bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2025–01–12
seventeen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz



  1. J Am Soc Mass Spectrom. 2025 Jan 05.
      Reproducibility in untargeted metabolomics data processing remains a significant challenge due to software limitations and the complex series of steps required. To address these issues, we developed Nextflow4MS-DIAL, a reproducible workflow for liquid chromatography-mass spectrometry (LC-MS) metabolomics data processing, validated with publicly available data from MetaboLights (MTBLS733). Nextflow4MS-DIAL automates LC-MS data processing to minimize human errors from manual data handling. The workflow supports software containerization, ensuring computational reproducibility and enabling collaborative research. Nextflow4MS-DIAL is compatible with any Unix-like system and supports multiple job schedulers, offering flexibility and ease of use. The Nextflow4MS-DIAL workflow is available under the permissive MIT license: https://github.com/Nextflow4Metabolomics/nextflow4ms-dial.
    Keywords:  LC-MS; Metabolomics; Nextflow; Reproducibility; Workflow
    DOI:  https://doi.org/10.1021/jasms.4c00364
  2. bioRxiv. 2024 Dec 20. pii: 2024.12.17.628765. [Epub ahead of print]
      Comprehensive global proteome profiling that is amenable to high throughput processing will broaden our understanding of complex biological systems. Here, we evaluated 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 slightly more 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.
    DOI:  https://doi.org/10.1101/2024.12.17.628765
  3. Molecules. 2024 Dec 16. pii: 5934. [Epub ahead of print]29(24):
      Targeted metabolomics and lipidomics are increasingly utilized in clinical research, providing quantitative and comprehensive assessments of metabolic profiles that underlie physiological and pathological mechanisms. These approaches enable the identification of critical metabolites and metabolic alterations essential for accurate diagnosis and precision treatment. Mass spectrometry, in combination with various separation techniques, offers a highly sensitive and specific platform for implementing targeted metabolomics and lipidomics in clinical settings. Nevertheless, challenges persist in areas such as sample collection, quantification, quality control, and data interpretation. This review summarizes recent advances in targeted metabolomics and lipidomics, emphasizing their applications in clinical research. Advancements, including microsampling, dynamic multiple reaction monitoring, and integration of ion mobility mass spectrometry, are highlighted. Additionally, the review discusses the critical importance of data standardization and harmonization for successful clinical implementation.
    Keywords:  lipidomics; mass spectrometry; metabolomics; precision medicine; targeted assay
    DOI:  https://doi.org/10.3390/molecules29245934
  4. J Proteome Res. 2025 Jan 07.
      The PRIDE database is the largest public data repository of mass spectrometry-based proteomics data and currently stores more than 40,000 data sets covering a wide range of organisms, experimental techniques, and biological conditions. During the past few years, PRIDE has seen a significant increase in the amount of submitted data-independent acquisition (DIA) proteomics data sets. This provides an excellent opportunity for large-scale data reanalysis and reuse. We have reanalyzed 15 public label-free DIA data sets across various healthy human tissues to provide a state-of-the-art view of the human proteome in baseline conditions (without any perturbations). We computed baseline protein abundances and compared them across various tissues, samples, and data sets. Our second aim was to compare protein abundances obtained here from the results of previous analyses using human baseline data-dependent acquisition (DDA) data sets. We observed a good correlation across some tissues, especially in the liver and colon, but weak correlations were found in others, such as the lung and pancreas. The reanalyzed results including protein abundance values and curated metadata are made available to view and download from the resource Expression Atlas.
    Keywords:  Expression Atlas; PRIDE; baseline expression; data independent acquisition; data reanalysis; mass spectrometry; proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00788
