bims-mascan 2026-03-22 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–03–22
twenty-one papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Untargeted Multiple Reaction Monitoring.
Tracer-assisted shotgun lipidomics (TASL): A quantitative workflow integrating stable-isotope tracing with global lipidome profiling.
Comparison of Liquid Chromatography- and Nano-Electrospray Ionization-Mass Spectrometry Approaches for Single-Cell Metabolomics.
Optimization of trap column properties and loading conditions for proteome profiling of single-cell-level sample inputs.
Systematic Evaluation of Depletion and Enrichment Technologies for Platelet-Free Plasma Proteomics.
Systematic Evaluation of the Impact of Storage Time on Label-Free Proteomics of Colorectal Adenocarcinoma Formalin-Fixed Paraffin-Embedded Tissues.
Top-Down Mass Spectrometry-Based Structural and Functional Proteomics: A Perspective.
Method design and automation of a forensic toxicology LC-QTOF-MS technique to meet high-throughput routine applications.
Glycoinformatic profiling of label-free intact heparan sulfate oligosaccharides.
Dissecting Metabolic Rewiring and Gene-Metabolite Interactions by Utilizing Untargeted Metabolomics and Single-Gene Knockouts in the Model Microorganism E. coli.
Comparing the quantitation of specialized pro-resolving mediators in plasma and serum using ELISA and LC-MS/MS.
Proteomic Analysis of Human Dentine Matrix Extracts by Peptide-Level High-pH Reversed-Phase Fractionation.
Metabolic Tracing Reveals IL-2 Driven Glutaminolysis and De Novo Pyrimidine Synthesis in Human Natural Killer Cells.
Therapeutic targeting of tricarboxylic acid cycle and oxidative phosphorylation: a critical analysis of metabolic interventions in cancer treatment.
Protein-Corona-Based Nanomagnetic Enrichment Strategy for In-Depth Secretome Profiling of Lung Adenocarcinoma Organoids.
Spatial Lipidomic Combining DESI Imaging and LESA Mass Spectrometry to Evaluate Metabolic Alterations in Obese Zebrafish.
Untargeted metabolomics data in the By-Band-Sleeve trial and ALSPAC: integrating clinical trial and population cohort data.
The carrying capacity for free fatty acids in plasma is exceedingly high in mammals, and this may be a liability for human metabolic health.
InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments.
Establishing an untargeted lipidomics workflow for cellular analysis: insights into endothelial cell function in anaphylaxis.
Protein Biomarkers as Molecular Tools for Forensic Biofluid Identification.

Anal Chem. 2026 Mar 17.

Untargeted Multiple Reaction Monitoring.

Winnie Uritboonthai, Aries Aisporna, Linh Hoang, Bill Webb, Elizabeth M Billings, Corey Hoang, Mirna Tobea, Chelsea C Cates-Gatto, Amanda J Roberts, Tony Teav, Rebecca Borreggine, Ioana Kyritsi, Hector Gallart-Ayala, Julijana Ivanisevic, Anna Popova, James R Williamson, Robert Plumb, Gary Siuzdak.

Multiple reaction monitoring (MRM) enables robust and sensitive quantification but traditionally requires predefined precursor-fragment transitions, limiting its use in discovery-driven studies. Here, we describe untargeted/micro/universal multiple reaction monitoring (uMRM), a workflow that converts high-resolution untargeted liquid chromatography-mass spectrometry/MS (LC-MS/MS) data into scheduled triple-quadrupole MRM transitions. Pooled-sample LC-MS and stepped-energy DDA MS/MS acquisitions (0, 10, 20, and 40 eV) are used to capture precursor and fragment information representative of each experimental set. Detected features undergo automated deisotoping and empirically validated in-source fragment filtering, followed by spline-based modeling of collision-energy-dependent fragmentation to define optimized precursor-fragment transitions. Transitions are scheduled using retention times observed in pooled samples and deployed on triple-quadrupole instruments without requiring nonlinear retention-time alignment or authentic standards. Across representative biological matrices, including urine, brain tissue, and cultured cells, uMRM enabled automated generation of quantitative MRM methods from untargeted discovery data. Benchmarking across seven triple-quadrupole platforms demonstrated strong agreement between uMRM-derived and experimentally optimized collision energies. By converting discovery-scale data sets into compact transition tables suitable for quantitative deployment, uMRM provides a reproducible approach for linking untargeted LC-MS/MS acquisition with targeted quantitation.

DOI: https://doi.org/10.1021/acs.analchem.5c06838
Anal Chim Acta. 2026 May 22. pii: S0003-2670(26)00267-9. [Epub ahead of print]1400 345317

Tracer-assisted shotgun lipidomics (TASL): A quantitative workflow integrating stable-isotope tracing with global lipidome profiling.

Hashmatullah Nasimi, Lya K K Holland, Knut K B Clemmensen, Jan Stenvang, Marja Jäättelä, Kenji Maeda, Mesut Bilgin.

