bims-mascan 2026-05-24 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–05–24
nineteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Lipid Class Prediction from MS1 Data using Gaussian Graphical Models.
The proteomics and phosphoproteomics landscape of melanoma under T cell attack.
omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation in bulk- and single-cell proteomics.
Improved peptide search for identification of SUMO and sequence-based modifications, in MaxSBM.
Bridging Simplicity and Depth in Single-Cell Proteomics: A Cost-Effective Workflow and an Expanded Framework for Data Evaluation.
A comprehensive evaluation of solid-phase extraction strategies for targeted and untargeted liquid chromatography - mass spectrometry metabolomic analysis.
Simultaneous determination of acylcarnitines, fatty acids, and amino acids by 3-nitrophenylhydrazine derivatization and ultra-high performance liquid chromatography/tandem mass spectrometry.
Tools and Technologies for Studying Cancer Metabolism.
Introduction to Cancer Metabolism.
Comparative performance of Chenomx and MagMet in NMR-based metabolite profiling with validation by liquid chromatography coupled with tandem mass spectrometry.
A platform for high-throughput and ultrasensitive immunopeptidomics.
Glutamine Metabolism: Role in Cancer Cell Proliferation and Survival.
Nature of False Peptide Identifications in Data-Independent Acquisition-Based Proteome Analysis.
N-glycopedia: libraries for native N-glycan structural analysis.
Ensuring Traceability in Nontargeted HRMS Annotation Workflows: An Urgent Call for Editorial Standards.
Robotic perturbation proteomics and AI agents enable scalable drug mechanism discovery.
Quantifying diclofenac - proven in theory, problematic in practice.
Glucuronidation metabolomic fingerprinting to map host-microbe metabolism.
The Warburg Effect and Aerobic Glycolysis in Tumors.

Anal Chem. 2026 May 20.

Lipid Class Prediction from MS1 Data using Gaussian Graphical Models.

Thomas Rix, Caroline Jane Sands, Alma Villaseñor, Ana Gradillas, Coral Barbas, Elena Chekmeneva, Elizabeth J Want, Timothy M D Ebbels.

Liquid chromatography-mass spectrometry (LC-MS) untargeted analysis enables comprehensive lipid profiling of biological samples. However, system-level interpretation is often limited by the large number of unannotated features. Assigning features to lipid classes provides a higher-level, yet informative, overview that complements detailed structural analysis and supports biological interpretation at the class level. Recent advances in the systematic prediction of chemical class using tandem mass spectrometry (MS2) help address this; however, a substantial proportion of features in untargeted LC-MS data sets are typically characterized only at the MS1 level. Here, we present a workflow to systematically predict the lipid class from MS1-only data in untargeted LC-MS, without requiring prior annotations or MS2. Motivated by previous research showing that Gaussian graphical models (GGMs) estimated from feature intensities can encode the lipid class structure, our method, GgmLipidClassifier (GLC), combines conventional accurate-mass database searching with a GGM-derived network structure in a unified scoring framework to predict lipid class according to the LIPID MAPS Structure Database (LMSD) ontology. Across three human serum and plasma data sets, GLC achieved overall accuracies of 82-90% at the LMSD main class-level and 72-86% at the lipid subclass level, with improved accuracy and reduced uncertainty compared to closest-m/z matching. GLC provides class predictions for most detected features and also generates prediction quality scores to support downstream interpretation. Applied to serum samples from an Alzheimer's disease study, lipid class enrichment based on GLC predictions was highly consistent with class enrichment derived from ground-truth lipid annotations. Importantly, GLC extended coverage to classes missing from the annotation set, revealing biologically plausible associations with Alzheimer's disease, including cholesterol and derivatives, vitamin D3 and derivatives, and plasmalogen glycerophosphoethanolamines. Overall, GLC provides robust lipid class predictions from MS1-only data, generating lipid class assignments for most detected features and complementing conventional analysis to support broader system-level interpretation.

DOI: https://doi.org/10.1021/acs.analchem.5c08067
Cell Rep Med. 2026 May 21. pii: S2666-3791(26)00246-6. [Epub ahead of print] 102829

The proteomics and phosphoproteomics landscape of melanoma under T cell attack.

