bims-mascan 2025-08-03 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–08–03
twenty-six papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

mzQuality: An Open-Source Software Tool for Quality Monitoring and Reporting of Targeted Mass Spectrometry Measurements.
Enhancing tandem mass spectrometry-based metabolite annotation with online chemical labeling.
SynchroSep-MS: Parallel LC Separations for Multiplexed Proteomics.
Determining carbon-carbon double bond position of unsaturated glycerophospholipids in human plasma NIST® SRM® 1950 by electron impact excitation of ions from organics-tandem mass spectrometry (EIEIO-MS/MS).
Drug-resistant Escherichia coli metabolomics via pseudo-targeted SWATH/IDA-MRM: Bridging high coverage and precision.
Data-Independent Acquisition (DIA)-Based Label-Free Redox Proteomics (DIALRP) Identifies Prominent Cysteine Oxidations in Translation Machinery in Prostate Cancer Cells Under Oxidative Stress.
Multiparametric Optimization of Data-Dependent Acquisition Towards More Holistic Bacterial Metabolite Coverage Through Molecular Networking.
High-throughput proteomic and phosphoproteomic analysis of formalin-fixed paraffin-embedded tissues.
Metabolic adaptations in cancer progression.
Rewiring cancer metabolism: oncogenic signaling pathways and targeted therapeutics.
Feasibility of amino acid profiling in long-term stored formalin-fixed paraffin-embedded colorectal neoplasia tissue.
Small Molecule Promoters of Endogenous Lipid Droplet Accumulation Drive Lysophagy.
Derivatization for success: a tutorial review of liquid chromatography mass spectrometry method optimization for determining amino compounds.
Single-Cell 5 μm-Resolution Dual-Polarity MALDI-MS Imaging without Matrix Reapplication.
Quantum Chemistry Calculation-Assisted Large-Scale Collision Cross Section Prediction Empowers Derivatization-Enhanced Multidimensional Metabolomics.
High-throughput plasma/serum proteomics by coupling nanoparticle protein corona-based sample preparation and capillary zone electrophoresis-tandem mass spectrometry.
Optimization of GC-MS run time for untargeted metabolomics: Trade-offs between speed, coverage, and repeatability.
Quantitative lipidomics for three-dimensional cell culture using deuterium oxide metabolic labeling.
Glucose metabolism and its direct action in cancer and immune regulation: opportunities and challenges for metabolic targeting.
Proteoform Identification Using Multiplexed Top-Down Mass Spectra.
Metabolic Adaptations in Cancer Progression: Optimization Strategies and Therapeutic Targets.
HIF-independent oxygen sensing via KDM6A regulates ferroptosis.
Improved Detection of Differentially Abundant Proteins through FDR-Control of Peptide-Identity-Propagation.
A diagnostic algorithm for inherited metabolic disorders using untargeted metabolomics.
AUTO-SP: Automated Sample Preparation for Analyzing Proteins and Protein Modifications.
Development and application of GlycanDIA workflow for glycomic analysis.

J Am Soc Mass Spectrom. 2025 Jul 25.

mzQuality: An Open-Source Software Tool for Quality Monitoring and Reporting of Targeted Mass Spectrometry Measurements.

Marielle van der Peet, Pascal Maas, Agnieszka Wegrzyn, Lieke Lamont, Ronan Fleming, Constance Bordes, Stéphanie Debette, Amy Harms, Thomas Hankemeier, Alida Kindt.

  Analyzing metabolites using mass spectrometry provides valuable insight into an individual's health or disease status. However, various sources of experimental variation can be introduced during sample handling, preparation, and measurement, which can negatively affect the data. Quality assurance and quality control practices are essential to ensuring accurate and reproducible metabolomics data. These practices include measuring reference samples to monitor instrument stability, blank samples to evaluate the background signal, and strategies to correct for changes in instrumental performance. In this context, we introduce mzQuality, a user-friendly, open-source R-Shiny app designed to assess and correct technical variations in mass spectrometry-based metabolomics data. It processes peak-integrated data independently of vendor software and provides essential quality control features, including batch correction, outlier detection, and background signal assessment, and it visualizes trends in signal or retention time. We demonstrate its functionality using a data set of 419 samples measured across six batches, including quality control samples. mzQuality visualizes data through sample plots, PCA plots, and violin plots, which illustrate its ability to reduce the effect of experiment variation. Compound quality is further assessed by evaluating the relative standard deviation of quality control samples and the background signal from blank samples. Based on these quality metrics, compounds are classified into confidence levels. mzQuality provides an accessible solution to improve the data quality without requiring prior programming skills. Its customizable settings integrate seamlessly into research workflows, enhancing the accuracy and reproducibility of the metabolomics data. Additionally, with an R-compatible output, the data are ready for statistical analysis and biological interpretation.

Keywords:  Data quality; Mass spectrometry; Metabolomics; R package; R-shiny app

DOI:  https://doi.org/10.1021/jasms.5c00073
Nat Commun. 2025 Jul 26. 16(1): 6911

Enhancing tandem mass spectrometry-based metabolite annotation with online chemical labeling.

Giovanni Andrea Vitale, Shu-Ning Xia, Kai Dührkop, Mohammad Reza Zare Shahneh, Heike Brötz-Oesterhelt, Yvonne Mast, Corinna Brungs, Sebastian Böcker, Robin Schmid, Mingxun Wang, Chambers C Hughes, Daniel Petras.

Metabolite identification in non-targeted mass spectrometry-based metabolomics remains a major challenge due to limited spectral library coverage and difficulties in predicting metabolite fragmentation patterns. Here, we introduce Multiplexed Chemical Metabolomics (MCheM), which employs orthogonal post-column derivatization reactions integrated into a unified mass spectrometry data framework. MCheM generates orthogonal structural information that substantially improves metabolite annotation through in silico spectrum matching and open-modification searches, offering a powerful new toolbox for the structure elucidation of unknown metabolites at scale.

DOI: https://doi.org/10.1038/s41467-025-61240-z
J Am Soc Mass Spectrom. 2025 Jul 30.

