bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2025–11–23
twenty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Targeted Metabolomic Methods for 13C Stable Isotope Labeling with Uniformly Labeled Glucose and Glutamine Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS).
  2. Benchmarking informatics workflows for data-independent acquisition single-cell proteomics.
  3. Scoring information integration with statistical quality control enhanced cross-run analysis of data-independent acquisition proteomics data.
  4. MetScribeR: A Semiautomated Tool for Data Processing of In-House LC-MS Metabolite Reference Libraries.
  5. Enhancing metabolite coverage using dedicated mobile phases for individual polarity modes in HILIC-MS.
  6. Using Untargeted Mass Spectrometry-Based Proteomics to Identify Putative Microprotein Biomarkers from Patient Tampons.
  7. Optimizing multiomics sample preparation: comparative evaluation of extraction protocols for HepG2 cells.
  8. Proteomics data imputation with a deep model that learns from many datasets.
  9. Branched chain amino acids and their aberrant metabolism in cancer.
  10. Quantifying Mitochondrial Metabolism and Metabolic Fluxes in Soft Agar Cultures.
  11. Structural annotation of acylcarnitines detected in SRM 1950 using collision-induced dissociation and electron-induced dissociation.
  12. From stress to homeostasis: Mass spectrometry-based insights into the unfolded protein response (UPR) and proteostasis.
  13. Identification and Characterization of Microproteins/ MicroORFs.
  14. Proteome-wide profiling of S-nitrosylated proteins using the SNOTRAP probe and mass spectrometry-based detection.
  15. Improving the Detection of Analyte Degeneracies in Untargeted Liquid Chromatography-Tandem Mass Spectrometry Data.
  16. Hyphenation of Trapped Ion Mobility to Two-Dimensional Mass Spectrometry for Protein Analysis in Complex Biomixtures.
  17. Senescence-Induced Lipidome Alterations in Mesenchymal Stromal Cells.
  18. Improving Metabolite Annotations in On-Tissue Chemical Derivatization Mass Spectrometry Imaging by Functional Group Filtering and Hydrogen-Deuterium Exchange.
  19. The SIMPLIFY Protocol: A Monophasic Extraction System Suitable for Exposomics, Metabolomics, Lipidomics, and Proteomics Research.
  20. Discrepancies in Biomarker Identification in Peak Picking Strategies in Untargeted Metabolomics Analyses of Cells, Tissues, and Biofluids.
  21. Protocol for investigating mitochondrial structure, function, and metabolism in human cervical cancer cells.
  22. Standardized sample preparation of paediatric bronchoalveolar lavage fluid for mass spectrometry based proteomic analysis.
  1. J Proteome Res. 2025 Nov 20.
    Targeted Metabolomic Methods for 13C Stable Isotope Labeling with Uniformly Labeled Glucose and Glutamine Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS).
    Erin Q Jennings, W Kimryn Rathmell, Jeffrey C Rathmell.
      Heavy carbon labeling has emerged as a popular way to study metabolic diseases. However, most carbon labeling techniques use untargeted mass spectrometry, which typically requires dependence on a research core and specialized software. By combining published 13C labeling patterns and known enzyme reactions, an optimized targeted mass spectrometry method was generated to measure stable isotope labeling with carbon-13 through glycolysis, the tricarboxylic acid cycle, the hexosamine biosynthetic pathway, and glutaminolysis using uniformly labeled glucose or glutamine. This method provides a novel and adaptable approach to investigate pointed hypotheses on the utilization of glucose or glutamine in disease states and models.
    Keywords:  carbon tracing; stable isotope labeling; tandem mass spectrometry; targeted metabolomics
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00514
  2. Nat Commun. 2025 Nov 21. 16(1): 10276
    Benchmarking informatics workflows for data-independent acquisition single-cell proteomics.
    Jianwei Wang, Yi Huang, Fanghua Lu, Qinqin Xu, Zhuo Yang, Yirong Jiang, Shaowen Shi, Jianzhang Pan, Yi Yang, Qun Fang.
