bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2025–10–26
twenty papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Evaluation of a Prototype Orbitrap Astral Zoom Mass Spectrometer for Quantitative Proteomics─Beyond Identification Lists.
  2. AlphaDIA enables DIA transfer learning for feature-free proteomics.
  3. JUMPshiny: A User-Friendly Platform for Comprehensive Analysis and Visualization of Quantitative Proteomics Data.
  4. Development of a robust sulfo-phospho-vanillin assay for sample normalization in LC-MS-based lipidomics.
  5. DIATAGeR: Triacylglycerol annotation of data-independent acquisition based lipidomics.
  6. Considering Measurement Time and Depth in diaPASEF Plasma Proteomics.
  7. A machine learning framework for classifying lipids in untargeted metabolomics using mass-to-charge ratios and retention times.
  8. Comparative Study of DDA and DIA Acquisition Modes on UPLC-Q-Orbitrap Mass Spectrometry Using Huaihua Powder.
  9. Optimisation of electron-induced dissociation parameters for molecular annotation of glycerides and phospholipids in fast LC-MS.
  10. Easy Proteomics Sample Preparation: Technical Repeatability and Workflow Optimization Across 8 Biological Matrices in a New Core Facility Setting.
  11. Spatiotemporal and quantitative analyses of phosphoinositides - fluorescent probe-and mass spectrometry-based approaches.
  12. Recent developments in plasma sample preparation methods for targeted metabolomics studies with liquid chromatography mass spectrometry.
  13. Stable isotope tracing in human plasma-like medium reveals metabolic and immune modulation of the glioblastoma microenvironment.
  14. Quantitative Reactivity Profiling of Functional Arginine Residues in Human Cancer Cell Line Proteomes.
  15. Lipid metabolic reprogramming in colorectal cancer: Insights to mechanisms and therapeutics.
  16. Direct MS enabled discovery of lipid signatures with diagnostic implications in colorectal cancer.
  17. Computational design and evaluation of optimal bait sets for scalable proximity proteomics.
  18. Evaluation of ion mobility, uni- and multidimensional liquid chromatography for non-target screening of phenolic compounds in wheat flag leaves.
  19. New Method Enhanced Extraction of Protein Signatures of Renal Cell Carcinoma from Proteomics Data.
  20. Development and validation of a five-channel multiplex LC-ESI-MS/MS method for the quantification of carboxylic acids in urine.
  1. J Proteome Res. 2025 Oct 22.
    Evaluation of a Prototype Orbitrap Astral Zoom Mass Spectrometer for Quantitative Proteomics─Beyond Identification Lists.
    Chris Hsu, Nicholas Shulman, Hamish I Stewart, Johannes Petzoldt, Anna Pashkova, Deanna L Plubell, Eduard Denisov, Bernd Hagedorn, Nicolaie E Damoc, Brendan X MacLean, Philip M Remes, Jesse D Canterbury, Alexander A Makarov, Christian Hock, Vlad Zabrouskov, Christine C Wu, Michael J MacCoss.
      Mass spectrometry instrumentation continues to evolve rapidly, yet quantifying these advances beyond conventional peptide and protein detections remains challenging. Here, we evaluate a modified Orbitrap Astral Zoom mass spectrometer (MS) prototype and compare its performance to the standard Orbitrap Astral MS. Across a range of acquisition methods and sample inputs, the prototype instrument outperformed the standard Orbitrap Astral MS in precursor and protein identifications, ion beam utilization, and quantitative precision. To enable meaningful cross-platform comparisons, we implemented an ion calibration framework that converts signal intensity from arbitrary units to ions per second. This benchmarking strategy showed that the prototype sampled 23.1% more ions per peptide than the original Orbitrap Astral MS. This increase in the ion beam utilization resulted in improved sensitivity and quantitative precision. To make these metrics broadly accessible, we added new metrics to the Skyline document grid to report the number of ions measured in a spectrum at the apex of the elution peak or the sum of ions between the peak integration boundaries. Taken together, our results demonstrate the Orbitrap Astral Zoom prototype as a high-performance platform for data-independent acquisition proteomics and establish a generalizable framework for evaluation of MS performance based on the number of ions detected for each analyte. Data are available on Panorama Public and ProteomeXchange under the identifier PXD064536.
    Keywords:  DIA; Orbitrap Astral Zoom; instrument comparisons; ion calibration; liquid chromatography; mass spectrometry; proteomics; quantitative results
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00593
  2. Nat Biotechnol. 2025 Oct 21.
    AlphaDIA enables DIA transfer learning for feature-free proteomics.
    Georg Wallmann, Patricia Skowronek, Vincenth Brennsteiner, Mikhail Lebedev, Marvin Thielert, Sophia Steigerwald, Mohamed Kotb, Oscar Despard, Tim Heymann, Xie-Xuan Zhou, Maximilian T Strauss, Constantin Ammar, Sander Willems, Magnus Schwörer, Wen-Feng Zeng, Matthias Mann.
