bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2026–04–26
twenty-six papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Miniaturized chromatography-based lipidomics methods towards single-cell analysis.
  2. METLIN 960 K: An Empirical Tandem Mass Spectrometry Data Resource.
  3. Confident Structural Annotation of Bile Acid Isomers Combining Electron-Activated Dissociation or Ion Mobility with High-Resolution Mass Spectrometry in Metabolomics.
  4. Evaluation of Data-Independent Acquisition-Based Phosphoproteomics Analysis in Mammalian and Bacterial Systems.
  5. Differential protein expression analysis of quantitative mass spectrometry data using DEqMS.
  6. Structure-informed deep generation enables de novo metabolite annotation in untargeted metabolomics.
  7. Practical considerations for optimizing untargeted metabolomics with Synapt XS HRMS: A tutorial.
  8. Direct Comparison of MRM-MS and PRM-MS Methods for Quantitative Ganglioside Analysis.
  9. [Glutamine metabolic reprogramming in regulating the occurrence and development of osteosarcoma].
  10. Development of a multi-targeted metabolomics platform for semi-quantification of faecal metabolites: a proof-of-concept analysis in human faeces.
  11. A Novel Workflow for Fast Elucidation of Drug Metabolites for Screening-Combining In Silico Metabolite Prediction With Trapped Ion Mobility QTOF-MS.
  12. A Potassium Phosphoramidate-Based Strategy for Compiling and Profiling the Noncanonical N-Phosphoproteome of Nasopharyngeal Carcinoma.
  13. Beyond Reanalysis: Critical Issues in Data Reuse for Solid Tumor Proteomics.
  14. Lipid droplet biogenesis as a metabolic switch regulating ferroptosis sensitivity in cancer cells.
  15. Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.
  16. FlashMRM: An Automated Platform for MRM Method Generation and Iterative Optimization Based on High-Resolution Spectra.
  17. Evolving LC-MS hyphenations: strengths, limitations, and emerging trends in bioanalytical applications.
  18. Metabolic and lipidomic stability in EDTA plasma from hospitalized patients: impact of refrigerated storage time and additional freeze-thaw cycle.
  19. Deeper is not always better in plasma proteomics.
  20. Enabling Next-Generation Mass Spectrometry-Based Proteomics: Standards, Proteoform Resolution, and FAIR, Reproducible, and Quantitative Analysis.
  21. Multidimensional and Hybrid Separation Strategies for Body Fluids and Tissues: Comparative Performance and Translational Considerations.
  22. Best Practices in GC-MS and GC × GC-MS-Based Metabolomics and Volatile Analyses: An International Survey.
  23. High-throughput single-vesicle imaging platform for direct extracellular vesicle profiling of human plasma.
  24. Proteogenomics: decoding cancer in multiple dimensions.
  25. Urinary Proteomics: Biological Foundations, Analytical Frameworks, and Clinical Translation Across Human Diseases.
  26. Your field needs your data! Raising the standard of quality control and data accessibility in reproductive proteomics.
  1. Anal Bioanal Chem. 2026 Apr 21.
    Miniaturized chromatography-based lipidomics methods towards single-cell analysis.
    Klidel Fae Rellin, Michael Witting.
      Lipidomics provides detailed insight into lipid metabolism and cellular function, but conventional workflows typically rely on bulk samples that mask cellular heterogeneity. Advances in analytical chemistry are enabling lipid analysis from extremely limited material, driving the development of miniaturized chromatography-mass spectrometry (LC-MS) workflows for low-input and single-cell studies. This review summarizes recent progress in miniaturized chromatography-based lipidomics. Reducing chromatographic scale improves ionization efficiency, sensitivity, and separation performance while minimizing sample consumption. We discuss key enabling technologies, such as low-flow electrospray interfaces and the integration of ion mobility (IM) spectrometry as an orthogonal separation dimension for lipid identification. Methodological considerations for low-input lipidomics are also addressed, particularly sample preparation and quantitative challenges at picogram-scale analyte levels. Finally, we highlight future directions in automation, microfluidics, and multidimensional separations. Together, these developments position miniaturized chromatography as a critical platform for advancing single-cell lipidomics and high-resolution studies of lipid metabolism.
    Keywords:  Ion mobility; Lipidomics; Liquid chromatography; Mass spectrometry; NanoLC; Single cell lipidomics
    DOI:  https://doi.org/10.1007/s00216-026-06488-0
  2. Anal Chem. 2026 Apr 21.
    METLIN 960 K: An Empirical Tandem Mass Spectrometry Data Resource.
    Winnie Uritboonthai, Aries E Aisporna, Linh Hoang, Corey Hoang, Elizabeth M Billings, Gary Siuzdak.
      METLIN 960 K represents the largest collection of experimentally acquired small-molecule MS/MS spectra currently available. We introduce a reengineered publicaly accessible METLIN platform integrating high-resolution tandem mass spectrometry (MS/MS) data for over 960,000 empirically validated molecular standards. This scale was enabled by a high-throughput experimental framework integrating acoustic droplet ejection with high-throughput LC-MS/MS acquisition, allowing systematic empirical generation of MS/MS spectra from authentic standards. In addition to scale, METLIN 960 K provides a uniquely standardized MS/MS data set, with spectra acquired under controlled and consistent conditions across ionization modes and collision energies, enabling reproducible spectral comparison and machine-learning applications. Each compound is characterized by MS/MS spectra acquired in both positive and negative ionization modes across four collision energies (0, 10, 20, and 40 eV), enabling comprehensive fragmentation coverage and improved structural annotation. Designed as a reference library for XCMS-METLIN and compatible with machine-learning workflows, METLIN 960 K supports high-fidelity spectral matching, neutral loss analysis, and filtering of misannotations, including annotation of in-source fragments and biologically synchronized ranking of candidate metabolites. The platform also provides empirically derived MRM transitions on all standards (via METLIN-MRM), supporting quantitative method development across a chemically diverse range of metabolites, natural products, lipids, peptides, pharmaceuticals, and toxicants. A redesigned interface enables efficient querying by exact mass, formula, or structure with direct access to curated spectra and metadata. Two additional resources enhance identification: (1) METLIN Core, a high-frequency-use subset for rapid searching, and (2) > 1.02 million additional structures without MS/MS data for hypothesis generation. Derived exclusively from authentic standards, METLIN 960 K (https://metlin.scripps.edu) provides the largest publicly available empirical MS/MS database, delivering high-confidence annotation for both untargeted and targeted mass spectrometry workflows.
