bims-mascan 2026-06-07 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–06–07
29 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Protocol for untargeted lipidomics of human serum using LC-TIMS-PASEF.
Microwave-Assisted Meta-Chloroperoxybenzoic Acid Epoxidation for Qualitative and Quantitative Analysis of Unsaturated Lipid Double-Bond Positional Isomers by Liquid Chromatography-Tandem Mass Spectrometry.
A multiplexed LC-MS/MS assay for comprehensive screening of amino acid metabolism disorders.
MetaDilutionR: An R Package for Data-Driven Determination of Optimal Plasma Dilution in Untargeted Metabolomics to Achieve Maximum Metabolite Coverage with ESI Linearity.
Confident Identification and Quantification of Mouse Brain Tissues Reveals Sirtuin 5-Dependent Regulation.
Targeted Livestock Metabolomics: A Review of Liquid chromatography-Mass Spectrometry-Based Approaches.
Neutral molecular networks: Polarity-independent tool for mass spectrometry data.
Prioritizing peptides for targeted mass spectrometry experiments using deep learning.
LipidCruncher: an open-source platform for processing, visualizing, and analyzing lipidomic data.
IMFrag: A Tool to Recognize In-Source Fragmentation in Ion Mobility-Enabled Data-Independent Acquisition Workflows.
Ordering molecular diversity in untargeted metabolomics via molecular community networking.
A Unified Mass Spectrometry Workflow Integrating Bottom-Up and Native Top-Down Proteomics via Nanodroplet Enzymatic Digestion.
Development and Validation of a Streamlined Workflow for Proteomic Analysis of Proteins and Post-translational Modifications from Dried Blood.
MS-Net: Multi-Similarity-Based Network Annotation for Untargeted Metabolomics.
Stable isotope labeling LC-MS for profiling the amine submetabolome in a D-lactate treated cell model.
Mapping the interactome of human tRNA methyltransferase TRMT1 using dual proximity labeling.
Comprehensive optimization of chromatographic separation and iterative data acquisition for liquid chromatography-tandem mass spectrometry non-targeted plasma metabolomics: Enhancing feature-based molecular networking.
In-Situ Metabolic Profiling of Single Living Cells by Liquid Secondary Ion Mass Spectrometry.
Exploring Acylcarnitine Metabolism Using Reverse Metabolomics.
Untargeted microLC-HRMS/MS for Measuring Endogenous Steroids in Mouse Plasma and Human Serum Samples Applied to Studying Polycystic Ovary Syndrome.
A liquid chromatography-mass spectrometry method to quantify total Coenzyme A concentration and isotopic labeling.
Development and validation of a quantitative ultra-performance liquid chromatography quadrupole time-of-flight method for urine organic acid analysis.
Ion chromatography-ultra-high-resolution mass spectrometry reveals EZH2-driven reprogramming of nucleic acid and protein methylation.
High-Throughput Reactive Desorption Electrospray Ionization Mass Spectrometry for Targeted Derivatization and Analysis of Poorly Ionized Small Molecules.
Proteomics in bone malignancies: from bulk profiling to single-cell and ultra-low-input technologies.
Integrated Protein Precipitation-Solid-Phase Extraction Workflow for Deep Top-Down Proteomic Analysis of Human Serum.
Spermine is an endogenous iron chelator that inhibits ferroptosis.
Trends in Computational Metabolomics: A Perspective on Five Years of Software Development, Challenges, and Opportunities (2021-2025).
Simultaneous UHPLC-MS/MS profiling of eight phosphate metabolites reveals divergent metabolic reprogramming under hypoxic stress.

STAR Protoc. 2026 Jun 01. pii: S2666-1667(26)00260-1. [Epub ahead of print]7(2): 104607

Protocol for untargeted lipidomics of human serum using LC-TIMS-PASEF.

Fernanda Sousa Monteiro, Adriana Zardini Buzatto.

  Here, we present a protocol for untargeted lipidomics of human serum. We describe steps for project design, sample preparation, and reversed-phase liquid chromatography coupled with trapped ion mobility spectrometry and parallel accumulation serial fragmentation (LC-TIMS-PASEF). We then detail procedures for quality control and data processing. The protocol enables comprehensive lipid profiling from 5 μL of serum, yielding annotations across glycerophospholipids, glycerolipids, sphingolipids, sterols, and fatty acyls. The resulting datasets support statistical analysis and integration with complementary omics approaches.

Keywords:  Mass Spectrometry; Metabolomics; Protocols in Metabolomics and Lipidomics

DOI:  https://doi.org/10.1016/j.xpro.2026.104607
J Sep Sci. 2026 Jun;49(6): e70460

Microwave-Assisted Meta-Chloroperoxybenzoic Acid Epoxidation for Qualitative and Quantitative Analysis of Unsaturated Lipid Double-Bond Positional Isomers by Liquid Chromatography-Tandem Mass Spectrometry.

Danyang Liu, Hongjin Chen, Yong Huang, Ting Zhou.

  Unsaturated lipid double-bond (C═C) positional isomers are closely related to lipid structure and biological function, but their reliable qualitative and quantitative analysis remains challenging by conventional liquid chromatography-mass spectrometry (LC-MS) methods. In this study, we developed a microwave-assisted meta-chloroperoxybenzoic acid (m-CPBA) epoxidation method coupled with LC-tandem MS for the identification and quantification of unsaturated lipid C═C positional isomers. The reaction conditions were optimized to facilitate the formation of fully epoxidized products, simplify the chromatographic peak profiles, and enhance their suitability for targeted quantitative analysis. The developed method showed good analytical performance, including satisfactory linearity (r2 > 0.99), precision (relative standard deviation <15%), recovery (75.5%-106.5%), and acceptable matrix effects (86.0%-117.3%). The method was successfully applied to rat brain tissue and plasma samples from a rat depression model, enabling the quantification of multiple unsaturated lipids and the characterization of their C═C positional isomers in complex biological matrices. These results demonstrate that microwave-assisted m-CPBA epoxidation provides a rapid, efficient, and practical strategy for LC-MS-based analysis of unsaturated lipid isomers.

Keywords:  biological matrices; derivatization; diagnostic fragmentation; fatty acids; phospholipids

DOI:  https://doi.org/10.1002/jssc.70460
Talanta Open. 2025 Dec;12 100523

A multiplexed LC-MS/MS assay for comprehensive screening of amino acid metabolism disorders.

Jisha Chandran, Madan Gopal Ramarajan, Vykuntaraju Kammasandra Nanjundagowda, Aruna Gowdra, Jayesh Warade, Anikha Bellad, Kannan Rangiah.

  Advancements in liquid chromatography-tandem mass spectrometry (LC-MS/MS) are redefining the landscape of clinical diagnostics, particularly in the context of newborn screening for inborn errors of metabolism. Conventional analytical platforms are often limited by a small number of analytes and require multiple platforms for subsequent analyses, thereby impacting timely diagnosis and management. This study describes the development of a multiplexed targeted mass spectrometry-based assay for simultaneous detection and quantitation of amines, including amino acids and their derivatives, from plasma, urine and dried blood spots. The method utilizes a single step derivatization strategy based on one step click chemistry that simplifies sample preparation while improving analytical sensitivity allowing for detection of analytes at sub-picomolar concentrations. Furthermore, we adopted a strategy of generating heavy isotopically labeled standards by chemically modifying the corresponding light standards using a stable isotope labeled derivatizing agent, enabling their use as internal standards for quantification. This approach can offer a cost-effective and scalable solution for the early detection and management of inherited metabolic disorders, particularly in cases where accurate detection of amines is critical.