      The scale of data generated for mass-spectrometry-based proteomics and modern acquisition strategies poses a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data-independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm that performs machine learning directly on the raw signal and is particularly suited for detecting patterns in data produced by time-of-flight instruments. Benchmarking demonstrates competitive identification and quantification performance. While the method supports empirical spectral libraries, we propose a search strategy named DIA transfer learning that uses fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine-specific and experiment-specific properties, enabling the generic DIA analysis of any post-translational modification. AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.
    DOI:  https://doi.org/10.1038/s41587-025-02791-w
  3. Proteomics. 2025 Oct 21. e70061
    JUMPshiny: A User-Friendly Platform for Comprehensive Analysis and Visualization of Quantitative Proteomics Data.
    Aijun Zhang, Yingxue Fu, Zuo-Fei Yuan, Long Wu, Dehui Kong, Ling Li, Zhiping Wu, Pjotr Prins, Junmin Peng, Xusheng Wang.
      Mass spectrometry-based quantitative proteomics has revolutionized our understanding of biological processes and unveiled the molecular mechanisms underlying various diseases. The analysis and visualization of quantitative proteomics data remain complex and require user-friendly tools with robust analytical capacities. In this study, we introduce JUMPshiny, a novel, interactive, and comprehensive web-service, that is built on R-Shiny and designed for processing and presenting quantitative proteomics data. JUMPshiny includes a wide range of visualizations and offers a streamlined workflow, including experimental design, data exploration, batch normalization, differential analysis, and enrichment analysis. Through examples, we demonstrate automated quality control, interactive data visualization, and customizable statistical analyses. Built on the R-Shiny framework, JUMPshiny integrates established libraries and packages to ensure computational robustness and reproducibility. Overall, JUMPshiny represents a powerful platform for proteomics data analysis for the research community. JUMPshiny is available at https://jumpshiny.genenetwork.org. The source code is available under MIT license at: https://github.com/Wanglab-UTHSC/JUMP_shiny.
    Keywords:  JUMPshiny; R packages; bioinformatics tools; data analysis; data visualization; mass spectrometry; quantitative proteomics; software; web application
    DOI:  https://doi.org/10.1002/pmic.70061
  4. Anal Chim Acta. 2025 Dec 08. pii: S0003-2670(25)01046-3. [Epub ahead of print]1378 344652
    Development of a robust sulfo-phospho-vanillin assay for sample normalization in LC-MS-based lipidomics.
    Fernanda Sousa Monteiro, Adriana Zardini Buzatto, Liang Li.
       BACKGROUND: Lipidomics, the comprehensive analysis of lipid profiles in biological samples, is crucial for understanding cellular processes and disease mechanisms. However, variations in the amounts or concentrations of samples can significantly impact the accuracy of lipid quantification in comparative studies. To address this challenge, the normalization of samples before processing and analysis is essential. This study introduces a robust normalization method for lipidome analysis, leveraging an improved sulfo-phospho-vanillin (SPV) analysis technique.
    RESULTS: The enhanced SPV method provides a reliable colorimetric assay for determining total lipid content across diverse sample types. By optimizing the detection limits and reducing the required sample amount for the assay, we successfully applied this method to saliva and cellular samples where the concentrations of the starting materials can have large variations. Our findings demonstrate that SPV normalization significantly improves lipid feature detection and intensity consistency across samples without introducing analytical bias. This normalization process facilitates more accurate comparisons of lipid profiles.
    SIGNIFICANCE: Implementing SPV-based normalization presents a practical solution for enhancing the accuracy of lipidome analyses in comparative studies. This approach is not only effective but also accessible for research laboratories, requiring a relatively simple workflow and standard UV-visible spectroscopy equipment.
    Keywords:  Lipidomics; Liquid chromatography; Mass spectrometry; Sample normalization; UV–Visible spectrometry
    DOI:  https://doi.org/10.1016/j.aca.2025.344652
  5. Anal Chim Acta. 2025 Dec 08. pii: S0003-2670(25)01092-X. [Epub ahead of print]1378 344698
    DIATAGeR: Triacylglycerol annotation of data-independent acquisition based lipidomics.
    Victor C L Lee, Khanh C K Nguyen, Linglan Zhu, Chloe A K White, Yong Jin Lim, Tao Huan, Thomas J Velenosi.
       BACKGROUND: Triacylglycerols (TGs) are the most abundant lipids in the human body and the primary source of energy storage. TGs are comprised of three fatty acyls with various lengths and double bond composition, complicating structural annotation when performing lipidomics by LCMS. Data-independent acquisition (DIA) based lipidomics enables a continuous and unbiased acquisition of all TGs, creating the potential for more comprehensive TG analysis. However, TG identification in DIA lipidomics data is challenging due to the difficulty analyzing multiplexed tandem mass spectra (MS2).
