bims-mascan 2025-11-30 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–11–30
24 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Comprehensive Quantitative Profiling of Less Polar Lipids in Human Plasma Using Validated Reversed-Phase UHPSFC/MS/MS.
Multiplexed Data-Independent Acquisition-Based Proteomics Enabled by TMTpro Complementary Ions.
Chiral High-Performance Liquid Chromatography (HPLC) in Targeted Metabolomics.
Mass Spectrometry for Lysine Methylation: Principles, Progress, and Prospects.
MetRS*-based deep cell-selective tissue proteomics and secretomics in vivo.
Laser Desorption-Rapid Evaporative Ionization Mass Spectrometry (LD-REIMS): A New Tool for the High-Throughput Metabolomic and Lipidomic Profiling of Live Cells.
AI redefines mass spectrometry chemicals identification: retention time prediction in metabolomics and for a Human Exposome Project.
SILAC-based assessment of S-palmitoylated proteins in mammalian cells by metabolic labeling and click-chemistry.
Integrated Metabolomics and Lipidomics of Tissue and Serum Reveal Mechanistic Pathways and Lipid Signatures Distinguishing Meningioma Grades.
Mass spectrometry-based proteomics of FFPE tissues: progress, limitations, and clinical translation barriers.
Storing Mass-Spectrometry Data in Simple Databases Enables Flexible and Intuitive Exploration without Time or Space Penalties.
Interplay between cancer cell lipotypes and disease states.
Liver microsomal fraction induced small molecule isotope labeling via catalyzed H/D and 16O/18O isotope exchange reactions: A novel approach for obtaining isotope-labeled compounds.
Imaging Cellular Metabolic Rewiring with SuMMIT-SRS.
Quantification of esterified oxylipins following HILIC-fractionation of lipid classes.
Macropinocytosis enables metabolic recycling of extracellular DNA in cancer cells.
Enhanced cell surface enrichment and data-independent acquisition for in-depth profiling of multiple myeloma cell lines.
SMAnalyst: A Web Server for Spatial Metabolomic Data Analysis and Annotation.
Quantitative Analysis of the Arabidopsis Leaf Secretory Proteome via TMT-Based Mass Spectrometry.
Revisiting the role of metabolic reprogramming as a contributor to prostate cancer disease progression.
Separating the wheat from the chaff: Determination of confidently secreted proteins by including information on their cellular abundance.
Hypoxia Rewires Histone Methylation in Glioblastoma Cells via Enzyme Reprogramming Despite Disruption of One-Carbon Metabolism.
Alternative Ion-Pairing Modifiers Should Be Investigated in Low-Input and Single-Cell Proteomics.
Immunopeptidomics Workflow for Isolation and LC-MS/MS Analysis of MHC Class I-Bound Peptides Under Hypoxic Conditions.

Anal Chem. 2025 Nov 23.

Comprehensive Quantitative Profiling of Less Polar Lipids in Human Plasma Using Validated Reversed-Phase UHPSFC/MS/MS.

Zuzana Lásko, Veronika Šubrtová, Ondřej Peterka, Robert Jirásko, Michal Holčapek.

The quantitative analysis of lipids is challenging due to their structural diversity and the coexistence of numerous isomers in biological samples. Here, we present a robust and validated method based on reversed-phase ultrahigh-performance supercritical fluid chromatography-tandem mass spectrometry (RP-UHPSFC/MS/MS) for the high-throughput profiling of less polar lipids in human plasma. The sample preparation workflow involves an initial Folch-based extraction, followed by chemical derivatization with benzoyl chloride, and the final liquid-liquid extraction into hexane to isolate less polar acylated analytes. This strategy enables the sensitive profiling of six major lipid classes (TG, DG, MG, SE, ST, and FA) in positive ion mode in less than 18 min. The use of two C18 columns in series provides high chromatographic resolution, sufficient to separate isomeric species, including cis/trans and positional isomers of double bonds in fatty acids. The method was rigorously validated for five lipid classes, except free FA, and the implementation of response factors was shown to be essential for the accurate quantification of sterol esters. A total of 147 lipid species were quantified in NIST SRM 1950 human plasma, demonstrating its suitability for large-scale lipidomic studies focused on less polar lipids.

DOI: https://doi.org/10.1021/acs.analchem.5c04668
Anal Chem. 2025 Nov 26.

Multiplexed Data-Independent Acquisition-Based Proteomics Enabled by TMTpro Complementary Ions.

Zicong Wang, Peng-Kai Liu, Haiyan Lu, Lingjun Li.

Data-independent acquisition (DIA) has emerged as a powerful approach in quantitative proteomics, offering more comprehensive and reproducible proteome coverage than the conventional data-dependent acquisition (DDA) method. However, applying multiplexed isobaric labeling to DIA has been challenging due to ratio distortion caused by coisolation and cofragmentation interference. Here, we present a 3-plex TMTpro complementary ion (TMTproC)-based DIA strategy that leverages complementary ions in isobaric labeling to achieve accurate quantification without increasing spectral complexity. By implementing a 4-Da spacing between complementary ions, we significantly reduce isotopic envelope overlap and simplify deconvolution. We systematically optimized higher-energy collisional dissociation (HCD) settings for complementary ion generation and validated this approach using tryptic bovine serum albumin (BSA) peptides labeled at 1:1:1, 10:5:1, and 1:5:10 ratios, achieving median peptide-level ratios within 10% of expected values and median coefficients of variation (CVs) below 4% across triplicates. We further demonstrated this method by applying TMTproC labeling across a 10-fold dynamic range to the yeast proteome in a strong human proteome background. The results exhibited high quantification precision and minimal ratio distortion. Overall, TMTproC-DIA provides a robust, versatile, and scalable solution for high-throughput DIA-based proteomics.

