bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2026–07–19
23 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Instrument-Software Synergy in Proteomics: Systematic Evaluation across Mass Spectrometry Platforms, Search Engines, and Rescoring Methods.
  2. Mass Spectrometry as a Tool for Metabolomics in Cancer.
  3. High-Throughput Single-Cell Proteomics Enabled by Integrating nPOP Workflow with Quantitative Hyperplexing.
  4. aiSysMet: AI-Powered Systems Metabolomics for Biomarker Discovery.
  5. High-throughput acoustic FFPE proteomics reveals ATP5IF1-associated mitochondrial alterations in acral melanoma.
  6. Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.
  7. Navigating the glycomics landscape with CE-MS: advances in sample preparation and analytical strategies.
  8. A nanogram-sensitive workflow for oligonucleotide mass spectrometry using ion-pair-free nanoflow HILIC and RNase benchmarking.
  9. Enrichment-Free Deep Proteomics Enables Proteome-Scale Analysis of Methionine Oxidation.
  10. Organ-Derived Spectral Libraries Improve Sensitivity in DIA Plasma Peptidomics.
  11. Standardised dual-flow cytometry workflows for quantitative assessment of mitochondrial superoxide and mitochondrial mass in live cancer cells and primary multiple myeloma cells.
  12. Structure-Based Comparative Metabolomics Identifies LysoPE 15:0 as a Candidate Metabolite Marker of Influenza Virus Infection Dynamics.
  13. Evaluating DIA LiP-MS Analysis Workflows with a Hybrid LiP Proteome Benchmark.
  14. Chemoselective Metabolomics via a Modular Reactivity-Encoding Platform.
  15. Proteomic investigation of signaling dynamics: from static maps to network rewiring.
  16. Spatial metabolomics - a metabolic microscope for respiratory medicine.
  17. Agentic AI-Assisted Coding Offers a Unique Opportunity to Instill Epistemic Grounding during Software Development.
  18. The Interdependence of Carbon Substrates and Metabolic Pathways Enables the Metabolic Plasticity of Cancer Cells.
  19. Network-based integration of metabolomics data from large-scale repositories.
  20. Establishing Orbitrap-Based Methods for Multi-Element Compound-Specific Isotope Analysis of Per- and Polyfluoroalkyl Substances.
  21. Shipping Conditions May Impact the Reliability of At-Home Microsampling Devices.
  22. Ketogenic diet mediates intestinal tumorigenesis through lipids not ketones.
  23. N-acetylated amino acids: an overlooked layer of metabolic regulation.
  1. J Proteome Res. 2026 Jul 14.
    Instrument-Software Synergy in Proteomics: Systematic Evaluation across Mass Spectrometry Platforms, Search Engines, and Rescoring Methods.
    Sander Heyndrickx, Robbin Bouwmeester, Arthur Declercq, Robbe Devreese, Magnus Palmblad, Wout Bittremieux, Tess Av Afanasyeva, Joel Lapin, Arzu Tugce Guler, Pierre-Olivier Schmit, Christine Carapito, Lennart Martens, Ralf Gabriels.
      Mass spectrometry-based proteomics has advanced through parallel improvements in instrumentation (mass spectrometers) and software (data analysis), yet whether these improvements interact synergistically or provide diminishing returns remains unclear. Here, we systematically evaluate instrument-software coevolution across eight mass spectrometry platforms, three generations of search engines, and multiple rescoring approaches, yielding 72 unique instrument-software combinations spanning from 2004 to 2024. Our results reveal that instrumentation and software improvements produce synergistic rather than substitutive benefits. Here, machine learning-based rescoring consistently recovers identifications from low-intensity precursors that produce noisier, more challenging spectra. Crucially, because of increased sensitivity and speed, modern instruments detect more low-intensity precursors, thereby increasing the population of challenging spectra for which rescoring provides the greatest benefit. However, this expanded detection depth comes at a cost: recovered low-abundance peptides exhibit inherently higher quantification error, creating a fundamental trade-off between proteome coverage and quantification accuracy. Together, these findings provide a systematic overview of how instrument and software advances have jointly shaped proteomics performance over the past two decades.
    Keywords:  data analysis; evaluation; instruments; machine learning; mass spectrometry; peptide identification; peptide quantification; proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00295
  2. Adv Exp Med Biol. 2026 ;1501 525-554
    Mass Spectrometry as a Tool for Metabolomics in Cancer.
    João S Patrício, Ana C L Guerreiro, Paulo A Gameiro, Judit Morello.
      Mass spectrometry (MS) is a highly sensitive and high-throughput analytical technology that has become central to investigating the metabolic alterations in cancer cells, enabling the discovery of diagnostic biomarkers and potential therapeutic targets. This chapter provides an overview of the key principles of MS-based metabolomics, covering the entire experimental workflow-from sample collection and preparation to data acquisition, preprocessing, normalization, statistical modeling, and pathway enrichment analysis. We also discuss current limitations and outline future directions to enhance data reproducibility, metabolite annotation, and clinical translation of MS-based results.
    Keywords:  Biomarkers; Cancer diagnosis; MS-based metabolomics; Mass spectrometry (MS)
    DOI:  https://doi.org/10.1007/978-3-032-12166-0_19
  3. Anal Chem. 2026 Jul 17.
    High-Throughput Single-Cell Proteomics Enabled by Integrating nPOP Workflow with Quantitative Hyperplexing.