  5. Anal Chem. 2025 Jan 05.
      Many analytical methods have been developed for performing targeted metabolomics. By combining multiple analytical techniques, comprehensive coverage of the metabolome can be achieved. We combined multiple analytical techniques to comprehensively and quantitatively characterize the widely studied NIST human plasma reference material, SRM 1950. Our goal was to provide a large, well-validated list of confident metabolite concentration values (i.e., benchmarks) to assist the metabolomics community in its calibration and comparison efforts. We used four analytical platforms: high-resolution NMR spectroscopy, direct injection tandem MS (DI-MS/MS), liquid chromatography tandem MS (LC-MS/MS), and inductively coupled plasma MS (ICP-MS). Eight validated analytical assays were run, yielding accurate quantitative measurements for 728 unique metabolites or metabolite species. Through computer-aided literature mining, we identified another 330 unique metabolites previously quantified in SRM 1950. We compared NIST-certified values along with literature-derived concentrations/ranges to the metabolite concentrations measured by our four platforms and eight assays. From these assays/platforms, we generated a list of high-confidence concentration values of 1058 metabolites or metabolite species in SRM 1950 including data for 60 amino acids/related compounds, 48 bile acids, 72 amines/sugars/alcohols, 21 metals, 8 catecholamines, 11 vitamins, 92 organic acids, 40 fatty acids/steroids/nucleobases/indole derivatives, 5 polyfluorinated compounds, 7 carotenoids, 39 acylcarnitines, 76 oxylipins, 13 sterols, and 566 lipids/lipid species. This data set represents the most complete quantitative characterization of SRM 1950. An online database (SRM1950-DB) containing 1058 plasma metabolites/metabolite species in SRM 1950, their structures, HMDB IDs, mass, chemical class, concentrations, references, and reliability is freely available at https://srm1950-data.wishartlab.com.
    DOI:  https://doi.org/10.1021/acs.analchem.4c05018
  6. Cell Rep. 2025 Jan 03. pii: S2211-1247(24)01481-5. [Epub ahead of print]44(1): 115130
      Tumor cells must optimize metabolite acquisition between synthesis and uptake from a microenvironment characterized by hypoxia, lactate accumulation, and depletion of many amino acids, including arginine. We performed a metabolism-focused functional screen using CRISPR-Cas9 to identify pathways and factors that enable tumor growth in an arginine-depleted environment. Our screen identified the SLC-family transporter SLC7A5 as required for growth, and we hypothesized that this protein functions as a high-affinity citrulline transporter. Using isotope tracing experiments, we show that citrulline uptake and metabolism into arginine are dependent upon expression of SLC7A5. Pharmacological inhibition of SLC7A5 blocks growth under low-arginine conditions across a diverse group of cancer cell lines. Loss of SLC7A5 reduces tumor growth and citrulline import in a mouse tumor model. We identify a conditionally essential role for SLC7A5 in arginine metabolism, and we propose that SLC7A5-targeting therapeutic strategies in cancer may be effective in the context of arginine limitation.
    Keywords:  CP: Cancer; CP: Metabolism; CRISPR screening; SLC7A5; amino acid transport; arginine; cancer metabolism; citrulline
    DOI:  https://doi.org/10.1016/j.celrep.2024.115130
  7. Anal Chim Acta. 2025 Jan 22. pii: S0003-2670(24)01292-3. [Epub ahead of print]1336 343491
       BACKGROUND: Untargeted metabolomics requires robust and reliable strategies for data processing to extract relevant information form the underlying raw data. Multiple platforms for data processing are available, but the choice of software tool can have an impact on the analysis. This study provides a comprehensive evaluation of four workflows based on commonly used metabolomics software tools: XCMS, Compound Discoverer, MS-DIAL, and MZmine. These tools were applied to a dataset derived from bovine saliva samples spiked with small polar molecules analyzed by anion exchange chromatography coupled to high resolution mass spectrometry.
    RESULTS: The analysis revealed significant differences in the number and overlap of detected features, with only approximately 8 % of the features included in all four peak tables. Among the overlapping features, MS-DIAL demonstrated the greatest similarity to manual integration, while XCMS and MZmine also performed well. In contrast, Compound Discoverer had issues to reliably integrate high baseline peaks. This study also explores various post-processing strategies, including missing value imputation, transformation, scaling, and filtering. The assessment of missing values indicated that they primarily originated from low abundance, making imputation with small values the most effective approach. No clear evidence suggested that transformation is necessary for downstream statistical analyses. Auto scaling emerged as the most suitable strategy for data scaling. Low thresholds for blank filtering were found to be the most effective in enhancing data quality. The optimization of filtering thresholds required a careful balance to remove unnecessary information while retaining vital data.
    SIGNIFICANCE AND NOVELTY: This work provides an overview of commonly applied strategies in untargeted metabolomics analysis, emphasizing the importance of careful workflow selection and optimization. It serves as a resource for refining data processing strategies to achieve accurate and reliable results, while also offering fresh insights into the challenges encountered throughout the untargeted metabolomics processing pipeline.