DOI: https://doi.org/10.1021/acs.analchem.5c03563
Methods Mol Biol. 2026 ;2994 197-229

Chiral High-Performance Liquid Chromatography (HPLC) in Targeted Metabolomics.

Xiaoqing Fu, Michael Lämmerhofer.

  Enzymatic reactions usually occur with a high substrate, product, or substrate-product stereoselectivity. In many instances, information on the stereochemical disposition of biologically active metabolites is indispensable for a complete understanding of biological processes and metabolic states. This raised interest in structural isomerism and stereoisomerism in metabolomics and lipidomics. While structural and geometrical isomers, as well as diastereomers, can be principally distinguished by achiral liquid chromatography (LC) and also ion-mobility mass spectrometry (MS), enantiomers require enantioselective separation methods. While untargeted enantioselective metabolomics is mostly based on the derivatization of metabolites with a chiral derivatizing agent, followed by LC separation on achiral columns and hyphenation with high-resolution mass spectrometry, targeted enantioselective LC with chiral stationary phases mostly employs robust triple quadrupole (QqQ) mass spectrometry for detection. It is a powerful technology, yet it focuses primarily on a broad, metabolite class-wide coverage. This chapter describes four distinct fields of application of targeted enantioselective LC-tandem mass spectrometry assays for important and representative metabolite classes. The first one deals with the enantioselective analysis of oxylipins using polysaccharide-based chiral columns, with protocols applicable for platelets and plasma; the second focuses on 3-hydroxy fatty acid enantiomer separations in plasma and platelets; the third application provides two workflows for proteinogenic enantioselective amino acid analysis; and the fourth application proposes methodologies for enantioselective short branched-chain fatty acid analysis. Critical aspects are discussed.

Keywords:  3-Hydroxy fatty acid; Amino acid; Branched alkanoic acids; Chiral derivatizing agent; Chiral stationary phase; LC–MS; Lipidomics; Mass spectrometry; Metabolite; Metabolomics; Oxylipin

DOI:  https://doi.org/10.1007/978-1-0716-5023-3_11
Biomedicines. 2025 Nov 19. pii: 2825. [Epub ahead of print]13(11):

Mass Spectrometry for Lysine Methylation: Principles, Progress, and Prospects.

Mackenzie G Cumming, Kyle K Biggar.

  Lysine methylation is a regulatory post-translational modification with diverse roles across both histone and non-histone proteins. Despite its biological relevance, comprehensive characterization of lysine methylation remains analytically challenging due to its low stoichiometry, subtle mass changes, and the absence of standardized, robust enrichment strategies. Mass spectrometry (MS) has become the cornerstone of methylation analysis, supporting both targeted and proteome-wide investigations. In this review, we examine the evolution of MS-based workflows for lysine methylation, including advances in ionization and fragmentation techniques, high-resolution mass analyzers, and acquisition strategies such as data-independent acquisition (DIA) and parallel accumulation-serial fragmentation (PASEF). We evaluate bottom-up, middle-down, and top-down proteomic approaches and discuss enrichment methods ranging from immunoaffinity and chromatography to chemical derivatization. Particular attention is given to persistent challenges, including proteolytic constraints and isobaric interference, that complicate confident site-level resolution. Finally, we highlight emerging solutions and future directions aimed at improving the sensitivity, specificity, and reproducibility of lysine methylation profiling. Together, this synthesis provides a forward-looking roadmap for optimizing MS workflows in methyllysine proteomics.

Keywords:  lysine methylation; mass spectrometry; methyllysine; proteomics

DOI:  https://doi.org/10.3390/biomedicines13112825
Methods Cell Biol. 2026 ;pii: S0091-679X(24)00185-7. [Epub ahead of print]200 105-117

MetRS*-based deep cell-selective tissue proteomics and secretomics in vivo.

Jonathan J Swietlik, Felix Meissner.

  Combining MetRS*-based cell-selective protein labeling with mass spectrometry-based proteomics is a powerful approach for investigating intercellular communication within tissues. Cell-selective labeling overcomes limitations of cell sorting techniques and facilitates cell type-specific proteome and secretome analyses in vivo. Our recent work has showcased the application of this method for the comprehensive proteomic characterization of cellular proteins in tissues, as well as released proteins in the bloodstream. Here, we present experimental guidelines for MetRS*-based cell-selective proteomics experiments in vivo and a detailed sample preparation protocol for tissues and body fluids.

Keywords:  Azidonorleucine; BONCAT; Cell-selective proteomics; GINCAT; MetRS*; Secretomics; in vivo secretomics

DOI:  https://doi.org/10.1016/bs.mcb.2024.08.002
Anal Chem. 2025 Nov 27.

Laser Desorption-Rapid Evaporative Ionization Mass Spectrometry (LD-REIMS): A New Tool for the High-Throughput Metabolomic and Lipidomic Profiling of Live Cells.

Stefania Maneta-Stavrakaki, Aurelien Tripp, Daniel Simon, Yuchen Xiang, Adrienn Molnar, Athanasios Tsalikis, Efstathios Andreas Elia, Josephine Bunch, Julia Balog, George Poulogiannis, Zoltan Takats.