    Kunpei Cai, Qing Zeng, Chuanxi Huang, Jing Yang, Fuchu He, Yun Yang.
      Single-cell proteomics (scProteomics) has emerged as a powerful approach to dissect cellular heterogeneity and dynamic molecular mechanisms at unprecedented resolution. However, achieving high proteome coverage and quantitative accuracy while maintaining high throughput remains a major challenge. In this study, we established a high-throughput scProteomics workflow that integrates a modified nanoproteomic sample preparation (nPOP) workflow with an IBT16-TMTpro 16 quantitative hyperplexing strategy. Through systematic optimization of chromatographic and mass spectrometric conditions, we established a label-free workflow for high-sensitivity and high quantification accuracy. On average, more than 3000 protein groups were identified from individual 293T and HeLa cells on timsTOF SCP. When applied to single cholangiocarcinoma (CCA) cells and matched paracancerous cells dissociated from human fresh-frozen CCA tissue, approximately 2000 protein groups were quantified per cell, revealing distinct metabolic and translational regulation patterns consistent with previously reported molecular features of CCA subtypes. To achieve high-throughput scProteomics, we then established an nPOP-based IBT16-TMTpro 16 quantitative hyperplexing workflow. Across four human cell lines (293T, HeLa, A549, and LM3), our quantitative hyperplexing strategy achieved over 95% labeling efficiency and consistently identified 1400 to 2000 protein groups from single cells. In comparison to conventional multiplexing methods, our hyperplexing strategy not only enhanced proteome depth but also achieved ultrahigh-throughput (∼2000 single cells per day when using Orbitrap Astral Zoom). Overall, our label-free and quantitative hyperplexing workflows provide an efficient and scalable platform for large-scale scProteomics studies and clinical applications.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01570
  4. Bioinformatics. 2026 Jul 15. pii: btag520. [Epub ahead of print]
    aiSysMet: AI-Powered Systems Metabolomics for Biomarker Discovery.
    Habtom Ressom, Linge Yan, Hongyu Ao, Xinran Zhang, Sara Hashemi, Rency Varghese, Bardia Nezami, Dawit Mengistu.
       MOTIVATION: Metabolomics plays an essential role in the growing systems biology approaches to unravel the relationships between metabolites and diseases. Liquid chromatography-mass spectrometry (LC-MS) is central to this effort because it can profile many metabolites from limited material. Yet, in a typical untargeted LC-MS-based metabolomics study, the majority of detected peaks remain unannotated, largely due to incomplete spectral libraries and uncertainties in peak picking, alignment, and the handling of isotopes and adducts. These limitations hinder seamless integration with other omics layers.
    RESULTS: We developed an AI-powered platform (aiSysMet) that uses statistical, machine learning, and deep learning methods for metabolomics data processing, metabolite annotation, and integrative analysis of multi-omics data. The platform's interactive and modular web interface allows users to easily build data analysis pipelines that can be executed in the cloud.
    AVAILABILITY: aiSysMet is freely available for non-commercial users on https://tools.omicscraft.com/aiSysMet.
    DOI:  https://doi.org/10.1093/bioinformatics/btag520
  5. Mol Cell Proteomics. 2026 Jul 13. pii: S1535-9476(26)00116-7. [Epub ahead of print] 101620
    High-throughput acoustic FFPE proteomics reveals ATP5IF1-associated mitochondrial alterations in acral melanoma.
    Diana Lashidua Fernández-Coto, Marisol Ayala, Ramiro Alonso, Alicia Palmqvist, Ma Fernanda García, Scarlett F Balderas-López, Henriett Oskolas, Lukas Christersson, Livia Fulöp, Balazs Szigeti, Victor H Olivera-Rodríguez, Matilda Marko-Varga, Peter Horvatovich, Johan Malm, Leticia Szadai, Roberto Herrera-Goepfert, Attila Marcell Szasz, Sergio Encarnación-Guevara, György Marko-Varga, Jeovanis Gil.
      Formalin-fixed, paraffin-embedded (FFPE) archives underpin dermatopathology and translational oncology, enabling clinically annotated melanoma cohorts, but cohort-scale proteomics remains limited by labor and variability in upstream processing. We established a plate-scale, acoustic FFPE proteomics workflow and integrated it with AI-assisted digital pathology to support composition-aware molecular profiling from routine sections. The optimized pipeline reduces handling steps, is designed to improve reproducibility, and supports rapid parallel processing in a 96-well format. Deep data-independent acquisition mass spectrometry of 40 primary melanomas spanning acral lentiginous, lentigo maligna, superficial spreading, and nodular subtypes quantified more than 8,200 protein groups and a mean of 5,200 proteins per tumor. Proteome profiles resolved melanoma subtypes and defined an acral lentiginous melanoma program enriched for translation/biogenesis and extracellular matrix/adhesion processes with relative depletion of lipid and fatty-acid metabolism and peroxisomal pathways. Supervised feature selection highlighted TNC, POSTN, EIF4A1, and ARF4 and uncovered selective depletion of ATP5IF1, a regulator of mitochondrial ATP synthase, in acral lentiginous melanoma. ATP5IF1 depletion persisted after adjustment for mitochondrial proxies and QuPath-derived tumor content, and coincided with higher glycolysis relative to Complex V. This pathology-integrated, high-throughput FFPE proteomics framework enables scalable retrospective discovery and suggests subtype-specific metabolic alterations consistent with mitochondrial remodeling in an underrepresented melanoma subtype.
