bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2025–11–16
seventeen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Optimizing MS Parameters for Data-Independent Acquisition (DIA) to Enhance Untargeted Metabolomics.
  2. Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting (IMPACT): Bridging Untargeted and Targeted Flux Analysis.
  3. The role of metaorganismal lipid metabolism in human health and disease.
  4. Disc-Hub: a python package for benchmarking machine learning strategies in DIA-MS identification.
  5. The ProteomeXchange consortium in 2026: making proteomics data FAIR.
  6. Enhancing Hydroxyl Metabolite Analysis through In Situ Derivatization-Enabled Desorption Electrospray Ionization-Mass Spectrometry.
  7. Plasma Fractionation and Mixture Improves Coverage in Proteomic Analysis.
  8. Breaking barriers in crosslinking mass spectrometry with enhanced throughput and sensitivity using Orbitrap Astral.
  9. Use of Synthetic Standard Peptides in Standardized Digests to Evaluate Both Sample and Instrument Suitability in Proteomics.
  10. Proteoform Mapping Calls for a Combined Effort From Bottom-Up and Top-Down Proteomics.
  11. ALDOA-Mediated Metabolic Reprogramming is a Targetable Vulnerability for Ferroptosis Sensitization in Cancer.
  12. Koina: Democratizing machine learning for proteomics research.
  13. Metabolomics and metabolites in cancer diagnosis and treatment.
  14. A novel derivatization reagent for ultra-sensitive determination of free fatty acids in complex matrices by UHPLC-QQQ-MS.
  15. Assessment of a Protease-Free Method for Body Fluid Identification in Sexual Assault Evidence by Targeted High-Resolution Mass Spectrometry.
  16. Unraveling virus-host interactions: Current advances in viral lipidomics.
  17. Matrix effects in untargeted LC-MS metabolomics: From creation to compensation with post-column infusion of standards.
  1. J Proteome Res. 2025 Nov 12.
    Optimizing MS Parameters for Data-Independent Acquisition (DIA) to Enhance Untargeted Metabolomics.
    Frederico G Pinto, Alexander D Giddey, Rouda S B Almarri, Omer S Alkhnbashi, Timothy J Garrett, Mohammed J Uddin, Nelson C Soares.
      Data-Independent Acquisition (DIA) has emerged as a powerful mass spectrometry (MS) strategy for comprehensive metabolomics. This study presents a novel short gradient (13 min) nanosensitive analytical method for human plasma analysis using DIA LC-MS/MS, focusing on in-depth optimization of MS parameters to maximize data quality and metabolite coverage. Key MS parameters, including scan speed, isolation window width, resolution, automatic gain control, and collision energy, were systematically tuned to balance the sensitivity and specificity while minimizing interferences. The optimized method enabled the detection of 2,907 features with 675 annotated compounds, leveraging recent progress in nano-LC-MS/MS for multiomics applications and showcasing the possibility of combining proteomics and metabolomics within a single chromatographic system. Ultimately, a comparison was performed between the data acquired through the DIA and DDA MS approaches in the context of untargeted metabolomics. This optimized analytical method yields more robust and reproducible results, thereby expanding the potential for meaningful discoveries across diverse biological fields.
    Keywords:  data-independent acquisition; human plasma; mass spectrometry; metabolomics
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00622
  2. Bioinformatics. 2025 Nov 14. pii: btaf591. [Epub ahead of print]
    Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting (IMPACT): Bridging Untargeted and Targeted Flux Analysis.
    Collin Starke, Janneke Hendriks, Judith Wahrheit, Frank Fluitman, Jules Beekwilder, Florence Miramella Schempp, Karsten Hiller.
       SUMMARY: The "Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting" (IMPACT) is a comprehensive, fully modular platform designed for both targeted and untargeted metabolomics analysis in stable-isotope labeling experiments. It facilitates the accurate calculation of mass isotopomer distributions (MIDs) and the annotation of unknown metabolites with a contextualization algorithm, addressing the challenges in metabolomics research. IMPACT integrates an entire preprocessing pipeline for LC-MS data, including peak picking, feature grouping, and peak filling, along with advanced features for isotope detection, MID calculation, and its core feature contextualization, enabling metabolite integration into biological pathway networks. The platform supports various file formats and offers user-friendly online access, making it accessible for researchers seeking to elucidate metabolic pathways and networks with precision and reliability.