   BACKGROUND: Shotgun lipidomics provides a quantitative, steady-state overview of global lipidomes, but offers limited insight into metabolic dynamics. Tracer lipidomics yields time-resolved quantitative information on specific biosynthetic pathways, but labeling can perturb lipidomes, making labeled time-course samples unsuitable for steady-state comparisons. Here, we introduce Tracer-Assisted Shotgun Lipidomics (TASL), a strategy that integrates stable-isotope tracing with shotgun lipidomics in a single workflow, enabling time-resolved analysis while retaining labeled samples as inputs for steady-state lipidome profiling. This is achieved through a minimally perturbing strategy where cells are pre-equilibrated in an unlabeled precursor before switching to the isotopically labeled precursor at the same concentration.
RESULTS: As a proof of concept, TASL was applied to HCT116 colorectal cancer cells and three drug-resistant variants, sampled over 24 h following the switch from unlabeled l-serine to l-serine-(13C315N) to label de novo synthesized sphingolipids. Leveraging the enhanced statistical power of this design, global steady-state analysis revealed accumulation of dihydrosphingolipid species lacking the canonical 4,5-trans double bond in their long-chain base as the most prominent alteration shared across drug-resistant cell lines. Time-resolved analysis of the de novo sphingolipid biosynthesis pathway subsequently identified a pronounced bottleneck at dihydroceramide desaturation, diverting flux toward dihydrosphingomyelin despite an otherwise intact pathway.
SIGNIFICANCE: Together, TASL provides a generalizable and minimally perturbing framework for integrating global steady-state lipidomics with time-resolved pathway analysis, and can be readily extended to other tracers, pathways, and biological systems to study metabolic rewiring at the lipidome scale.

Keywords:  Cancer; Drug resistance; Lipid metabolism; Mass spectrometry; Sphingolipids; Tracer-assisted shotgun lipidomics; l-serine-((13)C(3)(15)N) labeling

DOI:  https://doi.org/10.1016/j.aca.2026.345317
Anal Chem. 2026 Mar 18.

Comparison of Liquid Chromatography- and Nano-Electrospray Ionization-Mass Spectrometry Approaches for Single-Cell Metabolomics.

Abigail Cook, Claire Davison, Jordan Pascoe, Harpreet Atwal, George Mayson, Ahmed Ali, Dany Jv Beste, Melanie Bailey.

Live single-cell metabolomics is a rapidly growing area of research, which offers the potential to provide unique insights into cellular function and heterogeneity. Single-cell isolation approaches based on capillary sampling are in principle compatible with either nano-electrospray ionization-mass spectrometry (nano-ESI-MS), where the cell is lysed and sprayed directly into a mass spectrometer, or liquid chromatography-mass spectrometry (LC-MS) for metabolomics analysis. However, there are no data indicating which approach can provide the best performance (metabolite coverage, reproducibility and sensitivity) for single-cell metabolomics. In this work, we have developed and then compared two semitargeted metabolomics methods (direct nano-ESI-MS and LC-MS) for detecting amino acids and other hydrophilic metabolites in single macrophages. Interestingly, our results show that, even when using analytical-flow LC-MS, the coverage of metabolites is superior to the nano-ESI-MS method. We applied both methodologies to single THP-1 macrophages infected with fluorescent Mycobacterium bovis bacillus Calmette-Guérin (BCG), the vaccine strain of Mycobacterium tuberculosis. Infected cells were identified under a microscope and sampled into glass capillaries. Our results show that the LC-MS approach provides a much clearer distinction between infected and control cells than using nano-ESI-MS. LC-MS detected enrichment of several compounds in infected cells, including methionine, cysteine and taurine, highlighting reprogramming of sulfur metabolism during mycobacterial infection. These findings establish a robust analytical framework for spatially resolved single-cell metabolomics and underscore its potential for uncovering infection-driven metabolic heterogeneity, with broad applications in infectious disease research, drug discovery, and clinical diagnostics.

DOI: https://doi.org/10.1021/acs.analchem.5c06318
Anal Bioanal Chem. 2026 Mar 19.

Optimization of trap column properties and loading conditions for proteome profiling of single-cell-level sample inputs.

Siqi Huang, Thy Truong, Chao Wang, Xiaofeng Xie, H-J Lavender Lin, Ryan T Kelly.

  Nanoflow liquid chromatography-mass spectrometry has become indispensable for profiling limited and heterogeneous biological samples, yet overall analytical sensitivity, robustness, and throughput often remain constrained by the mechanisms used to transfer samples onto the analytical column. Although trap-and-elute workflows are widely deployed to improve loading efficiency, sample cleanup, and column longevity, a systematic evaluation of how trap column properties and loading parameters influence proteome coverage, particularly under low-input and high-throughput conditions, is needed. Here, we assess trap column inner diameter, particle size, packing material, loading flow rate, and sample concentration using both bulk-prepared digests and single cells. We demonstrate that incorporating a trap column markedly mitigates performance losses associated with large-volume loading with minimal impact on peak width, peak area, or identification depth. Notably, variations in trap column geometry and loading speed exert only minimal influence on chromatographic quality or proteome depth, indicating that trap-and-elute workflows afford considerable flexibility in the LC system design. These findings establish practical guidelines for optimizing trap column configurations and highlight the suitability of trap-and-elute strategies for high-sensitivity, high-throughput proteomics.

