bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2026–02–01
nineteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. An adaptive, continuous-learning framework for clinical decision-making from proteome-wide biofluid data.
  2. Correction to "A Robust Strategy for High-Throughput and Deep Proteomics by Combining Narrow-Window Data-Independent Acquisition and Isobaric Mass Tagging".
  3. Comprehensive evaluation of reversed-phase and hydrophilic interaction liquid chromatography - mass spectrometry for analysis of gangliosides.
  4. Comprehensive Curation and Harmonization of Small-Molecule MS/MS Libraries in Spectraverse.
  5. Subcellular mass spectrometry reveals proteome remodeling in an asymmetrically dividing (frog) embryonic stem cell.
  6. mspms: an R package and GUI for multiplex substrate profiling by mass spectrometry.
  7. A Novel Compact Multi-Reflecting Time-of-Flight Mass Spectrometer.
  8. IsoPS-DIA: Dual Functionality of Absolute Targeted Quantification and Global Proteome Profiling.
  9. Retrieval-Augmented Language Models Enable Scalable Chemical Source Classification in Metabolomics Workflows.
  10. ProteoGyver: A Fast, User-Friendly Tool for Routine QC and Analysis of MS-based Proteomics Data.
  11. Integrated HPLC-UV validation and LC-MS/MS optimization with molecular networking and chemometrics for advanced characterization of chlorogenic acids in Ilex guayusa.
  12. Less missing values-evaluation of proteomics workflows for the quantification of (small) proteins.
  13. Integrated targeted and untargeted analysis of polar peptides in foods using hydrophilic interaction liquid chromatography-data-independent acquisition-mass spectrometry.
  14. Relevance analysis of N-glycan variations in C2C12 cells and mouse serum under simulated microgravity using a quaternary phosphonium hydrazide labeling strategy-based mass spectrometry quantitation approach.
  15. Phospholipid Profiling Established by Structure-Rich Fragments for Molecular Species Level Shotgun Analysis.
  16. Introduction of 4D-Proteomics: A Breakthrough in Analyzing the Plant Proteomes During Stress Conditions.
  17. Integrating mass spectrometry-based multi-omic signatures of extracellular vesicles: from discovery to clinical translation.
  18. Peroxisome deficiency impacts metabolites of lysine, lipid, and polyamine metabolism in Saccharomyces cerevisiae.
  19. MIMI: Molecular Isotope Mass Identifier for stable isotope-labeled Fourier transform ultra-high mass resolution data analysis.
  1. Nat Commun. 2026 Jan 27. 17(1): 1105
    An adaptive, continuous-learning framework for clinical decision-making from proteome-wide biofluid data.
    Johannes B Müller-Reif, Vincent Albrecht, Vincenth Brennsteiner, Jakob M Bader, Peter V Treit, Nicolai J Wewer Albrechtsen, Susanne Pangratz-Führer, Matthias Mann.
      Mass spectrometry (MS)-based proteomics provides deep molecular insights from patient samples, but clinical use has been limited by missing values, static biomarker panels, and the need for targeted assay development. We present a framework - Adaptive Diagnostic Architecture for Personalized Testing by Mass Spectrometry (ADAPT-MS) - that enables direct diagnostic and prognostic interpretation of discovery-mode proteomics data at the level of individual samples. ADAPT-MS dynamically retrains simple, robust classifiers based on the proteins quantified in each sample, eliminating the need for imputation or fixed panels. Applied to plasma and cerebrospinal fluid datasets across diseases and clinical centers, it achieves high performance and generalizability using robust, transparent and generalizable statistical models. A single proteomic measurement can support multiple diagnostic questions via retrospective cohort matching, with each classification taking only seconds. As population-scale proteomics datasets grow, this approach lays the foundation for scalable, real-time, and personalized diagnostics directly from proteome-wide data. Such a community effort may help to transform discovery proteomics into a routine clinical tool.
    DOI:  https://doi.org/10.1038/s41467-025-67968-y
  2. J Proteome Res. 2026 Jan 29.
    Correction to "A Robust Strategy for High-Throughput and Deep Proteomics by Combining Narrow-Window Data-Independent Acquisition and Isobaric Mass Tagging".
    Chaewon Kang, Jiwon Hong, Hokeun Kim, JeongSu Jo, Jun-Hyeong Seo, Jeong-Won Lee, Sang-Won Lee.