    RESULTS: In this study, we present DIATAGeR, an R package aimed to improve and automate TG identifications to the molecular species level in DIA-based lipidomics. With DIATAGeR, TGs are identified using a TG-centric approach, where each TG in the reference database is considered as an analysis target, searched in DIA spectra, and scored using a logistic regression machine learning algorithm. Additionally, DIATAGeR uses a false discovery rate (FDR) correction calculated by a target-decoy approach to improve the confidence of TG identification and limit false positives due to interference from unrelated ions. The performance of DIATAGeR was validated in a lipidomic study of liver and plasma samples from mice with metabolic dysfunction-associated steatohepatitis (MASH) and healthy controls. All 9 TG standards were annotated at an FDR <0.1 in both datasets. When benchmarked against MS-DIAL, TGs identified by DIATAGeR contained 18 % and 12 % more even-carbon fatty acyls in liver and plasma datasets, respectively.
    SIGNIFICANCE: DIATAGeR is a valuable tool for streamlining complex TG annotation in DIA-lipidomics data. It supports vendor-neutral MS spectra data formats and offers a customizable reference database. By combining TG-centric and target-decoy approaches, DIATAGeR showed improvements in TG identification by addressing primary challenges associated with multiplexed MS2 spectra. DIATAGeR is freely available at https://github.com/Velenosi-Lab/DIATAGeR.
    Keywords:  Data-independent acquisition; Lipidomics; Machine learning; Mass spectrometry; R package; Triacylglycerol annotation
    DOI:  https://doi.org/10.1016/j.aca.2025.344698
  6. J Proteome Res. 2025 Oct 22.
    Considering Measurement Time and Depth in diaPASEF Plasma Proteomics.
    Eva R Smit, Carmen van der Zwaan, Stijn A Groten, Maartje van den Biggelaar, Arie J Hoogendijk, Pieter Langerhorst.
      Mass spectrometry (MS)-based plasma proteomics is a powerful approach to unraveling the biology or pathophysiology in large clinical cohorts. Implementation of data-independent acquisition in combination with ion mobility approaches (diaPASEF) has enabled high-throughput analysis with increasing proteomic depth. DIA-based methods are dependent upon experimental or in-silico-generated spectral libraries, yet there is a lack of consensus in the field about which library produces superior results in regard to protein and peptide identifications and quantifications. Here, we evaluated approaches for building a spectral library in plasma proteomics on a timsTOF HT system. Furthermore, the relationship between measurement time, library depth, and number of protein and peptide identification for high-throughput plasma proteomics applications was assessed. As expected, an increase in the measurement time invested in the spectral library enhanced the number of identifications. At the protein level, in silico libraries provided decreased depth compared to the most extensive experimental library. However, the experimental library enhanced the number of peptide identifications by 14% compared to that of the in silico library. With the field increasingly moving to peptide-centric approaches, an experimental library allows for deeper assessment in peptide-based studies.
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00637
  7. Metabolomics. 2025 Oct 18. 21(6): 151
    A machine learning framework for classifying lipids in untargeted metabolomics using mass-to-charge ratios and retention times.
    Christelle Colin-Leitzinger, Yonatan Ayalew Mekonnen, Isis Narvaez-Bandera, Vanessa Y Rubio, Dalia Ercan, Eric A Welsh, Lancia N F Darville, Min Liu, Hayley D Ackerman, Julian Avila-Pacheco, Clary B Clish, Kevin Hicks, John M Koomen, Nancy Gillis, Brooke L Fridley, Elsa R Flores, Oana A Zeleznik, Paul A Stewart.
       INTRODUCTION: The identification of unknown metabolites remains a major challenge in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS). This process typically depends on comparing mass spectral or chromatographic data to reference databases or deciphering complex fragmentation in tandem mass spectra. While current machine learning methods can predict metabolite structures using MS/MS (MS2) data, no approaches, to our knowledge, use only mass-to-charge ratio (m/z) and retention time (RT) from LC-MS data.
    OBJECTIVE: To explore the potential of using the mass-to-charge ratio (m/z) and retention time (RT) from LC-MS data as standalone predictors for metabolite classification and propose a modeling framework which can be implemented internally on standalone datasets.
    METHODS: We trained machine learning models on 20 mouse lung adenocarcinoma tumor samples with 7,353 features and validated them on a dataset of 81 samples with 22,000 features. A total of 120 combination of preprocessors and models were assessed. Features were classified as "lipid" or "non-lipid" based on the Human Metabolome Database (HMDB) taxonomy, and model performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), and area under the precision-recall curve (PR). We replicate the process in an independent dataset generated using human plasma samples.
    RESULTS: We classified untargeted LC-MS features as "lipid" or "non-lipid" per the HMDB super class taxonomy and evaluated model performance. A framework including steps to choose the preprocessors and models for metabolite classification was designed. In our lab, tree-based models demonstrated superior performance across all metrics, achieving high accuracy, AUC, and PR which was consistent with the independent dataset.