Keywords:  Mass spectrometry; Nano LC; Single-cell proteomics

DOI:  https://doi.org/10.1007/s00216-026-06440-2
J Proteome Res. 2026 Mar 17.

Systematic Evaluation of Depletion and Enrichment Technologies for Platelet-Free Plasma Proteomics.

Salem Al Siblani, Jean Armengaud, Clément Lozano.

  Plasma proteomics is a rapid, noninvasive, and highly informative approach for identifying disease biomarkers. However, the wide dynamic range of protein concentration limits the depth of liquid chromatography-tandem mass spectrometry proteomics. To address this challenge, we evaluated, with an Orbitrap Astral instrument using DIA, the performance of several protein depleting and enriching technologies on platelet-free plasma. Specifically, we assessed: perchloric acid depletion, immunodepletion, ProteoMiner, MagNet-SAX, ENRICHplus, Proteonano and the combination of ProteoMiner and immunodepletion. All methods were assessed in terms of proteomic depths, protein quantification precision, and functional analysis. Proteonano exhibited the most effective enrichment for the lowest abundance proteins, confidently identifying 299 proteins with mapped blood concentrations below 106 pg/L. Immunodepletion yielded the highest proteome coverage in the moderate abundance range (660 confident proteins). Also, ENRICHplus quantitative profile closely matched that of the neat plasma (93% correlation). Additionally, high repeatability (median coefficient of variation) was demonstrated by MagNet-SAX (13%), ProteoMiner (15%), and immunodepletion (16%). Combining ProteoMiner and immunodepletion reduced the plasma protein dynamic range, enabling deeper low abundance protein analysis but decreased repeatability. These results obtained on platelet-free plasma deviate from previously reported results on platelet-rich plasma, highlighting the crucial sample preparation stage for plasma proteomics.

Keywords:  ASTRAL; DIA; ENRICHplus; MagNet-SAX; PCA-N; ProteoMiner; Proteonano; immunodepletion; plasma proteomics; platelet-free plasma

DOI:  https://doi.org/10.1021/acs.jproteome.5c01056
J Proteome Res. 2026 Mar 18.

Systematic Evaluation of the Impact of Storage Time on Label-Free Proteomics of Colorectal Adenocarcinoma Formalin-Fixed Paraffin-Embedded Tissues.

Yingjie Li, Li Zhou, Li Xia, Qin Fu, Xiaoyu Wu, Shundong Dai, Wenwen Xia, Liyuan Meng.

  Mass spectrometry (MS)-based proteomics has empowered comprehensive protein profiling of biological specimens. However, formalin-fixed paraffin-embedded (FFPE) tissues─critical resources for clinical biomarker discovery-remain underexplored in the setting of long-term storage (>15 years). Herein, we systematically evaluated the impact of storage time on proteomic analyses of 80 colorectal adenocarcinoma (CRC) FFPE samples, which were stratified by two key variables: storage time (>15 years vs <1 year) and tissue type (tumor vs adjacent normal tissue). We adopted a standardized protein extraction strategy, and subsequent proteomic profiling was performed via data-dependent acquisition and data-independent acquisition MS workflows. Our results demonstrated that FFPE tissue storage time impacts protein extraction efficiency, peptide yields, PTM identification, and protein quantification. The impacts were more pronounced on the peptide level. However, the biological enrichments (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis) from the global proteome profile and from differentially expressed proteins in CRC tissues were independent of archival time. Five clinically relevant biomarkers of CRC were further validated via immunohistochemistry. Collectively, our findings confirm that FFPE tissues retain stability for proteomic analyses even following >15 years of storage, thereby providing critical insights for leveraging archival FFPE biobanks to advance clinical proteomics and archival pathology research.

Keywords:  DDA; DIA; FFPE tissue; colorectal adenocarcinoma; protein cross-linking; proteomics; storage time

DOI:  https://doi.org/10.1021/acs.jproteome.5c01070
J Mass Spectrom. 2026 Apr;61(4): e70044

Top-Down Mass Spectrometry-Based Structural and Functional Proteomics: A Perspective.

Kellye A Cupp-Sutton, Si Wu.

A wide range of protein modifications (e.g., truncations, amino acid substitutions, and posttranslational modifications, PTMs) create diverse proteoforms that govern physiological regulation and support cellular homeostasis. As such, understanding proteoform structure is fundamental to elucidating biological function and disease mechanisms. Traditional high-resolution methods (e.g., NMR, cryo-EM, and X-ray crystallography) offer powerful structural information but remain limited in their capacity to interrogate the structural effects of proteoform heterogeneity. Mass spectrometry (MS)-based structural proteomics allows the evaluation of protein structure in native and native-like conditions. Typically, MS-based structural proteomics methods implement bottom-up proteomics in which proteins are digested into small peptides prior to MS detection. This digestion, however, may obscure proteoform structural information such as coordinating PTMs. Top-down proteomics, on the other hand, analyzes intact proteoforms directly to preserve the connectivity between proteoform structure and function. In this perspective, we discuss the application of top-down MS-based proteomics for interrogating intact proteoform structures and outline future directions for the field. We highlight the potential of top-down structural proteomics as a powerful and complementary approach to bottom-up proteomics and traditional biochemical strategies, enabling comprehensive characterization of the intact structural proteome.