Understanding the dynamic cellular metabolism is essential for gaining deeper insights into inter- and intracellular functions. In recent years, mass spectrometry (MS) has become the technology of choice for the biochemical characterization and profiling of cell lines, particularly when coupled with separation techniques such as liquid chromatography (LC-MS). However, these methods typically involve extensive sample preparation with potent organic solvents, which is labor-intensive, time-consuming, and incompatible with direct analysis of intact, live cells. Here, we propose the use of the ambient ionization technique Laser Desorption-Rapid Evaporative Ionization Mass Spectrometry (LD-REIMS) incorporated in an automated platform, for the high-throughput profiling of live or frozen cell monolayers, with minimal pretreatment. Validation experiments using 10 breast and colorectal cancer cell lines confirmed high accuracy, repeatability, and molecular coverage of the method, with over 400 metabolites and lipids detected and identified, including saccharides, amino acids, fatty acids, and glycerophospholipids. Of these, 144 were further confirmed and quantified with LC-MS/MS and standard compounds. We also applied the method to establish lipidomic differences across the isogenic MCF10A cells harboring either WT or MUT PIK3CA. Finally, we conducted time-series experiments on hypoxic cells, which revealed significant dynamic changes in metabolism, including lactate accumulation due to anaerobic glycolysis.

DOI: https://doi.org/10.1021/acs.analchem.5c04847
Front Public Health. 2025 ;13 1687056

AI redefines mass spectrometry chemicals identification: retention time prediction in metabolomics and for a Human Exposome Project.

Fenna C M Sillé, Carsten Prasse, Thomas Luechtefeld, Thomas Hartung.

  The comprehensive identification of environmental and endogenous chemicals in human biospecimens is a critical bottleneck for realizing the Human Exposome Project. Untargeted metabolomics, particularly liquid chromatography-high-resolution mass spectrometry (LC-HRMS), offers unparalleled coverage of small molecules, but most detected features remain unidentified due to limited spectral libraries and structural ambiguity. Retention time (RT) prediction-based on quantitative structure-retention relationships (QSRR) and enhanced by artificial intelligence (AI)-is an underutilized orthogonal parameter that can substantially improve metabolite annotation confidence. This review synthesizes advances in machine learning-based RT prediction, probabilistic calibration, and cross-platform harmonization for liquid chromatography and gas chromatography, including deep learning, graph neural networks, and transfer learning approaches. We evaluate workflows integrating RT prediction with mass-based searches and network-based annotation tools, highlighting their potential to refine candidate ranking and reduce false positives in environmental exposure assessment. The use of endogenous compounds as internal calibrants is discussed as a practical strategy for improving RT transferability across laboratories. We further outline how RT-aware annotation supports non-targeted screening of emerging contaminants, transformation products, and exposure biomarkers, thereby enhancing the interpretability and reproducibility of exposomics data. By integrating RT prediction, QSRR modeling, and AI into untargeted metabolomics pipelines, researchers can move from qualitative detection toward quantitative, inference-driven mapping of environmental influences on human health, strengthening the scientific foundation for environmental health policy and preventive public health strategies.

Keywords:  Human Exposome Project; artificial intelligence; environmental health; exposomics; quantitative structure–retention relationships; retention time prediction; untargeted metabolomics

DOI:  https://doi.org/10.3389/fpubh.2025.1687056
Methods Cell Biol. 2026 ;pii: S0091-679X(25)00001-9. [Epub ahead of print]200 211-243

SILAC-based assessment of S-palmitoylated proteins in mammalian cells by metabolic labeling and click-chemistry.

Anke Vandekeere, Sarah-Maria Fendt, Salvador Aznar Benitah, Miguel Martin-Perez.

  S-palmitoylation of cysteine residues is the only lipid-based posttranslational modification of proteins that is reversible and therefore has important implications in cellular function. S-palmitoylation has been associated with several cellular processes (e.g., cell signaling, protein transport, cell cycle, immune response, lipid metabolism, host-pathogen interaction) and human diseases, including neurological disorders, cancer, and infectious diseases. However, S-palmitoylation research has been hampered by the cumbersome experimental protocols necessary for its study. Currently, there are two main methodologies that, coupled with mass spectrometry (MS), allow the study of S-palmitoylated proteins proteome-wide. They mainly differ in the way of labeling palmitoylated proteins: one relies on "metabolic labeling" with a palmitic acid analog in living cells, while the other is based on "chemical labeling" of thiol groups derived from palmitoylated sites in extracted proteins. Although metabolic labeling is restricted to cultured cells, we will focus on this technique as it is more sensitive and specific than others. Here, we describe the protocol to measure palmitoylation in cancer cells using metabolic labeling coupled to SILAC-based mass spectrometry quantification, which can be applied to other mammalian cell models. Facilitating the use of this methodology will extend the knowledge of palmitoylation signaling and unravel potential therapeutic avenues for diseases in which this unexplored modification is implicated.

Keywords:  Mass spectrometry (MS); Metabolic labeling; Posttranslational modification; S-palmitoylation; Stable isotope labeling with amino acids in cell culture (SILAC)

DOI:  https://doi.org/10.1016/bs.mcb.2025.01.001
J Proteome Res. 2025 Nov 26.

Integrated Metabolomics and Lipidomics of Tissue and Serum Reveal Mechanistic Pathways and Lipid Signatures Distinguishing Meningioma Grades.

Ankit Halder, Suhisna Dutta, Sridhar Epari, Prakash Shetty, Aliasgar Moiyadi, Sanjeeva Srivastava.

