bims-mascan 2025-11-02 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–11–02
twenty-one papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Data-Independent Immunopeptidomics Discovery of Low-Abundant Bacterial Epitopes.
Adaptive Modeling of Tandem Mass Spectrometry Data: Creation of the METLIN 960K MRM Database.
A shrinkage-based statistical method for testing group mean differences in quantitative bottom-up proteomics.
Tear fluid proteomics: a comparative study of DIA and DDA mass spectrometry.
SICyLIA-cTMT dissects redox proteome dynamics with high accuracy and depth at microgram scale.
prm-PASEF-Based Quantification and Isomeric Model for Extended Coverage of Human Plasma Lipidome in Parkinson's Disease.
Aerith: Visualization and Annotation of Isotopic Enrichment Patterns of Peptides and Metabolites with Stable Isotope Labeling from Proteomics and Metabolomics.
Enhanced Spatial Proteomics and Metabolomics from a Single Tissue Section Using MALDI-MSI and LCM-microPOTS Platforms.
Accumulation of succinate suppresses de novo purine synthesis through succinylation-mediated control of the mitochondrial folate cycle.
Optimization of Data-Dependent Acquisition Parameters in Mass Spectrometry for Oligonucleotide Identification.
Lipid Metabolic Reprogramming During Progression to Castration-resistant Prostate Cancer Identified by Quantitative Proteomics.
From Identification to Insight: Making Full Use of the Diagnostic Potential of MS/MS Proteotyping in Clinical Microbiology Using Efficient Bioinformatics.
High-Frequency Microfluidic Fractionation for Compound-Resolved Bioactivity-Based Metabolomics.
Quantitative Targeted Analysis of Antibody Fc Glycosylation by Glyco-MRM.
Multimodal landscape of atherosclerotic plaques: A spatial omics approach with mass spectrometry imaging.
Analytical Methods for the Determination of Diamorphine (Heroin) in Biological Matrices: A Review.
Comprehensive lipid analysis of human meibum and tears.
The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis.
SMART 2.0 Statistical Metabolomics Analysis: An R Tool 2.0.
Trapped Ion Mobility Improves Annotation Accuracy in LC-HRMS Screening Applications for Exposomics.
Barcode-free hit discovery from massive libraries enabled by automated small molecule structure annotation.

J Proteome Res. 2025 Oct 30.

Data-Independent Immunopeptidomics Discovery of Low-Abundant Bacterial Epitopes.

Patrick Willems, An Staes, Laia Miret-Casals, Vadim Demichev, Simon Devos, Francis Impens.

  Mass spectrometry-based immunopeptidomics is a powerful approach to uncover peptides presented by human leukocyte antigen (HLA) molecules that can guide vaccine design and immunotherapies. While data-dependent acquisition (DDA) has been the standard for navigating through the complexity associated with nonenzymatic immunopeptide database searches, data-independent acquisition (DIA) is increasingly adopted in immunopeptidomics research. In this work, we compare diaPASEF to conventional ddaPASEF in terms of global immunopeptidome profiling and bacterial epitope discovery of the model intracellular pathogen Listeria monocytogenes. We show that DIA spectrum-centric workflows that search pseudo-MS/MS spectra complement DDA analysis by uncovering additional human and bacterial immunopeptides. Furthermore, we leveraged DIA-NN for generating and searching proteome-wide predicted HLA class I peptide spectral libraries, scoring approximately 150 million immunopeptide peptide precursors. This approach outperformed other spectrum-based methods in the identification of MHC class I peptides and recovered low-abundant peptide precursors missed by other methods. Taken together, our results demonstrate how both DIA spectrum- and peptide-centric immunopeptidomics analysis are promising strategies to identify low-abundant immunopeptides.

Keywords:  DIA-NN; Listeria monocytogenes; data-independent acquisition; mass spectrometry; spectral library

DOI:  https://doi.org/10.1021/acs.jproteome.5c00449
Anal Chem. 2025 Oct 28.

Adaptive Modeling of Tandem Mass Spectrometry Data: Creation of the METLIN 960K MRM Database.

Aries Aisporna, Bill Webb, Winnie Uritboonthai, Linh Hoang, Elizabeth M Billings, Corey Hoang, Robert Plumb, Gary Siuzdak.