Keywords:  Amines; Amino acid; Inborn errors of metabolism; LC-MS; Newborn Screening; Quantification

DOI:  https://doi.org/10.1016/j.talo.2025.100523
J Am Soc Mass Spectrom. 2026 Jun 03.

MetaDilutionR: An R Package for Data-Driven Determination of Optimal Plasma Dilution in Untargeted Metabolomics to Achieve Maximum Metabolite Coverage with ESI Linearity.

Keerthana Vinod Kumar, Aviral Singh, Sneha Rana, Rakesh Sahay, Pramod P Wangikar.

  High-resolution mass spectrometry (HRMS) instruments for untargeted metabolomics typically offer a linear dynamic range spanning approximately 4 orders of magnitude. However, biological samples contain metabolites spanning concentration ranges far exceeding this window, making dilution optimization critical for reliable quantification. Despite its importance, dilution selection in untargeted workflows is rarely standardized and is often determined empirically through manual inspection, leading to operation outside the linear dynamic range, ion suppression, and compromised reproducibility. To address this, we present MetaDilutionR, an open-source R package that standardizes dilution optimization by systematically evaluating electrospray ionization (ESI) linearity using plasma as a model. MetaDilutionR automates dilution assessments, applying user-adjustable slope and R2 thresholds to classify features as linear or nonlinear and executes the complete analysis via a single function call, ensuring algorithmic reproducibility across users and platforms. The package generates comprehensive outputs, including log2-transformed data, a summary of linear features with their optimal dilution ranges, nonlinear features highlighting potential ion suppression or detector saturation, detailed evaluations across dilution scenarios, and visual regression plot reports. Benchmarking against three established metabolomics workflows demonstrated that the R2-slope criterion of MetaDilutionR reduces false-positive linear assignments. Cross-platform applicability of the algorithm on an independent GC-MS data set confirmed consistent classification performance beyond LC-HRMS. By facilitating systematic identification of metabolite-specific optimal dilution conditions, MetaDilutionR enables metabolites to be quantified within their linear dynamic range─a prerequisite for reliable quantification─thereby enhancing reproducibility and consistency of downstream validation, making it readily integrable into existing metabolomics workflows.

Keywords:  linearity; metabolite coverage; serial dilution; untargeted metabolomics

DOI:  https://doi.org/10.1021/jasms.5c00419
bioRxiv. 2026 May 28. pii: 2026.05.26.726073. [Epub ahead of print]

Confident Identification and Quantification of Mouse Brain Tissues Reveals Sirtuin 5-Dependent Regulation.

Landgrave-Gomez Jorge, Bons Joanna, Vega-Hormazabal Genesis, Riley Rebeccah, Schilling Birgit, Verdin Eric.

Methylmalonylation is a non-enzymatic lysine post-translational modification derived from methylmalonyl-CoA, a reactive intermediate that accumulates during mitochondrial dysfunction and branched-chain amino acid catabolism. Although reported in models of methylmalonic acidemia, its broader distribution and functional relevance remain largely unexplored. Progress has been hindered by a key analytical challenge: methylmalonyl-and succinyl-lysine are isobaric (+100.0160 Da) and generate overlapping mass spectrometric fragmentation spectra, preventing confident identification in conventional proteomic workflows. Here, we establish a straightforward proteomic workflow that overcomes this barrier and enables confident identification and quantification of lysine methylmalonylation by combining antibody-based enrichment with data-independent acquisition mass spectrometry (DIA-MS). Anti-malonyl antibodies were used to enrich methylmalonylated peptides through cross-reactivity. Using synthetic peptide standards containing malonyl-, succinyl-, or methylmalonyl-lysine, we defined distinguishing analytical features including chromatographic retention time, ion mobility, and fragmentation patterns. Applying this approach to mouse brain tissues from Sirtuin-5 (SIRT5) knockout and wild-type mice, we identified 44 methylmalonylated peptides across 41 proteins, enriched in neuronal and myelin-associated proteins (NEFM, NEFL, MBP) and mitochondrial enzymes such as ADT1. Several sites were increased in SIRT5-deficient brains, consistent with regulation by this mitochondrial deacylase. Functional assays demonstrated that methylmalonylation of myelin basic protein (MBP) impairs lipid binding, linking this modification to myelin stability. Together, this workflow enables confident methylmalonylation identification and defines it as a widespread and regulated modification in the brain, providing a framework to study metabolically driven protein acylation in neurobiology and disease.
Significance: Lysine methylmalonylation has remained largely unexplored due to its isobaric overlap with succinylation, which prevents confident identification using conventional proteomic workflows. Here, we establish an integrated strategy combining antibody-based enrichment, data-independent acquisition mass spectrometry, and orthogonal analytical features to resolve these modifications with high confidence. Applying this approach to mouse brain tissue reveals a SIRT5-regulated methylmalonylome enriched in mitochondrial and myelin-associated proteins, including myelin basic protein (MBP). Functional assays demonstrate that methylmalonylation impairs MBP lipid binding, linking this modification to myelin stability. Beyond this specific application, our workflow provides a generalizable framework to resolve isobaric post-translational modifications and expands the study of metabolically driven protein acylation in neurobiology and disease.

DOI: https://doi.org/10.64898/2026.05.26.726073
J Agric Food Chem. 2026 Jun 02.

Targeted Livestock Metabolomics: A Review of Liquid chromatography-Mass Spectrometry-Based Approaches.

Kangkang Xu, Franz Berthiller, Rudolf Krska, Heidi E Schwartz-Zimmermann.

  With the advancement of sophisticated analytical techniques, such as nuclear magnetic resonance spectroscopy and mass spectrometry (MS), metabolomics has become a powerful tool for analyzing and quantifying small molecules in cells, tissues, and biofluids. Among MS-based methods, liquid chromatography-mass spectrometry (LC-MS) is widely used due to its high analyte coverage, sensitivity, and selectivity. Targeted metabolomics quantifies predefined metabolites, in contrast to untargeted approaches that profile all detectable metabolites. This review provides an overview of targeted LC-MS methods and applications in livestock metabolomics, focusing on ruminants and swine, and covering research from the past decade. We discuss sample preparation, LC-MS instrumentation, and method validation, as well as emerging trends including combined LC techniques, integration of targeted and untargeted approaches, and multiomics studies. Current limitations, future directions, and a general workflow for method development are also addressed.

Keywords:  LC-MS; analytical methods; animal metabolomics; derivatization; dilute and shoot; validation

DOI:  https://doi.org/10.1021/acs.jafc.5c16870
Anal Chim Acta. 2026 Sep 15. pii: S0003-2670(26)00658-6. [Epub ahead of print]1415 345708

Neutral molecular networks: Polarity-independent tool for mass spectrometry data.