    AVAILABILITY AND IMPLEMENTATION: IMPACT is implemented in Python 3.9 and R 4.3.2, with a front-end in Javascript utilizing the Cytoscape.js library for data visualization. It is available as a docker container and can be accessed online at https://impact.bioinfo.nat.tu-bs.de, providing a user-friendly interface for metabolomics data analysis.
    Keywords:  LC-MS; Labeling; MIDs; Metabolomics; Targeted; Untargeted
    DOI:  https://doi.org/10.1093/bioinformatics/btaf591
  3. Immunometabolism (Cobham). 2025 Oct;7(4): e00074
    The role of metaorganismal lipid metabolism in human health and disease.
    William J Massey, Jonathan Mark Brown.
      Most chronic diseases including coronary heart disease, obesity, diabetes, cancer, and multiple neurodegenerative diseases are driven by dysregulated lipid metabolism. In fact, many common drugs taken by millions including aspirin, statins, fibrates, and others improve health by reorganizing systemic lipid metabolism. Although we have a wealth of information on the enzymes and pathways maintaining lipid metabolic homeostasis in our human cells, there is much less known in regard to how our gut microbiome may coordinate with the host to control systemic lipid metabolism. With advances in untargeted metabolomics, there is a rapidly expanding list of gut microbe-derived lipid metabolites with unannotated function. Many of these bacterial lipids can be assimilated into host lipids and alter host lipid metabolic processes. Here, we discuss how gut microbe-derived lipids may be further metabolized by the host through metaorganismal metabolic pathways. We also discuss the untapped therapeutic potential for targeting metaorganismal lipid metabolism for the improvement of human health.
    Keywords:  lipid; metabolism; microbiome
    DOI:  https://doi.org/10.1097/IN9.0000000000000074
  4. Bioinform Adv. 2025 ;5(1): vbaf232
    Disc-Hub: a python package for benchmarking machine learning strategies in DIA-MS identification.
    Yiwen Yu, Xiaohui Wu, Jian Song.
       Motivation: Accurate analysis of data-independent acquisition (DIA) mass spectrometry data relies on machine learning to distinguish target peptides from decoy peptides. Different DIA identification engines adopt distinct binary classifiers and training workflows to accomplish this learning task. However, systematic comparisons of how different machine learning strategies affect identification performance are lacking. This absence of evaluation hinders optimal learning strategy selection, increases the risk of model underfitting or overfitting, and ultimately undermines the effectiveness and reliability of false discovery rate (FDR) control.
    Results: In this study, we benchmarked three training strategies and four classifiers on representative DIA datasets. Among them, K-fold training combined with a multilayer perceptron achieved the best balance between identification depth and FDR control. We have released the datasets and code through the Python package Disc-Hub, enabling rapid selection of optimal machine learning configurations for developing DIA identification algorithms.
    Availability and implementation: Disc-Hub is released as an open source software and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/yuyiwen-yiyuwen/Disc_Hub.
    DOI:  https://doi.org/10.1093/bioadv/vbaf232
  5. Nucleic Acids Res. 2025 Nov 06. pii: gkaf1146. [Epub ahead of print]
    The ProteomeXchange consortium in 2026: making proteomics data FAIR.
    Eric W Deutsch, Nuno Bandeira, Yasset Perez-Riverol, Vagisha Sharma, Jeremy J Carver, Luis Mendoza, Deepti J Kundu, Chakradhar Bandla, Selvakumar Kamatchinathan, Suresh Hewapathirana, Zhi Sun, Shin Kawano, Shujiro Okuda, Brian Connolly, Brendan MacLean, Michael J MacCoss, Tao Chen, Yunping Zhu, Yasushi Ishihama, Juan Antonio Vizcaíno.