      
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00014
  3. J Chromatogr A. 2026 Jan 22. pii: S0021-9673(26)00061-0. [Epub ahead of print]1770 466731
    Comprehensive evaluation of reversed-phase and hydrophilic interaction liquid chromatography - mass spectrometry for analysis of gangliosides.
    Miroslav Lísa, Roman Hájek.
      Gangliosides are biologically significant molecules with essential physiological roles in the organism. They are increasingly studied as biomarkers for disease diagnosis and progression. Due to their extreme structural diversity and typically low abundance in biological samples, efficient and sensitive liquid chromatography-mass spectrometry (LC-MS) methods are required for their analysis. In this study, we present a comprehensive comparison of the performance of two widely used LC-MS approaches: reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC). Both methods were independently optimized following an identical protocol, with a focus on chromatographic performance and MS response. Notably, salt concentration had a pronounced impact on ganglioside retention, peak shape, isomer separation, and MS signal intensity in both chromatographic modes. In HILIC, adjusting the initial gradient composition and profile enabled effective separation of gangliosides from highly abundant matrix lipids. The RP method demonstrated superior ganglioside signal intensity and a 3.5-fold reduction in analysis time. Both approaches allowed the separation of gangliosides based on the number of sialic acid and saccharide residues. Additionally, the RP method enabled separation according to ceramide composition. We established rules for RP separation based on the number of carbon atoms in the ceramide moiety, which can be used to predict ganglioside retention times. Based on our findings, each method offers distinct advantages for specific analytical goals. The HILIC approach is better suited for untargeted ganglioside profiling using accurate m/z and interclass separation, while the RP method is ideal for sensitive, high-throughput targeted analysis in complex biological matrices.
    Keywords:  Gangliosides; Hydrophilic interaction liquid chromatography; LC-MS; Lipidomics; Lipids; Retention behavior; Reversed-phase
    DOI:  https://doi.org/10.1016/j.chroma.2026.466731
  4. Anal Chem. 2026 Jan 25.
    Comprehensive Curation and Harmonization of Small-Molecule MS/MS Libraries in Spectraverse.
    Vishu Gupta, Hantao Qiang, Hsin-Hsiang Chung, Ehud Herbst, Michael A Skinnider.
      Reference libraries of tandem mass spectra (MS/MS) are widely used for metabolite identification in untargeted metabolomics and to train machine-learning models for metabolite annotation. However, public spectral libraries are scattered across disparate databases and contain spectra that are of low resolution or quality, missing critical metadata, or which have chemically incoherent annotations. Addressing these issues requires extensive preprocessing and considerable expertise in mass spectrometry, which presents a significant barrier to investigators interested in developing their own machine-learning models. Here, we present Spectraverse, a comprehensive and extensively curated library of public MS/MS spectra from small molecules. We assembled reference spectra from both major repositories and previously overlooked resources and then developed a preprocessing pipeline to harmonize metadata, standardize chemical structures, and remove low-quality or redundant spectra. These efforts led us to identify previously undocumented pitfalls in existing public libraries that may have confounded prior comparisons of machine-learning models or conversely have caused valid MS/MS spectra to have been discarded from the training sets of these models. The resulting resource affords the most comprehensive coverage of chemical space of any machine-learning-ready library of MS/MS spectra to date while also expanding the coverage of adducts and ionization modes encountered in metabolomics experiments. We intend to maintain and expand Spectraverse in order to encompass the growing number of publicly available reference MS/MS spectra that can be expected to accumulate in the future.
    DOI:  https://doi.org/10.1021/acs.analchem.5c06256
  5. Proc Natl Acad Sci U S A. 2026 Feb 03. 123(5): e2518372123
    Subcellular mass spectrometry reveals proteome remodeling in an asymmetrically dividing (frog) embryonic stem cell.
    Bowen Shen, Leena R Pade, Fei Zhou, Peter Nemes.