Nicola Dalla Valle, Pietro Franceschi, Mar Garcia-Aloy, Peter Robatscher, Sabine Oettl, Michael Oberhuber.

  Untargeted mass spectrometry-based metabolomics generates large-scale fragmentation data, typically analyzed separately in positive and negative ionization modes. The fragmentation patterns of the same molecule usually capture distinct but complementary structural information across polarities. Evaluating them simultaneously, rather than in separate molecular networks, can enhance the overall informativeness and speed the analysis. In this paper, we introduce the Neutral Molecular Network (NMN) concept, a novel strategy that unifies complementary fragmentation patterns from both polarities into a single "neutral pseudo-spectra". NMNs outperformed polarity-specific networks on several large-scale publicly available MS/MS libraries in terms of chemical reliability of spectral matches and clustering capability. The improved performances were further confirmed through the analysis of a biological untargeted dataset of Alternaria fungal extracts, where this approach facilitated the identification of previously uncharacterized toxin derivatives. NMN offers a polarity-independent framework for the structural interpretation of untargeted MS/MS data. This method can improve the efficiency of metabolite annotation pipelines and can be applicable to diverse metabolomics workflows.

Keywords:  Alternaria; Electrospray ionization; Mass spectrometry; Molecular network; Untargeted metabolomics

DOI:  https://doi.org/10.1016/j.aca.2026.345708
bioRxiv. 2026 May 26. pii: 2026.05.21.727053. [Epub ahead of print]

Prioritizing peptides for targeted mass spectrometry experiments using deep learning.

Shreyash Sonthalia, Priank Dasgupta, Chris Hsu, Bo Wen, Michael J MacCoss, William Stafford Noble.

One critical step in any targeted mass spectrometry experiment is selecting, from each protein of interest, a small number of peptides that respond well in the mass spectrometer and can serve as reliable proxies for protein quantification. Existing methods select target peptides either by relying on prior empirical measurements, limiting their applicability to previously observed peptides, or using machine learning to predict peptide behavior from sequence alone. However, current machine learning tools suffer from various limitations, including using detectability as an indirect proxy for intensity, relying on small training sets, or ignoring the precursor charge state. In this study, we introduce Bromo, a transformer-based deep learning model that ranks peptide precursors from a given protein by their relative response, taking charge state into account. Trained on millions of annotated peptide pairs derived from large-scale, publicly available data-independent acquisition mass spectrometry data, Bromo consistently outperforms existing sequence-based methods across diverse, independent datasets. Furthermore, we show that fine-tuning Bromo on experiment-specific data can account for differences in sample preparation, sample matrix, and instrument platform, all of which influence which peptides serve as optimal targets. This adaptability makes Bromo a practical tool for selecting target peptides for selected reaction monitoring and parallel reaction monitoring assay development across a wide range of experimental conditions.

DOI: https://doi.org/10.64898/2026.05.21.727053
BMC Bioinformatics. 2026 Jun 04.

LipidCruncher: an open-source platform for processing, visualizing, and analyzing lipidomic data.

Hamed Abdi, Yohannes A Ambaw, Zon Weng Lai, Ritchie Ly, Chandramohan Chitraju, Shubham Singh, Robert V Farese, Tobias C Walther.

   BACKGROUND: Advances in mass spectrometry (MS)-based lipidomics have led to a significant surge in data volume, underscoring a need for robust tools to efficiently evaluate and visualize these expansive datasets. While numerous software tools have been developed, current workflows are hindered by manual spreadsheet handling and insufficient data quality assessment prior to analysis. Here, we introduce LipidCruncher, an open-source, web-based platform designed to easily process, visualize, and analyze lipidomic data with high efficiency and rigor.
RESULTS: LipidCruncher consolidates key steps of the lipidomics analysis workflow, including data standardization, normalization, and stringent quality controls. The platform also provides advanced visualization and analysis tools that are tailored to interrogate lipidomic data and enable detailed and holistic data exploration. To illustrate LipidCruncher's utility, we analyzed lipidomic data from adipose tissue of mice lacking the triacylglycerol synthesis enzymes DGAT1 and DGAT2.
CONCLUSIONS: LipidCruncher fills a specific gap in the lipidomics analysis ecosystem by providing an integrated, quality-focused platform that accepts data from multiple sources and complements existing specialized tools. By bridging the critical divide between data generation and biological interpretation, LipidCruncher facilitates rigorous lipidomics analyses to accelerate the translation of complex lipid profiles into biological insights.

Keywords:  Bioinformatics; Computational biology; Lipidomics; Lipids; Mass spectrometry; Open-source software; Phospholipids; Scientific software; Sphingolipids; Sterols

DOI:  https://doi.org/10.1186/s12859-026-06483-3
Anal Chem. 2026 Jun 06.

IMFrag: A Tool to Recognize In-Source Fragmentation in Ion Mobility-Enabled Data-Independent Acquisition Workflows.

Ryan Nguyen, Anvitaa Anandkumar, Griffin Rangel, Jennifer Liem, Yvonne S Lin, Libin Xu.

High-resolution mass spectrometry is a powerful tool for untargeted analysis. However, in-source fragmentation (ISF) could lead to the misidentification of compounds in untargeted metabolomics or exposomics studies. To prevent misidentification and to strengthen compound identification through MS/MS spectral library matching, we developed IMFrag, a Jupyter notebook-based tool that utilizes structural information gained from ion mobility-mass spectrometry (IM-MS) as an orthogonal technique to differentiate independent precursor ions from fragments formed via ISF. We first examined l-tryptophan, which is an essential amino acid that undergoes extensive fragmentation during electrospray ionization (ESI). IM-enabled data-independent acquisition (IM-DIA) analysis revealed distinct mobility signatures for identical fragment ions formed at different instrument sites, enabling discrimination between ISFs generated prior to the IM drift tube and fragments produced via postmobility collision-induced dissociation. Similar patterns were observed for a structurally diverse collection of small molecules. Additional structural information could also be inferred from the IM-DIA workflow, such as unique dimers, protonation sites, and distinct ion types that were not apparent from LC-MS alone. These insights were shown to be useful when applied to healthy human plasma samples, which served as a more complex and biologically relevant matrix that contained ambiguities, such as coeluting, isobaric candidate structures. Thus, IMFrag was developed as an accessible framework for interrogating MS1 and post-IM MS2 chemical features in untargeted data sets and can be integrated into untargeted analysis pipelines or used to support the development of ISF-derived MS/MS spectral libraries.

DOI: https://doi.org/10.1021/acs.analchem.6c01388
Cell Rep Methods. 2026 Jun 02. pii: S2667-2375(26)00168-2. [Epub ahead of print] 101468

Ordering molecular diversity in untargeted metabolomics via molecular community networking.

Elizabeth A Coler, Alexey Melnik, Ali Lotfi, Dana Moradi, Ben Ahiadu, Paulo Wender Portal Gomes, Abubaker Patan, Vincent Charron-Lamoureux, Pieter C Dorrestein, Stephen Barnes, Vladimir Boginski, Alexander Semenov, Alexander A Aksenov.