      Recent years have seen a rise of single-cell proteomics by data-independent acquisition mass spectrometry (DIA MS). While diverse data analysis strategies have been reported in literature, their impact on the outcome of single-cell proteomic experiments has been rarely investigated. Here, we present a framework for benchmarking data analysis strategies for DIA-based single-cell proteomics. This framework provides a comprehensive comparison of popular DIA data analysis software tools and searching strategies, as well as a systematic evaluation of method combinations in subsequent informatic workflow, including sparsity reduction, missing value imputation, normalization, batch effect correction, and differential expression analysis. Benchmarking on simulated single-cell samples consisting of mixed proteomes and real single-cell samples with a spike-in scheme, recommendations are provided for the data analysis for DIA-based single-cell proteomics.
    DOI:  https://doi.org/10.1038/s41467-025-65174-4
  3. Commun Chem. 2025 Nov 20. 8(1): 364
    Scoring information integration with statistical quality control enhanced cross-run analysis of data-independent acquisition proteomics data.
    Mingxuan Gao, Shubham Gupta, Wenxian Yang, Rongshan Yu, Hannes L Röst.
      The peptide-centric strategy is widely applied in data-independent acquisition (DIA) proteomics to analyze multiplexed MS2 spectra. However, current software tools often rely on single-run data for peptide peak identification, leading to inconsistent quantification across heterogeneous datasets. Match-between-runs (MBR) algorithms address this by aligning peaks or elution profiles post-analysis, but they are often ad hoc and lack statistical frameworks for controlling peak quality, causing false positives and reduced quantitative reproducibility. Here we present DreamDIAlignR, a cross-run peptide-centric tool that integrates peptide elution behavior across runs with a deep learning peak identifier and alignment algorithm for consistent peak picking and FDR-controlled scoring. DreamDIAlignR outperformed state-of-the-art MBR methods, identifying up to 21.2% more quantitatively changing proteins in a benchmark dataset and 36.6% more in a cancer dataset. Additionally, DreamDIAlignR establishes an improved methodology for performing MBR compatible with existing DIA analysis tools, thereby enhancing the overall quality of DIA analysis.
    DOI:  https://doi.org/10.1038/s42004-025-01734-5
  4. J Proteome Res. 2025 Nov 17.
    MetScribeR: A Semiautomated Tool for Data Processing of In-House LC-MS Metabolite Reference Libraries.
    Adam M Tisch, Jason M Inman, Ewy A Mathé, Djawed Bennouna.
      One persistent challenge in untargeted metabolomics is the identification of compounds from their mass spectrometry (MS) signal, which is necessary for biological data interpretation. This process can be facilitated by building in-house libraries of metabolite standards containing retention time (RT) information, which is orthogonal and complementary to large, published MS/MS spectra repositories. Creating such libraries can require substantial effort and is time intensive. To streamline this process, we developed metScribeR, an R package with a Shiny application to accelerate the creation of RT and m/z libraries. metScribeR provides an easy, user-friendly interface for peak finding, filtering, and comprehensive quality review of the MS data. Uniquely, metScribeR does not require MS/MS spectral information and reports an identification probability estimate for each adduct. In our benchmarking, metScribeR required approximately 10 s of computational and manual effort per standard, showed a correlation of 0.99 between manual and metScribeR-derived RTs, and appropriately filtered out poor quality peaks. The metScribeR output is a.csv file including the identity, m/z, RT, and peak quality information for standards along with MS/MS spectra retrieved from MassBank of North America (MoNA). metScribeR is open source and available for download on GitHub at https://github.com/ncats/metScribeR.
    Keywords:  LC-MS; R; mass spectrometry; metabolite identification; metabolite libraries; metabolomics; software
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00548
  5. Anal Bioanal Chem. 2025 Nov 19.
    Enhancing metabolite coverage using dedicated mobile phases for individual polarity modes in HILIC-MS.
    Alena Langová, Malena Manzi, Jana Brejchová, Ondřej Kuda, Michal Holčapek, Robert Jirásko.
      We have developed a new hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) method with mobile phases optimized for high metabolite coverage in individual polarity modes. This dual mobile phase strategy expands the range of annotated metabolites and improves identification confidence, providing broader and more accurate metabolic profiles. The incorporation of a bioinert chromatographic system further enhances sensitivity. The bridged ethyl hybrid (BEH) amide column yields the best results for metabolomic analysis among the three chromatographic columns in this comparison. The method development involves investigating the effects of mobile phase composition, pH, and a medronic acid additive on the MS response under bioinert chromatographic conditions. The results highlight the important role of alkaline pH for the sensitive detection of polyphosphorylated metabolites, while demonstrating the redundancy of chelating additives in a fully bioinert system. Finally, the optimized method is applied to mouse plasma, pancreas, and liver samples to demonstrate its versatility and reliable performance in complex biological matrices, establishing it as a powerful tool for confident and reproducible metabolomics studies.