DOI: https://doi.org/10.1002/jms.70044
Forensic Sci Int. 2026 Mar 10. pii: S0379-0738(26)00106-4. [Epub ahead of print]384 112919

Method design and automation of a forensic toxicology LC-QTOF-MS technique to meet high-throughput routine applications.

Maria Sarkisian, Megan Farley, Luke N Rodda.

  The utility of a comprehensive and validated analytical technique is minimized without a holistic method design and automated data processing. Forensic toxicology laboratories require workflows that are analytically rigorous and legally defensible, yet traditional data processing is limited by manual review, software constraints, and increasing case complexity. To address these challenges, we developed and validated a method design and automated data processing framework for a previously published liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) technique to support high-throughput toxicology while embedding the laboratory's quality assurance program. The system integrates lookup tables, custom calculations, and automated flagging rules within the vendor software to assess calibration, retention time, internal standard stability, quality control performance, and measurement uncertainty. Its modular design enables automated data parsing, comparative evaluation against reference standards, quantitative determinations, and exception handling, with results exported in standardized formats compatible with laboratory information and case management systems. Implementation reduced review time from several days to three hours per batch, while improving consistency, reproducibility, and alignment with ANSI/ASB Standard 054 and ISO/IEC 17025:2017 requirements. This framework demonstrates how automation can simultaneously enhance efficiency, compliance, and defensibility in forensic toxicology and provides a scalable foundation for future integration with advanced computational and machine learning applications.

Keywords:  Data automation; Forensic toxicology; High-throughput analysis; LC-QTOF-MS; Quality assurance

DOI:  https://doi.org/10.1016/j.forsciint.2026.112919
Mol Cell Proteomics. 2026 Mar 12. pii: S1535-9476(26)00049-6. [Epub ahead of print] 101553

Glycoinformatic profiling of label-free intact heparan sulfate oligosaccharides.

Marissa L Maciej-Hulme, Jandi Kim, Elijah T Roberts, Yiqing Zhang, Anouk van der Velden, Dirk den Braanker, Cansu Yanginlar, Mark de Graaf, Ton Rabelink, Bernard van den Berg, Ellen van Omen, Rutger Maas, Anne-Els van de Logt, I Jonathan Amster, Johan van der Vlag.

  Heparan sulfates (HS) are a group of heterogenous linear, sulfated polysaccharides that play a role in in health and many diseases including cancer, cardiovascular, and kidney diseases. The structural variety of HS has greatly challenged the development and utility of HS analytics, particularly for native (non-depolymerized) structures, leaving a significant gap in HS technologies for clinical application. Mass spectrometry (MS)-based profiling with bioinformatics offers a top-down approach that can retain variety in large data sets. Using healthy human plasmas, we developed an MS glycoprofiling approach for native HS oligosaccharides, which retains the structural complexity of each individual HS chain and generates an HS 'index' (or Heparan-ome) for each patient. As a proof of concept, analysis of 53 plasma samples ranging from 4 groups of kidney disease patients revealed a new subset cluster (21%, 4/19) of membranous glomerulopathy (MG) patients with distinct HS profiles, highlighting the potential of HS glycoprofiling as a powerful new approach into clinical practice, which warrants future development into quantitative oliGAGomics and clinical diagnostics of kidney and other diseases.

Keywords:  glycomics; heparan sulfate; heparan sulfate profiling; kidney disease; mass spectrometry

DOI:  https://doi.org/10.1016/j.mcpro.2026.101553
J Am Soc Mass Spectrom. 2026 Mar 16.

Dissecting Metabolic Rewiring and Gene-Metabolite Interactions by Utilizing Untargeted Metabolomics and Single-Gene Knockouts in the Model Microorganism E. coli.

Xinru Pang, Li Chen, Huan Zhang, Shiqi Zhang, Jiangjiang Zhu.

  Central carbon metabolism, comprising glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway (PPP), is essential for Escherichia coli survival and growth. While disruptions in these pathways are known to affect cellular physiology, the system-wide metabolite-level consequences of single-gene knockouts remain incompletely understood. Using untargeted LC-MS metabolomics, we systematically profiled E. coli knockouts of TCA core enzymes, isoforms, subunits, bypass routes, and TCA-associated pathways. Core TCA knockouts separated into two major metabolic clusters, with cluster 1 strains displaying strong divergence in amino acid metabolism and cluster 2 retaining partial similarity to the parent strain. Isoform-specific deletions revealed differential roles of aconitases (ΔacnA vs ΔacnB) and fumarases (ΔfumA vs ΔfumC), while subunit knockouts of 2-oxoglutarate dehydrogenase (ΔsucA, ΔsucB) and succinate dehydrogenase (ΔsdhA-D) produced localized but distinct metabolite shifts, particularly around glutamate- and 2-oxoglutarate-linked metabolism. Bypass enzyme deletions (ΔaceA, ΔaceB, ΔglcB, and ΔmaeB) disrupted carbohydrate- and redox-related metabolites, underscoring their role as metabolic safety nets. Importantly, knockouts also triggered off-target effects in glycolysis, PPP, and the electron transport chain, highlighting the interconnectivity of central carbon metabolism. Our systematic approach demonstrated the possibility of utilizing comprehensive and untargeted metabolomics to map gene-metabolite associations and decipher potential metabolic interlinks.