  Molecules in living systems are not random but are shaped by biological necessity. Mass spectrometry (MS) is a powerful tool for exploring these complex molecular landscapes. Molecular networking links metabolites by spectral similarity, but conventional methods leave many nodes disconnected. We introduce molecular community networking (MCN), which identifies natural molecular clusters and prunes them to keep the strongest links. The approach increases connectivity to about 95% of molecules and better captures structurally related compounds, including distinct ion forms and in-source fragmentation ions. MCN also improves the mapping of molecular space, helping distinguish true novel molecules from artifacts. Using MCN, we discovered dipeptide-conjugated bile acids associated with Bifidobacterium breve and proposed structures for previously unexplored N-acyl amides that interact with G protein-coupled receptors. We also built a global metabolome map from public GNPS/MassIVE data, covering about 8.4 million molecular features, creating a "roadmap" for molecular diversity.

Keywords:  CP: computational biology; CP: metabolism; gas chromatography-mass spectrometry; liquid chromatography-mass spectrometry; metabolomics; molecular community networking; molecular networking

DOI:  https://doi.org/10.1016/j.crmeth.2026.101468
Anal Chem. 2026 Jun 03.

A Unified Mass Spectrometry Workflow Integrating Bottom-Up and Native Top-Down Proteomics via Nanodroplet Enzymatic Digestion.

Hsi-Wen Wang, Yong-Lun Hong, Ting-Yi Chiang, Szu-Hsueh Lai.

Bottom-up and native top-down proteomics provide complementary but traditionally disconnected views of protein composition and structure. Integrating these approaches into a single, streamlined workflow has remained challenging due to incompatible sample preparation and analytical requirements. Here we report a unified mass spectrometry platform that bridges bottom-up and native top-down analysis through nanodroplet-accelerated enzymatic digestion performed directly under native electrospray conditions. By tuning the extent of digestion within nanodroplets, intact proteins, protein assemblies, and proteolytic peptides are generated simultaneously and analyzed within a single experiment. This workflow enables simultaneous peptide-level identification and native proteoform analysis within a single mass spectrometric experiment. We demonstrate the generality of this approach using myoglobin, amphipathic β-casein isoforms, and the 800 kDa GroEL chaperonin complex, achieving rapid sequence coverage while preserving native structural information accessible by native top-down mass spectrometry. This integrated strategy provides a practical route for comprehensive proteoform and protein assembly characterization across multiple levels of structural organization.

DOI: https://doi.org/10.1021/acs.analchem.6c01549
J Proteome Res. 2026 Jun 05.

Development and Validation of a Streamlined Workflow for Proteomic Analysis of Proteins and Post-translational Modifications from Dried Blood.

Matthew W Foster, Youwei Chen, Marlene J Violette, Sarah M Shapley, Michael T Forrester, J Scott Mellors, Brett S Phinney, Robert S Plumb, J Will Thompson, Timothy J McMahon.

  It is increasingly recognized that the 'omic analysis of whole blood has applications for precision medicine and disease phenotyping. Despite this realization, whole blood is generally viewed as a challenging analytical matrix in comparison to plasma or serum. Moreover, proteomic analyses of whole blood have almost exclusively focused on (non)targeted analyses of protein abundances and much less on post-translational modifications (PTMs). Here, we developed a streamlined workflow for processing 20 microliters of venous blood collected by volumetric absorptive microsampling that incorporates serial trypsinization and N-glycopeptide and phosphopeptide enrichment and avoids laborious sample dry-down or cleanup steps. As many as 10,000 analytes (reported as protein groups, glycopeptidoforms, and phosphosites) can be quantified by liquid chromatography-tandem mass spectrometry in under 2 h of MS acquisition time. Using these methods, we explored the stability of "dried" and "wet" blood proteomes, as well as the effects of ex vivo inflammatory stimulus or phosphatase inhibition. Multiomics factor analysis enabled facile identification of analytes that contributed to interindividual variability of the blood proteomes, including N-glycopeptides that distinguish immunoglobulin heavy constant alpha 2 allotypes. Collectively, our results help to establish feasibility and best practices for the integrated MS-based quantification of proteins and PTMs from dried blood.

Keywords:  HILIC; IMAC; MOFA; Mitra device; Orbitrap Astral; VAMS; data-independent acquisition; microflow liquid chromatography; stepped collision energy

DOI:  https://doi.org/10.1021/acs.jproteome.6c00032
Anal Chem. 2026 May 30.

MS-Net: Multi-Similarity-Based Network Annotation for Untargeted Metabolomics.

Victor Pereira Francisco, Simon Duthen, Elise Crossay, Amélie Perez, Solweig Hennechart, Marion Alignan, Romain Valentin, Franck Milone-Delacourt, Guillaume Marti.

Confident metabolite annotation remains a critical bottleneck in untargeted LC-MS metabolomics, with experimental spectral libraries covering only 5-20% of detected features. While in silico tools generate extensive candidate lists per feature, top-ranked predictions frequently fail to reflect true molecular identities, leading to high false annotation rates. We present multi-similarity Network-based annotation (MS-Net), an accessible workflow that integrates mass spectral similarity networks, molecular structure similarity (Tanimoto metrics), and taxonomic knowledge to prioritize annotations within vast candidate spaces. High-confidence annotations from authentic standards, spectral libraries, and taxonomically filtered candidates seed iterative propagation throughout mass spectral similarity networks. The workflow employs a composite Link Score combining structural, spectral, and computational evidence to rescue correct annotations from lower-ranked positions. Applied to Cannabis sativa extracts (2595 features to 1297 after filtering), MS-Net assigned 1275 compounds from an initial candidate space of over 118,000 structures. Notably, 53% of final annotations were rescued from ranks 2-50, demonstrating correction of initial in silico ranking. The workflow successfully reconstructed known cannabinoid biosynthetic pathways, validating biological coherence. MS-Net is freely available as a KNIME workflow with complete documentation at https://forge.inrae.fr/metatoul/equipe-agromix/ms-net, enabling reproducible, offline annotation suitable for systems biology integration.

DOI: https://doi.org/10.1021/acs.analchem.6c01026
Anal Chim Acta. 2026 Aug 22. pii: S0003-2670(26)00599-4. [Epub ahead of print]1412 345649

Stable isotope labeling LC-MS for profiling the amine submetabolome in a D-lactate treated cell model.

Yue-Qiang Chen, Xiao-Xia Dong, Li Liu, Xiu-Ping Chen, Chen-Guo Feng, Li-Xing Zhan, Yue Su, Fang Zhang, Yin-Long Guo.