    Keywords:  Bioinert chromatography; HILIC; Mass spectrometry; Metabolite annotation; Metabolomics
    DOI:  https://doi.org/10.1007/s00216-025-06189-0
  6. Methods Mol Biol. 2026 ;2992 283-315
    Using Untargeted Mass Spectrometry-Based Proteomics to Identify Putative Microprotein Biomarkers from Patient Tampons.
    Gordon T Luu, Conrad Fihn, Laura M Sanchez.
      Mass spectrometry-based proteomics is a versatile technique that facilitates the study of microproteins as biomarkers and potential translational applications by utilizing liquid chromatography-trapped ion mobility-tandem mass spectrometry (LC-TIMS-MS/MS) and matrix assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS). Importantly, MS has been shown to be compatible with complex biological samples (i.e., mammalian biofluids and patient samples). Here we describe a workflow combining: (1) top-down proteomics for analysis of intact microproteins via MALDI-TOF MS and (2) bottom-up proteomics for analysis of enzymatically digested microproteins via LC-TIMS-MS/MS to annotate putative microproteins of interest from a complex biological sample: patient-derived tampons.
    Keywords:  Bioinformatics; Cheminformatics; LC–MS/MS; MALDI; Microproteins; Mss spectrometry
    DOI:  https://doi.org/10.1007/978-1-0716-5013-4_20
  7. Anal Bioanal Chem. 2025 Nov 19.
    Optimizing multiomics sample preparation: comparative evaluation of extraction protocols for HepG2 cells.
    Tilman F Arnst, Selina Hemmer, Claudia Fecher-Trost, Lea Wagmann, Markus R Meyer.
      Multiomics approaches enable a comprehensive characterization of complex biological systems by simultaneously investigating multiple molecular layers. Generating multiple omics datasets from a single sample is crucial to minimize biological variability and ensure cross-layer consistency, which is critical for robust downstream data analysis. However, existing workflows often require adaptation to the specific experimental context and instrumental setup. This study systematically compared two established protocols for the simultaneous extraction of metabolites, lipids, and proteins from HepG2 cells: (i) a biphasic extraction with subsequent overnight protein digestion from the interphase pellet, and (ii) a monophasic extraction involving on-bead protein digestion. For the monophasic approach, we further investigated the effects of bead size and digestion conditions. Metabolomics samples were analyzed using liquid chromatography coupled to high-resolution tandem mass spectrometry; lipidomics and proteomics samples were analyzed by nano-scale liquid chromatography coupled with ion mobility separation and high-resolution tandem mass spectrometry. Each method was evaluated in terms of total feature count, selectivity, reproducibility, handling complexity, and overall performance. While neither protocol was optimal across all criteria, the monophasic extraction using paramagnetic beads with shortened incubation time proved to be the most reproducible, efficient, and cost-effective solution for in-house multiomics workflows in HepG2 cells.
    Keywords:  HepG2 cells; Mass spectrometry; Metabolomics; Multiomics; Proteomics; Sample preparation
    DOI:  https://doi.org/10.1007/s00216-025-06235-x
  8. Mol Cell Proteomics. 2025 Nov 17. pii: S1535-9476(25)00560-2. [Epub ahead of print] 101461
    Proteomics data imputation with a deep model that learns from many datasets.
    Lincoln Harris, William S Noble.
      Missing values are a major challenge in the analysis of mass spectrometry proteomics data. Missing values hinder reproducibility, decrease statistical power for identifying differentially abundant proteins and make it challenging to analyze low-abundance proteins. We present Lupine, a deep learning-based method for imputing, or estimating, missing values in quantitative proteomics data. Lupine is, to our knowledge, the first imputation method that is designed to learn jointly from many datasets, and we provide evidence that this approach leads to more accurate predictions. We validated Lupine by applying it to tandem mass tag data from >1,000 cancer patient samples spanning ten cancer types from the Clinical Proteomics Tumor Atlas Consortium. Lupine outperforms the state-of-the-art for proteomics imputation, uniquely identifies differentially abundant proteins and Gene Ontology terms and learns a meaningful representation of proteins and patient samples. Lupine is implemented as an open-source Python package.