      The ProteomeXchange consortium of proteomics resources (http://www.proteomexchange.org) was established to standardize open data practices in the mass spectrometry (MS)-based proteomics field. Here, we describe the main developments in ProteomeXchange in the last 3 years. The six member databases of ProteomeXchange, spread out in three different continents, are the PRIDE database, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public. We provide updated data submission statistics, showcasing that the number of datasets submitted to ProteomeXchange resources has continued to accelerate every year. Through June 2025, 64 330 datasets had been submitted to ProteomeXchange resources, and from those, 30 097 (47%) just in the last 3 years. We also report on the improvements in the support for the standards developed by the Proteomics Standards Initiative, e.g. for Universal Spectrum Identifiers and for SDRF (Sample and Data Relationship Format)-Proteomics. Additionally, we highlight the increase in data reuse activities of public datasets, including targeted reanalyses of datasets of different proteomics data types, and the development of novel machine learning approaches. Finally, we summarize our plans for the near future, covering the development of resources for controlled-access human proteomics data, and for the support of non-MS proteomics approaches.
    DOI:  https://doi.org/10.1093/nar/gkaf1146
  6. Anal Chem. 2025 Nov 14.
    Enhancing Hydroxyl Metabolite Analysis through In Situ Derivatization-Enabled Desorption Electrospray Ionization-Mass Spectrometry.
    Yen-Chu Lin, Guan-Yuan Chen, Ya-Jin Jheng, Hsiao-Wei Liao.
      Unlocking the full potential of metabolomics hinges on significantly improving the detection of metabolites, particularly those containing hydroxyl groups, which often remain challenging to ionize. Our previous work established 2-(4-Boronobenzyl) isoquinolin-2-ium bromide (BBII) as a highly effective derivatization reagent for enhancing hydroxyl metabolite sensitivity in liquid chromatography-mass spectrometry. Building upon this, we herein introduce a novel and robust extension: BBII derivatization integrated with desorption electrospray ionization (DESI) for in situ hydroxyl metabolite detection. This innovative approach involves incorporating BBII directly into the DESI spray solvent, leading to significant sensitivity enhancement through instantaneous derivatization reaction. We demonstrate substantial signal increases ranging from 1.8- to 17.2-fold for hydroxyl metabolite reference standards, making previously undetectable compounds such as glucose, hexadecanol, and estradiol readily observable. When applied to mouse brain tissue sections for mass spectrometry imaging (MSI), BBII-DESI successfully revealed the distinct spatial distributions of representative hydroxyl metabolites, including glucose and cholesterol that are typically invisible to conventional methods. A key advantage of this methodology is the characteristic boron isotopic pattern of BBII-derivatized features, facilitating rapid and precise screening and identification. This BBII-assisted DESI strategy effectively unveils the "dark metabolome" of hydroxyl compounds, providing access to previously inaccessible metabolic information. Our method broadens the utility of ambient ionization techniques by enabling direct analysis of biological samples with minimal preparation, which is crucial for high-throughput applications. This advance offers substantial potential for accelerating biomarker discovery and disease diagnostics through direct visualization of metabolic alterations within native tissue environments, thereby marking a significant leap forward in spatial metabolomics applications for health and disease research.
    DOI:  https://doi.org/10.1021/acs.analchem.5c04858
  7. J Proteome Res. 2025 Nov 12.
    Plasma Fractionation and Mixture Improves Coverage in Proteomic Analysis.
    Ting Liu, Han Chen, Yuning Song, Xiang Ye, Dan Wang, Qiuhong Zhang, Huiru Liu, Jiaojiao Sha, Liangjia Du, Shanyu Qi, Zijin Geng, Qianqian Hu, Yanyang Wang, Minqi Cai, Dezhu Chen, Hongyan Song, Jie Pan, Yiqiang Chen, Tianze Ling, Cheng Chang, Bing Bai.
      Expanding plasma proteome coverage increases the success in proteomic discovery of blood biomarkers. Here we report that the sequential precipitation of plasma by increasing concentrations of acetonitrile (AcN) can fractionate proteins. Combining some of these fractions with other fractions from polyethylene glycol (PEG) precipitation and albumin depletion to have the mixed sample that included these partitioned fractions: 10% whole plasma, 20% 20%-AcN-pellet, 10% 40%-AcN-pellet, 20% 50%-AcN-supernatant, 20% 10%-PEG-precipitation-albumin-depletion, and 20% 20%-PEG-precipitation-albumin-depletion, has yielded identification of 5441 proteins, remarkably larger than the 2040 proteins detected in the whole plasma directly. This study provides nearly the largest plasma proteomics data sets and recommends this fractionation and mixture strategy as an efficient approach for expanded plasma proteomics coverage.