Giulia Franciosa, Agnete W P Jensen, Ana Martinez-Val, Ilaria Piga, Marco Donia, Jesper V Olsen.

  Understanding how tumor cells interact with tumor-infiltrating lymphocytes (TILs) is crucial for improving immunotherapy, yet protein-level changes remain largely unexplored. To address this, we profile the early responses of patient-derived melanoma cells co-cultured with matched autologous TILs. To distinguish tumor from TIL proteomes without physical sorting, we apply stable isotope labeling by amino acids in cell culture (SILAC) coupled with Orbitrap Astral data-independent acquisition (DIA) mass spectrometry (MS). This approach enables cell type-specific profiling of protein phosphorylation and degradation, alongside bulk analysis of the early newly synthesized proteome during active immune attack. Our analyses resolve interferon-γ-dependent changes in melanoma cells, identify the cytotoxic and regulatory T cell molecule (CRTAM) as a selective marker of reactive TILs, and reveal rapid tumor-intrinsic activation of DNA damage response-associated kinases, exposing potential therapeutic vulnerabilities. Overall, this framework provides a powerful resource for dissecting tumor-immune interactions to guide biomarker discovery and advance immunotherapy.

Keywords:  CRTAM; DNA-PK; TILs; cancer; cell signaling; immunotherapy; mass spectrometry; melanoma; phosphoproteomics; proteomics

DOI:  https://doi.org/10.1016/j.xcrm.2026.102829
Nat Commun. 2026 May 20.

omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation in bulk- and single-cell proteomics.

Alexandre Segers, Cristian Castiglione, Christophe Vanderaa, Lennart Martens, Davide Risso, Lieven Clement.

The unprecedented speed and sensitivity of mass spectrometry (MS) unlocked large-scale applications of proteomics and even enabled proteome profiling of single cells. However, this fast-evolving field is hindered by a lack of scalable dimensionality reduction tools that can compensate for substantial batch effects and missingness across MS runs. Therefore, we present omicsGMF, a fast, scalable, and interpretable matrix factorization method, tailored for bulk and single-cell proteomics data. Unlike current workflows that sequentially apply imputation, batch correction, and principal component analysis, omicsGMF integrates these steps into a unified framework, dramatically enhancing data processing and dimensionality reduction. Additionally, omicsGMF provides robust imputation of missing values, outperforming bespoke state-of-the-art imputation tools. We further demonstrate how this integrated approach increases statistical power to detect differentially abundant proteins in the downstream data analysis. Hence, omicsGMF is a highly scalable approach to dimensionality reduction in proteomics, that dramatically improves many important steps in proteomics data analysis.

DOI: https://doi.org/10.1038/s41467-026-73402-8
Mol Cell Proteomics. 2026 May 18. pii: S1535-9476(26)00085-X. [Epub ahead of print] 101589

Improved peptide search for identification of SUMO and sequence-based modifications, in MaxSBM.

Caroline Lennartsson, Pelagia Kyriakidou, Michael Lund Nielsen, Jesper Velgaard Olsen, Jürgen Cox, Ivo Alexander Hendriks.

  Post-translational modifications, such as SUMOylation and ubiquitination, regulate key cellular processes by covalently attaching to lysine residues. While mass spectrometry allows site-specific identification of PTMs, most existing search engines are optimized for small, non-fragmenting modifications and struggle to detect large, fragmenting protein-based modifiers. We refer to these as Sequence-Based Modifiers (SBMs). To overcome this limitation, we developed an SBM-specific search strategy within MaxQuant that accounts for the fragmentation behavior of SBMs during peptide identification. Using publicly available datasets, we validated our approach for SUMO2/3. Our analysis identified distinct diagnostic features and characteristic mass shifts associated with SBM fragmentation, referred to in this study as d-ions (diagnostic ions) and p-ions (PTM ions). By leveraging these features, our method improved the identification of SUMOylated peptides from human cell lines by ∼13%, SUMOylation sites in mouse embryonic cells by ∼22%, and in mouse adipocytes by ∼24%. Our search method improved spectral annotation of SBMs by up to 9 % increase in the median Andromeda score. Taken together, we highlight the potential of our SBM search to enhance the discovery of protein-based modifications.