    CONCLUSION: Our results demonstrate that metabolites can be classified as "lipid", "non-lipid" using only m/z and RT from untargeted LC-MS data, without requiring MS2 spectra. Although this study focused on lipid classification, the approach shows potential for broader application, which warrants further investigation across diverse compound classes, detection methods, and chromatographic conditions.
    Keywords:  LC–MS; Machine learning; Mass–to–charge ratio; Retention time; Unknown metabolites
    DOI:  https://doi.org/10.1007/s11306-025-02343-y
  8. J Vis Exp. 2025 Oct 03.
    Comparative Study of DDA and DIA Acquisition Modes on UPLC-Q-Orbitrap Mass Spectrometry Using Huaihua Powder.
    Di Wang, Lei Yin, Linglong Wang, Ruijun Zhang, Jia Nie, Maoyuan Jiang.
      This study systematically evaluates the comparative performance of two mass spectrometry acquisition modes, data-dependent acquisition (DDA) and data-independent acquisition (DIA), coupled with ultra-high-performance liquid chromatography-quadrupole-orbitrap high-resolution mass spectrometry (UPLC-Q-Orbitrap HRMS) for comprehensive chemical profiling of complex traditional Chinese medicine (TCM) formulations. Huaihua Powder, a classical formulated prescription, was employed as a model system for empirical assessment. Optimized DDA and DIA acquisition methods were separately established: the DDA method incorporated a targeted precursor ion selection strategy with customized fragmentation parameters, while the DIA method employed a segmented variable window strategy to cover the target m/z range, performing unbiased fragmentation on all precursor ions. Compound identification was executed using Compound Discoverer software. The comparative evaluation specifically focused on the performance characteristics of the two acquisition modes, encompassing the number of identified compounds, reproducibility, MS/MS spectral quality, and detection sensitivity for low-abundance active constituents. Results demonstrated that the DDA mode yielded a higher total number of detected compounds, whereas the DIA mode generated a greater proportion of high-confidence identifications (10.63 % with spectral match scores >0.8). Notably, the DIA approach exhibited significantly superior reproducibility in retention time and peak area for six representative compounds, with rutin showing >3-fold difference in retention time RSD between the two acquisition modes. However, DDA produced cleaner MS/MS spectra with distinct fragment ions, whereas DIA spectra exhibited interference from contaminant ions. Concurrently, DIA effectively detected low-abundance active constituents whose ion chromatograms and MS/MS fragments could not be extracted in DDA mode. This study contributes critical experimental evidence and analytical datasets to inform the selection of high-resolution mass spectrometry acquisition modes for complex TCM formulation research. Subsequent researchers may integrate the complementary advantages of both approaches to achieve dual objectives in comprehensive compound characterization.
    DOI:  https://doi.org/10.3791/69227
  9. Analyst. 2025 Oct 22.
    Optimisation of electron-induced dissociation parameters for molecular annotation of glycerides and phospholipids in fast LC-MS.
    Vincen Wu, Abraham Moyal, Alaa Othman, Nicola Zamboni.
      Electron-induced dissociation methods, particularly electron impact excitation of ions from organics (EIEIO), offer enhanced capabilities for lipid structural elucidation over traditional collision-induced dissociation (CID). Despite their analytical promise, the practicality of EIEIO within routine liquid chromatography-mass spectrometry (LC-MS) workflows remains largely unexplored. In this study, we optimised LC-EIEIO-MS analysis for the rapid and detailed structural annotation of glycerides and phospholipids. We evaluated the effects of reaction times, accumulation times, and electron kinetic energies using lipid standards from multiple classes and at varying concentrations. Our results revealed that short reaction times of 30 ms consistently yielded stronger diagnostic signals crucial for lipid class identification and sn-position discrimination at concentrations as low as 200 pg on column. To systematically infer the position of double bonds from EIEIO spectra, we introduced LipidOracle, a software that tests all possible isomers and correctly accounts for missing data, noise, and crowded spectra. We demonstrated that longer accumulation times of 200 ms were most effective for determining carbon-carbon double bond (CC) positions, particularly in polyunsaturated lipids. Finally, we evaluated the performance of EIEIO with liver and plasma extracts. Overall, we demonstrate that comprehensive lipid structural characterisation, including sn-position and double bond locations in fatty acyl chains, is achievable within typical LC-MS timescales (∼0.2 s). Our findings outline practical guidelines for high-throughput analysis of complex lipid samples by EIEIO.
    DOI:  https://doi.org/10.1039/d5an00567a
  10. Proteomics. 2025 Oct 24. e70064
    Easy Proteomics Sample Preparation: Technical Repeatability and Workflow Optimization Across 8 Biological Matrices in a New Core Facility Setting.
    Paraskevi Karousi, Maria Voumvouraki, Panagiota Efstathia Nikolaou, Ioannis Kollias, Foteini Paradeisi, Elena Sampanai, Vasiliki Gkalea, Ioannis Morianos, Jerome Zoidakis, Efstathios Kastritis, Nikolaos Thomaidis, Guillaume Médard, Julie Courraud.
