bims-mascan 2025-03-09 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–03–09
fourteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Limelight: An Open, Web-Based Tool for Visualizing, Sharing, and Analyzing Mass Spectrometry Data from DDA Pipelines.
Single-Cell Proteomics Using Mass Spectrometry.
MS-RT: A Method for Evaluating MS/MS Clustering Performance for Metabolomics Data.
Reversed-phase liquid chromatography/mass spectrometry approach for (un)targeted analysis of polar to mid-polar metabolites.
Affinity Purification Mass Spectrometry on the Orbitrap-Astral Mass Spectrometer Enables High-Throughput Protein-Protein Interaction Mapping.
A simplified optimization approach for sample preparation workflow in LC-MS-based quantitative proteomic analysis: Biological samples to peptides.
Bromine-Based Derivatization of Carboxyl-Containing Metabolites for Liquid Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry.
diaPASEF analysis for HLA-I peptides enables quantification of common cancer neoantigens.
A guide to reverse metabolomics-a framework for big data discovery strategy.
Functional Single-Cell Proteomics: Technology and Biological Applications.
Single-cell lipidomics: protocol development for reliable cellular profiling using capillary sampling.
CE-MS Metabolomic and LC-MS Proteomic Analyses of Breast Cancer Exosomes Reveal Alterations in Purine and Carnitine Metabolism.
Rapid and Accurate Identification of Microorganisms Using Ion Mobility-Mass Spectrometry.
Citrulline regulates macrophage metabolism and inflammation to counter aging in mice.

J Proteome Res. 2025 Mar 04.

Limelight: An Open, Web-Based Tool for Visualizing, Sharing, and Analyzing Mass Spectrometry Data from DDA Pipelines.

Michael Riffle, Alex Zelter, Daniel Jaschob, Michael R Hoopmann, Danielle A Faivre, Robert L Moritz, Trisha N Davis, Michael J MacCoss, Nina Isoherranen.

  Liquid chromatography-tandem mass spectrometry employing data-dependent acquisition (DDA) is a mature, widely used proteomics technique routinely applied to proteome profiling, protein-protein interaction studies, biomarker discovery, and protein modification analysis. Numerous tools exist for searching DDA data and myriad file formats are output as results. While some search and post processing tools include data visualization features to aid biological interpretation, they are often limited or tied to specific software pipelines. This restricts the accessibility, sharing and interpretation of data, and hinders comparison of results between different software pipelines. We developed Limelight, an easy-to-use, open-source, freely available tool that provides data sharing, analysis and visualization and is not tied to any specific software pipeline. Limelight is a data visualization tool specifically designed to provide access to the whole "data stack", from raw and annotated scan data to peptide-spectrum matches, quality control, peptides, proteins, and modifications. Limelight is designed from the ground up for sharing and collaboration and to support data from any DDA workflow. We provide tools to import data from many widely used open-mass and closed-mass search software workflows. Limelight helps maximize the utility of data by providing an easy-to-use interface for finding and interpreting data, all using the native scores from respective workflows.

Keywords:  DDA; data visualization; mass spectrometry; proteomics; server; software development

DOI:  https://doi.org/10.1021/acs.jproteome.4c00968
ArXiv. 2025 Feb 17. pii: arXiv:2502.11982v1. [Epub ahead of print]

Single-Cell Proteomics Using Mass Spectrometry.

Amanda Momenzadeh, Jesse G Meyer.

Single-cell proteomics (SCP) is transforming our understanding of biological complexity by shifting from bulk proteomics, where signals are averaged over thousands of cells, to the proteome analysis of individual cells. This granular perspective reveals distinct cell states, population heterogeneity, and the underpinnings of disease pathogenesis that bulk approaches may obscure. However, SCP demands exceptional sensitivity, precise cell handling, and robust data processing to overcome the inherent challenges of analyzing picogram-level protein samples without amplification. Recent innovations in sample preparation, separations, data acquisition strategies, and specialized mass spectrometry instrumentation have substantially improved proteome coverage and throughput. Approaches that integrate complementary -omics, streamline multi-step sample processing, and automate workflows through microfluidics and specialized platforms promise to further push SCP boundaries. Advances in computational methods, especially for data normalization and imputation, address the pervasive issue of missing values, enabling more reliable downstream biological interpretations. Despite these strides, higher throughput, reproducibility, and consensus best practices remain pressing needs in the field. This mini review summarizes the latest progress in SCP technology and software solutions, highlighting how closer integration of analytical, computational, and experimental strategies will facilitate deeper and broader coverage of single-cell proteomes.
J Proteome Res. 2025 Mar 05.