Keywords:  PTM; SUMO; SUMOylation; data dependent acquisition; endogenous; label free quantification; mass spectrometry; peptide identification; proteomics; sequence-based modifiers; ubiquitin; ubiquitination

DOI:  https://doi.org/10.1016/j.mcpro.2026.101589
J Proteome Res. 2026 May 19.

Bridging Simplicity and Depth in Single-Cell Proteomics: A Cost-Effective Workflow and an Expanded Framework for Data Evaluation.

Shuxin Chi, Jason Rogalski, Huan Zhong, Esperanza Garcia Martinez, Arpa Ebrahimi, Rachel Wong, Melanie L Bailey, Marco A Marra, Claudia S Maier, Terrance P Snutch, Leonard J Foster.

  Single-cell proteomics (SCP) offers direct insight into functional protein states that drive cellular heterogeneity, complementing genomic and transcriptomic analyses. Although recent reports have demonstrated improved proteome coverage, their reliance on specialized instrumentation limits the broader adoption. Additionally, current evaluation practices remain largely centered on protein and peptide identification counts, which alone do not fully reflect data quality or biological interpretability. Here, we describe an accessible, label-free SCP workflow that implements easily accessible laboratory equipment: a single-cell dispenser, conventional multiwell plates, and an incubator with water-bath-based humidity control. Using trapped ion mobility spectrometry─time-of-flight mass spectrometry (timsTOF), we systematically optimized key sample preparation variables, including trypsin concentration, incubation time, reduction/alkylation, digestion conditions, and plate types, which together maximize data quality and reproducibility. We further introduce a data quality framework that moves beyond identification counts, emphasizing quantitative consistency and biological interpretability via individual protein coverage completeness across cells, coefficients of variation across technical replicates, peptide-to-protein ratios, and single-cell-to-bulk correlations. Collectively, our approach lowers technical barriers to accessing SCPs while enabling more rigorous, interpretable, and scalable SCP analysis across diverse research contexts.

Keywords:  data visualization; liquid chromatography−mass spectrometry; sample preparation; single-cell proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.6c00046
Talanta. 2026 May 09. pii: S0039-9140(26)00612-0. [Epub ahead of print]309 129956

A comprehensive evaluation of solid-phase extraction strategies for targeted and untargeted liquid chromatography - mass spectrometry metabolomic analysis.

Eva Cífková, Miroslav Lísa, Zdeňka Neuerová, Ondřej Keresteš.

  Sample preparation remains one of the most critical and challenging steps in liquid chromatography - mass spectrometry (LC-MS) metabolomic analysis, as it directly affects metabolite recovery and analytical accuracy and reliability. In this study, we systematically compared solid-phase extraction (SPE) strategies for serum sample preparation in both targeted and untargeted metabolomic approaches. A total of 71 metabolites with diverse structural and polarity characteristics were analyzed using various SPE formats, including dispersive SPE with multiple sorbents and extraction modes, SPE spin columns, SPE pipette tips, and conventional SPE cartridges. Sorbents within the same extraction mode yielded comparable results. Hydrophilic interaction liquid chromatography sorbents demonstrated the highest performance across a wide range of polarities, whereas reversed-phase sorbents favored moderately polar compounds. Ion-exchange sorbents exhibited limited suitability for broad metabolite coverage due to strong pH dependence but improved recovery of ionizable compounds when combined with other sorbents. While different SPE formats showed similar extraction efficiency, their repeatability varied, with spin columns outperforming conventional cartridges. SPE exhibited mitigation of matrix effects in targeted analysis, particularly for highly polar metabolites, compared to protein precipitation (PPT). Although PPT offered higher efficiency in the untargeted workflow, SPE increased feature coverage by up to 50%. Among commercial products, hydrophilic-lipophilic balanced (HLB) sorbents delivered superior efficiency and repeatability, with HLB-packed spin columns providing the most universal and robust performance for LC-MS metabolomic analysis. The results of this systematic evaluation offer practical guidance for selecting appropriate sample preparation strategies tailored to specific analytical goals and target metabolites.

