bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2025–04–20
thirteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids.
  2. Ex Vivo Measurement of Stable Isotope-Labeled Fatty Acid Incorporation Into Phospholipids in Isolated Mice Muscle.
  3. Skylite: Skyline-Based Lipid Isomer Retention Time Evaluation for Lipidomics in Metabolic Dysfunction-Associated Steatohepatitis.
  4. DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis.
  5. Bifunctional Tagging through N-Doped Ozonide for Charge Switching and Isomeric Characterization of Glycerophospholipids Using Tandem Mass Spectrometry.
  6. MS1FA: Shiny App for the Annotation of Redundant Features in Untargeted Metabolomics Datasets.
  7. UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
  8. Defining Spectral Quality in Mass Spectrometry-Based Proteomics: A Retrospective Review.
  9. Lipid metabolism dysregulation for bone metastasis and its prevention.
  10. Adopting ambient ionization mass spectrometry into bioanalytical laboratories.
  11. A Reproducibility Crisis for Clinical Metabolomics Studies.
  12. Development and Clinical Evaluation of a Multiplexed Health Surveillance Panel Using Ultra High-Throughput PRM-MS in an Inflammatory Bowel Disease Cohort.
  13. Deep Visual Proteomics maps proteotoxicity in a genetic liver disease.
  1. Expert Rev Proteomics. 2025 Apr 14.
    Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids.
    Annie Ha, Michael Woolman, Matthew Waas, Meinusha Govindarajan, Thomas Kislinger.
       INTRODUCTION: Cancer is the second leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.
    AREAS COVERED: We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.
    EXPERT OPINION: Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still need to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.
    Keywords:  Biofluid; Blood; Cancer; Data-independent acquisition; Proteomics; Urine; biomarker discovery; mass spectrometry
    DOI:  https://doi.org/10.1080/14789450.2025.2491355
  2. Bio Protoc. 2025 Apr 05. 15(7): e5257
    Ex Vivo Measurement of Stable Isotope-Labeled Fatty Acid Incorporation Into Phospholipids in Isolated Mice Muscle.
    Tomoki Sato, Shinji Miura.
      With the advancement of liquid chromatography-mass spectrometry (LC-MS/MS), the quantification of glycerophospholipid (PL) molecules has become more accessible, leading to the discovery of numerous enzymes responsible for determining the acyl groups attached to these molecules. Metabolic tracer experiments using radioisotopes and stable isotopes are powerful tools for defining the function of metabolic enzymes and metabolic flux. We have established an ex vivo muscle experimental system using stable isotope-labeled fatty acids to evaluate fatty acid incorporation into PL molecules. Here, we describe a method to incorporate fatty acids with stable isotope labels into excised skeletal muscle and detect the PL molecules containing labeled acyl chains by LC-MS/MS. Key features • Quantify the metabolism of fatty acids into phospholipid acyl chains. • Enable measurements in excised muscle samples. • Assess the effects of genetic recombination of acyltransferases.
    Keywords:  Acyl chain; Free-fatty acid; Liquid chromatography–mass spectrometry; Phospholipid; Skeletal muscle; Stable isotope tracer
    DOI:  https://doi.org/10.21769/BioProtoc.5257
  3. Anal Chem. 2025 Apr 14.
    Skylite: Skyline-Based Lipid Isomer Retention Time Evaluation for Lipidomics in Metabolic Dysfunction-Associated Steatohepatitis.
    Jan Philipp Menzel, Fabienne E Birrer, Deborah Stroka, Mojgan Masoodi.
      Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disorder worldwide and can progress to steatohepatitis. Elevated de novo lipogenesis (DNL) is a key contributor to hepatic steatosis. Fatty acid (FA) desaturation produces several unsaturated lipid isomers that are structurally very similar but have diverse biological functions. However, due to their structural similarity, many conventional mass spectrometry approaches cannot detect such metabolic alterations. Thus, we introduce the Skylite (Skyline-based lipid isomer retention time evaluation) workflow using conventional liquid chromatography-mass spectrometry (LC-MS) to identify important isomer features. Retention times of isomeric phosphatidylcholines are compared with the well-characterized human plasma reference standard NIST 1950. Retention time trends correlate well with fixed-charge derivatized FA in liquid chromatography and ozone-induced dissociation mass spectrometry data. The interpretation is supported by double bond diagnostic fragments in LC-MS/MS experiments of epoxidized hydrolyzed fatty acids. We investigate hepatic lipid profiles, focusing on esterified fatty acids in two mouse models of metabolic dysfunction-associated steatohepatitis (MASH). Out of 37 phosphatidylcholine sum compositions, the workflow identifies 123 lipid features. Importantly, CCl4-induced and melanocortin-4 receptor knockout mice on a western diet (WD) have significantly higher levels of mead acid, branched-chain fatty acid, and n-7 PUFA incorporated into phosphatidylcholines. While the MASH mouse liver tissues contain notable amounts of n-7 PUFA, no n-10 PUFA were detected, potentially indicating a unique desaturation pattern. The screening for altered lipid isomer profiles bridges the gap between high-throughput analyses and specialized structure-resolved techniques.
