bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2025–09–14
fifteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz



  1. J Vis Exp. 2025 Aug 19.
      Mass spectrometry (MS)-based proteomics data, including Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA), are widely used in biological research. However, the application of these datasets in validation studies is still limited due to the lack of clear demonstrations on how to effectively search and analyze proteomic data. To fill this gap, we selected one DDA and one DIA dataset deposited in the PRoteomics IDEntifications Database (PRIDE) data repository to better illustrate the proteomic data analysis workflow and downstream post-processing of protein search results. For demonstration purposes, we used two free computational tools: FragPipe (v22.0) for DDA datasets and DIA-NN (2.1.0) for DIA datasets. Post-processing steps, such as generating volcano plots and lists of dysregulated proteins, were demonstrated using R code. This study provides basic protocols for searching and analyzing proteomic data, serving as an essential beginner's guide to effectively handle proteomic datasets. Through this work, we aim to empower researchers with the knowledge necessary to leverage proteomic data in their biological investigations.
    DOI:  https://doi.org/10.3791/68707
  2. J Proteome Res. 2025 Sep 12.
      Orbitrap (OT)-based mass spectrometer platforms are a gold standard in high-resolution mass spectrometry, where their primary disadvantage is slower-scanning speed in comparison to time-of-flight or linear ion trap mass analyzers. In this study, we utilize long OT transients to extend the precursor dynamic range by modifying the selected ion monitoring method to multiplex several precursor m/z ranges into a single scan, which we call "multiple accumulation precursor mass spectrometry". Our approach requires no software or hardware modifications and hides the additional ion accumulation steps during the time it takes to make other Orbitrap measurements, producing precursor spectra with nearly 2× dynamic range and essentially no consequences. We collected data using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methods to evaluate a range of approaches. With DDA, MAP-MS precursor quantification improves with higher quality measurements. At the same time, DIA detection is enhanced by up to 11% when combining precursor and tandem mass spectra for peptide detection.
    Keywords:  data-dependent acquisition; data-independent acquisition; mass spectrometry; multiplexing; orbitrap mass analyzer; peptide identification optimization; peptide quantification optimization
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00469
  3. Anal Chem. 2025 Sep 12.
      Mass spectrometry (MS) is a well-established technology in biological research, enabling the sensitive and precise quantitative analysis of complex samples. While traditional LC-MS systems provide robust performance for targeted analyses, their reliance on chromatographic separation limits throughput, rendering large-scale studies inefficient. The emergence of Acoustic Ejection Mass Spectrometry (AEMS) has revolutionized high-throughput workflows by eliminating chromatography and enabling direct nanoliter-scale sampling, achieving hundreds to thousands of measurements per hour. However, AEMS's full potential remains constrained by software limitations─existing tools lack robust automated processing capabilities for critical tasks such as peak detection, integration, and multimodal data analysis (e.g., multiple reaction monitoring, precursor ion, and neutral loss scans). To address this gap, we developed rtmsEcho, an open-source R package that extends our previously published rtms framework. This specialized solution provides direct access to AEMS data, enabling customizable processing of both MRM and full-scan acquisitions (precursor ion and neutral loss modes) while automating shot-to-peak association and spectral analysis. By streamlining data extraction and quantification, rtmsEcho enhances efficiency and reproducibility in high-throughput applications, including drug discovery, quality control, and clinical diagnostics. This innovation bridges a critical gap in AEMS data analysis, allowing researchers to fully leverage the speed and precision of next-generation mass spectrometry.