MS-RT: A Method for Evaluating MS/MS Clustering Performance for Metabolomics Data.

Xianghu Wang, Yasin El Abiead, Deepa D Acharya, Christopher J Brown, Ken Clevenger, Jie Hu, Ashley Kretsch, Carla Menegatti, Quanbo Xiong, Wout Bittremieux, Mingxun Wang.

  The clustering of tandem mass spectra (MS/MS) is a crucial computational step to deduplicate repeated acquisitions in data-dependent experiments. This technique is essential in untargeted metabolomics, particularly with high-throughput mass spectrometers capable of generating hundreds of MS/MS spectra per second. Despite advancements in MS/MS clustering algorithms in proteomics, their performance in metabolomics has not been extensively evaluated due to the lack of database search tools with false discovery rate control for molecule identification. To bridge this gap, this study introduces the MS1-retention time (MS-RT) method to assess MS/MS clustering performance in metabolomics data sets. Here, we validate MS-RT by comparing MS-RT to established proteomics clustering evaluation approaches that utilize database search identifications. Additionally, we evaluate the performance of several MS/MS clustering tools on metabolomics data sets, highlighting their advantages and drawbacks. This MS-RT method and the MS/MS clustering tool benchmarking will provide valuable real world practical recommendations for tools and set the stage for future advancements in metabolomics MS/MS clustering.

Keywords:  MS-RT method; benchmark clustering; clustering tools; completeness; metabolomics; purity; tandem mass spectrometry

DOI:  https://doi.org/10.1021/acs.jproteome.4c00881
Talanta. 2025 Feb 28. pii: S0039-9140(25)00343-1. [Epub ahead of print]291 127853

Reversed-phase liquid chromatography/mass spectrometry approach for (un)targeted analysis of polar to mid-polar metabolites.

Eva Cífková, Zdeňka Neuerová, Miroslav Lísa.

  Herein, we aim to establish a straightforward and versatile reversed-phase liquid chromatography/mass spectrometry (RP-LC/MS) methodology for analyzing a wide range of polar and mid-polar metabolites utilizing a single instrument, column, and mobile phase. We present a comprehensive evaluation of three C18 columns compatible with aqueous solutions using 19 mobile phases in terms of the number of detected metabolites, chromatographic performance, and MS response. The RP-LC/MS platform utilizes the HSS T3 column with a mobile phase consisting of 0.2 % formic acid, acetonitrile, and propan-2-ol, effectively separating polar and mid-polar metabolites through various mobile phase gradients. Our developed method outperforms the conventional hydrophilic interaction liquid chromatography metabolomic method, yielding a higher number of detected metabolites and better chromatographic performance. The RP-LC/MS platform demonstrates excellent intrabatch and interbatch retention time repeatability (<0.8 %). Furthermore, the determined concentrations of metabolites show strong agreement with certified and published concentrations of metabolites in the SRM 1950 plasma sample. We successfully annotate 71 polar metabolites, 36 acylcarnitines, 23 endocannabinoids, 42 oxylipins, and 16 fatty acids in plasma, placenta, and brain samples. The developed RP-LC/MS approach represents a robust and adaptable technique for the targeted or untargeted analysis of polar and mid-polar metabolites employing a single chromatographic column and mobile phase. This is achieved through the simple modification of the gradient program and MS conditions. Consequently, this methodology offers a highly valuable tool for conducting comprehensive, large-scale metabolomic investigations on a variety of biological samples.

Keywords:  Liquid chromatography; Mass spectrometry; Metabolomics; Reversed-phase; Targeted analysis; Untargeted analysis

DOI:  https://doi.org/10.1016/j.talanta.2025.127853
J Proteome Res. 2025 Mar 02.

Affinity Purification Mass Spectrometry on the Orbitrap-Astral Mass Spectrometer Enables High-Throughput Protein-Protein Interaction Mapping.