   BACKGROUND: Alterations in the amine submetabolome are closely associated with cellular metabolic status and are important for understanding metabolic regulation under intervention conditions. In this study, a stable isotope labeling derivatization combined with liquid chromatography-mass spectrometry (LC-MS) was employed to characterize alterations in the cellular amine submetabolome. Optimization of chromatographic separation and cellular metabolic quenching procedures improved the separation performance of amine metabolites while enabling more accurate preservation of the metabolic state at the time of sampling.
RESULTS: The established method was applied to a D-lactate treated BEAS-2B cell model to evaluate amine submetabolome alterations under different compound intervention conditions. The results demonstrated that selenomethionine (SeMet) and berberine alleviated D-lactate induced abnormalities in the amine submetabolome and showed metabolic regulatory effects consistent with tumor-suppressive trends observed under in vivo experiments.
SIGNIFICANCE: Overall, this analytical strategy enables characterization of the amine submetabolome in complex biological samples and provides a reliable methodological reference for metabolome-based investigations of cellular metabolic alterations.

Keywords:  Amine submetabolome; BEAS-2B cells; D-lactate; LC–MS; Lung cancer; Stable isotope labeling

DOI:  https://doi.org/10.1016/j.aca.2026.345649
bioRxiv. 2026 May 19. pii: 2026.05.18.725941. [Epub ahead of print]

Mapping the interactome of human tRNA methyltransferase TRMT1 using dual proximity labeling.

Angel D'Oliviera, Sophie Olson, Harrison Bernhard, Yanbao Yu, Jeffrey S Mugridge.

Transfer RNA methyltransferase 1 (TRMT1) installs N2-methylguanosine and N2,N2-dimethylguanosine modifications at position 26 of mammalian tRNAs, supporting tRNA structure, translation, and cellular response to redox stress. However, the local environment and interactome of TRMT1 in the cell is poorly defined. Here, we use APEX2-based proximity labeling of the N- and C-terminus of TRMT1, coupled with label-free quantitative proteomics to map candidate TRMT1-proximal proteins in HEK293T cells. Mass spectrometry data was acquired using both data-independent acquisition (DIA) and data-dependent acquisition (DDA) methods, and it was found that DIA substantially increased proximity proteome coverage, reproducibility, and the number of significantly enriched candidate hits compared to the DDA method. N- and C-terminal APEX2-TRMT1 constructs captured largely overlapping proteomes, suggesting the dual-labeling strategy provides a robust map of proximal proteins. Analysis of the significant TRMT1-proximal proteins reveals enrichment in RNA processing and ribonucleoprotein-associated factors, in addition to hits connected to tRNA modification, tRNA biogenesis, and redox-associated biology. These data provide a proteome-scale view of TRMT1-associated cellular proteins and environments, and lay the groundwork for future validation of functional TRMT1 interaction networks.
Significance: Fusing APEX2 enzyme to both N-terminal and C-terminal of the bait enhanced the sensitivity for identification of protein interactions.Combining APEX2-based endogenous labeling with DIA mass spectrometry increases reproducibility and depth of proximity proteome.The study provides a rich source of potential interacting or proximally close proteins to TRMT1, which warrants further validation studies.

DOI: https://doi.org/10.64898/2026.05.18.725941
J Chromatogr A. 2026 May 27. pii: S0021-9673(26)00457-7. [Epub ahead of print]1783 467128

Comprehensive optimization of chromatographic separation and iterative data acquisition for liquid chromatography-tandem mass spectrometry non-targeted plasma metabolomics: Enhancing feature-based molecular networking.

Jiamin Yang, Xiaona Lai, Qing Liu, Yiwen Tao, Hongbo Huang, Bo Ding.

  Liquid chromatography-tandem mass spectrometry (LC-MS/MS) non-targeted plasma metabolomics is essential for biomarker discovery, yet the lack of standardized analytical protocols often compromises data reliability. In this study, we systematically optimized the entire metabolomics workflow, focusing on the integration of feature-based molecular networking (FBMN) to enhance metabolite annotation. Critical parameters, including protein precipitation chemistry, chromatographic selectivity, and data acquisition strategies, were comprehensively assessed. Optimal extraction was achieved using a methanol/ethanol (1:1, v/v) mixture at a 1:4(v/v) sample-to-solvent ratio. For chromatographic separation, an XBridge BEH C18 column outperformed alternatives. Superior peak capacity and ionization efficiency were obtained using 0.1% formic acid (or 10 mM ammonium acetate with 0.1% formic acid) in positive mode, and 10 mM ammonium formate with 0.1% acetic acid in negative mode. While the timing of internal standard addition did not significantly alter the metabolic profile, instrument signal stability became a critical factor for sequences exceeding one week. Notably, the transition to a 100 mm column and the implementation of iterative data-dependent acquisition (DDA) on quality control samples significantly expanded the FBMN size and the number of uniquely annotated compounds. This optimized, robust workflow provides a standardized framework for improving metabolite coverage and annotation confidence in large-scale clinical investigations.

Keywords:  Chromatographic optimization; FBMN; Iterative acquisition; Non-targeted plasma metabolomics; Protein precipitation

DOI:  https://doi.org/10.1016/j.chroma.2026.467128
Anal Chem. 2026 May 31.

In-Situ Metabolic Profiling of Single Living Cells by Liquid Secondary Ion Mass Spectrometry.

Yuxin Liu, Jilin Tang, Yifan Liang, Qun Luo, Bo Guan, Lirong Liang, Fuyi Wang, Yanyan Zhang.

Single-cell mass spectrometry enables label-free and high-throughput molecular analysis of individual cells. However, conventional vacuum-based secondary ion mass spectrometry (SIMS) faces challenges in probing metabolism of single living cells under native physiological conditions. Here, we introduce a liquid SIMS platform coupled with a vacuum-compatible cell-culture device, which allows in-situ metabolomic profiling of single living cells in their native culture environment without any pretreatment. This platform uniquely enables direct nanoscale interfacial characterization, and we report for the first time the determination, via MS depth profiling, of a lipid bilayer with a SiN-equivalent thickness of ∼8.6 nm in a single living human nonsmall cell lung cancer (A549) cell. As a proof of concept, we applied this method to investigate metabolomic changes linked to cisplatin resistance in A549 cells. Our findings indicate upregulation of cholesterol, phosphatidylcholine, and low-unsaturation fatty acids in resistant cells, and we demonstrate that inhibiting cholesterol synthesis effectively reduces drug resistance. This work underscores the potential of liquid SIMS for in-situ metabolic profiling during complex biological processes.

DOI: https://doi.org/10.1021/acs.analchem.6c00906
Anal Chem. 2026 Jun 04.

Exploring Acylcarnitine Metabolism Using Reverse Metabolomics.

Allison J Keys, Daniel J Wiener, Sara M Pacini, Chengze Sun, Lindsay E Sandusky, Huaiyang Zhong, Emily C Gentry.

Untargeted mass spectrometry (MS) is a valuable tool for studying human metabolism and identifying small molecule disease biomarkers. However, annotation of chemical structures and validation of findings across numerous cohorts remains challenging. Reverse metabolomics employs a structure-driven approach to overcome these issues by searching spectra of known structures against an entire repository of untargeted LC-MS/MS data to see where metabolites of interest are found. This work uses reverse metabolomics to study acylcarnitine (AC) metabolism in humans and other animals. Here, a library of 76 ACs was chemically synthesized then searched against public metabolomics data to explore where metabolites of interest are detected. From this analysis, it was determined that acylcarnitines are most frequently observed in human and mouse samples, with about 90% of all searched AC structures present in both blood and fecal samples from these species. This work identified positive associations between certain AC structures and disease, indicating their capacity as health biomarkers. Machine learning was applied, determining that AC presence and absence data can accurately predict healthy versus unhealthy individuals with good precision and recall, albeit the models lack disease specificity. Overall, our findings suggest that AC profiles can serve as valuable biomarkers for disease detection throughout the entire lifespan and should be examined for their potential beyond current clinical screening protocols.