    Keywords:  AI-Digital Pathology; ATP synthase inhibitor IF1; Acral melanoma; Adaptive focused acoustics; Oxidative phosphorylation; Plate-scale FFPE proteomics
    DOI:  https://doi.org/10.1016/j.mcpro.2026.101620
  6. J Proteomics. 2026 Jul 11. pii: S1874-3919(26)00111-9. [Epub ahead of print]331 105708
    Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.
    Dirk Winkelhardt, Julia P Schessner, Sven Berres, Alireza Nameni, Armin Soleymaniniya, Ayla Schröder, Caroline Jachmann, Douwe Schulte, Georg Wallmann, Henry Webel, Johannes Rainer, Katerina Nastou, Laurent Gatto, Magnus Palmblad, Maike Weber, Omar Shouman, Pavel Sinitcyn, Ralf Gabriels, Robbe Devreese, Tim Van Den Bossche, Tine Claeys, Wout Bittremieux, Julian Uszkoreit.
      Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.
    Keywords:  Artificial intelligence in bioinformatics; Community-driven development; Computational mass spectrometry; Proteomics
    DOI:  https://doi.org/10.1016/j.jprot.2026.105708
  7. Analyst. 2026 Jul 13.
    Navigating the glycomics landscape with CE-MS: advances in sample preparation and analytical strategies.
    Karthika Korumadathil Shaji, Peter L Horvatovich, Guinevere S M Lageveen-Kammeijer.
      Glycosylation is one of the most structurally diverse and biologically consequential co- and post-translational modifications, yet its analytical characterisation remains challenging due to extensive isomerism, microheterogeneity, branching structure and the presence of labile residues. Among the available analytical platforms, capillary electrophoresis (CE), particularly when coupled to mass spectrometry (CE-MS), offers exceptional separation efficiency at nanolitre sample loadings and can resolve glycan variants that remain obscured in conventional LC- or MALDI-based workflows. This review provides a comprehensive overview of recent advances that have expanded the utility of CE and CE-MS in glycomics. We discuss practical considerations in enzymatic and chemical glycan release and highlight how the workflow format and clean-up influence recovery, quantitative precision and downstream compatibility. A major section is dedicated to the critical evaluation of major reducing-end derivatisation chemistries, including reductive amination, hydrazide and Michael-addition labelling, stable isotope, isobaric, and emerging instant-labelling strategies as well as permethylation, focusing on how labelling modulates electrophoretic mobility, isomer resolution, ionisation efficiency and MS/MS fragmentation. We outline current CE-MS methodologies, focusing on background electrolyte design, capillary coatings, sample injection modes, and the latest developments in sheath-flow, sheathless, nanoflow, and microfluidic interfaces. Performance benchmarks, including sensitivity, isomer resolution, robustness, and quantitative precision, are evaluated alongside recent innovations such as dopant enriched gases and integrated CE-MS cartridges. Finally, we assess the opportunities and remaining barriers for the broader adoption of CE-MS in biomedical, clinical, and biopharmaceutical glycomics. Continued advances in MS interface design, automation, and MS-compatible labelling chemistries are expected to further transform CE-MS into a routinely and widely deployable platform for high-resolution glycan characterisation.
    DOI:  https://doi.org/10.1039/d6an00304d
  8. Nucleic Acids Res. 2026 Jul 03. pii: gkag691. [Epub ahead of print]54(13):
    A nanogram-sensitive workflow for oligonucleotide mass spectrometry using ion-pair-free nanoflow HILIC and RNase benchmarking.
    Yuyang Qi, Chengkang Li, Nur Yesiltac-Tosun, Jannick Schicktanz, Leona Rusling, Steffen Kaiser, Samuel Wein, Stefanie Kaiser.
      RNA modifications regulate diverse cellular processes, yet comprehensive characterization of modified RNA sequences remains technically challenging. Mass spectrometry provides direct chemical information on RNA, but current oligonucleotide-based workflows typically require micrograms of RNA input and often rely on ion-pairing reagents for chromatographic separation, limiting their applicability to scarce or native RNA samples. Here, we establish a sensitive oligonucleotide mass spectrometry workflow that combines ion-pair-free nanoflow hydrophilic interaction liquid chromatography with systematic benchmarking of controlled RNA cleavage strategies. We compared RNase T1, RNase 4, and colicin E5 and evaluated how reaction conditions influence cleavage specificity, fragment length distribution, and terminal chemistries of RNA hydrolysates. The resulting workflow enables robust LC-MS/MS analysis using standard MS-compatible buffers and supports confident oligonucleotide identification through NucleicAcidSearchEngine (NASE) database searching. Using this approach, we achieved high sequence coverage from nanogram-scale RNA inputs, enabling modification analysis of 25-50 ng native yeast tRNAPhe and sequence verification of 250 ng synthetic mRNA. Together, this work establishes a sensitive and broadly applicable platform for oligonucleotide mass spectrometry and provides practical guidance for RNase selection and digestion strategies. The method expands the applicability of RNA MS to low-input samples and supports future studies of RNA sequence and modification landscapes.