    Keywords:  Anion exchange chromatography; Data treatment; Mass spectrometry; Metabolomics; Processing
    DOI:  https://doi.org/10.1016/j.aca.2024.343491
  8. Int J Mol Sci. 2024 Dec 14. pii: 13413. [Epub ahead of print]25(24):
      Ferroptosis, a novel form of cell death discovered in recent years, is typically accompanied by significant iron accumulation and lipid peroxidation during the process. This article systematically elucidates how tumor metabolic reprogramming affects the ferroptosis process in tumor cells. The paper outlines the basic concepts and physiological significance of tumor metabolic reprogramming and ferroptosis, and delves into the specific regulatory mechanisms of glucose metabolism, protein metabolism, and lipid metabolism on ferroptosis. We also explore how complex metabolic changes in the tumor microenvironment further influence the response of tumor cells to ferroptosis. Glucose metabolism modulates ferroptosis sensitivity by influencing intracellular energetic status and redox balance; protein metabolism, involving amino acid metabolism and protein synthesis, plays a crucial role in the initiation and progression of ferroptosis; and the relationship between lipid metabolism and ferroptosis primarily manifests in the generation and elimination of lipid peroxides. This review aims to provide a new perspective on how tumor cells regulate ferroptosis through metabolic reprogramming, with the ultimate goal of offering a theoretical basis for developing novel therapeutic strategies targeting tumor metabolism and ferroptosis.
    Keywords:  ferroptosis; glucose metabolism; lipid metabolism; protein metabolism; tumor metabolic reprogramming
    DOI:  https://doi.org/10.3390/ijms252413413
  9. Redox Biol. 2024 Dec 31. pii: S2213-2317(24)00458-0. [Epub ahead of print]80 103480
      Dormant disseminated tumor cells (DTCs) remain viable for years to decades before establishing a clinically overt metastatic lesion. DTCs are known to be highly resilient and able to overcome the multiple biological hurdles imposed along the metastatic cascade. However, the specific metabolic adaptations of dormant DTCs remain to be elucidated. Here, we reveal that dormant DTCs upregulate de novo lipogenesis and favor the activation and incorporation of monounsaturated fatty acids (MUFAs) to their cellular membranes through the activation of acyl-coenzyme A synthetase long-chain family member 3 (ACSL3). Pharmacologic inhibition of de novo lipogenesis or genetic knockdown of ACSL3 results in lipid peroxidation and non-apoptotic cell death through ferroptosis. Clinically, ACSL3 was found to be overexpressed in quiescent DTCs in the lymph nodes of breast cancer patients and to significantly correlate with shorter disease-free and overall survival. Our work provides new insights into the molecular mechanisms enabling the survival of dormant DTCs and supports the use of de novo lipogenesis inhibitors to prevent breast cancer metastasis.
    Keywords:  Breast cancer; Ferroptosis; Lipid metabolism; Lipid peroxidation; Metastasis; Monounsaturated fatty acids activation; Tumor cell dormancy
    DOI:  https://doi.org/10.1016/j.redox.2024.103480
  10. Anal Chem. 2025 Jan 07.
      The integrative multiomics characterization of minute amounts of clinical tissue specimens has become increasingly important. Here, we present an approach called Integral-Omics, which enables sequential extraction of metabolites, lipids, genomic DNA, total RNA, proteins, and phosphopeptides from a single biopsy-level tissue specimen. We benchmarked this method with various samples, applied the workflow to perform multiomics profiling of tissues from six patients with colorectal cancer, and found that tumor tissues exhibited suppressed ferroptosis pathways at multiomics levels. Together, this study presents a methodology that enables sequential extraction and profiling of metabolomics, lipidomics, genomics, transcriptomics, proteomics, and phosphoproteomics using biopsy tissue specimens.
    DOI:  https://doi.org/10.1021/acs.analchem.4c04421
  11. Cancer Lett. 2025 Jan 02. pii: S0304-3835(25)00005-9. [Epub ahead of print]611 217441
      Metabolic reprogramming is a hallmark of cancer, crucial for malignant transformation and metastasis. Chronic lymphocytic leukaemia (CLL) and prostate cancer exhibit similar metabolic adaptations, particularly in glucose and lipid metabolism. Understanding this metabolic plasticity is crucial for identifying mechanisms contributing to metastasis. This review considers glucose and lipid metabolism in CLL and prostate cancer, exploring their roles in healthy and malignant states and during disease progression. In CLL, lipid metabolism supports cell survival and migration, with aggressive disease characterised by increased fatty acid oxidation and altered sphingolipids. Richter's transformation and aggressive lymphoma, however, exhibit a metabolic shift towards increased glycolysis. Similarly, prostate cell metabolism is unique, relying on citrate production in the healthy state and undergoing metabolic reprogramming during malignant transformation. Early-stage prostate cancer cells increase lipid synthesis and uptake, and decrease glycolysis, whereas metastatic cells re-adopt glucose metabolism, likely driven by interactions with the tumour microenvironment. Genetic drivers including TP53 and ATM mutations connect metabolic alterations to disease severity in these two malignancies. The bone microenvironment supports the metabolic demands of these malignancies, serving as an initiation niche for CLL and a homing site for prostate cancer metastases. By comparing these malignancies, this review underscores the importance of metabolic plasticity in cancer progression and highlights how CLL and prostate cancer may be models of circulating and solid tumours more broadly. The metabolic phenotypes throughout cancer cell transformation and metastasis, and the microenvironment in which these processes occur, present opportunities for interventions that could disrupt metastatic processes and improve patient outcomes.
