bims-mascan 2026-11-01 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–11–01
twenty-one papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Enhancing untargeted lipidomics using offline two-dimensional LC-MS and systematic evaluation of data processing tools.
JMod: Joint modeling of mass spectra for empowering multiplexed DIA proteomics.
An Optimized Protocol for TurboID-Based Proximity Labeling and Sample Preparation for Mass Spectrometry in Arabidopsis thaliana.
A Practical Guide to Dereplication in Natural Products Metabolomics.
Data processing in proteomics and metabolomics: liquid chromatography-mass spectrometry-based omics with divergent computational paradigms.
Automated plate-to-peptide protocol for mass spectrometry sample preparation.
In situ polymerized monolith tips for reproducible, format-flexible proteomic sample preparation applied to biofluids.
Simplifying low-input archival formalin-fixed paraffin-embedded proteomics: a detergent-free workflow in archival kidney tissue.
Spatial lipidomics in gynecologic malignancies: molecular identification, cellular attribution, and mechanistic validation.
Integrated Functional and Metabolomic Profiling of Melanoma Cells Treated with Redox-Active Compounds.
Detecting and Quantifying Variability in Mass Spectrometry-Based Metabolomics Datasets.
Data-Driven Mechanistic Modeling of Untargeted Metabolome Data.
Duplex Paternò-Büchi Isobaric Reagents for One-Step C═C-Resolved Lipid Isomer Quantification.
Mass Spectrometry Imaging-Guided Proteomics, an Emerging Frontier in Spatial Biology.
A roadmap for quantifying metabolism in patients with cancer.
Highly sensitive determination of polyunsaturated fatty acids by permanently charged derivatization-liquid chromatography-electrospray ionization tandem mass spectrometry.
A High-Resolution Mass Spectrometry Workflow for Xenobiotic Metabolite Discovery and Toxicity Prioritization: Disperse Orange 61 as a Case Study.
Mass Spectrometry-Based Quantification in Snake Venomics and Antivenomics: Current Trends and Future Perspectives.
Quantitative Top-Down Proteomics: A Review.
A Comprehensive GC-MS Toolbox for Targeted and Untargeted Sterol Analysis.
Mass Spectrometry-Based Omics: An Integrated Framework for Analytical Quality, Data Integrity, and Biological Interpretation.

Anal Chim Acta. 2026 Dec 01. pii: S0003-2670(26)01268-7. [Epub ahead of print]1425 346318

Enhancing untargeted lipidomics using offline two-dimensional LC-MS and systematic evaluation of data processing tools.

Fernanda Sousa Monteiro, Adriana Zardini Buzatto, Liang Li.

  Although two-dimensional liquid chromatography-mass spectrometry (2DLC-MS) has been explored to improve lipidomic coverage, its practical implementation remains limited by challenges in fractionation design and interpretation of complex datasets. Here we report an optimized offline hydrophilic interaction chromatography-reversed-phase liquid chromatography (HILIC-RPLC) 2DLC-MS workflow for untargeted lipidomics. A simplified five-fraction HILIC strategy was developed to enhance lipid class separation while maintaining practical analytical throughput. Optimization of solvent composition, mobile phases, and gradient conditions improved compatibility between chromatographic dimensions and enabled efficient fractionation prior to RPLC-MS analysis. Compared with conventional one-dimensional RPLC-MS, the proposed workflow reduced lipid co-elution and ion suppression, resulting in approximately two-fold higher lipid feature coverage in biological samples including serum, tissue, and cultured cells. Chromatographic fractionation information was further used as an orthogonal parameter to evaluate lipid annotation reliability. Using this framework, three lipidomics software platforms-MetaboScape, MS-DIAL, and LipidScreener-were systematically evaluated for processing 2DLC-MS datasets. The results reveal substantial variability among software tools and demonstrate that integrating chromatographic fractionation with data processing improves confidence in untargeted lipidomic analysis.

Keywords:  Data processing and annotation; Hydrophilic interaction chromatography; Lipidomics; Reversed-phase LC; Two-dimensional LC

DOI:  https://doi.org/10.1016/j.aca.2026.346318
bioRxiv. 2026 Aug 31. pii: 2025.05.22.655512. [Epub ahead of print]

JMod: Joint modeling of mass spectra for empowering multiplexed DIA proteomics.

Kevin McDonnell, Daniel J Geiszler, Nathan Wamsley, Jason Derks, Sarah Sipe, Zachary A Cohen, Lina Kozhaya Warinner, Maddy Yeh, Eunice Koo, Andrew Leduc, Theodore J Zwang, Harrison Specht, Nikolai Slavov.