DOI: https://doi.org/10.1021/acs.analchem.6c01418
J Am Soc Mass Spectrom. 2026 Jun 05.

Untargeted microLC-HRMS/MS for Measuring Endogenous Steroids in Mouse Plasma and Human Serum Samples Applied to Studying Polycystic Ovary Syndrome.

Nathan Ghafari, Danko Brukner, Sherin A Nawaito, Danielle E Berbrier, Charlotte W Usselman, Nicolas Pilon, Lekha Sleno.

  Steroids play essential roles in regulating metabolism, response to stress, electrolyte balance, and reproductive function; however, their analysis in complex biological matrices remains challenging. Low endogenous concentrations, structural similarities, and poor ionization efficiencies can limit their detection, and conventional workflows frequently require large sample volumes and/or chemical derivatization, often restricting quantitative applications to targeted analyses. A workflow was developed for the untargeted analysis of endogenous steroids in serum or plasma samples using microflow liquid chromatography coupled to high-resolution tandem mass spectrometry. The Evosep One LC platform, applied widely for bottom-up proteomics, was coupled to a Sciex ZenoTOF 7600 quadrupole-time-of-flight system to enable the separation and detection of unconjugated and sulfated steroids in low-volume serum and plasma samples. The method was tested with a range of analytical standards, demonstrating efficient chromatographic separation and detection with high-accuracy MS/MS for structural confirmation. A total of 13 unconjugated steroids were confirmed and detected from female mouse plasma and/or human serum in positive mode, whereas three sulfated steroids were detected exclusively in human serum using negative mode. The sulfated steroids were confirmed using in vitro incubations of the parent steroids. Considerable interspecies differences were observed, consistent with the known literature on steroid metabolism. In a transgenic mouse model developed to mimic a metabolic subtype of polycystic ovary syndrome (PCOS), significant alterations in corticosteroids were detected. In human serum samples, dehydroepiandrosterone and androstenedione were significantly elevated in PCOS patients compared with healthy volunteers. The observed relative changes in both species showed similarities to the steroid perturbation patterns previously reported in PCOS.

Keywords:  endogenous steroids; high-resolution tandem mass spectrometry; microflow liquid chromatography; plasma; polycystic ovary syndrome; serum

DOI:  https://doi.org/10.1021/jasms.6c00096
bioRxiv. 2026 May 20. pii: 2026.05.19.726225. [Epub ahead of print]

A liquid chromatography-mass spectrometry method to quantify total Coenzyme A concentration and isotopic labeling.

Amelia L Taylor, Nathaniel W Snyder, Caroline R Bartman.

Coenzyme A is an essential cofactor synthesized from pantothenate, cysteine, and ATP, and is involved in numerous processes of cellular metabolism through its ability to carry activated acyl groups. Coenzyme A participates in catabolism of carbohydrate, fat and amino acids; biosynthesis of fatty acids, cholesterol and heme; and protein modification including acetylation and 4-phosphopantetheinylation. Despite CoA's critical functions, the regulation of CoA levels and the rate of CoA synthesis in different cell types and disease states are not well understood. One reason for this gap is that many acyl-CoA species are analytically challenging to measure due to factors including instability, poor ionization, and the wide range of biochemical properties conferred by different acyl chain lengths. In addition, most current methods do not support analysis of CoA isotopic labeling, which is required to quantify CoA synthesis rate or to measure absolute concentration using isotope-labeled internal standards. Here, we describe a method to quantify the concentration and isotopic labeling of total CoA, defined as the sum of CoASH plus all acyl-CoA species. Acyl-CoA species are hydrolyzed using sodium hydroxide to remove acyl chains, then CoA is derivatized on the thiol with N-ethylmaleimide (NEM). Following protein precipitation and solid phase extraction, samples are analyzed by liquid chromatography-mass spectrometry. This method is linear in a wide range that captures mouse tissue CoA levels, with accuracy within 15% error and precision below 15% relative standard deviation for both pure standards and tissue samples. We applied this method to measure total CoA concentration in five tissues from male and female mice, and total CoA synthesis rate in mouse liver via infusion of 13 C- 15 N-pantothenate. Overall, this method offers a tractable approach to measure total CoA concentration and isotopic labeling to enable study of total CoA synthesis rates and concentrations in health and disease.

DOI: https://doi.org/10.64898/2026.05.19.726225
Am J Clin Pathol. 2026 Jun 04. pii: aqag061. [Epub ahead of print]165(6):

Development and validation of a quantitative ultra-performance liquid chromatography quadrupole time-of-flight method for urine organic acid analysis.

Yi Xiao, Michael Wakefield, Mari Ishak Gabra, Lidong Zhai, Edward Ki Yun Leung.

   OBJECTIVES: Urine organic acid analysis is essential for diagnosing inborn errors of metabolism and is conventionally performed using multistep, labor-intensive sample preparation and gas chromatography-mass spectrometry (GC-MS). We sought to develop and validate a quantitative ultra-performance liquid chromatography quadrupole time-of-flight (UPLC-QTOF) method with a "dilute-and-shoot" approach.
METHODS: 20 µL of calibrator, quality control material, or urine specimen, normalized by creatinine concentration, was mixed with mobile phase A (0.05% formic acid in water) and internal standards to a final volume of 440 µL. The supernatant was injected onto a Waters ACUITY Premier HSS T3 UPLC column, with data acquired in MSE mode on a Waters Xevo G3 QTOF mass spectrometer and quantification achieved using both linear and quadratic regressions.
RESULTS: The method quantifies 27 analytes and separates diagnostically important isomers in 20 minutes. Repeatability and reproducibility were 12% or less coefficient of variation, with no carryover observed. Spike-recovery studies demonstrated recoveries between 85% and 115%, and concordant results were obtained from 51 urine specimens vs the conventional GC-MS method. No matrix effect was identified except for 3-hydroxyglutaric acid. Compared with other UPLC-QTOF methods, improved chromatographic performance was achieved with the Premier HSS T3 column, while MSE high-resolution MS data provided fragmentation information to support higher-confidence compound identification. Compared with conventional GC-MS methods, this method requires substantially lower specimen volume and simplified sample preparation.
CONCLUSIONS: This UPLC-QTOF dilute-and-shoot urine organic acid method demonstrated acceptable analytical and clinical performance. Continued optimization will be pursued to expand the panel and support the diagnosis of a broader range of inborn errors of metabolism.

Keywords:  UPLC-QTOF; clinical mass spectrometry; high-resolution mass spectrometry; inborn errors of metabolism; mass spectrometry validation; urine organic acid

DOI:  https://doi.org/10.1093/ajcp/aqag061
Anal Chim Acta. 2026 Aug 22. pii: S0003-2670(26)00603-3. [Epub ahead of print]1412 345653

Ion chromatography-ultra-high-resolution mass spectrometry reveals EZH2-driven reprogramming of nucleic acid and protein methylation.

