bims-mascan 2026-03-29 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–03–29
sixteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

What Does Next-Generation Mass Spectrometry Offer for Proteomics? A Comprehensive Platform Comparison.
Direct Infusion Acoustic Droplet Ejection Mass Spectrometry: Enabling High-Throughput Shotgun Lipidomics.
Decoding the Metabolic Signatures of Neurodegeneration Diseases: Advances in Mass Spectrometry-Based Metabolomics.
Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage.
Protocol to purify fatty acid metabolites with two approaches for quantification using liquid chromatography-mass spectrometry.
A Deep Quantitative Proteome Turnover Platform for Human iPSC-derived Neurons.
Circular and Long Non-Coding RNAs in Cancer Metabolism: Dual Perspective of Biomarkers and Therapeutic Targets.
Enhanced Untargeted Metabolomics Based on High-Resolution Mass Spectrometry Reveals Global Rewiring Due to Mitochondrial Dysfunction in Yeast.
Unified Multitask Modeling for Retention Time Prediction Across Chromatographic Conditions.
Impact of Variations in Commercial Standard Operating Procedure on Plasma Proteome Recovery.
Navigating Challenges in Mass Spectrometry Analysis of Endogenous and Synthetic Protein Modifications.
Fully Automated Serum LC-MS/MS Platform and Pediatric Reference Intervals for Organic Acids, Amino Acids, and Acylcarnitines in Children (Ages 0-6 Years): Toward Quantitative Diagnosis of Inborn Errors of Metabolism.
AI redefine untargeted metabolomics: estimating chemical amounts for a Human Exposome Project.
Untargeted metabolomics for candidate marker nomination during late-stage cocoa fermentation: a framework toward objective endpoint assessment.
The oncogenic control of nucleotide synthesis.
Cell-specific isotope labeling identifies myo -inositol transfer between neurons and oligodendroglia to support myelin repair.

J Proteome Res. 2026 Mar 25.

What Does Next-Generation Mass Spectrometry Offer for Proteomics? A Comprehensive Platform Comparison.

Filipa Blasco Tavares Pereira Lopes, Daniela Schlatzer, Tara Sudhadevi, Anantha Harijith, Marzieh Ayati, Mehmet Koyutürk, Mark R Chance.

  Next-generation mass spectrometry platforms (Orbitrap Astral, timsTOF Ultra) are reshaping proteomics by enhancing analytical depth and sensitivity. We compared these platforms against Orbitrap Exploris 480 using neonatal mouse lung tissues from a bronchopulmonary dysplasia model (n = 12), employing four acquisition strategies: Exploris 480 DDA/DIA, Astral HR-DIA, and timsTOF Ultra DIA-PASEF. All platforms identified ∼4000 proteins in common, with 98% proteome coverage of data-dependent acquisition (DDA) identifications using data-independent (DIA) methods and 92% concordance between next-generation systems. Orbitrap Astral and timsTOF Ultra quantified >225,000 peptides and 13,000 proteins, representing ∼800% and ∼300% greater depth than Exploris 480 DDA, respectively. Furthermore, new-generation platforms cut recommended sample size by ∼66%. Enhanced proteome depth improved subcellular compartment annotations from 30% (DDA) to 66% (next-generation platforms) and reactome pathway coverage from 58% (DDA) to 90% (next-generation platforms). Differential expression analysis identified up to four times more phenotype-associated proteins in DIA data sets, enriched in mitochondrial, ribosomal, and extracellular components, with up to 44 enriched pathways. Importantly, proteins uniquely detected showed no functional annotation bias. These findings demonstrate that DIA acquisition on multivendor next-generation platforms provides superior proteome coverage and more complete systems biology assessment without introducing bias, enabling enhanced understanding of complex biological systems.

Keywords:  DDA; DIA; Orbitrap Astral; bronchopulmonary dysplasia; data-dependent acquisition; data-independent acquisition; proteomics; timsTOF Ultra

DOI:  https://doi.org/10.1021/acs.jproteome.5c01007
Anal Chem. 2026 Mar 25.

Direct Infusion Acoustic Droplet Ejection Mass Spectrometry: Enabling High-Throughput Shotgun Lipidomics.

Yu Zhang, Amy Harms, Lucas Jurado-Fasoli, Jonatan R Ruiz, Nicolas Drouin, Thomas Hankemeier.