Parallelization of data acquisition substantially increases the throughput of mass spectrometry-based proteomics. However, parallelization also increases the density of mass spectra and consequently the overlap between ions, frustrating their analysis. To improve sequence identification and quantification from such spectra, we developed an open-source software for Joint Modeling of mass spectra (JMod). JMod models overlapping peaks as linear super-positions of their components in both MS1 and MS2 space, which permits multiplexed DIA with smaller mass offsets to increase the multiplexing capacity and thus proteomics through-put for a given plexDIA tag. This enables 9-plexDIA using 2 Da offset PSMtags, increasing throughput 9-fold while preserving quantitative accuracy and coverage depth. Furthermore, we use JMod to deconvolve simultaneous labeling by mass tags and heavy amino acids, thus increasing the throughput of metabolic pulse experiments measuring protein synthesis and degradation rates in single cells from mouse liver. By supporting enhanced decoding of highly multiplexed DIA spectra, JMod provides an open and flexible software that increases the throughput of sensitive proteomics.

DOI: https://doi.org/10.1101/2025.05.22.655512
Bio Protoc. 2026 Oct 20. 16(20): e5852

An Optimized Protocol for TurboID-Based Proximity Labeling and Sample Preparation for Mass Spectrometry in Arabidopsis thaliana.

Li-Dan Zhang, Yu-Qi Ye, Dan-Ting Zhuo, Qiu-Ping Yan, Ya-Ling Ye, Hongbo Li, Caiji Gao, Lei Feng.

  Plant proximity labeling proteomics enables the identification of transient and weak intracellular protein interactions that are undetectable via traditional biochemical assays. Conventional enrichment pipelines suffer severe quantitative bias caused by urea-derived chemical artifacts and high mass spectrometry background signals from sample- and carrier-derived impurities. This protocol describes a complete standardized workflow for biotinylated protein extraction, enrichment, and LC-MS/MS sample preparation optimized for Arabidopsis seedlings. The procedure integrates controlled urea dilution and pre-desalting to suppress non-enzymatic protein modifications, introduces acetylation blocking of streptavidin magnetic beads to mitigate streptavidin degradation during on-bead digestion, and applies two-step on-bead trypsin digestion to improve peptide coverage. Multi-point sample retention and dual protein quantification are implemented throughout all experimental stages to ensure full-process quality control. Subsequent data processing pipelines using Spectronaut for data-independent acquisition (DIA) data and MaxQuant for data-dependent acquisition (DDA) data are also detailed for consistent proteome quantification. This workflow delivers higher protein recovery and better signal-to-noise ratios than standard protocols while offering flexible adaptation for various crop proximity labeling and affinity enrichment proteomic research. Key features • Complete standardized preprocessing pipeline specially optimized for Arabidopsis seedlings undergoing TurboID-based proximity labeling proteomics. • Urea dilution and pre-desalting to suppress artificial protein carbamylation. • Magnetic bead acetylation blocking to mitigate streptavidin degradation during on-bead digestion, coupled with two-step tryptic digestion for higher peptide coverage. • Full-process multi-point quality control, compatible with DIA (Spectronaut) and DDA (MaxQuant) mass spectrometry analysis.

Keywords:  Arabidopsis; Biotinylated protein; On-bead digestion; Proximity labeling; Quantitative proteomics; Streptavidin magnetic bead; TurboID

DOI:  https://doi.org/10.21769/BioProtoc.5852
J Nat Prod. 2026 Oct 23. 89(10): 2764-2782

A Practical Guide to Dereplication in Natural Products Metabolomics.

Jiangpeiyun Jin, Neha Garg.

  Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics has become a central approach for characterizing the chemical diversity of biological systems, particularly in natural product discovery. However, the scale and complexity of untargeted LC-MS/MS datasets present a persistent challenge: only a small fraction of detected features can be confidently assigned to known structures, leaving much of the metabolome unexplored. Dereplication and annotation tools have rapidly expanded to address this bottleneck, including molecular networking, spectral library matching, substructure discovery, chemical class prediction, in silico structure prediction, and repository-scale spectral searching. For beginners, these growing computational tools can be difficult to navigate since individual tools differ in input requirements, confidence levels, and optimal use cases. This review provides a practical overview of major annotation strategies in LC-MS/MS-based metabolomics and emphasizes how complementary tools can be integrated into coherent workflows. We first introduce core approaches for feature organization and dereplication, then discuss advanced strategies for substructure analysis, chemical classification, de novo annotation, and genome-informed discovery. Finally, representative applications from studies on different biological systems illustrate how integrative workflows enable prioritization, structural characterization, and discovery of previously unknown chemistry and biochemical reactions.

Keywords:   in silico tools; annotation; computational metabolomics; dark metabolites; mass spectrometry; metabolomics; natural product discovery

DOI:  https://doi.org/10.1021/acs.jnatprod.6c00896
Brief Bioinform. 2026 Sep 01. pii: bbag591. [Epub ahead of print]27(5):

Data processing in proteomics and metabolomics: liquid chromatography-mass spectrometry-based omics with divergent computational paradigms.