    Keywords:  LC-MS/MS; Plasma; acetonitrile; albumin depletion; mass spectrometry; polyethylene glycol; proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00814
  8. Nat Commun. 2025 Nov 10. 16(1): 9877
    Breaking barriers in crosslinking mass spectrometry with enhanced throughput and sensitivity using Orbitrap Astral.
    Fränze Müller, Micha J Birklbauer, Julia Bubis, Karel Stejskal, Viktoria Dorfer, Karl Mechtler.
      Crosslinking mass spectrometry is an essential tool for probing protein-protein interactions and structural organization. We here compare Orbitrap Astral and Orbitrap Eclipse instruments using Cas9 crosslinked with PhoX and DSSO under standardized chromatographic and acquisition conditions. The Astral identifies over 40% more unique residue pairs, largely due to increased MS1 sensitivity and efficient detection of low-abundance precursors. Implementation of high-field asymmetric ion mobility spectrometry further increases identifications by 30% through improved precursor filtering. On the Astral, single higher-energy collisional dissociation consistently outperforms stepped fragmentation, particularly at low sample amounts, whereas the Eclipse shows minimal dependence on fragmentation strategy. Gradient optimization experiments demonstrate that longer separations enhance identifications in purified crosslinked samples, while gains plateau in complex backgrounds, indicating the need for enrichment or isolation strategies. Column comparisons show that pore size and particle diameter affect separation efficiency, with the Aurora Ultimate column yielding sharper peaks and more crosslink identifications than PepMap. Together, these findings emphasize that instrument choice, fragmentation mode, and chromatographic design directly influence crosslinking performance. The Astral's combination of sensitivity and scan speed supports comprehensive detection of low-abundance crosslinks, providing deeper structural coverage of protein interaction networks.
    DOI:  https://doi.org/10.1038/s41467-025-64844-7
  9. J Proteome Res. 2025 Nov 13.
    Use of Synthetic Standard Peptides in Standardized Digests to Evaluate Both Sample and Instrument Suitability in Proteomics.
    Leonard B Collins, Taufika Islam Williams, Alexandria L Sohn, Jaclyn G Kalmar, Michael S Bereman, David C Muddiman.
      Whole proteome digests are routinely used to diagnose chromatograph and mass spectrometer outputs to ensure suitability for the analysis of complex matrices, but their usage inherently fails to prove system reliability under varying "load" conditions. There is a need for a reliable, predictive tool that can explain variation in both instrument response and downstream identification results from a whole proteome analysis. We designed an experiment using a hybrid sample of standardized materials to create such an approach, which could then lead to a new system suitability test for bottom-up proteomics. The standard HeLa protein digest was combined with Promega 6 × 5 LC-MS/MS Peptide Reference Mix and diluted to create a range of sample mass loadings and reference peptide concentrations. Data were collected using data-dependent (DDA) and data-independent acquisition methods, and reference peptide peak abundances were correlated to the number of protein identifications (IDs), peptide groups (PGs), and peptide spectrum matches (PSMs) found by Proteome Discoverer. An asymptotic relationship explained decreasing IDs, PGs, and PSMs identified from the HeLa digest with decreasing 6 × 5 Peptide abundances. By linking the mass spectrometer measurement of ion abundance with downstream results obtained from a complex matrix, we successfully used the hybrid standardized sample to mathematically define new system suitability thresholds.
    Keywords:  ID-based metrics; ID-free metrics; bottom-up proteomics; data-dependent acquisition (DDA); isotopologues; quality control (QC); system suitability
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00415
  10. Proteomics. 2025 Nov 11. e70077
    Proteoform Mapping Calls for a Combined Effort From Bottom-Up and Top-Down Proteomics.
    Yuri E M van der Burgt.
      Proteomics has demonstrated that each protein consists of various proteoforms that provide an additional layer of biological data next to gene-, transcript-, or protein expression levels. As a result, proteome analyses are increasingly being complemented with proteoform maps. Identification and quantification of the type, site, and dynamics of a proteoform modification contribute to a better understanding of human biology. Testing the hypothesis that proteoform-resolved data can provide novel tools in precision medicine requires robust and high-throughput proteoform measurements. The prime candidate for this purpose is top-down proteomics (TDP), commonly performed with mass spectrometry. In this Special Issue: Top-Down Proteomics, Fornelli et al. report a comparative analysis of various SDS-removal methods that are needed for proteoform mapping in TDP experiments. Their work provides technical guidance on an important aspect of the TDP sample preparation process.
