bims-mascan 2026-04-12 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–04–12
eighteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Carafe2 enables high quality in silico spectral library generation for timsTOF data-independent acquisition proteomics.
An orthogonal extraction workflow for high-depth plasma proteomics with metabolomic compatibility from low microliter-scale samples.
Rapid Analysis of NAD and Other Phosphorylated Metabolites in Complex Biological Samples by Hydrophilic Interaction Liquid Chromatography Coupled with Tandem Mass Spectrometry.
Mapping the mammalian dark metabolome by in vivo isotope tracing.
Ferroptosis as an approach to leverage cancer metabolism.
Pan-Metabolomics Repository Mapping of the Carnitine Landscape.
Azetidination-based photocycloaddition enables regioisomer-resolved annotation of cholesteryl esters by negative-ion-mode LC-MS/MS.
A Chemically Derivatized in Silico Mass Spectral Library for Fine-Structure Annotation of Phosphoinositides.
Metabolomics across scales: from single cells to population studies.
A metabolomics and lipidomics atlas of pulmonary large cell neuroendocrine carcinoma.
MetaDIA: A DDA-free Database Reduction Strategy for DIA Human Gut Metaproteomics.
Investigation into oximation methods to improve the selective detection of small α-ketoacids in cell extracts by GC-EI-MS.
Deep profiling of porcine brain gangliosides enabled by TiO2 magnetic nanoparticle-based enrichment and advanced lipidomic analysis.
Early metabolic alterations in pediatric obesity: Depot-specific insights from untargeted adipose tissue metabolomics.
Mitochondrial ACSS1 Links Acetate Metabolism to Pyrimidine Biosynthesis in Nutrient-Stressed B-Cell Lymphomas.
Metabolic profiling reveals pyrimidine synthesis enzyme CAD as a central carbon metabolism signaling node in cancer cell proliferation.
High-Throughput Small-Scale Platform for Synthesis, Characterization, and Modeling of Per- and Polyfluoroalkyl Substances Analogs.
Development of a Quantitative Serial LC-MS/MS Method for Gut Microbiota Metabolomics.

bioRxiv. 2026 Mar 31. pii: 2026.03.27.714846. [Epub ahead of print]

Carafe2 enables high quality in silico spectral library generation for timsTOF data-independent acquisition proteomics.

Bo Wen, J Sebastian Paez, Chris Hsu, Daniele Canzani, Alexis Chang, Nicholas Shulman, Brendan X MacLean, Matthew D Berg, Judit Villén, William E Fondrie, Lindsay K Pino, Michael J MacCoss, William S Noble.

Data-independent acquisition (DIA) proteomics enables reproducible and systematic peptide detection and quantification, and trapped ion mobility spectrometry (TIMS) on the timsTOF platform further improves DIA by synchronizing ion mobility separation with quadrupole precursor sampling. Analyzing the highly multiplexed spectra generated by DIA typically relies on spectral libraries, and fully leveraging the additional ion mobility dimension requires these libraries to include accurate retention time, fragment ion intensity, and ion mobility annotations. Existing in silico spectral library generation tools either lack ion mobility support entirely or rely on models trained on data-dependent acquisition (DDA) data, that can introduce a mismatch that may not capture unique experiment-specific biases when applied to each respective timsTOF dataset. Carafe is a software tool that uses deep learning models to generate high-quality, experiment-specific in silico libraries by training directly on DIA data. In this study, we extend Carafe to generate libraries for timsTOF DIA data, which involves fine-tuning retention time (RT), fragment ion intensity, and ion mobility prediction models using timsTOF DIA data. Carafe2 operates directly on native timsTOF raw data (Bruker .d directories) without the need for data conversion. We demonstrate the performance of Carafe2 across a wide range of DIA applications, including global proteome, phosphoproteome, and plasma proteome datasets. Comparing Carafe2 fine-tuned RT, fragment ion intensity, and ion mobility prediction models with pretrained DDA models, we find that Carafe2 models outperform pretrained models on a variety of DIA datasets. We then demonstrate the utility of in silico libraries generated by Carafe2 for peptide detection on several different types of timsTOF DIA datasets by comparing with the libraries generated with DDA-trained AlphaPeptDeep models, DIA-NN built-in models, and empirical spectral libraries generated from DDA experiments.

DOI: https://doi.org/10.64898/2026.03.27.714846
Anal Methods. 2026 Apr 07.

An orthogonal extraction workflow for high-depth plasma proteomics with metabolomic compatibility from low microliter-scale samples.

Chuanping Zhao, Huiting Hu, Xiaohong Qian, Wanjun Zhang, Weijie Qin.