Amy Zheng, Bin Liao, Liang Zhang, Tao Huan.

  Proteomics and metabolomics are two foundational pillars of omics research. Proteomics focuses on the large-scale, comprehensive studies of the entire set of proteins encoded by the genome. Metabolomics targets the systematic study of chemically diverse small molecules that represent the integrated biochemical output of cellular processes and organismal physiology. Although both fields rely heavily on liquid chromatographic separation coupled with mass spectrometry (LC-MS), their fundamental chemical and biological differences shape every stage of data processing and interpretation. In this review, we move beyond a descriptive comparison of existing pipelines and develop a task-level framework to understand why proteomics and metabolomics have evolved different bioinformatic paradigms despite sharing the same analytical platform. We examine how the degree of prior molecular knowledge shapes feature extraction, identification, confidence assessment, and quantification. We show that the constrained, sequence-defined nature of the proteome enables database-driven peptide identification, rigorous false discovery rate control, and increasingly standardized quantitative workflows. In contrast, we highlight how the open, chemically heterogeneous, and incompletely enumerated metabolome has required peak-shape-based feature detection, retention-time and mass-to-charge ratio-based alignment, extensive signal correction, and confidence-tiered structural annotation. By examining these workflows side by side, we identify shared challenges and field-specific bottlenecks and discuss opportunities for cross-fertilization between proteomics and metabolomics. Ultimately, this review provides a framework for an in-depth understanding of why two LC-MS-based omics fields have developed divergent computational solutions and how future bioinformatic advances may benefit both communities.

Keywords:  bioinformatics; cross-field learning; data processing; metabolomics; molecular annotation; proteomics

DOI:  https://doi.org/10.1093/bib/bbag591
STAR Protoc. 2026 Oct 28. pii: S2666-1667(26)00570-8. [Epub ahead of print]7(4): 104917

Automated plate-to-peptide protocol for mass spectrometry sample preparation.

Sibilla Sander, Sophie Buchser, Michael Stumpe, Joern Dengjel.

  Although advances in mass spectrometry now support proteomic screens of thousands of samples and sample preparation has been automated to match, most current protocols still start from pre-prepared cell lysates. Here, we present a protocol for automated mass spectrometry sample preparation starting at the cell culture plate level. We describe steps for cell seeding in 96-well plates, in-plate cell lysis, and single-pot solid-phase-enhanced sample preparation. This streamlined approach significantly reduces manual handling while yielding highly reproducible measurements from low cell numbers.

Keywords:  Cell culture; Cell separation/fractionation; Proteomics

DOI:  https://doi.org/10.1016/j.xpro.2026.104917
Mol Cell Proteomics. 2026 Oct 26. pii: S1535-9476(26)00192-1. [Epub ahead of print] 101696

In situ polymerized monolith tips for reproducible, format-flexible proteomic sample preparation applied to biofluids.

Kathrin Korff, Lukas T Henneberg, Denys Oliinyk, Nils Eikmeier, Anton M Schüle, Tim Heymann, Alicia-Sophie Schebesta, Vincent Albrecht, Michael Roehrl, Christoph J O Kaiser, Matthias Mann, Johannes B Müller-Reif.

  Sample preparation increasingly sets the throughput and reproducibility of mass spectrometry (MS)-based proteomics. StageTips (stop-and-go extraction tips) and variants thereof have long been common implements to purify samples, and we recently extended the concept to solid-phase extraction capture (SPEC) tips, in which the entire digestion takes place in sub-microliter volumes. We replace the hand-packed bed with a monolith photopolymerized directly inside the pipette tip from a defined recipe (pSPEC), implemented here with strong anion-exchange (SAX) chemistry. A liquid-handling robot casts 384 tunable tips in minutes, at low cost and in any format, adding negligibly to the workflow's variance. Across biofluids, pSPEC added ∼20% more identifications than in-solution digestion of plasma and reached 3,500 protein groups from a single injection of healthy urine and 4,800 from saliva at 100 samples per day, depths usually requiring depletion or fractionation. The same light-cast approach should extend to single cells, affinity capture, and population-scale studies.

Keywords:  Bodyfluids; High-throughput sample processing; Polymer monolith; Proteomics; Sample preparation

DOI:  https://doi.org/10.1016/j.mcpro.2026.101696
Mol Cell Proteomics. 2026 Oct 27. pii: S1535-9476(26)00190-8. [Epub ahead of print] 101694

Simplifying low-input archival formalin-fixed paraffin-embedded proteomics: a detergent-free workflow in archival kidney tissue.

Eleni Skandalou, Yuying Sammy Choi, Stian Bakke Hansen, Hans-Peter Marti, Øystein Eikrem, Jessica Furriol.