      Subcellular proteomics maps protein localization within restricted domains of a cell, complementing high-resolution imaging by expanding the number of proteins that can be profiled at once. Achieving this at depth from subcellular inputs remains challenging. Here, we advance microprobe capillary electrophoresis-mass spectrometry (CE-MS) with trapped ion mobility spectrometry and data-independent acquisition (diaPASEF) to quantify more than a thousand proteins from opposite poles of an asymmetrically dividing embryonic blastomere in live Xenopus laevis embryos. From ~200 pg of HeLa digest-approximately 80% of a cell-the technology identified 1,035 proteins with high reproducibility in quantification (coefficient of variation <15% across technical triplicates). With microprobe sampling in vivo, we quantified 808-1,022 proteins from opposite poles of the dorsal-animal (D1) blastomere before division, and we traced how these spatial distributions are retained or remodeled in the descendant D1.1 (neural-fated) and D1.2 (epidermal-destined) cells. To decouple subcellular distributions from dorsal-ventral axis cues, we perturbed patterning by ultraviolet ventralization. These results establish microprobe CE-MS for deep subcellular proteomics in intact embryos and reveal spatially distinct protein distributions during early fate specification. These spatial proteome differences appear consistent with early lineage tendencies yet precede and likely bias, rather than fix, later fate decisions that depend on gastrula-stage inductive signals.
    Keywords:  Xenopus; blastomere; mass spectrometry; proteomics; subcellular
    DOI:  https://doi.org/10.1073/pnas.2518372123
  6. BMC Bioinformatics. 2026 Jan 24.
    mspms: an R package and GUI for multiplex substrate profiling by mass spectrometry.
    Charlie Bayne, Brianna Hurysz, David J Gonzalez, Anthony O'Donoghue.
       BACKGROUND: Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS) is a powerful method for determining the substrate specificity of proteolytic enzymes, which is essential for developing protease inhibitors, diagnostics, and protease-activated therapeutics. However, the complex datasets generated by MSP-MS pose significant analytical challenges and have limited accessibility for non-specialist users.
    RESULTS: We developed mspms, a Bioconductor R package with an accompanying graphical interface, to streamline the analysis of MSP-MS data. Mspms standardizes workflows for data preparation, processing, statistical analysis, and visualization. The tool is designed for accessibility, serving advanced users through the R package and broader audiences through a web-based interface. We validated mspms using data from four well-characterized cathepsins (A-D), demonstrating that it reliably captures expected substrate specificities.
    CONCLUSIONS: mspms is the first publicly available, comprehensive platform for MSP-MS data analysis downstream of peptide identification and quantification. It integrates preprocessing, normalization, statistical testing, and visualization into a single, transparent, and user-friendly framework, making it a valuable resource for the protease research community. The package is distributed via Bioconductor, and a graphical interface is available online for interactive use.
    Keywords:  Bioconductor; Bioinformatics tools; Computational proteomics; Data visualization; Mass spectrometry data analysis; Multiplex substrate profiling; Protease specificity; R package
    DOI:  https://doi.org/10.1186/s12859-026-06373-8
  7. J Am Soc Mass Spectrom. 2026 Jan 26.
    A Novel Compact Multi-Reflecting Time-of-Flight Mass Spectrometer.
    Anatoly N Verenchikov, Jason Wildgoose, Sergey N Kirillov, Aleksey V Vorobyev, Vasily V Makarov, Lee A Gethings, Robert P Tonge, Matthew E Daly, William J Johnson, James I Langridge.
      Mass spectrometry is an indispensable tool for the rapid and in-depth analysis of complex mixtures across diverse biologically important fields including metabolomics, lipidomics, and proteomics. These applications demand high speed instruments with subppm mass measurement accuracy over a wide dynamic range of sample concentrations. Here, we introduce an liquid chromatography-mass spectrometry/MS (LC-MS/MS) quadrupole time-of-flight mass spectrometer featuring a novel collision cell, a high dynamic range detector, and a compact multireflecting orthogonal time-of-flight analyzer. This innovative instrument achieves high analytical performance, acquiring full mass range spectra at 100,000 Full Width Half Maximum (FWHM) resolution up to 100 spectra/s acquisition speed. The instrument achieves excellent linearity within a dynamic range of 105, with a correlation coefficient R2 = 0.984. The speed, resolution and dynamic range are in excellent balance as demonstrated by the analysis of isotopically labeled lipids in human blood plasma.
    Keywords:  high resolution; lipidomics; multireflecting time-of-flight
    DOI:  https://doi.org/10.1021/jasms.5c00321
  8. Anal Chem. 2026 Jan 27.
    IsoPS-DIA: Dual Functionality of Absolute Targeted Quantification and Global Proteome Profiling.