SynchroSep-MS: Parallel LC Separations for Multiplexed Proteomics.

Noah M Lancaster, Li-Yu Chen, Bingnan Zhao, Benton J Anderson, Mitchell D Probasco, Vadim Demichev, Daniel A Polasky, Alexey I Nesvizhskii, Katherine A Overmyer, Scott T Quarmby, Joshua J Coon.

Achieving high throughput remains a challenge in MS-based proteomics for large-scale applications. We introduce SynchroSep-MS, a novel method for parallelized, label-free proteome analysis that leverages the rapid acquisition speed of modern mass spectrometers. This approach employs multiple liquid chromatography columns, each with an independent sample, simultaneously introduced into a single mass spectrometer inlet. A precisely controlled retention time offset between sample injections creates distinct elution profiles, facilitating unambiguous analyte assignment. We modified the DIA-NN workflow to effectively process these unique parallelized data, accounting for retention time offsets. Using a dual-column setup with mouse brain peptides, SynchroSep-MS detected approximately 16,700 unique protein groups, nearly doubling the peptide information obtained from a conventional single proteome analysis. The method demonstrated excellent precision and reproducibility (median protein %RSDs less than 4%) and high quantitative linearity (median R2 greater than 0.96) with minimal matrix interference. SynchroSep-MS represents a new paradigm for data collection and the first example of label-free multiplexed proteome analysis via parallel LC separations, offering a direct strategy to accelerate throughput for demanding applications such as large-scale clinical cohorts and single-cell analyses without compromising peak capacity or causing ionization suppression.

DOI: https://doi.org/10.1021/jasms.5c00207
J Pharm Biomed Anal. 2025 Jul 24. pii: S0731-7085(25)00422-4. [Epub ahead of print]266 117081

Determining carbon-carbon double bond position of unsaturated glycerophospholipids in human plasma NIST® SRM® 1950 by electron impact excitation of ions from organics-tandem mass spectrometry (EIEIO-MS/MS).

Sara Martínez, Ana Gradillas, Hana Cermakova, Michael Witting, Coral Barbas.

  Understanding the structural diversity and biological functions of unsaturated fatty acyl chains (FAC) esterified in complex lipids -such as glycerolipids (GL), glycerophospholipids (GP) or sphingolipids (SP)- requires precise knowledge of the degree of unsaturation, location, and geometrical isomerism of the carbon-carbon double bonds (CC). However, the complex isomeric nature of lipids, combined with the inherent limitations of conventional tandem mass spectrometry (MS/MS) in structural elucidation, remains a major challenge in accurate CC elucidation. To overcome this, advanced MS/MS strategies, such as electron impact excitation of ions from organics (EIEIO) have emerged, generating diagnostic fragment ions that enable unambiguous CC localization. In the present study, we conducted a qualitative structural analysis of the CC positions in esterified FAC of GP present in NIST® Human Plasma Standard Reference Material, SRM 1950, employing RP-UHPLC-ESI(+)-EIEIO-QTOF-MS/MS. Interpretation of ESI(+)-EIEIO-MS/MS spectra enabled CC determination in 120 unsaturated GP, revealing a predominance of ω-6 and ω-3 FAC. These results offer new insights into the FAC distribution of this reference material, enhancing the structural annotation confidence level. By integrating such detailed molecular information, EIEIO-MS/MS proves to be a powerful approach for in-depth lipid structural elucidation in complex biological matrices, thereby contributing to methodological advancements and supporting its future application in translational lipidomics.

Keywords:  Double bond position; Electron impact excitation of ions from organics (EIEIO); Electron-induced dissociation (EID); Lipid annotation; Lipidomics; MS/MS fragmentation; NIST SRM 1950

DOI:  https://doi.org/10.1016/j.jpba.2025.117081
J Adv Res. 2025 Jul 30. pii: S2090-1232(25)00581-8. [Epub ahead of print]

Drug-resistant Escherichia coli metabolomics via pseudo-targeted SWATH/IDA-MRM: Bridging high coverage and precision.

Xi Jia, Liang Zhao, Saiwa Liu, Jingjing Du, Zhinan Wang, Lirui Ge, Jian Xu, Kexin Cui, Yu Ga, Xiaowei Li, Jianzhong Shen, Xi Xia.

   INTRODUCTION: Metabolic reprogramming plays a significant role in the emergence, progression, and response to antibiotic pressure in bacterial resistance. Current metabolomics approaches face significant limitations: untargeted methods lack quantitative precision, while targeted analyses suffer from limited coverage. These technical constraints hinder comprehensive evaluation of metabolic contributions to antibiotic activity and resistance evolution, creating a critical knowledge gap in understanding treatment outcomes for resistant bacteria.
OBJECTIVE: To establish a metabolomics method with comprehensive coverage, excellent reproducibility, high sensitivity, and wide dynamic range for elucidating the dynamic relationships between bacterial metabolic reprogramming, antibiotic activity, and resistance phenotype development.
METHODS: We employed a complementary liquid chromatography system incorporating reverse-phase liquid chromatography, hydrophilic interaction liquid chromatography, and metal-sensitive liquid chromatography. Coupled with high-resolution mass spectrometry and utilizing three complementary data acquisition modes - full scan, information-dependent acquisition (IDA), and sequential window acquisition of all theoretical mass spectra (SWATH) - we developed a novel pseudo-targeted metabolomics approach based on triple quadrupole mass spectrometry, designated as SWATH/IDA-MRM. This optimized method was subsequently applied to investigate metabolic reprogramming in Escherichia coli strains harboring the resistance genes mcr-1, blaNDM-1, blaNDM-5, and the dual combination mcr-1 + blaNDM-1.
RESULTS: Our analytical platform successfully identified 3,529 metabolic features using six complementary chromatographic separation conditions, achieving broader metabolite coverage than conventional targeted metabolomics. Comparative evaluation against untargeted approaches revealed marked improvements in analytical performance, including enhanced linearity, reproducibility, detection sensitivity, and dynamic range, along with superior capacity for discriminating metabolic profiles between sample groups. Application to antibiotic-resistant E. coli strains revealed substantial metabolic flux alterations in resistant versus susceptible strains, with predominant perturbations in nucleotide metabolism, amino acid metabolism, energy metabolism, lipid metabolism, and redox balance pathways.
CONCLUSION: The developed SWATH/IDA-MRM platform represents a significant methodological advancement for investigating the complex interplay between microbial metabolic adaptation and antimicrobial responses. This integrated analytical approach enables systematic characterization of resistance-associated metabolic reprogramming, thereby establishing a framework for developing targeted therapeutic strategies against pathogen-specific metabolic vulnerabilities.

