bims-mascan 2026-09-13 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–09–13
23 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

MRM Processor: An Integrated Workflow for Automated MRM Data Processing via Relative Retention Time Correction.
MetaboAnnotate: An AI-powered Multiagent Framework for Integrating Annotation Tools for Untargeted Metabolomics.
ExchangeXplorer: an R/Shiny application for processing and visualization of LC-HDX-MS data in metabolomics, lipidomics, and exposomics.
Hierarchical mixed-effects model for batch effect correction in primary-tissue single-cell metabolomics by mass spectrometry.
A simple, anesthesia-free infusion technique for in vivo metabolic tracing.
Quantitative RNA modification mapping by mass spectrometry with isobaric tags and nucleobase fragment analysis.
An Instrumental Optimization of a Label-Free Proteomic Method for Trace Protein Input.
A Draft Map of E. coli Proteoforms.
Mass spectrometry imaging in plants: Methods and applications.
The data substrate of exposome intelligence: an interoperability profile for untargeted metabolomics.
A Survey of Burkholderiales Secondary Metabolites Using Library-Scale Stable Isotope Labeling Reveals Selective Production of Methyl-Esterified 3-Hydroxybutyrate Oligomers.
Machine Learning-Enhanced LC-HRMS Workflows for Suspect and Nontargeted Screening of Pesticide Residues in Fruits and Vegetables.
Dangerous liaisons: interactions between cancer cells and adipocytes as drivers of cancer progression.
Thiol-Ene Click-Derived Porous-Layer Open-Tubular Column for Online Purification and Multiplex LC-MS/MS Analysis of Neurotransmitters.
Expanded evaluation of a robust on-line sample cleanup-LC-MS platform suited for new approach methodologies (NAM).
Multi-Center Study on Sample Preparation and Feature Selection Effects in Reproducible Mass Spectrometry Imaging for Clinical Translation.
Non-target screening using fast comprehensive two-dimensional liquid chromatography-high-resolution mass spectrometry II: the mosaic workflow - development of a scalable and open-source non-target screening data processing workflow for LC×LC-HRMS and its application to wastewater effluent.
Uncovering the signaling networks of disseminated glioblastoma cells in vivo with INSIGHT.
Fast and Scalable Sample Preparation for Integrated Metabolome and Exposome-Wide Association Studies (MWAS/ExWAS).
Gut microbiome and metabolite profiles are associated with subsequent histologic MASH in a longitudinal mouse model.
Modulating pressure in the Orbitrap improves sensitivity and mass resolution in charge detection mass spectrometry.
Acetyl-CoA-dependent processes are preferentially supported by local metabolite synthesis.
A Simplified and Robust LC-MS/MS Method for Simultaneous Quantification of Six Steroid Hormones Associated With Polyendocrine Metabolic Ovarian Syndrome.

Anal Chem. 2026 09 08. 98(35): 25770-25781

MRM Processor: An Integrated Workflow for Automated MRM Data Processing via Relative Retention Time Correction.

Yao-Yu Chen, Na An, Xin-Yi Wei, Quan-Fei Zhu, Yu-Qi Feng.

Targeted metabolomics using multiple reaction monitoring (MRM) provides sensitive and selective quantification, but large-scale data processing remains challenged by retention time (RT) drift, incorrect peak detection, and subjective integration. Here, we present the MRM Processor, an integrated, automated workflow that leverages relative retention time (RRT)-driven RT correction, derivative-based signal characterization, chromatographic peak integration, and calibration-based quantification. By dynamically updating analyte RTs using designated internal standards and evaluating chromatographic signals with derivative patterns, the workflow improves peak localization while reducing the level of manual intervention. Comprehensive validation using bile acid standards, matrix-spiked biological samples, and a pediatric sepsis fecal cohort demonstrated robust RT correction, accurate peak detection, reliable quantitative performance, and superior peak classification compared with existing data processing tools. Additional validation using structurally diverse metabolite standards further defined the current applicability boundary of the RRT correction strategy. The MRM Processor is freely available and compatible with standard MRM data acquired by liquid chromatography-tandem mass spectrometry (LC-MS/MS).

DOI: https://doi.org/10.1021/acs.analchem.6c02846
Anal Chem. 2026 09 08. 98(35): 25617-25625

MetaboAnnotate: An AI-powered Multiagent Framework for Integrating Annotation Tools for Untargeted Metabolomics.

Yan Zhou Chen, Brandon Mukadziwashe, Frederick Zhang, Soha Hassoun.

Metabolite annotation remains a major bottleneck in untargeted metabolomics, limiting biological interpretation of large-scale mass spectrometry data sets. Although substantial advances have been made through spectral libraries, machine learning-based annotation models, and community benchmarking efforts, many recently developed tools remain difficult to incorporate into routine workflows because they are distributed as research-oriented software with complex dependencies and nonstandard interfaces. Here, we present MetaboAnnotate, a web-based framework that uses a large language model (LLM) in a multiagent system to orchestrate multiple metabolite annotation tools through a unified natural-language interface. The system enables users to submit MS/MS spectra and execute multitool annotation workflows without local installation or programming expertise. The current implementation integrates complementary methods, including SIRIUS, FLARE, JESTR, and DiffMS. Evaluation on the CASMI 2016 and CASMI 2022 benchmarks shows that agreement among independent annotation tools substantially reduces false discovery rates and improves annotation accuracy. Application to a fecal metabolomics data set further demonstrates the utility of multitool consensus for identifying high-confidence putative metabolites. MetaboAnnotate is available at: https://hassounlab.cs.tufts.edu/MetaboAnnotate/.