    DOI:  https://doi.org/10.1021/acs.analchem.5c08031
  3. ACS Omega. 2026 Apr 14. 11(14): 21878-21889
    Confident Structural Annotation of Bile Acid Isomers Combining Electron-Activated Dissociation or Ion Mobility with High-Resolution Mass Spectrometry in Metabolomics.
    Thomas A Brunet, Rémy De Boni, Justine Massias, Luiz F M Rodrigues, Guillaume Rossignol, Arnaud Salvador, Yohann Clément, Carlos A Sorgi, Sophie Ayciriex.
      Accurate identification of lipid isomers remains a major challenge in metabolomics, particularly for bile acids (BAs), whose biological functions critically depend on the number, position, and stereochemistry of hydroxyl groups as well as on the nature of their conjugation. Conventional tandem mass spectrometry (MS/MS) with collision-induced dissociation (CID) often fails to resolve isomeric lipids differing only in subtle structural features, leading to an underestimation of lipidome complexity. Here, we present an analytical framework integrating electron-activated dissociation (EAD) or ion mobility spectrometry (IM) with high-resolution liquid chromatography-mass spectrometry to improve annotation of BA isomers in untargeted metabolomics. EAD provides nonergodic fragmentation that preserves labile bonds and reveals site-specific details, while IM separates ions by their collision cross section (CCS), supplying orthogonal structural information. Using synthetic standards and human plasma extracts, we show that EAD-derived diagnostic ions markedly enhance structural discrimination and enable consistent and reproducible identification of BA isomers compared to CID-only workflows, while CCS measurements alone lack sufficient robustness to reliably distinguish closely related BA isomers across measurements. The integrated data set enables confident assignment of positional and conjugation variants, contributing to a more complete representation of BA diversity. This multidimensional approach offers a robust platform for structural lipidomics and supports the establishment of transferable spectral and mobility libraries to advance metabolome annotation accuracy.
    DOI:  https://doi.org/10.1021/acsomega.5c12361
  4. J Proteome Res. 2026 Apr 21.
    Evaluation of Data-Independent Acquisition-Based Phosphoproteomics Analysis in Mammalian and Bacterial Systems.
    Sophie Hongfei Liu, Daniel H Call, Reta Birhanu Kitata, Issac K Attah, Rosalie K Chu, Marina A Gritsenko, Vishant Boradia, Christoph Grundner, Le Z Day, Jon M Jacobs.
      Recent advancements in data-independent acquisition (DIA) workflows have greatly increased the depth and throughput of mass spectrometry-based proteomics. However, many of these advancements remain undercharacterized for phosphoproteomics, particularly in bacteria that have fewer protein phosphorylation events. We evaluated the impact of instrument/gradient length (Orbitrap Astral, 15 min; Orbitrap Exploris, 90 min), analysis tools (DIA-NN 1.9.2, DIA-NN 2.3, FragPipe, Spectronaut), and library search strategies (spectral-library versus library-free) on phosphoproteomic coverage and quantification in a human cell line and bacterial lysate. The 15 min Astral analysis identified similar numbers of phosphopeptides compared to a 90 min Exploris acquisition method across all analysis tools, demonstrating a substantial advantage in throughput. Within the Astral workflow, Spectronaut provided the highest phosphoproteome coverage, whereas DIA-NN 1.9.2, DIA-NN 2.3, and FragPipe exhibited less quantitative variation. We compared library-free and spectral-library search strategies using the Mycobacterium tuberculosis (Mtb) H37Rv strain. We observed greater phosphopeptide identifications yet different phosphopeptide profiles via library-free searches in comparison to spectral-library searches. When comparing Mtb harvested at different growth phases, phosphosite fold-changes were consistent, whereas statistical significance varied between tools. This work can be informative for workflow selection in DIA phosphoproteomics studies, especially for biological samples with low phosphorylation frequencies.
    Keywords:  Astral; DIA-NN; FragPipe; Mycobacterium tuberculosis; Spectronaut; data-independent acquisition; library-free; mass spectrometry; phosphoproteomics; spectral-library
    DOI:  https://doi.org/10.1021/acs.jproteome.5c01142
  5. Nat Protoc. 2026 Apr 22.
    Differential protein expression analysis of quantitative mass spectrometry data using DEqMS.
    Yafeng Zhu, Olena Berkovska, Lingshuo Wang, Mei Yang, Henrik J Johansson, Georgios Mermelekas, Mahshid Zarrineh, Dong Yin, Lukas M Orre, Janne Lehtiö.
      DEqMS is an R package-based statistical tool for differential protein expression analysis in quantitative mass spectrometry-based proteomics. It implements a robust Bayesian method for accurate variance estimation that accounts for the number of mass spectrometry features used for protein quantification (number of peptide precursors or peptide spectrum matches). Originally validated for data-dependent acquisition proteomics, DEqMS now extends to data-independent acquisition workflows, as demonstrated using both spike-in and real-world datasets. Given a peptide- or protein-level quantification table with mass spectrometry feature count as inputs, DEqMS outputs a protein- or gene-level results table containing fold changes and multiple statistics (t-values, P value, among others) adjusted according to mass spectrometry feature count. Here we detail the use of the DEqMS R package. This updated workflow broadens DEqMS's applicability, enabling researchers with basic R programming knowledge to identify proteins with significantly altered abundance between sample groups across diverse quantitative proteomics datasets. DEqMS is available to install at https://bioconductor.org/packages/DEqMS/ .