Keywords:  Mass spectrometry; Metabolomics; Sample preparation; Solid phase extraction; Targeted analysis; Untargeted analysis

DOI:  https://doi.org/10.1016/j.talanta.2026.129956
J Chromatogr A. 2026 May 15. pii: S0021-9673(26)00434-6. [Epub ahead of print]1782 467105

Simultaneous determination of acylcarnitines, fatty acids, and amino acids by 3-nitrophenylhydrazine derivatization and ultra-high performance liquid chromatography/tandem mass spectrometry.

Hamidreza Ardalani, Srinivas Reddy Dannarm, Peter Spégel.

  Acylcarnitines (ACs) play a pivotal role in metabolism, most notably by facilitating the transport of fatty acids (FAs) into mitochondria for β-oxidation, a key step in cellular energy production. Dysregulation of AC, FA, and amino acid (AA) levels has been linked to various metabolic disorders, including cardiovascular diseases, neurodegenerative conditions, and cancer. Consequently, monitoring these metabolites in blood samples provides valuable insights into metabolic health and disease progression. In this study, we developed a method for the quantification of carnitine, seven ACs, fifteen FAs, and thirteen AAs in human serum using reversed-phase ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). By employing 3-nitrophenylhydrazine (3-NPH) derivatization, we achieved high detectability for ACs, with limits of detection (LODs) ranging from 0.01-0.27 ng/mL for ACs, 0.22-1.76 ng/mL for FAs and 0.17-18.25 ng/mL for AAs. Recovery rates ranged from 92-126% for ACs, 56-116% for FAs and 86-115% for AAs. Inter- and intra-day precision were below 20% for all metabolites except two FAs. This method provides a reliable and sensitive tool for the simultaneous analysis of ACs, FAs, and AAs in serum, with potential applications in clinical diagnostics and metabolic research.

Keywords:  Acylcarnitine; Amino acids; Fatty acids; LC-MS/MS; Nitrophenylhydrazine

DOI:  https://doi.org/10.1016/j.chroma.2026.467105
Cancer Treat Res. 2026 ;195 237-247

Tools and Technologies for Studying Cancer Metabolism.

Ayush Madan, Subhajit Mandal, Soukat Chandra, Joy Das, Biplab Debnath.

  Tools for studying cancer metabolism include mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy for metabolomics, metabolic imaging (PET, MRI, MRS) for in vivo analysis, and metabolic flux analysis (MFA) with stable isotope tracers to track metabolic pathways. Other technologies involve microfluidic systems for simulating tumor environments and fluorescence-activated cell sorting (FACS)-based methods for analyzing immune cell metabolism. Multiple analytical platforms that facilitate the detection of metabolites in cells and living organisms have been utilized to study cancer metabolism. In this section, we will discuss how these techniques have contributed to the study of cancer metabolism and how they have led to advances in our understanding of metabolic reprogramming and biological phenotypes.

Keywords:  Analytical platforms; Cancer metabolism; Metabolites

DOI:  https://doi.org/10.1007/978-3-032-21861-2_12
Cancer Treat Res. 2026 ;195 1-20

Introduction to Cancer Metabolism.

Gomathi Mohan, Mukesh Meena, Manzer H Siddiqui, Gaurav Raturi, Pracheta Janmeda, Priya Chaudhary.

  The fact that tumor cells have a distinct metabolic phenotype from their normal equivalents is becoming more widely recognized. Tumor metabolism exhibits a complex ecological network due to the presence of multiple metabolic compartments interconnected through the transfer of catabolites. Tumor cells exhibit markedly elevated rates of metabolism for fatty acids, glutamine, acetate, hydroxybutyrate, pyruvate, lactate, and glucose compared to nontumor cells. Tumor cells can generate adenosine triphosphate (ATP) as the fundamental energy unit due to their metabolic flexibility and unpredictability, which aids in maintaining the redox balance and distributing resources to essential biosynthetic activities necessary for cell proliferation, growth, and survival. Experimental data indicate that cancer growth may be induced by metabolic cross talk between cell populations exhibiting distinct, synergistic metabolic characteristics. Thus, emphasizing the metabolic variations between tumor and normal cells presents a suitable approach for anticancer strategies. Cancer cells adapt their metabolism and influence the metabolic processes of surrounding cells within the microenvironment of the tumor to ensure their proliferation and survival. This process drives disease progression; specifically, we identify targetable metabolic weaknesses that can be intervened upon.