Multiple Reaction Monitoring (MRM) remains the gold standard for quantitative mass spectrometry but continues to be constrained by the limited availability of high-quality transitions and collision energy (CE) values for many biologically and chemically relevant molecules. Here, we present the METLIN 960K MRM library, a 960,000-compound transition resource derived entirely from empirically acquired MS/MS data. MRM transitions were generated in both positive and negative ionization modes using an empirical spline-based pipeline refined by AI BioSync, an XCMS enhancement that provides a framework of AI and machine-learning tools designed to decipher spectral data for biological and analytical relevance. Central to this approach is spline fitting of CE-dependent intensity profiles from experimental MS/MS data collected at four discrete energies (0, 10, 20, and 40 eV), enabling continuous CE modeling and precise prediction of optimal fragmentation conditions. Supervised learning models were used within AI BioSync to refine spline fitting across diverse chemical classes, improving reproducibility and predictive accuracy. Validation across more than 100 authentic compounds, including rare metabolites and diverse small molecules, demonstrated robust detection down to 1 nM, confirming both sensitivity and scalability. This framework also holds immediate applicability for preclinical drug development studies, where authentic metabolite and impurity standards are often unavailable. Unlike prior methods reliant on in silico fragmentation or heuristic rules, all transitions are derived directly from experimental MS/MS data using absolute intensities. The resulting precursor m/z-centric METLIN 960K MRM library (https://metlin.scripps.edu) greatly expands the chemical space accessible to targeted quantitation, providing a scalable, vendor-independent path for sensitive and specific molecular detection across research, clinical, and applied applications.

DOI: https://doi.org/10.1021/acs.analchem.5c04639
BMC Bioinformatics. 2025 Oct 31. 26(1): 269

A shrinkage-based statistical method for testing group mean differences in quantitative bottom-up proteomics.

Namgil Lee, Hojin Yoo, Juhyoung Kim, Heejung Yang.

   BACKGROUND: In bottom-up proteomics using data-independent acquisition mass spectrometry (DIA-MS), quantitative measurements are obtained following multiple steps of protein fragmentation and ionization, which introduces cumulative errors and impairs the effectiveness of classical statistical methods. This study proposes an alternative statistical approach for testing group mean differences at the peptide level in quantitative bottom-up proteomics.
RESULTS: We present a novel probabilistic graphical model, that accounts for the non-normality of empirical distributions and the correlations between fragment ion quantities. Based on the model, we propose a new statistical method that improves upon the classical feature-based approach by incorporating distribution-free shrinkage estimation of covariance matrices and bootstrap-based estimation of degrees-of-freedom. Simulated experiments demonstrate that the proposed method outperforms the four most widely used classical methods in terms of specificity, sensitivity, and accuracy, particularly when the data distribution closely resembles real MS data, and under conditions of small sample sizes. Numerical analysis of real quantitative tandem mass spectrometry data reveals that the proposed method effectively identifies candidate peptides exhibiting changes in mean quantity following treatment with the kinase inhibitor Staurosporine.
CONCLUSIONS: The proposed statistical method offers an effective alternative to classical approaches for differential analysis of peptides in quantitative bottom-up proteomics using DIA-MS. The R software package MDstatsDIAMS is available at https://github.com/namgillee/MDstatsDIAMS .

Keywords:  Differential analysis; Ionization efficiency; Shrinkage estimation; Tandem mass spectrometry

DOI:  https://doi.org/10.1186/s12859-025-06275-1
J Mass Spectrom Adv Clin Lab. 2025 Dec;38 26-36

Tear fluid proteomics: a comparative study of DIA and DDA mass spectrometry.

Saleh Ahmed, Jeremy Altman, Garrett Jones, Drew Mayernik, Eliza Williams, Amr Mahmoud, Tae Jin Lee, Wenbo Zhi, Shruti Sharma, Ashok Sharma.

   Background: Mass spectrometry is a powerful technique for tear fluid proteomics, offering critical insights into its complex molecular composition. Traditional data-dependent acquisition (DDA) often favors high-abundance proteins because it selects only the most intense precursor ions within a given window during each scan cycle. A newer approach, data-independent acquisition (DIA), addresses this by fragmenting all precursor ions within defined mass windows, offering broader coverage and improved quantification. This study presents a systematic comparison of DDA and DIA workflows to assess their relative performance in detecting tear fluid proteins.
Methods: Tear fluid samples were collected from healthy individuals using Schirmer strips, processed using in-strip protein digestion, and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS). DDA and DIA workflows were compared for proteomic depth, reproducibility, and data completeness. Quantification accuracy was assessed using serial dilutions of tear fluid in a complex biological matrix.
Results: DIA identified 701 unique proteins and 2,444 peptides, outperforming DDA, which identified 396 unique proteins and 1,447 peptides. Across eight replicates, DIA exhibited greater data completeness (78.7% for proteins and 78.5% for peptides) compared with DDA (42% for proteins and 48% for peptides). Reproducibility was markedly improved with DIA, with a median coefficient of variation (CV) of 9.8% for proteins and 10.6% for peptides, compared to 17.3% and 22.3%, respectively, for DDA. Quantification accuracy was also enhanced, with superior consistency across the dilution series.
Conclusion: Overall, DIA provides deeper, more reproducible, and more accurate proteome profiling of tear fluid than DDA, making it well suited for biomarker discovery.

Keywords:  Biomarkers; Mass spectrometry; Proteomics; Schirmer strip; Tear fluid

DOI:  https://doi.org/10.1016/j.jmsacl.2025.10.001
Cell Rep Methods. 2025 Oct 29. pii: S2667-2375(25)00246-2. [Epub ahead of print] 101210

SICyLIA-cTMT dissects redox proteome dynamics with high accuracy and depth at microgram scale.