DOI: https://doi.org/10.1021/acs.analchem.6c01717
Anal Chim Acta. 2026 11 01. pii: S0003-2670(26)01016-0. [Epub ahead of print]1421 346066

ExchangeXplorer: an R/Shiny application for processing and visualization of LC-HDX-MS data in metabolomics, lipidomics, and exposomics.

Tomas Cajka, Jiri Hricko, Lucie Rudl Kulhava, Veronika Hola, Michaela Paucova, Michaela Novakova, Oliver Fiehn.

   BACKGROUND: Reliable annotation remains a major challenge in untargeted LC-MS-based metabolomics, lipidomics, and exposomics. Liquid chromatography-hydrogen/deuterium exchange-mass spectrometry (LC-HDX-MS) provides orthogonal structural information by revealing the number of exchangeable hydrogens within a molecule, thereby supporting functional-group assignment, distinguishing isomeric structures, and reducing false-positive annotations. However, broader adoption of LC-HDX-MS for small-molecule analysis has been limited by the lack of dedicated software for systematic data processing and interpretation.
RESULTS: We developed ExchangeXplorer, an open-source R/Shiny application for processing and visualizing LC-HDX-MS data from small molecules. The software accepts feature tables generated by MS-DIAL, mzmine, and related workflows, automatically pairs unlabeled and HDX-labeled features, calculates deuterium-induced mass shifts, and exports results for downstream annotation. Additional modules provide visualization of paired MS1 and MS/MS spectra, chromatographic validation using extracted ion chromatograms, estimation of exchangeable hydrogens from molecular structures, and generation of m/z-retention time target lists. Evaluation using 163 reference compounds representing metabolites, lipids, pharmaceuticals, and exposome-related chemicals showed that incorporation of experimentally determined exchangeable-hydrogen counts reduced PubChem isomer candidates by an average of 65%. Application to human plasma and serum datasets further demonstrated utility in complex biological matrices.
SIGNIFICANCE: ExchangeXplorer provides a dedicated framework for integrating HDX-derived information into untargeted LC-MS annotation workflows, improving confidence in small-molecule characterization and reducing candidate-space complexity.

Keywords:  Exposomics; Hydrogen/deuterium exchange; LC–MS; Lipidomics; Metabolomics; Structural characterization

DOI:  https://doi.org/10.1016/j.aca.2026.346066
Anal Chim Acta. 2026 11 01. pii: S0003-2670(26)00992-X. [Epub ahead of print]1421 346042

Hierarchical mixed-effects model for batch effect correction in primary-tissue single-cell metabolomics by mass spectrometry.

Chunfei Zhong, Jiewei Deng, Guihua Deng, Yuehua Wu, Yunyun Yang, Chunyan Hao, Tiangang Luan.

  Single-cell mass spectrometry (MS) metabolomics is a powerful tool for profiling metabolites in primary tissues. However, daily instrumental drift and sample preparation introduce severe batch effects, which are highly intertwined with interindividual biological variation. Additionally, single cells are fully consumed after MS detection, making conventional quality control (QC) unavailable for error correction. Herein, we established a hierarchical mixed-effects correction strategy tailored to the three-level nested structure of analytical days, individual samples and single cells. A total of 1355 single cells were used for model training and 871 independent cells for validation using zebrafish liver samples. Variance decomposition revealed day-level batch effects accounted for 32.0% of total variation and sample variation for 23.3%. After correction, the average batch silhouette coefficient dropped from 0.333 to 0.081, while 98.4% of metabolite pairwise correlations were retained. Compared with ComBat, Harmony and z-score scaling, our method achieves a better trade-off between removing technical artifacts and preserving metabolic profiles. As a QC-free workflow, this approach is reliable and widely applicable to single-cell MS datasets from diverse tissues and analytical platforms.

Keywords:  Batch-effect correction; Hierarchical mixed-effects model; Mass spectrometry; QC-free batch normalization; Single-cell metabolomics

DOI:  https://doi.org/10.1016/j.aca.2026.346042
Sci Adv. 2026 Sep 11. 12(37): eaeg1157

A simple, anesthesia-free infusion technique for in vivo metabolic tracing.

Yumi Kim, Samantha Caldwell, Meredith Long, Dominique Abrahams, Margi Baldwin, Gina M DeNicola.

Accurate metabolic flux analysis requires tracer delivery that preserves physiological metabolism. Current methods may distort metabolism through isoflurane anesthesia, surgical stress, or complex procedures. We demonstrate that isoflurane anesthesia profoundly alters serum and tissue metabolism across multiple pathways. In serum, acylcarnitines and fatty acids were broadly decreased, whereas amino acid metabolites and select nucleotide species were increased. Across multiple organs, isoflurane induced coordinated metabolic remodeling and distinct tissue-specific responses, including glycolytic remodeling in the brain and amino acid accumulation in the pancreas. To address these metabolic disturbances, we established a nonsurgical tail vein catheterization method completed in minutes under brief isoflurane anesthesia that enables multihour tracer infusion in awake, freely moving mice. Using U-13C6-cystine infusion, this method achieved robust cysteine labeling and downstream labeling comparable to jugular infusion while maintaining circulating cystine pools closer to physiological levels. This platform provides a practical approach for in vivo stable isotope tracing under more physiological conditions.