    DOI:  https://doi.org/10.1016/j.mcpro.2025.101461
  9. Trends Cancer. 2025 Nov 18. pii: S2405-8033(25)00255-9. [Epub ahead of print]
    Branched chain amino acids and their aberrant metabolism in cancer.
    Morgan L Brown, Xuanyan Cai, M Celeste Simon.
      Cancer cells require sufficient nutrients to support biomass generation, rapid proliferation, and survival. Thus, extensive reprogramming of amino acid metabolism is necessary for tumor initiation and progression under strenuous conditions. One metabolic pathway that has garnered attention is branched chain amino acid (BCAA) catabolism, a pathway that is highly altered across malignancies. This review examines current insights into how circulating BCAAs and their aberrant catabolic enzymes impact both cancer cells and the surrounding tumor microenvironment.
    Keywords:  branched chain amino acids; cancer metabolism; nutrient supplementation; tumor microenvironment
    DOI:  https://doi.org/10.1016/j.trecan.2025.10.004
  10. Methods Mol Biol. 2026 ;2990 127-143
    Quantifying Mitochondrial Metabolism and Metabolic Fluxes in Soft Agar Cultures.
    Birte Dowerg, Fangfang Chen, Thekla Cordes.
      Anchorage-independent cultures provide insights into cell proliferation, differentiation, and tumorigenesis beyond traditional two-dimensional models by mimicking parts of the extracellular matrix (ECM). The soft agar colony formation assay enables cells to proliferate in a three-dimensional manner resulting in metabolic phenotypes that are distinct from traditional monolayer cultures. Here, we established a soft agar colony formation assay with subsequent cell isolation to analyze mitochondrial metabolism, metabolic fluxes, morphology, and gene expression within the same sample. We applied mass spectrometry and tracing approaches to decipher carbon utilization for tricarboxylic acid (TCA) cycle metabolism. We also quantified the alteration of immune-related genes in response to inflammatory stimuli in soft agar cultures that might be relevant to autoimmune diseases, which are frequently associated with inflammatory environments and may contribute insights into chronic inflammation and immune cell survival that parallel tumorigenic processes. Our methodology offers a robust model to better understand cell metabolism and function of anchorage-independent cultures that may contribute to the development of new treatment strategies.
    Keywords:  Anchorage-independent cultures; Extracellular matrix; Mass spectrometry; Metabolic flux; Metabolism; Metabolite extraction; Mitochondria; Soft agar; Stable isotope tracer
    DOI:  https://doi.org/10.1007/978-1-0716-4997-8_11
  11. Anal Bioanal Chem. 2025 Nov 20.
    Structural annotation of acylcarnitines detected in SRM 1950 using collision-induced dissociation and electron-induced dissociation.
    Valentina Ramundi, Michael Witting.
      Acylcarnitines are important intermediates in fatty acid metabolism, shuttling acyl groups into mitochondria for β-oxidation and energy production. As biomarkers, their concentrations are utilized to diagnose metabolic and cardiovascular diseases, insulin resistance, and neurodegenerative disorders. However, in-depth structural characterization of acylcarnitines is limited by conventional collision-induced dissociation (CID), which yields fragments predominantly from the carnitine headgroup with minimal information regarding the fatty acyl chains. In this case, we employed a novel quadrupole time-of-flight (QToF) mass spectrometer, the Sciex ZenoTOF 8600, with CID and electron-induced dissociation (EID) to carry out in-depth analysis of acylcarnitines detected in National Institute of Standards and Technology (NIST) SRM 1950 reference plasma. With increased sensitivity and reduced accumulation times (95 ms) on the 8600 platform, we were able to confidently annotate 35 acylcarnitines, including isomeric species and functional groups such as hydroxylations and double bonds. EID provided comprehensive structural information, enabling the discrimination of isomers such as valeryl-, isovaleryl-, and 2-methylbutyryl-carnitine (Car 5:0), as well as the positioning of methyl branching via diagnostic fragments. Hydroxylated species, such as Car 16:0;3OH, were confirmed by the presence of diagnostic ions and matched to reference standards by retention time. Moreover, EID enabled the localization of double bonds within unsaturated species (e.g., Car 18:1, Car 18:2) via fragmentation patterns that are indicative of unsaturation positions, following established lipid fragmentation mechanisms. This work demonstrates that EID offers significant advancements for the structural elucidation of acylcarnitines, delivering enhanced sensitivity and deeper insights into isomeric and functional diversity.