Keywords:  Escherichia coli; central carbon metabolism; gene-metabolite interaction; metabolic rewiring; single-gene knockout model; untargeted metabolomics

DOI:  https://doi.org/10.1021/jasms.5c00454
Prostaglandins Leukot Essent Fatty Acids. 2026 Mar 06. pii: S0952-3278(26)00009-8. [Epub ahead of print]209 102731

Comparing the quantitation of specialized pro-resolving mediators in plasma and serum using ELISA and LC-MS/MS.

Brendon Naicker, Vindya Ramanayake-Mudiyanselage, Tanya Maxey, Victoria J Tyrrell, James Horn, Paul D Kennedy, Daniel Tew, Jeff Johnson, Garret A FitzGerald, Robert C Murphy, Kirk Maxey, Valerie B O'Donnell, Miguel A Gijón.

  Quantitation of oxylipins is generally performed using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Researchers can also use commercial enzyme-linked immunosorbent assays (ELISAs), due to convenience or where access to instrumentation is limited. However, oxylipins are small molecules with many isomers and metabolites that are difficult to fully discriminate using ELISAs, with many potential sources of cross-reactivity. In this paper, the use of ELISAs for oxylipin analysis is compared with LC-MS/MS, with two specialized pro-resolving mediators (SPM) as examples. By reviewing the literature, we show that ELISAs report significantly higher levels of resolvin D1 (RvD1) and resolvin D2 (RvD2) than LC-MS/MS. Also, we show experimentally that in plasma and serum samples where these lipids were not detected using LC-MS/MS, a positive result could be obtained using ELISA, and that these signals increase with improper sample processing. In conclusion, while ELISA could be a useful technique for detecting the presence of oxylipins, positive signals need verification using LC-MS/MS.

Keywords:  Enzyme-linked immunosorbent assay; Liquid chromatography-mass spectrometry; Resolvin

DOI:  https://doi.org/10.1016/j.plefa.2026.102731
J Proteome Res. 2026 Mar 16.

Proteomic Analysis of Human Dentine Matrix Extracts by Peptide-Level High-pH Reversed-Phase Fractionation.

Mennatuallah Khalil, Bushra Gull, Sara Alhimairi, Alexander D Giddey, Mohamed Jamal.

  Proteomics research has increasingly focused on human cells, tissues, and fluids; however, comprehensive data on dental tissues remain limited. Dentine, a mineralized component of teeth, contains structural proteins and bioactive molecules that can modulate pulp cell activity and support regeneration when released. Understanding its protein composition is therefore essential. Previous studies have identified relatively few dentine proteins, and technical challenges have hindered reproducibility. Traditional extraction methods also rely on strong acids that lack clinical relevance. In this technical note, we introduce a workflow combining EDTA-based dentine extraction under clinically relevant conditions with peptide-level fractionation using high-pH reversed-phase chromatography. This approach was compared with unfractionated samples, SDS-PAGE protein-level fractionation, and strong cation exchange (SCX) peptide-level fractionation, all followed by LC-MS/MS. Data are available via ProteomeXchange (PXD070849). This workflow enabled the identification of 514 proteins compared with 238 (unfractionated), 428 (SDS-PAGE), and 193 (SCX). High-pH reversed-phase chromatography contributed 217 unique identifications, exceeding those from other techniques. Although used in other proteomic systems, this methodology has not previously been applied to dentine matrix extracts and represents a promising approach for improving protein discovery.

Keywords:  dentine; dentine matrix; extracellular matrix; mass spectrometry; proteomics; regenerative endodontics

DOI:  https://doi.org/10.1021/acs.jproteome.5c01067
J Biol Chem. 2026 Mar 12. pii: S0021-9258(26)00237-1. [Epub ahead of print] 111367

Metabolic Tracing Reveals IL-2 Driven Glutaminolysis and De Novo Pyrimidine Synthesis in Human Natural Killer Cells.

Karen Slattery, Gearóid Conlon, Ethan Collins, Derek P Nolan, Clair M Gardiner, Geraldine M O'Connor.

Natural Killer (NK) cells are innate lymphocytes that are key to intrinsic cancer immunosurveillance and an important target for cancer immunotherapy. Understanding fundamental human NK cell metabolism provides opportunities for optimising NK cell therapies. Little is known about how glutamine, an important cell nutrient and carbon source, is utilised by human NK cells. To address this, we performed U13C-glutamine tracing experiments by Liquid Chromatography Mass Spectrometry (LCMS) and Gas Chromatography Mass Spectrometry (GCMS) analysis of human NK cells stimulated with IL-2 for 18 hours to provide a global overview of glutamine usage by these cells. Our results show that glutamine is taken up by resting NK cells and that this increases further upon IL-2 stimulation. Metabolite labelling analysis identified that IL-2 results in greater conversion of glutamine to glutamate, allowing for anaplerotic flux into the TCA cycle. The fate of the glutamine-derived carbons diverged at oxaloacetate (OAA) allowing both bioenergetic and biosynthetic outcomes - some carbons continued around the TCA cycle while others were exported, converted to aspartate and subsequently used for pyrimidine synthesis. Nucleotide synthesis by IL-2 activated NK cells was found to be essential for expression of the activation marker CD69. The data indicate that glutamine is a key nutrient taken up by human NK cells, and that IL-2 drives glutaminolysis. Subsequent glutamate is used to support the TCA cycle, generating energy and providing intermediates for de novo pyrimidine synthesis.

