bims-mascan 2025-02-16 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–02–16
twenty papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Isobaric Tagging and Data Independent Acquisition as Complementary Strategies for Proteome Profiling on an Orbitrap Astral Mass Spectrometer.
Development and Validation of Targeted Metabolomics Methods Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for the Quantification of 235 Plasma Metabolites.
Workflow4Metabolomics (W4M): A User-Friendly Metabolomics Platform for Analysis of Mass Spectrometry and Nuclear Magnetic Resonance Data.
Molecular Structure and Mass Spectral Data Quality-Driven Processing of High-Resolution Mass Spectrometry for Quantitative Analysis.
Cloud-Enabled Scalable Analysis of Large Proteomics Cohorts.
Development of an Oxylipin Library Using Liquid Chromatography-Ion Mobility Quadrupole Time-of-Flight: Application to Mouse Brain Tissue.
ProteoArk: A One-Pot Proteomics Data Analysis and Visualization Tool for Biologists.
Recent advances in analytical methods and bioinformatic tools for quantitative glycomics.
Multiplatform Lipid Analysis of the Brain of Aging Mice by Mass Spectrometry.
Groomed Fingerprint Sebum Sampling: Reproducibility and Variability According to Anatomical Collection Region and Biological Sex.
Mass Spectrometry-Based Proteomics Technologies to Define Endogenous Protein-Protein Interactions and Their Applications to Cancer and Viral Infectious Diseases.
Tackling pressure fluctuations in ultra-high performance liquid chromatography to robustly resolve and analyse polar metabolites.
TRAF3 loss protects glioblastoma cells from lipid peroxidation and immune elimination via dysregulated lipid metabolism.
Acetyl-CoA synthesis in the skin is a key determinant of systemic lipid homeostasis.
Microflow LC-MS Bottom-Up Proteomics Using 1.5 mm Internal Diameter Columns.
Serine starvation suppresses the progression of esophageal cancer by regulating the synthesis of purine nucleotides and NADPH.
SIGNIFICANT IMPACT OF CONSUMABLE MATERIAL AND BUFFER COMPOSITION FOR LOW-CELL NUMBER PROTEOMIC SAMPLE PREPARATION.
Untargeted Metabolomics and Proteomics-Based Research of the Long-Term Exercise on Human Body.
Ion Mobility-Coupled Mass Spectrometry for Metallophore Detection.
Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes.

J Proteome Res. 2025 Feb 12.

Isobaric Tagging and Data Independent Acquisition as Complementary Strategies for Proteome Profiling on an Orbitrap Astral Mass Spectrometer.

Xinyue Liu, Shane L Dawson, Steven P Gygi, Joao A Paulo.

  Comprehensive global proteome profiling that is amenable to high throughput processing will broaden our understanding of complex biological systems. Here, we evaluate two leading mass spectrometry techniques, Data Independent Acquisition (DIA) and Tandem Mass Tagging (TMT), for extensive protein abundance profiling. DIA provides label-free quantification with a broad dynamic range, while TMT enables multiplexed analysis using isobaric tags for efficient cross-sample comparisons. We analyzed 18 samples, including four cell lines (IHCF, HCT116, HeLa, MCF7) under standard growth conditions, in addition to IHCF treated with two H2O2 concentrations, all in triplicate. Experiments were conducted on an Orbitrap Astral mass spectrometer, employing Field Asymmetric Ion Mobility Spectrometry (FAIMS). Despite utilizing different acquisition strategies, both the DIA and TMT approaches achieved comparable proteome depth and quantitative consistency, with each method quantifying over 10,000 proteins across all samples, with marginally higher protein-level precision for the TMT strategy. Relative abundance correlation analysis showed strong agreement at both peptide and protein levels. Our findings highlight the complementary strengths of DIA and TMT for high-coverage proteomic studies, providing flexibility in method selection based on specific experimental needs.

Keywords:  Astral; DIA; FAIMS; IHCF; TMTpro

DOI:  https://doi.org/10.1021/acs.jproteome.4c01107
Molecules. 2025 Feb 05. pii: 706. [Epub ahead of print]30(3):

Development and Validation of Targeted Metabolomics Methods Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for the Quantification of 235 Plasma Metabolites.

Kangkang Xu, Franz Berthiller, Barbara U Metzler-Zebeli, Heidi E Schwartz-Zimmermann.