Sergio Lilla, Samuel Atkinson, Sonja Radau, Ulla-Maja Bailey, Atul Shahaji Deshmukh, Jiska van der Reest, Joanna Kirkpatrick, Thomas MacVicar, Sara Zanivan.

  Cysteine oxidative modifications are critical signaling events regulating cellular functions, but their low abundance and dynamic nature pose technical challenges. We developed the SICyLIA-TMT workflow, which sequentially labels reduced and reversibly oxidized cysteines with light and heavy iodoacetamide (IAA) within the same sample. The inclusion of tandem mass tags (TMTs) enables simultaneous quantification of oxidative modification dynamics and protein levels across multiple conditions using micrograms of material. To improve the detection of low-abundance oxidized cysteines, a dedicated TMT channel serves as a carrier for heavy IAA-labeled peptides (SICyLIA-cTMT), enhancing quantification and enabling precise stoichiometry calculations. We demonstrate the workflow's applicability to cultured cells and full organs under stress. SICyLIA-cTMT achieves unprecedented depth and accuracy in redox proteome analysis while reducing mass spectrometry time. Combining SICyLIA-TMT with latest mass spectrometry technologies further halves the acquisition time without compromising coverage, improving throughput and enabling comprehensive studies of oxidative signaling.

Keywords:  CP: biotechnology; cancer; cysteine oxidation; fibroblasts; mass spectrometry; obesity; oxidative signaling; post-translational modification; redox proteomics; redox stress

DOI:  https://doi.org/10.1016/j.crmeth.2025.101210
Anal Chem. 2025 Oct 27.

prm-PASEF-Based Quantification and Isomeric Model for Extended Coverage of Human Plasma Lipidome in Parkinson's Disease.

Dhanwin Baker, Gabriel Gonzalez Escamilla, Daniel Janitschke, Yvan Devaux, Nils Schröter, Sergiu Groppa, Laura Bindila.

This study introduces a clinical lipidomics platform leveraging fragment-based quantification on parallel reaction monitoring (PRM)-parallel accumulation serial fragmentation (PASEF) for lipid quantification. An isomeric model, termed "SN regression model", built on specific PASEF-fragment ion patterns, was developed for the quantification of coeluting sn positional isomers without prior derivatization. This PASEF-isomeric lipidomics aids in the resolution and quantification of 176 lipid isomers coeluting in chromatography and/or ion mobility dimensions, expanding the lipidome quantitative coverage to 481 plasma lipids covering 14 lipid subclasses with CV <40% for 32 plasma replicates. We demonstrated the method's advantage for clinical research by detailed quantitative lipidomic phenotyping of patients with Parkinson's disease, enabling the delineation of new biochemical pathways affected by the disorder and stratification of patients. The method's amenability for high-throughput deep quantitative coverage of highly structurally resolved lipidome has implications for improving the diagnosis and understanding of the distinct metabolic alterations in Parkinson's disease subgroups and, generally, for disorders associated with lipid dysregulation.

DOI: https://doi.org/10.1021/acs.analchem.5c02340
Anal Chem. 2025 Oct 28.

Aerith: Visualization and Annotation of Isotopic Enrichment Patterns of Peptides and Metabolites with Stable Isotope Labeling from Proteomics and Metabolomics.

Yi Xiong, Ryan S Mueller, Shichao Feng, Xuan Guo, Chongle Pan.

Stable isotope probing (SIP) traces the metabolism of biological cells using isotopically heavy substrates (e.g., 13C, 15N, or 2H). Confident identification of the metabolic products of isotopic labeling remains a challenge due to the difficulties in simulating, visualizing and annotating the isotopic patterns of partially labeled peptides and metabolites found in mass spectrometry (MS) data. Here, we present Aerith, an R package designed to visualize data of simulated and observed isotopic envelopes of peptides and metabolites with user-defined formula and atom % enrichment levels. Aerith models the isotopic distributions of the fragment ion series of a peptide by sequentially convoluting isotopic envelopes of monomeric units using a convolution algorithm. Aerith simulates fine isotopic structures of a compound using Monte Carlo simulation via the multinomial distribution, and calculates the isotopic envelopes of metabolites with known chemical formulas using an FFT-based algorithm. These algorithms provide accurate simulation of the isotopic envelopes of SIP-labeled peptides and metabolites with high computational efficiency. Aerith evaluates peptide-spectrum matches through multiple robust and commonly used scoring functions to compare experimental and theoretical spectra. These algorithms were implemented in C++ and accessed in R via Rcpp to ensure real-time interactivity and significantly improve computational efficiency compared to native R code. We present case studies to demonstrate Aerith's utility in resolving isotopic fine structures and envelopes for glucose, penicillin, and microbial peptides containing natural and enriched isotopes. By providing visualization of isotopically labeled peptides and metabolites, Aerith enables precise annotation of their mass spectra and manual validation of their identifications in proteomic and metabolomic SIP studies.