  Archival formalin-fixed paraffin-embedded (FFPE) tissue is the largest clinically annotated human biospecimen resource, yet proteomic analysis of low-input microdissected regions remains limited. We performed a streamlined workflow enabling deep proteome coverage from laser-microdissected archival FFPE kidney tissue using standard laboratory infrastructure. We systematically compared extraction with n-dodecyl-β-D-maltoside followed by single-pot, solid-phase-enhanced sample preparation (DDM + SP3), detergent-free triethylammonium bicarbonate followed by SP3 cleanup (TEAB + SP3), and direct TEAB processing without offline bead-based cleanup. Across the experimental conditions evaluated, elimination of detergent-based cleanup was associated with increased proteome depth in low-input FFPE samples. A direct TEAB workflow reproducibly yielded up to 5500 proteins from 27000 μm2 microdissected regions of decade-old biopsies. Hydrophobicity profiling and functional enrichment analyses showed similar physicochemical and cellular-compartment coverage across workflows, with no evidence of major workflow-associated bias under the conditions evaluated. Comparable proteome coverage was observed across platforms, although not directly controlled due to differences in liquid chromatography-mass spectrometry (LC-MS) configuration. The required input corresponds to common microanatomical units and does not rely on automation or specialized microfluidics. This workflow provides an accessible framework for compartment-resolved proteomics in kidney tissue, enabling retrospective and rare-disease proteomic research from long-term FFPE repositories. Further studies are needed to assess our workflow's applicability to other scarce archival specimens.

Keywords:  Formalin-fixed paraffin-embedded (FFPE) tissue; Laser capture microdissection; Low-input proteomics; Mass spectrometry proteomics; Protein extraction method

DOI:  https://doi.org/10.1016/j.mcpro.2026.101694
Front Mol Biosci. 2026 ;13 1957466

Spatial lipidomics in gynecologic malignancies: molecular identification, cellular attribution, and mechanistic validation.

Jingyan Zhao, Wei Ma.

  Spatial lipidomics can place lipid identities within histopathologic compartments, but ion maps are frequently interpreted beyond what their analytical and biological validation can support. Here, we critically synthesize direct spatial lipid evidence in ovarian, endometrial, and cervical cancers and use spatial metabolomics, mass spectrometry imaging, spatial transcriptomics or proteomics, and non-spatial lipid biology only as explicitly graded support. We organize inference as an eight-gate chain: pathology-defined sampling, analytical quality control, structural identification, cellular attribution, metabolic flux, perturbation and rescue, patient-level validation, and prospective clinical utility. Within this framework, lipid uptake, de novo synthesis, desaturation, fatty-acid oxidation, membrane remodeling, prostaglandin E2 signaling, and ferroptosis are interpreted as mechanistic programs downstream of spatially anchored observations. Disease ecology determines which programs are plausible: omental adipocyte-tumor exchange dominates ovarian cancer, obesity-estrogen-insulin and PI3K signaling frame endometrial cancer, and persistent high-risk human papillomavirus infection shapes membrane and inflammatory remodeling in cervical cancer. Evidence remains highly asymmetric. Ovarian cancer has the broadest spatial literature; endometrial evidence relies principally on one 64-patient DESI-MSI cohort; and cervical evidence is limited to very small phospholipid and oleate studies. Spatial lipidomics is therefore ready to localize disease-specific niches, prioritize causal experiments, and define testable patient subsets, but not yet to support routine screening, diagnosis, treatment selection, or surveillance. Closing the gap requires orthogonal structural confirmation, cell-resolved source assignment, isotope tracing, functional rescue, and locked multicenter validation.

Keywords:  ferroptosis; gynecologic malignancies; lipid metabolism; mass spectrometry imaging; metabolomics; spatial lipidomics; tumor microenvironment

DOI:  https://doi.org/10.3389/fmolb.2026.1957466
Methods Mol Biol. 2027 ;3089 389-402

Integrated Functional and Metabolomic Profiling of Melanoma Cells Treated with Redox-Active Compounds.

Marcella Bonanomi, Daniela Gaglio.

  Metabolic plasticity is a hallmark of melanoma progression and therapy resistance. This chapter describes a reproducible protocol for comprehensive metabolic profiling of melanoma cells exposed to redox-active compounds such as emodin and aloe-emodin. The method integrates cell proliferation assays, mitochondrial functional analysis using Seahorse extracellular flux measurements, reactive oxygen species (ROS) quantification, LC-MS-based metabolomics, stable-isotope tracing, and 13C metabolic flux analysis (13C-MFA). The workflow enables quantitative characterization of treatment-induced alterations in energy metabolism, redox balance, and central carbon fluxes in melanoma cell lines with distinct metabolic phenotypes. The procedures are presented in a step-by-step format suitable for researchers unfamiliar with the individual techniques and can be adapted to other cancer models or metabolic perturbations.