Plasma proteomics serves as the cornerstone of clinical biomarker development, yet standalone profiling is plagued by three critical bottlenecks: poor detection of low-abundance proteins, overlapping signals across pathologically similar diseases, and the lack of upstream/downstream molecular context to elucidate underlying mechanisms. These limitations collectively result in a low translation rate of protein biomarkers into clinical practice. A major hurdle to overcoming these challenges lies in technical barriers that hinder deep proteome coverage and the seamless integration of metabolomics into large-scale plasma proteomic studies. Conventional approaches require separate sample preparation for each omics layer, doubling the experimental workload, while sequential extraction of proteins and metabolites inevitably compromises either proteome depth or metabolite stability. Herein, we report an orthogonal extraction workflow that achieves enhanced proteomic profiling depth while preserving metabolomic compatibility from a mere 10 µL of plasma. We demonstrate that a ternary solvent mixture (TSM) of methanol, acetonitrile, and acetone exhibits distinct chemical orthogonality to perchloric acid (PCA) precipitation, with each strategy recovering reproducible yet partially non-overlapping subsets of plasma proteins. By combining orthogonal extraction with PCA and TSM to deplete high-abundance proteins, we expanded plasma proteome coverage to 1136 proteins-representing a 49% increase compared to PCA alone and a 2.6-fold enhancement relative to the standard neat plasma processing protocol. This marked improvement in low-abundance protein detection enabled efficient coverage of 101 FDA-approved circulating biomarkers. For metabolomics, the workflow retained comprehensive coverage (1174 metabolites) comparable to standard methanol extraction, with superior qualitative reproducibility and quantitative consistency across technical replicates. This workflow may enable protein-centric biomarker discovery augmented by orthogonal metabolic insights from microliter-scale samples, providing a transformative tool to surmount the limitations of standalone proteomics and accelerate the clinical translation of candidate biomarkers.

DOI: https://doi.org/10.1039/d6ay00195e
Anal Chem. 2026 Apr 08.

Rapid Analysis of NAD and Other Phosphorylated Metabolites in Complex Biological Samples by Hydrophilic Interaction Liquid Chromatography Coupled with Tandem Mass Spectrometry.

Adela Pravdova, Maximilian Kleinert, John Henderson, Eleni Kafkia, David Pladevall-Morera, Caio Y Yonamine, Jonas T Treebak, Tetiana Brodiazhenko, Ilya Terenin, Jan Jakub Zylicz, Thomas Moritz, Ondrej Hodek.

Nucleotides and coenzymes play critical roles in energy metabolism and cellular signaling and as building blocks of nucleic acids. This work addresses the challenges in the measurement of the phosphorylated metabolites using hydrophilic interaction liquid chromatography coupled with mass spectrometry, which facilitates the separation and detection of polar metabolites. Here, we present optimized HILIC-MS/MS methods for rapid analysis of polar metabolites including nucleotides and their derivatives in complex biological matrices, such as murine adipose, skeletal, and liver tissues, human plasma, and bacteria. The developed methodologies enable separation of key nucleotides and other phosphorylated metabolites within 6 min and cofactors such as NAD+, NADH, NADP+, and NADPH within 4 min. Validation of these methods demonstrated high accuracy, precision, and sensitivity and stresses the substantial impact of matrix effects. The applicability of the methods was also tested on 13C-labeling experiments with mouse pluripotent stem cells. Additionally, sample pretreatment techniques, such as liquid-liquid extraction and solid-phase extraction, were evaluated as a tool to decrease the negative impact of matrix effects in complex samples. This work enhances the analytical capabilities for nucleotide quantification in metabolomics, facilitating the study of metabolic pathways and disease markers.

DOI: https://doi.org/10.1021/acs.analchem.6c00721
bioRxiv. 2026 Apr 02. pii: 2026.03.31.713900. [Epub ahead of print]

Mapping the mammalian dark metabolome by in vivo isotope tracing.

Michael R MacArthur, Justine Raeber, Wenyun Lu, Hantao Qiang, Amelia V Schueppert, Lucas B Ayres, Ricardo A Cordova, Michael D Neinast, Edmundo Leiva, Vanha N Pham, Jenna E AbuSalim, Connor S R Jankowski, Laith Z Samarah, Asael Roichman, Christian G Peace, Daniil G Ivanov, Gianna L Renzo, Anna M Oschmann, Julien F Ayroles, Sarah J Mitchell, Xi Xing, Kellen Olszewski, Hahn Kim, Joshua D Rabinowitz, Michael A Skinnider.