Lia R Serrano, Adrian Pelin, Tabiwang N Arrey, Nicolaie E Damoc, Alicia L Richards, Yuan Zhou, Noah M Lancaster, Trenton M Peters-Clarke, Anna Pashkova, Gwendolyn M Jang, Manon Eckhardt, Scott T Quarmby, Martin Zeller, Daniel Hermanson, Hamish Stewart, Christian Hock, Alexander Makarov, Vlad Zabrouskov, Nevan J Krogan, Joshua J Coon, Danielle L Swaney.

  Classical proteomics experiments offer high-throughput protein quantification but lack direct evidence of the spatial organization of the proteome, including protein-protein interaction (PPIs) networks. While affinity purification mass spectrometry (AP-MS) is the method of choice for generating these networks, technological impediments have stymied the throughput of AP-MS sample collection and therefore constrained the rate and scale of experiments that can be performed. Here, we build on advances in mass spectrometry hardware that have rendered high-flow liquid chromatography separations a viable solution for faster throughput quantitative proteomics. We describe our methodology using the Orbitrap-Astral mass spectrometer with 7 min, high-flow separations to analyze 216 AP-MS samples in ∼29 h. We show that the ion-focusing advancements, rapid mass analysis, and sensitive ion detection facilitate narrow-bin data-independent acquisition on a chromatographically practical timescale. Further, we highlight several aspects of state-of-the-art confidence-scoring software that warrant reinvestigation given the analytical characteristics of the Orbitrap-Astral mass spectrometer through comparisons with an enrichment-based thresholding technique. With our data, we generated an interaction map between 998 human proteins and 59 viral proteins. These results hold promise in expediting the throughput of AP-MS experiments, enabling more high-powered PPI studies.

Keywords:  AP-MS; Astral; PPIs; host–pathogen

DOI:  https://doi.org/10.1021/acs.jproteome.4c01040
Arch Pharm (Weinheim). 2025 Mar;358(3): e2400911

A simplified optimization approach for sample preparation workflow in LC-MS-based quantitative proteomic analysis: Biological samples to peptides.

Surendra Fartade, Tarang Jadav, Niraj Rajput, Pinaki Sengupta.

  Quantitative proteomics, an integral subfield within proteomics, is pivotal for elucidating complex biological processes. By integrating with other omics data, quantitative proteomics facilitates system-level analysis and significantly advances our understanding of cellular networks and disease mechanisms. The ongoing advancements in quantitative proteomics technology significantly boost its importance by improving analytical accuracy. This review focuses on quantitative proteomics employing liquid chromatography-mass spectrometry (LC-MS), a cornerstone technique renowned for its sensitivity, selectivity, accuracy, and throughput. The efficacy of LC-MS proteomics is heavily reliant on sample preparation, which encompasses protein extraction, total protein estimation, reduction, alkylation, digestion, and cleanup. For the very first time, this article provides a detailed examination of sample preparation methods offering insights and guidelines that researchers can utilize to refine their experimental protocols which were not critically evaluated before. By optimizing sample preparation workflows, researchers can enhance the robustness and reproducibility of their proteomic studies. By understanding the complexities of sample preparation in quantitative proteomics, researchers can optimize their experimental workflow to improve the robustness and reproducibility of their results. This review provides a comprehensive overview of sample preparation strategies in quantitative proteomics using LC-MS, discussing the underlying principles and key considerations for each step. By delving into the complexities of sample preparation, this article aims to aid researchers in optimizing their workflows to achieve robust and reproducible results, which ultimately drive innovations and breakthroughs in biomedical research and healthcare.

Keywords:  LC‐MS; bioanalysis; biomarker discovery; quantitative proteomics

DOI:  https://doi.org/10.1002/ardp.202400911
J Am Soc Mass Spectrom. 2025 Mar 07.

Bromine-Based Derivatization of Carboxyl-Containing Metabolites for Liquid Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry.

Kaylie I Kirkwood-Donelson, Prashant Rai, Lalith Perera, Michael B Fessler, Alan K Jarmusch.