    DOI:  https://doi.org/10.1093/nar/gkag691
  9. DNA Res. 2026 Jul 18. pii: dsag011. [Epub ahead of print]
    Enrichment-Free Deep Proteomics Enables Proteome-Scale Analysis of Methionine Oxidation.
    Hiromasa Mitsui, Yusei Okuda, Ryo Konno, Daisuke Nakajima, Osamu Ohara, Yusuke Kawashima.
      Advances in mass spectrometry (MS)-based proteomics have enabled the large-scale characterization of posttranslational modifications (PTMs) through affinity-based enrichment. However, this technique introduces a bias towards selectively enrichable modifications, thus, leaving oxidative modifications underexplored. Methionine oxidation (methionine sulfoxide) is an important indicator of cellular redox status, but its systematic analysis remains challenging because no enrichment method is available and artifactual oxidation can occur during sample preparation. Here, we developed an enrichment-free proteomic strategy for large-scale detection of methionine oxidation using a deep LC-MS platform. By optimizing acquisition conditions, we identified more than 260k precursors in a single-shot analysis. Under these conditions, methionine oxidation was efficiently detected, whereas many other PTMs remained poorly detected. To improve data reliability, we established a sample preparation workflow that minimized artifactual oxidation. Accordingly, we identified more than 3,500 methionine-oxidized proteins. Integration of methionine oxidation and expression proteomics across subcellular compartments revealed redox patterns under low-serum conditions, including increased mitochondrial oxidation and decreased endoplasmic reticulum oxidation. These changes are associated with metabolic reprogramming and altered antioxidant capacity. Overall, this study established an enrichment-free framework for the proteome-scale methionine oxidation analysis, and demonstrated that integrating oxidation and expression data enables the spatially resolved interpretation of cellular redox states.
    Keywords:  Methionine oxidation/Methionine sulfoxide/Oxidative stress/Proteomics/Enrichment-free analysis
    DOI:  https://doi.org/10.1093/dnares/dsag011
  10. Proteomics. 2026 Jul 11. e70165
    Organ-Derived Spectral Libraries Improve Sensitivity in DIA Plasma Peptidomics.
    Yusei Okuda, Ryo Konno, Tomomi Taguchi, Makoto Itakura, Ami Tamari, Takashi Matsui, Takeshi Miyatsuka, Osamu Ohara, Yusuke Kawashima, Yoshio Kodera.
      Plasma contains diverse bioactive peptides that play crucial roles in maintaining homeostasis and regulating disease responses. However, the presence of peptides derived from high-abundance proteins such as albumin makes comprehensive analysis of native peptides secreted by organs challenging. This study aimed to establish a highly sensitive plasma peptidomic approach by combining data-independent acquisition (DIA) with spectral libraries of plasma and organs. First, peptides were extracted from plasma and eleven organ types using a high-yield peptide extraction method, the differential solubilization method. These peptides were then measured via data-dependent acquisition (DDA) analysis using a timsTOF HT for constructing an empirical spectral library. Subsequently, DIA-MS data from plasma samples were measured and analyzed using this spectral library. This strategy achieved identification of, on average, over 5500 peptides per run, with over 2000 organ-derived peptides including 19 known bioactive peptides. The novel strategy proposed here enables highly sensitive quantitative analysis of organ-derived peptides in plasma, linking them to their secreting organs. It is expected to substantially contribute not only to the discovery of biomarkers and novel bioactive peptides but also to elucidating the pathophysiology of systemic diseases.
    Keywords:  DIA‐MS; mouse; peptidome; peptidomics; plasma
    DOI:  https://doi.org/10.1002/pmic.70165
  11. Front Cell Dev Biol. 2026 ;14 1854542
    Standardised dual-flow cytometry workflows for quantitative assessment of mitochondrial superoxide and mitochondrial mass in live cancer cells and primary multiple myeloma cells.
    Eugenia Giglio, Martina Giuseffi, Marzia Sichetti, Marisabel Mecca.
      Mitochondrial metabolism plays a critical role in carcinogenesis and cancer progression. Quantitative assessment of mitochondrial function in live cells remains technically challenging because existing biochemical assays lack single-cell resolution, and microscopy-based approaches are limited in throughput and quantitative reproducibility. Here we describe a robust and reproducible standardised dual-flow cytometry protocol for simultaneous quantitative assessment of mitochondrial superoxide production and mitochondrial mass in live cancer cells and primary patient-derived multiple myeloma plasma cells using MitoSOX Green and MitoTracker Red. The protocol provides a step-by-step workflow comprising preparation of cultured cancer cells or isolation of primary CD138+ plasma cells, optimised probe staining, viability discrimination, standardised flow cytometry acquisition and gating, and quantitative fluorescence normalisation. Compared with conventional mitochondrial assays requiring cell lysis or imaging-based analysis, this approach enables high-throughput, quantitative mitochondrial profiling at single-cell resolution in heterogeneous populations while preserving cellular integrity. The procedure incorporates defined staining conditions, instrument calibration guidance, quality-control criteria and normalisation strategies to improve reproducibility across experiments and laboratories. The workflow yields robust fluorescence measurements with low technical variability and enables discrimination of mitochondrial oxidative activity relative to mitochondrial content, facilitating analysis of mitochondrial dysfunction, oxidative stress responses and treatment-induced mitochondrial perturbations. The method is compatible with multiparametric flow cytometry and can be adapted to diverse cell types and experimental systems. The complete protocol requires ∼6-8 h for cultured cells or 8-12 h when primary cell isolation is included and can be implemented by researchers with standard cell culture and flow cytometry expertise.