Despite decades of biochemical study, a comprehensive map of the mammalian metabolome remains elusive. Mass spectrometry-based metabolomics detects thousands of small molecule-associated signals in mammalian tissues, but it is currently unclear how many of these reflect products of endogenous metabolism. Here, we leverage systematic in vivo isotope tracing to infer the biosynthetic origins of unidentified metabolites. We administered 26 different isotopically labelled nutrients to mice, measured circulating and tissue metabolite labelling by mass spectrometry, and developed a statistical framework to infer the number of carbon atoms incorporated from each of these precursors into more than 4,000 putative metabolites. We show this information can be harnessed for biosynthesis-aware structure elucidation using a multimodal AI model that co-embeds isotopic labelling patterns with chemical structures. This approach revealed several previously unrecognized families of mammalian metabolites, including cysteine-derived alkylthiazolidines, dithioacetal mercapturic acid derivatives, short-chain N-acyltaurines, acylglycyltaurines, and N-oxidized taurines. It further uncovered a family of mevalonate-derived isoprenoid metabolites that includes 2,3-dihydrofarnesoic acid, which is markedly depleted in both mouse and human aging. Age-related depletion of these isoprenoids is driven by impaired coenzyme A synthesis. Our work establishes the biosynthetic precursors for thousands of unidentified metabolites and reveals multiple previously unrecognized branches of mammalian metabolism.

DOI: https://doi.org/10.64898/2026.03.31.713900
Trends Cell Biol. 2026 Apr 09. pii: S0962-8924(26)00039-5. [Epub ahead of print]

Ferroptosis as an approach to leverage cancer metabolism.

Jenny Jin, Jiachen Hu, Mingyue Li, Wei Gu, Xuejun Jiang, Brent R Stockwell.

  Ferroptosis is a cell death process defined by the iron-mediated peroxidation of membrane phospholipids that overwhelms the cell's innate antioxidant capabilities. Sitting at the nexus of iron, lipid, reactive oxygen species stress responses, and cellular metabolism, ferroptosis is intricately tied to these pathways. The burgeoning field of cancer metabolism has revealed that cancer cells exhibit changes in ferroptosis-relevant metabolic pathways, thereby opening an important avenue of investigation into whether tumors can have characteristic metabolic alterations that render them exquisitely sensitive to ferroptotic cell death. In this review, we highlight recent findings in the metabolic pathways linking ferroptosis and oncogenesis, as well as implications for future cancer therapeutic strategies.

Keywords:  cancer metabolism; ferroptosis; lipid metabolism; lipidomics; metabolomics; oncogenic signaling

DOI:  https://doi.org/10.1016/j.tcb.2026.03.008
bioRxiv. 2026 Mar 31. pii: 2026.03.27.714844. [Epub ahead of print]

Pan-Metabolomics Repository Mapping of the Carnitine Landscape.

Helena Mannochio-Russo, Patrick C Ferreira, Kine Eide Kvitne, Abubaker Patan, Victoria Deleray, Julius Agongo, Harsha Gouda, Wilhan D Gonçalves Nunes, Shipei Xing, Jasmine Zemlin, Martijn van Faassen, Erin R Reilly, Imhoi Koo, Andrew D Patterson, Shirley M Tsunoda, Mingxun Wang, Dionicio Siegel, Lindsey A Burnett, Pieter C Dorrestein.

Carnitines are a structurally diverse class of metabolites formed by conjugation of L-carnitine with fatty acids, amino acids, xenobiotics, and microbial metabolites. They play roles in transport, mitochondrial and peroxisomal metabolism, detoxification, and systemic signaling, yet their chemical diversity remains incompletely defined. We applied a pan-repository data mining strategy of LC-MS/MS data across GNPS/MassIVE, MetaboLights, and Metabolomics Workbench using MassQL diagnostic fragment ion filtering to systematically extract acylcarnitine spectra. This yielded a library of 34,222 unique MS/MS spectra representing 2,857 atomic compositions, corresponding to 3,872,050 detections. These datasets provide an MS/MS library for annotation, discovery, and contextualization of acylcarnitines, enabling identification of previously unknown carnitines, such as dihydroferulic acid conjugated carnitines and supporting future exploration of this metabolite class across host metabolism, diet, microbial activity, pharmacological exposures, and metabolic dysregulation.

DOI: https://doi.org/10.64898/2026.03.27.714844
Anal Chim Acta. 2026 Jun 08. pii: S0003-2670(26)00342-9. [Epub ahead of print]1402 345392

Azetidination-based photocycloaddition enables regioisomer-resolved annotation of cholesteryl esters by negative-ion-mode LC-MS/MS.