DOI: https://doi.org/10.1016/j.jbc.2026.111367
Biochem Pharmacol. 2026 Mar 12. pii: S0006-2952(26)00221-2. [Epub ahead of print] 117888

Therapeutic targeting of tricarboxylic acid cycle and oxidative phosphorylation: a critical analysis of metabolic interventions in cancer treatment.

Yawei Han, Yuhao Wei, Yuanting Xie, Mengzhu Zheng.

  Metabolic reprogramming is a hallmark of cancer cells, characterized by distinct alterations in cellular metabolism that emerge during malignant transformation. Enhanced activities of the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS) in tumor cells support their elevated biosynthetic demands for essential biomolecules, including nucleotides, amino acids, and fatty acids. These cancer-specific metabolic reprogramming not only generates mutant targets that are directly druggable, but also induces targets associated with synthetic lethality effects. In this review, we systematically elucidate the molecular dysregulation mechanism of the TCA cycle and the key enzyme OXPHOS, integrate the preclinical and clinical data of existing dysregulated enzyme inhibitors, and also propose a therapeutic approach using metabolic synthetic lethality as a strategy to overcome the toxicity and acquired resistance of targeted therapies in order to achieve selective potentiation of cancer cells on top of conventional targeted therapies. Furthermore, we critically analyze the structural optimization of key inhibitors, providing medicinal chemistry insights into their design, optimization, and mechanisms of action, which are essential for developing next-generation therapeutics with improved efficacy and selectivity. Through comprehensive analysis of altered tumor metabolism, we aim to provide novel insights and perspectives for drug design and target selection in cancer therapeutics.

Keywords:  Cell metabolic; Metabolic synthetic lethality; OXPHOS; TCA cycle

DOI:  https://doi.org/10.1016/j.bcp.2026.117888
Anal Chem. 2026 Mar 18.

Protein-Corona-Based Nanomagnetic Enrichment Strategy for In-Depth Secretome Profiling of Lung Adenocarcinoma Organoids.

Wenhui Wu, Yuebo Zhang, Zizhang Guo, Hanqing Zhang, Yi Li, Haiping Hao, Hui Ye.

Secreted proteins are critical mediators of intercellular communication that shape the tumor microenvironment (TME). While patient-derived organoids (PDOs) provide physiologically relevant models that preserve patient heterogeneity, comprehensive secretome profiling remains challenging. This difficulty arises from the low abundance of secreted proteins and high interference from exogenous media components essential for organoid maintenance. Here, we report a Protein corona nanomagnetic bead-based PDO Secretome Profiling workflow (PPSP) that integrates magnetic-bead enrichment with high-resolution mass spectrometry data-independent acquisition (HRMS-DIA). By leveraging the protein corona effect to mitigate background interference, PPSP significantly enhances detection depth. In a proof-of-concept application to lung adenocarcinoma (LUAD) PDOs, the workflow identified 1376 proteins in a single sample─a 4.9-fold increase over direct analysis─and uncovered 1095 proteins undetectable by conventional methods. Notably, PPSP enabled the detection of both known LUAD biomarkers and previously uncharacterized secreted proteins associated with patient survival. This robust and scalable platform facilitates the in-depth profiling of the PDO secretome and the identification of clinically relevant signaling proteins.

DOI: https://doi.org/10.1021/acs.analchem.5c07672
J Lipid Res. 2026 Mar 12. pii: S0022-2275(26)00045-3. [Epub ahead of print] 101019

Spatial Lipidomic Combining DESI Imaging and LESA Mass Spectrometry to Evaluate Metabolic Alterations in Obese Zebrafish.

Simon Isfort, Nadia Mercader, Mojgan Masoodi.

  Mass Spectrometry Imaging (MSI) enables spatial mapping of metabolites but often lacks in-situ structural confirmation. To address this, we validated a workflow combining histological staining, Desorption Electrospray Ionization (DESI)-MSI for spatial metabolic mapping and Liquid Extraction Surface Analysis (LESA)-MS2 for structural identification. The integrated approach allows comprehensive in situ detection and structural confirmation of metabolites using MS2 spectra, eliminating ambiguity and allowing confident molecular identification. We applied this workflow to full body sections of adult zebrafish (Danio rerio) fed a customized high-fat, high-cholesterol diet (HFD). Our multimodal imaging approach showed high analytical reproducibility during validation across tissues and highlighted the tissue specific lipidome signature. Unsupervised clustering of DESI-MSI data accurately identified adipose depots based solely on their lipid signature which were then confirmed by histology. Receiver operating characteristic (ROC) analysis and subsequent LESA-MS2 molecular confirmation led to identification of 52 lipids in adipose tissue, discriminating from non-adipose regions and included Di- and Triglycerides (DAGs and TAGs), free fatty acids (FFAs) and oxidized FFAs. This study establishes an optimized spatial lipidomics workflow and provides the first spatially resolved lipidomic profile of zebrafish adipose tissue. The integrated approach is broadly applicable to scenarios where sample material is limited, such as clinical biopsies or organoid models.