      Bottom-up proteomics relies on efficient and repeatable sample preparation for accurate protein identification and precise quantification. This study evaluates the performance of adapted SPEED (Sample Preparation by Easy Extraction and Digestion) protocol, a simplified, detergent-free approach tailored for various biological matrices, including lysis-resistant samples. Protein extraction and denaturation steps were refined for 8 biological matrices enabling standardized, cheap, and scalable proteomics analysis on 96-well plates. For tissue samples requiring downstream applications like Western blotting, we used a low-detergent RIPA buffer. Notably, the protocols demonstrate remarkable down-scalability, enabling robust proteomics measurements from as few as 3000 cells per sample for preparation and even down to 300 cells per LC-MS/MS analysis. Key advancements include a 30-min nanoLC-MS/MS run, achieving a 15-20 samples-per-day throughput, and leveraging the power of diaPASEF using thoroughly optimized DIA-windows to enhance proteome coverage. These adaptations streamline workflows, enabling proteomics analyses in matrices with challenging physical and biochemical properties. This study underscores the importance of early-stage optimization and feasibility testing in proteomics pipelines to inform study design and sample selection. By showcasing robust, scalable adaptations of the SPEED protocol, we provide a foundation for reproducible, high-throughput proteomic studies across diverse biological contexts.
    Keywords:  SPEED protocol; bottom‐up proteomics; diaPASEF; technical repeatability; tryptic peptides
    DOI:  https://doi.org/10.1002/pmic.70064
  11. FEBS Lett. 2025 Oct 22.
    Spatiotemporal and quantitative analyses of phosphoinositides - fluorescent probe-and mass spectrometry-based approaches.
    Hiroaki Kajiho, Shin Morioka, Junko Sasaki, Takehiko Sasaki.
      Comprehensive understanding of phosphoinositide signaling requires both spatiotemporal visualization and precise quantitative analysis of individual lipid species. Phosphoinositides, a family of phosphorylated derivatives of phosphatidylinositol (PI), are structurally diverse lipid messengers that orchestrate a wide range of cellular functions, including membrane trafficking, cytoskeletal dynamics, and signal transduction. Due to their dynamic metabolism and compartment-specific localization, their analysis demands complementary strategies that integrate live-cell imaging with molecular quantification. In this review, we first summarize the development and application of fluorescence-based probes designed to monitor the distribution and dynamics of phosphoinositides in living cells, highlighting their specificity, targeting mechanisms, and limitations. We then provide an overview of recent advances in mass spectrometry-based methodologies that enable high-sensitivity, isomer-resolved quantification of phosphoinositides in biological specimens, including improvements in lipid extraction, derivatization, and chromatographic separation. Together, these dual approaches offer synergistic insights into the biochemical and cellular regulation of phosphoinositide signaling.
    Keywords:  PH domain; fluorescent probe; intracellular localization; lipidomics; mass spectrometry; phosphoinositide
    DOI:  https://doi.org/10.1002/1873-3468.70200
  12. J Pharm Biomed Anal. 2025 Oct 17. pii: S0731-7085(25)00543-6. [Epub ahead of print]268 117202
    Recent developments in plasma sample preparation methods for targeted metabolomics studies with liquid chromatography mass spectrometry.
    Phaedra Verding, Yvan Vander Heyden, Ann Van Eeckhaut, Debby Mangelings.
      Targeted metabolomics focuses on the quantification of a selected set of metabolites of interest from biological samples. The obtained data is used to better understand the physiological and pathophysiological state of an organism. Plasma samples are commonly analyzed with liquid chromatography coupled to mass spectrometry but require prior sample preparation. In preclinical studies sample volumes are often limited and metabolites of interest are typically present at low concentrations. As a result, there is a growing need for miniaturized sample preparation methods with high extraction efficiency. This review discusses key aspects in sample preparation highlighting the limitations associated with the more conventional methods, such as limited sample clean-up, low selectivity, long extraction times, limited extraction efficiency and the use of hazardous or toxic organic solvents. To address their challenges, novel sample preparation strategies have been developed. Examples that will be discussed are solid phase microextraction, microextraction by packed sorbents, 3D-printed sorbents, molecularly imprinted polymers, magnetic nanoparticles, magnetic ionic liquid-based liquid-liquid microextraction, dispersive liquid-liquid microextraction, nanoconfined liquid phase nanoextraction and supported liquid membrane-electromembrane extraction. A critical evaluation of these sample preparation methods is presented in the context of targeted metabolomics. Furthermore, inspiration is found in untargeted metabolomics and other bioanalytical applications for alternative sample preparation methods that may hold potential for plasma in targeted metabolomics.
    Keywords:  Liquid Chromatography – Mass Spectrometry; Microextraction; Plasma; Sample preparation; Targeted metabolomics
    DOI:  https://doi.org/10.1016/j.jpba.2025.117202
  13. Neuro Oncol. 2025 Oct 25. pii: noaf248. [Epub ahead of print]
    Stable isotope tracing in human plasma-like medium reveals metabolic and immune modulation of the glioblastoma microenvironment.