Andrea Cerrato, Enrico Taglioni, Chiara Cavaliere, Aldo Laganà, Elena Lucà, Carmela Maria Montone, Anna Laura Capriotti.

   BACKGROUND: Cholesteryl esters (CE) play central roles in lipid transport and storage, yet their structural characterization remains challenging due to their extreme hydrophobicity and poor ionization efficiency. Conventional CE lipidomics workflows typically rely on positive-ion-mode analysis and report CE at the sum-composition level, without resolving double-bond (C]C) positional isomerism. Chemical derivatization strategies enabling isomer-resolved analysis of CE are therefore highly desirable but remain largely unexplored.
RESULTS: In the present study, the use of the aza-Paternò-Büchi (aPB) reaction with 6-azauracil was extended to CE, enabling negative-ion-mode LC-HRMS/MS analysis with annotation of C]C bond regiochemistry. Optimization of the derivatization conditions allowed efficient reaction of highly hydrophobic CE while maintaining compatibility with electrospray ionization. Tandem mass spectrometry revealed a previously unreported set of diagnostic fragment ions that proved particularly suitable for quantitative applications. The workflow enabled both relative and absolute quantitation of CE regioisomers with good linearity, repeatability, and trueness over a wide dynamic range, using a single dominant diagnostic ion to simplify data processing. Application to complex biological matrices of clinical interest demonstrated the feasibility of the approach, providing direct access to CE regioisomer distributions in human plasma and bovine liver.
SIGNIFICANCE: This study establishes aPB-based derivatization as a viable and complementary strategy for structurally resolved CE analysis, expanding the analytical toolbox for lipidomics studies where detailed molecular structure is critical for biological interpretation.

Keywords:  Aza-Paternò-Büchi; Double bond location; High-resolution mass spectrometry; Lipidomics

DOI:  https://doi.org/10.1016/j.aca.2026.345392
Anal Chem. 2026 Apr 05.

A Chemically Derivatized in Silico Mass Spectral Library for Fine-Structure Annotation of Phosphoinositides.

Zhengjiu An, Ye Liu, Jian Ni, Jun Ding.

Phosphoinositides (PIPx) are structurally complex lipids with essential roles in cellular signaling and disease. Their biological functions critically depend on subtle molecular characteristics, including headgroup identity, acyl-chain composition, and regioisomerism. However, comprehensive structural annotation of PIPx species by mass spectrometry remains challenging due to their intrinsically low abundance, extensive isomerism, and limited availability of reference spectra. Herein, we report a chemically derivatized in silico mass spectral library that enables the fine-structure annotation of PIPx. A chemical derivatization strategy using (4-(diazomethyl)phenyl)-N,N-dimethylmethanamine (DMPDA) markedly improves the liquid chromatographic behavior and ionization efficiency of PIPx species, resulting in up to a 10-fold increase in detection sensitivity. More importantly, the resulting DMPDA-PIPx derivatives exhibit reprogrammed fragmentation behavior in tandem mass spectrometry, generating diagnostic ions that differentiate phosphate positional isomers as well as acyl-chain composition and sn-positional variants. General fragmentation rules were established and applied to 1,736,028 simulated DMPDA-PIPx structures, yielding an in-depth in silico mass spectral library that spans millions of PIPx structures. Integration of chemical derivatization with in silico library-based spectral matching enables automated annotation of PIPx isomers that are indistinguishable using conventional MS/MS approaches. Application of this workflow to aging mouse tissues reveals pronounced organ-specific heterogeneity in PIPx profiles and distinct tissue-specific remodeling of PIPx isomers during aging.

DOI: https://doi.org/10.1021/acs.analchem.6c00638
Nature. 2026 04;652(8109): 313-320

Metabolomics across scales: from single cells to population studies.

Theodore Alexandrov, Nicola Zamboni.

Metabolomics has matured into a powerful approach for probing metabolism, offering readouts that closely reflect cellular and organismal function in health and disease. Here we highlight two rapidly advancing frontiers: single-cell metabolomics and population-scale metabolomics. Single-cell metabolomics resolves the metabolic states of individual cells, uncovering cell-to-cell heterogeneity and spatial organization within tissues. Population-scale profiling profiles metabolites across large cohorts, enabling the discovery of markers of disease, environmental exposures and genetic variation. Although these approaches operate at different scales, they face shared challenges-including metabolite identification, quantification and multimodal data integration-and offer common advantages, such as the ability to capture non-genetic influences on phenotype and to scale to high throughput. We propose that continued advances in scalability will bring these domains together, enabling the construction of comprehensive metabolic atlases that chart cellular and interindividual variation and provide training data for foundation models of metabolism. By integrating cellular and population-level insights, single-cell and population-scale metabolomics promise to advance our understanding of metabolism across biology, medicine and pharmacology.