    DOI:  https://doi.org/10.1021/acs.analchem.5c03730
  4. Cell. 2025 Sep 02. pii: S0092-8674(25)00929-8. [Epub ahead of print]
      Single-cell metabolomics (SCM) promises to reveal metabolism in its complexity and heterogeneity, yet current methods struggle with detecting small-molecule metabolites, throughput, and reproducibility. Addressing these gaps, we developed HT SpaceM, a high-throughput SCM method combining cell preparation on custom glass slides, small-molecule matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (MS), and batch processing. We propose a unified framework covering quality control, characterization, structural validation, and differential and functional analyses. Profiling HeLa and NIH3T3 cells, we detected 73 small-molecule metabolites validated by bulk liquid chromatography tandem MS (LC-MS/MS), achieving high reproducibility and single-cell resolution. Interrogating nine NCI-60 cancer cell lines and HeLa, we identified cell-type markers in subpopulations and metabolic hubs. Upon inhibiting glycolysis in HeLa cells, we observed emerging glucose-centered metabolic coordination and intra-condition heterogeneity. Overall, we demonstrate how HT SpaceM enables robust, large-scale SCM across over 140,000 cells from 132 samples and provide guidance on how to interpret metabolic insights beyond population averages.
    Keywords:  LC-MS/MS; MALDI-imaging mass spectrometry; NCI-60; SpaceM; co-abundance; heterogeneity; high-throughput; reproducibility; single-cell metabolomics; small-molecule metabolites
    DOI:  https://doi.org/10.1016/j.cell.2025.08.015
  5. Int J Mol Sci. 2025 Aug 30. pii: 8466. [Epub ahead of print]26(17):
      Metabolism is a tightly controlled, but plastic network of pathways that allow cells to grow and maintain homeostasis. As a normal cell transforms into a malignant cancer cell and proliferates to establish a tumor, it utilizes a variety of metabolic pathways that support growth, proliferation, and survival. Cancer cells alter metabolic pathways in different contexts, leading to complex metabolic heterogeneity within a tumor. There is an unmet need to characterize how cancer cells alter how they use resources from the environment to evolve, spread to other sites of the body, and survive current standard-of-care therapies. We review key techniques and methods that are currently used to study cancer metabolism and provide drawbacks and considerations in using one over another. The goal of this review is to provide a methods' guide to study different aspects of cell and tissue metabolism, how they can be applied to cancer, and discuss future perspectives on advancements in these areas.
    Keywords:  13C-metabolic flux analysis; Seahorse metabolic flux analysis; cancer metabolism; fluorescent probes; genetically encoded fluorescent biosensors; isotope tracing; untargeted metabolomics
    DOI:  https://doi.org/10.3390/ijms26178466
  6. EMBO Mol Med. 2025 Sep 12.
      Bead-based enrichment is a promising strategy to improve depth in plasma proteomics by overcoming the dynamic range barrier. However, its robustness against pre-analytical variation has not been sufficiently characterized. Here, we systematically evaluate five plasma proteomics workflows, including three bead-based methods, a neat workflow, and a precipitation protocol using spike-ins of low-abundance proteins and defined cellular contaminants. We find that bead-based approaches enhance detection of low-abundance proteins but can be highly susceptible to systematic bias from platelet and PBMC contamination. This can inflate results by thousands of proteins, potentially explaining some of the high literature-reported numbers. A perchloric acid-based workflow shows resistance to erythrocyte and platelet-derived contamination. We investigate how centrifugation conditions, anticoagulant choice, and buffer-bead combinations modulate contamination profiles and demonstrate that bias can be mitigated by optimized sample handling. Altogether, we identify more than 13,000 different protein groups, including cellular components from the circulating proteome. Our results provide a quantitative framework for assessing workflow performance under variable sample quality and offer guidance for both biomarker discovery and quality control in clinical proteomics studies.
    Keywords:  Bead-Based Enrichment; Biomarker Validation; Plasma Proteomics; Pre-Analytical Bias; Sample Quality