Mohamed M Y Kaddah, Teresa W-M Fan, Jahid M M Islam, Penghui Lin, Teresa A Cassel, Richard Cunningham, Tom Brown, Richard M Higashi, Andrew N Lane.

  ENHANCER OF ZESTE: homolog 2 (EZH2), a histone H3K27 trimethyltransferase, is a key epigenetic regulator frequently dysregulated in cancer. To determine its impact on nucleotide biosynthesis and nucleic acid methylation in intact cells requires highly sensitive, isomer-resolving analytical workflows. We developed a targeted ion chromatography-ultra-high-resolution Fourier transform mass spectrometry (IC-UHR-FTMS) workflow with lower limits of quantification down to 9 fmol on-column to determine changes in methylation of DNA, total RNA, and mRNA in A549 cells following EZH2 knockdown (KD). Using dual stable isotope tracers, l-methionine-(methyl-13C) and l-glutamine-(15N2), in a multiplexed stable isotope-resolved metabolomics (SIRM) design, we quantified positionally-resolved 13C/15N labeling of methylated nucleotides and their precursors. EZH2 KD reduced 15N incorporation into deoxynucleotides, indicating impaired de novo synthesis from glutamine. It also attenuated 15N and/or 13C labeling of nucleotides and methylated nucleotides in total RNA and mRNA at various atomic positions, reflecting global losses in biosynthesis and S-adenosylmethionine (SAM)-dependent methylation. Notably, AMP methylation at N6 and 2'-O positions was most responsive to EZH2 KD, implicating reduced capped-RNA translation. Some of the EZH2 KD-induced changes in RNA methylation corresponded with the altered expression of their writer or eraser enzymes. This study demonstrates multiplex stable isotope tracers-coupled IC-UHR-MS as a powerful tool for comprehensive tracing of methylation dynamics in mammalian cells and reveals EZH2's role in metabolic-epitranscriptomic regulation by modulating SAM availability via glutamine-fueled de novo purine biosynthesis and RNA methylation.

Keywords:  EZH2; Ion chromatography (IC); Isotopologue analysis; Methylated nucleotides; RNA methylation; Stable isotope-resolved metabolomics (SIRM); Ultra-high-resolution mass spectrometry (UHR-MS)

DOI:  https://doi.org/10.1016/j.aca.2026.345653
Anal Chem. 2026 Jun 01.

High-Throughput Reactive Desorption Electrospray Ionization Mass Spectrometry for Targeted Derivatization and Analysis of Poorly Ionized Small Molecules.

Yunfei Feng, Kitmin Chen, Kai-Hung Huang, Joseph V Caruso, Christopher J Welch, R Graham Cooks, Christina R Ferreira, Nicolás M Morato.

Mass spectrometry (MS) analysis of some biologically relevant compounds is limited because of their inherently poor ionization efficiency. This challenge can often be overcome by incorporating easily ionized moieties via functional-group-targeted chemical derivatization. However, the wide variety of derivatization agents and conditions can make method development cumbersome. In this study, we employ an automated high-throughput (HT) platform based on desorption electrospray ionization (DESI) MS to rapidly screen (1 sample per second) and select appropriate derivatization strategies for poorly ionized analytes leveraging accelerated on-the-fly microdroplet reactions. This approach allowed the rapid identification of efficient derivatization strategies that were then readily applied to the qualitative and quantitative analyses of molecules of biological importance. Specifically, hydroxysteroids (e.g., cholesterol, testosterone, and cholecalciferol) were imaged in tissue sections using 4-formyl-1-methylpyridinium benzenesulfonate without the loss of the intrinsic spatial resolution of DESI, while 4-borono-N,N,N-trimethylbenzenaminium allowed sensitive and HT quantitation of urinary 3-methoxy-4-hydroxyphenylglycol, a metabolite whose urine levels have been correlated with neurological disorders.

DOI: https://doi.org/10.1021/acs.analchem.6c00291
J Transl Med. 2026 Jun 03.

Proteomics in bone malignancies: from bulk profiling to single-cell and ultra-low-input technologies.

Jianming Chen, Jiaqiao Luo, Ning Ni, Jinhao Zhang, Lei Jiang, Hongbao Wu, Shenghao Wen, Ninghao Hu, Jiaran Li, Gang Deng, Shuxiang Song, Xiaomeng Shi.

   BACKGROUND: Bone malignancies, particularly high-grade primary bone sarcomas, remain clinically challenging due to early dissemination, marked heterogeneity, and limited progress in systemic therapies for metastatic or relapsed disease. While genomics and transcriptomics have clarified structural complexity and transcriptional programs, they provide only an indirect view of the functional machinery that ultimately drives invasion, immune escape, and therapy resistance. Proteomics and phosphoproteomics offer a complementary and often non-redundant layer by reporting protein abundance, pathway activity states, and actionable targets at the execution level. However, the strength of proteomics evidence is currently uneven across bone malignancy entities, with osteosarcoma providing the most mature cohort-scale multi-omics context, while emerging cohort-scale proteomics is increasingly available for selected entities such as Ewing sarcoma.
MAIN BODY: In this Review, we synthesize recent advances in mass spectrometry-based proteomics for bone malignancies, using osteosarcoma as the primary exemplar where cohort-scale proteomics / phosphoproteomics-integrated studies are most mature, while selectively incorporating evidence from other bone tumors and skeletal metastasis contexts. To address evidence heterogeneity explicitly, we highlight where conclusions are supported by cohort-scale tumor proteomics/proteogenomics versus where evidence remains exploratory, model-driven, or cross-entity extrapolations). We summarize key results from bulk tissue proteomics, circulating proteomics, extracellular vesicle (EV) and secretome profiling, and highlight how these data have revealed recurrent biological axes including extracellular matrix remodeling and cell-matrix signaling, metabolic rewiring and stress-response programs, and immune/stromal contexture. We further discuss how multi-omics integration refines molecular subtyping, links bulk signatures to functional dependencies, and supports biomarker prioritization with translational intent (e.g., secretome-informed circulating candidates). We additionally provide a practical comparison of proteomics workflows (label-free DDA/DIA, isobaric labeling, phosphoproteomics, top-down, and low-input/single-cell methods) and summarize key barriers to clinical implementation (pre-analytics, standardization, QC governance, and assay/regulatory considerations).
CONCLUSIONS: Finally, we outline emerging single-cell and ultra-low-input proteomics technologies and propose a staged roadmap for their implementation in bone tumor research, emphasizing feasibility atlases, integrated multi-omics cohorts, and translation into targeted assays and clinically deployable risk models. Collectively, available evidence suggests that proteomics may become an increasingly important pillar for precision medicine in bone malignancies by bridging molecular alterations to actionable functional states and cellular mechanisms. Real-world translation will require fit-for-purpose study designs, harmonized SOPs and multicenter benchmarking, and disciplined down-selection of discovery signatures into validated targeted assays that demonstrably add value beyond existing clinical predictors.