DOI: https://doi.org/10.1038/s41586-026-10277-1
Biomed Pharmacother. 2026 Apr 07. pii: S0753-3322(26)00360-4. [Epub ahead of print]198 119327

A metabolomics and lipidomics atlas of pulmonary large cell neuroendocrine carcinoma.

Ilona Tietzova, Jiri Hricko, Rebecca C Rancourt, Marc A Schneider, Michael Allgäuer, Tomas Cajka.

  Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rare and aggressive lung cancer with limited therapeutic options and poorly defined metabolic features. To establish a comprehensive molecular overview, we generated the first metabolomics and lipidomics atlas of LCNEC using paired tumor and adjacent non-tumor lung tissues from 34 patients. Untargeted multiplatform liquid chromatography-mass spectrometry profiled 1052 metabolites, revealing extensive remodeling of amino acid, nucleotide, and lipid metabolism. Tumor tissues showed pronounced accumulation of both D- and L-2-hydroxyglutaric acid, indicating altered α-ketoglutarate metabolism independent of IDH1/2 mutations. Newly identified N-lactoyl-amino acids, formed via CNDP2-mediated condensation of lactate and amino acids, were uniformly elevated, suggesting enhanced lactoyl conjugation under elevated lactate levels. Lipidomic profiling revealed widespread reprogramming, including increased phosphatidylcholines, ether-linked phospholipids, polyunsaturated bis(monoacylglycero)phosphates, long-chain triacylglycerols, cholesteryl esters, and acylcarnitines, indicative of lysosomal remodeling and altered mitochondrial fatty acid transport. In addition, the nicotine metabolite cotinine was quantified as an objective biomarker of smoking exposure, revealing discrepancies between measured cotinine levels and self-reported smoking status in several patients. This highlights the value of metabolomics for independently verifying clinical information. Collectively, these data define a hybrid metabolic phenotype bridging features of small and non-small cell lung cancer while revealing unique metabolic signatures of LCNEC. The resulting atlas provides a foundational resource for biomarker discovery and the development of metabolism-based therapeutic strategies in this understudied lung cancer subtype.

Keywords:  Atlas; LC-MS; Lipidomics; Lung cancer; Metabolomics; Oncometabolites; Resource

DOI:  https://doi.org/10.1016/j.biopha.2026.119327
Genomics Proteomics Bioinformatics. 2026 Apr 04. pii: qzag029. [Epub ahead of print]

MetaDIA: A DDA-free Database Reduction Strategy for DIA Human Gut Metaproteomics.

Haonan Duan, Zhibin Ning, Zhongzhi Sun, Tiannan Guo, Yingying Sun, Daniel Figeys.

  Microbiomes, especially within the gut, are complex and may comprise hundreds of species. The identification of peptides in metaproteomics presents a substantial challenge, as it involves matching peptides to mass spectra within an enormous search space for complex and unknown samples. This poses difficulties for both the accuracy and the speed of identification. Specifically, analysis of data-independent acquisition (DIA) datasets has relied on libraries constructed from prior data-dependent acquisition (DDA) results. However, this method is resource-intensive, consumes samples, and limits identification to peptides previously identified. These limitations restrict the application of DIA in metaproteomics research. We introduced a novel strategy to reduce the search space by utilizing species abundance and functional abundance information from the microbiome to score each peptide and prioritize those most likely to be detected. Using this strategy, we have developed and optimized a workflow called MetaDIA for the analysis of microbiome data generated by DIA, which operates independently of DDA assistance. Our approach successfully created a smaller, yet sufficient database for DIA data search in metaproteomics. The results demonstrated strong consistency with the traditional DDA-based library approach at both protein and functional levels. MetaDIA is readily accessible as an open-source project hosted on GitHub (https://github.com/northomics/MetaDIA).

Keywords:  Data independent acquisition; Data-dependent acquisition-free; Human gut microbiome; Metaproteomics; diaPASEF

DOI:  https://doi.org/10.1093/gpbjnl/qzag029
J Chromatogr B Analyt Technol Biomed Life Sci. 2026 Apr 05. pii: S1570-0232(26)00148-0. [Epub ahead of print]1277 125059

Investigation into oximation methods to improve the selective detection of small α-ketoacids in cell extracts by GC-EI-MS.

Sadie J Gale, Danisha Johal, Seth J Parker.