DOI: https://doi.org/10.1021/acs.analchem.5c03207
Anal Chem. 2025 Oct 31.

Enhanced Spatial Proteomics and Metabolomics from a Single Tissue Section Using MALDI-MSI and LCM-microPOTS Platforms.

Marija Veličković, Le Z Day, Kevin J Zemaitis, Isaac Kwame Attah, Kristin E Burnum-Johnson, Christopher R Anderton, Dušan Veličković.

Spatially resolved mass spectrometry (MS)-based multiomics workflows are becoming more utilized for revealing the complex biology that occurs within tissues. However, these approaches commonly require multiple independent tissue sections to analyze the metabolite and protein compositions of these samples. This poses a significant challenge in preserving cell- or region-specific molecular fidelity, as variations between tissue sections can compromise the accurate correlation of molecular data. Here, we developed workflows for comprehensive multiomics profiling from a single tissue section (STS) using different MS modalities. We enhanced the functionality of an electrically insulated substrate by employing metal-assisted approaches that enabled both MS-based untargeted spatial metabolomics and proteomics from STS. This allowed metabolite imaging using matrix-assisted laser desorption/ionization-MS imaging (MALDI-MSI), without compromising it for subsequent proteome profiling with laser capture microdissection (LCM)-based technology. Specifically, implementing copper tape as a backing for polyethylene naphthalate (PEN) slides enabled the detection of >140 metabolites across a poplar root tissue section using MALDI-trapped ion mobility spectrometry time-of-flight (timsTOF)-MS. Afterward, we detected 6571 unique proteins from two distinct root regions by leveraging LCM technology coupled to our microdroplet based sample preparation approach. We also developed an alternative workflow utilizing gold-coated PEN substrates for imaging with MALDI-Fourier-transform ion cyclotron resonance (FTICR)-MS, which permitted the profiling of >170 metabolites and the identification of 6542 unique proteins across a single poplar root tissue section. These results were comparable to using each omics analysis independently. These approaches offer new opportunities for high-resolution molecular profiling of multiple omics levels across biological tissues.

DOI: https://doi.org/10.1021/acs.analchem.5c05005
Mol Cell. 2025 Oct 28. pii: S1097-2765(25)00819-6. [Epub ahead of print]

Accumulation of succinate suppresses de novo purine synthesis through succinylation-mediated control of the mitochondrial folate cycle.

Mushtaq A Nengroo, Austin T Klein, Heather S Carr, Olivia Vidal-Cruchez, Umakant Sahu, Daniel J McGrail, Nidhi Sahni, Niklas B Thompson, Peter A Faull, Peng Gao, John M Asara, Hardik Shah, Marc L Mendillo, Issam Ben-Sahra.

  The de novo purine synthesis pathway is fundamental for nucleotide production, yet the role of mitochondrial metabolism in modulating this process remains underexplored. Here, we identify that succinate dehydrogenase (SDH) is essential for maintaining de novo purine synthesis. Genetic or pharmacological inhibition of SDH suppresses purine synthesis, contributing to a decrease in cell proliferation. Mechanistically, SDH inhibition elevates succinate, which in turn promotes the succinylation of serine hydroxymethyltransferase 2 (SHMT2) within the mitochondrial tetrahydrofolate (THF) cycle. This post-translational modification lowers formate output, depriving cells of one-carbon units needed for purine assembly. In turn, cancer cells activate the purine salvage pathway, a metabolic compensatory adaptation that represents a therapeutic vulnerability. Notably, co-inhibition of SDH and purine salvage induces pronounced antiproliferative and antitumoral effects in preclinical models. These findings reveal a signaling role for mitochondrial succinate in tuning nucleotide metabolism and highlight a dual-targeted strategy to exploit metabolic dependencies in cancer.

Keywords:  TCA cycle; cancer; formate; mitochondrial metabolism; nucleotide metabolism; succinate

DOI:  https://doi.org/10.1016/j.molcel.2025.10.002
J Am Soc Mass Spectrom. 2025 Oct 27.

Optimization of Data-Dependent Acquisition Parameters in Mass Spectrometry for Oligonucleotide Identification.

Audriy Jebet, Xipeng Ma, Liqing He, Adam J Anthony, Seongho Kim, Eugene Mueller, Craig J McClain, Xiang Zhang.

  Accurate characterization of RNAs and their chemical modifications is critical for understanding RNA biology and post-transcriptional regulation. Mass spectrometry using data-dependent acquisition (DDA) is a crucial tool for identifying oligonucleotides (OGN) in epitranscriptomics. In this study, the key DDA parameters on an Orbitrap Fusion Lumos mass spectrometer were optimized, and an iterative mass exclusion MS/MS acquisition method was developed to enhance the OGN identification. Optimal performance was achieved with full MS resolving power of 120,000 and an MS/MS resolving power of 15,000, top 15 MS/MS scans, and 30% normalized HCD collision energy. Applying these settings to analyze RNase T1 digested E. coli rRNA resulted in the identification of an average of 358 unique OGNs and 58% rRNA sequence coverage. Our findings highlight the importance of tailored DDA parameter optimization and establish a robust workflow for confident OGN identification in MS-based epitranscriptomics.