    DOI:  https://doi.org/10.1038/s41596-026-01349-7
  6. Nat Commun. 2026 Apr 20.
    Structure-informed deep generation enables de novo metabolite annotation in untargeted metabolomics.
    Hongmiao Wang, Haosong Zhang, Zheng-Jiang Zhu.
      Metabolite annotation, especially the discovery of unknown metabolites, remains a fundamental challenge in mass spectrometry-based untargeted metabolomics due to limited reference mass spectra. Here we present MetGenX, a structure-informed encoder-decoder neural network that enables efficient and controllable generation of metabolite structures directly from MS2 spectra. By reformulating the spectrum-to-structure task as a structure-to-structure generation problem, MetGenX significantly improves generation accuracy and chemical space coverage. In independent tests, it achieved top-1 accuracy of 55.9% on 1388 NIST MS2 spectra and 68.5% on 1681 spectra from real biological samples, outperforming existing in silico tools. Its structure-informed design ensures robust performance across both positive and negative ionization modes without retraining. Applying a multi-step annotation workflow to mouse liver untargeted metabolomics data, MetGenX identified two previously uncharacterized metabolites absent from major human metabolome databases. These results demonstrate MetGenX's strong potential to advance de novo metabolite annotation and facilitate the discovery of uncharacterized chemical entities.
    DOI:  https://doi.org/10.1038/s41467-026-72149-6
  7. J Chromatogr A. 2026 Apr 14. pii: S0021-9673(26)00321-3. [Epub ahead of print]1778 466991
    Practical considerations for optimizing untargeted metabolomics with Synapt XS HRMS: A tutorial.
    Lars Kaiser, Oliver Riester, Hans-Peter Deigner.
      Metabolomics, the systematic study of small molecules in biological samples, has rapidly emerged as a key discipline for understanding metabolism across organisms. LCHRMS displays the dominant technique for global metabolomics due to its high resolution and fast detection. In addition, some instruments offer the option of separating analytes based on their mobility in a carrier gas (ion mobility separation). However, transferring existing protocols between instruments is often limited by differences in hardware, ion mobility capabilities, and instrument generations. In particular, traveling wave ion mobility spectrometry (TWIMS) can induce ion heating and fragmentation, primarily affecting labile polar metabolites (<500 Da), requiring individual adjustment of instrument settings. Because of component redesigns, parameters of previous systems cannot be directly transferred to the Synapt XS; this tutorial provides an overview of modifiable settings. We focus on the application of Synapt XS HRMS for untargeted metabolomics of polar and non-polar metabolites across diverse biological matrices. However, the procedures described for sample preparation, chromatographic separation, and data analysis are largely transferable to other high-performance devices. The tutorial includes step-by-step protocols for sample preparation, chromatographic separation of polar and non-polar compounds, adjustment of TWIMS parameters for labile metabolites, and the use of Progenesis QI, including creation of in-house spectral libraries and integration of existing fragment databases. Additionally, strategies for data acquisition modes and initial quality control and preprocessing are presented and discussed. Together, these methods provide a framework for highly sensitive, reproducible metabolomics analyses and, apart from MS-specific parameters, are broadly applicable to modern mass spectrometers from various manufacturers with comparable performance.
    Keywords:  High-resolution mass spectrometry; Ion heating; LC-MS protocols; Synapt XS; Traveling wave ion mobility; Untargeted metabolomics
    DOI:  https://doi.org/10.1016/j.chroma.2026.466991
  8. J Am Soc Mass Spectrom. 2026 Apr 24.
    Direct Comparison of MRM-MS and PRM-MS Methods for Quantitative Ganglioside Analysis.
    Akeem Sanni, Abderrahmane Koraich, Judith Nwaiwu, Arvin Saffarian Delkhosh, Yehia Mechref.
      Gangliosides are structurally diverse, low-abundance glycosphingolipids central to neuronal signaling and cancer progression; however, their quantitative analysis is hindered by low ionization efficiency, structural heterogeneity, and complex isomeric patterns. Targeted mass spectrometry (MS) represents a powerful approach for resolving these challenges, yet systematic comparisons of multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) for native gangliosides remain limited. Here, we developed and optimized a targeted LC-MS/MS workflow to directly evaluate PRM on a Q-Exactive HF Orbitrap versus MRM on a TSQ Vantage triple quadrupole. Collision-energy optimization revealed distinct fragmentation behaviors across platforms, identifying optimal normalized collision energies (NCE) for PRM of 28 for GD1a and 25 for GD2, GT1b, GM1, and GQ1b, whereas optimal CE values for MRM were 35 for GD1a, GD2, and GT1b, and 30 for GQ1b. Additionally, PRM enabled multiplexing up to 15 transitions per analyte, improving signal-to-noise up to ∼4-fold and reducing %RSD through postacquisition transition summation. High-energy collision dissociation (HCD) used in PRM generated a richer array of fragment ions, including informative cross-ring cleavages and low-mass diagnostic ions, providing superior structural confidence compared to CID fragmentation in MRM. Notably, PRM uniquely enabled quantification of GM1, which exceeded the mass range of the triple quadrupole instrument. Applied to post-mortem human brain tissue extracts, PRM distinguished GD1a and GD1b isomers with high specificity. These findings establish PRM as a robust, highly sensitive, and structurally informative platform for comprehensive ganglioside profiling in complex biological matrices.
    Keywords:  CID; HCD; LC-MS/MS; MRM; PRM; gangliosides
    DOI:  https://doi.org/10.1021/jasms.6c00071
  9. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2025 Dec 28. pii: 1672-7347(2025)12-2425-13. [Epub ahead of print]50(12): 2425-2437
    [Glutamine metabolic reprogramming in regulating the occurrence and development of osteosarcoma].
    Yaxuan Xu, Xinyu Zhao, Yuyu Gao, Yujun Lian, Yingchun He, Huai Tao.