    Milan R Savani, Mohamad El Shami, Kenji Miki, Lauren C Gattie, Bailey C Smith, William H Hicks, Jacob O Weiss, Skyler S Oken, Lavanya N Katta, Tracey Shipman, Maged T Ghoche, Lauren G Zacharias, Misty S Martin-Sandoval, Eric Y Montgomery, Yi Xiao, Diana D Shi, Jeremy N Rich, Timothy E Richardson, Pascal O Zinn, Bradley C Lega, Thomas P Mathews, Ralph J DeBerardinis, Kalil G Abdullah, Samuel K McBrayer.
       BACKGROUND: In vivo stable isotope tracing is useful for natively surveying glioma metabolism but can be difficult to implement. Stable isotope tracing is tractable using in vitro glioma models, but most models lack nutrient conditions and cell populations relevant to human gliomas. This limits our ability to study glioma metabolism in the presence of an intact tumor microenvironment (TME) and immune-metabolic crosstalk.
    METHODS: We optimized an in vitro stable isotope tracing approach for human glioma explants and glioma stem-like cell (GSC) lines that integrates human plasma-like medium (HPLM). We performed 15N2-glutamine tracing in GSC monocultures and human IDH-wildtype glioblastoma explants and developed an analytical framework to evaluate microenvironment-dependent metabolic features that distinguish them. We also conducted spatial transcriptomics to assess transcriptional correlates to metabolic activities.
    RESULTS: HPLM culture preserved glioma explant viability and stemness while unmasking metabolic and immune programs suppressed by conventional culture conditions. Stable isotope tracing in HPLM revealed TME-dependent and TME-independent features of tumor metabolism. Tissue explants recapitulated tumor cell-intrinsic metabolic activities, such as synthesis of immunomodulatory purines. Unlike GSC monocultures, tissue explants captured tumor cell-extrinsic activities associated with stromal cell metabolism, as exemplified by astrocytic GDP-mannose production in heterocellular explants. Finally, glioma explants displayed tumor subtype-specific metabolic reprogramming, including robust pyrimidine degradation in mesenchymal cells.
    CONCLUSIONS: We present a tractable approach to assess glioma metabolism in vitro under physiologic nutrient levels and in the presence of an intact TME. This platform opens new avenues to interrogate glioma metabolism and its interplay with the immune microenvironment.
    Keywords:  Glioma; metabolism; organoids; preclinical models; stable isotope tracing
    DOI:  https://doi.org/10.1093/neuonc/noaf248
  14. Angew Chem Int Ed Engl. 2025 Oct 20. e202515603
    Quantitative Reactivity Profiling of Functional Arginine Residues in Human Cancer Cell Line Proteomes.
    Wenbo Zhao, Yuliang Tang, Yihui Gao, Qi Ding, Qiang Li, Wenyang Li, Xiaoguang Lei.
      Arginine, a critical amino acid for protein structure and function, is involved in enzyme catalysis and macromolecular interactions. However, selectively targeting its reactive guanidine group has been challenging. Here, we utilized a probe, AP-1, based on phenylglyoxal, which demonstrated remarkable chemical selectivity and reactivity toward arginine residues. Using activity-based protein profiling (ABPP), we explored the human proteome across four cancer cell lines, obtaining quantitative data for approximately 17 000 arginine residues. This analysis led to the identification of several previously unreported hyperreactive arginine residues, including R43 of PKM, R171 of LDHA, R172 of LDHB, R341 of CKB, R168 of EIF4A1, and R118 of FUBP1, which are crucial for protein function. Notably, the mutation of CKB's R341 inhibited cell proliferation and migration by downregulating energy supply. We also introduced ArGO-LDHA-1, a covalent inhibitor targeting LDHA's hyperreactive arginine residues, showing potential to enhance chemotherapy efficacy. This work highlights the biological significance of arginine residues and provides a platform for large-scale profiling of arginine reactivity.
    Keywords:  Activity‐based protein profiling; Arginine; Chemical probes; Mass spectrometry; Proteomics
    DOI:  https://doi.org/10.1002/anie.202515603
  15. World J Gastrointest Oncol. 2025 Oct 15. 17(10): 109398
    Lipid metabolic reprogramming in colorectal cancer: Insights to mechanisms and therapeutics.
    Chen-Dong Wang, Bi-Xiang Zhang, Jia Song.
      Colorectal cancer (CRC) exhibits profound lipid metabolic reprogramming, a hallmark of malignant transformation that supports tumorigenesis, immune evasion, and therapeutic resistance. Dysregulated lipid metabolism in CRC involves altered fatty acid synthesis, uptake, oxidation, and cholesterol metabolism, which collectively drive cancer cell proliferation, metastasis, and interactions with the tumor microenvironment (TME). This review synthesizes current insights into lipid metabolic rewiring in CRC, its role in shaping immunosuppressive TME dynamics, and emerging therapeutic strategies targeting lipid pathways.