High-throughput lipidomics is increasingly important for large-scale studies and clinical applications. While shotgun lipidomics enables rapid analysis, it suffers from limitations such as carryover, ion suppression, and limited structural specificity. Acoustic droplet ejection mass spectrometry (ADE-MS) presents a novel approach, enabling touchless nanoliter-scale sample introduction at high speed, precision, and accuracy. Initially designed for single-droplet injection, ADE-MS was adapted for direct infusion with stable signals. In this study, we developed and benchmarked a scalable workflow based on ADE-MS/MS with parallel reaction monitoring (PRM) on a ZenoTOF MS platform implemented in a 384-well format. By optimizing solvent composition, droplet parameters, and MS acquisition settings, the workflow enabled reproducible quantification of over 1000 polar and nonpolar lipid species across 14 subclasses, with low sample consumption and a total run time of approximately five minutes per sample. Applying this method to NIST SRM 1950 plasma, a total of 731 lipid species were quantified. The method demonstrated robust analytical performance in terms of linearity, precision, reproducibility, and recovery across 384-well microplates. Cross-platform comparison with a validated hydrophilic interaction liquid chromatography (HILIC)-MS/MS method using NIST SRM 1950 plasma demonstrated strong agreement (R2 > 0.80 for most subclasses) and substantially higher throughput, achieving over 200 lipid identifications per minute and a daily capacity exceeding 280 samples. The applicability of this workflow was demonstrated by identifying 656 differential lipid features associated with progressive lipidomic dysregulation across body mass index categories.

DOI: https://doi.org/10.1021/acs.analchem.5c05763
Metabolites. 2026 Mar 20. pii: 206. [Epub ahead of print]16(3):

Decoding the Metabolic Signatures of Neurodegeneration Diseases: Advances in Mass Spectrometry-Based Metabolomics.

Md Abdul Hakim, Li Li.

  The dysregulation of multiple metabolic pathways is a potential contributor to the development of neurodegenerative diseases. Understanding early-stage metabolic alterations is crucial for identifying targets associated with disease development and progression. Recent advances in mass spectrometry-based metabolomics now allow investigators to conduct a comprehensive analysis of small-molecule metabolites in complex biological systems, providing valuable insights regarding the biochemical mechanisms underlying neurodegeneration. This review presents the latest advances in mass spectrometry-based metabolomic approaches and their applications in studying neurodegenerative diseases. We discuss methodology improvements in metabolomics, including sample preparation, chromatography separations, ionization, and fragmentation. These improvements enable broader detection and more accurate identification of metabolites. We also review developments in bioinformatics tools for large-scale data processing, structural annotation, and pathway analysis. Furthermore, the signature metabolites associated with major neurodegenerative diseases and the key metabolic pathways involved are summarized. Finally, we address current analytical and biological challenges in mass spectrometry-based metabolomics while exploring its future directions in translational research.

Keywords:  biomarker discovery; mass spectrometry; metabolomic pathways; metabolomics; neurodegenerative diseases

DOI:  https://doi.org/10.3390/metabo16030206
Nat Methods. 2026 Mar 23.

Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage.

Nikita Levin, Cemil Can Saylan, Joel Lapin, Yana Demyanenko, Kevin L Yang, John Sidda, Alexey I Nesvizhskii, Mathias Wilhelm, Shabaz Mohammed.

Bottom-up proteomics relies predominantly on collision-induced dissociation (CID) for peptide sequencing, which has achieved remarkable sensitivity and efficiency now enabling single-cell analysis. However, CID shows limitations in characterizing post-translational modifications and complex proteoforms. Here we have developed an integrated mass spectrometry platform enabling automated collision-, electron- and photon-based fragmentation techniques. Using multi-enzyme deep proteomics workflows, we generated comprehensive datasets to train a unified Prosit deep learning model predicting spectra across all dissociation methods. This publicly available model, now integrated into FragPipe's MSBooster module, increased protein identifications by >10% on average for both data-dependent and data-independent acquisition across all fragmentation techniques. We demonstrate that alternative approaches, particularly electron-induced and ultraviolet photodissociation, which generate richer, more informative spectra, achieve identification efficiency competitive with CID while providing superior sequence coverage. This work establishes a framework enabling routine application of advanced fragmentation techniques in standard proteomics pipelines.