  Plasma contains metabolites with diverse physicochemical properties, ranging from highly polar to highly apolar, and concentrations spanning at least nine orders of magnitude. Plasma metabolome analysis is valuable for monitoring health and evaluating medical interventions but is challenging due to the metabolome's diversity and complexity. This study aims to develop and validate targeted LC-MS/MS methods for quantifying 235 mammalian metabolites from 17 compound classes in porcine plasma without prior derivatization. Utilizing reversed-phase and hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry, each analyte is identified and quantified using two selected reaction monitoring (SRM) transitions. Fast polarity switching and scheduled SRM enhance the metabolome coverage and throughput, enabling the analysis of one sample in about 40 min. A simple "dilute and shoot" sample preparation protocol was employed, with samples injected at two dilution levels to align metabolite concentrations within calibration curve ranges. Validation in porcine plasma included assessments of carryover, linearity, detection and quantification limits, repeatability and recovery. The method was further applied to plasma samples from various animal species, demonstrating its applicability to human and animal studies. This study establishes two robust LC-MS/MS methods for comprehensive porcine plasma metabolome quantification, advancing large-scale targeted metabolomics in biomedical research.

Keywords:  HILIC; animal metabolomics; metabolomics; plasma metabolites; reversed-phase high-performance liquid chromatography; targeted HPLC-MS/MS methods

DOI:  https://doi.org/10.3390/molecules30030706
Curr Protoc. 2025 Feb;5(2): e70095

Workflow4Metabolomics (W4M): A User-Friendly Metabolomics Platform for Analysis of Mass Spectrometry and Nuclear Magnetic Resonance Data.

Cédric Delporte, Marie Tremblay-Franco, Yann Guitton, Cécile Canlet, Ralf J M Weber, Helge Hecht, Elliott James Price, Jana Klánová, Charlotte Joly, Céline Dalle, Julien Saint-Vanne, Etienne Thévenot, Isabelle Schmitz, Sylvain Chéreau, Sylvain Dechaumet, Binta Diémé, Franck Giacomoni, Gildas Le Corguillé, Mélanie Pétéra, Florence Souard.

  Various spectrometric methods can be used to conduct metabolomics studies. Nuclear magnetic resonance (NMR) or mass spectrometry (MS) coupled with separation methods, such as liquid or gas chromatography (LC and GC, respectively), are the most commonly used techniques. Once the raw data have been obtained, the real challenge lies in the bioinformatics required to conduct: (i) data processing (including preprocessing, normalization, and quality control); (ii) statistical analysis for comparative studies (such as univariate and multivariate analyses, including PCA or PLS-DA/OPLS-DA); (iii) annotation of the metabolites of interest; and (iv) interpretation of the relationships between key metabolites and the relevant phenotypes or scientific questions to be addressed. Here, we will introduce and detail a stepwise protocol for use of the Workflow4Metabolomics platform (W4M), which provides user-friendly access to workflows for processing of LC-MS, GC-MS, and NMR data. Those modular and extensible workflows are composed of existing standalone components (e.g., XCMS and CAMERA packages) as well as a suite of complementary W4M-implemented modules. This tool suite is accessible worldwide through a web interface and is hosted on UseGalaxy France. The extensible Virtual Research Environment (VRE) provided offers pre-configured workflows for metabolomics communities (platforms, end users, etc.), as well as possibilities for sharing among users. By providing a consistent ecosystem of tools and workflows through Galaxy, W4M makes it possible to process MS and NMR data from hundreds of samples using an ordinary personal computer, after step-by-step workflow optimization. © 2025 Wiley Periodicals LLC. Basic Protocol 1: W4M account creation, working history preparation, and data upload Support Protocol 1: How to prepare an NMR zip file Support Protocol 2: How to convert MS data from proprietary format to open format Support Protocol 3: How to get help with W4M (IFB forum) and how to report a problem on the GitHub repository Basic Protocol 2: LC-MS data processing Alternate Protocol 1: GC-MS data processing Alternate Protocol 2: NMR data processing Basic Protocol 3: Statistical analysis Basic Protocol 4: Annotation of metabolites from LC-MS data Alternate Protocol 3: Annotation of metabolites from NMR data.

Keywords:  GC–MS; LC–MS; NMR; annotation; chemometrics; statistics; untargeted metabolomics

DOI:  https://doi.org/10.1002/cpz1.70095
Rapid Commun Mass Spectrom. 2025 Feb 10. e10000

Molecular Structure and Mass Spectral Data Quality-Driven Processing of High-Resolution Mass Spectrometry for Quantitative Analysis.

Fabien Fontaine, Luca Morettoni, Ken Anderson, Bernard Choi, Ismael Zamora, Kevin P Bateman.

   RATIONALE: LC-MS-based quantification is traditionally performed using selected or multiple reaction monitoring (SRM/MRM) acquisition functions on triple quadrupole (QQQ) instruments resulting in both high sensitivity and selectivity. This workflow requires a previously identified reaction or transition from a precursor ion to a fragment ion to be monitored to obtain the needed selectivity for the compound of interest. High-resolution mass spectrometry (HRMS) has long sought to be a viable alternative for quantitatipve workflows but has been unable to broadly compete, mainly due to the lack of suitable data processing software.
METHODS: The approach we developed agnostically and automatically identifies all ions related to the compound being analyzed in both the MS and MSMS data, acquired with data-dependent or data-independent methods. The algorithm automatically selects optimal parameters (ion extraction window, ions to sum, etc.) to provide the best overall method to meet the acceptance criteria defined by the user (accuracy/precision).
RESULTS: The results obtained are directly compared to QQQ data collected from the same set of samples and show that the automated HRMS approach is as good as and, in some cases, better than the traditional QQQ approach in terms of selectivity, sensitivity, and dynamic range.
CONCLUSIONS: This new methodology enables the use of generic methods for data collection for quantitative analysis using high-resolution mass spectrometry. With this approach, data collection is faster, and the processing algorithm provides quality equal to or better than the current QQQ methodology. This enables an overall reduction in cycle time and improved assay performance versus current HRMS-based quantitative analysis as well as traditional QQQ workflows.