Keywords:  Bacterial metabolism; Bla(NDM); Pseudo-targeted metabolomics; mcr-1

DOI:  https://doi.org/10.1016/j.jare.2025.07.051
J Proteome Res. 2025 Jul 31.

Data-Independent Acquisition (DIA)-Based Label-Free Redox Proteomics (DIALRP) Identifies Prominent Cysteine Oxidations in Translation Machinery in Prostate Cancer Cells Under Oxidative Stress.

Daiki Kobayashi, Tomoyo Takami, Masaki Matsumoto.

  Oxidative stress is a key factor in numerous physiological and pathological processes, including aging, cancer, and neurodegenerative diseases. Protein cysteine residues are particularly susceptible to oxidative stress-induced modifications that can alter their structure and function, thereby affecting intracellular signaling pathways. In this study, we performed a data-independent acquisition mass spectrometry (DIA-MS)-based label-free redox proteomics method, termed DIALRP, to comprehensively analyze cysteine oxidative modifications in the prostate cancer cell line DU145 under oxidative stress induced by menadione (MND). Of 10,821 cysteine-containing peptides identified, we successfully quantified the redox changes in 3665 peptides. We also observed that 1407 peptides were significantly oxidized in response to MND treatment. Gene ontology analysis revealed that a group of translation-related molecules was most enriched among highly MND-sensitive cysteine-containing proteins. Notably, our data demonstrated that MND-induced oxidative stress inhibits EIF2, EIF6, and EEF2 complex formation, suggesting that these complex inhibitions become functional factors for a dramatic reduction in translation activity. Our results show that DIALRP is utilized as a robust and cost-effective approach for investigating redox-regulated cellular processes. Moreover, these findings provide significant insights into translation regulation under oxidative stress and provide a valuable framework for future studies on redox-mediated cellular processes.

Keywords:  cancer; data-independent acquisition; oxidative stress; redox proteomics; translation factors

DOI:  https://doi.org/10.1021/acs.jproteome.5c00339
Int J Microbiol. 2025 ;2025 4388417

Multiparametric Optimization of Data-Dependent Acquisition Towards More Holistic Bacterial Metabolite Coverage Through Molecular Networking.

Adivhaho Khwathisi, Amidou Samie, Asfatou Ndama Traore, Ntakadzeni Edwin Madala.

  Prokaryotic organisms rely on a limited array of metabolites for survival, which varies according to their natural environment. For example, soil-borne bacteria produce diverse metabolites, such as antibiotics, to thrive in their competitive surroundings, inhibiting the growth of nearby competing bacteria. The structural diversity of these compounds offers great analytical challenges, since there is no universal acquisition setting that can be applied to achieve their comprehensive coverage. Therefore, the use of a single experimental setup inevitably hinders the comprehensive metabolite coverage, which would affect the outputs. To address this, we propose employing a design of experiment (DoE) approach through the central composite design (CCD) to enhance the metabolite detection and broaden the coverage of the data-dependent acquisition (DDA) mode of the UHPLC-qTOF-MS technique. Our study reveals that altering collision energy significantly enhances metabolite coverage compared to adjusting the DDA threshold of detection. Furthermore, the ability of global natural product social (GNPS)-based molecular network models to annotate metabolites is greatly influenced by data acquisition settings, particularly affecting MS2 data. Interestingly, molecular networks constructed from averaged spectral data obtained through randomly selected DDA settings outperform those generated using customized settings through DoE modeling. This study demonstrates that in untargeted LC-MS metabolomics, both collision energy and intensity threshold independently enhance metabolite coverage in untargeted metabolomics. However, their combined use results in even greater coverage. Consequently, we recommend adopting group-based optimization over single-point optimization for more comprehensive metabolite coverage and in-depth exploration. However, caution should be taken in order to balance between robust data and redundancy.

Keywords:  LC-MS (liquid chromatography-mass spectrometry); bacterial metabolites; data-dependent acquisition (DDA); metabolite profiling; metabolomics; molecular networking

DOI:  https://doi.org/10.1155/ijm/4388417
Mol Cell Proteomics. 2025 Jul 29. pii: S1535-9476(25)00143-4. [Epub ahead of print] 101044

High-throughput proteomic and phosphoproteomic analysis of formalin-fixed paraffin-embedded tissues.

Moe Haines, John R Thorup, Simone Gohsman, Claudia Ctortecka, Chelsea Newton, Dan C Rohrer, Galen Hostetter, D R Mani, Michael A Gillette, Shankha Satpathy, Steven A Carr.

Formalin-fixed, paraffin-embedded (FFPE) patient tissues are a valuable resource for proteomic studies with the potential to associate derived molecular insights with clinical annotations and outcomes. Here we present an optimized, partially automated, plate-based workflow for FFPE proteomics combining pathology-guided macrodissection, xylene-free deparaffinization using Adaptive Focused Acoustics (AFA) sonication for lysis and decrosslinking, optimized S-Trap digestion and cleanup of peptides, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) using Exploris 480, Orbitrap Astral, and timsTOF HT instrumentation. The workflow enables analysis of up to 96 dissected FFPE tissue samples or 10 μm scrolls, identifying 8,000-10,000 unique proteins per sample with median CVs <20%. Application to lung adenocarcinoma (LUAD) FFPE blocks confirms the platform's effectiveness in processing complex, clinically relevant samples, achieving deep proteome coverage and quantitative robustness comparable to TMT-based methods. Using the Orbitrap Astral with short, 24-minute gradients, the workflow identifies up to 10,000 unique proteins and 11,000 fully localized phosphosites in LUAD FFPE tissue, demonstrating the ability to derive biologically relevant phosphoprotein/peptide results from clinically derived FFPE tumor samples. This high-throughput, scalable workflow advances biomarker discovery and proteomic research in archival tissue samples.