DOI: https://doi.org/10.1038/s41592-026-03042-9
STAR Protoc. 2026 Mar 22. pii: S2666-1667(26)00112-7. [Epub ahead of print]7(2): 104459

Protocol to purify fatty acid metabolites with two approaches for quantification using liquid chromatography-mass spectrometry.

Tomomi Hashidate-Yoshida, Masaki Yamada, Katsuyuki Nagata, Yasuyuki Kihara, Yoshihiro Kita, Hideo Shindou.

  Fatty acid metabolites, such as eicosanoids and docosanoids, play important biological roles and are strictly regulated by diverse enzymes. Here, we present two approaches for purifying their metabolites, including the phospholipid mediator platelet-activating factor (PAF), for quantification using liquid chromatography-mass spectrometry (LC-MS). We describe steps for tissue collection, lipid extraction, and lipid mediator purification. We then detail procedures for data analysis. This protocol allows the selection of a suitable technique for analyzing target molecules. For complete details on the use and execution of this protocol, please refer to Yamamoto et al.1.

Keywords:  Cell Membrane; Health Sciences; Metabolism; Signal Transduction

DOI:  https://doi.org/10.1016/j.xpro.2026.104459
bioRxiv. 2026 Mar 16. pii: 2026.03.14.711828. [Epub ahead of print]

A Deep Quantitative Proteome Turnover Platform for Human iPSC-derived Neurons.

Ashley M Frankenfield, Jamison Shih, Tao Zhang, Jiawei Ni, Wan Nur Atiqah Binti Mazli, Edwin Lo, Yansheng Liu, Jiou Wang, Ling Hao.

Quantitative evaluation of protein turnover in human neurons is crucial for understanding neuron homeostasis and guiding drug development for neurological diseases. However, measuring protein turnover in postmitotic neurons remains challenging due to the high dynamic range of protein half-lives and limited proteome coverage in SILAC (Stable Isotope Labeling by Amino acids in Cell culture) experiments. Despite broad applications of dynamic SILAC proteomics to measure protein turnover in rodent tissues and primary neurons, few studies have measured protein half-lives in human neurons with limited proteome coverage. Here, we established a comprehensive platform to quantify protein half-lives in human induced pluripotent stem cell (iPSC)-derived neurons. By integrating optimized dynamic SILAC labeling in human neuron cultures, extensive peptide fractionation, optimized data-dependent and data-independent LC-MS/MS acquisition methods, and a streamlined computational pipeline, we achieved deep and accurate measurement of 10,792 protein half-lives from 162,854 unique peptides. We then compared the protein turnover and abundances in iPSC-derived glutamatergic cortical neurons and spinal motor neurons, revealing globally conserved proteome dynamics alongside subtype-specific differences consistent with specialized neuronal functions. To enable broad community access, we created NeuronProfile ( www.neuronprofile.com ), an interactive web platform for exploring protein turnover, abundance, and subcellular location in human neurons. Together, this work provides a comprehensive analytical platform to assess human neuronal proteostasis and a foundational resource for neurological disease research and therapeutic development.

DOI: https://doi.org/10.64898/2026.03.14.711828
Noncoding RNA. 2026 Mar 19. pii: 11. [Epub ahead of print]12(2):

Circular and Long Non-Coding RNAs in Cancer Metabolism: Dual Perspective of Biomarkers and Therapeutic Targets.

Francesca Pia Carbone, Stefania Hanau, Nicoletta Bianchi.

  Background/Objectives: Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to sustain proliferation, survive under metabolic stress, and develop therapeutic resistance. While oncogenic signaling pathways regulating cancer metabolism have been extensively studied, increasing evidence indicates that non-coding RNAs (ncRNAs) play essential roles in coordinating metabolic adaptation. This review aims to synthesize current knowledge on long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) as important but relatively less characterized regulators of cancer metabolic adaptation and discuss their potential as biomarkers and therapeutic targets. Methods: We analyzed their roles across multiple types of cancer, prioritizing studies that integrate ncRNA profiling with metabolomics and mechanistic investigations, with particular attention to their diagnostic, prognostic, and predictive value. Results: LncRNAs and circRNAs regulate major metabolic pathways, including glycolysis, mitochondrial function, glutaminolysis, lipid metabolism, and redox balance. They act through transcriptional and epigenetic mechanisms, protein scaffolding, peptide encoding, and miRNA sponging, frequently converging on key regulators such as HIF-1α, c-Myc, p53, AMPK, and mTOR. However, many reported associations remain largely correlative, with limited integration of quantitative metabolic flux analyses and insufficient validation in physiologically relevant models. Conclusions: Although lncRNAs and circRNAs constitute an important context-dependent regulatory layer linking oncogenic signaling to metabolic reprogramming, future studies should combine ncRNA perturbation with stable isotope tracing, fluxomics, spatial metabolomics, long-read sequencing, and single-cell approaches to define causal and spatially resolved metabolic functions. Such integrative strategies may improve biomarker development and support ncRNA-informed, metabolism-oriented therapeutic interventions.