    Keywords:  Acylcarnitines; Electron-induced dissociation (EID); High-resolution mass spectrometry; Structural elucidation; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/s00216-025-06234-y
  12. Adv Biol Regul. 2025 Oct 30. pii: S2212-4926(25)00054-5. [Epub ahead of print] 101127
    From stress to homeostasis: Mass spectrometry-based insights into the unfolded protein response (UPR) and proteostasis.
    Lea A Barny, Lars Plate.
      The unfolded protein response (UPR) is a central regulator of proteostasis, coordinating cellular adaptation to endoplasmic reticulum (ER) stress. It is comprised of three signaling branches: ATF6 (activating transcription factor 6), IRE1 (inositol-requiring enzyme 1), and PERK (protein kinase RNA-like ER kinase), which mediate transcriptional and translational reprogramming of the proteostasis network. These pathways display both functional redundancy and branch-specific activities. Dysregulated UPR signaling contributes to diverse pathologies: in cancer, UPR activation supports uncontrolled proliferation and treatment resistance, whereas in aging, proteostasis decline and diminished UPR responsiveness are hallmarks. Traditional approaches, including transcriptomics and western blotting, have been widely used to monitor UPR activity, but they offer limited insight into its regulation at the protein level. In contrast, liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics allows comprehensive, branch-specific profiling of UPR signaling. Recent advances, including data-independent acquisition (DIA) MS and automated sample preparation, have further improved sensitivity, reproducibility, and detection of low-abundance UPR target proteins. Proteomics thus provides a systematic and scalable framework to interrogate UPR regulation across cell types and disease models. When integrated with complementary datasets, protein-level measurements can uncover context-dependent molecular signatures of UPR activity, offering insights into disease mechanisms and guiding the rational design of targeted pharmacological interventions. Future work integrating high-resolution LC-MS/MS proteomics with tissue and single-cell analyses will further clarify the role of the UPR in health and disease.
    Keywords:  Activating transcription factor 6; Bottom-up proteomics; ER stress; Inositol requiring enzyme 1; Protein kinase R-like ER kinase; Proteomics automation; Proteostasis
    DOI:  https://doi.org/10.1016/j.jbior.2025.101127
  13. Methods Mol Biol. 2026 ;2992 41-49
    Identification and Characterization of Microproteins/ MicroORFs.
    Na Ding, Juan Xu, Jiaxin Yu.
      Microproteins and microOpen Reading Frames (microORFs) are small proteins encoded by short, often overlooked, coding regions in the genome. These peptides are typically under 100 amino acids in length and are believed to play critical roles in cellular processes such as stress response, metabolic regulation, and protein quality control. However, their discovery has been hindered by limitations in traditional proteomics and gene annotation pipelines. In this chapter, we present a novel method for the systematic identification of microproteins and microORFs across a range of eukaryotic species using an integrative approach that combines ribosome profiling, mass spectrometry-based proteomics, and advanced bioinformatics tools. Our method enhances the sensitivity of microprotein detection and allows for the validation of candidate microORFs in vivo. This methodology provides a comprehensive platform for microprotein discovery, offering new insights into gene regulation and cellular function.
    Keywords:  Bioinformatics; Cellular Function; Mass Spectrometry; MicroORFs; Microproteins; Ribosome Profiling; Small Peptides
    DOI:  https://doi.org/10.1007/978-1-0716-5013-4_4
  14. Nat Protoc. 2025 Nov 19.
    Proteome-wide profiling of S-nitrosylated proteins using the SNOTRAP probe and mass spectrometry-based detection.
    Hongmei Yang, Haitham Amal, Steven R Tannenbaum, Stuart A Lipton.