The analysis of small carboxyl-containing metabolites (CCMs), such as tricarboxylic acid (TCA) cycle intermediates, provides highly useful information about the metabolic state of cells. However, their detection using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) methods can face sensitivity and specificity challenges given their low ionization efficiency and the presence of isomers. Ion mobility spectrometry (IMS), such as trapped ion mobility spectrometry (TIMS), provides additional specificity, but further signal loss can occur during the mobility separation process. We, therefore, developed a solution to boost CCM ionization and chromatographic separation as well as leverage specificity of IMS. Inspired by carbodiimide-mediated coupling of carboxylic acids with 4-bromo-N-methylbenzylamine (4-BNMA) for quantitative analysis, we newly report the benefits of this reagent for TIMS-based measurement. We observed a pronounced (orders of magnitude) increase in signal and enhanced isomer separations, particularly by LC. We found that utilization of a brominated reagent, such as 4-BNMA, offered unique benefits for untargeted CCM measurement. Derivatized CCMs displayed shifted mobility out of the metabolite and lipid region of the TIMS-MS space as well as characteristic isotope patterns, which were leveraged for data mining with Mass Spectrometry Query Language (MassQL) and indication of the number of carboxyl groups. The utility of our LC-ESI-TIMS-MS/MS method with 4-BMA derivatization was demonstrated via the characterization of alterations in CCM expression in bone marrow-derived macrophages upon activation with lipopolysaccharide. While metabolic reprogramming in activated macrophages has been characterized previously, especially with respect to TCA cycle intermediates, we report a novel finding that isomeric itaconic, mesaconic, and citraconic acid increase after 24 h, indicating possible roles in the inflammatory response.

DOI: https://doi.org/10.1021/jasms.5c00023
Mol Cell Proteomics. 2025 Mar 03. pii: S1535-9476(25)00036-2. [Epub ahead of print] 100938

diaPASEF analysis for HLA-I peptides enables quantification of common cancer neoantigens.

Denys Oliinyk, Hem R Gurung, Zhenru Zhou, Kristin Leskoske, Christopher M Rose, Susan Klaeger.

Human leukocyte antigen class I (HLA-I) molecules present short peptide sequences from endogenous or foreign proteins to cytotoxic T cells. The low abundance of HLA-I peptides poses significant technical challenges for their identification and accurate quantification. While mass spectrometry (MS) is currently a method of choice for direct system-wide identification of cellular immunopeptidome, there is still a need for enhanced sensitivity in detecting and quantifying tumor specific epitopes. As gas phase separation in data-dependent MS data acquisition (DDA) increased HLA-I peptide detection by up to 50%, here, we aimed to evaluate the performance of data-independent acquisition (DIA) in combination with ion mobility (diaPASEF) for high-sensitivity identification of HLA presented peptides. Our streamlined diaPASEF workflow enabled identification of 11,412 unique peptides from 12.5 million A375 cells and 3,426 8-11mers from as low as 500,000 cells with high reproducibility. By taking advantage of HLA binder-specific in-silico predicted spectral libraries, we were able to further increase the number of identified HLA-I peptides. We applied SILAC-DIA to a mixture of labeled HLA-I peptides, calculated heavy-to-light ratios for 7,742 peptides across 5 conditions and demonstrated that diaPASEF achieves high quantitative accuracy up to 4-fold dilution. Finally, we identified and quantified shared neoantigens in a monoallelic C1R cell line model. By spiking in heavy synthetic peptides, we verified the identification of the peptide sequences and calculated relative abundances for 13 neoantigens. Taken together, diaPASEF analysis workflows for HLA-I peptides can increase the peptidome coverage for lower sample amounts. The sensitivity and quantitative precision provided by DIA can enable the detection and quantification of less abundant peptide species such as neoantigens across samples from the same background.

DOI: https://doi.org/10.1016/j.mcpro.2025.100938
Nat Protoc. 2025 Feb 28.

A guide to reverse metabolomics-a framework for big data discovery strategy.

Vincent Charron-Lamoureux, Helena Mannochio-Russo, Santosh Lamichhane, Shipei Xing, Abubaker Patan, Paulo Wender Portal Gomes, Prajit Rajkumar, Victoria Deleray, Andrés Mauricio Caraballo-Rodríguez, Kee Voon Chua, Lye Siang Lee, Zhao Liu, Jianhong Ching, Mingxun Wang, Pieter C Dorrestein.