    Keywords:  MitoSOX; MitoTracker; ROS; flow cytometry; live cancer cells; mitochondrial mass; mitochondrial superoxide
    DOI:  https://doi.org/10.3389/fcell.2026.1854542
  12. Molecules. 2026 Jun 29. pii: 2275. [Epub ahead of print]31(13):
    Structure-Based Comparative Metabolomics Identifies LysoPE 15:0 as a Candidate Metabolite Marker of Influenza Virus Infection Dynamics.
    Junxiao Wang, Yuting Li, Bin Wang, Wenxia Fang, Yushen Du, Fei Xu.
      Influenza virus outbreaks remain a persistent public health concern, yet traditional metabolomics methods are inadequate for addressing key analytical challenges of "dark matter" in influenza research. By integrating quantitative MS1 data, MS2-derived fragmentation trees and molecular fingerprints, structure-based comparative metabolomics enhances predictive capability for chemical structures, and enables the discovery of candidate metabolic markers without the need for database spectra. In this study, we established a C57BL/6J mouse model of H1N1 infection (with PBS as control) and performed structure-based comparative metabolomics on fecal samples using liquid chromatography-mass spectrometry (LC-MS). Quantitative analysis of MS1 data identified 40 differential metabolites, while qualitative analysis of MS2 data enabled their structural annotation. A candidate metabolite marker, LysoPE 15:0, along with other potential metabolic markers, was annotated and validated using Mirror plot, CFM-ID, and sim-Rank-Network. Our findings demonstrate that structure-based comparative metabolomics enables library spectra-free annotation of metabolomic "dark matter" and provides a methodological workflow for discovering candidate metabolite markers in other diseases.
    Keywords:  candidate metabolite marker discovery; influenza virus; structure-based comparative metabolomics
    DOI:  https://doi.org/10.3390/molecules31132275
  13. Anal Chem. 2026 Jul 16.
    Evaluating DIA LiP-MS Analysis Workflows with a Hybrid LiP Proteome Benchmark.
    Shanshan Li, Shijia Yuan, Huiting Luo, Jingyi Xu, Zhaoyu Zhang, Ronghui Lou, Hebin Liu, Chengpin Shen, Wenqing Shui.
      Emerging as a powerful structural proteomics approach, limited proteolysis mass spectrometry (LiP-MS) has been widely employed to interrogate proteome-wide protein structural alterations, identify drug targets and drug-binding pockets, and probe protein-protein interactions. However, LiP-MS-based proteomics data analysis is fundamentally different from that of conventional proteomics informatics. LiP-MS relies on peptide-centric analysis in order to pinpoint structural regions or residues within a protein that exhibit conformational changes. The presence of a large number of semitryptic peptides substantially increases LiP-MS data complexity. Moreover, there is a lack of consensus on the statistical criteria for defining structural changes. To evaluate informatics workflows for DIA-based LiP-MS, we generated a high-quality benchmark data set comprising more than 170,000 LiP peptides with defined composition. We then performed a comprehensive assessment of major DIA analysis platforms incorporating different spectral libraries, and introduced DIA-LiPQuan, an informatics pipeline tailored to DIA LiP-MS quantification and downstream analysis. Data reanalysis by DIA-LiPQuan with in silico libraries allows sensitive and robust detection of both site-specific structural remodeling of proteins and drug-bound protein targets from the cellular proteome. Collectively, our study provides a valuable benchmark resource and informatics package for LiP-MS data mining, which would facilitate its broader applications in structural proteomics and drug discovery.
    DOI:  https://doi.org/10.1021/acs.analchem.6c02411
  14. Adv Sci (Weinh). 2026 Jul 17. e76599
    Chemoselective Metabolomics via a Modular Reactivity-Encoding Platform.
    Xin Tao, Cang-Man Zhang, Ru-Jie Yang, Min Ren, Zhuo-Ling Zhong, Kang-Hui Wang, Rui-Xue Hu, Jia-Yue Liu, Jun-Yi Zhou, Hou-Kai Li, Jian-Bo Wan.