Keywords:  ATP; Cancer cells; Disease progression; Metabolic cross talk; Tumor metabolism

DOI:  https://doi.org/10.1007/978-3-032-21861-2_1
Talanta. 2026 May 15. pii: S0039-9140(26)00667-3. [Epub ahead of print]309 130011

Comparative performance of Chenomx and MagMet in NMR-based metabolite profiling with validation by liquid chromatography coupled with tandem mass spectrometry.

Wesam A Ali, Anju Annie Thomas, Lydia Belkacem, Huda E Mustafa, Habiba Alsafar, Selena E Richards.

  In this work, two computational approaches for metabolite quantification in serum samples using 1H NMR spectroscopy were evaluated: the spectral matching method (MSM) implemented in MagMet and the non-linear least squares method (MNLLS) implemented in Chenomx. The comparison focused on their underlying methodologies, including deconvolution algorithms and user workflows, to assess their relative performance and suitability for metabolomics data analysis. As various analyses (e.g. pattern recognition, classification, biomarker discovery, and pathway analysis) rely on the precision and consistency of input features (e.g., metabolite concentrations), selecting a robust quantification method is essential. Variability in quantification can introduce noise and impact the stability and comparability of analytical outputs. To validate performance, MSM (MagMet) and MNLLS (Chenomx) were benchmarked against quantitative NMR (qNMR) and liquid chromatography-tandem mass spectrometry (LC-MS/MS), the latter serving as the primary reference due to its high sensitivity and broad metabolite coverage (Gika et al., 2014) [1]. Although LC-MS/MS may be affected by matrix effects and ion suppression; these factors are well characterized and routinely mitigated through isotope-labeled internal standards and validated analytical workflows. Moreover, LC-MS offers substantially higher sensitivity than NMR, typically by two to three orders of magnitude, enabling the detection and quantification of hundreds to thousands of metabolites within a single analysis (Nagana Gowda and Raftery, 2022) [2]. qNMR was included as a complementary technique to provide orthogonal validation rather than serving as the sole benchmark. Ten independent serum control samples from a healthy reference group were analyzed to account for natural biological variability, enhancing the generalizability of the findings. The comparison was structured around four criteria: (i) quantitative performance, (ii) computational stability, (iii) usability and processing time, and (iv) method-based similarity via partial least squares-discriminant analysis (PLS-DA). This work differs from prior studies by integrating statistical validation, repeatability testing, and practical usability assessment, and by benchmarking computational quantification pipelines against experimentally grounded methods such as qNMR and LC-MS/MS [3-5]. The selected approach is expected to demonstrate improved consistency in quantification relative to the alternative, contributing to more reliable biological interpretations and more reproducible analytical outcomes across datasets.

Keywords:  Bioinformatics; Metabolomics; Multivariate analysis; Quantitative NMR

DOI:  https://doi.org/10.1016/j.talanta.2026.130011
Mol Cell Proteomics. 2026 May 18. pii: S1535-9476(26)00086-1. [Epub ahead of print] 101590

A platform for high-throughput and ultrasensitive immunopeptidomics.

Adillah Gul, Laura Van Moortel, Patrick Willems, Ilke Aernout, Laura Pedró-Cos, Kia C Ferrell, Katie Boucher, An Staes, Simon Devos, Ine Lentacker, Bart Vandekerckhove, Caroline Demangel, Fabien Thery, Francis Impens.

Mass spectrometry (MS)-based immunopeptidomics is a powerful approach for untargeted discovery of peptides presented on major histocompatibility complex (MHC) molecules, which can guide the selection of vaccine antigens and immunotherapy targets. First-generation immunopeptidomics workflows require processing of hundreds of millions of cells using lengthy, manual procedures. More recent approaches focus on increasing either sensitivity or throughput, but rarely combine both aspects. Here, we describe a semi-automated immunopeptidomics platform that combines high sensitivity with high throughput by implementing highly optimized conditions for isolation of MHC class I and II peptides in a 96-well positive-pressure device. Lysis in a small volume of 100 μl allows efficient MHC capture in a 96-well filter plate with optimal pore size for automated washing, elution and C18 purification steps. Upon analysis of 25% of the eluate from 16 million cells, our workflow identified over 13,500 MHC I and 6,000 MHC II peptides on a timsTOF SCP mass spectrometer, operating in DDA-PASEF mode. Exploring the sensitivity limits of our platform, we identified up to 1,000 MHC I peptides, including hundreds of predicted binders, from as few as 20,000 JY cells. Validating the platform's performance for quantitative biological discovery, we report the identification of known and novel bacterial immunopeptides from U937 macrophages infected with Listeria monocytogenes or Bacillus Calmette-Guérin (BCG). Together, our optimized immunopeptidomics platform enables robust immunopeptide detection from lower-input samples in a high-throughput fashion, enabling its use for biological applications where sample amounts are limiting.