DOI: https://doi.org/10.1126/sciadv.aeg1157
Nat Commun. 2026 Aug 08. pii: 9538. [Epub ahead of print]17(1):

Quantitative RNA modification mapping by mass spectrometry with isobaric tags and nucleobase fragment analysis.

Junzhou Wu, Jingjing Sun, Yifeng Yuan, Shiuan Nee Goh, Hazel Chay, Dwijapriya, Valérie de Crécy-Lagard, Peter C Dedon.

RNA modifications regulate RNA stability, translation, stress responses, and disease processes, yet their function remains poorly understood due to technical limitations in sequence analysis. Here, we present an RNA-specific isobaric tandem mass tagging (RMT) platform for omic-scale quantitative mapping of RNA modifications. The platform combines RNA-specific tags adapted from proteomics with an end-to-end workflow spanning sample preparation through data processing. Validation using synthetic oligonucleotides and total tRNA from Pseudomonas aeruginosa yielded reproducible quantification, with coefficients of variation below 5%. Together with nucleobase fragment analysis, we identified and quantified 24 RNA modifications in PA14 tRNAs, including previously undescribed m2A38 and Gm/Cm39, and assigned their corresponding writer enzymes. Further analyses of tRNAs from writer knockout strains and stressed cells revealed dynamic modification patterns, modification interdependencies, and their potential roles in stress adaptation. This method provides a robust, cost-effective platform for quantitative RNA modification mapping, enabling deeper biological insights.

DOI: https://doi.org/10.1038/s41467-026-76537-w
Anal Chem. 2026 09 08. 98(35): 25721-25740

An Instrumental Optimization of a Label-Free Proteomic Method for Trace Protein Input.

Dongyoon Shin, Sumin Lee, Sohyun Yang, Jeongwoo Hong, Da-Yeon Lee, Young June Jeon, Amos Chungwon Lee, Youngsoo Kim, Han Suk Ryu, Junho Park.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics of trace-level samples, such as tens of cells or spatially resolved tissue regions, offers unique biological insights but is often constrained by the requirement for specialized, costly instrumentation. In this study, we developed a scalable workflow for the deep proteomic analysis of low- to ultralow-input samples by systematically optimizing a widely adopted Orbitrap and UHPLC platform to maximize sensitivity, precision, and throughput. This optimized workflow identified over 5600 proteins from 5 ng of peptides and 3400 proteins from 20 sorted cells, achieving a throughput of 30 analyses per day while maintaining deep proteome coverage and high quantitative reproducibility. Furthermore, by applying this method to spatially resolved proteomics, we identified over 6100 proteins from microscale regions of interest (ROIs) within a formalin-fixed, paraffin-embedded (FFPE) tissue. A data-driven normalization strategy was employed to correct for variable cellularity across tissue regions, effectively revealing intratumor heterogeneity and distinct molecular and functional signatures, including pathway activations not apparent in parallel spatial transcriptomic analysis. Ultimately, this accessible, high-performance method substantially lowers the instrumentation barrier for the deep proteomic profiling of trace-level biological samples.

DOI: https://doi.org/10.1021/acs.analchem.6c02686
Anal Chem. 2026 09 08. 98(35): 25961-25971

A Draft Map of E. coli Proteoforms.

Jorge A Colón-Rosado, Fei Fang, Liangliang Sun.

Top-down proteomics (TDP) enables direct characterization of intact proteoforms, providing protein-level insights into molecular diversity arising from post-translational modifications and sequence variations. Despite this advantage, proteome coverage in TDP remains limited relative to bottom-up proteomics (BUP). To expand coverage, we developed an integrated multidimensional approach combining sequential protein extraction, size-exclusion chromatography (SEC) fractionation, and capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) and reversed-phase liquid chromatography (RPLC)-MS/MS. This approach identified 743 proteoform families and 10,613 proteoforms from E. coli cells through hundreds of MS runs. By incorporating previous E. coli TDP data sets from our group, we identified 14,932 proteoforms from 985 proteoform families, covering 43% of the E. coli proteome. The data represent the highest proteome coverage of cells by MS-based TDP, creating a draft map of E. coli proteoforms. The results offer strong evidence that MS-based TDP can reach high proteome coverage.

DOI: https://doi.org/10.1021/acs.analchem.6c03571
Curr Opin Plant Biol. 2026 Sep 09. pii: S1369-5266(26)00105-6. [Epub ahead of print]94 102962

Mass spectrometry imaging in plants: Methods and applications.

Yonghui Dong, Youwei Lu, Asaph Aharoni.

Mass spectrometry imaging (MSI) has emerged as a powerful platform for spatial metabolomics, enabling direct mapping of metabolites within plant tissues. However, successful application of MSI in plants remains strongly dependent on effective sample preparation, as the unique structural features of plant tissues pose significant analytical challenges. In this review, we highlight recent advances in sample preparation strategies designed to address these limitations across diverse plant tissues. We then discuss how the methodological and technological innovations improve spatial resolution, sensitivity, specificity, and analytical throughput. Building on this technical progress, we survey the expanding biological applications of MSI in plant science, from decoding plant-environment interactions and resolving gene function to tracing the dynamics of metabolism through stable isotope labeling. By linking chemical distributions with biological processes, MSI is playing an increasingly important role in uncovering how metabolic pathways are organized and regulated within plant tissues, ultimately enabling a spatially resolved understanding of plant metabolism.