    DOI:  https://doi.org/10.1021/acs.analchem.4c06503
  4. Nat Commun. 2025 Apr 14. 16(1): 3530
    DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis.
    Zhiwei Liu, Pu Liu, Yingying Sun, Zongxiang Nie, Xiaofan Zhang, Yuqi Zhang, Yi Chen, Tiannan Guo.
      Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly pivotal in quantitative proteomics. In this study, we present DIA-BERT, a software tool that harnesses a transformer-based pre-trained artificial intelligence (AI) model for analyzing DIA proteomics data. The identification model was trained using over 276 million high-quality peptide precursors extracted from existing DIA-MS files, while the quantification model was trained on 34 million peptide precursors from synthetic DIA-MS files. When compared to DIA-NN, DIA-BERT demonstrated a 51% increase in protein identifications and 22% more peptide precursors on average across five human cancer sample sets (cervical cancer, pancreatic adenocarcinoma, myosarcoma, gallbladder cancer, and gastric carcinoma), achieving high quantitative accuracy. This study underscores the potential of leveraging pre-trained models and synthetic datasets to enhance the analysis of DIA proteomics.
    DOI:  https://doi.org/10.1038/s41467-025-58866-4
  5. Anal Chem. 2025 Apr 17.
    Bifunctional Tagging through N-Doped Ozonide for Charge Switching and Isomeric Characterization of Glycerophospholipids Using Tandem Mass Spectrometry.
    Chia-Lung Tsai, Xi Chen, Ramidi G Reddy, Xin Yan.
      Glycerophospholipids (GPLs) are structurally diverse biomolecules that play crucial roles in cellular membranes, signaling, and metabolism. Electrospray ionization-tandem mass spectrometry (ESI-MS/MS) has been widely used for GPL identification due to its high sensitivity and specificity. However, this method often falls short in distinguishing isomeric lipids, such as those differing in the positions of carbon-carbon double bonds. Additionally, the ion types naturally generated during ESI are not always optimal for lipid detection and identification. In this work, we introduce a novel bifunctional tag, nitrophenyl pyrazole (DNPZ), which reacts with double bonds in lipids to form N-doped ozonides. In-situ tandem MS analysis of these modified lipids enables simultaneous identification of double-bond positional isomers and charge switching, facilitating the acquisition of comprehensive structural information. Our findings demonstrate that this approach significantly improves ionization efficiency of GPLs in negative ion mode and provides detailed insights into fatty acyl chain compositions and double-bond positions in GPLs. We have demonstrated that this method allows for the characterization of various lipid classes, lipids with multiple double bonds as well as polar lipid extracts from complex biological samples without the need for authentic lipid reference standards.
    DOI:  https://doi.org/10.1021/acs.analchem.5c00443
  6. Bioinformatics. 2025 Apr 15. pii: btaf161. [Epub ahead of print]
    MS1FA: Shiny App for the Annotation of Redundant Features in Untargeted Metabolomics Datasets.
    Ruibing Shi, Frank Klawonn, Mark Brönstrup, Raimo Franke.
       MOTIVATION: Untargeted metabolomics, the comprehensive analysis of small molecules in biological systems, has become an invaluable tool for understanding physiology and metabolism. However, the annotation of metabolomic data is often confounded by the presence of redundant features, which can arise from e.g. multimerization, in-source fragments (ISFs), and adducts.
    RESULTS: MS1FA uniquely integrates all major annotation approaches for redundant features within a single interactive platform. It combines correlation-based grouping with reliable ISF annotation using MS2 data and operates with MS1 data only, MS2 data only, or both. Additionally, it offers a distinctive method for grouping features based on relational criteria. As the only web-based platform with these capabilities, MS1FA provides easy access and allows users to explore and annotate the feature table interactively, with options to download the results.
    AVAILABILITY: MS1FA is freely accessible at https://ms1fa.helmholtz-hzi.de. The source code and data are available at https://github.com/RuibingS/MS1FA_RShiny_dashboard and are archived with the DOI 10.5281/zenodo.15118962.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btaf161
  7. Nat Cancer. 2025 Apr 18.
    UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Amy X Xie, Wesley Tansey, Ed Reznik.