Keywords:  HRMS; automated; quantitation; structure based

DOI:  https://doi.org/10.1002/rcm.10000
J Proteome Res. 2025 Feb 13.

Cloud-Enabled Scalable Analysis of Large Proteomics Cohorts.

Harendra Guturu, Andrew Nichols, Lee S Cantrell, Seth Just, Janos Kis, Theodore Platt, Iman Mohtashemi, Jian Wang, Serafim Batzoglou.

  Rapid advances in depth and throughput of untargeted mass-spectrometry-based proteomic technologies enable large-scale cohort proteomic and proteogenomic analyses. As such, the data infrastructure and search engines required to process data must also scale. This challenge is amplified in search engines that rely on library-free match between runs (MBR) search, which enable enhanced depth-per-sample and data completeness. However, to date, no MBR-based search could scale to process cohorts of thousands or more individuals. Here, we present a strategy to deploy search engines in a distributed cloud environment without source code modification, thereby enhancing resource scalability and throughput. Additionally, we present an algorithm, Scalable MBR, that replicates the MBR procedure of popular DIA-NN software for scalability to thousands of samples. We demonstrate that Scalable MBR can search thousands of MS raw files in a few hours compared to days required for the original DIA-NN MBR procedure and demonstrate that the results are almost indistinguishable to those of DIA-NN native MBR. We additionally show that empirical spectra generated by Scalable MBR better approximates DIA-NN native MBR compared to semiempirical alternatives such as ID-RT-IM MBR, preserving user choice to use empirical libraries in large cohort analysis. The method has been tested to scale to over 15,000 injections and is available for use in the Proteograph Analysis Suite.

Keywords:  bioinformatics; cloud search; computational biology; data-independent acquisition; mass spectrometry; match-between-runs; population proteomics; proteograph; proteomics; scalable computing

DOI:  https://doi.org/10.1021/acs.jproteome.4c00771
Anal Chem. 2025 Feb 09.

Development of an Oxylipin Library Using Liquid Chromatography-Ion Mobility Quadrupole Time-of-Flight: Application to Mouse Brain Tissue.

Maria Moran-Garrido, Ameer Y Taha, Ángel Gaudioso, Maria Dolores Ledesma, Coral Barbas.

Oxylipins are bioactive lipid mediators derived from polyunsaturated fatty acids (PUFAs) that play crucial roles in physiological and pathological processes. The analysis and identification of oxylipins are challenging due to the numerous isomeric forms. Ion mobility (IM), which separates ions based on their spatial configuration, combined with liquid chromatography (LC) and mass spectrometry (MS), has been proven effective for separating isomeric compounds. In this study, we developed an extensive oxylipin library containing information on retention time (RT), m/z, and CCS values for 74 oxylipin standards using LC-IM-QTOF-MS in positive and negative ionization modes. The oxylipins in the library were grouped into 15 isomer categories to evaluate the efficacy of IM in isomeric separation. Various adducts were investigated, including protonated, deprotonated, and sodiated forms. The ΔCCS% for more than 1000 isomeric pairs was calculated, revealing that 30% of these exhibited a ΔCCS% greater than 2%. Positive ionization mode demonstrated superior separation capabilities, with 274 isomer pairs achieving baseline separation (ΔCCS% >  4%). Sodium adducts significantly improved isomer separation. With the inclusion of LC separation, only nine oxylipins coeluted, forming six different isomeric pairs. CCS values for the adducts [M+Na]+ and [M+2Na-H]+ separated three of these isomeric pairs. The CCS values were compared to experimental libraries, confirming the high reproducibility of CCS measurements, with average errors below 2%. Applying this library to mouse brain samples, 19 different oxylipins were identified by matching RT, m/z, and CCS values. Coeluting isomers, 9- and 13-HODE, 8- and 12-HETE, and 15-oxo-ETE and 14(15)-EpETrE, were successfully separated and identified using drift time separation.

DOI: https://doi.org/10.1021/acs.analchem.4c06265
J Proteome Res. 2025 Feb 10.

ProteoArk: A One-Pot Proteomics Data Analysis and Visualization Tool for Biologists.