    DOI:  https://doi.org/10.1002/pmic.70077
  11. Adv Sci (Weinh). 2025 Nov 11. e11880
    ALDOA-Mediated Metabolic Reprogramming is a Targetable Vulnerability for Ferroptosis Sensitization in Cancer.
    Pengqi Wang, Kezhang He, Bowen Wang, Wei Zhou, Ying Zhang, Tianhua Ma, Sheng Ding.
      Ferroptosis presents great potential for cancer therapy, either alone or in combination with classical therapy. However, inducing ferroptosis by targeting canonical ferroptosis suppressors that directly inhibit lipid peroxidation non-selectively induces ferroptosis in both cancerous and normal cells, thereby limiting its therapeutic potential. In this study, it is revealed that aldolase A (ALDOA) reprograms lipid metabolism to resist ferroptosis in cancer cells and identifies ALDOA as a targetable vulnerability for ferroptosis sensitization. Cancer cells with ALDOA suppression exhibit increased susceptibility to ferroptosis-a response less obvious in normal cells. Mechanistically, ALDOA depletion induces significant accumulation of fructose 1,6-bisphosphate in cancer cells, thereby enhancing autophagy-dependent degradation of phospholipid-modifying enzymes. These alterations increase the ratio of phospholipids containing pro-ferroptotic polyunsaturated fatty acids over anti-ferroptotic monounsaturated fatty acids, culminating in heightened ferroptosis sensitivity. Moreover, ALDOA inhibitors selectively promote ferroptosis in cancer cells, both in vitro and in vivo. Collectively, the findings reveal that ALDOA-mediated metabolic reprogramming is a targetable vulnerability for ferroptosis sensitization in cancer.
    Keywords:  ALDOA; autophagy; cancer; ferroptosis; lipid metabolism; metabolic reprogramming
    DOI:  https://doi.org/10.1002/advs.202511880
  12. Nat Commun. 2025 Nov 11. 16(1): 9933
    Koina: Democratizing machine learning for proteomics research.
    Ludwig Lautenbacher, Kevin L Yang, Tobias Kockmann, Christian Panse, Wassim Gabriel, Dulguun Bold, Elias Kahl, Matthew Chambers, Brendan X MacLean, Kai Li, Fengchao Yu, Brian C Searle, Damien Beau Wilburn, Mohammad Reza Zare Shahneh, Yuhui Hong, Haixu Tang, Mingxun Wang, Ralf Gabriels, Robbin Bouwmeester, Robbe Devreese, Jesse Angelis, Eduard Sabidó, Tobias K Schmidt, Alexey I Nesvizhskii, Mathias Wilhelm.
      Recent developments in machine learning (ML) and deep learning have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML models are regularly published, the rate at which the community adopts these models is slow. This is in part due to a lack of findability and accessibility of these models as well as the technical challenges involved in incorporating these models into data analysis pipelines and demonstrating their reusability for end-users. Here we show Koina, an open-source decentralized and online-accessible model repository to facilitate publication of ML models. Koina enables ML model usage via an easy-to-use online interface, facilitating the integration of ML models in data analysis pipelines. Using the widely used FragPipe computational platform as an example, we demonstrate how Koina can be integrated with existing proteomics software tools and how these integrations improve data analysis.
    DOI:  https://doi.org/10.1038/s41467-025-64870-5
  13. Mol Biomed. 2025 Nov 14. 6(1): 109
    Metabolomics and metabolites in cancer diagnosis and treatment.
    Minyi Cai, Haiyan Liu, Chen Shao, Tingting Li, Jun Jin, Yahui Liang, Jinhu Wang, Ji Cao, Bo Yang, Qiaojun He, Xuejing Shao, Meidan Ying.