DOI: https://doi.org/10.1016/j.mcpro.2025.101044
Physiol Rev. 2025 Jul 28.

Metabolic adaptations in cancer progression.

Yiming Peng-Winkler, Sarah-Maria Fendt.

  Cancer cells reprogram their metabolism as they travel to distant organs to establish metastases, the leading cause of cancer-related mortality. While the metabolic state of primary tumors has been extensively studied, the specific metabolic alterations associated with metastases have only recently garnered significant attention. The metabolic dependencies that arise during the metastatic cascade, along with the adaptive metabolic shifts required for growth in a new microenvironment, present promising therapeutic targets. In this review, we provide an overview of cancer metabolism, followed by a detailed exploration of the metabolic changes occurring at each stage of metastasis and within common organs of metastatic spread. Lastly, we examine the potential and challenges of targeting metabolic pathways in cancer therapy.

Keywords:  Cancer; Metabolism; Metabolism-based therapy; Metastasis; Organ microenvironment

DOI:  https://doi.org/10.1152/physrev.00037.2024
Sci China Life Sci. 2025 Jul 25.

Rewiring cancer metabolism: oncogenic signaling pathways and targeted therapeutics.

Siying Lyu, Nina Gildor, Qing Zhang, Chengheng Liao.

  Metabolic reprogramming is a hallmark of cancer, playing a critical role in tumorigenesis by supporting cancer cell survival, proliferation, metastasis, and immune evasion. Oncogenic signaling pathways regulate key metabolic processes by orchestrating gene expression and enhancing metabolic enzyme activity, ensuring cancer cells meet their bioenergetic and biosynthetic demands. Here, we highlight the roles of major oncogenic metabolic signaling pathways, including phosphoinositide 3-kinase (PI3K)/AKT, Myc, p53, and hypoxia-inducible factor (HIF), in driving metabolic rewiring. We provide a conceptual framework to understand why metabolic reprogramming occurs in tumor cells, how metabolic alterations contribute to tumorigenesis, metastasis, and immune evasion, and the therapeutic implications of targeting these metabolic vulnerabilities in cancer.

Keywords:  cancer metabolism; cell proliferation; immune evasion; metastasis; oncogenic signaling pathways; target therapy

DOI:  https://doi.org/10.1007/s11427-025-2979-3
Metabolomics. 2025 Jul 27. 21(4): 102

Feasibility of amino acid profiling in long-term stored formalin-fixed paraffin-embedded colorectal neoplasia tissue.

Roza C M Opperman, Puck E Bruchner, Sofie Bosch, Tim G J de Meij, Evelien Dekker, Nanne K H de Boer, Eduard A Struys.

   INTRODUCTION: Metabolic processes play a role in cancer development, with faecal amino acids emerging as potential biomarkers for colorectal neoplasia. While fresh frozen tissue is preferred for metabolomic analysis, formalin-fixed paraffin-embedded (FFPE) tissue is more widely available.
OBJECTIVES: We aimed to evaluate amino acid profiles in FFPE tissue across different stages of the adenoma-carcinoma sequence.
METHOD: A panel of 20 amino acids was measured using liquid chromatography-tandem mass spectrometry.
RESULTS: Fourteen amino acids were detected, with proline elevated in colorectal carcinoma versus advanced (FC 2.33, p = 0.04) and non-advanced adenomas (FC 2.42, p = 0.02).
CONCLUSION: Despite analytical challenges, amino acid profiling in FFPE tissue is feasible.

Keywords:  Amino acids; Colorectal adenoma; Colorectal cancer; Formalin-fixed paraffin embedded; Liquid-chromatography tandem-mass spectrometry

DOI:  https://doi.org/10.1007/s11306-025-02301-8
Angew Chem Int Ed Engl. 2025 Jul 26. e202504595

Small Molecule Promoters of Endogenous Lipid Droplet Accumulation Drive Lysophagy.

Sai Kumari Vechalapu, Santhosh Duraisamy, Sathyapriya Senthil, Rakesh Kumar, Deepa Ajnar, Apparao Draksharapu, Suresh Kumar, Dharmaraja Allimuthu.

  Lipid droplets (LDs) are central in regulating metabolism in stress-induced conditions, including one triggered by nutrient deprivation. Genetic manipulation of the lipid metabolic network or supplementation of a high-fat diet/oleic acid (OA) are the traditional routes for voluntarily triggering LD formation in cells and animals to study the role of LDs in disease biology. We developed a new screening platform for identifying small molecule-based LD inducers, which identified linoleic acid (LOA, diunsaturated fatty acid) as a better tool than OA (monounsaturated fatty acid) in promoting LD formation in cells. Subsequent screening and validation discovered a novel heterocyclic compound and respective iron complex for promoting a rapid organization of endogenous lipids into droplets by mimicking desaturase function and modulating oxidative lipid metabolism. Notably, our mass spectral lipidomics analysis presented the overproduction of phosphatidylcholines and small triglycerides (TG), a hallmark of LDs. We uncovered that the abrupt levels of LD formation induced by our molecules promoted a unique cell death mechanism, lysophagy in cancer cells, to prevent their proliferation, movement, and colonization. Collectively, our work introduces new small molecules as powerful tools for reliably promoting LD accumulation in cells, a promising tool for studying the role in health and disease.

Keywords:  High‐content imaging; Lipid droplet; Lipidomics; Lysophagy; Small molecules

DOI:  https://doi.org/10.1002/anie.202504595
Anal Chim Acta. 2025 Oct 08. pii: S0003-2670(25)00748-2. [Epub ahead of print]1370 344354

Derivatization for success: a tutorial review of liquid chromatography mass spectrometry method optimization for determining amino compounds.