  Meningioma, the most prevalent primary intracranial tumor, presents significant clinical challenges due to unclear molecular mechanisms underlying its progression from low-grade (LG) to high-grade (HG) and lack of grade-specific biomarkers. Here, we employed high-resolution mass spectrometry-based integrated tissue metabolomics and lipidomics on ∼45 samples. Our findings highlight dysregulated pathways like nucleotide, choline, sphingolipid, and glycerophospholipid metabolism, with purine metabolism-related metabolites notably upregulated in tumor samples. We further performed targeted verification of a subset of purine metabolism-related metabolites using targeted metabolomics. Further, serum lipidomics profiling was performed on ∼75 samples to identify a set of candidate markers. A set of lipid markers was identified as dysregulated in both tissue and serum samples, showing the effects of tumor-associated metabolic changes. The major dysregulated lipid classes were phosphatidylcholines, phosphatidylethanolamines accounting for around 70%, with variations in saturation and carbon chain length. Additionally, machine-learning-based feature selection was used to identify a panel of lipid markers capable of distinguishing HG from LG samples. This analysis identified 18 top classifier lipids, two of which were also dysregulated in tissue samples. Longitudinal analysis of these lipids further emphasized their role in tumor progression. This exploratory study lays the foundation for further validation of candidate markers in a larger cohort of samples.

Keywords:  candidate markers; high-grade tumors; lipidomics; low-grade tumors; machine learning; meningioma; metabolomics; multiple reaction monitoring (MRM)

DOI:  https://doi.org/10.1021/acs.jproteome.5c00663
Clin Proteomics. 2025 Nov 28. 22(1): 45

Mass spectrometry-based proteomics of FFPE tissues: progress, limitations, and clinical translation barriers.

Sara Abdulmohsen AlHammadi, Lamar Nabil Nagshabandi, Huzaifa Muhammad, Hatouf H Sukkarieh, Ahmad Aljada.

   BACKGROUND: Formalin-fixed paraffin-embedded (FFPE) tissue proteomics has emerged as a promising approach for precision medicine, offering access to vast clinical archives. Despite technological advances enabling identification of thousands of proteins from FFPE samples, no proteomic diagnostic tests based on FFPE tissues have achieved regulatory approval for clinical diagnostics, raising fundamental questions about the translational viability of this approach.
MAIN BODY: This review critically evaluates the realistic barriers preventing clinical translation of FFPE proteomics and identifies targeted applications with genuine promise for near-term implementation. We demonstrate that while comprehensive discovery-based proteomics faces insurmountable challenges including validation failure rates exceeding 90%, targeted proteomic strategies focused on specific clinical questions show substantially greater potential. Current implementation barriers extend beyond technical limitations to encompass economic constraints (5-10-fold higher costs than immunohistochemistry), regulatory uncertainties, and fundamental incompatibilities with clinical laboratory workflows. The persistent emphasis on increasingly complex analytical platforms may represent misallocated resources given unresolved standardization and validation challenges.
CONCLUSION: Strategic redirection toward targeted proteomic applications addressing specific diagnostic needs, rather than comprehensive molecular profiling, offers the most viable pathway for clinical translation. Success will require prioritizing applications where FFPE proteomics provides unique, actionable information that justifies its complexity and cost relative to established methodologies. We propose specific criteria for identifying high-impact applications and outline a pragmatic roadmap for achieving clinical implementation within realistic timeframes.

Keywords:  Biomarker validation; Clinical proteomics; Clinical translation; FFPE tissues; Mass spectrometry; Precision medicine; Targeted proteomics

DOI:  https://doi.org/10.1186/s12014-025-09567-z
J Proteome Res. 2025 Nov 24.

Storing Mass-Spectrometry Data in Simple Databases Enables Flexible and Intuitive Exploration without Time or Space Penalties.

William Kumler, Sam LaRue, Anitra E Ingalls.

  Mass spectrometry (MS) generates large data sets that are stored in increasingly optimized and complex file types, demanding technical expertise to extract information rapidly and easily. We wondered whether a simple structured query language (SQL) database could hold raw MS data and allow for easily readable queries without incurring major penalties in the read time or disk space relative to other popular MS formats. Here, we describe a basic MS schema with intuitive database tables and fields that can outperform other formats for exploratory and interactive analysis according to six data subsets commonly extracted: single scans (both MS1 and MS2), ion chromatograms, retention time ranges, and fragmentation searches (both precursor and fragment search). Additionally, we compare SQLite, DuckDB, and Parquet implementations and find that they can perform these tasks in under a second, even when the files occupy over a gigabyte of data on the disk. We believe that this tidy data schema expands nicely to most forms of MS data and offers a way to transparently query data sets while preserving computational performance.

Keywords:  SQL; benchmarking; data storage; exploratory data analysis; human-centered design; liquid chromatography; mass spectrometry

DOI:  https://doi.org/10.1021/acs.jproteome.5c00721
Trends Cancer. 2025 Nov 25. pii: S2405-8033(25)00260-2. [Epub ahead of print]

Interplay between cancer cell lipotypes and disease states.

Xi Wang, Yilong Zou.

  While the initial transformation of cancer cells is driven by genetic alterations, tumor cell behaviors and functional states are dynamically regulated by cell-intrinsic factors including proteins, metabolites and lipids, and extrinsic microenvironmental factors. Emerging multi-omics technologies highlighted that cancer cells exhibit distinct lipidome compositions and employ specific lipid metabolic circuits for chemical conversions - collectively defined as 'lipotypes'. We review the interplay between cancer lipotypes and cellular states, focusing on interpreting how being at different positions along the spectra of representative lipid metabolic axes influences cancerous traits. We aim to instill a system biology perspective to integrate 'lipotypes' into the established 'genotype-phenotype' framework in cancer.

