bims-mascan 2025-02-02 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

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

Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics.
Automated High-Throughput Affinity Capture-Mass Spectrometry Platform with Data-Independent Acquisition.
A High-Throughput Integrated Nontargeted Metabolomics and Lipidomics Workflow Using Microelution Enhanced Matrix Removal-Lipid for Comparative Analysis of Human Maternal and Umbilical Cord Blood Metabolomes.
Using DIMet for Differential Analysis of Labeled Metabolomics Data: A Step-by-step Guide Showcasing the Glioblastoma Metabolism.
BET inhibition induces GDH1-dependent glutamine metabolic remodeling and vulnerability in liver cancer.
A multimodal imaging pipeline to decipher cell-specific metabolic functions and tissue microenvironment dynamics.
Advanced lipidomics using UHPLC-ESI-QTOF-MS/MS reveals novel lipids in hibernating syrian hamsters.
Current perspectives for metabolomics and lipidomics in dyslipidemia of acne vulgaris: a mini review.
OpenMS WebApps: Building User-Friendly Solutions for MS Analysis.
The Role of Mitochondrial Solute Carriers SLC25 in Cancer Metabolic Reprogramming: Current Insights and Future Perspectives.
Comprehensive untargeted polar metabolite analysis using solvent switching liquid chromatography tandem mass spectrometry.

Nat Commun. 2025 Jan 26. 16(1): 1051

Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics.

Urine Test Sample Working Group

Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography-mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research.

DOI: https://doi.org/10.1038/s41467-025-56337-4
J Proteome Res. 2025 Jan 27.

Automated High-Throughput Affinity Capture-Mass Spectrometry Platform with Data-Independent Acquisition.

Hui Jing, Paul L Richardson, Gregory K Potts, Sameera Senaweera, Violeta L Marin, Ryan A McClure, Adam Banlasan, Hua Tang, James E Kath, Shitalben Patel, Maricel Torrent, Renze Ma, Jon D Williams.

  Affinity capture (AC) combined with mass spectrometry (MS)-based proteomics is highly utilized throughout the drug discovery pipeline to determine small-molecule target selectivity and engagement. However, the tedious sample preparation steps and time-consuming MS acquisition process have limited its use in a high-throughput format. Here, we report an automated workflow employing biotinylated probes and streptavidin magnetic beads for small-molecule target enrichment in the 96-well plate format, ending with direct sampling from EvoSep Solid Phase Extraction tips for liquid chromatography (LC)-tandem mass spectrometry (MS/MS) analysis. The streamlined process significantly reduced both the overall and hands-on time needed for sample preparation. Additionally, we developed a data-independent acquisition-mass spectrometry (DIA-MS) method to establish an efficient label-free quantitative chemical proteomic kinome profiling workflow. DIA-MS yielded a coverage of ∼380 kinases, a > 60% increase compared to using a data-dependent acquisition (DDA)-MS method, and provided reproducible target profiling of the kinase inhibitor dasatinib. We further showcased the applicability of this AC-MS workflow for assessing the selectivity of two clinical-stage CDK9 inhibitors against ∼250 probe-enriched kinases. Our study here provides a roadmap for efficient target engagement and selectivity profiling in native cell or tissue lysates using AC-MS.

Keywords:  DIA; automation; chemoproteomics; kinase inhibitor; kinome

DOI:  https://doi.org/10.1021/acs.jproteome.4c00696
Anal Chem. 2025 Jan 30.

A High-Throughput Integrated Nontargeted Metabolomics and Lipidomics Workflow Using Microelution Enhanced Matrix Removal-Lipid for Comparative Analysis of Human Maternal and Umbilical Cord Blood Metabolomes.

Wenjie Wu, Ke Wang, Jianing Liu, Pui-Kin So, Ting-Fan Leung, Man-Sau Wong, Danyue Zhao.