DOI: https://doi.org/10.1016/j.mcpro.2026.101590
Cancer Treat Res. 2026 ;195 45-78

Glutamine Metabolism: Role in Cancer Cell Proliferation and Survival.

Aganta Chakraborty, Sanket Pramanik, Shrabasti Mullick, Rajarshi Nath, Mohini Mondal, Radheshyam Pal, Sumel Ashique, Mayukh Jana.

  Glutamine is crucial for cancer cell proliferation and survival because it fuels the TCA cycle, provides building blocks for macromolecules, and helps maintain redox balance by supporting antioxidant pathways like glutathione production. Cancer cells often become dependent on glutamine, a phenomenon known as glutaminolysis, and use it to produce energy and essential molecules for rapid growth. This metabolic addiction makes glutamine metabolism a significant target for cancer therapies. In recent years, some therapeutic drugs targeting glutamine metabolism to treat cancer have been developed. However, such drugs are not sufficiently effective. Targeting metabolic reprogramming may be an effective strategy to enhance cancer treatment efficacy. Glutamine serves as a vital nutrient for cancer cells. Inhibiting glutamine metabolism has shown promise in preventing tumor growth both in vivo and in vitro through various mechanisms.

Keywords:  Cancer microenvironment; Glutamine metabolism; Redox balance; Signaling pathways; TCA cycle; Therapeutic targets; Tumor proliferation

DOI:  https://doi.org/10.1007/978-3-032-21861-2_3
J Am Soc Mass Spectrom. 2026 May 18.

Nature of False Peptide Identifications in Data-Independent Acquisition-Based Proteome Analysis.

Daniil D Pomogaev, Mikhail V Gorshkov, Mark V Ivanov.

Data-independent acquisition (DIA) mass spectrometry is increasingly used in proteomics because it offers a shorter analysis time at higher proteome coverage. Most existing DIA search engines rely heavily on fragmentation spectra and produce identification results, even when precursor ions are not detected. Although the latter is well-known, the errors it can lead to have received little attention. In this work, we studied false identifications in DIA, primarily due to the lack of a corresponding precursor ion envelope. Using a DIA data set with known UPS proteins spiked into Escherichia coli (E. coli), we performed an extensive search against several hundred databases with in silico-generated UPS variants mimicking single amino-acid substitutions. We found that DIA search results often fail to distinguish between peptides with similar fragmentation patterns but different precursor masses. Peptides with substitutions near the N-terminus were more often misidentified by the search engine than those with substitutions near the C-terminus. This algorithmic shortcoming limits its applications in areas such as proteogenomics, post-translational modification detection, and proteoform analysis. To partially address this issue, we propose using identifications based only on the identified precursor and the narrow isolation windows.

DOI: https://doi.org/10.1021/jasms.5c00320
Nat Commun. 2026 May 20.

N-glycopedia: libraries for native N-glycan structural analysis.

Christopher Ashwood, Richard D Cummings.

Measurements of glycans modifying glycoproteins are hampered by the lack of standards that reflect the wide diversity in structure typically observed. To this end we exploit a large library of N-glycan standards comprised of a unique collection of 226 N-glycans including oligomannose, hybrid, and complex-type and apply a method employing porous graphitised carbon (PGC) and liquid chromatography mass spectrometry (PGC-LC-MS) to provide a high-resolution separation and characterisation of underivatized N-glycan structures. Chromatogram libraries arising from this study include retention time data, diagnostic fragments, and validated structural assignments, providing a robust platform for both targeted and discovery-based glycomics. Here we establish this generated data as an N-glycopedia, the resource in which researchers can compare this collective data to N-glycans under study and overcome the limitations of only having compositional data and predicted structures. The technology is easily expandable to include additional N-glycans as new standards become available.