Keywords:  13C metabolic labeling; Aloe-emodin; Emodin; LC–MS metabolomics; Melanoma; Metabolic plasticity; Redox homeostasis; Seahorse assay; Stable isotope tracing

DOI:  https://doi.org/10.1007/978-1-0716-5619-8_27
Biomolecules. 2026 Sep 25. pii: 1399. [Epub ahead of print]16(10):

Detecting and Quantifying Variability in Mass Spectrometry-Based Metabolomics Datasets.

Aleesa E Chua, Heather Desaire.

  Mass spectrometry-based metabolomics approaches generate high-dimensional datasets that enable comprehensive profiling of metabolites and other biomolecules. However, these datasets are influenced by multiple sources of variability, including both the biological variation of interest and unwanted technical or systematic variation introduced throughout experimental or analytical workflows. Distinguishing between these sources of variation is important for ensuring robust and reliable downstream interpretation. This review examines commonly used approaches for the detection and quantification of variability in MS-based omics datasets, including statistical and multivariate methods such as coefficient of variation analysis, principal component analysis, variance-based metrics and other quality assessment and visualization techniques used to evaluate data structure and reproducibility. The review further highlights how these approaches can be applied to identify systematic sources of variation to support the appropriate implementation of normalization strategies. Improved detection and characterization of variability enhance data quality, reproducibility, and interpretability, which thereby support more reliable biomarker discovery and broader omics applications.

Keywords:  data analysis; mass spectrometry; metabolomics; quality control; statistics; variation; visualization

DOI:  https://doi.org/10.3390/biom16101399
Methods Mol Biol. 2027 ;3083 133-162

Data-Driven Mechanistic Modeling of Untargeted Metabolome Data.

Meeri Mäkinen, Veronique Chotteau.

  Mathematical modeling can aid in understanding cell metabolism by allowing the construction of mechanistic metabolic models. The cell metabolism can be described as a biochemical reaction network. However, due to the reaction network size, identifying pathways used by the cells is challenging. Enumeration of all the possible pathways is challenging due to the number of combinations increasing exponentially with the reaction network size. Column generation (CG) is a data-driven approach for iteratively identifying a set of metabolic pathways. CG allows a reaction network of up to genome scale. In this chapter, we describe in detail how to carry out CG modeling for untargeted extracellular and intracellular metabolomic data. Metabolomics data are often scarce, and the metabolite concentrations from untargeted metabolomics are relative, posing challenges for modeling. We have demonstrated how to deal with these challenges and identify important metabolites and metabolic routes from untargeted metabolomic data [1]. Steps from initial key metabolite identification to reaction network modification, model construction, and model interpretation are explained. This allows the reader to apply the method to their own data and identify important metabolic pathways, where an interesting application can be to identify feed medium modifications to obtain better production for their mammalian cell-based bioprocess.

Keywords:  Bioproduction process modelling; CHO cells; Elementary flux modes; Mechanistic metabolic modelling; Untargeted metabolomics

DOI:  https://doi.org/10.1007/978-1-0716-5583-2_9
J Am Chem Soc. 2026 Oct 07. 148(39): 42508-42520

Duplex Paternò-Büchi Isobaric Reagents for One-Step C═C-Resolved Lipid Isomer Quantification.

Xi Chen, Gopal Reddy Ramidi, Nitya Shree, Dallas Freitas, Syuan-Ting Kuo, Huiying Guo, Luchen Wuyang, Aidan Slagter, Peiyu Cai, Ashok Polu, Mahua Choudhury, Xin Yan.

Lipid isomeromics, the systematic structural and quantitative profiling of lipid isomers, is needed to determine how subtle variations in lipid structure contribute to metabolic regulation and disease. Carbon-carbon double-bond (C═C) positional isomers can have distinct biological functions despite sharing identical masses and sum compositions; however, their quantitative analysis remains challenging. Here, we introduce duplex Paternò-Büchi (PB)-derived isobaric mass tags, termed AMPE-tags, that integrate lipid derivatization, C═C positional-isomer identification, and relative quantification in a single photochemical step. The tags combine a PB-reactive ketone, an isotope-encoded methylpiperazine reporter, and a mass-balancing group within one molecular scaffold. Photoirradiation installs the tag directly at lipid C═C bonds to form oxetane-containing PB products. Higher-energy collisional dissociation of intact PB products generates reporter ions for total-level quantification, whereas collision-induced dissociation produces C═C position-specific diagnostic ions that retain the reporter-bearing tag. Subsequent MS3 fragmentation of these diagnostic ions releases reporter ions directly associated with individual positional isomers, enabling isomer-level quantification without chromatographic resolution of C═C positional isomers. The duplex tags provided accurate relative quantification of unsaturated fatty acids over a 200-fold concentration-ratio range and in structurally diverse mixtures containing PB regioisomers and minor photochemical byproducts. Application to visceral adipose tissue from mice fed chow or high-fat diets identified 18 unsaturated fatty acids and 37 C═C positional isomers, revealing diet-associated isomer remodeling that was masked at the total-fatty-acid level, including opposing changes among FA 18:1 positional isomers. These PB-derived isobaric reagents provide a chemical platform for duplex lipid isomeromics and enable biologically informative quantification beyond conventional sum-composition lipid analysis.