      Understanding disease-associated metabolic reprogramming requires comprehensive interrogation of the chemically diverse metabolome. However, conventional liquid chromatography-mass spectrometry (LC-MS) workflows analyze metabolites in a largely non-discriminatory manner, resulting in systematic underrepresentation of specific functional and reactivity classes due to heterogeneous ionization efficiencies and matrix interference. Here, we report a chemoselective metabolomics strategy based on a modular reactivity-encoding platform (MREP) that enables functional group-resolved stratification of complex metabolomes. Four orthogonally designed alkyne-tagged probes selectively derivatize carboxyl, carbonyl, amine, and thiol functionalities under compatible conditions. The encoded metabolites are subsequently immobilized via azide-alkyne cycloaddition onto a unified solid-phase capture resin, which simultaneously removes matrix components and installs a diagnostic reporter module. This integrated encoding-capture architecture achieves high reaction orthogonality, near-quantitative conversion, and robust quantitative performance across structurally diverse metabolites. The resulting triazole derivatives exhibit markedly enhanced ionization efficiencies and generate a universal reporter-ion, enabling confident submetabolome classification and reconstruction. Application to serum and liver tissues from mice substantially expands the detectable chemical space, yielding 7 208 features and 1 573 annotated metabolites across four functional group-defined layers. Collectively, this work establishes the MREP framework as a versatile platform for reactivity-resolved interrogation of complex small-molecule systems.
    Keywords:  alkyne‐tagged probe; chemoselective metabolomics; click chemistry; reactivity encoding; submetabolome
    DOI:  https://doi.org/10.1002/advs.76599
  15. Biosci Rep. 2026 Aug 19. pii: BSR20250116. [Epub ahead of print]46(8):
    Proteomic investigation of signaling dynamics: from static maps to network rewiring.
    Ugo Dionne, Kosar Vafaee, Geoffrey G Hesketh, Anne-Claude Gingras.
      Cellular processes are controlled by interconnected networks of protein-protein interactions that can be dynamically regulated by post-translational modifications such as phosphorylation. Dysregulation of signaling pathways can drive cellular transformation and contribute to cancer treatment resistance. Mass spectrometry (MS)-based approaches have emerged as key technologies to study both protein function and their dynamic regulation at a network level. Modern proteomics allows investigators to study how signaling networks are rewired in response to genetic lesions, external cues, and targeted therapies, enabling the comparison of baseline (steady-state) networks to perturbed states. Here, we briefly describe key advancements in proteomics to study signaling dynamics, including affinity-purification combined with MS, proximity proteomics (e.g., BioID, APEX), and phosphoproteomics. We highlight how proteomics has led to the identification of comprehensive protein-protein interaction networks, to the delineation of protein subcellular localization maps and to discoveries regarding their dynamics and rewiring in disease. Finally, we comment on the future directions of proteomics to study signaling dynamics, enabled by next-generation MS instruments and AI-driven data analysis, and discuss how these developments are paving the way for clinical translation by bringing quantitative network biology into patient-relevant contexts.
    Keywords:  cancer; mass spectrometry; phoshorylation; protein dynamics; proteomics; signalling
    DOI:  https://doi.org/10.1042/BSR20250116
  16. Am J Respir Cell Mol Biol. 2026 Jul 11. pii: aanag143. [Epub ahead of print]
    Spatial metabolomics - a metabolic microscope for respiratory medicine.
    Matthew J Smith, Jesper Säfholm, Craig E Wheelock.
      Spatial metabolomics enables in situ, pixel-resolved mapping of small molecules across tissues, providing a powerful complement to conventional bulk metabolomics, which lacks cellular and anatomical resolution. By adapting mass spectrometry (MS) ionization approaches such as matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), spatial metabolomics generates high-resolution metabolic maps that link metabolite distributions to defined tissue regions, cellular niches, and biological functions. This "metabolic microscopy" enables localization of biosynthetic pathways, identification of region-specific metabolic signatures, and integration with histology and other spatial omics modalities. High-resolution MS platforms support untargeted discovery, while targeted approaches enable sensitive detection of low-abundance metabolites. In respiratory research, spatial metabolomics has begun to reveal regional heterogeneity in lung metabolism, including surfactant remodeling, lipid mediator localization, fibrosis-associated metabolic reprogramming, infection-specific airway responses, and tumor-associated phospholipid dysregulation. These studies highlight the capacity of the method to connect localized biochemistry with pulmonary physiology and disease mechanisms. However, challenges remain, including limited sensitivity, difficulties in metabolite identification and isomer discrimination, restricted quantification strategies, and incompatibility with commonly used FFPE tissue preservation, underscoring the need for standardized fresh-frozen tissue workflows. Future integration of spatial metabolomics with spatial transcriptomics and proteomics promises comprehensive, multi-layered metabolic mapping of the airways, enabling the cross talk between multiple orders of biology to be understood from specific tissue microenvironments. As part of a multimodal spatial biology framework, spatial metabolomics has strong potential to define disease endotypes, inform therapeutic target discovery, and generate spatial metabolic atlases that advance mechanistic understanding and precision medicine in respiratory disease.
    DOI:  https://doi.org/10.1093/ajrcmb/aanag143
  17. J Proteome Res. 2026 Jul 17.
    Agentic AI-Assisted Coding Offers a Unique Opportunity to Instill Epistemic Grounding during Software Development.
    Magnus Palmblad, Jared M Ragland, Benjamin A Neely.