DOI: https://doi.org/10.1038/s41467-026-73091-3
Anal Chem. 2026 May 21.

Ensuring Traceability in Nontargeted HRMS Annotation Workflows: An Urgent Call for Editorial Standards.

Francisco José Díaz-Galiano, Yann Guitton, María Murcia-Morales, Gaud Dervilly, Bruno Le Bizec.

Untargeted high-resolution mass spectrometry (HRMS) is widely used in metabolomics, exposomics, and chemical monitoring. However, compound annotation, a central element for interpreting untargeted data, is frequently reported without sufficient information to allow independent evaluation. This communication examines current annotation practices in untargeted HRMS studies and remarks the increasing lack of standardized reporting of metadata, structural identifiers, and confidence criteria. Annotations are often reduced to compound names and exact masses, sometimes relegated to Supporting Information or omitted entirely, despite their central role in data interpretation. Although several community initiatives and guidelines have proposed reporting recommendations and identification confidence frameworks, their application and enforcement remain inconsistent. As a result, annotation traceability is often insufficient to support reproducibility, interstudy comparability, or long-term data reuse. These limitations affect downstream applications, including meta-analyses, automated data mining, and regulatory-relevant fields such as food safety and exposure assessment. This article argues that improving annotation traceability is essential for the scientific robustness of untargeted HRMS workflows and emphasizes the role of journals, reviewers, and authors in ensuring that annotation information remains verifiable, reusable, and scientifically accountable.

DOI: https://doi.org/10.1021/acs.analchem.6c00439
bioRxiv. 2026 May 07. pii: 2026.05.04.722718. [Epub ahead of print]

Robotic perturbation proteomics and AI agents enable scalable drug mechanism discovery.

Yuming Jiang, Cameron S Movassaghi, Jesús Muñoz-Estrada, Niveda Sundararaman, Amanda Momenzadeh, Jesse G Meyer.

Large-scale mass spectrometry-based proteomic screening could reveal cellular mechanisms of drug action at systems resolution but remains limited by experimental complexity and the difficulty of extracting insight from high-dimensional datasets. Here, we describe an end-to-end platform that combines semi-automated sample preparation, rapid LC-MS/MS, and AI agent-based data analysis to enable scalable proteomic screening. In a screen of 172 compounds in HepG2 cells, we generated 1,232 proteomes with more than 8,700 quantified proteins in approximately three weeks. Agentic AI reduced data analysis and interpretation time to less than one day while translating proteomic measurements into structured mechanism-oriented summaries and experimentally testable hypotheses. Guided by this framework, we validated: (1) a cholesterol-lowering effect of methylene blue in vitro and (2) an association between loratadine exposure and increased circulating iron in matched electronic health record analyses. This work establishes a scalable platform for generating proteomic drug perturbation data and automatically converting that data into mechanistic insights and candidate translational hypotheses using AI.

DOI: https://doi.org/10.64898/2026.05.04.722718
Bioanalysis. 2026 May 21. 1-6

Quantifying diclofenac - proven in theory, problematic in practice.

Sarah Hofmann, Eva Schöning, Peter Huber, Felix Leger, Heike Wiese.

   AIM: During clinical sample analysis unforeseen problems arose using a previously validated assay for the quantification of diclofenac in the concentration range from 0.0500 to 50.0 ng/mL. Here, we describe the troubleshooting process leading to the identification of the root cause and resulting changes in assay development strategy for robust supported liquid extraction (SLE)-based assays for clinical application.
METHODS: Individual steps of the sample preparation workflow and subsequent analysis with liquid chromatography and mass spectrometry (LCMS) were examined. Troubleshooting results were tested using stored validation samples and pooled clinical samples for confirmation.
RESULTS: The root cause for altered assay performance could be identified as the SLE plate batch effect attributable to variability in the sorbent material (diatomaceous earth). Reducing the sample loading volume applied to the SLE plates yielded purer extracts and consistent chromatographic performance. Quantitative recovery and signal consistency were restored after the modification.
CONCLUSION: Assay robustness was improved by underloading SLE plates. After adjusting the method and re-validation, clinical samples were successfully analyzed. The internal method development strategy was adjusted to avoid full capacity loading of SLE plate with natural sorbents.