Keywords:  cancer progression and metastasis; lipid biosynthesis; lipid metabolism; lipidomics; lipotypes; storage and degradation; tumor microenvironment; uptake

DOI:  https://doi.org/10.1016/j.trecan.2025.10.009
Anal Chim Acta. 2026 Jan 01. pii: S0003-2670(25)01199-7. [Epub ahead of print]1381 344805

Liver microsomal fraction induced small molecule isotope labeling via catalyzed H/D and 16O/18O isotope exchange reactions: A novel approach for obtaining isotope-labeled compounds.

Boris Tupertsev, Sergey Osipenko, Tatiana Ikonnikova, Yury Kostyukevich.

   BACKGROUND: The use of stable isotope-labeled compounds in various omics studies, combined with chromatography-mass spectrometry (MS) techniques, has become a powerful strategy to enhance quantitative accuracy, identification confidence, and biological insight. However, current labeling strategies are often limited by high cst, operational complexity, and narrow applicability. Consequently, there is a growing need for novel methods that address both qualitative and quantitative challenges in biological sample analysis. As a promising alternative, this study explores an enzymatic labeling approach using liver microsomes as biocatalysts for stable isotope incorporation into small molecules.
RESULTS: We developed an enzymatic isotope labeling method using rat liver microsomal fraction to label progesterone and seven structurally related compounds with deuterium and oxygen-18. Labeling was performed in 50 mM phosphate-buffered saline (PBS, pH 7.4) using 99.8 % D2O and 50 % H218O at 37 °C in the absence of MgCl2 and NADPH. Compared to non-enzymatic controls, hydrogen/deuterium and oxygen-16/oxygen-18 exchanges were accelerated by up to 32- and 41-fold, respectively. Structure-dependent labeling patterns and mechanisms, as well as the rates and extents of heavy isotope incorporation, were evaluated using HPLC-MS/MS and NMR. Notably, 0.3 mg of deuterium-labeled progesterone was obtained in under two weeks without the need for total synthesis, demonstrating the method's practical utility and scalability for standard production.
SIGNIFICANCE: This low-cost enzyme-based labeling method facilitates rapid identification of functional groups and efficient preparation of labeled standards for quantitative MS analyses. It offers a practical, scalable alternative to traditional synthetic labeling techniques. With minimal setup and broad compounds applicability, the approach provides significant advantages for MS-based omics workflows in both research and applied analytical laboratories.

Keywords:  Internal standards; Liver microsomes; Mass spectrometry; Omics studies; Stable isotope labeling

DOI:  https://doi.org/10.1016/j.aca.2025.344805
bioRxiv. 2025 Oct 13. pii: 2025.10.10.681769. [Epub ahead of print]

Imaging Cellular Metabolic Rewiring with SuMMIT-SRS.

Yajuan Li, Zhi Li, Yuhan Li, Kevin Rhine, Sural Ranamukhaarachchi, Shuo Qin, Hongje Jang, Jorge Villazon, Zhiliang Bai, Stephanie I Fraley, Gene W Yeo, Rong Fan, Lingyan Shi.

  Cells dynamically rewire their metabolic pathways in response to physiological and pathological cues. Such plasticity is particularly critical in neurons, stem cells, cancer cells, and immune cells, where biosynthetic demands can shift rapidly. However, current metabolic imaging techniques using isotope labeling typically track only one metabolite at a time, limiting their ability to capture the rapid dynamics of complex metabolic networks including coordinated precursor utilization, crosstalk, and turnover. Here, we present Subcellular Multiplexed Metabolic Isotope Tracing Stimulated Raman Scattering microscopy (SuMMIT-SRS), a platform that enables simultaneous visualization of multiple metabolic dynamics at subcellular resolution. By exploiting the distinct vibrational signatures of carbon-deuterium bonds derived from multiple deuterated amino acids, lipids, and monosaccharide tracers, SuMMIT-SRS maps co-regulated DNA, RNA, protein, and lipid synthesis at the same time and resolves various individual amino acid-mediated metabolic pathways within intact cells and tissues. We demonstrate SuMMIT's broad utility across Drosophila fat body tissue and developing brain, tumor organoids, aged human neurons, and mouse liver, capturing cell type-specific metabolic rewiring under genetic and pathological perturbations. This approach extends SRS to multiplexed isotope tracing, offering a powerful tool to uncover dynamic and complex biosynthesis programs in development, health, and disease.

Keywords:  Metabolic rewiring; SRS; lipid; metabolism; multiplex; optical imaging; protein

DOI:  https://doi.org/10.1101/2025.10.10.681769
J Lipid Res. 2025 Nov 21. pii: S0022-2275(25)00213-5. [Epub ahead of print] 100950

Quantification of esterified oxylipins following HILIC-fractionation of lipid classes.

Luca M Wende, Laura Carpanedo, Lilli Scholz, Nadja Kampschulte, Annette L West, Philip C Calder, Nils Helge Schebb.