    Keywords:  Chronic lymphocytic leukaemia; Malignant transformation; Metabolic reprogramming; Metastasis; Microenvironment; Prostate cancer
    DOI:  https://doi.org/10.1016/j.canlet.2025.217441
  12. J Proteome Res. 2025 Jan 08.
      Recent improvements in methods and instruments used in mass spectrometry have greatly enhanced the detection of protein post-translational modifications (PTMs). On the computational side, the adoption of open modification search strategies now allows for the identification of a wide variety of PTMs, potentially revealing hundreds to thousands of distinct modifications in biological samples. While the observable part of the proteome is continuously growing, the visualization and interpretation of this vast amount of data in a comprehensive fashion is not yet possible. There is a clear need for methods to easily investigate the PTM landscape and to thoroughly examine modifications on proteins of interest from acquired mass spectrometry data. We present PTMVision, a web server providing an intuitive and simple way to interactively explore PTMs identified in mass spectrometry-based proteomics experiments and to analyze the modification sites of proteins within relevant context. It offers a variety of tools to visualize the PTM landscape from different angles and at different levels, such as 3D structures and contact maps, UniMod classification summaries, and site specific overviews. The web server's user-friendly interface ensures accessibility across diverse scientific backgrounds. PTMVision is available at https://ptmvision-tuevis.cs.uni-tuebingen.de/.
    Keywords:  Web server; open search; post-translational modifications; visualization
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00679
  13. Molecules. 2024 Dec 19. pii: 5993. [Epub ahead of print]29(24):
      Analyzing and detecting endogenous amino acids in blood is of crucial importance for the diagnosis of medical conditions and scientific research. Considering the lack of UV chromophores in most of these analytes and the presence of several interfering substances in plasma, the quantification of quite a few amino acids and related compounds presents certain technical challenges. As a blank plasma matrix lacking these endogenous substances does not exist, the surrogate matrix method is used, as well as isotopic internal standards for calibration, to ensure the accuracy and reliability of the study. Method validation was conducted for 48 target analytes, giving the following results: linearity (R2 at least 0.99), limit of quantification (from 0.65 to 173.44 μM), precision (intra-day and inter-day RSD for LQC ranged from 3.2% to 14.2%, for MQC from 2.0% to 13.6%, and for HQC from 1.6% to 11.3%), accuracy, recovery, and stability of the method (all complied with the guidelines). To further investigate the applicability of this method to large-scale sample analysis, the method was successfully applied to the analysis of amino acids in plasma samples collected from 20 control individuals, demonstrating its wide application scope for clinical diagnosis and metabolic research.
    Keywords:  HILIC-MS/MS; amino acids; non-derivatization; plasma; quantitative; surrogate matrix method
    DOI:  https://doi.org/10.3390/molecules29245993
  14. J Proteome Res. 2025 Jan 07.
      Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods is often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma.
    Keywords:  affinity purification; antibody; artificial intelligence; immunoprecipitation; machine learning; mass spectrometry; plasma; protein−protein Interactions; proteomics; supervised learning
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00160
  15. Nat Protoc. 2025 Jan 06.
      Aminoacyl-tRNA synthetases (aaRSs) provide an essential functional link between an mRNA sequence and the protein it encodes. aaRS enzymes catalyze a two-step chemical reaction that acylates specific tRNAs with a cognate α-amino acid. In addition to their role in translation, acylated tRNAs contribute to non-ribosomal natural product biosynthesis and are implicated in multiple human diseases. In synthetic biology, the acylation of tRNAs with a non-canonical α-amino acid or, more recently, a non-α-amino acid monomer is a critical first step in the incorporation of these monomers into proteins, where they can be used for fundamental and applied science. These endeavors all demand an understanding of aaRS activity and specificity. Here, we describe a liquid chromatography-mass spectrometry assay that directly monitors aaRS activity by detecting the intact acyl-tRNA product. After a simple tRNA acylation reaction workup, acyl- and non-acyl-tRNA molecules are resolved by using ion-pairing reverse-phase chromatography, and their exact masses are determined by using high-resolution time-of-flight mass spectrometry. Our assay is fast and simple, quantifies reaction yields as low as 0.23% and can also be used on tRNAs acylated with flexizyme to detect products that are undetectable by using standard techniques. The protocol requires basic expertise in molecular biology, liquid chromatography-mass spectrometry and RNase-free techniques. This protocol takes ≥5 h to complete, depending on the number of samples.