      Protein S-nitrosylation (SNO) is a ubiquitous post-translational modification, which regulates a broad range of functional parameters, including protein stability; enzymatic, transcriptional and ion channel activity; and cellular signal transduction. Aberrant protein SNO is associated with diverse pathophysiology, from cardiovascular, metabolic and respiratory disorders to neurodegeneration and cancer. Drugs that enhance or inhibit specific SNO reactions are being developed as potential disease-modifying therapeutics. However, owing to a lack of suitable approaches to monitor SNO proteins, which often exist at low abundance with ephemeral expression, a systematic understanding of their roles in disease remains elusive. Here we report a robust and proteome-wide approach for the exploration of the S-nitrosoproteome in human and mouse tissues, using the brain as an example, with a probe named SNOTRAP (a triphenylphosphine thioester linked to a biotin molecule through a polyethylene glycol spacer group) in conjunction with mass spectrometry (MS)-based detection. In this Protocol, we detail tissue sample preparation, synthesis of SNOTRAP under an argon atmosphere and subsequent MS-based identification and analysis of SNO proteins. In situ labeling of SNO proteins is achieved by the SNOTRAP probe, concomitantly yielding a disulfide-iminophosphorane as a labeling tag. The chemically tagged proteins can be digested, followed by streptavidin capture, release by triscarboxyethylphosphine and relabeling of the liberated free Cys with N-ethylmaleimide. This approach selectively enriches SNO-containing peptides at specific sites for label-free quantification by Orbitrap MS. It requires about 5 d for synthesis of the SNOTRAP probe, 2-2.5 d for sample preparation and about 5 d for nano-liquid chromatography-tandem MS measurement and analysis.
    DOI:  https://doi.org/10.1038/s41596-025-01282-1
  15. Anal Chem. 2025 Nov 21.
    Improving the Detection of Analyte Degeneracies in Untargeted Liquid Chromatography-Tandem Mass Spectrometry Data.
    Caitlin N Cain, Cheng-Hua Ma, Pavlo Popov, Robert T Kennedy.
      Advances in liquid chromatography-tandem mass spectrometry (LC-MS/MS) and chemometrics have driven the field of untargeted metabolomics forward. However, interpretation of these studies can be challenging, since these data sets often return thousands of features with only a small fraction identifiable. Most of these unidentified features arise from degeneracy, where a single analyte produces multiple features from adduct formation, in-source fragments, and isotopes. This work improves the detection and clustering of these degenerate features with a new peak shape consistency metric, termed lack-of-fit (LOF). This metric quantifies the residual error between two features within a time window, where a LOF <20% suggests degeneracy. To first evaluate metric performance, 21 analytes were spiked into brain dialysate and features were discovered using tile-based Fisher ratio (F-ratio) analysis. Incorporating the proposed LOF metric not only reduced the feature list and retained all the spiked analytes in the top 25 hits but also outperformed other data-driven degeneracy methods by ∼18-48%. LOF clustering was then applied to reduce the number of features detected in brain dialysate at different preconcentration levels and mobile phase gradient lengths. LOF clustering provided a 90% degenerate feature classification accuracy, surpassing existing LC-MS/MS data processing methods. Despite the chromatographic complexity, this metric could resolve features from coeluting compounds at lower resolutions (Rs ≤ 0.3) than the standard correlation-based method. LOF clustering reduced this untargeted data set by ∼79-86%, ultimately revealing ∼583 unique metabolites in brain dialysate. These results collectively demonstrate that the LOF metric can improve and strengthen the interpretation of untargeted metabolomics data.
    DOI:  https://doi.org/10.1021/acs.analchem.5c03704
  16. J Am Soc Mass Spectrom. 2025 Nov 18.
    Hyphenation of Trapped Ion Mobility to Two-Dimensional Mass Spectrometry for Protein Analysis in Complex Biomixtures.
    Callan Littlejohn, Meng Li, Christopher A Wootton, Mark P Barrow, Peter B O'Connor.
      The analysis of complex biological mixtures remains a significant challenge in mass spectrometry (MS), particularly when using conventional direct infusion MS/MS approaches due to inherent limitations in resolving power and spectral complexity. Here, we demonstrate the integration of trapped ion mobility spectrometry (TIMS) with two-dimensional mass spectrometry (2DMS) to enable high-resolution TIMS-MS/2DMS experiments for detailed protein characterization within mixtures. TIMS provides separation based on the ion's size-to-charge ratio, effectively reducing the occurrence of chimeric tandem mass spectra containing fragments from more than one precursor ion. This coupling allows for an improved peak capacity and reduced ambiguity in tandem spectral interpretation. When applied to a model protein mixture, the TIMS-MS/2DMS method allows resolution of near m/z species, including isomeric and isonucleonic species, and it was possible to assign secondary fragmentation with greater confidence.
    DOI:  https://doi.org/10.1021/jasms.5c00292
  17. J Proteome Res. 2025 Nov 17.
    Senescence-Induced Lipidome Alterations in Mesenchymal Stromal Cells.