DOI: https://doi.org/10.1016/j.pbi.2026.102962
Front Artif Intell. 2026 ;9 1901969

The data substrate of exposome intelligence: an interoperability profile for untargeted metabolomics.

Thomas Hartung.

  Untargeted metabolomics, anchored in high-resolution mass spectrometry, has matured into the central analytical platform of human exposomics. It can capture endogenous biology, diet, drugs, microbial chemistry, environmental contaminants, and their transformation products from a single biological sample. Yet exposome science remains stubbornly single-study: most untargeted exposomics publications stand alone, featuring tables, partial annotations, and semi-quantitative intensities that cannot be combined across cohorts. The bottleneck is no longer instrumentation or annotation; it is interoperability. Existing standards, including MSI, mQACC, BP4NTA, NORMAN, MERIT, mzML, mzTab-M, ISA-Tab, the Universal Spectrum Identifier, RefMet, ChEBI, MetaboLights, Metabolomics Workbench, GNPS/MassIVE, and the emerging GA4GH human exposome data standards, cover the necessary ingredients but do not yet compose a single, executable profile. I argue that the next stage of exposomics must move from FAIR deposition to meta-analysis-ready evidence: a four-layer stack of acquisition comparability, machine-readable reporting, evidence-aware annotation, and standardized summary statistics, validated by a living community benchmark. Cumulative exposome science depends on it.

Keywords:  FAIR data; data interoperability; exposome; federated learning; high-resolution mass spectrometry; meta-analysis; metabolite annotation; untargeted metabolomics

DOI:  https://doi.org/10.3389/frai.2026.1901969
J Ind Microbiol Biotechnol. 2026 Sep 10. pii: kuag026. [Epub ahead of print]

A Survey of Burkholderiales Secondary Metabolites Using Library-Scale Stable Isotope Labeling Reveals Selective Production of Methyl-Esterified 3-Hydroxybutyrate Oligomers.

Liana Zaroubi, Stephanie C Heard, Matthew Sutherland, Alessandra S Eustaquio, Roger G Linington.

  Large-scale untargeted bacterial metabolomics studies are often challenging to interpret, particularly for natural product discovery. Complex raw mass spectrometry data contains media components, biotransformation products, workup contaminants, and in-source fragments, making it difficult to resolve true biosynthesized bacterial products from background features. Incorporating stable isotope labels into untargeted workflows enables the selective detection of actively biosynthesized compounds, drastically reducing candidate mass spectrometry features to highlight novel or robustly expressed metabolites. Recently, our lab introduced IsoAnalyst, a parallel stable isotope labeling platform that links secondary metabolites to biosynthetic gene clusters by determining the incorporation rates of a panel of isotopically labeled building blocks. In this study we applied the IsoAnalyst protocol to a library of 114 Burkholderiales strains with the goals of profiling the specialized metabolite chemistry of this bacterial order and connecting detected natural products to their cognate BGCs based on their labeling profiles. While our strain-matching protocol, which evaluates correlations between detected metabolites and BiG-SCAPE-defined gene cluster families, did not yield confident BGC assignments, it successfully highlighted widely distributed metabolites with distinct labeling profiles. We targeted a set of these molecules for isolation, revealing two classes of compounds derived from primary biomolecule metabolism that are implicated in cellular stress responses. The first class of molecules were identified as lysophosphatidyl ethanolamine analogues while the second was characterized via NMR spectroscopy and mass spectrometry as methyl-esterified 3-hydroxybutyrate oligomers (HB6-OMe, HB7-OMe, and HB8-OMe), representing higher-degree polymer units than previously reported.

Keywords:  Burkholderiales; Stable isotope; gene cluster family; library-scale; metabolomics

DOI:  https://doi.org/10.1093/jimb/kuag026
Anal Chem. 2026 09 08. 98(35): 25557-25574

Machine Learning-Enhanced LC-HRMS Workflows for Suspect and Nontargeted Screening of Pesticide Residues in Fruits and Vegetables.

Sergio González, Gabriel Merino, Jairo Arturo Guerrero Dallos.

Analytical methodologies are essential for detecting and quantifying contaminants. While methods for known compounds are well established, identifying unexpected or unknown compounds remains challenging due to the lack of reference data. Several strategies have been proposed to integrate analytical information into data analysis workflows, but their implementation often requires programming skills and results are rarely presented in a format familiar to analytical chemists, such as the uncertainty budgets used in quantitative analysis. We developed an integrated workflow for suspect screening and nontargeted identification of pesticides in fruits and vegetables using QuEChERS extraction and HPLC-ESI-HRMS (Orbitrap) analysis. The workflow was developed using variable data-independent acquisition (vDIA) data and evaluates protonated and deprotonated species for compound identification. The workflow estimates missing identification properties using machine learning and combines their contributions into a single confidence value. Validation with fortified matrix extracts at different mass concentrations and with real samples containing pesticide residues showed performance comparable to existing software, particularly improving nontargeted identification at high concentrations (>6% increase in identified compounds). This approach reduces the input required for suspect screening and estimates properties for candidate compounds in nontargeted analysis. Results are reported with explicit consideration of the influence of identification properties, analogous to uncertainty budgets in chemical metrology. This workflow improves the interpretability and reliability of chemical identification and supports data-driven decision-making in routine analysis.