Keywords:  Adipose tissue; Dietary fat; Mass spectrometry Imaging; Obesity; Spatial Lipidomics; Zebrafish

DOI:  https://doi.org/10.1016/j.jlr.2026.101019
Wellcome Open Res. 2025 ;10 632

Untargeted metabolomics data in the By-Band-Sleeve trial and ALSPAC: integrating clinical trial and population cohort data.

Madeleine Smith, Lucy Goudswaard, David Hughes, Jane Blazeby, Chris Rogers, Graziella Mazza, Eleanor Gidman, Sophie FitzGibbon, Alix Groom, Susan Ring, Kate Northstone, Sarah Matthews, Gail White, Laurie Fabian, Nicholas Timpson, Laura Corbin.

   Introduction: Metabolomics is the study of measured metabolites and low-molecular weight molecules in a biological specimen, collectively known as the metabolome. Measuring the metabolome in populations is useful for investigating complex, polygenic and multifactorial traits as it can give insight into cellular metabolism and its perturbations in various states of health and disease. Here we present a description of metabolomics data generated using an untargeted mass-spectrometry approach in two studies: (1) the Avon Longitudinal Study of Parents and Children (ALSPAC) - a healthy, general population; and (2) the By-Band-Sleeve trial (BBS) - a pragmatic randomised controlled trial (RCT) of metabolic and bariatric surgery (MBS) plus a non-randomised observational sub-study.
Methods: Samples for this work were collected from ALSPAC participants at 30 years of age. Two sample collection efforts were made within BBS - firstly, from the RCT comparing the effectiveness of three types of MBS: the Roux-en-Y gastric bypass ("bypass"), laparoscopic adjustable gastric band ("band") and the sleeve gastrectomy ("sleeve"), and secondly from the non-randomised (observational) study of MBS. In both instances, samples were collected from patients before and after surgery. In total, 2128 samples were sent for mass-spectrometry (MS) metabolomics analysis by Metabolon (Discovery HD4 platform). Data underwent quality control (QC) via a standard pipeline using the R package metaboprep.
Results: After QC, the combined dataset consists of semi-quantitative data for 1176 features in 517 ALSPAC participants and 1062 BBS participants (1018 from the RCT and 44 from the non-randomised study) (1013 pre-surgery samples and 421 post-surgery samples).
Conclusion: Overall, we have provided a summary of MS data produced across two different study populations, described the QC procedures undertaken and provided some data validation analyses. Bringing together samples from these two studies in a single experiment offers a novel study design able to explore the biological implications of weight and intentional weight loss.

Keywords:  ALSPAC; By-Band-Sleeve; Metabolon; bariatric surgery; body mass index; mass-spectrometry; metabolic surgery; metabolomics; obesity

DOI:  https://doi.org/10.12688/wellcomeopenres.24917.2
J Physiol. 2026 Mar 18.

The carrying capacity for free fatty acids in plasma is exceedingly high in mammals, and this may be a liability for human metabolic health.

Gregory C Henderson.



Keywords:  diabetes; free fatty acid; insulin resistance; lipid metabolism; metabolic health; non‐esterified fatty acid

DOI:  https://doi.org/10.1113/JP290998
Nat Mach Intell. 2025 Mar 31. 7(4): 565-579

InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments.

Kevin Eloff, Konstantinos Kalogeropoulos, Amandla Mabona, Oliver Morell, Rachel Catzel, Esperanza Rivera-de-Torre, Jakob Berg Jespersen, Wesley Williams, Sam P B van Beljouw, Marcin J Skwark, Andreas Hougaard Laustsen, Stan J J Brouns, Anne Ljungars, Erwin M Schoof, Jeroen Van Goey, Ulrich Auf dem Keller, Karim Beguir, Nicolas Lopez Carranza, Timothy P Jenkins.

Mass spectrometry-based proteomics focuses on identifying the peptide that generates a tandem mass spectrum. Traditional methods rely on protein databases but are often limited or inapplicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to spectra without prior information, is valuable for diverse biological applications; however, owing to a lack of accuracy, it remains challenging to apply. Here we introduce InstaNovo, a transformer model that translates fragment ion peaks into peptide sequences. We demonstrate that InstaNovo outperforms state-of-the-art methods and showcase its utility in several applications. We also introduce InstaNovo+, a diffusion model that improves performance through iterative refinement of predicted sequences. Using these models, we achieve improved therapeutic sequencing coverage, discover novel peptides and detect unreported organisms in diverse datasets, thereby expanding the scope and detection rate of proteomics searches. Our models unlock opportunities across domains such as direct protein sequencing, immunopeptidomics and exploration of the dark proteome.