    Hsin-Ju Chan, Huan-Chi Chiu, Li-Yu Chen, Chi-Ting Lai, Chia-Yen Wang, Shr-Uen Lin, Sung-Liang Yu, Yu-Ju Chen.
      Current gene testing reveals only the mutation status yet lacks protein expression of the actual drug target. A comprehensive evaluation requires methods that integrate the absolute quantification of mutant proteins with global profiling of downstream signaling and resistance pathways. Here, we present an isotope pair-separated data-independent acquisition (IsoPS-DIA) strategy with a dual functionality of multiplexed absolute quantification and global proteome profiling in a single run. IsoPS-DIA features a dual-window design: narrow consecutive windows separate light/heavy-isotope-labeled peptide pairs to reduce coisolation interference and maximize usable fragment ions, while wide variable windows capture proteome-wide information. Using EGFR mutations (L858R, G719A, Del19) in lung cancer cell lines as a model, IsoPS-DIA achieved subfemtomole sensitivity (LOQ 36-222 amol), excellent linearity across 4 orders of magnitude (R2 = 0.998-0.999), and high reproducibility (median CV ∼ 3%). For the first time, the method quantified endogenous EGFR and KRAS driver mutations alongside their wild-type counterparts, revealing allele-specific expression heterogeneity not captured by genomic variant allele frequency (VAFs). Simultaneously, IsoPS-DIA achieved >6,000 protein coverage across six cell lines, uncovering variability in EGFR signaling cascades and actionable variants such as KRAS-G12S. Benchmarking against parallel reaction monitoring (PRM), fixed scanning window (Fix-DIA) and variable scanning windows DIA (Var-DIA) confirmed IsoPS-DIA's superior accuracy and reproducibility without compromising proteome coverage. IsoPS-DIA is compatible with both Orbitrap and quadrupole time-of-flight mass spectrometry (Q-TOF) platforms using standard software, and we provide an open-source window-design tool to facilitate adoption. These results demonstrate the unique capability of IsoPS-DIA to bridge genotype and proteotype through precise, scalable, and reproducible quantification, offering a broadly applicable platform for precision oncology and other applications.
    DOI:  https://doi.org/10.1021/acs.analchem.5c05651
  9. Anal Chem. 2026 Jan 29.
    Retrieval-Augmented Language Models Enable Scalable Chemical Source Classification in Metabolomics Workflows.
    Prajit Rajkumar, Runbang Tang, Harshada Sapre, Jasmine Zemlin, Victoria Deleray, Jeong In Seo, Siddharth Mohan, Shipei Xing, Harsha Gouda, Yasin El Abiead, Shirley M Tsunoda, Haoqi Nina Zhao, Pieter C Dorrestein.
      There is a growing need for scalable chemical classification to support the interpretation of exposomics and metabolomics data. While structural categorization has been largely automated, functional and exposure-based labeling of chemicals remains a manual and time-consuming process. Here, we present chemsource, a flexible framework that integrates large language models (LLMs) with retrieval-augmented generation (RAG) to automate chemical classification. chemsource retrieves descriptive text from Wikipedia or PubMed abstracts based on chemical names and prompts LLMs to assign user-defined categories based on the retrieved content. We demonstrate classification into five exposure categories: endogenous metabolites, food molecules, drugs, personal care products, industrial chemicals, and combinations of these possibilities. Benchmarking against manually curated labels for 4,953 compounds showed 75% overall agreement, with category-level recall exceeding 75% across all classes. Expert review indicated that most discrepancies could be attributed to prompt ambiguity and incomplete manual labels rather than model failure. To demonstrate the utility of chemsource in metabolomics workflow, we applied it to eight public untargeted metabolomics data sets, revealing distinct exposure patterns across human biospecimens, mouse tissues, environmental dust, and consumer product extracts. chemsource is customizable via prompt editing, enabling diverse classification tasks without requiring coding expertise. The tool is freely available as a Python package (https://pypi.org/project/chemsource/). Text retrieval is free; classification requires user-supplied LLM API access.
    DOI:  https://doi.org/10.1021/acs.analchem.5c05301
  10. Bioinformatics. 2026 Jan 30. pii: btag050. [Epub ahead of print]
    ProteoGyver: A Fast, User-Friendly Tool for Routine QC and Analysis of MS-based Proteomics Data.