      The capabilities of AI-assisted coding are progressing at a breakneck speed. Chat-based vibe coding has evolved into fully fledged AI-assisted, agentic software development using agent scaffolds, where the human developer creates a plan that agentic AIs implement. One current trend is utilizing documents beyond this plan such as project- and method-scoped documents. Here, we propose GROUNDING.md, a community-governed, field-scoped epistemic grounding document, using mass spectrometry-based proteomics as an example. This explicit field-scoped document encodes Hard Constraints (non-negotiable validity invariants empirically required for scientific correctness) and Convention Parameters (community-agreed defaults). In this framework, Hard Constraints are intended to function as field-scoped validity constraints that take precedence over lower-priority context when properly loaded, while Convention Parameters capture community-agreed defaults. In practice, GROUNDING.md will empower a non-domain expert to generate code, tools, and software that have best practices baked in at the ground level, providing confidence to the software developer but also to those reviewing or using the final product. It seems easier to have agentic AIs adhere to guidelines than humans, and this opportunity allows organizations to develop epistemic grounding documents in such a way that keeps domain experts in the loop in a future of democratized generation of bespoke software solutions.
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00398
  18. Adv Exp Med Biol. 2026 ;1501 1-42
    The Interdependence of Carbon Substrates and Metabolic Pathways Enables the Metabolic Plasticity of Cancer Cells.
    Inês Serpa-Gonçalves, Jacinta Serpa.
      The tumor microenvironment (TME) functions as a dynamic and co-evolving ecosystem, where malignant and non-malignant cells form a metabolically interdependent community. This ecological view reimagines tumors not as isolated cell masses, but as complex biotopes in which cellular interactions are integral to tumor initiation, growth, and progression. A hallmark of this adaptive environment is metabolic plasticity-an essential mechanism that enables tumor cells, including the metastatic ones, to reprogram their metabolism in response to fluctuating nutrient availability and environmental stressors. At the core of this reprogramming lies carbon metabolism, characterized by the selective and flexible utilization of key metabolites, including glucose, lactate, glutamine, cysteine, and fatty acids. These compounds support energy production, biomass synthesis, and redox balance, while also facilitating the export and repurposing of metabolic byproducts for signaling or reuse. This chapter presents a conceptual framework that explores the interdependence of central metabolic pathways, emphasizing how tumors coordinate energy generation, biosynthesis, and redox control to support malignant progression.
    Keywords:  Gluconeogenesis; Glutaminolysis; Glycolysis; Interconnected metabolic pathways; Metabolic dependence; Metabolism-targeted therapies; Pentose phosphate pathway (PPP)
    DOI:  https://doi.org/10.1007/978-3-032-12166-0_1
  19. Metabolomics. 2026 Jul 15. pii: 127. [Epub ahead of print]22(4):
    Network-based integration of metabolomics data from large-scale repositories.
    Cecilia Wieder, Eloisa Rocha Liedl, Thomas Payne, Ozgur Yurekten, Callum Martin, Felix Xavier Amaladoss, Noemi Tejera, Wanchang Lin, Yasin El Abiead, Pieter Dorrestein, Claire O'Donovan, Juan Antonio Vizcaíno, Warwick Dunn, Timothy Ebbels.
       INTRODUCTION: Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility.
    OBJECTIVES: This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content.
    METHODS: We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main .
    RESULTS: As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected.
    CONCLUSION: Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.
    Keywords:  Data integration; Harmonised annotation; Networks; Public data reuse; Repositories
    DOI:  https://doi.org/10.1007/s11306-026-02507-4
  20. Anal Chem. 2026 Jul 16.
    Establishing Orbitrap-Based Methods for Multi-Element Compound-Specific Isotope Analysis of Per- and Polyfluoroalkyl Substances.
    Juyoung Park, Linnea J Heraty, Neil C Sturchio, Deb P Jaisi, Mi-Ling Li.
      Per- and polyfluoroalkyl substances (PFAS) are globally distributed environmental contaminants whose persistence and toxicity necessitate advanced source identification strategies. Stable isotope analysis serves as a powerful forensic tool, providing intrinsic signatures to trace contaminant origins and transformation pathways. However, multi-element isotope characterization of PFAS remains underdeveloped, with most studies limited to carbon. Here, we present a robust method for multi-element (δ34S, δ15N, and δ18O) compound-specific isotope analysis of PFAS using Orbitrap mass spectrometry (MS). Using perfluorobutanesulfonamide (FBSA) as a model compound, we evaluated the isotopic characteristics of its NSO2- fragment generated via higher-energy collisional dissociation (HCD) and in-source fragmentation. Orbitrap δ34S measurements across diverse FBSA samples showed high precision and good alignment with elemental analyzer-isotope ratio mass spectrometry (EA-IRMS), effectively resolving isotopic variations among samples (ranging from -4.3‰ to +4.4‰). Simultaneous δ15N measurements and optimized δ18O measurements exhibited good agreement with EA-IRMS values, demonstrating robust multi-element capability. Across 3 orders of magnitude in concentrations of FBSA (0.01 to 10 μM), Orbitrap δ34S values measured using HCD remained stable (2 SD = 0.96‰), whereas δ34S measured using in-source fragmentation at 0.01 μM deviated from the mean δ34S values of higher concentrations by 2.9‰. In binary mixtures with perfluorohexanesulfonamide (FHxSA), HCD-based analysis of FBSA yielded consistent isotope values, with deviations observed only under equimolar, high PFAS loading conditions (7 μM each of FBSA and FHxSA). These results demonstrate that the developed framework provides a high-resolution platform for PFAS isotope analysis, enabling new opportunities in environmental forensics.