Untargeted metabolomics is evolving into a field of big data science. There is a growing interest within the metabolomics community in mining tandem mass spectrometry (MS/MS)-based data from public repositories. In traditional untargeted metabolomics, samples to address a predefined question are collected and liquid chromatography with MS/MS data are generated. We then identify metabolites associated with a phenotype (for example, disease versus healthy) and elucidate or validate their structural details (for example, molecular formula, structural classification, substructure or complete structural annotation or identification). In reverse metabolomics, we start with MS/MS spectra for known or unknown molecules. These spectra are used as search terms to search public data repositories to discover phenotype-relevant information such as organ/biofluid distribution, disease condition, intervention status (for example, pre- and postintervention), organisms (for example, mammals versus others), geography and any other biologically relevant associations. Here we guide the reader through a four-part process: (1) obtaining the MS/MS spectra of interest (Universal Spectrum Identifier) and (2) Mass Spectrometry Search Tool searches to find the files associated with the MS/MS that are in available databases, (3) using the Reanalysis Data User Interface framework to link the files with their metadata and (4) validating the observations. Parts 1-3 could take from hours to days depending on the method used for collecting MS/MS spectra. For example, we use MS/MS spectra from three small molecules: phenylalanine-cholic acid (a microbially conjugated bile acid), phenylalanine-C4:0 and histidine-C4:0 (two N-acyl amides). We leverage the Global Natural Products Social Molecular Networking-based framework to explore the microbial producers of these molecules and their associations with health conditions and organ distributions in humans and rodents.

DOI: https://doi.org/10.1038/s41596-024-01136-2
Methods Mol Biol. 2025 ;2902 145-159

Functional Single-Cell Proteomics: Technology and Biological Applications.

Pin-Rui Su, Miao-Ping Chien.

  Single-cell proteomics has the potential to decipher tumor heterogeneity, and methods such as Single-Cell ProtEomics by Mass Spectrometry (SCoPE-MS) allow profiling of several tens of single cells for more than 1,000 proteins per cell. Our laboratory has advanced state-of-the-art single-cell proteomics technology to enable linking proteomes of individual cells with phenotypes of interest. Here, we describe a microscopy-based functional single-cell proteomic profiling technology, called FUNpro, designed for general use in cultured cell lines. FUNpro enables high-throughput, real-time live-cell screening, identification, and isolation of single cells of interest, even when the phenotypes are dynamic or the cells are rare. This chapter outlines a generalized protocol for functional single-cell proteomic profiling and analysis of a standard cancer cell line, including an overview of the microscopy, cell culture, image analysis, photolabeling of cells of interest, fluorescence-activated cell sorting (FACS), and the single-cell proteomics procedures and analyses employed.

Keywords:  Microscopy; Phenotype-to-proteome linking; Photolabeling; SCoPE-MS; Single-cell proteomics

DOI:  https://doi.org/10.1007/978-1-0716-4402-7_9
Analyst. 2025 Mar 07.

Single-cell lipidomics: protocol development for reliable cellular profiling using capillary sampling.

Anastasia Kontiza, Johanna von Gerichten, Matt Spick, Emily Fraser, Catia Costa, Kyle D G Saunders, Anthony D Whetton, Carla F Newman, Melanie J Bailey.

Single-cell lipidomics enables detailed analysis of the lipidomes of cells, but is challenged by small sample volumes, the risk of background interference and a lack of validation data. In this study, we explore the effect of different sampling variables on the lipid profiles of single pancreatic cancer cells, detected using liquid chromatography-mass spectrometry (LC-MS). We use automated and manual capillary sampling methods to isolate living single cells and evaluate different sampling media, capillary tips, aspiration volume, and temperature and humidity control. We demonstrate that automated and manual capillary sampling yield comparable lipid profiles when key parameters are controlled. Our findings highlight that appropriate blank correction, capillary tip type, and the control of aspiration volumes are all critical to preserving detection sensitivity. Conversely, choice of sampling medium does not affect lipidomics results. We also set out suggested best practices for these methodological variables, laying a foundation for robust, adaptable workflows in single-cell lipidomics for applications such as biomarker discovery and metabolic research.

DOI: https://doi.org/10.1039/d5an00037h
J Proteome Res. 2025 Mar 04.

CE-MS Metabolomic and LC-MS Proteomic Analyses of Breast Cancer Exosomes Reveal Alterations in Purine and Carnitine Metabolism.

Emil A Zaripov, Abdullah Khraibah, Petr Kasyanchyk, Aliaksandra Radchanka, Nico Hüttmann, Maxim V Berezovski.