DOI: https://doi.org/10.1021/acs.analchem.5c06726
Genes Dev. 2026 Sep 09.

Dangerous liaisons: interactions between cancer cells and adipocytes as drivers of cancer progression.

Carolina Dehesa Caballero, Almut Schulze.

  Metabolic plasticity and flexibility are key characteristics that allow cancer cells to adapt and thrive in different environments. Specifically, cancer cells can dynamically change the routing of metabolic pathways in response to environmental changes and adapt their metabolic activity depending on local nutrient availability. The tumor microenvironment (TME) plays crucial roles in cancer development and progression. It is now widely accepted that different stromal cells, as well as soluble factors, including metabolites, derived from the TME support cancer cell proliferation and survival and drive migration, invasion, and the formation of metastases. Some cancer types grow in the proximity of adipose tissue (AT), which is mostly composed of mature adipocytes, a specialized cell type responsible for the storage and controlled release of lipids. In response to specific stimuli released by cancer cells, adipocytes can transform into cancer-associated adipocytes (CAAs). CAAs release signaling molecules, and provide fatty acids to cancer cells and other cell types in the TME, which can then utilize these fatty acids as fuel. The interaction between cancer cells and adipocytes creates a dynamic cross-talk that promotes disease progression through multiple mechanisms. In this review, we aim to provide an overview of the main factors in the CAA-cancer cell cross-talk, with a focus on the metabolic consequences of this interaction.

Keywords:  EMT; cancer-associated adipocytes; fatty acid transport; lipid droplets; metabolic flexibility and plasticity; metastasis; oxidative stress

DOI:  https://doi.org/10.1101/gad.353813.126
Anal Chem. 2026 09 08. 98(35): 26089-26103

Thiol-Ene Click-Derived Porous-Layer Open-Tubular Column for Online Purification and Multiplex LC-MS/MS Analysis of Neurotransmitters.

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

A thiol-ene-based porous stationary phase (PLOT-type coating) was developed for online sample cleanup and enrichment coupled to LC-MS/MS for the simultaneous determination of structurally diverse neurotransmitters in biological matrices. The resulting porous thiol-ene functionalized capillary column combines the inherently high permeability of the open-tubular architecture with tailored surface functionality, enabling direct injection and efficient handling of highly polar, low-abundance analytes without derivatization. The method allows multiplexed quantification of serotonin, dopamine, glutamic acid, γ-aminobutyric acid, and reduced glutathione in plasma, cell samples, and brain tissue within a simplified analytical workflow. Compared to conventional off-line protocols, the proposed approach minimizes sample preparation while maintaining high sensitivity and reproducibility, with recoveries ranging from 86% to 100% and matrix effects within 90-104%. The platform demonstrates reliable analytical performance across different biological matrices and is compatible with routine LC-MS/MS instrumentation. Overall, this work establishes the thiol-ene-porous capillary as a viable strategy for integrated sample processing in bioanalysis and expands its application to polar metabolite profiling in complex systems.

DOI: https://doi.org/10.1021/acs.analchem.6c04601
Anal Bioanal Chem. 2026 Sep 08.

Expanded evaluation of a robust on-line sample cleanup-LC-MS platform suited for new approach methodologies (NAM).

Marcus Kamande, Julie Bekkhus, Stian Kogler, Aysan Safavi, Mads T S Gisle Johnsen, Patrik Appelblad, Steven Ray Wilson, Hanne Røberg-Larsen.

  Sample preparation in bioanalysis can require significant numbers of (manual) steps and consumables. Such procedures can be bottlenecks regarding cost, throughput, and greenness. This is also the case for analysis of new approach methodologies (NAMs) such as organoids and organ-on-a-chip systems. In this context, approaches tailored for measuring drugs in NAM-related cell culture media (CCM) are being developed. For mass spectrometry-based analysis, automated filtration/filter backflush solid-phase extraction liquid chromatography AFFL-SPE-LC is utilized. Case studies have shown that the "AFFL" platform allows for vast reductions in sample preparation efforts, as the "self-cleaning" filtration features and SPE conditions remove potentially clogging/contaminating materials from samples of biological origin, e.g., the salt and protein content in CCM. Here, broader demonstrations of the AFFL platform's traits for CCM analysis are provided, employing an extended panel of hydrophobic small-molecule drugs. The investigated AFFL platform shows reproducible chromatographic performance, as well as fit-for-purpose inter-matrix and inter-column robustness. Hundred-scale injections can be performed with satisfactory repeatability (e.g., retention time RSDs < 1%), with limited interference from the various CCM matrices investigated. Practical NAM applications are also demonstrated. The approach has clear advantages in greenness, obtaining an AGREE prep score of 0.71, contrasting the < 0.55 scores of more conventional/commercial approaches. The plastic consumable usage of our approach is 28 g/100 samples, a nearly 20-fold improvement over a previously established benchmark (500 g/100 samples). Taken together, these results demonstrate that AFFL provides a robust, low-waste, and scalable LC-MS-based workflow for chemical analysis of NAM-derived samples.