      Comprehensively studying metabolism requires metabolite measurements. Such measurements, however, are often unavailable in large cohorts of tissue samples. To address this basic barrier, we propose a Bayesian framework ('UnitedMet') that leverages RNA-metabolite covariation to impute otherwise unmeasured metabolite levels from widely available transcriptomic data. UnitedMet is equally capable of imputing whole pool sizes and outcomes of isotope tracing experiments. We apply UnitedMet to investigate the metabolic impact of driver mutations in kidney cancer, identifying an association between BAP1 and a highly oxidative tumor phenotype. We similarly apply UnitedMet to determine that advanced kidney cancers upregulate oxidative phosphorylation relative to early-stage disease, that oxidative metabolism in kidney cancer is associated with inferior outcomes to anti-angiogenic therapy and that kidney cancer metastases demonstrate elevated oxidative phosphorylation. UnitedMet provides a scalable tool for assessing metabolic phenotypes when direct measurements are infeasible, facilitating unexplored avenues for metabolite-focused hypothesis generation.
    DOI:  https://doi.org/10.1038/s43018-025-00943-0
  8. Mass Spectrom Rev. 2025 Apr 18.
    Defining Spectral Quality in Mass Spectrometry-Based Proteomics: A Retrospective Review.
    Vilenne Frédérique, Appeltans Simon, Askenazi Manor, Valkenborg Dirk.
      Mass spectrometry-based proteomics is essential for advancing preventive and personalised medicine. Technological advancements have greatly increased both the number and sensitivity of spectra generated in a single experiment. Traditionally, spectra are identified using database search engines that depend on large and continuously expanding databases. This expansion enlarges the search space, posing challenges for controlling the false discovery rate in peptide identification. While many bioinformatic workflows employ rescoring algorithms as a post-processing step to manage false discoveries, preprocessing spectra offers a promising alternative. One such method, spectral quality assessment, classifies spectra as "high" quality (likely containing a peptide) or "low" quality (predominantly consisting of noise). This review provides a comprehensive perspective on spectral quality assessment, examining existing tools and their underlying principles. We discuss key considerations such as the definition of spectral quality, normalisation, the use of experimental training data, and future research in the field. By highlighting the potential of spectral quality assessment to improve peptide identification and reduce false discoveries, we aim to elaborate on its potential for the proteomics community.
    Keywords:  Mass spectrometry; Proteomics; Quality control
    DOI:  https://doi.org/10.1002/mas.21933
  9. Expert Rev Anticancer Ther. 2025 Apr 12.
    Lipid metabolism dysregulation for bone metastasis and its prevention.
    Jyoti Poswal, Chandi C Mandal.
       INTRODUCTION: Bone metastasis often develops in advanced malignancies. Lipid metabolic dysregulation might play pivotal role in cancer progression and subsequent deterioration of bone health at metastatic condition. In-depth understanding of lipid reprogramming in metastasized cancer cells and other stromal cells including bone marrow adipocyte (BMA) is an urgent need to develop effective therapy.
    AREA COVERED: This paper emphasizes providing an overview of multifaceted role of dysregulated lipids and BMA in cancer cells in association with bone metastasis by utilizing search terms lipid metabolism, lipid and metastasis in PubMed. This study extends to address mechanism linked with lipid metabolism and various crucial genes (e.g. CSF-1, RANKL, NFkB and NFATc1) involved in bone metastasis. This review examines therapeutic strategies targeting lipid metabolism to offer potential avenues to disrupt lipid-driven metastasis.
    EXPERT OPINION: On metastatic condition, dysregulated lipid molecules especially in BMA and other stromal cells not only favors cancer progression but also potentiate lipid reprogramming within cancer cells. Distinct dysregulated lipid-metabolism associated genes may act as biomarker, and targeting these is challenging task for specific treatment. Curbing function of bone resorption associated genes by lipid controlling drugs (e.g. statins, omega-3 FA and metformin) may provide additional support to curtail lipid-associated bone metastasis.
    Keywords:  Bone metastasis; Osteoblast; Osteoclast; Therapeutics; bone marrow adipocyte; bone microenvironment; disseminated cancer cell; lipid dysregulation
    DOI:  https://doi.org/10.1080/14737140.2025.2492784
  10. Bioanalysis. 2025 Apr 12. 1-5
    Adopting ambient ionization mass spectrometry into bioanalytical laboratories.
    Forough Doustkhahvajari, Stephanie Rankin-Turner.