    DOI:  https://doi.org/10.1021/acs.analchem.6c02558
  21. J Proteome Res. 2026 Jul 14.
    Shipping Conditions May Impact the Reliability of At-Home Microsampling Devices.
    Andrea Villanueva Raisman, Jakob Naess, David Kotol, Fredrik Edfors.
      Targeted proteomics offers high precision, reproducibility, and multiplexing capabilities for quantifying proteins in complex biological samples, making the technology into a powerful tool for biomarker discovery and clinical diagnostics. When combined with at-home microsampling, this approach has the potential to transform population-level screening and longitudinal studies and can pave the way for decentralized healthcare. One of the main advantages of this combined approach is that samples can be collected offsite and then transported and stored as dried blood spots (DBSs). Currently, innovative volumetric microfluidic devices have also enabled DBS microsampling of consistently precise volumes. However, the reliability of the molecular data obtained with this mode of sampling still needs to be investigated and validated. In this study, 72 deidentified blood samples from two individuals were collected using DBS microsampling device CapitainerB and analyzed using targeted mass spectrometry and quantitative recombinant protein standards (qRePSs). The DBSs were subjected to three simulated and realistic shipping environmental conditions and stored in two different types of containers to assess sample stability. The results suggest that shipping conditions and storage at high temperatures often observed during summer have an impact on protein identification and quantification results, highlighting the need for further research to ensure biomarker reliability for dispersed microsampling.
    Keywords:  DBS; mass spectrometry; microsampling
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00117
  22. Nature. 2026 Jul 15.
    Ketogenic diet mediates intestinal tumorigenesis through lipids not ketones.
    Jessica E S Shay, Fangtao Chi, Constantine N Tzouanas, Shixun Han, Xiao Zhang, Johanna Ten Hoeve, Kevin J Williams, Seda Neptun, Tolga Sever, Isabela Fuentes, Sangeeta N Bhatia, Gizem Calibasi-Kocal, Matthew G Vander Heiden, Alex K Shalek, Ömer H Yilmaz.
      Diet composition shapes tissue function and disease risk by modulating nutrient availability, metabolic state and cellular dynamics1. In the gastrointestinal tract, obesogenic high-fat diets enhance small-intestinal stem cell activity and tumorigenesis2. However, the impact of ketogenic diets (KDs), which contain even higher lipid content but reduce circulating insulin and induce ketogenesis, remains poorly understood3. This is particularly relevant for patients with familial adenomatous polyposis who face a high risk of small-intestinal tumours4. Here we combine dietary, genetic and metabolic manipulations in mouse models of spontaneous intestinal adenoma formation to dissect the role of systemic and epithelial ketogenesis in intestinal cancer. We show that KD accelerates tumour burden and shortens survival, independent of ketone metabolites. Through genetic manipulation of the ketogenic pathway, we modulate the production of local and systemic ketone metabolites; however, neither inhibition nor augmentation of the ketogenic enzyme 3-hydroxy-3-methylglutaryl-coenzyme A synthase 2 nor disruption of ketolysis altered tumorigenesis. Combined intestinal loss of PPARα/δ/γ attenuates KD-driven intestinal stem cell expansion, proliferation and clonogenicity, whereas inhibition of downstream fatty acid oxidation through CPT1A loss limits adenoma formation specifically under KD, linking tumour initiation to fatty acid oxidation of dietary lipids rather than lipid accumulation. These findings reveal that dietary lipid content, through fatty acid oxidation rather than ketone metabolism, influences intestinal tumorigenesis and highlight the need for nuanced consideration of dietary strategies for cancer prevention in genetically susceptible populations.
    DOI:  https://doi.org/10.1038/s41586-026-10779-y
  23. Mol Metab. 2026 Jul 16. pii: S2212-8778(26)00109-2. [Epub ahead of print] 102425
    N-acetylated amino acids: an overlooked layer of metabolic regulation.
    Dirk Weber, Achim Bub.
      N-acetylated amino acids (Ac-AAs) have been repeatedly reported in metabolomics studies of high-intensity exercise, cold-exposed brown adipose tissue (BAT), and various pathological conditions. Despite their recurrent detection, the origins and physiological functions of Ac-AAs remain poorly understood, and evidence is fragmented across diverse scientific disciplines. While Ac-AAs have traditionally been attributed to the degradation of N-terminally acetylated proteins, this mechanism alone cannot fully account for their diversity and context-dependent regulation. Instead, accumulating evidence supports a model in which Ac-AA formation is driven by elevated intracellular acetyl-CoA and amino acid availability. Under these conditions, Ac-AA formation may represent a previously unrecognized metabolic mechanism involved in acetyl-CoA and amino acid homeostasis. In this review, we provide an overview of Ac-AA alterations across physiological contexts, synthesize current evidence on their origins, regulation, and physiological functions, and propose a mechanistic framework for the role of Ac-AAs in metabolic regulation. By integrating findings across diverse scientific disciplines, this review establishes a foundation for a more consistent interpretation of Ac-AAs across physiological and pathological contexts.
    Keywords:  Acetyl-CoA; Amino acids; Metabolic regulation; N-acetylated amino acids
    DOI:  https://doi.org/10.1016/j.molmet.2026.102425
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