  Several oxylipins are lipid mediators derived from the oxidation of polyunsaturated fatty acids (PUFAs). The majority of oxylipins in biological samples occurs esterified in neutral lipids (nLs) and phospholipids (PLs). They are commonly quantified indirectly following alkaline hydrolysis providing excellent sensitivity but the information in which lipid classes the oxylipins occurred in is lost. The direct analysis of oxidized lipids is currently not sensitive enough to detect all esterified oxylipins. Here, a new hydrophilic interaction liquid chromatography (HILIC) based lipid class fractionation using solid-phase extraction (SPE) cartridges was developed separating lipids into nLs and 4 PL fractions using a single column. Esterified oxylipins in the fractions were quantified following alkaline hydrolysis to sensitively pinpoint in which lipid classes they are bound in plasma. The fractionation was extensively characterized for different lipid extracts demonstrating high separation efficiency and recovery using labeled standards and untargeted analysis of endogenous lipids. Esterified oxylipins in the fractions were quantitatively detected. Based on the results from two independent human plasma pools including SRM1950 it is shown that: hydroxy-linoleic acid- and hydroxy-α-linolenic acid-derived oxylipins are preferably bound to nLs whereas long chain hydroxy-PUFAs and PUFAs (i.e. ARA EPA and DHA) are predominantly esterified to phospholipid classes. Supplementation of n3-PUFAs for 12 months led to an increase in EPA- and -DHA-derived oxylipins in all lipid fractions with the highest increase of hydroxy-PUFAs in nLs. This demonstrates a precursor PUFA-dependent binding of oxylipins and a direct effect of diet on esterified oxylipins in plasma.

Keywords:  esterified oxylipins; fish oil; glycerophospholipids; human plasma; hydrophilic interaction liquid chromatography; lipidomics; nutrition; omega-3 fatty acids; oxidized lipids; solid-phase extraction

DOI:  https://doi.org/10.1016/j.jlr.2025.100950
bioRxiv. 2025 Oct 23. pii: 2025.10.22.684010. [Epub ahead of print]

Macropinocytosis enables metabolic recycling of extracellular DNA in cancer cells.

Wen-Hsuan Yang, Olivia T Stamatatos, Evdokia Michalopoulou, Ava Sann, Paolo Cifani, Linda Van Aelst, Justin R Cross, Tse-Luen Wee, Michael J Lukey.

Avid nutrient consumption is a metabolic hallmark of cancer and leads to regional depletion of key metabolites within the tumor microenvironment (TME). Cancer cells consequently employ diverse strategies to acquire the fuels needed for growth, including bulk uptake of the extracellular medium by macropinocytosis. Here, we show that breast and pancreatic cancer cells macropinocytically internalize extracellular DNA (exDNA), an abundant component of the TME, and deliver it to lysosomes for degradation. This provides a supply of nucleotides that sustains growth when de novo biosynthesis is impaired by glutamine restriction or pharmacological blockade. Mechanistically, this process is dependent on the non-redundant lysosomal equilibrative nucleoside transporter SLC29A3 (ENT3), which mediates the export of nucleosides from the lysosomal lumen into the cytosol. Accordingly, genetic ablation of SLC29A3 or pharmacological disruption of lysosomal function prevents exDNA scavenging and potently sensitizes breast tumors to antimetabolite chemotherapy in vivo . These findings reveal a previously unrecognized nutrient acquisition pathway through which cancer cells recycle exDNA into metabolic building blocks and highlight SLC29A3 as a mediator of metabolic flexibility and a potential target to improve chemotherapy response.

DOI: https://doi.org/10.1101/2025.10.22.684010
Methods Cell Biol. 2026 ;pii: S0091-679X(25)00205-5. [Epub ahead of print]200 151-169

Enhanced cell surface enrichment and data-independent acquisition for in-depth profiling of multiple myeloma cell lines.

Audrey Kishishita, Szu-Ying Chen, Abhilash Barpanda, Arun P Wiita.

   BACKGROUND: Multiple myeloma is a hematologic malignancy characterized by clonal plasma cell proliferation, with recent therapeutic advances focusing on immunotherapies targeting cell surface antigens. While several surface markers are well-characterized, there remains a critical need to identify additional specific targets for relapsed cases. Comprehensive surface proteome analysis is challenging due to the low abundance of surface proteins and limited cell numbers available from patient samples.
METHODS: We developed an optimized cell surface capture (CSC) protocol coupled with data-independent acquisition (DIA) mass spectrometry for comprehensive surfaceome profiling of multiple myeloma cell lines AMO1 and MM1.S. The method utilizes periodate-based oxidation of surface glycoproteins followed by biocytin hydrazide labeling and NeutrAvidin enrichment. Surface-labeled proteins were analyzed using DIA mode on an Orbitrap Eclipse Tribrid mass spectrometer with optimized parameters for low-input samples.
RESULTS: Our approach successfully identified and quantified 989 high-confidence surface proteins from minimal sample inputs (10-fold less than standard protocols), demonstrating excellent reproducibility between biological replicates (R=0.907-0.916). Comparative analysis revealed 790 shared proteins between cell lines, with 93 and 106 proteins uniquely expressed in AMO1 and MM1.S, respectively. Principal component analysis showed clear separation between cell lines (PC1=84.6% variance), highlighting distinct surface protein expression profiles. The method detected known myeloma markers including BCMA and identified additional potential therapeutic targets.
CONCLUSIONS: CSC-DIA methodology enables comprehensive surface proteome analysis from limited cell numbers, making it suitable for rare patient samples. This provides a robust platform for discovering novel surface antigens to advance personalized myeloma immunotherapy.

Keywords:  Blood cancer; Cell surface proteomics; Data-independent acquisition; Immunotherapy targets; Myeloma; Surfaceomics; Target discovery

DOI:  https://doi.org/10.1016/bs.mcb.2025.10.005
Biomolecules. 2025 Nov 06. pii: 1562. [Epub ahead of print]15(11):

SMAnalyst: A Web Server for Spatial Metabolomic Data Analysis and Annotation.

Zhanlong Mei, Xiaolian Ning, Haoke Deng, Lingyun Chen, Yun Zhao, Jin Zi.