    Molly E Ogle, Joseph L Corstvet, Reesha K Vayalakkara, Facundo M Fernández, Johnna S Temenoff.
      Mesenchymal stromal cells (MSCs) show great promise as a clinical treatment for a variety of diseases, but their susceptibility to senescence during culture reduces the therapeutic potential and limits cell expansion. In this study, we explored how MSC lipid metabolism is altered in culture over time using ultrahigh-performance liquid chromatography mass spectrometry. The proportion of cells with senescence-associated β-galactosidase (SA-β-gal) activity was evaluated during 12 days of culture expansion of MSCs from two human donors. Lipid profiles were evaluated in parallel using exact mass and tandem mass spectrometry spectral database matching to generate 237 unique lipid annotations. Lipid abundance generally increased across most lipid classes over serial culture; however, many changes were heterogeneous between donors. Despite donor differences, 12 lipids, including 4 triglycerides (TG), provided discrimination between cultures with less than 10% SA-β-gal+, those with 10-20% SA-β-gal+, and greater than 20% SA-β-gal+ senescence proportion regardless of donor. More specifically, TG composed of long-chain, highly unsaturated fatty acids was strongly associated with higher MSC senescence. These changes in bulk lipid profiles may inform future strategies to monitor early culture senescence during the expansion of MSCs.
    Keywords:  bioinformatics; lipidomics; mass spectrometry; mesenchymal stromal cells; senescence
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00355
  18. J Am Soc Mass Spectrom. 2025 Nov 19.
    Improving Metabolite Annotations in On-Tissue Chemical Derivatization Mass Spectrometry Imaging by Functional Group Filtering and Hydrogen-Deuterium Exchange.
    Anna Uhlmansiek, Josiah J Rensner, Young Jin Lee.
      Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables the direct visualization of metabolites from tissue sections with high spatial resolution. However, its application to untargeted spatial metabolomics is hindered by poor ionizing compounds and challenges in accurate metabolite annotation. On-tissue chemical derivatization (OTCD) is commonly employed to enhance the ionization of metabolites bearing specific functional groups, and platforms such as METASPACE facilitate high-throughput annotation of derivatized features. Nevertheless, distinguishing structural isomers for a large number of metabolites remains a major challenge, often resulting in incorrect annotations. To address this limitation, we developed an improved annotation workflow for OTCD-MALDI-MSI by integrating two filtering strategies. Functional group filtering leverages SMARTS-based substructure matching to retain only those metabolites that react with the applied OTCD reagent. In parallel, gas-phase hydrogen-deuterium exchange (HDX) in the MALDI source is used to determine the number of labile hydrogens for each feature, enabling the exclusion of annotations that are inconsistent with HDX behavior. We applied this workflow to MALDI-MSI of maize root sections using Girard's reagents T and P, along with the plant-specific COCONUT metabolite database. The combined filtering strategy reduced incorrect annotations by ∼67%, from ∼7.3 annotations per unique feature without filtering to ∼2.4 with filtering, substantially improving annotation accuracy and confidence. By coupling OTCD signal enhancement with structurally informed filtering, this workflow advances the utility of MALDI-MSI for untargeted spatial metabolomics, enabling more reliable and scalable metabolite profiling in complex biological tissues.
    Keywords:  Hydrogen−Deuterium Exchange; METASPACE; Mass Spectrometry Imaging; Matrix-Assisted Laser Desorption Ionization; Metabolite Annotation; On-Tissue Chemical Derivatization; Spatial Metabolomics
    DOI:  https://doi.org/10.1021/jasms.5c00293
  19. Anal Chem. 2025 Nov 19.
    The SIMPLIFY Protocol: A Monophasic Extraction System Suitable for Exposomics, Metabolomics, Lipidomics, and Proteomics Research.
    Anh Hoang Nguyen, Victor Castro-Alves, Emilia Holmström Flores, João Marcos G Barbosa, Matilda Kråkström, Päivikki Reinivuori, Otto Kauko, Alex Dickens, Matej Orešič, Tuulia Hyötyläinen.
