome; gas chromatography-mass spectrometry; gastrointestinal diagnostics; health monitoring; liquid chromatography-mass spectrometry; low cost; metabotyping; patient self-testing; personalized medicine; personalized nutrition; sample collection; short-chain fatty acids

DOI:  https://doi.org/10.1128/msystems.01459-25
J Forensic Sci. 2026 Mar 08.

Differentiation of cannabinoid isomers via Cu-mediated molecular ion formation and electrospray ionization-tandem mass spectrometry.

Alleigh N Couch, Christopher M Zall, J Tyler Davidson.

  Gas chromatography-electron ionization-mass spectrometry (GC-EI-MS) remains the primary analytical technique used for cannabis analysis in seized drug laboratories. Electron ionization (EI) mass spectra exhibit extensive fragmentation, enabling the identification of cannabinoids by comparison with reference EI mass spectral libraries. However, limitations such as thermal degradation and potential cannabinoid conversion can occur due to the elevated temperatures of the GC inlet. In contrast, liquid chromatography-mass spectrometry (LC-MS) uses a soft ionization technique, such as electrospray ionization (ESI), which predominantly yields the protonated molecule with minimal fragmentation. Even with collisional activation using tandem mass spectrometry (MS/MS) analysis, the product ion spectra are nearly identical for cannabinoid isomers, reducing the effectiveness of this technique for cannabinoid identification. In this study, copper (Cu) salts are used to induce cannabinoid molecular ion formation under ESI conditions, enabling cannabinoid isomer differentiation. Thirteen cannabinoids were analyzed in the presence of Cu, and the resulting MS/MS product ion spectra exhibited fragmentation analogous to cannabinoid EI mass spectra. To evaluate forensic applicability, the EI-like product ion spectra were searched against the NIST 20 EI-MS mass spectral library using NIST MS Search software. Spectral matches confirmed that this alternative approach can generate EI-like data under ESI-MS/MS conditions, improving cannabinoid isomer identification. Additionally, this method was applied to methanolic extracts of authentic cannabis plant material to ensure cannabinoid molecular ion formation in real-world samples. The developed method offers an alternative approach to traditional workflows, while providing spectral data consistent with those routinely interpreted by seized drug analysts.

Keywords:  NIST MS search; cannabinoids; electrospray ionization‐tandem mass spectrometry (ESI‐MS/MS); fragmentation; isomer differentiation; molecular ion formation

DOI:  https://doi.org/10.1111/1556-4029.70290
Genome Biol. 2026 Mar 11.

Proteomics and tracer metabolomics link GAPDH ISGylation to glycolytic control.

Denzel Eggermont, Lissa Eggermont, Nagihan Aslantaş, Fabien Thery, Katie Boucher, Antje Beling, Bart Ghesquière, Francis Impens.

   BACKGROUND: Ubiquitin-like protein ISG15 (interferon-stimulated gene 15) is implicated in the regulation of central carbon metabolism, but conflicting findings across experimental systems limit mechanistic insight. Here, we apply a multi-omics approach in cells ectopically expressing the ISGylation machinery independent of immune stimuli, to generate a systematic view of ISGylation in metabolic control.
RESULTS: ISGylation preferentially targets metabolic enzymes, with marked enrichment among glycolytic proteins, suppressing the energy-yielding phase of glycolysis. Tracer metabolomics reveals a bottleneck at glyceraldehyde-3-phosphate dehydrogenase (GAPDH), reflected by accumulation of upstream intermediates and depletion of downstream metabolites. This arises from multisite ISGylation of lysines near its catalytic and regulatory regions, which reduces enzymatic activity without disrupting tetramer assembly.
CONCLUSIONS: These findings identify GAPDH as a central metabolic checkpoint regulated by ISGylation and uncover a direct post-translational mechanism by which ISG15 controls energy metabolism.

Keywords:  GAPDH; Glycolysis; ISG15; Mass spectrometry; Metabolomics; Proteomics

DOI:  https://doi.org/10.1186/s13059-026-04034-w