Sample pretreatment for mass spectrometry (MS)-based metabolomics and lipidomics is normally conducted independently with two sample aliquots and separate matrix cleanup procedures, making the two-step process sample-intensive and time-consuming. Herein, we introduce a high-throughput pretreatment workflow for integrated nontargeted metabolomics and lipidomics leveraging the enhanced matrix removal (EMR)-lipid microelution 96-well plates. The EMR-lipid technique was innovatively employed to effectively separate and isolate non-lipid small metabolites and lipids in sequence using significantly reduced sample amounts and organic solvents. Our proposed methodology enables parallel profiling of metabolome and lipidome within a single sample aliquot using ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Following method development and optimization with representative metabolites at levels comparable to those detected in human blood, the optimized workflow was applied to prepare metabolome-lipidome from maternal and umbilical cord-blood sera prior to comprehensive profiling using three different UHPLC columns. Results indicate that, compared with conventional two-step metabolomics-lipidomics sample pretreatment workflow, this new approach substantially reduces sample amount and processing time, while still preserving metabolite profiles and revealing additional MS features. Over 2500 metabolites were annotated in human sera with >1000 shared across maternal and cord blood. The shared metabolites are closely linked to various physiological functions, including nutrient transfer, hormonal regulation, waste product clearance, and metabolic programming, underscoring the significant impact of maternal metabolic activities on neonatal metabolic health. In summary, the proposed workflow enables efficient sample pretreatment for nontargeted metabolomics-lipidomics using one single sample while achieving broad metabolite coverage, highlighting its remarkable applicability in clinical and preclinical research.

DOI: https://doi.org/10.1021/acs.analchem.4c03222
Bio Protoc. 2025 Jan 20. 15(2): e5168

Using DIMet for Differential Analysis of Labeled Metabolomics Data: A Step-by-step Guide Showcasing the Glioblastoma Metabolism.

Johanna Galvis, Joris Guyon, Thomas Daubon, Macha Nikolski.

  Stable-isotope resolved metabolomics (SIRM) is a powerful approach for characterizing metabolic states in cells and organisms. By incorporating isotopes, such as 13C, into substrates, researchers can trace reaction rates across specific metabolic pathways. Integrating metabolomics data with gene expression profiles further enriches the analysis, as we demonstrated in our prior study on glioblastoma metabolic symbiosis. However, the bioinformatics tools for analyzing tracer metabolomics data have been limited. In this protocol, we encourage the researchers to use SIRM and transcriptomics data and to perform the downstream analysis using our software tool DIMet. Indeed, DIMet is the first comprehensive tool designed for the differential analysis of tracer metabolomics data, alongside its integration with transcriptomics data. DIMet facilitates the analysis of stable-isotope labeling and metabolic abundances, offering a streamlined approach to infer metabolic changes without requiring complex flux analysis. Its pathway-based "metabologram" visualizations effectively integrate metabolomics and transcriptomics data, offering a versatile platform capable of analyzing corrected tracer datasets across diverse systems, organisms, and isotopes. We provide detailed steps for sample preparation and data analysis using DIMet through its intuitive, web-based Galaxy interface. To showcase DIMet's capabilities, we analyzed LDHA/B knockout glioblastoma cell lines compared to controls. Accessible to all researchers through Galaxy, DIMet is free, user-friendly, and open source, making it a valuable resource for advancing metabolic research. Key features • Glioblastoma tumor spheroids in vitro replicate tumors' three-dimensional structure and natural nutrient, metabolite, and gas gradients, providing a more realistic model of tumor biology. • Joint analysis of tracer metabolomics and transcriptomics datasets provides deeper insights into the metabolic states of cells. • DIMet is a web-based tool for differential analysis and seamless integration of metabolomics and transcriptomics data, making it accessible and user-friendly. • DIMet enables researchers to infer metabolic changes, offering intuitive and visually appealing "metabologram" outputs, surpassing conventional visual representations commonly used in the field.

Keywords:  Bioinformatics; Data integration; Differential analysis; Glioblastoma; Metabolomics; Transcriptomics

DOI:  https://doi.org/10.21769/BioProtoc.5168
Life Metab. 2024 Aug;3(4): loae016

BET inhibition induces GDH1-dependent glutamine metabolic remodeling and vulnerability in liver cancer.

Wen Mi, Jianwei You, Liucheng Li, Lingzhi Zhu, Xinyi Xia, Li Yang, Fei Li, Yi Xu, Junfeng Bi, Pingyu Liu, Li Chen, Fuming Li.