    DOI:  https://doi.org/10.1038/s44321-025-00309-0
  7. Nat Metab. 2025 Sep 08.
      Cancer cells are exposed to diverse metabolites in the tumour microenvironment that are used to support the synthesis of nucleotides, amino acids and lipids needed for rapid cell proliferation. In some tumours, ketone bodies such as β-hydroxybutyrate (β-OHB), which are elevated in circulation under fasting conditions or low glycemic diets, can serve as an alternative fuel that is metabolized in the mitochondria to provide acetyl-CoA for the tricarboxylic acid (TCA) cycle. Here we identify a non-canonical route for β-OHB metabolism that bypasses the TCA cycle to generate cytosolic acetyl-CoA. We show that in cancer cells that can metabolize ketones, β-OHB-derived acetoacetate in the mitochondria can be shunted into the cytosol, where acetoacetyl-CoA synthetase (AACS) and thiolase convert it into cytosolic acetyl-CoA. This alternative metabolic routing allows β-OHB to avoid oxidation in the mitochondria and to be used as a major source of cytosolic acetyl-CoA, even when other key cytosolic acetyl-CoA precursors such as glucose are available in excess. Finally, we demonstrate that ketone body metabolism, including this alternative AACS-dependent route, can support the growth of mouse KrasG12D; Trp53-/- pancreatic tumours grown orthotopically in the pancreas of male mice, as well as the growth of mouse B16 melanoma tumours in male mice fed a calorie-restricted diet. Together, these data reveal how cancer cells use β-OHB as a major source of cytosolic acetyl-CoA to support cell proliferation and tumour growth.
    DOI:  https://doi.org/10.1038/s42255-025-01366-y
  8. J Biol Chem. 2025 Sep 08. pii: S0021-9258(25)02545-1. [Epub ahead of print] 110693
      Fetal bovine serum (FBS) is an undefined additive that is ubiquitous to mammalian cell culture media and whose functional contributions to promoting cell proliferation remain poorly understood. Efforts to replace serum supplementation in culture media have been hindered by an incomplete understanding of the environmental requirements fulfilled by FBS. Here, we use a combination of live-cell imaging and quantitative lipidomics to elucidate the role of serum in supporting proliferation. We show that serum provides consumed factors that enable proliferation, with serum metal and lipid components serving as crucial metabolic resources. Despite access to a wide range of lipid classes available in serum, we find albumin-bound lipids are the primary species consumed by cancer cells. Furthermore, we find that supplementing with additives that contain necessary metals and any of the albumin-associated lipid classes can obviate the FBS requirement for cancer cell proliferation. Using this defined system, we investigated cancer cell lipid consumption dynamics, finding that albumin-associated lipids are primarily consumed through a mass-action mechanism with minimal competition within or amongst lipid classes. We also find that lipid scavenging is a dominant lipid acquisition route and is necessary for cancer cell proliferation. This work therefore identifies metabolic contributions of serum and provides a framework for building defined culture systems that sustain cell proliferation without the undefined contributions of serum.
    DOI:  https://doi.org/10.1016/j.jbc.2025.110693
  9. F1000Res. 2025 ;14 714
       Background: Subcellular localisation is a determining factor of protein function. Mass spectrometry-based correlation profiling experiments facilitate the classification of protein subcellular localisation on a proteome-wide scale. In turn, static localisations can be compared across conditions to identify differential protein localisation events.
    Methods: Here, we provide a workflow for the processing and analysis of subcellular proteomics data derived from mass spectrometry-based correlation profiling experiments. This workflow utilises open-source R software packages from the Bioconductor project and provides extensive discussion of the key processing steps required to achieve high confidence protein localisation classifications and differential localisation predictions. The workflow is applicable to any correlation profiling data and supplementary code is provided to help users adapt the workflow to DDA and DIA data processed with different database softwares.
    Results: The workflow is divided into three sections. First we outline data processing using the QFeatures infrastructure to generate high quality protein correlation profiles. Next, protein subcellular localisation classification is carried out using machine learning. Finally, prediction of differential localisation events is covered for dynamic correlation profiling experiments.
    Conclusions: A comprehensive start-to-end workflow for correlation profiling subcellular proteomics experiments is presented. R version: R version 4.5.0 (2025-04-11) Bioconductor version: 3.21.