      
    Keywords:  Mass spectrometry; ambient ionization; bioanalytical; biomarkers; clinical; immunoassay; metabolomics
    DOI:  https://doi.org/10.1080/17576180.2025.2490462
  11. Trends Analyt Chem. 2024 Nov;pii: 117918. [Epub ahead of print]180
    A Reproducibility Crisis for Clinical Metabolomics Studies.
    Darcy Cochran, Mai NourEldein, Dominika Bezdekova, Aaron Schram, Réka Howard, Robert Powers.
      Cancer is a leading cause of world-wide death and a major subject of clinical studies focused on the identification of new diagnostic tools. An in-depth meta-analysis of 244 clinical metabolomics studies of human serum samples highlights a reproducibility crisis. A total of 2,206 unique metabolites were reported as statistically significant across the 244 studies, but 72% (1,582) of these metabolites were identified by only one study. Further analysis shows a random disparate disagreement in reported directions of metabolite concentration changes when detected by multiple studies. Statistical models revealed that 1,867 of the 2,206 metabolites (85%) are simply statistical noise. Only 3 to 12% of these metabolites reach the threshold of statistical significance for a specific cancer type. Our findings demonstrate the absence of a detectable metabolic response to cancer and provide evidence of a serious need by the metabolomics community to establish widely accepted best practices to improve future outcomes.
    Keywords:  NMR; cancer biomarkers; clinical metabolomics; mass spectrometry; meta-analysis
    DOI:  https://doi.org/10.1016/j.trac.2024.117918
  12. bioRxiv. 2025 Apr 04. pii: 2025.04.02.646850. [Epub ahead of print]
    Development and Clinical Evaluation of a Multiplexed Health Surveillance Panel Using Ultra High-Throughput PRM-MS in an Inflammatory Bowel Disease Cohort.
    Qin Fu, Philip M Remes, Jihyeon Lee, Cristina Jacob, Dalin Li, Manasa Vegesna, Koen Raedschelders, Ali Haghani, Emebet Mengesha, Philip Debbas, Estelle Hoedt, Sandy Joung, Susan Cheng, Scott Peterman, Justyna Fert-Bober, Gil Y Melmed, Dermot P B McGovern, Christopher I Murray, Jennifer E Van Eyk.
      Despite advances in clinical proteomics, translating protein biomarker discoveries into clinical use remains challenging due to the technical complexity of the validation process. Targeted MS-based proteomics approaches such as parallel reaction monitoring (PRM) offer sensitive and specific assays for biomarker translation. In this study, we developed a multiplex PRM assay using the Stellar mass spectrometry platform to quantify 57 plasma proteins, including 21 FDA-approved proteins. Loading curves (11-points) were performed at 4 sample throughputs (100, 144, 180, and 300 samples per day) using independent, optimized, and scheduled PRM methods. Following optimization, an inflammatory bowel disease (IBD) cohort of plasma samples (493 IBD, 509 matched controls) was analyzed at a throughput of 180 SPD. To monitor system performance, the study also included 1,000 additional injections for system suitability tests, low-, middle-, and high-quality controls, washes, and blanks. Using this approach, we observed high quantifiability (linearity, sensitivity, reproducibility) in the PRM assay and consistent in data acquisition across a large cohort. We also validated the candidate IBD markers, C-reactive protein and orosomucoid protein, identified in a recent discovery experiment.
    DOI:  https://doi.org/10.1101/2025.04.02.646850
  13. Nature. 2025 Apr 16.
    Deep Visual Proteomics maps proteotoxicity in a genetic liver disease.
    Florian A Rosenberger, Sophia C Mädler, Katrine Holtz Thorhauge, Sophia Steigerwald, Malin Fromme, Mikhail Lebedev, Caroline A M Weiss, Marc Oeller, Maria Wahle, Andreas Metousis, Maximilian Zwiebel, Niklas A Schmacke, Sönke Detlefsen, Peter Boor, Ondřej Fabián, Soňa Fraňková, Aleksander Krag, Pavel Strnad, Matthias Mann.
      Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood1-3. We use spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis4,5, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudotime across fibrosis stages. We achieve proteome depth of up to 4,300 proteins from one-third of a single cell in formalin-fixed, paraffin-embedded tissue. This dataset reveals a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show α1-antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with artificial intelligence-guided image-based phenotyping across several disease stages, revealing a late-stage hepatocyte phenotype characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10 (also known as TRAIL) amounts. This phenotype may represent a critical disease progression stage. Our study offers new insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.
    DOI:  https://doi.org/10.1038/s41586-025-08885-4
This page is being maintained by Thomas Krichel. It was last updated on 2025‒04‒21 at 6:23.