Keywords:  cancer; circular RNA; metabolites; non-coding RNAs

DOI:  https://doi.org/10.3390/ncrna12020011
Int J Mol Sci. 2026 Mar 13. pii: 2624. [Epub ahead of print]27(6):

Enhanced Untargeted Metabolomics Based on High-Resolution Mass Spectrometry Reveals Global Rewiring Due to Mitochondrial Dysfunction in Yeast.

Fabrizio Mastrorocco, Luca De Martino, Igor Fochi, Graziano Pesole, Ernesto Picardi, Clara Musicco, Sergio Giannattasio.

  Mitochondrial dysfunction profoundly alters cellular metabolism, yet its systems-level consequences remain incompletely characterized. Here, we present a comprehensive untargeted metabolomics analysis of respiratory-deficient (ρ0) and competent (ρ+) Saccharomyces cerevisiae prototrophic cells using ultra-high-performance liquid chromatography coupled to Orbitrap Fusion™ Tribrid™ high-resolution mass spectrometry. By integrating hydrophilic interaction and reversed-phase chromatography in both ionization modes, we detected ~7000 features per chromatographic condition, of which ~12% were structurally annotated through MSn fragmentation and in silico spectral matching. Principal component analysis revealed distinct metabolic signatures between ρ0 and ρ+ cells, with ~73% of total variance explained by the first two components. Volcano plot and hierarchical clustering analyses identified a marked accumulation of phosphate-containing metabolites, sphingolipids, ceramides, and fatty acid residues in ρ0 cells, whereas amino acids, excluding arginine, cysteine, and aromatics, were enriched in ρ+ cells. Notably, branched-chain amino acid depletion in ρ0 cells correlated with impaired growth and mitochondrial stress. Pathway enrichment analysis, supported by transcriptomic integration, prompted us to further investigate reprogramming of polyamine biosynthesis and aromatic amino acid metabolism. Calibration curves constructed from certified standards validated the accuracy of the LC-MS platform and reinforced annotation confidence. Our findings demonstrate that advanced untargeted metabolomics, coupled with MS3 fragmentation and multi-omics integration, enables high-resolution mapping of metabolic reconfiguration under mitochondrial dysfunction, offering mechanistic insights into mitochondrial retrograde signaling and adaptation.

Keywords:  mass spectrometry; metabolite annotation; mitochondrial dysfunction; mitochondrial retrograde pathway; untargeted metabolomics; yeast

DOI:  https://doi.org/10.3390/ijms27062624
Anal Chem. 2026 Mar 26.

Unified Multitask Modeling for Retention Time Prediction Across Chromatographic Conditions.

Ziyao Xiong, Xujie Wang, Junqi Liu, Shan Cong, Qiong Yang, Hongchao Ji.

Retention time (RT) is a key parameter in liquid chromatography-mass spectrometry (LC-MS) workflows, supporting compound identification, feature alignment, and quality control. However, traditional RT prediction models are built for specific chromatographic conditions, resulting in fragmented knowledge and limited scalability. We introduce Uni-RT, a unified multitask learning framework that simultaneously learns from heterogeneous data sets to capture both shared molecular retention patterns and condition-specific differences. By leveraging data across multiple chromatographic setups, Uni-RT achieves higher accuracy and robustness than pooled or condition-specific models while greatly simplifying model deployment. Evaluation on 28 reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) data sets demonstrates that multitask learning provides a powerful and generalizable solution for integrating RT prediction into diverse applications.

DOI: https://doi.org/10.1021/acs.analchem.5c07973
J Proteome Res. 2026 Mar 25.

Impact of Variations in Commercial Standard Operating Procedure on Plasma Proteome Recovery.

Taku Kasai, Luisa Weiss, Jessica Wohlfahrt, Joan T Matamalas, Gabriel Shlayen, Zachary J Kirsch, Masanori Aikawa, Elena Aikawa, Sasha A Singh.