DOI: https://doi.org/10.1021/jacs.6c13813
Mass Spectrom Rev. 2026 Oct 29.

Mass Spectrometry Imaging-Guided Proteomics, an Emerging Frontier in Spatial Biology.

Kenneth Weke, Julian J Lum, David R Goodlett, Helena Pětrošová, Irena Dapic.

  Spatial heterogeneity is a defining feature of tissue organization, yet conventional proteomic analysis provides limited information about the spatial origins of identified proteins. Spatially guided proteomics addresses this limitation by using tissue imaging techniques to select regions of interest (ROIs) for localized analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Among the imaging modalities available for this purpose, mass spectrometry imaging (MSI) stands out for its ability to map diverse molecular species directly within tissue without predefined targets, allowing chemically distinct regions to guide tissue sampling. By bridging spatial molecular profiles with high protein coverage, MSI-guided proteomics offers comprehensive biological insight. This review examines the workflow from MSI data acquisition through ROI definition, image registration, tissue isolation, and sample preparation to LC-MS/MS analysis. We compare common MSI modalities, discuss methods for ROI isolation, and summarize advances in nanoproteomics methods that support analysis of tissue samples at the microscopic scale. We critically evaluate applications in cancer research, infectious disease, neuroscience, and environmental science. Finally, we discuss the challenges and future directions of MSI-guided proteomics. We conclude that ongoing developments in the MSI-guided proteomics workflow position it as an emerging strategy for investigating localized biological processes within heterogeneous tissues.

Keywords:  laser microdissection; limited sample; liquid chromatography tandem mass spectrometry; spatial omics; spatially guided proteomics

DOI:  https://doi.org/10.1002/mas.70048
Trends Cancer. 2026 Oct 30. pii: S2405-8033(26)00239-6. [Epub ahead of print]

A roadmap for quantifying metabolism in patients with cancer.

Baharan Meghdadi, Daniel R Wahl, Deepak Nagrath.

  Advancements in in vivo stable isotope tracing have revolutionized our understanding of cancer metabolism, transitioning the field from static metabolomic snapshots to dynamic metabolic pathway activities. However, interpreting raw fractional enrichment data presents significant biological and analytical challenges. This review systematically examines the methodological landscape of in vivo tracing, addressing the interpretive gap created by clinical constraints, such as single-timepoint surgical sampling. We dissect how secondary circulating tracers and the inherent heterogeneity of the tumor microenvironment can confound metabolic signals. We highlight the critical transition to metabolic flux analysis as the requisite computational step to overcome these challenges and to identify targetable metabolic vulnerabilities in precision oncology.

Keywords:  digital twins; in vivo isotope tracing; isotope tracing in patients; metabolic flux analysis; tumor heterogeneity; tumor metabolism

DOI:  https://doi.org/10.1016/j.trecan.2026.10.005
Anal Sci. 2026 Oct 27.

Highly sensitive determination of polyunsaturated fatty acids by permanently charged derivatization-liquid chromatography-electrospray ionization tandem mass spectrometry.

Kento Sonogi, Shimba Kawasue, Reiko Koga, Hitoshi Nohta, Hideyuki Yoshida.

  Polyunsaturated fatty acids (PUFAs) are essential dietary components. However, they must be obtained from food because the human body cannot synthesize them in sufficient quantities. Furthermore, PUFA content can vary slightly depending on the type of food and storage conditions, making it necessary to establish accurate and highly sensitive methods for their quantification. However, their sensitive determination remains challenging because of the poor ionization efficiency of conventional liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-MS analyses. In this study, we developed a highly sensitive LC with tandem mass spectrometry (LC-MS/MS) method for PUFA analysis based on permanently charged derivatization using TPP-AEP. This derivatization reagent has a quaternary ammonium structure, which is required for efficient ionization under mild electrospray ionization (ESI)-MS conditions. Consequently, the [M]⁺ ions of PUFAs derivatized with TPP-AEP were detected with high sensitivity. Solid-phase extraction (SPE) was used for sample preparation to remove impurities. Furthermore, a C18 column was used for LC separation with an acidic mobile phase to achieve good peak shapes and stable detection sensitivity in PUFA analysis. The developed method exhibited excellent linearity (r2 ≥ 0.9983) and achieved a low limit of quantification in the range of 2.80-3.29 ng/mL for the examined PUFAs (5,8,11,14,17-eicosapentaenoic acid, 4,7,10,13,16,19-docosahexaenoic acid, arachidonic acid, and linoleic acid). Application of the method to PUFAs extracted from muscle tissues of four marine fish species demonstrated that the combination of lipid extraction, alkaline hydrolysis, derivatization, SPE, and LC-MS/MS allowed for the quantification of both free and total PUFA content with high sensitivity.