      Excessive cellular proliferation and metabolic reprogramming are important characteristics of cancer cells. Cancer cells promote excessive proliferation and growth by altering coordinated metabolic pathways. In terms of glucose metabolism, most cancer cells exhibit increased glucose uptake and lactate production even under aerobic conditions, known as the Warburg effect. Increased glucose uptake not only provides energy but also supplies essential carbon sources for the biosynthesis of nucleotides, lipids, and proteins. During this process, decreased pyruvate dehydrogenase activity leads to disruption of the tricarboxylic acid cycle (TCA), thereby increasing tumor cell dependence on other nutrients. In addition to glucose, glutamine (Gln) is also a key metabolic substrate required for cancer cell growth and proliferation. It provides both carbon and nitrogen sources to support the synthesis of ribose, non-essential amino acids, citrate, and glycerol, and compensates for the reduced oxidative phosphorylation caused by the Warburg effect. In human plasma, Gln is one of the most abundant amino acids. Normal cells synthesize Gln through glutaminase (GLS), but the Gln synthesized by tumor cells is insufficient to meet the demands of rapid proliferation, resulting in "Gln dependence". Most cancers, including osteosarcoma, show significantly increased demand for Gln. Metabolic reprogramming enables tumor cells to gain survival advantages in maintaining redox homeostasis and biosynthesis while forming unique metabolic phenotypes. Focusing on key enzymes and transporters involved in Gln metabolism in osteosarcoma and identifying potential targets may provide new ideas and directions for drug development.
    Keywords:  amino acid transporters; glutaminase; glutamine metabolism; metabolic reprogramming; osteosarcoma
    DOI:  https://doi.org/10.11817/j.issn.1672-7347.2025.250321
  10. Microbiome Res Rep. 2026 ;5(1): 7
    Development of a multi-targeted metabolomics platform for semi-quantification of faecal metabolites: a proof-of-concept analysis in human faeces.
    Kayo Ikuta, Akihiro Kunisawa, Itaru Dekio, Arisa Ito, Qiuyi Wang, Kazuhiro Kawamura, Masaki Yamada, Sumi Nakamura, Yoshihiro Hayakawa, Takuma Higurashi, Junko Iida, Eiichiro Fukusaki, Toru Suzuki, Mitsuharu Matsumoto.
      Objectives: Targeted metabolomic analysis of faecal samples has been limited by narrow chemical coverage. Here, we established a multiplexed, triple quadrupole mass spectrometry (TQMS)-based targeted metabolomics workflow. This workflow allows accurate detection and semi-quantification of diverse faecal metabolites and provides a methodological platform for studying host-microbiome metabolic interactions. Methods: Faecal metabolomes from germ-free (GF) mice, ex-germ-free (Ex-GF) mice, and human participants were analysed using TQMS-based targeted metabolomics. The analysis comprised multiple methods targeting amino acids and their derivatives, carbohydrates, short-chain fatty acids, bile acids, lipid mediators, and phospholipids. Results: In total, 607 low-molecular-weight metabolites in 44 chemical categories were detected and semi-quantified. Faecal metabolomes of GF and Ex-GF mice were analysed, uncovering 341 intestinal microbiome-dependent metabolites. A proof-of-concept analysis using faecal samples from five patients with colorectal cancer demonstrated the successful application of this platform to human clinical material, highlighting its strong potential for future disease-oriented metabolomic investigations. Conclusion: We developed a multi-targeted faecal metabolomics platform that substantially expands the chemical space accessible to targeted analysis. This workflow provides a methodological foundation for future large-scale and translational studies.
    Keywords:  Intestinal microbiome; faeces; germ-free mice; metabolome; triple quadrupole mass spectrometry
    DOI:  https://doi.org/10.20517/mrr.2025.85
  11. Drug Test Anal. 2026 Apr 22.
    A Novel Workflow for Fast Elucidation of Drug Metabolites for Screening-Combining In Silico Metabolite Prediction With Trapped Ion Mobility QTOF-MS.
    Annette Zschiesche, Birgit Schneider, Ilona Nordhorn, Carsten Baessmann, Laura M Huppertz, Jürgen Kempf.
      Urine is one of the preferred matrices for standard toxicological analysis, which makes the inclusion of drug metabolites in targeted and untargeted screening mandatory. Mass spectrometry is key for substance identification, but updating methods for emerging substances like new psychoactive substances (NPS) is challenging due to the limited availability of reference standards for metabolites. This is particularly problematic for drugs that are barely or not detectable at all in urine. Insufficient metabolic knowledge and lack of spectral data carry the risk of false negatives. This study evaluates a non-targeted workflow using ultrahigh-performance liquid chromatography-trapped ion mobility spectrometry time-of-flight mass spectrometry (UHPLC-timsTOF-MS) and dedicated processing software (MetaboScape), integrating in silico metabolite prediction (BioTransformer), fragmentation (MetFrag), collision cross-section (CCS) prediction, and library searching. Quetiapine was selected as a model compound. Phase I metabolites were generated via pooled human liver microsomes (pHLMs) and analyzed by UHPLC-timsTOF-MS. Features were extracted and annotated with MetaboScape. The workflow successfully annotated 20 phase I metabolites in the pHLM assay, with 13 confirmed by library matching and 18 by BioTransformer. These metabolites were added to a targeted UHPLC-QTOF-MS method for analysis of 30 quetiapine-positive ante- and post-mortem urine samples from forensic casework. This revealed N-, O-dealkyl and carboxylated metabolites as the most abundant biomarkers in human urine. This integrated approach enables rapid and reliable metabolite detection, supports biomarker discovery, and facilitates routine screening updates, especially for substances without reference standards. Although not intended for exhaustive metabolic characterization, it offers practical applicability in evolving drug landscapes.
    Keywords:  CCS; PASEF; biomarkers; in silico prediction; trapped ion mobility spectrometry
    DOI:  https://doi.org/10.1002/dta.70062
  12. Anal Chem. 2026 Apr 21.
    A Potassium Phosphoramidate-Based Strategy for Compiling and Profiling the Noncanonical N-Phosphoproteome of Nasopharyngeal Carcinoma.