  Advancements in mass spectrometry and complementary technologies now enable comprehensive, high-resolution plasma proteomics. Plasma is a key biofluid for clinical research, harboring potential disease-informative biomarkers. Commercial sources of nondiseased, healthy donor plasma samples are often used for proteomic workflow development and as controls for clinical studies. The overarching assumption is that standard operating procedures for plasma preparation are comparable across different sources. In this study, we investigated the effectiveness of a particle-based protein enrichment strategy against a conventional proteomics workflow on plasma samples from five commonly used commercial sources. We aimed to characterize the extent of variability in plasma proteomes when factors such as freeze-thaw cycles, choice of anticoagulant, and operator-to-operator performance were accounted for in the study setup. Plasma samples were analyzed in data-independent acquisition mode by using two distinct instruments (Exploris 480, timsTOF HT) and search algorithms (CHIMERYS, DIA-NN). Plasma proteome enrichment yields ranged from 2.8- to 6.2-fold more compared to the conventional workflow, achieving yields exceeding 5000 proteins (timsTOF HT). Notably, the observed variability in proteome composition was largely attributable to differences in whole blood-to-plasma operating procedures across commercial sources. While these distinct proteomes remained undetectable with conventional workflows, particle-dependent proteome profiling successfully revealed the procedural differences.

Keywords:  data-independent acquisitions (DIA); dia-PASEF; lipoproteins; particle-dependent enrichment; plasma proteomics; platelets; preanalytical variables

DOI:  https://doi.org/10.1021/acs.jproteome.5c01012
Biomolecules. 2026 Feb 28. pii: 367. [Epub ahead of print]16(3):

Navigating Challenges in Mass Spectrometry Analysis of Endogenous and Synthetic Protein Modifications.

Caroline M Hanson, Dina L Bai, Jarrod A Marto.

  Mass spectrometry-based analysis of post-translational modifications (PTMs) is a key strategy for characterizing protein regulation and identifying disease-associated targets, with endogenous PTMs serving as biomarkers for disease diagnosis and therapeutic response. More recently, chemical proteomic strategies have adapted PTM-focused workflows to measure engagement of covalent and photoactivatable small-molecule probes, expanding the scope of ligand discovery for these disease-associated targets. This review provides an overview of mass spectrometry-based PTM analysis workflows, including LC-MS/MS acquisition and post-acquisition data processing, with an emphasis on how modification-specific physicochemical properties influence PTM detection and identification. Common analytical challenges that limit PTM identification, including variable MS/MS fragmentation behavior and modification site localization, are discussed using modifications such as phosphorylation and photoaffinity labeling probe adducts as representative examples. Recent advances in acquisition strategies and computational tools that improve spectral quality and confidence in PTM assignment are also summarized. Additionally, approaches for the analytical validation of modification events, such as metabolic labeling strategies, are described. Together, this review outlines key considerations, capabilities, and limitations of MS-based PTM profiling and provides a framework for interpreting PTM datasets to support their effective integration into downstream biochemical and disease target validation studies.

Keywords:  chimeric spectra; collision induced dissociation (CID); database search; diagnostic ion; dynamic exclusion; electron capture dissociation (ECD); electron transfer dissociation (ETD); error-tolerant search; fragment remnant; immonium ion; isobaric; labeling profile; open search; parallel reaction monitoring (PRM); peptide-spectrum match (PSM); positional isomer; precursor ion scanning; site-determining ion; synthetic modification

DOI:  https://doi.org/10.3390/biom16030367
Diagnostics (Basel). 2026 Mar 19. pii: 911. [Epub ahead of print]16(6):

Fully Automated Serum LC-MS/MS Platform and Pediatric Reference Intervals for Organic Acids, Amino Acids, and Acylcarnitines in Children (Ages 0-6 Years): Toward Quantitative Diagnosis of Inborn Errors of Metabolism.

Yasushi Ueyanagi, Daiki Setoyama, Tsuyoshi Nakanishi, Yuichi Mushimoto, Vlad Tocan, Hironori Kobayashi, Miki Matsui, Shinya Matsumoto, Akiyoshi Fujishima, Taeko Hotta, Ayumi Sakata, Yuya Kunisaki.