  Small α-ketoacids are important metabolic intermediates that influence metabolic states associated with disease. Measuring α-ketoacid levels and labeling in stable-isotope tracing studies using chromatography coupled to mass spectrometry is sometimes challenging due to their small mass, low cellular abundance, chemical instability, and enzymatic utilization post-quenching. Chemical derivatization is often employed to increase mass-to-charge, improve chromatographic separation in complex samples, and to stabilize α-ketoacids for detection by GC- or LC-MS. Methoximation is one of the most used derivatization strategies to modify α-keto groups present on metabolic α-ketoacids for GC-EI-MS. However, methoxime groups do not significantly increase analyte mass nor contribute to retention on the mid-polar GC columns widely used for metabolic labeling studies. In this study, we evaluate different oximation reagents to improve the selective detection of small α-ketoacid oxime-TBDMS derivatives by GC-EI-MS. We find that oximation using hydroxylamine, ethoxyamine, or O-benzylhydroxylamine offer comparable performance to methoxyamine with distinct effects on column retention and main fragment ion mass-to-charge. We also demonstrate how these approaches may be applied to cell extracts. Our results highlight hydroxylamine or O-benzylhydroxylamine as preferred derivatization reagents to increase alpha-ketoacid mass-to-charge or increase column retention, respectively.

Keywords:  Alpha-ketoacids; Gas chromatography; Mass spectrometry; Metabolomics; Stable-isotope tracing

DOI:  https://doi.org/10.1016/j.jchromb.2026.125059
Analyst. 2026 Apr 07.

Deep profiling of porcine brain gangliosides enabled by TiO2 magnetic nanoparticle-based enrichment and advanced lipidomic analysis.

Yichun Wang, Yu Xia.

Gangliosides are a class of glycosphingolipids highly enriched in the central nervous system and play key roles in neurological functions and pathologies. Deep profiling of gangliosides remains challenging due to their low abundance, high structural complexity, and the matrix effect. Recently, we have developed a method for cellular ganglioside enrichment using TiO2 magnetic nanoparticles; however, the large difference within the brain lipidome demands significant modification of the method. Herein, we introduce a tailored enrichment procedure which selectively depletes major brain-specific interfering lipids, thereby allowing the enrichment of gangliosides with up to four sialic acid residues. Furthermore, the integration of amide-hydrophilic interaction liquid chromatography with trapped ion mobility spectrometry and tandem mass spectrometry greatly facilitates the discovery of new ganglioside structures. When applied to porcine brain total lipid extract, we achieved the identification of 239 species across 40 subclasses, including newly discovered GD1c and O-Ac-GD1c, with 184 of them being characterized at the chain composition level. Compared to the fewer than 15 subclasses identified in brain gangliosides using non-enriched approaches, our data present the most extensive structural atlas of brain gangliosides reported to date. This approach holds promise for investigating the brain ganglioside metabolism involved in neurodevelopment, neurodegeneration, and other neurological contexts.

DOI: https://doi.org/10.1039/d6an00100a
J Clin Lipidol. 2026 Mar 14. pii: S1933-2874(26)00076-0. [Epub ahead of print]

Early metabolic alterations in pediatric obesity: Depot-specific insights from untargeted adipose tissue metabolomics.

Ángeles López-Gonzálvez, Cristina Muñoz-San Martín, Andrea Soria-Gondek, Belén Requena, Pablo Fernández-García, Lorena González, Marjorie Reyes-Farias, Marta Murillo, Aina Valls, Patricia Corrales, Francesc Villarroya, Rubén Cereijo, Laura Herrero, Coral Barbas, David Sánchez-Infantes, Carolina Gonzalez-Riano.

   BACKGROUND: Childhood obesity is associated with lifelong metabolic risk, yet depot-specific alterations in adipose tissue metabolism during early life remain poorly understood.
OBJECTIVE: This study aimed to characterize the metabolic differences between subcutaneous (sWAT) and visceral (vWAT) white adipose tissue in pediatric obesity using untargeted metabolomics.
METHODS: Adipose tissue samples were collected from 12 children with overweight/obesity (OW/OB) and 18 who were of normal weight (NW). Untargeted metabolomics was performed using capillary electrophoresis-mass spectrometry to profile polar metabolites in sWAT and vWAT, and free fatty acids (FFAs) were analyzed using liquid chromatography-mass spectrometry.
RESULTS: Comparison of children with OW/OB vs NW revealed pronounced depot-specific heterogeneity. vWAT in children with OW/OB exhibited 24 significantly altered metabolites compared with NW controls. This visceral profile was characterized by elevated ketone bodies (3-hydroxybutyrate and acetoacetic acid), tricarboxylic acid cycle intermediates (citric and pyruvic acids), and long-chain FFAs (palmitic and oleic acids). Concurrently, amino acid imbalances, specifically elevated leucine and arginine but reduced histidine and carnosine, suggested heightened mitochondrial stress and inflammation. In contrast, sWAT from children with OW/OB showed fewer variations (12 metabolites), defined primarily by elevated glutamate, leucine, and short-chain FFAs, reflecting a milder metabolic disruption. Direct comparison between depots revealed that vWAT was enriched in amino acids and carnitine, while sWAT showed relatively higher levels of glycolytic and ketone body intermediates in NW conditions.
CONCLUSION: Depot-specific metabolic differences are evident in pediatric obesity. vWAT in children with OW/OB displays a metabolic profile consistent with heightened lipotoxicity and mitochondrial stress, whereas sWAT exhibits fewer, less pronounced metabolic differences.