Keywords:  LC-MS/MS; Supported liquid extraction (SLE); diatomaceous earth; diclofenac; troubleshooting

DOI:  https://doi.org/10.1080/17576180.2026.2677733
Nat Commun. 2026 May 20.

Glucuronidation metabolomic fingerprinting to map host-microbe metabolism.

Nina R Boyle, Josh J Sekela, Mingxun Wang, Helena Mannochio-Russo, Jeong Joo Pyo, Min Soo Kim, Shuchang Tian, Imhoi Koo, Elliot S Friedman, Ceylan Tanes, Mallappa Anitha, Yuan Tian, Ethan W Morgan, Iain A Murray, Joseph P Zackular, Kyle Bittinger, James D Lewis, Gary H Perdew, Gary D Wu, Babette S Zemel, Pieter C Dorrestein, Jordan E Bisanz, Matthew R Redinbo, Andrew D Patterson.

Glucuronidation is an important detoxification pathway that operates in balance with gastrointestinal microbial β-glucuronidase (GUS) activity, which can regenerate bioactive metabolites from their glucuronidated forms. How this host-microbe interaction shapes the distribution and pool of glucuronidated metabolites (i.e., the glucuronidome) remains poorly understood. In this study, we employed pattern-filtering data science approaches in conjunction with untargeted LC-MS/MS metabolomics to map the glucuronidome in urine, serum, and colon/fecal samples from gnotobiotic and conventional mice, and in humans. We find that microbial colonization and GUS activity compress the colonic glucuronidome and expand urinary glucuronidome diversity, revealing a compartmental redistribution of glucuronidated metabolites. Reverse metabolomics of known glucuronidated chemicals and glucuronidation pattern filtering searches in public metabolomics datasets exposed the diversity of glucuronidated metabolites in human and mouse ecosystems. In summary, we present a glucuronidation fingerprint resource that provides broader access to and analysis of the glucuronidome. Together, this work establishes a scalable analytical framework and provides mechanistic insight into how microbial activity reshapes systemic glucuronidation, with implications for drug metabolism, diet-microbe interactions, and biomarker discovery.

DOI: https://doi.org/10.1038/s41467-026-73398-1
Cancer Treat Res. 2026 ;195 21-43

The Warburg Effect and Aerobic Glycolysis in Tumors.

Bhupender Nehra, Sourabh Suthar, Rishabh Kasnia, Sunaina Demiwal, Mohini Mondal, Himanshu Sharma, Naheed Mojgani.

  A basic metabolic characteristic of carcinogenesis, namely the Warburg effect, is defined by the preferred utilization of aerobic glycolysis for energy generation in cancer cells, even in the presence of oxygen. Here, rapid proliferation and carefully planned adaptation to fulfill biosynthetic, redox, and survival requirements of malignant transformation are byproducts due to this adjustment in energy metabolism. Aerobic glycolysis enables tumor cells to accumulate metabolic intermediates, i.e., essential for macromolecular synthesis, supports redox homeostasis through NADPH production, and modulates tumor microenvironment via acidifying extracellular pH. Thereby, it leads to the promotion of invasion and immune evasion. Further, the discovery of this phenomenon by Otto Warburg nearly a century ago laid the foundation for modern cancer metabolism research. Advances in molecular oncology have since elucidated the regulatory role of oncogenes (such as MYC, RAS, and PI3K), tumor suppressors (like p53 and LKB1), and transcriptional networks (e.g., HIF-1α) to enforce glycolytic dependency. Recent studies further highlight that the Warburg effect integrates with mitochondrial signaling, epigenetic modifications, and metabolic cross talk between cancer cells as well as stromal components to provide novel therapeutic opportunities. This book chapter explores biochemical, molecular, and physiological dimensions of the Warburg effect along with its mechanistic basis, role in tumor progression, and emerging strategies to exploit glycolytic addiction in cancer therapy.

Keywords:  Aerobic glycolysis; Cancer metabolism; Glucose uptake; Mitochondrial function; Tumor microenvironment; Warburg effect

DOI:  https://doi.org/10.1007/978-3-032-21861-2_2