  Spatial metabolomics is a rapidly advancing field offering powerful insights into metabolic heterogeneity in biological tissues. However, its widespread adoption is hindered by fragmented tools and the lack of comprehensive, open-source GUI software covering the full analytical workflow (quality control, preprocessing, identification, pattern, and differential analysis). To address this, we developed SMAnalyst, an open-source, integrated web-based platform. SMAnalyst consolidates core functionalities, including multi-dimensional data quality assessment (background consistency, intensity, missing values), a comprehensive metabolite annotation scoring system (mass accuracy, isotopic similarity, adduct evidence), and dual-dimension spatial pattern discovery (metabolite co-expression and pixel clustering). It also offers flexible differential analysis (cluster- or user-defined regions). With its intuitive GUI and modular workflow, SMAnalyst significantly lowers the analysis barrier, by providing a unified solution that eliminates the need for tool switching and advanced computational skills. Tested with a mouse brain dataset, SMAnalyst efficiently handles large-scale data (e.g., >14,000 pixels, >3000 ion peaks), effectively filling a critical gap in integrated analytical solutions for spatial metabolomics.

Keywords:  differential analysis; metabolite annotation; quality control; spatial metabolomics; spatial pattern discovery; web-based platform

DOI:  https://doi.org/10.3390/biom15111562
Bio Protoc. 2025 Nov 20. 15(22): e5508

Quantitative Analysis of the Arabidopsis Leaf Secretory Proteome via TMT-Based Mass Spectrometry.

Sakharam Waghmare, Lingfeng Xia, Suzanne McGill, Richard Burchmore, Rucha Karnik.

  In plants, the apoplast contains a diverse set of proteins that underpin mechanisms for maintaining cell homeostasis, cell wall remodeling, cell signaling, and pathogen defense. Apoplast protein composition is highly regulated, primarily through the control of secretory traffic in response to endogenous and environmental factors. Dynamic changes in apoplast proteome facilitate plant survival in a changing climate. Even so, the apoplast proteome profiles in plants remain poorly characterized due to technological limitations. Recent progress in quantitative proteomics has significantly advanced the resolution of proteomic profiling in mammalian systems and has the potential for application in plant systems. In this protocol, we provide a detailed and efficient protocol for tandem mass tag (TMT)-based quantitative analysis of Arabidopsis thaliana secretory proteome to resolve dynamic changes in leaf apoplast proteome profiles. The protocol employs apoplast flush collection followed by protein cleaning using filter-aided sample preparation (FASP), protein digestion, TMT-labeling of peptides, and mass spectrometry (MS) analysis. Subsequent data analysis for peptide detection and quantification uses Proteome Discoverer software (PD) 3.0. Additionally, we have incorporated in silico-generated spectral libraries using PD 3.0, which enables rapid and efficient analysis of proteomic data. Our optimized protocol offers a robust framework for quantitative secretory proteomic analysis in plants, with potential applications in functional proteomics and the study of trafficking systems that impact plant growth, survival, and health. Key features • Rapid and high-purity collection of Arabidopsis thaliana leaf apoplast flush. • Use of filter-aided sample preparation (FASP) for protein cleaning to obtain high-quality data. • Use of in-house-generated theoretical spectral libraries for efficient and rapid analysis of MS data.

Keywords:  Apoplast flush collection; Arabidopsis; Filter-aided sample preparation (FASP); Liquid chromatography–mass spectrometry (LC-MS); MSPepSearch; Proteome Discoverer; Quantitative plant proteomics; Spectral library; Tandem mass tag (TMT)

DOI:  https://doi.org/10.21769/BioProtoc.5508
Chin Med J (Engl). 2025 Nov 21.

Revisiting the role of metabolic reprogramming as a contributor to prostate cancer disease progression.

Hekang Ding, Qingwei Meng, Jun Zhou, Li Chen, Jiaoti Huang, Bing Zhao, Lingfan Xu.

   ABSTRACT: Prostate cancer (PCa) is one of the most common malignancies worldwide, and metabolic reprogramming plays a crucial role, particularly in tumor progression and therapeutic resistance. As PCa progresses into advanced stages, such as castration-resistant prostate cancer, significant alterations in tumor metabolic pathways, including glycolysis, amino acid utilization, and lipid acid metabolism, occur. These reprogrammed metabolic pathways support the survival and proliferation of tumor cells in altered tumor microenvironments. Glutamine metabolism is significant in advanced PCa because this pathway not only contributes to the tricarboxylic acid cycle by providing energy and carbon skeletons but also supports the synthesis of macromolecules such as nucleotides and lipids and acts as a key driver of therapeutic resistance. In addition, pioneer transcription factors, such as the androgen receptor, either regulate the activity of metabolic pathways or are influenced by specific signaling metabolites. Targeting metabolic vulnerability is an ideal therapeutic strategy for advanced PCa. The aim of this review was to describe distinct metabolic features in different stages of PCa and highlight how to improve therapeutic effects by targeting tumor metabolism.

Keywords:  Castration-resistant prostate cancer; Glutamine metabolism; Metabolic reprogramming; Prostate cancer; Targeted therapy

DOI:  https://doi.org/10.1097/CM9.0000000000003844
Methods Cell Biol. 2026 ;pii: S0091-679X(24)00209-7. [Epub ahead of print]200 245-261

Separating the wheat from the chaff: Determination of confidently secreted proteins by including information on their cellular abundance.

Gereon Poschmann, Ronja Marie Schwermer, Kai Stühler.