DOI: https://doi.org/10.1038/s42256-025-01019-5
Front Immunol. 2026 ;17 1711640

Establishing an untargeted lipidomics workflow for cellular analysis: insights into endothelial cell function in anaphylaxis.

María Isabel Delgado Dolset, Andrea Escolar-Peña, Sergio Fernández-Bravo, Antonio J García-Cívico, Lucía Pajares, René Neuhaus, Rosario González-Mendiola, Coral Barbas, Domingo Barber, José Julio Laguna, María M Escribese, Vanesa Esteban, Alma Villaseñor.

   Background: Cell metabolomics, including lipidomics, presents several challenges regarding analyzing limited cell populations and distinguishing cellular metabolites from background signals originated from a stimuli or after a treatment. To address this, we have developed a novel workflow for untargeted cell lipidomics analysis.
Methods: To study the impact of varying input cell numbers on the outcomes of untargeted cell lipidomics analysis, CD3+ cells isolated from a healthy donor at 6 different cell counts (50k, 100k, 250k, 500k, 750k, and 1M) were analyzed by liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (LC-QTOF-MS) in positive and negative electrospray ionization (ESI+ and ESI-, respectively) modes. After data quality assurance (QA), Spearman correlation analyses were carried out to select chemical signals derived from cells (ρ ≥ 0.7, p-value < 0.05). Then, this methodology was applied to human microvascular dermal endothelial cells (HMVEC-d), where a cell number calibration curve including 4 cell counts (25k, 50k, 75k, and 100k) was incorporated alongside the experimental samples to enable the analysis of cell-derived chemical signals. Here, the lipid response of HMVEC-d after contact with sera from patients at baseline and during the acute stage of anaphylaxis triggered by three different mechanisms was explored.
Results: For the CD3+ model, we found that although 1087 chemical signals (k) passed the QA, samples did not cluster according to their cell count when taking all signals into account. After correlation analyses, the widest cell count interval considered for correlation analyses (50k-to-1M; k = 70) showed clear clustering by cell number. The principal component analysis (PCA) models for ESI+ showed that for this cell count interval, the first component explained over 90% of the variance among samples. After applying the same methodology to HMVEC-d, we found k = 157 and k = 278 correlated chemical signals for ESI+ and ESI- in the cell curve (25k-100k). Statistical analysis identified 193 chemical signals that significantly (p-value < 0.05 and p-adjusted value < 0.2) differed between the acute and baseline stages of anaphylaxis. Without this correlation approach, 67 additional chemical signals would have been selected as significant. From the 193 chemical signals, 75 unique lipids were annotated, mainly including fatty acids, acyl carnitines, glycerophospholipids, and sphingolipids, all increased in the acute phase. These changes were associated with sphingolipid and glycosphingolipid metabolism, and ceramide and phospholipid signaling pathways.
Conclusions: This workflow for cell lipidomics analysis allows the selection of lipids derived from the intracellular content regardless external sources, supporting specific intracellular metabolism profiling.

Keywords:  LC-MS; cell count interval; correlations; immunometabolism; lipidomics; metabolites

DOI:  https://doi.org/10.3389/fimmu.2026.1711640
J Proteome Res. 2026 Mar 16.

Protein Biomarkers as Molecular Tools for Forensic Biofluid Identification.

Anjali Chhikara, Vinayak Gupta, Pallavi Kumari, Jyoti Dalal.

  Forensic proteomics has rapidly established a significant role in forensics, particularly when DNA analysis is insufficient. Proteomics has been frequently recognized for helping reveal useful information about the origin, state and context of forensic samples. Proteins are considered crucial biomarkers in biological samples because of their ability to elucidate cellular functions and post-mortem alterations. Combining advanced mass spectrometry and bioinformatics has increased the importance of proteomics in forensic sciences. The current study examined the use of proteomic technologies, mass spectrometry, and sophisticated software across the key domains of forensic research. The procedure for Post-Mortem Interval (PMI) estimation and body fluid classification proved to be more precise because of the use of machine learning. Peptide biomarkers have helped identify various species by samples of blood, saliva, and semen, and recognize brain, muscles, and skin tissues. Despite significant advancements, the wide acceptance of forensic proteomics remains problematic due to intricate sample stability, high equipment costs, and strict legal standards. Recent advancements in analytical sensitivity, data interpretation tools, and collaborative efforts toward robust protocols position forensic proteomics as an indispensable component of the forensic toolkit. This review indicates the increasing relevance of proteomics in forensic applications, especially relating to PMI estimation, body fluid differentiation, and disease profiling. It promises to significantly enhance the depth of evidentiary interpretation and contribute to more precise and equitable outcomes in the criminal justice system. Proteomic biomarkers need further validation across a range of environments, and standardized protocols should be developed and tested to ensure that proteomics is suitable for forensic use in the courts.

Keywords:  bioinformatics; body fluid identification; forensic proteomics; mass spectrometry; post-mortem interval; protein biomarkers

DOI:  https://doi.org/10.1021/acs.jproteome.5c00898