  Bromodomain and extra-terminal domain (BET) proteins, which function partly through MYC proto-oncogene (MYC), are critical epigenetic readers and emerging therapeutic targets in cancer. Whether and how BET inhibition simultaneously induces metabolic remodeling in cancer cells remains unclear. Here we find that even transient BET inhibition by JQ-1 and other pan-BET inhibitors (pan-BETis) blunts liver cancer cell proliferation and tumor growth. BET inhibition decreases glycolytic gene expression but enhances mitochondrial glucose and glutamine oxidative metabolism revealed by metabolomics and isotope labeling analysis. Specifically, BET inhibition downregulates miR-30a to upregulate glutamate dehydrogenase 1 (GDH1) independent of MYC, which produces α-ketoglutarate for mitochondrial oxidative phosphorylation (OXPHOS). Targeting GDH1 or OXPHOS is synthetic lethal to BET inhibition, and combined BET and OXPHOS inhibition therapeutically prevents liver tumor growth in vitro and in vivo. Together, we uncover an important epigenetic-metabolic crosstalk whereby BET inhibition induces MYC-independent and GDH1-dependent glutamine metabolic remodeling that can be exploited for innovative combination therapy of liver cancer.

Keywords:  BET; glutamate dehydrogenase 1; glutamine metabolism; oxidative phosphorylation; synthetic lethality

DOI:  https://doi.org/10.1093/lifemeta/loae016
Nat Protoc. 2025 Jan 29.

A multimodal imaging pipeline to decipher cell-specific metabolic functions and tissue microenvironment dynamics.

Sharavan Vishaan Venkateswaran, Peter Kreuzaler, Catherine Maclachlan, Greg McMahon, Gina Greenidge, Lucy Collinson, Josephine Bunch, Mariia Yuneva.

Tissue microenvironments are extremely complex and heterogeneous. It is challenging to study metabolic interaction between the different cell types in a tissue with the techniques that are currently available. Here we describe a multimodal imaging pipeline that allows cell type identification and nanoscale tracing of stable isotope-labeled compounds. This pipeline extends upon the principles of correlative light, electron and ion microscopy, by combining confocal microscopy reporter or probe-based fluorescence, electron microscopy, stable isotope labeling and nanoscale secondary ion mass spectrometry. We apply this method to murine models of hepatocellular and mammary gland carcinomas to study uptake of glucose derived carbon (13C) and glutamine derived nitrogen (15N) by tumor-associated immune cells. In vivo labeling with fluorescent-tagged antibodies (B220, CD3, CD8a, CD68) in tandem with confocal microscopy allows for the identification of specific cell types (B cells, T cells and macrophages) in the tumor microenvironment. Subsequent image correlation with electron microscopy offers the contrast and resolution to image membranes and organelles. Nanoscale secondary ion mass spectrometry tracks the enrichment of stable isotopes within these intracellular compartments. The whole protocol described here would take ~6 weeks to perform from start to finish. Our pipeline caters to a broad spectrum of applications as it can easily be adapted to trace the uptake and utilization of any stable isotope-labeled nutrient, drug or a probe by defined cellular populations in any tissue in situ.

DOI: https://doi.org/10.1038/s41596-024-01118-4
J Chromatogr A. 2025 Jan 17. pii: S0021-9673(25)00041-X. [Epub ahead of print]1743 465692

Advanced lipidomics using UHPLC-ESI-QTOF-MS/MS reveals novel lipids in hibernating syrian hamsters.

Carolina Gonzalez-Riano, Gonzalo León-Espinosa, Mamen Regalado-Reyes, Antonia García, Javier DeFelipe, Coral Barbas.