Mahammad Nisar, Sreelakshmi Pathappillil Soman, Sourav Sreelan, Levin John, Sneha M Pinto, Richard Kumaran Kandasamy, Yashwanth Subbannayya, Thottethodi Subrahmanya Keshava Prasad, Saptami Kanekar, Rajesh Raju, Rex Devasahayam Arokia Balaya.

  ProteoArk is a web-based tool that offers a range of computational pipelines for comprehensive analysis and visualization of mass spectrometry-based proteomics data. The application comprises four primary sections designed to address various aspects of mass spectrometry data analysis in a single platform, including label-free and labeled samples (SILAC/iTRAQ/TMT), differential expression analysis, and data visualization. ProteoArk supports postprocessing of Proteome Discoverer, MaxQuant, and MSFragger search results. The tool also includes functional enrichment analyses such as gene ontology, protein-protein interactions, pathway analysis, and differential expression analysis, which incorporate various statistical tests. By streamlining workflows and developing user-friendly interfaces, we created a robust and accessible solution for users with basic bioinformatics skills in proteomic data analysis. Users can easily create manuscript-ready figures with a single click, including principal component analysis, heatmaps (K-means and hierarchical), MA plots, volcano plots, and circular bar plots. ProteoArk is developed using the Django framework and is freely available for users [https://ciods.in/proteoark/]. Users can also download and run the standalone version of ProteoArk using Docker as described in the instructions [https://ciods.in/proteoark/dockerpage]. The application code, input data, and documentation are available online at https://github.com/ArokiaRex/proteoark. A tutorial video is available on YouTube: https://www.youtube.com/watch?v=WFMKAZ9Slq4&ab_channel=RexD.A.B.

Keywords:  bioinformatics tools; data analysis; data visualization; mass spectrometry; proteomics; web application

DOI:  https://doi.org/10.1021/acs.jproteome.4c00556
Anal Bioanal Chem. 2025 Feb 13.

Recent advances in analytical methods and bioinformatic tools for quantitative glycomics.

Jihong Lu, Shuhong Guo, Qiannan Liu, Nafisa Tursumamat, Shengyang Liu, Shuye Wu, Heming Li, Juan Wei.

  The significance of glycans in various biological processes has been widely acknowledged. Quantitative glycomics is emerging as an important addition to clinical biomarker discovery, as it helps uncover disease-associated glycosylation patterns that are valuable for diagnosis, prognosis, and treatment evaluation. Compared to glycoproteomics and other established omics approaches, quantitative glycomics exhibits greater methodological diversity and it encounters various challenges in automation and standardization. Nonetheless, numerous advancements have been made in this field over the past 5 years. Here, we have reviewed recent progress in analytical methods and software to improve mass spectrometry-based quantitative glycomics primarily on N- and O-glycosylation. The discussion is organized into four sections: stable isotopic labeling, isobaric labeling, label-free, and fluorescence labeling strategies, with a particular emphasis on quantitative data interpretation. Novel derivatization methods and advanced techniques have been developed for high-throughput and highly sensitive glycan quantification with high accuracy. However, due to variations in glycan derivatization and difficulties in structural identification, most glycomic quantification methods are tailored to specific applications, and manual inspection is frequently necessary for precise data interpretation. Therefore, further advancements in glycan sample preparation, structural characterization, and automated data interpretation are essential to facilitate comprehensive and accurate quantification across a wide array of glycans.

Keywords:  Bioinformatic tools; Glycosylation; Mass spectrometry; Quantitative glycomics

DOI:  https://doi.org/10.1007/s00216-025-05778-3
J Proteome Res. 2025 Feb 08.

Multiplatform Lipid Analysis of the Brain of Aging Mice by Mass Spectrometry.

Punyatoya Panda, Christina R Ferreira, Bruce R Cooper, Allison J Schaser, Uma K Aryal.

  Lipids are critical to brain structure and function, accounting for approximately 50% of its dry weight. However, the impact of aging on brain lipids remains poorly characterized. To address this, here we applied three complementary mass spectrometry techniques: multiple reaction monitoring (MRM) profiling, untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS), and desorption electrospray ionization-MS imaging (DESI-MSI). We used brains from mice of three age groups: adult (3-4 months), middle-aged (10 months), and old (19-21 months). Phospholipids such as phosphatidylcholine, phosphatidylethanolamine, and phosphatidylglycerol were more abundant, while phosphatidylinositol and phosphatidylserine were reduced in old mice compared to adults or middle-aged mice. Key lipids such as polyunsaturated fatty acids, including DHA, AA, HexCer, SHexCer, and SM, were among the most abundant lipids in aged brains. DESI-MSI revealed spatial lipid distribution patterns consistent with findings from MRM profiling and LC-MS/MS. Integration of lipidomic data with the recently published proteomics data from the same tissues highlighted changes in proteins and phosphorylation levels of several proteins associated with Cer, HexCer, FA, PI, SM, and SHexCer metabolism, aligning with the multiplatform lipid surveillance. These findings shed insight into age-dependent brain lipid changes and their potential contribution to age-related cognitive decline.