Keywords:  Automation; Cell culture medium; Inline; LC-MS; NAM; Solid-phase extraction

DOI:  https://doi.org/10.1007/s00216-026-06775-w
Mol Cell Proteomics. 2026 Sep 10. pii: S1535-9476(26)00154-4. [Epub ahead of print] 101658

Multi-Center Study on Sample Preparation and Feature Selection Effects in Reproducible Mass Spectrometry Imaging for Clinical Translation.

Juliana P L Gonçalves, Marta Grzeski, Gentiane Krasniqi, Carsten Hopf, Oliver Klein, Kristina Schwamborn.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) provides spatially resolved proteomic information that is valuable for understanding disease development, identifying biomarkers, and predicting treatment responses. Despite this potential, MALDI-MSI has not yet been adopted as part of routine diagnostic workflows, primarily due to challenges in analytical validation and robustness. Although sample preparation workflows for MALDI-MSI generally follow the same core steps, there is still a lack of robust data processing frameworks capable of minimizing technical artefacts, improving feature selection, and reducing inter-laboratory variability. To assess reproducibility and robustness of in situ proteomics across laboratories, we investigated formalin-fixed paraffin-embedded human tissue samples from spleen, intestine, and pancreas using MALDI-MSI. Two complementary workflows were evaluated: (i) decentralized sample preparation with data acquisition performed with one instrument, and (ii) centralized sample preparation with data acquisition carried out on instruments across participating laboratories. Data analysis focused on evaluating strategies for feature selection that preserved morphological information. Our findings demonstrate that, with adherence to a standardized protocol, reproducible results across different laboratories can be achieved. However, sample preparation remains a major source of variability, which can be mitigated through appropriate data analysis strategies, particularly in the choice of features used for analysis. Most importantly, biological differences between tissues were consistently identifiable across all datasets, further underscoring the reliability of the approach for clinical translation, especially when sources of variability are properly controlled.

DOI: https://doi.org/10.1016/j.mcpro.2026.101658
J Chromatogr A. 2026 Sep 02. pii: S0021-9673(26)00748-X. [Epub ahead of print]1787 467422

Non-target screening using fast comprehensive two-dimensional liquid chromatography-high-resolution mass spectrometry II: the mosaic workflow - development of a scalable and open-source non-target screening data processing workflow for LC×LC-HRMS and its application to wastewater effluent.

Oskar Munk Kronik, Ryland T Giebelhaus, Jan H Christensen, Selina Tisler, Giorgio Tomasi, Nikoline Juul Nielsen.

  Comprehensive two-dimensional liquid chromatography-high-resolution mass spectrometry (LC × LC-HRMS) offers improved group-type separation and mass spectral quality than one-dimensional LC-HRMS, but its widespread adaption remains limited, partly, due to labor-intensive and non-scalable data-processing workflows. Here, we present Mosaic, an open-source NTS workflow for LC × LC-HRMS that groups ions originating from a single compound, including adducts, isotopes, and in-source fragments, into single components within and across samples. Mosaic was evaluated using three componentization strategies: mass filtering (MF), non-negative matrix factorization (NMF), and their combination (MF+NMF). The workflow was validated using 78 LC × LC-HRMS chromatograms originating from wastewater effluents samples from six wastewater treatment plants, quality control samples, field blanks and three instrument blanks. Fourteen internal standards (IS), present in 75 chromatograms, were used to quantify detection performance, across-sample grouping efficiency, and the accurracy of automated peak volume extraction. Within-sample true positive rates of detection (TPRs) were 52% (NMF), 91% (MF), and 99% (MF+NMF), respectively. All detected IS were componentized correctly for the MF+NMF. Only the combined MF+NMF componentization approach could extract peak volumes comparable to those obtained by manual integration (p = 0.99). This performance relied on the generation of higher purity mass spectral loadings, achieved through correlation-based removal of interfering ions in both chromatographic dimensions as done in MF, and NMF's ability to resolve peaks and corresponding ions with varying relative abundances across samples. The Mosaic workflow provides an open-source, transparent, and scalable NTS solution for complex samples analysed by LC × LC-HRMS, enabling reliable detection and peak volume extraction for trace-level contaminants at ng/mL concentrations.

Keywords:  CHEMOMETRICS; LC×LC-HRMS; Mass filtering; Non-target screening; Wastewater effluent

DOI:  https://doi.org/10.1016/j.chroma.2026.467422
Nat Commun. 2026 Aug 08. pii: 9536. [Epub ahead of print]17(1):

Uncovering the signaling networks of disseminated glioblastoma cells in vivo with INSIGHT.

Ryuhjin Ahn, Alicia D D'Souza, Lawrence Long, Yufei Cui, Jennifer Gantchev, Gerard Baquer, Danielle Burgenske, Katrina K Bakken, Lauren L Ott, Brett L Carlson, Grace Zhou, Ishwar N Kohale, Charles A Whittaker, Heidi Temple, Tomer M Yaron-Barir, Jeffrey Wyckoff, Terry C Burns, Rachael A Vaubel, Cameron T Flower, Wei Huang, Ann Tuma, Jared L Johnson, Nathalie Y R Agar, Josie Ursini-Siegel, Sarkaria Jann, Forest M White.