    Kari Salokas, Salla Keskitalo, Markku Varjosalo.
      Mass spectrometry-based proteomics generates increasingly large datasets requiring rapid quality control (QC) and preliminary analysis. Current software solutions often require specialized knowledge, limiting their routine use. We developed ProteoGyver (PG), an accessible, lightweight software solution designed for rapid QC and preliminary proteomics data analysis. PG provides automated QC metrics, intuitive graphical reports, and streamlined workflows for whole-proteome and interactomics datasets, significantly lowering the barrier to regular QC practices. The platform includes additional tools such as MS Inspector for longitudinal chromatogram inspection and Colocalizer for microscopy data. PG is easily deployed as a Docker container or standalone Python installation. PG is open-source and freely available in dockerhub and source code in github at github.com/varjolab/Proteogyver. Availability PG image and source code are available in github and dockerhub under LGPL-2.1.
    DOI:  https://doi.org/10.1093/bioinformatics/btag050
  11. J Chromatogr A. 2026 Jan 22. pii: S0021-9673(26)00062-2. [Epub ahead of print]1769 466732
    Integrated HPLC-UV validation and LC-MS/MS optimization with molecular networking and chemometrics for advanced characterization of chlorogenic acids in Ilex guayusa.
    Thomas Garzón, Jefferson V Pastuña-Fasso, Cristian Quiroz-Moreno, Melanie Ochoa-Ocampo, Evencio J Medina-Villamizar, Nina Espinosa de Los Monteros-Silva, Karel Diéguez-Santana, Jessica L Cooperstone, Zulay Niño-Ruíz, José R Almeida, Noroska G S Mogollón.
      Chlorogenic acids (caffeoylquinic acid isomers, CQAs) are major phenolic constituents of Ilex guayusa, but their comprehensive profiling in complex plant matrices is hindered by co-elution, overlapping UV spectra, and isomeric similarity in MS/MS. Rather than aiming to fully resolve isomer-specific quantification by MS, here we present an integrated workflow that couples validated HPLC-UV quantification of the major CQA (5-CQA) with an optimized UPLC-MS/MS strategy designed to improve MS1 peak integrity and expand MS/MS coverage for higher-confidence structural annotation. The HPLC-UV method showed excellent performance for targeted quantification of 5-CQA, including strong linearity (r² = 0.998), selectivity, sensitivity (LOQ = 0.25 mg/L), precision, and recovery. For LC-MS/MS, FastDDA acquisition (top-5 vs. top-15 precursors) revealed the expected trade-off between fragmentation depth and MS1 peak quality; however, post-acquisition raw-data merging restored MS1 fidelity and increased the number of detected features by 43%, enabling high-confidence annotation rather than quantitative discrimination of 16 metabolites and the propagation of oxidized CQA-related derivatives using feature-based molecular networking. Multivariate analyses (PCA, volcano plots, HCA) indicated that geographic location exerted the strongest influence on the metabolite composition, followed by sunlight exposure and plant age. Overall, the proposed workflow provides a practical framework that integrates robust chromatographic quantification with MS acquisition and data-processing optimization, thereby enhancing structural characterization and biological interpretation, rather than complete isomer-resolved quantification, of chlorogenic-acid-related chemistry across complex plant-derived and natural product matrices.
    Keywords:  Ilex guayusa; Metabolomic fingerprinting; Metabolomics; Validation of method
    DOI:  https://doi.org/10.1016/j.chroma.2026.466732
  12. Microlife. 2026 ;7 uqag002
    Less missing values-evaluation of proteomics workflows for the quantification of (small) proteins.
    Jürgen Bartel, Vaikhari Kale, Dennis Joshua Pyper, Harald Schwalbe, Sandra Maaß.
      Quantitative information on protein abundance is crucial to understand biological processes and is therefore frequently gathered in proteomic studies. However, the quality of a quantitative proteomic dataset is greatly affected by the number of missing values, which need to be minimized to produce robust and meaningful data. In this context, small proteins (≤100 amino acids) pose specific analytical challenges, which hinder their efficient identification and quantitative characterization in complex proteomes. In this study, methods for sample preparation and MS-data processing are systematically evaluated for their contribution to identification and quantification of small proteins of Clostridioides difficile 630 Δerm. Results show that small protein enrichment can enhance the number of identified and quantified proteins also for low abundant small proteins. Through application of spectral libraries for identification of MS spectra the number of robustly quantified proteins is increased and a lower limit of their detection is reached. Additionally, the dataset presented here is currently the most comprehensive protein repository for C. difficile covering 84.7% of the predicted proteome and 61.4% of all predicted small proteins of this important pathogen.