Keywords:  DESI imaging; aging; proteomics; targeted lipidomics; untargeted lipidomics

DOI:  https://doi.org/10.1021/acs.jproteome.4c00688
Molecules. 2025 Feb 06. pii: 726. [Epub ahead of print]30(3):

Groomed Fingerprint Sebum Sampling: Reproducibility and Variability According to Anatomical Collection Region and Biological Sex.

Madeline Isom, Eden P Go, Heather Desaire.

  Sebum lipids, accessible via groomed latent fingerprints, may be a valuable, underappreciated sample source for future biomarker research. Sampling sebum lipids from the skin is painless for patients, efficient for researchers, and has already demonstrated the potential to contain disease biomarkers. However, before sebum sampling can be implemented in routine studies, more information is needed regarding sampling reproducibility and variability. This information will enable researchers to choose the best practices for sebum-based studies. Herein, we use our recently established workflow for the collection and analysis of groomed fingerprints to assess the reproducibility of lipid profiles obtained via mass spectrometry. Using 180 fingerprint samples collected from 30 participants, we also assess lipid changes according to biological sex and anatomical grooming region (cheek, neck, and forehead) via supervised and unsupervised classification. The results demonstrate that this sampling protocol achieves satisfactory reproducibility, and negligible differences exist between male and female groomed fingerprint lipids. Moreover, the anatomical grooming region can impact the fingerprint lipid profile: cheek- and forehead-groomed fingerprints are more similar to one another than either collection site is to neck-groomed fingerprints. This information will inform future sebum-based biomarker investigations, enabling researchers to collect meaningful lipidomic datasets from groomed fingerprint samples.

Keywords:  biomarkers; fingerprint; lipidomics; lipids; machine learning; mass spectrometry; noninvasive sampling; sebum; skin

DOI:  https://doi.org/10.3390/molecules30030726
Mass Spectrom Rev. 2025 Feb 09.

Mass Spectrometry-Based Proteomics Technologies to Define Endogenous Protein-Protein Interactions and Their Applications to Cancer and Viral Infectious Diseases.

Clinton Yu, Rithika Adavikolanu, Robyn M Kaake, Lan Huang.

  An intricate network of protein assemblies and protein-protein interactions (PPIs) underlies nearly every biological process in living systems. The organization of these cellular networks is highly dynamic and intimately tied to the genomic and proteomic landscapes of a cell. Disruptions in normal PPIs can impair cellular functions and contribute to the development of human diseases. In recent years, targeting PPIs has emerged as an attractive strategy for drug discovery. Consequently, the identification and characterization of endogenous PPIs-those occurring naturally under physiological conditions-has become crucial for unraveling the molecular mechanisms driving human pathology and for laying the groundwork for novel diagnostics and therapeutics. Owing to numerous technological advancements, mass spectrometry (MS)-based proteomics has transformed the study of PPIs at the systems-level. This review focuses on proteomics approaches that enable the characterization of physiologically relevant endogenous interactions, spanning complex-centric to structure-centric analyses. Additionally, their applications to define native PPIs in the contexts of cancer and viral infectious diseases is highlighted.

Keywords:  Affinity Purification Mass Spectrometry (AP‐MS); Cancer; Co‐Fractionation Mass Spectrometry (CF‐MS); Cross‐Linking Mass Spectrometry (XL‐MS); Proximity Labeling Mass Spectrometry (PL‐MS); genome tagging; immunoprecipitation (IP); protein footprinting; protein‐protein interactions (PPIs); virology

DOI:  https://doi.org/10.1002/mas.21926
J Biol Chem. 2025 Feb 10. pii: S0021-9258(25)00131-0. [Epub ahead of print] 108283

Tackling pressure fluctuations in ultra-high performance liquid chromatography to robustly resolve and analyse polar metabolites.

James R Krycer, Manuel Plan, Thomas Stoll, Andrew R Laskary, Mark P Hodson, James E Hudson.

The success of modern metabolomics analysis depends on the separation of metabolites in complex samples using methods such as liquid chromatography and mass spectrometry. Herein, we present a protocol for resolving a broad range of polar metabolites, based on hydrophilic interaction liquid chromatography with a zwitterionic bonded phase (HILICz). In optimising this protocol, we encountered pressure fluctuations, a widespread problem that impacts metabolite analysis, restricts batch sizes, and imposes instrument downtime, ultimately incurring substantial time and financial expense. Thus, we use this opportunity as a case study to demonstrate the steps taken to overcome such pressure fluctuations, resulting in a protocol that robustly and consistently resolves polar metabolites in large batches of samples (>100 samples, equating to >40 hours of run-time). This consistency is essential to address the growing demand for repeatable in-depth metabolomics analysis of complex samples.