Dysregulation of intracellular signaling networks underpins cancer. Yet, resolving signaling networks within distinct or rare cell types in cancer in vivo has been unattainable. Here we develop INSIGHT by integrating cell sorting with mass spectrometry to enable quantitative phosphoproteomics and proteomics of discrete cell types from fixed tissues. Using INSIGHT, we map the signaling network within disseminating glioblastoma cells from patient-derived xenografts implanted in mice. Disseminating tumor cells undergo a proteome-wide shift from proliferative to mesenchymal, neural progenitor-like cell states. In parallel, signaling network and global kinase activity are rewired, transitioning from cell cycle-associated circuitries to those governing synaptic function, neuronal migration, and ion channel activity. Changes begin at the tumor margin and persist in distant brain parenchyma. Hornerin and phosphorylation of Ca²⁺-permeable GluA2 at Y876 were identified as mediators of glioblastoma progression. INSIGHT enables systems-level dissection of cell-type-specific signaling circuitries in vivo across wide range of biological systems.

DOI: https://doi.org/10.1038/s41467-026-76587-0
Anal Chem. 2026 09 08. 98(35): 26044-26053

Fast and Scalable Sample Preparation for Integrated Metabolome and Exposome-Wide Association Studies (MWAS/ExWAS).

Julia Füreder, Amira Müller, Giulia Guerra, Sabina Sieri, Max Lennart Feuerstein, Benedikt Warth.

Broad coverage exposome- and metabolome-wide association studies rely on advanced instrumentation, typically anchored in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to investigate exposure-effect associations. Sample preparation for biological fluids is a delicate matter, as it is essential to strike a pragmatic balance between sensitivity, chemical coverage, robustness, and time efficiency to meet the requirements of large-scale epidemiological studies. Here, we established a protein precipitation workflow in a scalable, high-throughput format for human plasma and urine. Different protocols were evaluated utilizing a xenobiotic mixture containing >200 exposure compounds based on extraction recovery, repeatability, and applicability for nontargeted analysis (NTA) and suspect screening. The tested high-throughput options included a protein precipitation workflow in 96-well plates and a phospholipid removal plate. For urine, a dilute-and-shoot approach was additionally tested. The plate-based protein precipitation resulted in superior performance with acceptable extraction recoveries (RE, 60-140%) for >70% of the highly diverse analyte panel in plasma and >80% in urine, and acceptable repeatability with relative standard deviations of <30% for more than 95% of analytes in plasma and 80% in urine. NTA results exhibited only a small number of altered annotated features compared to a tube-based protocol used for benchmarking, while increasing the overall time efficiency by a factor of five. To further test the protocol, it was applied to the NIST plasma standard reference material SRM1950. A total of 22 toxicants were identified and (semi-)quantified, with most concentrations comparable to available reference values. The results demonstrate the suitability of the established protocol for combined MWAS/ExWAS.

DOI: https://doi.org/10.1021/acs.analchem.6c03820
mSystems. 2026 Sep 10. e0072026

Gut microbiome and metabolite profiles are associated with subsequent histologic MASH in a longitudinal mouse model.

Stephany Flores Ramos, Kelly Fogelson, Valentina B Muti, Allegra T Aron, Rodolfo A Salido, R Alexander Richter, Helena Mannochio-Russo, Jingjing Hu, Mojgan Hosseini, Rohit Loomba, Pieter C Dorrestein, Rob Knight, Amir Zarrinpar.

  Detecting metabolic dysfunction-associated steatohepatitis (MASH) before fibrosis develops remains a major challenge. We used a longitudinal streptozotocin/high-fat diet (STAM/HFD) mouse model to identify microbial and metabolic features that emerge before histological MASH onset. Integrative 16S rRNA and untargeted LC-MS/MS analyses revealed microbial and metabolite shifts weeks before disease development. Lachnospiraceae and Oscillospiraceae were enriched in non-MASH mice and were negatively correlated with fatty acids elevated under MASH conditions. Bile acid and fatty acid metabolites enriched under MASH and non-MASH states varied with collection time, showing that diurnal variation strongly shapes biomarker profiles. Analysis of a human MAFLD cohort confirmed higher abundances of Lachnospiraceae bacteria in healthy participants. Our results identify early microbial and metabolic features that are associated with subsequent histologic MASH onset and reveal how temporal factors influence biomarker detection. These time-sensitive features, together with cross-species concordance in a human MAFLD cohort, lay the groundwork for developing and validating microbiome-based diagnostics and circadian-informed interventions that target the gut-liver axis to prevent metabolic liver disease.
IMPORTANCE: Metabolic dysfunction-associated steatohepatitis (MASH) often develops silently, limiting opportunities for early intervention before fibrosis occurs. In this study, we identify gut microbial taxa and stool metabolites that are associated with future histologic MASH development in a longitudinal mouse model. We show that members of the Lachnospiraceae and Oscillospiraceae families are enriched in mice that remain disease-free and that members of the Lachnospiraceae family exhibit similar patterns in a human MAFLD cohort. Importantly, we demonstrate that time of sample collection has a strong effect on metabolomic profiles, with disease-associated lipid and bile acid signals detectable only at specific sampling times. These findings highlight circadian timing as a critical variable in microbiome-based biomarker discovery and support the development of time-aware diagnostic strategies for early MASH risk assessment.