    Yuqiu Wang, Minchu Tang, Xiaole Song, Wenfan Xie, Yunjie Yin, Jing Gao, Yansheng Liu, Hu Zhou, Hongmeng Yu.
      Protein N-phosphorylation, especially in eukaryotes, plays a critical role in cell signal transduction and tumorigenesis. However, the N-phosphoproteome has not been extensively profiled due to its low abundance and chemical lability. Herein, we developed a potassium phosphoramidate (PPA)-based strategy for the generation of high-quality N-phosphorylation spectral libraries to profile the N-phosphoproteome. This approach relies on the chemical phosphorylation of basic amino acid residues (lysine, arginine, and histidine) by PPA, followed by peptide-level fractionation, phosphopeptide enrichment, spectral library generation, and data-independent acquisition mass spectrometry (DIA-MS) analysis. To develop this method, the feasibility and reproducibility were first validated using N-phosphorylated bovine serum albumin (N-pho-BSA). Phosphoproteome analysis of HEK293T lysates further demonstrated that the PPA-based library data-independent acquisition (PAlibDIA) achieved superior coverage and quantification reproducibility compared with conventional data-dependent acquisition (DDA) and direct DIA. This PAlibDIA approach was then employed to characterize N-phosphosites in human nasopharyngeal carcinoma (NPC), resulting in 493 pHis, 714 pLys, and 557 pArg sites; 85% are novel sites that were not previously reported. Further data analysis revealed that differentially regulated N-phosphosites were associated with RNA splicing, chromatin remodeling, nucleosome assembly, and multiple signaling pathways. Together, our PAlibDIA has great potential for comprehensive and in-depth analysis of the N-phosphoproteome, offering new opportunities to uncover regulatory mechanisms and identify potential therapeutic targets.
    DOI:  https://doi.org/10.1021/acs.analchem.5c06517
  13. Proteomes. 2026 Apr 07. pii: 16. [Epub ahead of print]14(2):
    Beyond Reanalysis: Critical Issues in Data Reuse for Solid Tumor Proteomics.
    Federica Franzetti, Nicole Giugni, Manuel Airoldi, Heather Bondi, Tiziana Alberio, Mauro Fasano.
      Proteomics represents a fundamental layer for understanding the molecular complexity of solid tumors by quantifying protein abundance and capturing proteoforms and post-translational modifications undetected in genomics or transcriptomics analyses. As mass spectrometry-based technologies and public proteomics repositories have expanded, opportunities for large-scale data reuse have grown accordingly. Nevertheless, data availability has not been translated into straightforward reuse: differences in experimental design, acquisition strategies, quantification workflows and metadata quality still limit the reproducibility and cross-study comparability. In this review, proteomics data reuse is defined as the systematic reanalysis and integration of publicly available datasets to support precision oncology applications such as biomarker assessment and antibody-drug conjugate target prioritization. We discuss reuse as an end-to-end analytical process, focusing on data analysis workflows, harmonization strategies, and the impact of heterogeneous experimental and analytical choices on interoperability. The increased application of artificial intelligence in proteomics data integration and reuse is also addressed, highlighting its analytical potential while underscoring the risks of overinterpretation when biological context and data structure are not adequately considered. Using colorectal and prostate cancer as representative examples, we illustrate how proteomics data reuse can support biological discovery and translational research, while critically examining the factors that limit robustness and clinical relevance.
    Keywords:  data harmonization; data standards; precision oncology; proteoforms; proteomics data reuse; public repositories; solid tumors
    DOI:  https://doi.org/10.3390/proteomes14020016
  14. FEBS J. 2026 Apr 24.
    Lipid droplet biogenesis as a metabolic switch regulating ferroptosis sensitivity in cancer cells.
    Abdulsamie Hanano, Amal Yousfan.
      Fatty acids (FAs) are essential for cellular growth and homeostasis; however, their excessive accumulation induces lipotoxicity. To prevent FA-induced damage, eukaryotic cells sequester surplus FAs within cytosolic lipid droplets (LDs), dynamic organelles central to lipid storage, metabolism, and signaling. Emerging evidence indicates that LDs suppress ferroptosis, an iron-dependent programmed cell death, by channeling polyunsaturated fatty acids (PUFAs) away from membrane phospholipids, thereby limiting lipid peroxidation. Nonetheless, the molecular mechanisms linking LD biogenesis to ferroptosis susceptibility remain poorly defined. In a recent study published in The FEBS Journal, Kump et al., provided mechanistic insights into how triacylglycerol (TGs) biosynthesis and LD assembly regulate ferroptosis in cancer cells as a function of PUFA availability. Here, we discuss and contextualize their principal findings.
    Keywords:  Acyl‐CoA diacylglycerol acyltransferase; ferroptosis; lipid droplets; lipid peroxidation; polyunsaturated fatty acids
    DOI:  https://doi.org/10.1111/febs.70567
  15. Genome Biol. 2026 Apr 21.
    Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.
    Jian Song, Amanda Momenzadeh, Hebin Liu, Chengpin Shen, Jesse G Meyer, Xiaohui Wu.
      diaPASEF improves ion utilization and sensitivity by synchronizing quadrupole isolation with trapped ion mobility separation, making it suitable for single-cell proteomics. We present Full-DIA, a deep learning-driven software that enhances proteome coverage, quantitative accuracy, and analysis speed over DIA-NN for single-cell diaPASEF data. Notably, Full-DIA generates a missing-value-free protein matrix under stringent global FDR control, enabling downstream analyses without data gaps. Applied to LPS-treated and cell-cycle datasets, this matrix yields pathway enrichment results with fewer off-target and more biologically relevant pathways. Full-DIA highlights the potential of deep learning for four-dimensional diaPASEF analysis and offers a solution to missing values.
    DOI:  https://doi.org/10.1186/s13059-026-04087-x
  16. Anal Chem. 2026 Apr 22.
    FlashMRM: An Automated Platform for MRM Method Generation and Iterative Optimization Based on High-Resolution Spectra.