      Advancing our understanding of human health and disease requires comprehensive analytical approaches capable of capturing the complex interplay between endogenous metabolism and environmental exposures. A major challenge in clinical research is the ability to capture multidimensional data, particularly a broad range of biochemical profiles, due to limitations of biological resources, time, and budget. In this study, we introduce the SIMPLIFY Protocol, a unified monophasic extraction method that enables the simultaneous extraction of chemical exogenous products and endogenous molecules. The method was evaluated against in-house extraction techniques, including protein precipitation with methanol (MeOH) and acetonitrile (ACN), and the Folch method using various sample types, particularly certified reference materials. We demonstrate that the SIMPLIFY Protocol not only performs comparably to our in-house methods but also offers enhanced versatility for additional applications such as derivatization and proteomics. The analyte abundances and reproducibility with this method strongly correlate with those from in-house-established techniques across diverse sample types. The method encompasses a broad spectrum of compounds, effectively profiling approximately 800 identified compounds, including polar compounds (e.g., amino acids), semipolar compounds (e.g., polyfluorinated compounds, bile acids, and lysophophatidylcholine), and nonpolar compounds (e.g., cholesteryl ester), with some limitations in extracting triacylglycerols. By maintaining simplified workflow and minimizing biological and resource consumption of multiple extractions, this method supports high-throughput exposomics/metabolomics and lipidomics studies. Furthermore, its streamlined design facilitates (semi)automation, making it highly suitable for large-scale clinical studies, where efficiency, cost-effectiveness, and sample availability are critical factors.
    DOI:  https://doi.org/10.1021/acs.analchem.5c05322
  20. J Proteome Res. 2025 Nov 20.
    Discrepancies in Biomarker Identification in Peak Picking Strategies in Untargeted Metabolomics Analyses of Cells, Tissues, and Biofluids.
    Iqbal Mahmud, Taylor A Harmon, Laurel E Meke, Timothy J Garrett.
      Different software and algorithms are available for peak picking in nontargeted metabolomics, and each may have its own strengths and limitations. The choice of the peak picking method can significantly influence the results obtained, including the number and identity of metabolites detected, their quantification, and subsequent biomarker analysis. The impact of peak picking by different tools in an untargeted metabolomics-based biomarker study is largely understated. This study compares two popular open-source software tools for peak picking in untargeted metabolomics of cancer cells, tissues, and biofluids: XCMS and MZmine 2. The investigation evaluates the impact of these peak picking algorithms on biomarker identification after careful noise filtering by blank feature filtering (BFF). We found significant discrepancy between the results obtained from XCMS and MZmine 2, regardless of the sample types, solvent gradient phases, retention time, or mass-to-charge ratio (m/z) tolerances used. Notably, this study revealed significant disagreement between peak picking tools in the context of metabolite-based biomarker study after BFF and highlighted the importance of carefully evaluating and selecting appropriate peak picking tools to ensure reliable and accurate results in untargeted metabolomics research.
    Keywords:  MZmine; XCMS; biomarker; peak picking; untargeted metabolomics
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00434
  21. STAR Protoc. 2025 Nov 19. pii: S2666-1667(25)00608-2. [Epub ahead of print]6(4): 104202
    Protocol for investigating mitochondrial structure, function, and metabolism in human cervical cancer cells.
    Sachin Shetty, Shama Prasada Kabekkodu.
      Mitochondria regulate a variety of biological activities, including metabolism, oxidative stress, and cell death. Here, we present a protocol for the investigation of mitochondrial structure, function, and metabolism in human cervical cancer cells. We describe steps for staining and visualizing mitochondria using confocal microscopy to assess morphology, mass, membrane potential, calcium, reactive oxygen species (ROS), and lipid droplet accumulation. We then detail procedures for isolating mitochondria and performing metabolomic analysis of mitochondrial metabolites via mass spectrometry. For complete details on the use and execution of this protocol, please refer to Adiga et al.1.
    Keywords:  cancer; cell biology; molecular biology
    DOI:  https://doi.org/10.1016/j.xpro.2025.104202
  22. Mol Cell Pediatr. 2025 Nov 20. 12(1): 21
    Standardized sample preparation of paediatric bronchoalveolar lavage fluid for mass spectrometry based proteomic analysis.
    Nadine Freitag, Dirk Schramm, Anja Stefanski, Christina B Schroeter, Kai Stühler, Gereon Poschmann, Marc D Driessen.
      
    Keywords:  Biomarker discovery; Bronchoalveolar lavage fluid; Bronchoscopy; Child; LC-MS/MS; Paediatric respiratory disease; Proteomics; Pulmonary proteome; Sample preparation
    DOI:  https://doi.org/10.1186/s40348-025-00205-0
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