Keywords:  LC-MS; Orbitrap Fusion Lumos mass spectrometer; epitranscriptomics; oligonucleotides; optimization

DOI:  https://doi.org/10.1021/jasms.5c00250
Cancer Genomics Proteomics. 2025 Nov-Dec;22(6):22(6): 940-952

Lipid Metabolic Reprogramming During Progression to Castration-resistant Prostate Cancer Identified by Quantitative Proteomics.

Hyunchae Sim, Subin Bae, Chai Won Park, So Young Choi, Kwang-Hyeon Liu, Eun Hye Lee, Bum Soo Kim, Jae-Wook Chung, Yun-Sok Ha, Jun Nyung Lee, Wonhwa Lee, Tae Gyun Kwon, Sangkyu Lee.

   BACKGROUND/AIM: The progression of hormone-sensitive prostate cancer (HSPC) to castration-resistant prostate cancer (CRPC) as a result of resistance to androgen deprivation therapy (ADT) remains a major challenge in prostate cancer treatment.
MATERIALS AND METHODS: To explore the underlying mechanisms, we performed deep comparative proteomic profiling of HSPC and CRPC cell lines. LNCaP and C4-2 cell lines were cultured in isotopically labeled medium, combined, and digested, followed by liquid chromatography-mass spectrometry (LC-MS/MS) and bioinformatic analyses.
RESULTS: Using SILAC-based proteomic analysis, 3,578 proteins were identified, with 2,474 quantified. In C4-2 cells, 41 proteins were significantly up-regulated, while 201 were down-regulated (fold-change >1.5 or <1.5-1, p<0.05). KEGG pathway analysis linked the increased proteins to fatty acid metabolism and biosynthesis of unsaturated fatty acids. Lipidomic analysis showed a significant rise in fatty acids like DHA, palmitic acid, stearic acid, and arachidic acid, aligning with the proteomic findings.
CONCLUSION: These results suggest that fatty acids play a key role in HSPC's progression to CRPC, possibly indicating that CRPC cells themselves may generate fatty acids.

Keywords:  Prostate cancer; castration-resistant prostate cancer; lipid metabolism; proteomics

DOI:  https://doi.org/10.21873/cgp.20548
J Proteome Res. 2025 Oct 29.

From Identification to Insight: Making Full Use of the Diagnostic Potential of MS/MS Proteotyping in Clinical Microbiology Using Efficient Bioinformatics.

Thilo Muth, Roger Karlsson, Yi-Kuo Yu, Gelio Alves.

  Accurate and rapid pathogen identification is critical for modern diagnostics, driving the need for technologies that combine speed, specificity, and functional insight. In this context, mass spectrometry (MS)-based proteomics is rapidly emerging as a transformative approach in clinical microbiology, offering comprehensive characterization of microorganisms and direct identification of protein biomarkers relevant to infectious disease diagnostics. While MS1-based approaches such as MALDI-TOF Biotyper have revolutionized species-level microbial identification through rapid and cost-effective workflows, they remain fundamentally limited in resolving strain-level differences and in detecting antimicrobial resistance (AMR) determinants. In contrast, so-called proteotyping enables the identification of organisms and resistance-associated proteins based on peptide sequence information obtained by tandem mass spectrometry (MS/MS), most commonly using shotgun proteomics. However, the clinical utility of proteotyping depends on the availability of efficient and accurate bioinformatics tools capable of handling large databases, disambiguating shared peptide sequences, and providing taxonomic and functional assignments with clinical relevance. This perspective highlights the diagnostic potential of MS/MS proteotyping and argues that advances in bioinformatics pipelines are needed for moving from microbial identification to actionable clinical insight.

Keywords:  AMR detection; bioinformatics workflows; clinical proteomics; diagnostic microbiology; mass spectrometry-based diagnostics; pathogen detection; proteotyping; taxonomic inference

DOI:  https://doi.org/10.1021/acs.jproteome.5c00733
Anal Chem. 2025 Oct 25.

High-Frequency Microfluidic Fractionation for Compound-Resolved Bioactivity-Based Metabolomics.

Christian Geibel, Julian Schubert, Simon B Knoblauch, Albert Hernandez, Leonardo Boldt, Dana C Schneider, Stilianos Papadopoulos Lambidis, Giovanni Andrea Vitale, Jakub Fleischer, Manuela Haussmann, Harald Gross, Mingxun Wang, Heike Brötz-Oesterhelt, Daniel Petras.