  A nanosheath-flow capillary electrophoresis mass spectrometry (CE-MS) system with electrospray ionization was used to profile cationic metabolite cargo in exosomes secreted by nontumorigenic MCF-10A and tumorigenic MDA-MB-231 breast epithelial cells. An in-house-produced sheath liquid interface was developed and machined from PEEK to enable nanoflow volumes. Normalization of CE-MS peak areas to the total UV signal was employed to enhance quantitative accuracy and reduce variability. CE-MS-based metabolomics revealed increased purine synthesis intermediates and increased carnitine synthesis metabolites in MDA-MB-231-derived exosomes, with pathway enrichment indicating the activation of de novo purine pathways and upregulation of carnitine metabolism. In addition, nano-LC-MS-based proteomics revealed differential expression of ecto-5'-nucleotidase (NT5E) and mitochondrial aldehyde dehydrogenase (ALDH9A1), demonstrating metabolic alterations in related enzymatic steps. This study demonstrates the application of nanosheath-flow CE-MS for comprehensive and quantitative exosome metabolomics, uncovering metabolic reprogramming in purine and carnitine pathways between normal and cancerous breast cell lines and providing insight into exosome-mediated signaling of breast cancer metabolism.

Keywords:  CE–MS; ESI; LC–MS; bioinformatics; breast cancer; exosomes; extracellular vesicles; metabolomics; proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.4c00795
Int J Mass Spectrom. 2025 Apr;pii: 117421. [Epub ahead of print]510

Rapid and Accurate Identification of Microorganisms Using Ion Mobility-Mass Spectrometry.

Ahmed M Hamid.

  Accurate identification of microorganisms to the strain and substrain levels in clinical and environmental samples is essential to provide an appropriate anti-biotherapy to the patients and reduce the prescription of broad-spectrum antimicrobials to minimize antibiotic resistance. Unfortunately, the current diagnosis methods are often slow, expensive, or laborious, which limits their use in resource-limited regions. Therefore, there is a strong unmet need for new technologies that can rapidly identify microorganisms in complex samples to complement the existing commercially available technologies. This Young Scientist Perspective demonstrates the value of combining the attributes of ion mobility-mass spectrometry and ambient ionization, enabling the rapid and accurate discrimination of bacteria to the species level after only a four-hour culturing period and showing that various bacterial species can have different isomers and conformers of their biomarkers. However, to discriminate closely-related bacterial strains, we needed to include other separation techniques in our workflow, such as liquid chromatography. Also, we utilized whole organism fingerprints, which include metabolites, lipids, and peptides, using our optimized workflow and machine learning to analyze a wide set of E. coli strains in artificially contaminated urine samples. Moreover, the various challenges for the routine identification of microorganisms using our optimized techniques in medical, environmental, and security fields and future outlooks are discussed.

Keywords:  ambient ionization; bacterial discrimination; ion mobility; mass spectrometry; omics

DOI:  https://doi.org/10.1016/j.ijms.2025.117421
Sci Adv. 2025 Mar 07. 11(10): eads4957

Citrulline regulates macrophage metabolism and inflammation to counter aging in mice.

Zhangdan Xie, Moubin Lin, Beizi Xing, Hongmiao Wang, Haosong Zhang, Zimu Cai, Xinyu Mei, Zheng-Jiang Zhu.

Metabolic dysregulation and altered metabolite concentrations are widely recognized as key characteristics of aging. Comprehensive exploration of endogenous metabolites that drive aging remains insufficient. Here, we conducted an untargeted metabolomics analysis of aging mice, revealing citrulline as a consistently down-regulated metabolite associated with aging. Systematic investigations demonstrated that citrulline exhibited antiaging effects by reducing cellular senescence, protecting against DNA damage, preventing cell cycle arrest, modulating macrophage metabolism, and mitigating inflammaging. Long-term citrulline supplementation in aged mice yielded beneficial effects and ameliorated age-associated phenotypes. We further elucidated that citrulline acts as an endogenous metabolite antagonist to inflammation, suppressing proinflammatory responses in macrophages. Mechanistically, citrulline served as a potential inhibitor of mammalian target of rapamycin (mTOR) activation in macrophage and regulated the mTOR-hypoxia-inducible factor 1α-glycolysis signaling pathway to counter inflammation and aging. These findings underscore the significance of citrulline deficiency as a driver of aging, highlighting citrulline supplementation as a promising therapeutic intervention to counteract aging-related changes.

DOI: https://doi.org/10.1126/sciadv.ads4957