DOI: https://doi.org/10.1016/j.jbc.2025.108283
J Clin Invest. 2025 Feb 11. pii: e178550. [Epub ahead of print]

TRAF3 loss protects glioblastoma cells from lipid peroxidation and immune elimination via dysregulated lipid metabolism.

Yu Zeng, Liqian Zhao, Kunlin Zeng, Ziling Zhan, Zhengming Zhan, Shangbiao Li, Hongchao Zhan, Peng Chai, Cheng Xie, Shengfeng Ding, Yuxin Xie, Li Wang, Cuiying Li, Xiaoxia Chen, Daogang Guan, Enguang Bi, Jian-You Liao, Fan Deng, Xiaochun Bai, Ye Song, Aidong Zhou.

  Glioblastoma (GBM) is a highly aggressive form of brain tumor characterized by dysregulated metabolism. Increased fatty acid oxidation (FAO) protects tumor cells from lipid peroxidation-induced cell death, although the precise mechanisms involved remain unclear. Herein, we report that loss of tumor necrosis factor receptor-associated factor 3 (TRAF3) in GBM critically regulates lipid peroxidation and tumorigenesis by controlling the oxidation of polyunsaturated fatty acids (PUFAs). TRAF3 is frequently repressed in GBM due to promoter hypermethylation. TRAF3 interacts with enoyl-CoA hydratase 1 (ECH1), an enzyme catalyzing the isomerization of unsaturated fatty acids (UFAs), and mediates K63-linked ubiquitination of ECH1 at Lys214. ECH1 ubiquitination impedes TOMM20-dependent mitochondrial translocation of ECH1, which otherwise promotes the oxidation of UFAs, preferentially the PUFAs, and limits lipid peroxidation. Overexpression of TRAF3 enhances the sensitivity of GBM to ferroptosis and anti-PD-L1 immunotherapy in mice. Thus, the TRAF3-ECH1 axis plays a key role in the metabolism of PUFAs, and is crucial for lipid peroxidation damage and immune elimination in GBM.

Keywords:  Brain cancer; Cancer immunotherapy; Cell biology; Fatty acid oxidation; Metabolism

DOI:  https://doi.org/10.1172/JCI178550
Cell Rep. 2025 Feb 10. pii: S2211-1247(25)00055-5. [Epub ahead of print]44(2): 115284

Acetyl-CoA synthesis in the skin is a key determinant of systemic lipid homeostasis.

Phuong T T Nguyen, Mia Shiue, Nina Kuprasertkul, Pedro Costa-Pinheiro, Luke T Izzo, Laura V Pinheiro, Hayley A Affronti, Gabriel Gugiu, Shivani Ghaisas, Joyce Y Liu, Jordan C Harris, Charles W Bradley, John T Seykora, Xiaolu Yang, Taku Kambayashi, Clementina Mesaros, Brian C Capell, Kathryn E Wellen.

  ATP-citrate lyase (ACLY) generates cytosolic acetyl-coenzyme A (acetyl-CoA) for lipid synthesis and is a promising therapeutic target in diseases with altered lipid metabolism. Here, we developed inducible whole-body Acly-knockout mice to determine the requirement for ACLY in normal tissue functions, uncovering its crucial role in skin homeostasis. ACLY-deficient skin upregulates the acetyl-CoA synthetase ACSS2; deletion of both Acly and Acss2 from the skin exacerbates skin abnormalities, with differential effects on two major lipid-producing skin compartments. While the epidermis is depleted of barrier lipids, the sebaceous glands increase production of sebum, supplied at least in part by circulating fatty acids and coinciding with adipose lipolysis and fat depletion. Dietary fat supplementation further boosts sebum production and partially rescues both the lipoatrophy and the aberrant skin phenotypes. The data establish a critical role for cytosolic acetyl-CoA synthesis in maintaining skin barrier integrity and highlight the skin as a key organ in systemic lipid regulation.

Keywords:  ACLY; ACSS2; CP: Metabolism; acetyl-CoA; adipose; epidermis; lipid metabolism; sebaceous glands; skin; skin barrier

DOI:  https://doi.org/10.1016/j.celrep.2025.115284
ACS Omega. 2025 Feb 04. 10(4): 4094-4101

Microflow LC-MS Bottom-Up Proteomics Using 1.5 mm Internal Diameter Columns.

Siddharth Jadeja, Denis K Naplekov, Mykyta R Starovoit, Kateřina Plachká, Harald Ritchie, Jason Lawhorn, Hana Sklenářová, Juraj Lenčo.