    Keywords:  Clostridioides difficile; SEPs; database search; low molecular weight proteome; mass spectrometry; peptidomics; sProteins; spectral library
    DOI:  https://doi.org/10.1093/femsml/uqag002
  13. Anal Bioanal Chem. 2026 Jan 28.
    Integrated targeted and untargeted analysis of polar peptides in foods using hydrophilic interaction liquid chromatography-data-independent acquisition-mass spectrometry.
    Boudewijn Hollebrands, Germaine Thong, Hans-Gerd Janssen.
      A hydrophilic interaction liquid chromatography-data-independent acquisition mass spectrometry (HILIC-DIA-MS) workflow was developed for simultaneous targeted semi-quantification of polar peptides and untargeted profiling of both peptides and other polar compounds in complex food matrices. The method uses a zwitterionic HILIC column optimized for separation of short polar peptides that are challenging to retain on reversed-phase columns. Many of these peptides contain charged amino acids and contribute to basic taste modalities such as umami and saltiness. Both targeted peptide analysis and comprehensive untargeted profiling were achieved by applying DIA-MS detection. This data acquisition mode was shown to be reproducible and sensitive while enabling retrospective data processing. High-resolution MS1 scans (60.000 FWHM), combined with fast MS2 scans and DIA mass windows of 15 m/z yielded highly repeatable and selective LC-MS profiles, allowing differentiation of structural isomers (e.g., alpha-glutamyl (umami) and gamma-glutamyl (kokumi)). The method was validated using taste-relevant dipeptides, demonstrating low detection limits (0.1-0.9 µM), good intra-day and inter-day precision, and high recovery (96%) in commercial soy sauce and yeast extract matrices. The workflow was further applied to the relative quantification of peptides and the untargeted profiling of characteristic molecular features in cheese, ham, and extracts from dried food ingredients. The integration of targeted and untargeted analyses demonstrates the suitability of HILIC-DIA-MS for comprehensive characterization of polar compounds in food systems.
    Keywords:  Food peptides; HILIC-DIA-MS; Polar bioactive compounds; Retrospective analysis; Untargeted profiling
    DOI:  https://doi.org/10.1007/s00216-026-06322-7
  14. Anal Bioanal Chem. 2026 Jan 28.
    Relevance analysis of N-glycan variations in C2C12 cells and mouse serum under simulated microgravity using a quaternary phosphonium hydrazide labeling strategy-based mass spectrometry quantitation approach.
    Qian-Ru Ma, Jie-Cheng Lu, Yan-Qing Li, Hong-Jie Lan, Yan Liu, Yu-Fen Zhao.
      Mass spectrometry combined with stable isotope labeling is a powerful technique for detecting disease-related changes in glycosylation patterns and identifying potential biomarkers. However, stable isotope labeling reagents that simultaneously offer high sensitivity, low cost, and stable sialic acid modifications remain scarce. In this study, we developed a convenient and cost-effective microwave-assisted method for synthesizing a stable isotopic quaternary phosphonium hydrazide labeling reagent pair, 14N/15N-P4HZD, for the quantitation difference analysis of N-glycans using HPLC-ESI-HRMS with high sensitivity and convenience. This strategy features high labeling efficiency, excellent reproducibility, and strong linearity (R2 = 0.9984) within a dynamic range spanning two orders of magnitude. The reagent pair is compatible with multiple ion source mass spectrometers and front-end chromatographic separation technologies. In particular, it enhances the ionization efficiency of sialylated N-glycans and facilitates their detection. The relative quantification method has been effectively applied to analyze the variations in N-glycomic profiles from two muscular atrophy models induced by simulated microgravity, specifically the C2C12 cell and hindlimb unloading mouse serum. We discover that these variations display characteristic relevance in both models. N-Glycans Man3GlcNAc3Fuc1 and Man3GlcNAc4Gal1Fuc1Sia1 exhibit their potential as biomarkers for the early diagnosis of muscular atrophy. The mass spectrometry method based on the 14N/15N-P4HZD reagent pair offers a convenient and feasible strategy for the difference analysis of N-glycomics, demonstrating significant potential for application in the discovery of clinical biomarkers.