Keywords:  Biomarkers; Bone sarcoma; Data-independent acquisition (DIA); Extracellular vesicles; Liquid biopsy; Osteosarcoma; Phosphoproteomics; Proteogenomics; Proteomics; Single-cell proteomics

DOI:  https://doi.org/10.1186/s12967-026-08363-z
J Proteome Res. 2026 Jun 03.

Integrated Protein Precipitation-Solid-Phase Extraction Workflow for Deep Top-Down Proteomic Analysis of Human Serum.

Ning Liu, Meng Zhang, Huiwen Qin, Heng Zhao, Yinlong Zhu, Yuanzhi Xie, Jing Liu, Fangjun Wang, Zheyi Liu, Cheng Chang.

  Top-down proteomics of human serum is hindered by extreme dynamic range and cumbersome prefractionation. Here, we establish a fully solution-based protein precipitation-solid-phase extraction (PP-SPE) workflow for deep, low-molecular-weight-biased proteoform profiling. Using systematically optimized combinations of five precipitants and three sorbents, the workflow enabled identification of 4470 proteoforms from 480 proteins with excellent reproducibility (R > 0.96). Compared with the gel-based PEPPI method, PP-SPE provided substantially greater depth, including >10-fold more <5 kDa proteoforms, while shortening the overall sample preparation workflow and offering a format that is readily adaptable to plate-based platforms, thus providing a robust front end for high-throughput serum top-down analyses.

Keywords:  human serum; protein precipitation; proteoform; solid-phase extraction (SPE); top-down proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.6c00159
Nature. 2026 Jun 03.

Spermine is an endogenous iron chelator that inhibits ferroptosis.

Man Li, Xuexin Yu, Shuqin Ouyang, Xiaohong Chen, Huiqi Yu, Yuanji Liu, Ziwen Li, Chunhua Yu, Rui Kang, Christine Gaillet, Ludovic Colombeau, Raphaël Rodriguez, Libing Song, Guido Kroemer, Daolin Tang, Jun Li.

Ferroptosis is an iron-dependent form of cell death driven by lipid peroxidation1. Here we identify spermine-a polyamine derived from spermidine2-as an endogenous iron chelator that directly suppresses ferroptosis. Integrating metabolomics, stable isotope tracing and biophysical studies of the interaction between spermine and Fe2+ ions, we demonstrate that aldehyde dehydrogenase 18 family member A1 (ALDH18A1) promotes an alternative glutamine-dependent pathway for de novo spermine synthesis. This process limits iron availability and lipid peroxidation in hepatocellular carcinoma. Genetic or pharmacological inhibition of ALDH18A1-through knockout, short hairpin RNA delivered using adeno-associated virus (AAV), or the small molecule inhibitor YG1702-triggers ferroptosis and impairs both spontaneous and chemically induced hepatocarcinogenesis. Conversely, supplementation of spermine protects against ferroptosis-associated ischaemia-reperfusion injury across multiple tissues, including the liver, intestine and kidneys. These findings uncover a pathophysiologically relevant metabolic circuit in which spermine-mediated iron chelation suppresses ferroptosis.

DOI: https://doi.org/10.1038/s41586-026-10597-2
Anal Chem. 2026 May 31.

Trends in Computational Metabolomics: A Perspective on Five Years of Software Development, Challenges, and Opportunities (2021-2025).

Daniel Domingo-Fernández, David Healey, Tobias Kind, August Allen, Viswa Colluru, Biswapriya Biswavas Misra.

Metabolomics software development has accelerated rapidly, yet no recent systematic analysis has quantified how the landscape is evolving across computational methods, geographies, and the research community's technology adoption. There is a strong need within the metabolomics research community to keep pace with the rapid expansion of accessible and free computational tools and resources. Given the absence of such a treatise since 2021 and the surge in advances in ion mobility mass spectrometry (IM-MS), single-cell and spatial metabolomics, and multimodal omics-based discovery, we offer a curated database that aggregates 746 mass spectrometry- and spectroscopy-based tools across 37 categories from data preprocessing to metabolite annotation. We report four structural shifts that redefine the field's trajectory. First, machine learning (ML) adoption in tools increased by 2.4-fold from 10.9% (2021) to 26.6% (2025). Second, annotation as a category commands the most tools (16.8%) and the highest ML investment among any of the proposed tool categories. The dominant strategy has shifted from library matching (2021) to spectrum prediction (2024) and, more recently, to de novo structure generation (2025), thereby progressively reducing the reliance on accessible experimental spectral reference databases. Third, Python has displaced R as the dominant programming language, with a sharp inflection in 2023 coinciding with the ML surge, while web server-only tools have sharply declined. Fourth, transformer architectures grew significantly, and in 2025, the first few large language model (LLM)-based and other multimodal metabolomics tools emerged, signaling a transition from task-specific classifiers toward pretrained, transferable representations. Concurrently, adoption of preprints as a publishing venue also rose by 2.5-fold, and, notably, mentions of benchmarking and explainability each increased by 8-18-fold, indicating a growing community-wide need and maturation. This computational metabolomics database is now made available here: https://github.com/enveda/computational-metabolomics-review.

DOI: https://doi.org/10.1021/acs.analchem.6c00361
J Chromatogr B Analyt Technol Biomed Life Sci. 2026 May 30. pii: S1570-0232(26)00255-2. [Epub ahead of print]1281 125166

Simultaneous UHPLC-MS/MS profiling of eight phosphate metabolites reveals divergent metabolic reprogramming under hypoxic stress.

Huixin Tan, Yuxin Zhang, Jingxin Fan, Lu Bai, Pingxiang Xu, Xiaorong Li, Xuelin Zhou, Ming Xue.

  Phosphate-containing metabolites serve as critical regulators of energy homeostasis and signal transduction under hypoxic stress. However, their simultaneous quantification is technically challenging due to high polarity and non-specific adsorption. In this study, a sensitive UHPLC-MS/MS method was established for the simultaneous quantification of eight key phosphate metabolites, including adenosine triphosphate (ATP), phosphoenolpyruvate (PEP) and glucose-1-phosphate (P1G), in biological matrices. By compressing the analytical run time to <4 min per sample, this high-throughput platform demonstrated excellent linearity (r2 > 0.990) for all analytes. The method satisfied rigorous bioanalytical validation standards, exhibiting intra- and inter-day precision (RSD) of 2.2%-11.5% and accuracy of 87.0%-109.5%. Application of this method to in vitro and in vivo hypoxia models demonstrated its capability to capture distinct, context-dependent metabolic adaptations. Specifically, the platform differentiated the energetic trajectories of neuronal cells and cardiomyocytes under hypoxia and profiled metabolic shifts in mouse brain tissue. This study provides a reliable analytical platform for quantifying highly polar phosphate metabolites, offering a robust tool for monitoring metabolic pool dynamics under physiological and pathological stress.

Keywords:  Energy metabolism; Hypoxia; Phosphate metabolites; UHPLC-MS/MS

DOI:  https://doi.org/10.1016/j.jchromb.2026.125166