  Background/Objectives: Conventional diagnosis of inborn errors of metabolism (IEMs) requires multiple specimen types-urine organic acids, plasma amino acids, and serum acylcarnitines-analyzed on distinct analytical platforms. This multi-assay approach is labor-intensive and limits timely clinical decision making. We aimed to develop a fully automated serum-based LC-MS/MS platform for integrated quantitative metabolite profiling and to establish pediatric reference intervals (RIs) to support diagnostic interpretation. Methods: A fully automated LC-MS/MS system integrated with the CLAM-2030 automated pretreatment module was developed to enable simultaneous quantification of 25 organic acids, 8 amino acids, and 21 acylcarnitines. Analytical performance was assessed for linearity, limits of detection and quantification, precision and accuracy. Serum samples from 296 non-IEM children aged 0-6 years were analyzed to establish pediatric RIs using Box-Cox transformation and Gaussian modeling. Clinical utility was evaluated in sera from 89 patients diagnosed with IEM using z-score-based logistic regression models. Results: The method demonstrated excellent performance, with linearity (r2 > 0.99) across calibration ranges, limits of detection and quantification defined by signal-to-noise ratios > 3 and >10, and intra- and inter-assay precision < 15% CV for all 54 analytes. Twenty-one analytes met the acceptance criterion of ±20% accuracy at all quality-control levels. Pediatric RIs provided a quantitative framework for interpreting the metabolic abnormalities. In IEM patients, disease-specific metabolites were consistently outside the established ranges, and z-score-based logistic regression models successfully distinguished major IEM categories, including organic acidemias and long-chain fatty acid oxidation disorders. Conclusions: This fully automated, serum-based LC-MS/MS platform provides a clinically practical and quantitative framework for integrated metabolic profiling using pediatric RIs, supporting diagnosis and monitoring of IEMs in pediatric settings.

Keywords:  acylcarnitines; amino acids; inborn errors of metabolism (IEMs); liquid chromatography–tandem mass spectrometry (LC–MS/MS); organic acids; pediatric reference intervals

DOI:  https://doi.org/10.3390/diagnostics16060911
Front Public Health. 2026 ;14 1775284

AI redefine untargeted metabolomics: estimating chemical amounts for a Human Exposome Project.

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

  The Human Exposome Project aims to map the totality of environmental exposures, but its success relies on transforming qualitative detections into quantitative data. Following our review on AI-driven metabolite identification, this second installment addresses the next critical bottleneck: estimating chemical concentrations in untargeted metabolomics without authentic standards. Translating LC-HRMS signal intensities into absolute concentrations is hindered by the vast variability in ionization efficiency and matrix effects, particularly for xenobiotics where reference standards are unavailable. We review emerging strategies that leverage artificial intelligence-ranging from descriptor-based regression to deep learning on molecular point clouds-to predict ionization response factors. We further evaluate a "matrix-embedded" calibration approach that utilizes ubiquitous endogenous metabolites (e.g., amino acids, lipids) as internal anchors to normalize response scales across studies. These innovations enable "tiered semi-quantification," allowing the classification of exposures into biologically relevant ranges (e.g., nanomolar vs. micromolar). This stratification facilitates direct integration with toxicological frameworks, such as the Threshold of Toxicological Concern (TTC) and high-throughput bioactivity data (e.g., ToxCast), for rapid risk prioritization. By integrating quantitative AI prediction models with robust quality assurance, untargeted metabolomics can evolve from a qualitative discovery tool into a quantitative engine for exposure science, providing the necessary evidence to link complex chemical exposures to human health outcomes.

Keywords:  Human Exposome Project; Threshold of Toxicological Concern (TTC); artificial intelligence; concentration prediction; exposomics; ionization efficiency; quantitative structure–retention relationships; untargeted metabolomics

DOI:  https://doi.org/10.3389/fpubh.2026.1775284
Food Chem. 2026 Mar 21. pii: S0308-8146(26)01142-8. [Epub ahead of print]512 148984

Untargeted metabolomics for candidate marker nomination during late-stage cocoa fermentation: a framework toward objective endpoint assessment.

Ana M León-Inga, Daniela Burgos-Toro, María José Chica, Ludger A Wessjohann, Mónica P Cala, Miguel Fernández-Niño.