Microbore columns with a 1.0 mm inner diameter (i.d.) have gained popularity in microflow liquid chromatography-mass spectrometry (LC-MS) workflows for exploratory proteomics applications due to their high throughput, robustness, and reproducibility. However, obtaining highly efficient separation using these columns remains unachievable, primarily due to significant radial flow heterogeneity caused by uneven particle packing density across the column cross-section. In this study, we evaluated the integration of a 1.5 mm i.d. column, which offers greater packing uniformity and reduced radial flow dispersion, into a microflow LC-MS setup for bottom-up proteomics analysis. The performance of the 1.5 mm i.d. column was compared with that of the 1.0 mm i.d. column using protein samples of varying complexity. The results demonstrate that 1.5 mm i.d. columns provide superior chromatographic separation and better compatibility with conventional-flow LC systems, yielding higher reproducibility and comparable protein and peptide identifications to the 1.0 mm i.d. columns at higher sample amounts. These findings suggest that 1.5 mm i.d. columns could be a suitable alternative to 1.0 mm i.d. columns for microflow LC-MS/MS proteomic analysis, particularly in laboratories with only conventional-flow LC systems.

DOI: https://doi.org/10.1021/acsomega.4c10591
Cancer Metab. 2025 Feb 13. 13(1): 10

Serine starvation suppresses the progression of esophageal cancer by regulating the synthesis of purine nucleotides and NADPH.

Hui Jie, Jing Wei, Zhuoling Li, Min Yi, Xinying Qian, Yan Li, Chunqi Liu, Chuan Li, Liang Wang, Pengchi Deng, Lunxu Liu, Xiaobo Cen, Yinglan Zhao.

  Serine metabolism provides important metabolic intermediates that support the rapid proliferation of tumor cells. However, the role of serine metabolism in esophageal squamous cell carcinoma (ESCC) and the underlying mechanism remains unclear. Here, we show that serine starvation predominantly inhibits ESCC cell proliferation by suppressing purine nucleotides and NADPH synthesis. Mechanistically, serine depletion led to the accumulation of aminoimidazole carboxamide ribonucleoside (AICAR), an intermediate metabolite of de novo purine synthesis, and AMP/ATP ratio. These increases activated 5'-AMP-activated kinase (AMPK), which subsequently inhibited the mTORC1 pathway by phosphorylating Raptor at Ser792. Moreover, serine depletion decreased NADPH level followed by elevated reactive oxygen species (ROS) production and DNA damage, which induced p53-p21 mediated G1 phase cell cycle arrest. Conversely, serine starvation activated transcription factor 4 (ATF4)-mediated robust expression of phosphoserine aminotransferase 1 (PSAT1) which in turn promoted compensatory endogenous serine synthesis, thus maintaining ESCC cell survival under serine-limited conditions. Accordingly, serine deprivation combined with PSAT1 inhibition significantly suppressed ESCC tumor growth both in vitro and in vivo. Taken together, our findings demonstrate that serine starvation suppresses the proliferation of ESCC cells by disturbing the synthesis of purine nucleotides and NADPH, and the combination of serine deprivation and PSAT1 inhibition significantly impairs ESCC tumor growth. Our study provides a theoretical basis for targeting serine metabolism as a potential therapeutic strategy for ESCC.

Keywords:  AMPKα / mTORC1 pathway; ESCC cell proliferation; Purine synthesis; Serine starvation

DOI:  https://doi.org/10.1186/s40170-025-00376-4
Anal Chem. 2025 Feb 11.

SIGNIFICANT IMPACT OF CONSUMABLE MATERIAL AND BUFFER COMPOSITION FOR LOW-CELL NUMBER PROTEOMIC SAMPLE PREPARATION.

Christopher Kune, Sylvia Tielens, Dominique Baiwir, Maximilien Fléron, Denis Vandormael, Gauthier Eppe, Laurent Nguyen, Gabriel Mazzucchelli.

Proteomics, essential for understanding gene and cell functions, faces challenges with peptide loss due to adsorption onto vial surfaces, especially in samples with low peptide quantities. Using HeLa tryptic digested standard solutions, we demonstrate preferential adsorption of peptides, particularly hydrophobic ones, onto polypropylene (PP) vials, leading to nonuniform signal loss. This phenomenon can alter protein quantification (e.g., Label-Free Quantification, LFQ) if no appropriate data processing is applied. Our study is based on understanding this adsorption phenomenon to establish recommendations for minimizing peptide loss. To address this issue, we evaluated the nature of surface material and buffer additives to reduce peptide-surface noncovalent binding. Here, we report that using vials made from polymer containing polar monomeric units such as poly(methyl methacrylate) (PMMA) or polyethylene terephthalate (PET) drastically reduces the hydrophobic peptide loss, increasing the global proteomics performance (4-fold increase in identified peptides for the single-cell equivalent peptide content range). Additionally, the incorporation of nonionic detergents like poly(ethylene oxide) (PEO) and n-Dodecyl-Beta-Maltoside (DDM) at optimized concentrations (0.0001% and 0.0075%, respectively) improves the overall proteomic performance and consistency, even across different vial materials. Implementing these recommendations on 0.2 ng/μL HeLa tryptic digest results in a 10-fold increase in terms of peptide signal. Application to True Single-Cell sample preparation without specialized instrumentation dramatically improves the performance, allowing for the identification of approximately 650 proteins, a stark contrast to none detected with classical protocols.