Riin Rebane, Ivo Leito.

  The accurate determination of amino compounds in complex matrices is essential for a wide range of applications, from bioanalysis to industry. Liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful analytical technique for their identification and quantification. However, the low ionization efficiency and poor retention of some amino compounds present a challenge in mass spectrometric detection, making derivatization a crucial step. This tutorial review provides a comprehensive guide to improving LC-MS methods through derivatization, enhancing detection and quantification. Key aspects of the optimization method are covered, including selection of suitable derivatization reagents, optimization of reaction and chromatographic conditions, and other practical aspects. Drawing on an extensive literature review and experience, this tutorial offers researchers valuable insights into refining LC-MS workflows for methods using derivatization, mainly to determine amino acids and biogenic amines. Serving as both an instructional guide and a practical reference, this study aims to advance the effectiveness of LC-MS-based methods for amino compound determination.

Keywords:  Amino acids; Derivatization; Liquid chromatography; Mass spectrometry

DOI:  https://doi.org/10.1016/j.aca.2025.344354
Anal Chem. 2025 Jul 29.

Single-Cell 5 μm-Resolution Dual-Polarity MALDI-MS Imaging without Matrix Reapplication.

Yanyan Chen, Rui Shi, Jianing Wang, Chengyi Xie, Yuanyuan Song, Ruxin Li, Luyao Wen, Thomas Ka-Yam Lam, Zhu Yang, Zongwei Cai.

High-resolution mass spectrometry imaging (MSI) plays a vital role in lipidomics, yet challenges persist in analyzing lipids at the single-cell level due to limitations in spatial resolution and lipid coverage. While existing strategies based on a single matrix application step for dual-polarity provide high lipid coverage from the same sample and enable easy sample preparation, matrix depletion limits their spatial resolution to 10 μm, preventing their application to single-cell imaging. Here, we present a single-cell/subcellular resolution strategy for dual-polarity matrix-assisted laser desorption and ionization mass spectrometry imaging (MALDI-MSI) that eliminates the need for matrix reapplication. This approach achieves 5 μm spatial resolution while maintaining lipid coverage comparable to multistep single-cell imaging methods. This is enabled by a fine-tuned matrix deposition technique that fully utilizes the high sensitivity of N-(1-naphthyl)-ethylenediamine dihydrochloride (NEDC) in dual polarities and optimized acquisition conditions, allowing single-deposition workflows without the need for washing, repreparation, or image recalibration. This single-cell resolution MALDI-MSI strategy successfully imaged a broader range of lipid species with distinctive spatial detail in mouse kidney tissue and lung carcinoma cells (A549). Using spatial probabilistic latent semantic analysis (PLSA), we identified three distinct lipid distribution patterns within a single-cell population in both polarities, and histogram analysis revealed substantial cell-to-cell lipidomic heterogeneity. This strategy overcomes limitations of traditional dual-polarity MSI and provides a powerful tool for advancing cellular lipidomics, elucidating disease mechanisms, and investigating environmental toxicology.

DOI: https://doi.org/10.1021/acs.analchem.5c03289
Angew Chem Int Ed Engl. 2025 Jul 29. e202507483

Quantum Chemistry Calculation-Assisted Large-Scale Collision Cross Section Prediction Empowers Derivatization-Enhanced Multidimensional Metabolomics.

Jian Sun, Junmeng Luo, Ming Gao, Fang Wang, Wenjing Nie, Moran Chen, Suming Chen.

  Derivatization-enhanced multidimensional metabolomics combined with ion mobility mass spectrometry will greatly improve the accuracy and coverage of metabolic analysis. However, accurate prediction of the large-scale collision cross section (CCS) of derivatized metabolites without relying on standards and the establishment of multidimensional analytical methods faces great challenges. Here, we propose quantum chemistry calculation-assisted machine learning strategies applicable to the accurate prediction of the CCS of derivatized sterols, develop C═C bond-targeted N-Me derivatization methods for unsaturated sterols, and create a large-scale, 4D information database of derivatized sterol lipids (n = 4891) by combining retention time and fragment ion prediction. Furthermore, a high-coverage unsaturated sterolomics at the isomer level was established on this basis, which quantitatively revealed the tissue-specific distribution patterns of over 100 sterol lipids. This study provides a key foundation for derivatization-enhanced metabolomics and provides important techniques and information for metabolic and functional studies of sterols.

Keywords:  Lipids; Machine learning; Mass spectrometry; Quantum chemistry; Sterolomics

DOI:  https://doi.org/10.1002/anie.202507483
Anal Methods. 2025 Jul 29.

High-throughput plasma/serum proteomics by coupling nanoparticle protein corona-based sample preparation and capillary zone electrophoresis-tandem mass spectrometry.

Qianyi Wang, Seyed Amirhossein Sadeghi, Fei Fang, Dejin Zheng, Chenxiang Luo, Guangyao Gao, Qianjie Wang, Bin Gu, Liangliang Sun.

A high-throughput workflow for bottom-up proteomics (BUP) of human plasma using capillary zone electrophoresis-tandem mass spectrometry (CZE-MS/MS) and nanoparticle protein corona-assisted sample preparation is presented. The streamlined approach enabled the identification and quantification of hundreds of proteins from plasma/serum samples in 3.5 hours from sample to data. Nanoparticles with varied physiochemical properties studied in this work captured different pools of the plasma/serum proteome in the protein coronas, and the protein corona-based sample preparation approach enabled the measurement of low-abundance proteins compared to the approach without nanoparticles. Applying this high-throughput workflow to serum samples of a mouse NUT carcinoma (NC) cancer model allowed the determination of differentially expressed serum proteins between NC bearing mice and healthy controls. By comparing our quantitative proteomics data with published transcriptomics data, we revealed a handful of potential serum protein biomarkers of NC cancer (e.g., secreted phosphoprotein 1, SPP1). We expect this high-throughput workflow, with additional improvement in the speed of the mass spectrometer, will be useful for advancing the discovery of new protein biomarkers of diseases (e.g., cancer) using plasma/serum samples.