Keywords:  Derivatization; Fish muscle tissues; LC–MS/MS; Polyunsaturated fatty acid

DOI:  https://doi.org/10.1007/s44211-026-00980-4
J Am Soc Mass Spectrom. 2026 Oct 27.

A High-Resolution Mass Spectrometry Workflow for Xenobiotic Metabolite Discovery and Toxicity Prioritization: Disperse Orange 61 as a Case Study.

Kirsten E Overdahl, Georgia Roberts, Emily C Gentry, Suramya Waidyanatha, June Dunnick, Alan K Jarmusch.

Extensive characterization of xenobiotic metabolites remains a major challenge in mass spectrometry-based exposomics, as untargeted high-resolution mass spectrometry (HRMS) can detect extensive chemical diversity but often lacks sufficient structural annotation and prioritization strategies for unknown metabolites. Here, we developed a built-for-purpose HRMS-based workflow combining untargeted metabolomics, in silico metabolite prediction, structural annotation, and toxicity prioritization to characterize xenobiotic biotransformation products using Disperse Orange 61 (DO61) as a case study. Sprague-Dawley rats were exposed to DO61 via oral gavage, and liver, plasma, and urine samples were analyzed by ultra-high performance liquid chromatography (UHPLC) coupled to HRMS in positive and negative ionization modes. Statistically significant features were prioritized and screened against a BioTransformer-generated suspect library containing predicted Phase I, Phase II, reductive, and microbial transformations. HRMS-based annotation, supported by bromine isotope pattern analysis and MS/MS fragmentation, identified putative DO61 metabolites, while SIRIUS-derived molecular formula predictions provided additional confidence through fragmentation-based structural evaluation. The workflow experimentally confirmed reductive azo cleavage as an early DO61 transformation, producing two aromatic amine products, and revealed extensive downstream metabolism of the parent compound and both cleavage products. Computational toxicity prediction further prioritized confirmed and putatively annotated metabolites with potential mutagenic relevance for a future evaluation. This workflow demonstrates how integrating untargeted HRMS with computational approaches can facilitate metabolite discovery, improve structural annotation confidence, and prioritize metabolites for toxicological evaluation, providing a hypothesis discovery strategy for characterizing xenobiotic metabolism and supporting exposure and hazard assessment within the field of exposomics research.

DOI: https://doi.org/10.1021/jasms.6c00325
Life (Basel). 2026 Sep 30. pii: 1636. [Epub ahead of print]16(10):

Mass Spectrometry-Based Quantification in Snake Venomics and Antivenomics: Current Trends and Future Perspectives.

Nisha Reghu, Bipin Gopalakrishnan Nair, Muralidharan Vanuopadath.

  Snake venomics and antivenomics approaches have increasingly relied on mass spectrometry to characterize the venom components and to evaluate the antivenom neutralization potential. However, accurate quantification remains essential for distinguishing biologically relevant toxins from snake venom and for translating proteomic data into meaningful clinical interpretations. This review article examines the role of quantitative analysis in snake venomics and antivenomics, with special emphasis on massspectrometry-based strategies used to estimate toxin abundance. This review summarizes the main applications and limitations of mass spectrometry in venom research covering bottom-up and top-down proteomics, venom databases, toxin quantification, and how MS-based quantification determines the efficacy of antivenom through antivenomics approaches. This review also explains why abundance alone cannot determine the biological or therapeutic importance of a toxin. The article discusses label and label-free based quantification, absolute quantification approaches, top-down venomics, mass spectrometry-based antivenomics, and the integration of proteo-transcriptomic workflows in snake venom research. These approaches can be affected by several factors including the problem of measuring toxin isoforms that share similar protein structures and toxin components that are important for antivenom recognition within complex venom samples. A conceptual overview is provided of how inaccurate quantification can influence venom proteome comparisons among similar and different species, as well as the interpretation of antivenom binding and neutralization potential. Reliable mass-spectrometry-based quantification will be critical for linking the venom composition with toxicity, antivenom performance evaluations in preclinical settings, and evidence-based strategies for snake venom research and snakebite management.

Keywords:  LC-MS/MS; antivenomics; mass spectrometry; proteomics; quantification; snake venom; venomics

DOI:  https://doi.org/10.3390/life16101636
Mass Spectrom Rev. 2026 Oct 29.

Quantitative Top-Down Proteomics: A Review.