    Keywords:   N-Glycomics; HPLC-ESI-HRMS; Muscle atrophy; Phosphonium hydrazide; Simulated microgravity; Stable isotope relative quantitation
    DOI:  https://doi.org/10.1007/s00216-026-06329-0
  15. Rapid Commun Mass Spectrom. 2026 Apr 30. 40(8): e70038
    Phospholipid Profiling Established by Structure-Rich Fragments for Molecular Species Level Shotgun Analysis.
    Rong Chen, Amber H Jannasch, Bruce R Cooper, Jonathan H Shannahan, Christina R Ferreira.
       RATIONALE: Accurate identification of phospholipid molecular species remains a major challenge in shotgun lipidomics because conventional tandem mass spectrometry typically resolves only one structural moiety at a time. This structural ambiguity limits confident lipid biomarker discovery and biological interpretation. Improving structural specificity without sacrificing analytical speed is therefore critical for lipidomics and disease-related studies.
    METHODS: Electrospray ionization tandem mass spectrometry was performed using direct infusion on a triple quadrupole mass spectrometer operated in multiple reaction monitoring (MRM) mode. MRMs were designed based on structure-rich phospholipid fragments containing both the headgroup and one fatty acyl chain. Lipids were extracted from mouse liver and brain tissues and analysed without chromatographic separation, and normal-phase LC was used for lipid headgroup confirmation only.
    RESULTS: Structure-rich MS/MS transitions enabled molecular species identification of both diacyl and ether phospholipids. 15 PUFA-containing phospholipids were identified as candidate biomarkers differentiating healthy and metabolic syndrome mouse livers, revealing opposing regulation among structurally similar species supported by complementary fragmentation and LC evidence. In mouse brains, three ether lipid biomarkers were discovered, including plasmalogens and plasmanyl lipids, with distinct disease-associated trends.
    CONCLUSION: This study demonstrates that structure-rich MS/MS transitions substantially improve phospholipid structural specificity in shotgun lipidomics while maintaining high throughput. The method enables reliable identification of individual lipid species with minimal isomer interference and is readily compatible with existing workflows. This strategy offers a practical path toward more precise lipid biomarker discovery and mechanistic insight into metabolic disease.
    Keywords:  mass spectrometry; metabolic syndrome; phospholipids; plasmalogens; shotgun lipidomics
    DOI:  https://doi.org/10.1002/rcm.70038
  16. Physiol Plant. 2026 Jan-Feb;178(1):178(1): e70735
    Introduction of 4D-Proteomics: A Breakthrough in Analyzing the Plant Proteomes During Stress Conditions.
    Ravi Gupta.
      Proteomics is defined as the identification, quantification, and characterization of the complete set of proteins expressed in a cell or tissue under specific conditions. The last two decades have witnessed rapid advancements in proteomics technologies, including the development of the Data-Independent Acquisition (DIA) mode, which has significantly improved the sensitivity, reproducibility, and depth of proteome coverage. These advancements, together with the development of cutting-edge data analysis tools, have undoubtedly facilitated the identification of stress-responsive proteins and potential biomarkers in different organisms. However, the identification of such stress-responsive proteins, particularly in plants, remains relatively challenging because of the presence of various high-abundance proteins such as RuBisCO, which hinders the identification and subsequent characterization of these stress-responsive proteins due to their low abundance. More recently, a four-dimensional (4D) proteomics approach has been introduced, which includes "ion mobility" as the fourth dimension to classical quantitative proteomics. This 4D-proteomics method utilizes trapped ion mobility spectrometry (TIMS) combined with parallel accumulation-serial fragmentation (PASEF), which significantly enhances the sensitivity and coverage of proteomics experiments, thus allowing the detection of low-abundance proteins. This review highlights the evolution of proteomic technologies, the development of the 4D proteomics workflow, and their potential application in unraveling the molecular mechanisms underlying plant responses to environmental stress conditions. In essence, this review article provides a comprehensive overview of the state-of-the-art in proteomics, emphasizing its transformative impact on plant science research and its potential to understand crop stress resilience.