DOI: https://doi.org/10.1039/d5ay00721f
J Pharm Biomed Anal. 2025 Jul 16. pii: S0731-7085(25)00409-1. [Epub ahead of print]266 117068

Optimization of GC-MS run time for untargeted metabolomics: Trade-offs between speed, coverage, and repeatability.

Özge Cansın Zeki, Cemil Can Eylem, Emirhan Nemutlu.

  Optimizing run time in gas chromatography-mass spectrometry (GC-MS) based metabolomics is essential for balancing metabolite coverage, reproducibility, and practical workflow constraints. In this study, three GC-MS methods with different run times, short (26.7 min), a standard method based on the established Fiehn protocol (37.5 min), and long (60 min), were evaluated across three biological matrices: cell culture, plasma, and urine. All methods were applied using identical injection volumes and derivatization protocols. The number of annotated metabolites in the short and standard methods was comparable: 138 vs. 156 in cell culture, 147 vs. 168 in plasma, and 186 vs. 198 in urine. The long method provided higher metabolite coverage (196 in cell culture, 175 in plasma, 244 in urine), largely due to improved chromatographic resolution and deconvolution, which also increased the number of unannotated features. Although the proportion of high-filling (0.75-1) annotated metabolites was similar across all methods (∼79-90 %), repeatability was slightly better in the standard and long methods (RSD ∼20-24 %) than in the short method (RSD ∼23-30 %). Notably, since derivatized samples must be analyzed within 24 h, the short method presents a practical advantage by enabling completion of full batch analysis within this time constraint. Overall, while the short and standard methods offer similar identification performance, the long method enhances analytical depth.

Keywords:  Cell culture; Data analysis; GC-MS; Metabolomics; Plasma; Untargeted; Urine

DOI:  https://doi.org/10.1016/j.jpba.2025.117068
Talanta. 2025 Jul 21. pii: S0039-9140(25)01102-6. [Epub ahead of print]297(Pt A): 128612

Quantitative lipidomics for three-dimensional cell culture using deuterium oxide metabolic labeling.

Jonghyun Kim, Kyoung-Jin Choi, Sung Bum Park, Yoon-Ju Na, Ki Young Kim, Tae-Young Kim.

  Three-dimensional (3D) cell culture offers a more physiologically relevant model than traditional two-dimensional culture, yet standardized methods for lipid quantification in 3D systems are lacking. This study presents a novel quantitative lipidomic approach combining 3D culture with deuterium oxide (D2O) metabolic labeling to provide comprehensive insights into metabolic alterations. Using a hydrogel-based 3D system, we cultured preadipocytes and adipocytes, incorporating macrophage co-culture to induce insulin resistance. Relative lipid quantification was achieved using D2O labeling for global omics relative quantification (DOLGOReQ). This method enabled the quantification of hundreds of lipids across major categories, including glycerolipids, glycerophospholipids, fatty acyls, and sphingolipids, while also revealing cell-type-specific D-labeling efficiencies. DOLGOReQ analysis revealed that macrophage co-culture significantly reduced long-chain free fatty acids and triacylglycerols (TGs). Quantitative correlation analysis between TGs and free fatty acids indicated that the macrophage-mediated TG reduction stemmed from decreased free fatty acid availability, the precursors for lipid synthesis. Furthermore, macrophages increased D-labeling efficiency, suggesting enhanced lipolysis contributing to TG reduction. DOLGOReQ not only facilitates relative quantification of lipid changes but also provides valuable insights into lipid turnover dynamics. These findings establish DOLGOReQ as a powerful tool for investigating global lipid metabolism changes induced by external stimuli in 3D cell culture.

Keywords:  Deuterium oxide; Lipidomics; Relative quantification; Three-dimensional cell culture; Turnover

DOI:  https://doi.org/10.1016/j.talanta.2025.128612
J Biomed Sci. 2025 Jul 29. 32(1): 71

Glucose metabolism and its direct action in cancer and immune regulation: opportunities and challenges for metabolic targeting.

Bo-Syong Pan, Che-Chia Hsu, Hsin-En Wu, Yuan-Ru Chen, Xiaobo Zhou, Shu-Chi Wang, Chia-Yang Li, Hui-Kuan Lin.

  Glucose metabolism is a pivotal hub for cellular energy production and the generation of building blocks that support cell growth, survival, and differentiation. Cancer cells undergo metabolic reprogramming to sustain rapid proliferation, survive in harsh microenvironments, and resist therapies. Beyond producing energy and building blocks to meet cancer cell demands, glucose metabolism generates numerous metabolites that serve as signaling molecules, orchestrating signaling pathways and epigenetic modifications that regulate cancer cell phenotypes and immunity. In this review, we discuss how glucose, through its metabolism and direct actions, influences diverse biological processes driving cancer progression and therapeutic resistance, while also exploring metabolic vulnerabilities in cancer for therapeutic strategies.

Keywords:  Cancer therapy; Glucose metabolism; Glucose sensor; Immune regulation; Immunotherapy resistance; Metabolic targeting; Tumor microenvironment; Warburg effect

DOI:  https://doi.org/10.1186/s12929-025-01167-1
Proteomics. 2025 Jul 30. e70020

Proteoform Identification Using Multiplexed Top-Down Mass Spectra.

Zhige Wang, Xingzhao Xiong, Xiaowen Liu.