      Cancer is a leading cause of death worldwide. Metabolic reprogramming in cancers plays an important role in tumor initiation, malignant progression and therapeutic response. Based on this, significant progress has been made in the development of the metabolite-based early cancer detection and targeted interventions. Over the past decade, metabolomics has been widely applied to detect metabolic alterations in tumor cells as well as their microenvironment. However, an up-to-date systematic review to summarize the current metabolomic and metabolites in cancer, especially their connections to cancer diagnostics/prognostic biomarkers and therapeutic strategies, is lacking. Here, we first introduced the platforms and analytical processes of metabolomics, as well as their application in different biological matrix of tumor patients. Then, we summarized representative cancer studies in which specific metabolites was found to be act as diagnostic or prognostic/stratification biomarkers. Furthermore, we reviewed the current therapeutic strategies targeting cancer metabolism, particularly the drugs/compounds that are either market-approved or in clinical trials, and also analyzed the potential of metabolites in personalizing precision treatment. Finally, we discussed the key challenges in this field, including the technical limitations of metabolomics and the clinical limitations of therapeutic targeting cancer metabolism, and further explored the future directions such as multi-omics perspective and lifestyle interventions. Taken together, we provides a comprehensive overview from technological platforms of metabolomics to translational applications of metabolites, facilitating the discovery of novel biomarkers and targeting strategies for precision oncology.
    Keywords:  Biomarkers; Cancer; Metabolites; Metabolomics; Therapeutic targets
    DOI:  https://doi.org/10.1186/s43556-025-00362-8
  14. Talanta. 2025 Nov 07. pii: S0039-9140(25)01595-4. [Epub ahead of print]299 129104
    A novel derivatization reagent for ultra-sensitive determination of free fatty acids in complex matrices by UHPLC-QQQ-MS.
    Yuying Lin, Yiran Zhao, Chengfeng Gao, Haoyuan Zeng, Yue Zhuo, Yi Zhun Zhu, Na Li, Jian-Lin Wu, Xiqing Bian.
      Plant oils are vital dietary sources of ω-3, ω-6, and ω-9 fatty acids, but their accurate quantification remains challenging due to their low abundance and complex matrix interference. Herein, we developed a novel derivatization method using 1-(2-diisopropylaminoethyl) piperazine (DPATP) coupled with ultra-high performance liquid chromatography-tandem triple quadrupole mass spectrometry (UHPLC-QQQ-MS/MS). This method offers the simultaneous analysis of 11 fatty acids with a remarkable 300-fold sensitivity enhancement, and limits of quantification as low as 0.4 fg. Comprehensive optimization revealed that liquid-liquid extraction (LLE) outperformed solid-phase extraction (SPE) in recovery efficiency. The derivatization reaction (30 °C for 10 min) was optimized to improve chromatographic resolution and ionization efficiency. Method validation confirmed good linearity, high precision, and acceptable accuracy. Minimal matrix effects and satisfactory recoveries further underscored the method's robustness. The established derivatization-UHPLC-QQQ-MS/MS approach was successfully applied to quantify FFAs in plant oils. This DPAPT-derivatization LC-MS platform is a powerful tool for ultra-sensitive FFA analysis, promising broad applicability in food science, lipidomics, clinical diagnostics, and environmental analysis.
    Keywords:  Analytical method development; Derivatization-LC-MS; Fatty acid profiling; Mass spectrometry; Ultra-sensitive detection
    DOI:  https://doi.org/10.1016/j.talanta.2025.129104
  15. J Proteome Res. 2025 Nov 12.
    Assessment of a Protease-Free Method for Body Fluid Identification in Sexual Assault Evidence by Targeted High-Resolution Mass Spectrometry.
    Catherine O Brown, Glendon J Parker, Christian G Westring, Phillip B Danielson, Kevin M Legg.