    Keywords:  LOPIT; QFeatures; Subcellular spatial proteomics; bandle; correlation profiling; mass spectrometry; pRoloc; protein localisation
    DOI:  https://doi.org/10.12688/f1000research.165543.1
  10. J Proteome Res. 2025 Sep 08.
      Plasma proteomics has regained attention in recent years through advancements in mass spectrometry instrumentation and sample preparation as well as new high-throughput affinity-based technologies. Here, we evaluate the analytical performance of the new Olink Reveal platform, a proximity extension assay (PEA)-based technology quantifying 1034 proteins and covering many biological pathways, in particular immune system processes. Using spiked-in recombinant Interleukin-10 (IL-10) and vascular endothelial growth factor D (VEGF-D) in the NIST SRM 1950 plasma standard, we assessed the linearity, sensitivity, precision, and accuracy of the Olink Reveal assay. The results demonstrated strong linear relationships (R2 0.922-0.953) for both IL-10 and VEGF-D across spiked-in concentrations, confirming the robust technical performance for these two proteins in the Olink Reveal platform. The resulting data contain no sensitive or personally identifiable information and are therefore suitable for use in benchmarking and software development. The data are publicly available in the PRIDE repository with identifier PAD000009.
    Keywords:  Olink Reveal; affinity proteomics; plasma
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00571
  11. Cell Syst. 2025 Sep 10. pii: S2405-4712(25)00229-7. [Epub ahead of print] 101396
      Intestinal epithelial damage predisposes to disorders like inflammatory bowel disease (IBD), with organoid transplantation emerging as a potential treatment. However, it is not known how well organoids recapitulate in vivo intestinal epithelial cells (IECs). We employed deep visual proteomics (DVP), integrating AI-guided cell classification, laser microdissection, and ultra-high-sensitivity proteomics at the single-cell level to generate an in-depth proteome resource of IECs directly isolated from the human colon and organoids. While in vitro organoids display high proliferation and low functional signatures, xenotransplantation induces a remarkable shift toward an in vivo-like phenotype. We recapitulated this transition by modifying culture conditions. Our data provide a comprehensive spatial proteomics resource and validate xenotransplanted organoids as suitable models for studying human IEC behavior with unprecedented molecular detail and demonstrate their clinical potential for patients with IBD and other intestinal disorders. A record of this paper's transparent peer review process is included in the supplemental information.
    Keywords:  IBD; colon; mass spectrometry; organoids; proteomics; spatial proteomics
    DOI:  https://doi.org/10.1016/j.cels.2025.101396
  12. Nat Protoc. 2025 Sep 08.
      Metabolism is a fundamental process that shapes the pharmacological and toxicological profiles of drugs, making metabolite identification and analysis critical in drug development and biological research. Global Natural Products Social Networking (GNPS) is a community-driven infrastructure for mass spectrometry data analysis, storage and knowledge dissemination. GNPS2 is an improved version of the platform offering higher processing speeds, improved data analysis tools and a more intuitive user interface. Molecular networking based on tandem mass spectrometry spectral alignments, combined with other tools in the GNPS2 analysis environment, enables the discovery of candidate drug metabolites without prior knowledge, even from complex biological matrices. This protocol represents an extension of a previously established protocol for fundamental molecular networking in GNPS, with a specific focus on metabolism studies. This article uses the example of the drug sildenafil to identify candidate metabolites obtained from liquid chromatography-quadrupole time-of-flight mass spectrometry analysis of liver microsomal fractions and mice plasma to guide the reader through a step-by-step process consisting of five GNPS2-based analytical workflows. It demonstrates how the tools in GNPS2 can be used not only to identify candidate drug metabolites from in vitro studies but also to evaluate the translational relevance of these in vitro findings to humans by using reverse metabolomics. We provide a step-by-step analytical approach based on published studies to showcase how GNPS2 can be effectively applied in drug metabolism studies.