  Top-down mass spectrometry (TDMS) is the method of choice for analyzing intact proteoforms, as well as their posttranslational modifications and sequence variations. In top-down tandem mass spectrometry (TD-MS/MS) experiments, multiple proteoforms are often co-fragmented, resulting in multiplexed TD-MS/MS spectra. Due to their increased complexity, compared to spectra from single proteoforms, multiplexed TD-MS/MS spectra present significant challenges for proteoform identification and quantification. Here we present TopMPI, a new computational tool specifically designed for the identification of multiplexed TD-MS/MS spectra. Experimental results demonstrate that TopMPI substantially increases the sensitivity and accuracy of proteoform identification in multiplexed TD-MS/MS spectral analysis compared to existing tools. SUMMARY: Top-down mass spectrometry (TDMS) is a powerful technique for analyzing intact proteoforms; however, identifying multiple co-fragmented proteoforms from multiplexed tandem mass spectrometry (MS/MS) spectra remains a significant challenge. In this paper, we introduce TopMPI, a new computational tool specifically designed to identify multiplexed TD-MS/MS spectra using a two-round database search strategy. Compared to existing tools, TopMPI significantly improves the sensitivity and accuracy of proteoform identification from multiplexed MS/MS spectra. The development of TopMPI enhances the identification of low abundance proteoforms in complex biological samples and increases the potential of TDMS for discovering proteoform biomarkers in disease studies.

Keywords:  database searching; multiplexing; proteoform identification; top‐down proteomics

DOI:  https://doi.org/10.1002/pmic.70020
Cancers (Basel). 2025 Jul 15. pii: 2341. [Epub ahead of print]17(14):

Metabolic Adaptations in Cancer Progression: Optimization Strategies and Therapeutic Targets.

Agnieszka Dominiak, Beata Chełstowska, Grażyna Nowicka.

  As tumor research has deepened, the deregulation of cellular metabolism has emerged as yet another recognized hallmark of cancer. Tumor cells adapt different biochemical pathways to support their rapid growth, proliferation, and invasion, resulting in distinct anabolic and catabolic activities compared with healthy tissues. Certain metabolic shifts, such as altered glucose and glutamine utilization and increased de novo fatty acid synthesis, are critical early on, while others may become essential only during metastasis. These metabolic adaptations are closely shaped by, and in turn remodel, the tumor microenvironment, creating favorable conditions for their spread. Anticancer metabolic strategies should integrate pharmacological approaches aimed at inhibiting specific biochemical pathways with well-defined dietary interventions as adjunctive therapies, considering also the role of gut microbiota in modulating diet and treatment responses. Given the established link between the consumption of foods rich in saturated fatty acids and sugars and an increased cancer risk, the effects of diet cannot be ignored. However, current evidence from controlled and multicenter clinical trials remains insufficient to provide definitive clinical recommendations. Further research using modern omics methods, such as metabolomics, proteomics, and lipidomics, is necessary to understand the changes in the metabolic profiles of various cancers at different stages of their development and to determine the potential for modifying these profiles through pharmacological agents and dietary modifications. Therefore, clinical trials should combine standard treatments with novel approaches targeting metabolic reprogramming, such as inhibition of specific enzymes and transporters or binding proteins, alongside the implementation of dietary restrictions that limit nutrient availability for tumor growth. However, to optimize therapeutic efficacy, a precision medicine approach should be adopted that balances the destruction of cancer cells with the protection of healthy ones. This approach, among others, should be based on cell type-specific metabolic profiling, which is crucial for personalizing oncology treatment.

Keywords:  amino acids utilization; anticancer metabolic strategies; dietary interventions; fatty acid synthesis; glucose metabolism; gut microbiota; metabolic reprogramming; precision oncology; tumor metabolism; tumor microenvironment

DOI:  https://doi.org/10.3390/cancers17142341
Mol Cell. 2025 Jul 15. pii: S1097-2765(25)00580-5. [Epub ahead of print]

HIF-independent oxygen sensing via KDM6A regulates ferroptosis.

Alexander M Minikes, Pei Liu, Hua Wang, Jiachen Hu, Hanan Alwaseem, Yueming Li, Xuejun Jiang.

  Ferroptosis, a metabolic cell death process driven by iron-dependent phospholipid peroxidation, is implicated in various pathologies, including cancer. While metabolic factors such as glucose, lipids, and multiple amino acids have all been demonstrated to modulate ferroptosis, the role of oxygen, another fundamental metabolic component, in ferroptosis is not fully understood. Here, we show that cells acclimated to a low oxygen environment develop marked resistance to ferroptosis, and this resistance is independent of canonical oxygen-sensing pathway mediated by prolyl hydroxylases (PHDs) and HIF transcription factors. Instead, hypoxia suppresses ferroptosis by inhibiting KDM6A, a tumor suppressor and oxygen-dependent histone demethylase, leading to reduced expression of its transcriptional targets, including lipid metabolic enzymes ACSL4 and ETNK1, thus rewiring cellular phospholipid profile to a ferroptosis-resistant state. Relevant to cancer, pharmacological inhibition of the oncogenic histone methyltransferase EZH2, which opposes KDM6A activity, restored ferroptosis sensitivity of xenograft bladder tumor tissues harboring KDM6A mutation.

Keywords:  ACSL4; ETNK1; KDM6A; KMT2D; bladder cancer; cancer therapy; ferroptosis; hypoxia; lipid metabolism; oxygen sensing

DOI:  https://doi.org/10.1016/j.molcel.2025.07.001
J Proteome Res. 2025 Jul 30.

Improved Detection of Differentially Abundant Proteins through FDR-Control of Peptide-Identity-Propagation.

Alexander J Solivais, Hannah Boekweg, Lloyd M Smith, William S Noble, Michael R Shortreed, Samuel H Payne, Uri Keich.

  The goal of proteomics is to identify and quantify peptides and proteins within a biological sample. Almost all algorithms for the identification of peptides in LC-MS/MS data employ two steps: peptide/spectrum matching and peptide-identity-propagation (PIP), also known as match-between-runs. PIP can routinely account for up to 40% of all results, with that proportion rising as high as 75% in single-cell proteomics. Unlike peptide identities derived through peptide/spectrum matches, for which error estimation has been strictly enforced for decades, peptide identities derived through PIP have not historically been subject to statistical evaluation. As an indispensable component of label-free quantification, PIP needs a statistically rigorous method for estimating its false-discovery rate (FDR). We present a method for FDR control of PIP, called PIP-ECHO, and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different benchmark data sets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that only PIP-ECHO can accurately control the FDR of PIP at 1% across all data sets. When analyzing a spike-in data set, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis, yielding substantially more differentially abundant proteins than either MaxQuant or IonQuant.