Specialized metabolites represent a prolific source of potential drug candidates. However, the process from detecting bioactivity in a crude metabolite extract to unambiguously identifying the active agent is a tedious and expensive endeavor. Speeding up this procedure is crucial, as new drugs, such as antibiotics, are urgently needed. Furthermore, the systematic functional assessment of complex metabolome samples represents a key bottleneck in nontargeted metabolomics, which once solved, holds the potential to fundamentally advance our systematic understanding of biology. To tackle this central bioanalytical challenge, we developed a compound-resolved bioactivity-based metabolomics workflow that combines nontargeted liquid chromatography tandem mass spectrometry (LC-MS/MS), high frequency fractionation on microfluidic devices and subsequent readout with luminescent bioreporter strains. Central for this workflow is a custom high-speed (∼1 Hz frequency) fractionation device that spots the mobile phase onto a microfluidic paper-analytical device (μPAD) in parallel to MS/MS data acquisition. Subsequently, the μPAD can be overlaid with a bioreporter strain, which displays cellular stress by expressing luciferase. The luminescence signal can then be correlated to MS signals through their chromatographic profiles. We evaluated five different luciferase-expressing bioreporter strains which provide information about different antibacterial modes of action, and tested the workflow with different antibiotic standards and mixtures thereof, as well as crude extracts from the known antibiotic producer Saccharopolyspora erythraea. Our results demonstrated high sensitivity (up to 1 ng/spot, depending on compound and bioreporter) and the rapid identification of multiple antimicrobial compounds out of crude extracts, highlighting the practicality and high-throughput capability of this compound-resolved bioactivity-based metabolomics approach.

DOI: https://doi.org/10.1021/acs.analchem.5c04612
J Proteome Res. 2025 Oct 28.

Quantitative Targeted Analysis of Antibody Fc Glycosylation by Glyco-MRM.

Yue Ma, Yang Song, Ji Luo, Jingxia Wang, Yi-Wei Tang, Dapeng Zhou.

  The N-glycoforms of Fc domain critically regulate binding affinity of IgG1 to Fcγ receptor IIIA (FcγRIIIa), Fcγ receptor IIb (FcγRIIb), and complement components. Quantifying antigen-specific IgG1 glycopeptides may provide precise insights into the pathogenesis of severe viral infections and autoantibody-mediated diseases. Here, we developed a liquid chromatography (LC) coupled with mass spectrometry (MS) by the multiple reaction monitoring (MRM) method to analyze IgG1 glycosylation profiles. Calibration curves were generated for six glycopeptides with integrated isotope-labeled internal standards, yielding lower limits of quantification (LLOQ) of G2 (200 pg/mL, 70.92 pM), G0F (500 pg/mL, 189.39 pM), G0NF (40 pg/mL, 14.05 pM), G2S (2.5 ng/mL, 802.87 pM), G1 (500 pg/mL, 187.98 pM), and G1N (100 pg/mL, 34.93 pM). For absolute IgG1 quantification, the LLOQ was determined as 1.26 μg/mL (8.43 nM). Application of calibration curve-based assays to influenza and COVID-19 infected individuals (within 3 months after infection) revealed distinct glycosylation profiles: influenza infected individuals exhibited significantly reduced core-fucosylation (28%), while both disease groups showed elevated galactosylation levels. This methodology provides a platform for laboratory-developed tests to track glycosylation alterations using widely accessible liquid chromatography-mass spectrometry (LC-MS) equipment.

Keywords:  B4GalTs; FUT8; Fc glycosylation; IgG1; MRM; mass spectrometry

DOI:  https://doi.org/10.1021/acs.jproteome.5c00560
Anal Chim Acta. 2025 Dec 15. pii: S0003-2670(25)01117-1. [Epub ahead of print]1379 344723

Multimodal landscape of atherosclerotic plaques: A spatial omics approach with mass spectrometry imaging.

Robin Joshi, Soon Yew Tang, Ujjalkumar Subhash Das, Daniel J Boehmler, Antonijo Mrčela, Ronan Lordan, E James Petersson, Aalim M Weljie, Garret A FitzGerald.

  Atherosclerotic plaques are complex and heterogeneous structures, originating as fatty streaks in the vasculature and formed by the accumulation of lipids and foam cells. Over time, these lesions progress as inflammation, smooth muscle cell proliferation and phenotypic switching, and extracellular matrix deposition contribute to plaque growth, culminating in their fracture, reactive thrombogenesis, and a cardiovascular event such as myocardial infarction and stroke. Traditional bulk mass spectrometry (MS) analysis has yielded critical insights into the molecular mechanisms of plaque formation and disease progression, but it is unable to determine the spatial heterogeneity and microenvironmental complexity within the lesion. Recent advances in mass spectrometry imaging (MSI) based omics, including spatial lipidomics, proteomics, and metabolomics, have enabled unprecedented visualization of molecular distribution in atherosclerotic plaques at cellular resolution. These techniques promise to elucidate the distinct cellular crosstalk, lesion vulnerability, and sex-specific disease mechanisms that contribute to plaque development and rupture. This review examines the recent advances in MS-based spatial omics and their application to atherosclerotic plaques in both experimental models and human samples. We highlight recent findings, explore their implications for precision medicine and translational research, and discuss current challenges in sample preparation and data integration. Despite challenges, we suggest approaches for integration of MS-based spatial omics using artificial intelligence (AI) to enhance data integration, interpretation, and translational applications in atherosclerosis research. These advances promise to broaden our understanding of atherosclerosis and identify novel therapeutic targets to limit the burden of cardiovascular disease.