    Keywords:  Colorectal cancer; High-fat diet; Lipid metabolism reprogramming; Targeted drugs; Tumor microenvironment
    DOI:  https://doi.org/10.4251/wjgo.v17.i10.109398
  16. J Pharm Biomed Anal. 2025 Oct 15. pii: S0731-7085(25)00539-4. [Epub ahead of print]268 117198
    Direct MS enabled discovery of lipid signatures with diagnostic implications in colorectal cancer.
    Ran Zheng, Lianjie Xiong, Jiaru Wang, Xingyu Shang, Hehong Sun, Yanhong Chang, Xinxin Du, Rui Su.
      Lipid metabolites are promising biomarkers for detection of colorectal cancer (CRC). However, current methods often involve complex sample preparation and long analysis times, which can affect the stability and integrity of clinical samples, especially tissues. This creates a need for faster, more accurate lipid profiling approaches. Comprehensive analysis of lipid changes and related metabolic pathways is key to understanding CRC development and improving diagnostic accuracy. Herein, we employed internal extractive electrospray ionization mass spectrometry (iEESI-MS) to analyze lipid profiles from CRC and healthy tissue groups combined with multivariate statistical analyses. Our analysis identified 40 significantly altered lipid species spanning nine major classes, comprising glycerophospholipids (phosphatidylcholines (PC), phosphatidylethanolamines (PE), phosphatidylglycerols (PG), phosphatidic acids (PA), and phosphatidylserines (PS), sphingomyelins (SM) and ceramides (Cer), and triacylglycerols (TG) and diacylglycerols (DG)). The lipid signature PC (36:4) demonstrated efficacy in discriminating CRC from normal tissues with highest area under the curve (AUC) values of 0.95. The optimal model selected included 10 lipid metabolites with high AUC value of 0.985, a sensitivity of 0.967, and a specificity of 0.95, suggesting that the lipidomic model has potential clinical applications. Pathway enrichment analysis further revealed statistically significant perturbations (p < 0.01) in glycerophospholipid metabolism pathways and glycerolipid metabolism pathways, suggesting their potential involvement in CRC pathogenesis. The iEESI-MS approach demonstrated the capability to rapidly detect CRC-specific lipid biomarkers and perturbed metabolic pathways, offering a promising tissue diagnostic tool that bridges lipid metabolism research with clinical applications in precision medicine for CRC detection.
    Keywords:  Colorectal cancer; Diagnosis; IEESI-MS; Lipid signatures; Tissue analysis
    DOI:  https://doi.org/10.1016/j.jpba.2025.117198
  17. Nat Commun. 2025 Oct 22. 16(1): 9333
    Computational design and evaluation of optimal bait sets for scalable proximity proteomics.
    Vesal Kasmaeifar, Saya Sedighi, Anne-Claude Gingras, Kieran R Campbell.
      The spatial organization of proteins within eukaryotic cells underlies essential biological processes and can be mapped by identifying nearby proteins using proximity-dependent biotinylation approaches such as BioID. When applied systematically to hundreds of bait proteins, BioID has localized thousands of endogenous proteins in human cells, generating a comprehensive view of subcellular organization. However, the need for large bait sets limits the scalability of BioID for context-dependent spatial profiling across different cell types, states, or perturbations. To address this, we develop a benchmarking framework with multiple complementary metrics to assess how well a given bait subset recapitulates the structure and coverage of a reference BioID dataset. We also introduce GENBAIT, a genetic algorithm-based method that identifies optimized bait subsets predicted to retain maximal spatial information while reducing the total number of baits. Applied to three large BioID datasets, GENBAIT consistently selected subsets representing less than one-third of the original baits while preserving high coverage and network integrity. This flexible, data-driven approach enables intelligent bait selection for targeted, context-specific studies, thereby expanding the accessibility of large-scale subcellular proteome mapping.
    DOI:  https://doi.org/10.1038/s41467-025-64383-1
  18. J Chromatogr A. 2025 Oct 11. pii: S0021-9673(25)00803-9. [Epub ahead of print]1764 466459
    Evaluation of ion mobility, uni- and multidimensional liquid chromatography for non-target screening of phenolic compounds in wheat flag leaves.
    Oskar Munk Kronik, Ewa Makowicz, Jan H Christensen, Nikoline Juul Nielsen, André de Villiers.