Keywords:  FDR; false-discovery rate; label-free quantification; match-between-runs; peptide-identity-propagation

DOI:  https://doi.org/10.1021/acs.jproteome.5c00065
Metabolomics. 2025 Jul 27. 21(4): 101

A diagnostic algorithm for inherited metabolic disorders using untargeted metabolomics.

Qian Gao, Adnan Khan, Mette Christensen, Xiaomin Zhou, Allan Lund, Sabine Weller Grønborg, Flemming Wibrand, Elsebet Østergaard, Thomas Moritz.

   INTRODUCTION: Untargeted metabolomics is a powerful tool for detecting perturbations in biological systems, offering significant potential for screening for rare inherited metabolic disorders (IMDs). However, the rarity and vast diversity of these diseases, results in limited availability of samples and incomplete metabolic pathway knowledge for each condition. Current diagnostic procedures rely heavily on manual interpretation, which is time-consuming, and data driven approaches are insufficient for small sample sizes.
OBJECTIVES: To develop a diagnostic algorithm for IMDs addressing the challenges posed by small sample sizes and continuously evolving datasets.
METHODS: 77 IMD patients (35 different IMDs) and 136 control samples were collected from Copenhagen University Hospital, Rigshospitalet. The metabolome was analyzed using liquid chromatography-mass spectrometry. An algorithm partially based on sparse hierarchical clustering was designed to generate IMD-specific metabolic signatures from metabolomics data, enabling comparison with undiagnosed patient samples to provide diagnostic predictions. An iterative improvement strategy was employed, where new data are continuously integrated to refine the IMD-specific signatures. The algorithm's performance was evaluated through both the current study and a case study using literature-derived data.
RESULTS: The algorithm demonstrated iterative improvement with each training round, correctly identifying the diagnosis within top 3 potential IMDs in 60% of samples (top 1 in 42%). The case study applied the method to literature-based data comprising 95 IMD samples (11 different IMDs) and 68 controls, yielding a correct diagnosis in 73.5% of cases.
CONCLUSION: These results demonstrate that the algorithm provides a flexible, data-driven framework for continuous improvement in IMD diagnosis, even with limited number of samples.

Keywords:  Diagnosis; IMD signature; Inherited metabolic disorders; Metabolomics; Unsupervised

DOI:  https://doi.org/10.1007/s11306-025-02302-7
Anal Chem. 2025 Jul 28.

AUTO-SP: Automated Sample Preparation for Analyzing Proteins and Protein Modifications.

T Mamie Lih, Liyuan Jiao, Lijun Chen, Jongmin Woo, Yuefan Wang, Hui Zhang.

Liquid chromatography (LC) tandem mass spectrometry (MS/MS) is one of the widely used proteomic techniques to study the alterations occurring at the protein level as well as post-translational modifications (PTMs) of proteins that are relevant to different physiological or pathological statuses. The mass spectrometric analysis of peptides digested from proteins (bottom-up proteomics) has emerged as one of the major approaches for proteomics. In this approach, proteins are first cleaved by one or more proteases into peptides for MS analysis, and peptides with PTMs are further enriched, followed by the LC-MS/MS analysis. To achieve a reproducible and quantitative proteomic characterization, a well-established protease digestion and PTM peptide enrichment protocol is critical. In this study, we developed AUTO-SP, a sample preparation platform providing automated protocols for BCA analysis, protein digestion, and PTM enrichment for protein and PTM analyses. We utilized patient-derived xenograft (PDX) breast cancer tumor tissues (basal-like and luminal subtypes) to demonstrate the efficacy of AUTO-SP. The protein amount was quantified, and proteins were further digested by using AUTO-SP for each PDX sample. Based on the data-independent acquisition (DIA)-MS data, we observed that samples of the same breast cancer subtypes were highly correlated (≥0.98). Additionally, >25,000 phosphopeptides and >14,000 ubiquitinated peptides were identified in the PDX samples when using AUTO-SP for PTM enrichment, while unique pathways were enriched from the differentially expressed ubiquitinated peptides of basal-like and luminal subtypes. AUTO-SP demonstrated its efficacy to provide a reliable and reproducible sample preparation procedure for MS-based proteomic and PTM analyses.

DOI: https://doi.org/10.1021/acs.analchem.5c00886
Nat Commun. 2025 Aug 01. 16(1): 7075

Development and application of GlycanDIA workflow for glycomic analysis.

Yixuan Xie, Xingyu Liu, Li Yi, Shunyang Wang, Zongtao Lin, Chenfeng Zhao, Siyu Chen, Faith M Robison, Benson M George, Carlito B Lebrilla, Ryan A Flynn, Benjamin A Garcia.

Glycans modify protein, lipid, and even RNA molecules to form the regulatory outer coat on cells called the glycocalyx. The changes in glycosylation have been linked to the initiation and progression of many diseases. Herein, we report a DIA-based glycomic workflow, termed GlycanDIA, to identify and quantify glycans with high sensitivity and precision. The GlycanDIA workflow combines higher energy collisional dissociation (HCD)-MS/MS and staggered windows for glycomic analysis, which facilitates the sensitivity in identification and precision in quantification compared to conventional glycomic methods. To facilitate its use, we also develop a generic search engine, GlycanDIA Finder, incorporating an iterative decoy searching for confident glycan identification from DIA data. Our results demonstrate that GlycanDIA can distinguish glycan composition and isomers from N-glycans, O-glycans, and human milk oligosaccharides (HMOs), while it also reveals information on low-abundant modified glycans. With the improved sensitivity and precision, we perform experiments to profile N-glycans from RNA samples, which have been underrepresented due to their low abundance. Using this integrative workflow to unravel the N-glycan profile in cellular and tissue glycoRNA samples, we find that RNA-glycans have different abundant forms as compared to protein-glycans and there are also tissue-specific differences, suggesting their distinct functions in biological processes.

DOI: https://doi.org/10.1038/s41467-025-61473-y