    Keywords:  PASEF; ion‐mobility; low‐abundance proteins; post‐translational modifications; proteomics
    DOI:  https://doi.org/10.1111/ppl.70735
  17. Clin Transl Immunology. 2026 ;15(1): e70078
    Integrating mass spectrometry-based multi-omic signatures of extracellular vesicles: from discovery to clinical translation.
    Akila Wijerathna-Yapa, Dimitri Aubert, Arutha Kulasinghe.
      Extracellular vesicles (EVs) are increasingly recognised as key mediators of intercellular communication and disease progression. Their capacity to carry bioactive molecules, namely proteins, lipids and metabolites, reflects the physiological and pathological states of their cells of origin, making them surrogates for diagnostic, prognostic and therapeutic endpoints. Recent advances in mass spectrometry have enabled comprehensive, high-resolution profiling of EVs across multiple omics layers. Proteomics has uncovered both conserved and disease-specific protein markers; lipidomics has revealed structurally distinct membrane compositions influencing EV stability and function; and metabolomics has captured dynamic snapshots of cellular metabolism. However, significant challenges persist for standardisation and interpretation of EVs, which include variation in EV isolation purity, scalability, EV heterogeneity and cross-study comparability. This perspective critically synthesises findings from recent EV multi-omics studies and proposes a conceptual framework for integrating these omics layers to better define EV identity and functionality. We highlight emerging clinical applications and outline future directions involving single-vesicle omics and the rational engineering of therapeutic EVs. The integration of multi-omics approaches with translational aims holds promise for advancing EVs from experimental tools to new pillars of precision medicine.
    Keywords:  exosomes; extracellular vesicles; liquid biopsy; mass spectrometry; multi‐omics
    DOI:  https://doi.org/10.1002/cti2.70078
  18. Histochem Cell Biol. 2026 Jan 29. 164(1): 6
    Peroxisome deficiency impacts metabolites of lysine, lipid, and polyamine metabolism in Saccharomyces cerevisiae.
    Tjasa Kosir, Daniel Baptista Alves Malheiro, Lea Giørtz Johnsen, Hirak Das, Bettina Warscheid, Morten Danielsen, Ida J van der Klei.
      Peroxisomes are eukaryotic organelles primarily known for their conserved roles in fatty acid β-oxidation and hydrogen peroxide detoxification. These organelles are also involved in a diverse range of other metabolic and non-metabolic functions. We recently compared the transcriptome and proteome of Saccharomyces cerevisiae wild-type and peroxisome-deficient (pex3) cells. This study uncovered the major processes and metabolic pathways that are influenced by peroxisomes. Here we performed a mass spectrometry-based analysis of intracellular metabolites of the same two strains. This led to the identification of 160 compounds, of which seven exhibited significant differences between wild-type and pex3 cells (glycerol-3-phosphate, carnitine, pantothenate, acetyl-spermidine, propionyl-carnitine, and aminolevulinic acid). Notably, we observed elevated lysine levels in pex3 cells, consistent with previous findings, which confirms the reliability and accuracy of our analytical approach. In addition, changes in carnitine compounds were measured, aligning with the proposed occurrence of a carnitine shuttle across the peroxisomal membrane. By integration of the current metabolomic data with the previously obtained transcriptomic and proteomic data, we provide a broader view of the metabolic impact of peroxisome deficiency. We show that, in addition to the well-known function of yeast peroxisomes in lipid and fatty acid degradation, these organelles are also involved in lipid synthesis. Furthermore, our study revealed that peroxisome deficiency affects polyamine homeostasis.
    Keywords:  Carnitine; Glycerol-3-phosphate; Lysine; Metabolomics; Peroxisomes; Polyamines; Yeast
    DOI:  https://doi.org/10.1007/s00418-025-02456-4
  19. BMC Bioinformatics. 2026 Jan 28.
    MIMI: Molecular Isotope Mass Identifier for stable isotope-labeled Fourier transform ultra-high mass resolution data analysis.
    Nabil Rahiman, Michael A Ochsenkühn, Shady A Amin, Kristin C Gunsalus.
      
    Keywords:  FT-ICR; Isotope labeling; Mass spectrometry; Metabolomics
    DOI:  https://doi.org/10.1186/s12859-025-06348-1
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