DOI: https://doi.org/10.1021/acs.analchem.4c03709
Appl Biochem Biotechnol. 2025 Feb 12.

Untargeted Metabolomics and Proteomics-Based Research of the Long-Term Exercise on Human Body.

Wenqian Zhuang, Yang Wang, Xin Xu, Jingjing Zhao.

  Regular long-term exercise can benefit the body and reduce the risk of several diseases, such as cardiovascular disease, diabetes, and obesity. However, the proteomic and metabolomic changes, as well as the physiological responses associated with long-term exercise, remain incompletely understood. To investigate the effects of long-term exercise on the human body, 14 subjects with long-term exercise habits and 10 subjects without exercise habits were selected for this study. Morning urine samples were collected and analyzed for untargeted metabolomics and proteomics using liquid chromatography-mass spectrometry. A total of 404 differential metabolites and 394 differential proteins were screened in this research, and the analysis results indicated that long-term exercise may affect energy metabolism, amino acid synthesis and metabolism, nucleotide metabolism, steroid hormone biosynthesis, and the inflammatory response. These findings offer a more comprehensive understanding of the molecular effects of long-term exercise on the human body and provide a basis for future research exploring the underlying mechanisms.

Keywords:  Liquid chromatography mass spectrometry; Long-term exercise; Metabolomics; Proteomics

DOI:  https://doi.org/10.1007/s12010-025-05195-3
J Nat Prod. 2025 Feb 10.

Ion Mobility-Coupled Mass Spectrometry for Metallophore Detection.

Bailey A Bell, Josephine M Anderson, Scott R Rajski, Tim S Bugni.

Metal chelating small molecules (metallophores) play significant roles in microbial interactions and bacterial survival; however, current methods to identify metallophores are limited by low sensitivity, a lack of metal selectivity, and/or complicated data analysis. To overcome these limitations, we developed a novel approach for detecting metallophores in natural product extracts using ion mobility-coupled mass spectrometry (IM-MS). As a proof of concept, marine bacterial extracts containing known metallophores were analyzed by IM-MS with and without added metals, and the data were compared between conditions to identify metal-binding metabolites. Ions with changes in both mass and mobility were specific to metallophores, enabling their identification within these complex extracts. Additionally, we compared the use of direct infusion (DI) and liquid chromatography (LC) separation with IM-MS. For most samples, DI outperformed LC by minimizing the time required for data collection and simplifying analysis. However, for some samples, LC improved the detection of metallophores likely by reducing ion suppression. IM-MS was then used to identify 10 metallophores in an extract from a marine Micromonospora sp. Overall, incorporating IM-MS facilitated the rapid detection of metal-binding natural products in complex bacterial extracts through the comparison of mass and mobility data in the presence and absence of metals.

DOI: https://doi.org/10.1021/acs.jnatprod.4c00911
Mol Cell Proteomics. 2025 Feb 05. pii: S1535-9476(25)00022-2. [Epub ahead of print] 100924

Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes.

Wassim Gabriel, Rebecca Meelker Gonzalez, Sophia Laposchan, Erik Riedel, Gönül Dündar, Brigitte Poppenberger, Mathias Wilhelm, Chien-Yun Lee.

  Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteome-wide scale. Although mass spectrometry has enabled the identification of citrullination sites in complex biological samples, it faces significant challenges, including limited enrichment tools and a high rate of false positives due to the identical mass with deamidation (+0.9840 Da) and errors in monoisotopic ion selection. These issues often necessitate manual spectrum inspection, reducing throughput in large-scale studies. In this work, we present a novel data analysis pipeline that incorporates the deep learning model Prosit-Cit into the MS database search workflow to improve both the sensitivity and precision of citrullination site identification. Prosit-Cit, an extension of the existing Prosit model, has been trained on ∼53,000 spectra from ∼2,500 synthetic citrullinated peptides and provides precise predictions for chromatographic retention time and fragment ion intensities of both citrullinated and deamidated peptides. This enhances the accuracy of identification and reduces false positives. Our pipeline demonstrated high precision on the evaluation dataset, recovering the majority of known citrullination sites in human tissue proteomes and improving sensitivity by identifying up to 14 times more citrullinated sites. Sequence motif analysis revealed consistency with previously reported findings, validating the reliability of our approach. Furthermore, extending the pipeline to a tissue proteome dataset of the model plant Arabidopsis thaliana enabled the identification of ∼200 citrullination sites across 169 proteins from 30 tissues, representing the first large-scale citrullination mapping in plants. This pipeline can be seamlessly applied to existing proteomics datasets, offering a robust tool for advancing biological discoveries and deepening our understanding of protein citrullination across species.

Keywords:  Prosit; citrullination; deep learning; mass spectrometry; proteomics

DOI:  https://doi.org/10.1016/j.mcpro.2025.100924