    Zecang You, Ao Guo, Changzhi Shi, Jiahu Deng, Jingzhi Yao, Jia Tu, Junjie Yang, Shipei Xing, Mingliang Fang.
      High-throughput targeted analysis in exposomics relies on multiple reaction monitoring (MRM) using liquid chromatography-triple quadrupole mass spectrometry (LC-TQMS), yet its method development remains limited by dependence on chemical standards, labor-intensive parameter generation, and complex matrix interferences (INTF). Here, we introduce FlashMRM, a web-based platform for the automated generation and optimization of MRM parameters by leveraging high-resolution mass spectrometry (HRMS) databases and experimentally preacquired biosample HRMS data. The pseudotranslation from the large-scale HRMS database to the TQMS database (TQDB) was achieved through mass unit normalization, retention time prediction, and cross-instrument collision energy conversion algorithms. FlashMRM integrated Pseudo TQDB containing over 25,000 candidate analytes and INTF TQDB, which encompasses ∼44,000 potential interference MS features in biological matrices, utilizing a dual-weighted scoring model to balance the sensitivity and specificity. Leveraging a library of pesticides with highly diverse chemical structures as a representative model, FlashMRM generated transitions for 255 pesticides with sensitivity comparable to experimentally developed MRM transitions while increasing the specificity score from 0.60 to 0.68. At a spiked concentration of 10 ng/L in urine, the number of Top 5 detectable transitions increased from 194 in experiments to 233 using FlashMRM with optimized specificity weighting, enhancing trace-level detection under biomatrix interference. In the analysis of human urine samples, FlashMRM achieved high-confidence TQMS detection for 55 of 99 targeted exposure biomarkers. FlashMRM enables sensitive and accurate large-scale targeted screening on TQMS without standards and extensive preanalysis injections, integrating optimized scoring algorithms and built-in databases within a user-friendly web platform.
    DOI:  https://doi.org/10.1021/acs.analchem.6c00179
  17. Talanta. 2026 Apr 16. pii: S0039-9140(26)00415-7. [Epub ahead of print]308 129759
    Evolving LC-MS hyphenations: strengths, limitations, and emerging trends in bioanalytical applications.
    Bhavna P Kapse, Rishabh P Soni.
      Hyphenated analytical techniques, particularly liquid chromatography-mass spectrometry (LC-MS), play a central role in addressing the increasing complexity of modern bioanalytical challenges. Recent advancements in LC-MS hyphenation strategies have significantly improved sample preparation efficiency, analyte enrichment, and overall analytical performance. The integration of ion mobility spectrometry provides an additional orthogonal separation dimension, particularly effective for drug metabolism research and isomer discrimination. Furthermore, capillary electrophoresis hyphenation enables effective analysis of polar and charged metabolites, while ambient ionization approaches such as DESI and DART support rapid screening with minimal sample preparation. Tandem setups involving UV, fluorescence, and NMR provide essential layers of structural confirmation. Additionally, the shift toward microfluidic lab-on-a-chip platforms enables miniaturization, thereby reducing sample volume requirements and increasing analytical throughput. Despite these advances, challenges related to instrumental complexity, data interpretation, and regulatory harmonization continue to limit broader translation into routine practice. Collectively, these developments highlight the transformative potential of LC-MS while also emphasizing critical gaps that will shape future bioanalytical advancements.
    Keywords:  Capillary electrophoresis–mass spectrometry (CE–MS); Ion mobility spectrometry (IMS); Liquid chromatography–mass spectrometry (LC–MS); Tandem mass spectrometry (MS/MS)
    DOI:  https://doi.org/10.1016/j.talanta.2026.129759
  18. Metabolomics. 2026 Apr 23. pii: 56. [Epub ahead of print]22(3):
    Metabolic and lipidomic stability in EDTA plasma from hospitalized patients: impact of refrigerated storage time and additional freeze-thaw cycle.
    Sander Johannes Thorbjørnsen Guttorm, Liana Gukasyan, Christian Prebensen, Niclas G Karlsson, Aleksander Rygh Holten, Dag Kvale, Helge Rootwelt, Katja Benedikte Prestø Elgstøen, Erik Koldberg Amundsen.
       BACKGROUND: Global metabolomics and lipidomics are increasingly applied in clinical research. Storage variability can compromise analyte stability and omics data quality, especially in clinical settings. While previous studies have focused on healthy individuals, the stability of the metabolome and lipidome in patient samples remains underexplored.
    OBJECTIVE: To identify metabolites and lipids most susceptible to be affected by post-centrifugation storage time and a single additional freeze-thaw cycle in EDTA plasma from hospitalized patients.
    METHODS: EDTA plasma samples from 20 patients acutely hospitalized in a medical ward were collected (K2EDTA, 5 mL, with gel) and stored at 4 °C for 0, 24, and 72 h post-centrifugation. All samples underwent one additional freeze-thaw cycle and were analyzed using global liquid chromatography-mass spectrometry (LC-MS) metabolomics and lipidomics.
    RESULTS: Approximately 90% of the global metabolome and lipidome remained stable and robust to the pre-analytical factors induced. Metabolomic profiles showed a storage time-dependent increase in differentially abundant features, while most lipidomic alterations occurred within the first 24 h. A total of 116 annotated compounds exceeded a Cohen's d effect size threshold of ± 0.25, including lactate, hypoxanthine, oxoproline, fatty acid(20:4), and lysophosphatidylcholine(16:0).
    CONCLUSION: The plasma metabolome and lipidome are largely robust to common storage conditions in patient samples. However, refrigerated plasma storage time post-centrifugation and an additional freeze-thaw cycle can induce biologically significant changes in specific metabolites and lipids. This study is among the first to evaluate metabolomic and lipidomic stability using clinical samples and our data supports a sample collection that can be easily implemented in clinical laboratory workflows.
    Keywords:  EDTA plasma; Lipidome; Metabolome; Storage stability; Storage time
    DOI:  https://doi.org/10.1007/s11306-026-02428-2
  19. Nat Biotechnol. 2026 Apr 22.
    Deeper is not always better in plasma proteomics.