      Non-target screening (NTS) of plant secondary metabolites is analytically challenging due to the complexity of mixtures with structurally similar compounds and isomers. This study evaluates the added value of ion mobility spectrometry (IMS) and comprehensive two-dimensional liquid chromatography (LC × LC) for enhancing separation, mass spectral quality, chromatographic structure and from access to collisional cross section (CCS) values in NTS workflows. Four LCHRMS configurations were compared: RPLCHRMS, HILICHRMS, RPLC × HILICHRMS, and HILIC × RPLCHRMS, each with and without travelling wave (TW)IMS. Data were acquired in data-independent acquisition (DIA) mode. Phenolic compounds in wheat flag leaf extracts were analyzed as a representative case. The results show that mass spectral purity improved up to 74 times when hyphenating RPLC with IMS for peaks with intensities <1.5 × 104. The compound groups: flavones, flavan-3-ols, benzoic acid derivatives and phenolic acids were separated into distinct retention time regions in RPLC × HILIC, while co-eluting in RPLC and HILIC. Only two of ten isomeric pairs were partially resolved by IMS, with resolutions of 0.50 and 0.63, whereas RPLC achieved resolution values of 29 and 13 for the same pairs. Notably, the only isomers with IMS resolution ≥ 0.5 had m/z > 447. We showed that large [Formula: see text] deviations (-9.47 %) were observed when several deprotomers of a molecule were created and separated in IMS. Deprotomers are not accommodated for in the machine learning prediction models, which limits their use for such molecules. The complementary strengths of IMS and LC × LC should be exploited strategically to address specific analytical goals in plant metabolite screening.
    Keywords:  2D-LC; Ion mobility; Mass spectral quality; Structured chromatogram
    DOI:  https://doi.org/10.1016/j.chroma.2025.466459
  19. J Proteome Res. 2025 Oct 20.
    New Method Enhanced Extraction of Protein Signatures of Renal Cell Carcinoma from Proteomics Data.
    Hongyi Liu, Zhuo Ma, T Mamie Lih, Lijun Chen, Yingwei Hu, Yuefan Wang, Zhenyu Sun, Yuanyu Huang, Yuanwei Xu, Hui Zhang.
      In this study, we generated label-free data-independent acquisition (DIA)-based liquid chromatography (LC)-mass spectrometry (MS) proteomics data from 261 renal cell carcinomas (RCC) and 195 normal adjacent tissues (NAT). The RCC tumors included 48 nonclear cell renal cell carcinomas (non-ccRCC) and 213 ccRCC. A total of 219,740 peptides and 11,943 protein groups were identified, with 9,787 protein groups per sample on average. We adopted a comprehensive approach to select representative samples with different mutations, considering histopathological, immune, methylation, and non-negative matrix factorization (NMF)-based subtypes, along with clinical characteristics (gender, grade, and stage) to capture the complexity and diversity of ccRCC tumors. We identified a protein signature containing 55 proteins that distinguish RCC tumors from NATs. Furthermore, a protein signature containing 39 proteins that differentiate different RCC tumor subtypes was also identified. Our findings offer an extensive perspective of the proteomic landscape in RCC, illuminating specific proteins that serve to distinguish RCC tumors from NATs and among various RCC tumor subtypes.
    Keywords:  clear cell renal cell carcinoma; clinical proteomic tumor analysis consortium (CPTAC); data-independent acquisition (DIA); non-clear cell renal cell carcinoma; proteomics; renal cell carcinoma
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00414
  20. Anal Chim Acta. 2025 Dec 08. pii: S0003-2670(25)01045-1. [Epub ahead of print]1378 344651
    Development and validation of a five-channel multiplex LC-ESI-MS/MS method for the quantification of carboxylic acids in urine.
    Wan-Hui Lu, Ju-Yu Chen, Te-I Weng, Ching-Hua Kuo, Guan-Yuan Chen.
       BACKGROUND: Carboxylic acids are vital metabolites widely present in living organisms, playing critical roles in various biological processes. Alterations in carboxylic acid profiles are often associated with inborn errors of metabolism (IEMs), which can lead to severe consequences or even mortality without timely treatment. Monitoring these metabolites through time-series analysis is essential for understanding disease progression. However, achieving high-throughput analysis of carboxylic acids using conventional chromatographic techniques remains a significant challenge.
    RESULTS: This study introduces a 5-channel multiplex method using liquid chromatography‒electrospray ionization‒tandem mass spectrometry (LC‒ESI‒MS/MS) to quantify essential carboxylic acids in urine. The method employs butanol isotopes to form butyl derivatives (D0-, D3-, D5-, D7-, and D9-carboxylic acid butyl ester). Validation results demonstrated high accuracy (85.22 %-115.16 %) for mixture estimation, with analyte quantification accuracies ranging from 79.1 % to 116.6 % and precisions below 15 % for most compounds. The validated method was applied to four urine samples in a simulated time-series study, highlighting its effectiveness in tracking metabolic changes.
    SIGNIFICANCE: The innovative multiplex strategy significantly enhances the throughput and precision of carboxylic acid quantification. By enabling efficient time-series analyses, this approach contributes to the study of IEMs and accelerates clinical diagnostics, offering promising applications in both research and healthcare.
    Keywords:  Carboxylic acid; Chemical isotope labeling; Inborn error metabolism; LC-MS/MS; Multiplex analysis
    DOI:  https://doi.org/10.1016/j.aca.2025.344651
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