    DOI:  https://doi.org/10.1038/s41596-025-01237-6
  13. J Invest Dermatol. 2025 Sep 04. pii: S0022-202X(25)02410-8. [Epub ahead of print]
      Liquid chromatography-mass spectrometry (LC-MS) is an evolving tool for comprehensive proteomic analyses across tissues. Despite the widespread use of LC-MS in dermatology, full-thickness human skin remains challenging to analyse. The skin extracellular matrix (ECM) presents two major obstacles: the extensive crosslinking complicates protein extraction and the high abundance of ECM proteins can mask lower-abundance proteins, reducing identification numbers. These limitations hinder progress in skin proteomics research. To address these challenges, we adapted the Trifluoroacetic acid (TFA)-based SPEED method for skin samples. TFA does not disrupt most crosslinks, allowing for removal of abundant crosslinked ECM proteins. This enhanced proteome coverage, increased the number of identified protein groups to over 6,200 in healthy human skin. The improved sensitivity enabled the use of minimally invasive 2 mm punch biopsies, potentially facilitating greater patient enrolment compared to commonly used larger punch biopsies. We anticipate that these advancements in sample preparation will pave the way for analysis tools like machine learning in skin proteomics, which require large datasets with high identification numbers. Furthermore, we extend this approach to tape strip proteomics and identify up to 2,300 proteins in healthy skin, providing a cost-effective, scalable, and sensitive alternative to current workflows.
    Keywords:  Mass spectrometry; SPEED; Skin biopsies; tape strips; ubiquitin
    DOI:  https://doi.org/10.1016/j.jid.2025.08.033
  14. J Agric Food Chem. 2025 Sep 11.
      The sweetness of Stevia rebaudiana (Bertoni) leaves is attributed to steviol glycosides (SGs). A method to analyze more extensively and decipher the structures of SGs is needed to understand (i) the biochemical determinism of SG diversity and (ii) the variability of their sensorial attributes. SGs from 20 genotypes of S. rebaudiana (Bertoni) were extracted via liquid extraction and tentatively identified by using LC-ESI-HRMS untargeted metabolomics, enabling the annotation of 114 SGs at different confidence levels. We identified the presence of original malonylated-SGs (up to 36%), acetylated-SGs, and other unidentified families, and confirmed the structure of malonyl-Reb A through 1D and 2D-NMR analyses after purification. Targeted identification and absolute quantification of 16 SGs were performed using analytical standards. Statistical analyses were applied to SG-based classification of stevia within 5 classes. This approach, combining targeted and untargeted metabolomics, may facilitate genotype selection for breeding programs and industrial applications based on SG proportions.
    Keywords:  Stevia; mass spectrometry; steviol glycosides; untargeted metabolomics
    DOI:  https://doi.org/10.1021/acs.jafc.5c10084
  15. Methods. 2025 Sep 10. pii: S1046-2023(25)00201-4. [Epub ahead of print]
      The tricarboxylic acid cycle (TCA), also known as the Krebs Cycle or the citric acid cycle, is an essential metabolic pathway involved in energy production that is often impacted by disease, making it of key interest to identify effective, affordable, and simple ways to monitor the impact of disease on TCA metabolism. 13C-based stable isotope labeling is a useful technique to track pathway alterations in living hosts. However, infusion-based methodologies are slow and expensive despite achieving steady-state labeling. Bolus-based methods are cheaper, faster, and compatible with biohazardous models, but require optimization to achieve maximum labeling. Herein, we performed bolus-based stable isotope labeling experiments in mouse models to identify the optimal dosage amount, label administration length, fast length prior to label administration, 13C-labeled precursor, and route of administration for the TCA cycle in the esophagus, heart, kidney, liver, plasma, and proximal colon. 13C-glucose at a concentration of 4 mg/g administered via intraperitoneal injection followed by a 90 min label incorporation period achieved the best overall TCA labeling. For most organs, a 3 h fast prior to label administration improved labeling, but labeling in the heart was better with no fasting period, showcasing the need to optimize methodology on an organ-by-organ basis. We also identified that bolus administration of glucose provided little impact on metabolism compared to vehicle control. The experiments outlined here provide critical information for designing in vivo stable isotope labeling experiments for the study of the TCA cycle.
    Keywords:  Bolus; Carbon-13; Fasting; Glucose; Labeling; Mice; TCA cycle
    DOI:  https://doi.org/10.1016/j.ymeth.2025.09.004