Kellye A Cupp-Sutton, Mai A Atallah, Zhitao Zhao, Samin Anjum, Trishika Chowdhury, Anju Teresa Sunny, Patrycja Szamweber, Si Wu.

  Top-down proteomics approaches have emerged as powerful strategies for investigating the effects of proteoform modifications, such as amino acid substitutions, truncation, and post-translational modifications (PTMs), on protein function and activity in biological systems. In addition to proteoform structure, proteoform abundance also influences cellular biochemistry by regulating signaling pathways and altering protein-protein interaction dynamics. Thus, accurate measurement of proteoform abundance is critical for examining cellular phenotypes and disease states. The application of quantitative proteomics strategies to top-down proteomics, however, has been challenging due to difficulties in extracting and solubilizing intact proteins, the wide dynamic range in proteoform abundance, limited sensitivity for low‑abundance species, and the need for advanced separations and robust deconvolution algorithms. Recent advances in top-down proteomics strategies, including methods to improve proteoform mass spectrometry data collection, sample preparation, and data analysis, have laid the groundwork for quantitative top-down proteomics. Here, we provide a comprehensive review of current quantitative top-down proteomics strategies and examine the applications, advantages, and challenges, including label-free, metabolic labeling, chemical tag labeling, and targeted quantification strategies.

Keywords:  chemical tag labeling; label‐free quantitation; metabolic labeling; quantitative top‐down proteomics; targeted mass spectrometry

DOI:  https://doi.org/10.1002/mas.70051
J Lipid Res. 2026 Oct 26. pii: S0022-2275(26)00209-9. [Epub ahead of print] 101179

A Comprehensive GC-MS Toolbox for Targeted and Untargeted Sterol Analysis.

Ludwig Huber, Jonathan P Sirois, Jonas Huhn, Oliver Scherf-Clavel, Stefan Berghoff, Christoph Müller.

  Sterols represent a structurally distinct class of lipophilic natural lipids defined by a rigid tetracyclic scaffold. They are essential for cellular architecture and function, serving as key structural components of biological membranes, precursors for bioactive molecules, and regulators of diverse signaling pathways. However, their high structural similarity and closely related physicochemical properties hamper chromatographic resolution, while the limited availability of reference standards restricts accurate identification and quantification. As a consequence, sterols remain underrepresented in many research areas, including human biomarker studies. To address this, we established a comprehensive sterol analytical toolbox that integrates validated commercially available and in-house synthesized reference standards and is released as an open access sterol library for the research community. The presented library comprises 110 sterols of plant, fungal, and mammalian origin, including six sterol acids, seven epoxy sterols, and 22 oxysterols. Each compound was systematically characterized by chromatographic separation on four different GC-columns, determination of relative retention times and Kováts indices, full scan electron ionization (EI) mass spectra, and optimized multiple reaction monitoring transitions for quantitative analysis. The resulting dataset enabled the establishment of sterol-specific EI-MS fragmentation signatures and retention increment principles, providing a robust framework for the reliable identification of known and previously uncharacterized sterols.

Keywords:  Kováts index; Lipids; cholesterol biosynthesis; cholesterol metabolism; ergosterol biosynthesis; oxysterol; phytosterols; steroid hormones

DOI:  https://doi.org/10.1016/j.jlr.2026.101179
Molecules. 2026 Oct 10. pii: 3604. [Epub ahead of print]31(20):

Mass Spectrometry-Based Omics: An Integrated Framework for Analytical Quality, Data Integrity, and Biological Interpretation.

Andrzej Gawor, Ewa Bulska.

  Results obtained with mass spectrometry-based omics depend on every stage of the work, from sample collection to data processing. Quality guidance exists for individual omics fields and for individual stages, but it rarely states when a dataset is ready for statistical analysis or how far its interpretation may go. We present a step-by-step protocol for proteomics, metabolomics, lipidomics, glycomics/glycoproteomics, and metallomics that brings existing recommendations and international standards together in six steps: (1) defining the intended use and the target claim, (2) checking that each specimen is fit for analysis, (3) preparing samples and acquiring data under control, (4) checking the dataset before release, (5) analysing and interpreting the data, including the use of tools based on artificial intelligence, and (6) verifying the findings and reporting. For each step, the protocol lists the required input, the actions to be taken, a checkpoint with acceptance criteria set in advance, the corrective action to be taken when the criteria are not met, and the record to be kept. Five claim levels show which conclusions the collected evidence supports. Separate criteria are given for qualitative, semi-quantitative, and quantitative studies, and numerical values from the literature are offered as starting points that each laboratory should replace with its own limits. The protocol can be used as a practical guide for planning, carrying out, and reporting omics studies and for deciding when a finding needs further verification.

Keywords:  artificial intelligence; claim strength; data release; fit-for-purpose; mass spectrometry; omics; quality assurance; quality control; system suitability; traceability

DOI:  https://doi.org/10.3390/molecules31203604