  One important strategy of cells to communicate with their environment is the release of proteins which can serve as signals for other cells nearby or at distant places in a complex organism. A first step in the characterization and investigation of cell-cell communication in this context is to figure out which proteins are released from cells under defined experimental conditions. Here, we present an approach that will give rise to a high-quality secretome and detects proteins that will be confidently released by cultured cells. This approach is based on the separate preparation of proteins from conditioned medium and corresponding cell lysates. After protein digestion and quantitative mass spectrometric analysis, protein abundances are compared and proteins showing a significantly higher abundance in the secretome are identified. We assume that these proteins have a higher probability of being released by well-directed processes and not simply by contamination of (dead) cells. We show an optimized protocol in which samples from primary human normal dermal fibroblasts (NHDF) are prepared with the single-pot solid-phase-enhanced sample preparation (SP3) method from only 450μL conditioned medium along with one-hour gradient separations and data-independent mass spectrometric data acquisition.

Keywords:  Cell-cell communication; Classical secretion; Contaminants; Leaderless secretion; Mass spectrometry; Secretome; Secretomics

DOI:  https://doi.org/10.1016/bs.mcb.2024.09.004
Biochemistry. 2025 Nov 26.

Hypoxia Rewires Histone Methylation in Glioblastoma Cells via Enzyme Reprogramming Despite Disruption of One-Carbon Metabolism.

Hui Tang, Pei Xu, Jason Herring, Kangling Zhang.

Hypoxia is a hallmark of the tumor microenvironment that profoundly alters the cellular metabolism and epigenetic regulation. In this study, we investigated how oxygen limitation reprograms histone methylation in glioblastoma cells by integrating stable isotope tracing with high-resolution proteomics and epigenomics. Using deuterium-labeled serine and the RQMID-MS platform, we demonstrated that hypoxia impairs methyl group transfer from serine to histones due to the downregulation of the vitamin B12 transporter TCN2, which is critical for homocysteine remethylation and SAM synthesis. Despite this blockade in one-carbon metabolism, global histone methylation patterns were not uniformly suppressed. Instead, we observed site-specific changes driven by altered expression of methyltransferases and demethylases, particularly decreased KMT1F (H3K9 methylation) and KMT2B (H3K4 methylation) and increased KDM2A (H3K36 demethylation), KDM3A (H3K9 demethylation), and KMT5A/SETD8 (H4K20 monomethylation). These findings reveal that the histone methylation landscape under hypoxia is governed by a compensatory interplay between one-carbon metabolism and chromatin-modifying enzyme regulation.

DOI: https://doi.org/10.1021/acs.biochem.5c00632
J Proteome Res. 2025 Nov 24.

Alternative Ion-Pairing Modifiers Should Be Investigated in Low-Input and Single-Cell Proteomics.

Colten D Eberhard, Cameron Braswell, Benjamin C Orsburn.

  A recent study demonstrated a substantial increase in the peptide signal and corresponding proteome coverage when employing 0.5% acetic acid (AA) as the ion pairing modifier in place of the 0.1% formic acid traditionally used in shotgun proteomics. Given the strictly limited material and counterintuitive observations by others in the emerging field of single-cell proteomics, we chose to investigate this alternative modifier in the analysis of subnanogram proteome dilutions. When digest standards as low as 20 pg total load on the column were evaluated, AA led to increased proteome coverage at every peptide load assessed. Relative improvements were more apparent at lower concentrations, with a 20 pg peptide digest demonstrating a striking 1.8-fold increase to over 2000 protein groups identified in a 30 min analysis. Furthermore, we find that this increase in signal can be leveraged to reduce ramp times, leading to 1.7× more scans across each peak and improvements in quantification, as measured by %CVs. These results can be reproduced on multiple trapped ion mobility instruments. When evaluating single cancer cells, approximately 13% more peptide groups were identified on average when employing AA in the place of FA. These results suggest that ion pairing modifiers and other additives warrant re-evaluation in the context of low-input and single-cell proteomics. All vendor raw and processed data are available through ProteomeXchange as PXD046002 and PXD051590.

Keywords:  DIA; alternative buffers; diaPASEF; single-cell proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.5c00930
Bio Protoc. 2025 Nov 20. 15(22): e5505

Immunopeptidomics Workflow for Isolation and LC-MS/MS Analysis of MHC Class I-Bound Peptides Under Hypoxic Conditions.

Hala Estephan, Ester M Hammond, Eleni Adamopoulou.

  Immunopeptidomics enables the identification of peptides presented by major histocompatibility complex (MHC) molecules, offering insights into antigen presentation and immune recognition. Understanding these mechanisms in hypoxic conditions is crucial for deciphering immune responses within the tumor microenvironment. Current immunopeptidomics approaches do not capture hypoxia-induced changes in the repertoire of MHC-presented peptides. This protocol describes the isolation of MHC class I-bound peptides from in vitro hypoxia-treated cells, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. It describes optimized steps for cell lysis, immunoaffinity purification, peptide elution, and MS-compatible preparation under controlled low-oxygen conditions. The method is compatible with various quantitative mass spectrometry approaches and can be adapted to different cell types. This workflow provides a reliable and reproducible approach to studying antigen presentation under hypoxic conditions, thereby enhancing physiological relevance and facilitating deeper immunological insights. Key features • Enables isolation of MHC class I-bound peptides from cells cultured under hypoxic conditions. • Designed for low-input samples and optimized for maintaining cell viability during extended hypoxic exposure. • Compatible with label-free LC-MS/MS for detailed immunopeptidome analysis. • Adaptable to all human and murine cell lines commonly used in cancer and immunology research.

Keywords:  Antigen presentation; Hypoxia; Immunopeptidomics; MHC class I; MHC peptide; Mass spectrometry; Tumor microenvironment

DOI:  https://doi.org/10.21769/BioProtoc.5505