      Serological screening, including immunological lateral flow assays, remains common for body fluid identification in sexual assault investigations but lacks the sensitivity and specificity of modern DNA profiling. To address this gap, alternative molecular approaches, including MS-based proteomics, have been explored. However, adoption is hindered by lengthy bottom-up workflows and reliance on research-grade instrumentation. Here, a streamlined, protease-free assay for the identification of saliva and seminal fluid in sexual assault evidence is described. Casework-type body fluid samples were extracted in a single step and analyzed by targeted DDA on a Q Exactive MS with a 25-min separation and data search using Byos software. The 96-well plate format used is amenable to higher-throughput automation. Discovery data sets included 50 saliva and 60 semen samples (including samples from 5 vasectomized males). This resulted in the identification of 7 saliva biomarkers (PRB1, PRB2, PRB4, PRH1, STATH, HTN1, and SMR3B) and 5 seminal fluid biomarkers (SEMG1, SEMG2, PSA, PAP, and PIP). Peptide standards were synthesized to confirm the discovery results and to develop a targeted assay. The method was successfully validated using 168 forensic casework-type samples, including diluted, laundered, and environmentally challenged samples on a variety of substrates.
    Keywords:  forensics; mass spectrometry; proteomics; serology; sexual assault screening
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00711
  16. iScience. 2025 Nov 21. 28(11): 113750
    Unraveling virus-host interactions: Current advances in viral lipidomics.
    Belen Requena, Coral Barbas, Carolina Gonzalez-Riano.
      Viruses significantly alter host lipid metabolism to facilitate their replication, assembly, and immune evasion. Lipidomics, a mass spectrometry-driven field, enables the comprehensive profiling of virus-induced lipid remodeling and provides crucial insights into host-pathogen interactions. This review provides a comprehensive overview of cutting-edge lipidomic research in viral infections, focusing on studies published from January 2023 onwards. Emphasis is placed on recent advancements in understanding key respiratory viruses (e.g., SARS-CoV-2), bloodborne pathogens (HIV, HCV), and emerging viral threats such as West Nile or the Dengue viruses. We examine the latest analytical platforms, annotation techniques, and biological findings, highlighting how specific alterations in glycerophospholipid, sphingolipid, and sterol pathways reveal novel diagnostic and therapeutic opportunities. While challenges in standardization, isomer annotation, and clinical translation persist, emerging MS technologies and computational strategies promise to overcome these limitations. Integrating lipidomics with systems biology approaches will be crucial for advancing precision virology and developing next-generation antiviral therapies.
    Keywords:  biological sciences; biotechnology; omics
    DOI:  https://doi.org/10.1016/j.isci.2025.113750
  17. J Chromatogr A. 2025 Nov 01. pii: S0021-9673(25)00852-0. [Epub ahead of print]1765 466508
    Matrix effects in untargeted LC-MS metabolomics: From creation to compensation with post-column infusion of standards.
    Pingping Zhu, Amy C Harms, Pascal Maas, Manisha Bakas, Julia Josette Whien, Anne-Charlotte Dubbelman, Thomas Hankemeier.
      Matrix effect is a well-known issue affecting accuracy and repeatability in metabolomics studies using liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). Post-column infusion of standards (PCIS) is a promising strategy to monitor and correct matrix effect but has been rarely reported in untargeted metabolomics. The major challenges lie in selecting appropriate PCISs and identifying the most suitable PCIS to correct the matrix effect experienced by each feature. In this study, we aim to present a method for selecting suitable PCISs for matrix effect compensation based on the artificial matrix effect (MEart) created by post-column infusion of compounds that disrupt the ESI process. Our hypothesis is that the suitable PCIS for a given analyte can be identified by comparing the PCISs' ability in MEart compensation. We evaluated this approach using 19 stable-isotopically labeled (SIL) standards spiked in plasma, urine, and feces. PCISs selected based on MEart were compared to those selected by biological matrix effect (MEbio), with 17 out of 19 SIL standards (89 %) showing consistent PCIS selection, demonstrating the effectiveness of MEart in identifying suitable PCISs. Applying MEart-selected PCISs to correct for the MEbio resulted in improved MEbio for most of the SILs affected by matrix effect and maintained MEbio for those experiencing no matrix effect. We demonstrated the efficacy of MEart in selecting suitable PCISs for MEbio correction within an LC-PCIS-MS method. Importantly, since MEart can be assessed for any detected feature, its application holds great potential for identifying suitable PCISs for matrix effect correction in untargeted metabolomics.
    Keywords:  Artificial matrix; LC-MS; Matrix effect correction; Post-column infusion; Untargeted metabolomics
    DOI:  https://doi.org/10.1016/j.chroma.2025.466508
This page is being maintained by Thomas Krichel. It was last updated on 2025‒11‒16 at 8:21.