  Mammalian hibernation offers a unique model for exploring neuroprotective mechanisms relevant to neurodegenerative diseases. In this study, we employed untargeted lipidomics with iterative tandem mass spectrometry (MS/MS) to profile the brain lipidome of Syrian hamsters across different hibernation stages: late torpor, arousal, and euthermia (control). Previously, a lipid species identified as methyl-PA(16:0/0:0) showed a significant increase during torpor, but its precise structure was unresolved due to technological constraints. Leveraging iterative MS/MS and advanced lipid annotation tools (LipidAnnotator and MS-DIAL), we accurately annotated 377 lipid species, including the re-identification of methyl-PA(16:0/0:0) as methylated lysophosphatidic acid (PMeOH 16:0/0:0). This reannotation led to the discovery of two additional lipids during torpor: PMeOH 18:0/0:0 and PMeOH 18:1/0:0. Verification involved manual inspection of MS/MS spectra and Kendrick Mass Defect plots. The lipid alterations observed during torpor suggest biochemical adaptations to maintain membrane fluidity and protect against oxidative stress under hypothermic conditions. Elevated levels of PMeOH lipids and their lyso-forms may play roles in cell survival signalling. Additionally, a decrease in phosphatidic acid species and an increase in diacylglycerol species imply a metabolic shift favouring diacylglycerol production, potentially activating protein kinase C signalling pathways. The increased levels of monogalactosyl diglyceride lipids during torpor suggest a role in neuroprotection by enhancing oligodendrocyte function and myelination. Our comprehensive lipidomic profiling provides detailed insights into lipid dynamics associated with hibernation and underscores the potential of advanced MS/MS methodologies in lipidomics for developing therapeutic strategies against neurodegenerative diseases.

Keywords:  Brain lipidome; DDA; Hibernation; Novel lipids; Untargeted lipidomics

DOI:  https://doi.org/10.1016/j.chroma.2025.465692
Front Med (Lausanne). 2024 ;11 1538373

Current perspectives for metabolomics and lipidomics in dyslipidemia of acne vulgaris: a mini review.

Liang Wu, Sheng-Cai Zhu, Yang He, Yun-Xia Zhu, Xiao-Liang Ou-Yang, Deng Zhang, Chun-Ming Li.

  Acne vulgaris (AV) is a common inflammatory disorder involving the pilosebaceous unit. Many studies have reported that people with AV have higher levels of total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-c) compared to healthy controls. Hence, they concluded that an unhealthy lipid profile is an independent risk factor for AV. Recent research in metabolomics and lipidomics has been propelled by rapid advancements in technologies including computational methods and mass spectrometry. Using metabolomics and lipidomics approach, a broad range of structurally diverse lipid species were detected and important lipid biomarkers were identified that are vital to the pathogenesis of AV. In this review, we will describe the recent progress in dyslipidemia of AV using metabolomics and lipidomics advances. We will begin with a literature overview of dyslipidemia of AV, followed by a short introduction of metabolomics and lipidomics. Finally, we will focus on applying metabolomics and lipidomics in dyslipidemia of AV.

Keywords:  acne vulgaris; dyslipidemia; lipidomics; metabolomics; phosphatidylcholines; sphingomyelins

DOI:  https://doi.org/10.3389/fmed.2024.1538373
J Proteome Res. 2025 Jan 29.

OpenMS WebApps: Building User-Friendly Solutions for MS Analysis.

Tom David Müller, Arslan Siraj, Axel Walter, Jihyung Kim, Samuel Wein, Johannes von Kleist, Ayesha Feroz, Matteo Pilz, Kyowon Jeong, Justin Cyril Sing, Joshua Charkow, Hannes Luc Röst, Timo Sachsenberg.

  Liquid chromatography-mass spectrometry (LC-MS) is an indispensable analytical technique in proteomics, metabolomics, and other life sciences. While OpenMS provides advanced open-source software for MS data analysis, its complexity can be challenging for nonexperts. To address this, we have developed OpenMS WebApps, a framework for creating user-friendly MS web applications based on the Streamlit Python package. OpenMS WebApps simplifies MS data analysis through an intuitive graphical user interface, interactive result visualizations, and support for both local and online execution. Key features include workspace management, automatic generation of input widgets, and parallel execution of tools, resulting in high performance and ready-to-use solutions for online and local deployment. This framework benefits both researchers and developers: scientists can focus on their research without the burden of complex software setups, and developers can rapidly create and distribute custom WebApps with novel algorithms. Several applications built on the OpenMS WebApps template demonstrate its utility across diverse MS-related fields, enhancing the OpenMS ecosystem for developers and a wider range of users. Furthermore, it integrates seamlessly with third-party software, extending its benefits to developers beyond the OpenMS community.

Keywords:  OpenMS; Streamlit; mass spectrometry; metabolomics; proteomics; pyOpenMS; web applications

DOI:  https://doi.org/10.1021/acs.jproteome.4c00872
Int J Mol Sci. 2024 Dec 26. pii: 92. [Epub ahead of print]26(1):

The Role of Mitochondrial Solute Carriers SLC25 in Cancer Metabolic Reprogramming: Current Insights and Future Perspectives.