  The cutting test remains the conventional standard for determining the endpoint of cocoa fermentation. However, it is subjective, operator-dependent, and lacks biochemical precision. To explore molecular criteria that can complement this traditional approach, we applied an untargeted, multi-platform metabolomics (GC-MS and LC-MS) to characterize biochemical transitions during the last 48 h of fine-flavor cocoa fermentation. Metabolic fingerprints suggest a stage-dependent separation driven by polar and moderately polar metabolites, while lipid profiles remained largely unchanged. Twenty-nine confidently annotated compounds showed consistent temporal dynamics across all platforms, representing sugars, organic acids, peptides, polyphenols, and nitrogenous bases. Using rigorous criteria, including fold change behavior, statistical significance, annotation confidence, and reproducibility, we selected twelve metabolites as candidate indicators whose trends robustly distinguish the late stages of fermentation. This study presents a reproducible workflow for nominating candidate markers of cocoa fermentation completion, which can be transferred, expanded, and validated across different fermentation systems.

Keywords:  Cocoa fermentation; Fermentation-stage indicators; Fine-flavor cocoa; Putative molecular markers; Untargeted metabolomics

DOI:  https://doi.org/10.1016/j.foodchem.2026.148984
Oncogenesis. 2026 Mar 26.

The oncogenic control of nucleotide synthesis.

Olivia Vidal-Cruchez, Issam Ben-Sahra.

Proliferating cancer cells reprogramme metabolism to secure nucleotides and other macromolecules required for biomass accumulation and genome duplication. Beyond serving as DNA/RNA precursors, nucleotides act as energy currencies, second messengers, glycosyl donors, and modulators of cytoskeletal dynamics; sustaining adequate pools is therefore indispensable for tumour growth and progression. Oncogenic lesions, such as loss of TP53 or LKB1, hyperactive PI3K-AKT-mTORC1, and MYC or RAS, coordinate transcriptional programmes, substrate transport, and post-translational control of rate-limiting enzymes to elevate de novo purine and pyrimidine synthesis and shape salvage use. These circuits integrate glycolysis, the pentose-phosphate pathway, folate-dependent one-carbon metabolism, and glutamine/aspartate provisioning to channel carbon and nitrogen into ring assembly. In this review, we organize this landscape into an environment-shaped routing model that explains when tumours favour de novo versus salvage and how therapies reroute flux. We synthesise current mechanisms by which oncogenes and tumour suppressors regulate nucleotide synthesis in cancer and outline therapeutic implications, including inhibitors of pathway enzymes (e.g., DHODH, IMPDH), strategies that restrict precursor availability, and rational combinations with targeted agents or DNA-damaging therapies to exploit replication stress and metabolic vulnerabilities. Together, these insights highlight nucleotide metabolism as a central, drug-responsive nexus linking oncogenic signalling to malignant proliferation.

DOI: https://doi.org/10.1038/s41389-026-00608-2
bioRxiv. 2026 Mar 22. pii: 2026.03.19.712965. [Epub ahead of print]

Cell-specific isotope labeling identifies myo -inositol transfer between neurons and oligodendroglia to support myelin repair.

Kayla Adkins-Travis, Mun-Gu Song, Michaela Schwaiger-Haber, Kevin Cho, Ronald Fowle-Grider, Stephen L Johnson, Leah P Shriver, Gary J Patti.

Neurons and glial cells are biochemically coupled through the exchange of nutrients, but our knowledge of which metabolites are transferred between them remains limited due to technical challenges. Here, we introduce a strategy to label specific cell types with isotopic tracers so that metabolite transfer can be measured directly in the intact brain. By engineering neurons in mice to metabolize 13 C-labeled cellobiose, a glucose dimer that wild-type cells cannot catabolize, we selectively track neuron-derived metabolites by using mass spectrometry-based metabolomics. Applying this approach enabled us to identify myo -inositol as a critical metabolite synthesized by neurons and transferred to oligodendrocyte progenitor cells (OPCs) via the SLC5A3 transporter. The transfer of myo -inositol from neurons to OPCs promotes OPC proliferation and differentiation by enhancing phosphatidylinositol synthesis and upregulating expression of myelin-associated genes. During demyelination, deficient nutrient transfer can be rescued by dietary supplementation of myo -inositol, which accelerates myelin repair. These findings establish a generalizable technology for tracing intercellular metabolite transfer in vivo and identify a previously unrecognized mechanism of myo -inositol transfer from neurons to glial cells in support of CNS regeneration, revealing a potential metabolic target for therapeutic intervention in neurodegenerative disease.

DOI: https://doi.org/10.64898/2026.03.19.712965