Keywords:  Artificial intelligence; Atherosclerosis; Lipids; Mass spectrometry imaging (MSI); Proteins; spatial omics

DOI:  https://doi.org/10.1016/j.aca.2025.344723
Toxics. 2025 Oct 13. pii: 867. [Epub ahead of print]13(10):

Analytical Methods for the Determination of Diamorphine (Heroin) in Biological Matrices: A Review.

Ahmed Ibrahim Al-Asmari.

  Diamorphine (DIM, heroin) is a semi-synthetic opioid that undergoes rapid conversion to 6-monoacetylmorphine and morphine, producing short-lived biomarkers that are difficult to capture during the process. This review critically explores the evolution of analytical techniques for quantitative DIM analysis in biological matrices from 1980 to 2025. It synthesizes findings across blood, plasma, urine, hair, sweat, and postmortem samples, emphasizing matrix-specific challenges and forensic applicability. Unlike previous opioid reviews that primarily focused on metabolites, this work highlights analytical methods capable of successfully detecting diamorphine itself alongside its key metabolites. This review examines 32 studies spanning three decades and compares three core analytical methods: gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC) with optical detection and liquid chromatography-mass spectrometry (LC-MS). Key performance metrics include sensitivity, sample preparation workflow, hydrolysis control, metabolite coverage, matrix compatibility, automation potential and throughput. GC-MS remains the workhorse for hair and sweat ultra-trace screening after derivatization. HPLC with UV, fluorescence or diode-array detection enables robust quantification of morphine and its glucuronides in pharmacokinetic and clinical settings. LC-MS facilitates the multiplexed analysis of DIM, its ester metabolites and its conjugates in a single, rapid run under gentle conditions to prevent ex vivo degradation. Recent advances such as high-resolution mass spectrometry and microsampling techniques offer new opportunities for sensitive and matrix-adapted analysis. By integrating validation parameters, forensic applicability, and evolving instrumentation, this review provides a practical roadmap for toxicologists and analysts navigating complex biological evidence.

Keywords:  forensics; metabolites; opiates; opioids; pharmacokinetics; toxicology

DOI:  https://doi.org/10.3390/toxics13100867
Sci Rep. 2025 Oct 30. 15(1): 35556

Comprehensive lipid analysis of human meibum and tears.

Makiko Terai, Keisuke Jojima, Megumi Ogawa-Sawai, Akio Kihara.

  Abnormalities in the tear film lipid layer, which plays a critical role in preventing water evaporation and protecting the corneal surface, lead to dry eye disease. The lipids in this layer include both meibum lipids (from the meibomian glands) and phospholipids of other origins. Meibum lipids include cholesteryl esters, wax monoesters, wax diesters (WdiEs), (O-acyl)-ω-hydroxy fatty acids (OAHFAs), and cholesteryl OAHFAs. Nonetheless, the exact composition of these lipid classes remains largely unclear. Here, we analyze the composition of cholesteryl esters, wax monoesters, WdiEs, OAHFAs, cholesteryl OAHFAs, phosphatidylcholines, and sphingomyelins in human meibum and tears using multiple reaction monitoring mode liquid chromatography-tandem mass spectrometry, which is highly sensitive, selective, and quantitative. This revealed that the WdiEs in meibum and tears fall within the type 1ω and 2ω classes. Among the lipids examined, the type 1ω WdiEs in particular comprised diverse species. The lipid composition of most of the lipid classes, except for the phosphatidylcholines, was similar in meibum and tears. The findings of this comprehensive lipid analysis contribute to elucidating the overall composition of human meibum and tear lipids.

Keywords:  Meibum; Multiple reaction monitoring; Tandem mass spectrometry; Tear; Tear film lipid layer; Wax diester

DOI:  https://doi.org/10.1038/s41598-025-23048-1
Nat Commun. 2025 Oct 27. 16(1): 9322

The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis.

Annelaura Bach Nielsen, Lasse Fjordside, Lylia Drici, Maud Eline Ottenheijm, Christine Rasmussen, Anna J Henningsson, Lene Holm Harritshøj, Matthias Mann, Helene Mens, Anne-Mette Lebech, Nicolai J Wewer Albrechtsen.