Amer Ahmed, Giorgia Natalia Iaconisi, Daria Di Molfetta, Vincenzo Coppola, Antonello Caponio, Ansu Singh, Aasia Bibi, Loredana Capobianco, Luigi Palmieri, Vincenza Dolce, Giuseppe Fiermonte.

  Cancer cells undergo remarkable metabolic changes to meet their high energetic and biosynthetic demands. The Warburg effect is the most well-characterized metabolic alteration, driving cancer cells to catabolize glucose through aerobic glycolysis to promote proliferation. Another prominent metabolic hallmark of cancer cells is their increased reliance on glutamine to replenish tricarboxylic acid (TCA) cycle intermediates essential for ATP production, aspartate and fatty acid synthesis, and maintaining redox homeostasis. In this context, mitochondria, which are primarily used to maintain energy homeostasis and support balanced biosynthesis in normal cells, become central organelles for fulfilling the heightened biosynthetic and energetic demands of proliferating cancer cells. Mitochondrial coordination and metabolite exchange with other cellular compartments are crucial. The human SLC25 mitochondrial carrier family, comprising 53 members, plays a pivotal role in transporting TCA intermediates, amino acids, vitamins, nucleotides, and cofactors across the inner mitochondrial membrane, thereby facilitating this cross-talk. Numerous studies have demonstrated that mitochondrial carriers are altered in cancer cells, actively contributing to tumorigenesis. This review comprehensively discusses the role of SLC25 carriers in cancer pathogenesis and metabolic reprogramming based on current experimental evidence. It also highlights the research gaps that need to be addressed in future studies. Understanding the involvement of these carriers in tumorigenesis may provide valuable novel targets for drug development.

Keywords:  cancer; metabolic reprogramming; metabolism; mitochondria; mitochondrial carriers

DOI:  https://doi.org/10.3390/ijms26010092
Talanta. 2025 Jan 16. pii: S0039-9140(25)00096-7. [Epub ahead of print]287 127610

Comprehensive untargeted polar metabolite analysis using solvent switching liquid chromatography tandem mass spectrometry.

Jake P Violi, Connor R Philips, David S Gertner, Mika T Westerhausen, Matthew P Padula, David P Bishop, Kenneth J Rodgers.

  Metabolomics analyses enable the examination and identification of endogenous biochemical reaction products, revealing information on the metabolic pathways and processes active within a living cell or organism. Determination of metabolic shifts can provide important information on a treatment or disease. Unlike other omics fields that typically have analytes of the same chemical class with common building blocks, those that fall under the nomenclature of metabolites encompass a wide array of different compounds with very diverse physiochemical properties. Development of a comprehensive metabolomic pipeline therefore can be a troublesome and complicated process for the analyst. Often single liquid chromatography-mass spectrometry methods on unfractionated samples are carried out in order to be time-efficient, however this could potentially produce data with a low number of identifiable metabolites. In the present studies, we developed a comprehensive polar metabolomics pipeline for cell-based metabolomics. SH-SY5Y neuroblastoma cells were selected as the sample matrix for method development since they are one of the most widely used cell lines for human neurotoxicity studies. This was accomplished by investigating and optimising different mass spectrometry source and chromatographic conditions to enhance the signal of polar metabolites. Optimised hydrophilic interaction liquid chromatography (HILIC) based metabolomic methods at different pH values were examined in positive, negative, and polarity switching modes to determine which combination yielded the highest number of confidently identified metabolites. Additionally, the use of sequentially running two methods was also compared to determine the degree of overlap and whether there is merit in running two separate methods on one sample. It was determined that solvent switching between two optimised methods, acidic chromatographic conditions in positive mode and basic chromatographic conditions in negative mode, yielded the highest number of unique identifiable metabolites. This could be run in a single analytical batch due to the large pH range of the column. A quick switch method in-between each method allowed both conditioning the column and preparation of the MS source conditions for the sequential method.

Keywords:  Chromatography; HILIC; LC-MS; Metabolomics; Method development; Optimisation

DOI:  https://doi.org/10.1016/j.talanta.2025.127610