    Huanhuan Gao, Kathrin Korff, Matthias Mann, Tiannan Guo.
      
    DOI:  https://doi.org/10.1038/s41587-026-03106-3
  20. Proteomes. 2026 Apr 21. pii: 20. [Epub ahead of print]14(2):
    Enabling Next-Generation Mass Spectrometry-Based Proteomics: Standards, Proteoform Resolution, and FAIR, Reproducible, and Quantitative Analysis.
    Rui Vitorino.
      Recent advances in mass spectrometry, data-independent acquisition, proteoform-resolving workflows, and multi-omics integration have significantly expanded the scale and scope of proteomics. However, the reuse and translational application of these datasets are limited by inconsistent standards, insufficient metadata, and inadequate computational interoperability. Proteoform-centric approaches provide higher molecular resolution by capturing intact protein variants and patterns of post-translational modification. Computational methods, including selected applications of machine learning and large language models (LLMs), are increasingly used for tasks such as spectral prediction and pattern discovery in clinical proteomics datasets. Despite these advancements, FAIR (Findable, Accessible, Interoperable, and Reusable) data practices, proteoform biology, and AI analytics are often pursued independently. This work presents an integrated framework for next-generation proteomics in which standardization and FAIR (Findable, Accessible, Interoperable, and Reusable) principles establish machine-actionable foundations for proteoform-resolved analysis and computational inference. It examines community efforts to promote data sharing and interoperability, as well as strategies for characterizing proteoforms using bottom-up, middle-down, and top-down approaches. It also highlights emerging AI and ML applications within the proteomics workflow. The framework emphasizes the importance of treating proteoforms as primary computational entities and adopting FAIR practices during data collection to enable reproducible and interpretable modeling. Finally, it introduces an architectural model that integrates FAIR infrastructures and proteoform resolution. In addition, practical recommendations for making AI-ready proteomics, including a minimal community checklist to support reproducibility, benchmarking, and translational scalability, are provided.
    Keywords:  AI/ML; FAIR infrastructures; proteoform-centric biology
    DOI:  https://doi.org/10.3390/proteomes14020020
  21. Biomed Chromatogr. 2026 Jun;40(6): e70445
    Multidimensional and Hybrid Separation Strategies for Body Fluids and Tissues: Comparative Performance and Translational Considerations.
    Uttam Prasad Panigrahy, Yaso Deepika Mamidisetti, Kuchukuntla Mounika, Jainendra Kumar Battineni, Divya Amaravadi, M Akiful Haque.
      Analytical technologies for body fluids and tissues have advanced substantially over the past decade, particularly in chromatographic, electrophoretic and ion-mobility-based separation strategies designed to address matrix complexity and structural isomerism. Improvements in stationary-phase chemistry, multidimensional liquid chromatography, capillary electrophoresis and mobility-integrated mass spectrometry have enhanced peak capacity, structural discrimination, remains the selection and validation of separation strategies capable of minimizing coelution, ion suppression and quantitative variability in heterogeneous matricesremains the selection and validation of separation strategies capable of minimizing coelution, ion suppression and quantitative variability in heterogeneous matrices. This review examines recent progress in multidimensional chromatography, mobility-resolved workflows, miniaturized separation systems and hybrid imaging-separation platforms, with emphasis on comparative performance, practical implementation constraints and translational reproducibility. Applications across plasma, serum, urine, saliva, cerebrospinal fluid (CSF), tissues and spatially resolved analyses are discussed in the context of analyte chemistry and matrix-dependent method selection. Future progress will depend on standardized multidimensional workflows, validated performance metrics, harmonized reporting frameworks and data-driven optimization strategies that support robust clinical translation. By focusing on separation-centred decision-making and quality assurance, this review provides guidance for analytical scientists seeking reliable molecular characterization of complex human biospecimens.
    Keywords:  body fluids; chromatography; mass spectrometry; metabolomics; microfluidics; tissues
    DOI:  https://doi.org/10.1002/bmc.70445
  22. Anal Chem. 2026 Apr 24.
    Best Practices in GC-MS and GC × GC-MS-Based Metabolomics and Volatile Analyses: An International Survey.
    Ryland T Giebelhaus, Michael Herold, James J Harynuk, David E Alonso, Bashar Amer, Corey D Broeckling, Pantelis Charisiadis, Raquel Cumeras, A Paulina de la Mata, Warwick Dunn, Flavio A Franchina, Helen Gika, Jennifer A Kirwan, Isabella H Muti, Lukáš Najdekr, Estelle Pujos-Guillot, Pierre-Hugues Stefanuto, Teresa Steininger-Mairinger, Alpesh Thakker, Oliver Fiehn.
      Standardized quality assurance and quality control (QA/QC) practices are essential for reproducible GC-MS metabolomics, yet systematic documentation of current laboratory practices has been lacking. Here, as part of the Metabolomic Quality Assurance and Quality Control Consortium (mQACC), we surveyed 85 laboratories from 27 countries to characterize QA/QC implementation and establish evidence-based recommendations. Respondents represented diverse applications, with 79% performing untargeted analysis, 60% conducting targeted analyses, and 44% conducting both. While single column chromatography is clearly the norm, 24% of the participants used multidimensional chromatography to improve the separation of complex mixtures. Electron ionization with autotuning dominated >95% of the respondents, but more than 30% of the laboratories at least occasionally also used chemical ionization. While most laboratories used low-resolution mass spectrometers, almost half of the laboratories also performed GC-MS analyses on high-resolution QTOF or Orbitrap instruments. A strong consensus emerged on critical QA/QC practices: >90% of laboratories use internal standards for quality control, perform regular leak checks, and maintain injector systems through routine component replacement, spanning column (exchange/cuts), liners, syringes, and septa. Routine monitoring (>50%) involves method blanks, peak shape assessments, and systematic evaluation of intensity drifts, carryovers, and contamination. Retention indices coupled with mass spectral library matching served as the primary annotation approach (60%). Overall, a consensus of best practices in QA/QC and reporting emerged, providing evidence-based recommendations for high-quality GC-MS metabolomics.