Lyme neuroborreliosis (LNB), a nervous system infection caused by tick-borne spirochetes of the Borrelia burgdorferi sensu lato complex, is among the most frequent bacterial infections of the nervous system in Europe. Early diagnosis and continuous monitoring remain challenging due to limited sensitivity and specificity of current methods and requires invasive lumbar punctures, underscoring the need for improved, less invasive diagnostic tools. Here, we apply mass spectrometry-based proteomics to analyse 308 cerebrospinal fluid (CSF) samples and 207 plasma samples from patients with LNB, viral meningitis, controls and other manifestations of Lyme borreliosis. Diagnostic panels of regulated proteins are identified and evaluated through machine learning-assisted proteome analyses. In CSF, the classifier distinguishes LNB from viral meningitis and controls with AUCs of 0.92 and 0.90, respectively. In plasma, LNB is distinguished from controls with an AUC of 0.80. Our findings suggest a potential diagnostic role for machine learning-assisted proteomics in adults with LNB.

DOI: https://doi.org/10.1038/s41467-025-64903-z
Anal Chem. 2025 Oct 31.

SMART 2.0 Statistical Metabolomics Analysis: An R Tool 2.0.

Yu-Jen Liang, Chih-Ting Yang, Chia-Wei Chen, Yin-Chun Lin, Shu-Yao Lin, Yi Sheng Wang, Hsin-Chou Yang.

Metabolomics has experienced significant growth and increased popularity due to technological advancements. We introduced an integrated tool for untargeted metabolomics analysis, SMART 1.0, that streamlined the entire analysis process, from initial data preprocessing to subsequent association analysis. With SMART 2.0, we enhanced SMART 1.0 by introducing new analytical modules in targeted metabolomics analysis, data normalization, quality control assessment, and advanced dimensionality reduction and classification methods. Additionally, SMART 2.0 offers integrative omics pathway analysis and postanalysis tasks such as peak identification and concentration calibration. We also explored the potential of using large language models for peak annotation and have found the results to be promising. This study employs narcotics data and breast cancer data as demonstrative examples to illustrate the new functionalities. The codes, a user guide, and example data can be downloaded at https://github.com/YuJenL/SMART.

DOI: https://doi.org/10.1021/acs.analchem.5c03225
Anal Chem. 2025 Oct 31.

Trapped Ion Mobility Improves Annotation Accuracy in LC-HRMS Screening Applications for Exposomics.

Carolin Huber, Nadin Ulrich, Martin Krauss.

Ion mobility techniques coupled to mass spectrometry, such as trapped ion mobility (TIMS), are promoted to separate analytes from coeluting matrix interferences and to resolve isomers based on their corresponding CCS values. Complementary to the retention time (RT) dimension revealed from liquid chromatography, the collision cross section (CCS) serves as a robust and matrix-independent parameter. We evaluated the advantages of TIMS in the screening of human samples, such as urine, serum, breastmilk, and matrices relevant for exposure analysis, such as dust and wastewater. We conducted a screening library for 769 environmental contaminants, which resulted in a total of 948 CCS values (594 positive and 354 negative ionization modes). We screened for the potential co-occurrence of interfering compounds originating from five different matrix backgrounds, leading to peaks with similar m/z and RT but differences in the mobilograms. For all matrices combined, 112 peaks with different mobility values relative to the reference standard were found. Our evaluation highlights the benefits of TIMS in reducing the number of inconclusive assignments through the separation of coeluting compounds and background noise and gaining a high MS2 coverage for low-abundant ions. These advantages are beneficial especially for suspect screening applications, where broader RT windows are necessary.

DOI: https://doi.org/10.1021/acs.analchem.5c04665
Nat Commun. 2025 Oct 27. 16(1): 9479

Barcode-free hit discovery from massive libraries enabled by automated small molecule structure annotation.

Edith van der Nol, Nils Alexander Haupt, Qing Qing Gao, Benthe A M Smit, Martin Andre Hoffmann, Martin Engler-Lukajewski, Marcus Ludwig, Sean McKenna, J Miguel Mata, Olivier J M Béquignon, Gerard van Westen, Tiemen J Wendel, Sylvie M Noordermeer, Sebastian Böcker, Sebastian Pomplun.

Affinity-selection platforms are powerful tools in early drug discovery, but current technologies - most notably DNA-encoded libraries (DELs) - are limited by synthesis complexity and incompatibility with nucleic acid-binding targets. We present a barcode-free self-encoded library (SEL) platform that enables direct screening of over half a million small molecules in a single experiment. SELs combine tandem mass spectrometry with custom software for automated structure annotation, eliminating the need for external tags for the identification of screening hits. We develop efficient, high-diversity synthesis protocols for a broad range of chemical scaffolds and benchmark the platform in affinity selections against carbonic anhydrase IX, identifying multiple nanomolar binders. We further apply SELs to flap endonuclease 1 (FEN1) - a disease related DNA-processing enzyme inaccessible to DELs - and discover potent inhibitors. Taken together, screening barcode-free libraries of this scale all at once represents an important development, enables access to novel target classes, and promises substantial impact on both academic and industrial early drug discovery.

DOI: https://doi.org/10.1038/s41467-025-65282-1