    DOI:  https://doi.org/10.1038/s41596-024-01086-9
  16. bioRxiv. 2024 Dec 17. pii: 2024.12.13.628374. [Epub ahead of print]
      Aging results in a progressive decline in physiological function due to the deterioration of essential biological processes, such as transcription and RNA splicing, ultimately increasing mortality risk. Although proteomics is emerging as a powerful tool for elucidating the molecular mechanisms of aging, existing studies are constrained by limited proteome coverage and only observe a narrow range of lifespan. To overcome these limitations, we integrated the Orbitrap Astral Mass Spectrometer with the multiplex tandem mass tag (TMT) technology to profile the proteomes of three brain tissues (cortex, hippocampus, striatum) and kidney in the C57BL/6JN mouse model, achieving quantification of 8,954 to 9,376 proteins per tissue (cumulatively 12,749 across all tissues). Our sample population represents balanced sampling across both sexes and three age groups (3, 12, and 20 months), comprising young adulthood to early late life (approximately 20-60 years of age for human lifespan). To enhance quantitative accuracy, we developed a peptide filtering strategy based on resolution and signal-to-noise thresholds. Our analysis uncovered distinct tissue-specific patterns of protein abundance, with age and sex differences in the kidney, while brain tissues exhibit notable age changes and limited sex differences. In addition, we identified both proteomic changes that are linear with age (i.e., continuous) and that have a non-linear pattern (i.e., non-continuous), revealing complex protein dynamics over the adult lifespan. Integrating our findings with early developmental proteomic data from brain tissues highlighted further divergent age-related trajectories, particularly in synaptic proteins. This study not only provides a robust data analysis workflow for TMT datasets generated using the Orbitrap Astral mass spectrometer but also expands the proteomic landscape of aging, capturing proteins with age and sex effects with unprecedented depth.
    DOI:  https://doi.org/10.1101/2024.12.13.628374
  17. Mol Genet Metab. 2024 Dec 30. pii: S1096-7192(24)00893-X. [Epub ahead of print]144(3): 109009
      The 3-methylglutaconic aciduria (3-MGA-uria) syndromes comprise a heterogeneous group of inborn errors of metabolism defined biochemically by detectable elevation of 3-methylglutaconic acid (3-MGA) in the urine. In type 1 (or primary) 3-MGA-uria, distal defects in the leucine catabolism pathway directly cause this elevation. Secondary 3-MGA-uria syndromes, however, are unrelated to leucine metabolism-specific defects but share a common biochemical phenotype of elevated 3-MGA. It is currently thought that this accumulation is due to an underlying buildup of acetyl-CoA in the mitochondria from impaired function of the TCA cycle with ensuing formation of trans-3-methylglutaconyl CoA and its subsequent byproducts, including 3-MGA. In these disorders, urine 3-MGA levels are known to be fluctuant and at times undetectable by standard urine organic acid analysis (UOA), thereby reducing the utility of this biochemical screening method. Here, we retrospectively evaluated a cohort of nine patients with confirmed 3-MGA-uria syndromes. It was observed that UOA analysis obtained from three separate patients did not identify detectable 3-MGA levels. This inherent limitation highlights the need for a more sensitive clinical modality. Untargeted metabolomics profiling is a rapidly emerging technology that is being used to detect and characterize biochemical abnormalities in many inborn errors of metabolism. Untargeted metabolomics profiling performed on plasma samples in this cohort identified significant elevations of 3-MGA in all nine individuals. This high degree of clinical sensitivity demonstrates the promising potential for untargeted metabolomics analysis as both an effective biochemical screening tool for 3-MGA-uria syndromes and a functional method to assist with validation of genomic variants of uncertain significance in these disorders.
    Keywords:  3-methylglutaconic aciduria; Mitochondrial disorders; Untargeted metabolomics
    DOI:  https://doi.org/10.1016/j.ymgme.2024.109009