    DOI:  https://doi.org/10.1021/acs.analchem.5c06918
  23. Nat Commun. 2026 Apr 23.
    High-throughput single-vesicle imaging platform for direct extracellular vesicle profiling of human plasma.
    Chungmin Han, Arek V Melkonian, Justin C Rolando, Sara Whiteman, David R Walt.
      Extracellular vesicles (EVs) are nanoscale particles secreted by cells that carry diverse biomolecules reflecting their cell of origin. Single-EV imaging approaches have enabled precise characterization of heterogeneous EV populations; however, their broader application is limited by low-throughput workflows and cumbersome EV isolation procedures. Here, we introduce a streamlined, high-throughput imaging platform capable of analyzing protein expression of individual intact EVs directly from unprocessed biological samples at the single-vesicle level. Our approach employs a functionalized glass surface optimized for high-throughput single-EV imaging, facilitating specific capture of EVs and enabling integration with existing automation technologies. We evaluate the platform's analytical capabilities by characterizing various recombinant EV samples and demonstrate its clinical utility by analyzing EVs in a total of 191 human plasma samples with high-throughput efficiency. This technology will offer a pathway for high-precision and large-scale characterization of EVs in clinical samples.
    DOI:  https://doi.org/10.1038/s41467-026-72179-0
  24. MedScience. 2026 Apr 20.
    Proteogenomics: decoding cancer in multiple dimensions.
    Qian Liu, Yimin Chen, Hu Zhou.
      A key challenge in cancer precision oncology is the limited ability of genomic analyses to accurately predict changes in protein expression or function, even though proteins serve as the main targets of numerous modern therapies. Bridging this gap necessitates precise quantification of proteins and their post-translational modifications (PTMs). Recent advances in mass spectrometry (MS)-based proteomics now enable large-scale, quantitative characterization of proteins and PTMs in tumor tissues. To link genomic aberrations to cancer phenotypes, the emerging field of proteogenomics integrates proteomic data, including PTMs, with genomic, epigenomic, and transcriptomic information. This comprehensive approach offers a deeper understanding of cancer biology at multiple levels. This review highlights recent advancements in MS-based proteomics, key discoveries in cancer proteogenomics, and the transformative potential of this field in decoding the complexities of cancer across diverse dimensions.
    Keywords:  cancer; multi-omics; precision oncology; proteogenomics; proteomics
    DOI:  https://doi.org/10.1007/s11684-026-1222-2
  25. Genomics Proteomics Bioinformatics. 2026 Apr 22. pii: qzag028. [Epub ahead of print]
    Urinary Proteomics: Biological Foundations, Analytical Frameworks, and Clinical Translation Across Human Diseases.
    Fanjie Meng, Yuxuan Xiao, Xincheng Li, Jiawen Chen, Bin Hu, Jun Wang, Kezhong Chen.
      Urinary proteomics has swiftly emerged as a formidable tool for the identification of non-invasive biomarkers and the surveillance of diseases. The progression in high-resolution mass spectrometry and data-independent acquisition techniques has facilitated the urinary proteome in providing detailed insights into intricate pathophysiological processes impacting multiple organ systems. This review synthesizes the recent advancements in urinary proteomics, detailing the analytical methodologies utilized, the challenges associated with standardization, and the normalization strategies crucial to the discipline. We undertake a comparative analysis of data-dependent and data-independent acquisition methodologies and examine their complementary roles in clinical workflows for biomarker discovery and translation. Additionally, we highlight both common and disease-specific proteomic signatures across a spectrum of disorders, including oncological, renal, cardiovascular, metabolic, and neurodegenerative diseases. We also investigate the role of artificial intelligence and multi-omics integration in supporting predictive modeling. Lastly, we discuss the ongoing developments in regulatory and implementation frameworks, such as data privacy regulations and clinical validation standards, that are positioning urinary proteomics as a key component of preventive and precision medicine.
    Keywords:  Biomarker discovery; Mass spectrometry; Multi-omics integration; Precision medicine; Urinary proteomics
    DOI:  https://doi.org/10.1093/gpbjnl/qzag028
  26. Reprod Fertil Dev. 2026 May 11. pii: RD26034. [Epub ahead of print]38(7):
    Your field needs your data! Raising the standard of quality control and data accessibility in reproductive proteomics.
    David A Skerrett-Byrne.
      The field needs your data. Despite rapid progress in reproductive proteomics, a major barrier to scientific advancement remains the limited availability and transparency of proteomic datasets. Although more than 2000 sperm proteomics studies are indexed on PubMed, fewer than 414 datasets have been deposited in ProteomeXchange, leaving the majority of published findings effectively inaccessible for reanalysis. This Viewpoint highlights the urgent need for improved data stewardship, standardised quality control and open access to raw mass spectrometry files across reproductive biology. In this article, I outline how transparent false discovery rate control, true biological replication and clearly defined quantitative thresholds are essential for generating robust and interpretable proteomic outputs. I further discuss how interactive data platforms, such as ShinyApps, can substantially improve the accessibility and usability of these complex reproductive proteomic datasets. Using recent examples, I demonstrate how public data reanalysis can uncover species-conserved pathways, improve proteome coverage, validate biological functions and enable new discoveries and insights far beyond the aims of the original studies. Finally, I present a practical roadmap for authors, reviewers and journals to ensure that reproductive proteomics embraces the FAIR data principles, and moves towards a culture where sharing raw data, comprehensive metadata and interactive applications becomes standard practice. To support implementation, a concise checklist is provided to summarise key criteria for data availability, quality control and metadata reporting. Improving data accessibility and quality will not only strengthen individual studies, but will accelerate discovery and create a more robust, connected and future-proof foundation for reproductive biology.
    Keywords:  Shiny app; data reuse; data stewardship; open science; publicly accessible data; quality control; reproductive proteomics; roadmap
    DOI:  https://doi.org/10.1071/RD26034
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