Keywords:  circadian rhythm; gut-liver axis; insulin signaling; metabolic dysfunction-associated steatohepatitis (MASH); metabolomics; microbiome; time-restricted feeding (TRF)

DOI:  https://doi.org/10.1128/msystems.00720-26
Nat Commun. 2026 Aug 13. pii: 9690. [Epub ahead of print]17(1):

Modulating pressure in the Orbitrap improves sensitivity and mass resolution in charge detection mass spectrometry.

Eduard H T M Ebberink, Elena Giaretta, Arjan Barendregt, Jan Fiala, Victor C Yin, Tobias P Wörner, Kyle L Fort, Alexander A Makarov, Albert J R Heck.

Native mass spectrometry has become a key method for studying macromolecular assemblies, providing insights into structures, stoichiometries, and binding interactions. A key aspect for the transmission of electrospray-generated bioparticles into the mass analyzer is the use of gas for collisional cooling and ion desolvation. However, in Orbitrap-based mass spectrometry, the elevated pressure may negatively affect ions, as collisions with background gas can destabilize ion trajectories, potentially leading to incorrect mass determination. These effects are amplified when the pressure in the collision cell is high, as required for large biological assemblies, and when ions are measured for (ultra)long acquisition times in the Orbitrap, as required for high-resolution mass spectrometry. To address these issues, we modified a standard Q Exactive™ UHMR by installing a pulsed valve to control gas flow and limit gas leakage into the Orbitrap. This way, ion transmission and desolvation are maintained while the ultrahigh vacuum in the mass analyzer is enhanced during acquisition. We show that this improves ion detection of various assemblies, including adeno-associated viruses, IgM, and plasmid DNA, with superior mass accuracy and resolving power. The pulsed valve implementation will benefit nearly all mass measurements, setting the stage for next-generation Orbitrap-based, single-ion mass spectrometry.

DOI: https://doi.org/10.1038/s41467-026-76691-1
Sci Adv. 2026 Sep 11. 12(37): eaee4935

Acetyl-CoA-dependent processes are preferentially supported by local metabolite synthesis.

Julianna G Supplee, Pedro Costa-Pinheiro, Karl Wessendorf-Rodriguez, Stephanie Stransky, Qihua Yang, Christina Demetriadou, Nivitha Murali, Joy Li, Jennifer L Pennise, Mariola M Marcinkiewicz, Simone Sidoli, Nathaniel W Snyder, Christian M Metallo, Ronen Marmorstein, Kathryn E Wellen.

The cell nucleus is an active metabolic site. Numerous enzymes best known for their roles in cytosolic or mitochondrial pathways also function in the nucleus, where they contribute to gene regulation and DNA replication and repair. Although metabolites can diffuse through nuclear pores, it remains unclear the extent to which the nucleus and cytosol operate as continuous versus distinct metabolic spaces. Both compartments require acetyl-CoA-for example, for histone acetylation and lipid synthesis-and the acetyl-CoA generating enzyme ATP-citrate lyase (ACLY) resides in both locations, but the significance of its dual localization is incompletely understood. Using cell lines in which ACLY is localized to either compartment, we find that ACLY in either location supports fatty acid synthesis and histone acetylation, yet compartment-localized ACLY enables finer control. Nuclear ACLY preserves histone H3K23 acetylation under glucose limitation and modulates specific transcriptional programs, whereas cytosolic ACLY most efficiently supports lipid biosynthetic fluxes. Thus, local synthesis defines a preferential metabolic fate, providing more precise regulation.

DOI: https://doi.org/10.1126/sciadv.aee4935
Biomed Chromatogr. 2026 Oct;40(10): e70609

A Simplified and Robust LC-MS/MS Method for Simultaneous Quantification of Six Steroid Hormones Associated With Polyendocrine Metabolic Ovarian Syndrome.

Mengxue Feng, Yan Song, Xueqing Qian.

  Polyendocrine metabolic ovarian syndrome (PMOS), formerly known as polycystic ovary syndrome (PCOS), is characterized by dysregulation of multiple steroid hormones. Accurate quantification of these analytes is essential for laboratory evaluation; however, currently may involve complex sample-preparation workflows or provide insufficient analytical coverage, particularly for dehydroepiandrosterone sulfate (DHEAS). A liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay was established for the simultaneous measurement of six steroid hormones associated with PMOS. Sample preparation combined liquid-liquid extraction with protein precipitation. Chromatographic separation was completed within 6 min. Validation followed international recommendations and included linearity, sensitivity, precision, accuracy, matrix effects, selectivity, and specificity. All analytes showed excellent linearity (R = 0.9959-0.9995), with intra- and inter-day CVs ≤ 5.6%. Spike recovery ranged from 85.2% to 114.9%. Accuracy was further supported by third-party QC materials and successful participation in an external quality assessment program. The assay provided an expanded linear range for DHEAS (20-20,000 ng/mL). This validated LC-MS/MS assay provides rapid, sensitive, and reproducible quantification of six PCOS-related steroid hormones and is suitable for routine laboratory implementation.

Keywords:  dehydroepiandrosterone sulfate (DHEAS); liquid chromatography–tandem mass spectrometry (LC–MS/MS); polycystic ovary syndrome (PCOS); polyendocrine metabolic ovarian syndrome (PMOS); serum; steroids

DOI:  https://doi.org/10.1002/bmc.70609