Keywords:  Adipose tissue; CE-MS; LC-MS; Pediatric obesity; Untargeted metabolomics

DOI:  https://doi.org/10.1016/j.jacl.2026.03.003
Cancer Lett. 2026 Apr 07. pii: S0304-3835(26)00251-X. [Epub ahead of print] 218488

Mitochondrial ACSS1 Links Acetate Metabolism to Pyrimidine Biosynthesis in Nutrient-Stressed B-Cell Lymphomas.

Johnvesly Basappa, Aaron R Goldman, Cosimo Lobello, Shengchun Wang, David Rushmore, Olga Melnikov, Neil V Sen, Vinay S Mallikarjuna, Priyanka Jain, Masoud Edalati, David S Nelson, Kathy Q Cai, Pin Lu, Reza Nejati, Hossein Borghaei, Pradeep K Gupta, Kavindra Nath, Kathryn E Wellen, Mariusz A Wasik.

  Acetate serves as an alternative carbon source in nutrient-limited tumors, yet its role in supporting nucleotide biosynthesis remains poorly understood. Here, we identify the mitochondrial enzyme ACSS1 as a key metabolic driver in mantle cell lymphoma (MCL), diffuse large B-cell lymphoma (DLBCL), and chronic lymphocytic leukemia (CLL). ACSS1 is frequently overexpressed and catalyzes the conversion of acetate to mitochondrial acetyl-CoA, sustaining oxidative metabolism and biosynthesis under nutrient stress. Genetic silencing of ACSS1 impairs mitochondrial respiration and disrupts acetate incorporation into acetyl-CoA, TCA cycle intermediates, glutamate, and aspartate, while markedly reducing 13C-acetate labeling of dihydroorotate and orotate, intermediates in de novo pyrimidine synthesis. Untargeted metabolomics reveal enrichment of pyrimidine biosynthesis pathways in ACSS1-high cells. Notably, acetate or uridine supplementation rescues the growth of ACSS1-deficient cells, confirming a functional link between acetate metabolism and nucleotide synthesis. Importantly, in vivo studies using two different MCL xenografts demonstrate that ACSS1 knockdown profoundly suppresses tumor growth, indicating that ACSS1 is required not only for metabolic adaptation of lymphoma cells in vitro but also in vivo. Collectively, our results uncover an ACSS1-dependent mitochondrial acetate-pyrimidine axis that sustains lymphoma growth and represents a previously unrecognized therapeutic vulnerability.

Keywords:  ACLY; ACSS1; ACSS2; CAD; DHODH; acetate metabolism; cancer metabolism; oncometabolite

DOI:  https://doi.org/10.1016/j.canlet.2026.218488
Mol Cell. 2026 Apr 07. pii: S1097-2765(26)00192-9. [Epub ahead of print]

Metabolic profiling reveals pyrimidine synthesis enzyme CAD as a central carbon metabolism signaling node in cancer cell proliferation.

Chao Qin, Zhenhao An, Shu Feng, Wen Fu, Xinchi Xie, Ali Can Savas, Taolin Xie, Charles Brenner, Takeshi Saito, Pinghui Feng.

  Rapid cancer cell proliferation requires extensive macromolecular biosynthesis, yet how distinct anabolic pathways are coordinated remains incompletely understood. Here, we report that the trifunctional carbamoyl-phosphate synthase, aspartate transcarbamoylase, and dihydroorotase (CAD) activates key glycolytic enzymes to support biosynthesis and cancer cell proliferation. When cancer proteomics datasets were queried, a CAD activation signature was identified in diverse tumors. Metabolomics analysis revealed that CAD fuels central carbon metabolism, specifically the pentose phosphate pathway (PPP) and serine synthesis pathway (SSP). Mechanistically, CAD deamidates and activates glucose-6-phosphate dehydrogenase (G6PD) and phosphoglycerate dehydrogenase (PHGDH), rate-limiting enzymes of the PPP and SSP, respectively, which are fully recapitulated by the glutaminase domain of CAD. Functional interrogation of cancer-associated CAD mutations and human hepatocellular carcinoma tumors predicts the metabolic signature endowed by G6PD and PHGDH deamidation. Simultaneous inhibition of G6PD and PHGDH effectively impeded tumor formation. This work identifies CAD as a central carbon metabolism signaling node and a potential therapeutic target.

Keywords:  CAD; Cancer metabolism; G6PD; PHGDH; central carbon metabolism; deamidation; pyrimidine synthesis; the pentose phosphate pathway; the serine synthesis pathway

DOI:  https://doi.org/10.1016/j.molcel.2026.03.016
Environ Sci Technol Lett. 2025 Oct 14. 12(10): 1437-1444

High-Throughput Small-Scale Platform for Synthesis, Characterization, and Modeling of Per- and Polyfluoroalkyl Substances Analogs.

Kai-Hung Huang, Namita Narendra, Kaili Yap, Nicolás M Morato, Kitmin Chen, Yunfei Feng, R Graham Cooks, Tillmann Kubis, Christina R Ferreira.

Per- and polyfluoroalkyl substances (PFAS) are a global challenge due to their exceptional thermal and chemical durability which leads to environmental persistence, bioaccumulation, and toxicity. Tackling this challenge is a complex endeavor as the ever-expanding number of emerging PFAS hinders their monitoring while current countermeasures remain limited. Thus, there is a need for rapid strategies that can transform PFAS into safer, degradable analogs or expand libraries for untargeted monitoring. Here, we describe the implementation of a high-throughput (1 Hz) desorption electrospray ionization mass spectrometry (HT-DESI-MS) platform for the chemical transformation of perfluorocarboxylic acids (PFCAs) via a data-driven workflow that led to 915 new PFCA analogs (89% success rate) and revealed reactivity trends. Tandem mass spectrometry (MS/MS) enabled online structural confirmation and diagnostic fragment identification, supporting standard-free LC-MS/MS analysis. Further integration with ion mobility spectrometry (IMS) provided drift time measurements correlating with molecular size and shape, adding a new dimension that can improve feature annotation in untargeted PFAS analysis. Complementary quantum mechanical calculations of dipole moment and HOMO- LUMO gap predicted polarity and electronic reactivity, guiding analog selection. Collectively, this workflow combines rapid synthesis, structural annotation, and multidimensional profiling, with potential to discover safer PFAS and enhance environmental monitoring.

DOI: https://doi.org/10.1021/acs.estlett.5c00699
ACS Omega. 2026 Mar 31. 11(12): 19431-19439

Development of a Quantitative Serial LC-MS/MS Method for Gut Microbiota Metabolomics.

Takanobu Yoshida, Tomoya Shintani, Daisuke Sasaki, Christopher J Vavricka, Yasushi Matsuki, Akihiko Kondo, Tomohisa Hasunuma.

The significance of gut microbiota in human health has gained increasing attention. Accordingly, metabolomics has been used to elucidate host-microbiota interactions. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an ideal choice for metabolome analysis of gut microbiota due to its quantitative capabilities. However, conventional LC-MS/MS requires multiple columns, multiple mobile phases, and complex procedures to optimize conditions for each target metabolite. To address these limitations, we developed a quantitative serial LC-MS/MS method, termed the Kobe University Serial LC-MS/MS Analysis using Multiple columns with a Single mobile phase (KUSLAMS). This platform integrates two columns (PFPP and C18) and a derivatization method for seamless, high-throughput quantification of 215 metabolites, including amino acids, nucleotides, carboxylic acids, amines, and fatty acids. Reproducibility for repeated analysis was assessed using 82 intracellular gut microbiota metabolites, for which new analytical methods were developed. Among these, 64 metabolites were detected with coefficients of variation (CV) below 15%. The application of KUSLAMS to an in vitro gut microbiota culture system with and without inulin revealed differences in the concentrations of 21 intracellular and 14 extracellular metabolites. Notably, several metabolites exhibited increased intracellular and decreased extracellular concentrations, suggesting a possible link between intracellular accumulation and extracellular depletion, although this interpretation is exploratory. These results indicate that KUSLAMS allows for the simultaneous monitoring of intra- and extracellular metabolite dynamics. Together, these findings demonstrate that KUSLAMS is a robust and versatile platform for the exploration of microbiota-derived metabolites relevant to human health.

DOI: https://doi.org/10.1021/acsomega.5c12997