bims-mascan 2025-12-14 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–12–14
eighteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Data independent acquisition mass spectrometry (DIA-MS) in cancer studies: a concise review.
Integrating endogenous TurboID and data-independent acquisition mass spectrometry for in vivo proximity labeling.
Enhanced Identifications and Quantification through Retention Time Down-Sampling in Fast-Cycling diagonal-PASEF Methods.
From identification to quantification: A step-by-step workflow for intact glycopeptide analysis by mass spectrometry.
Quantitative Proteomics of Nitrosylated Proteins in Melanoma Using the Biotin-Switch Technique Combined With Tandem Mass Tag Labeling.
In-cell Proteomics Enables High-Resolution Spatial and Temporal Mapping of Early Xenopus tropicalis Embryos.
A resource to empirically establish drug exposure records directly from untargeted metabolomics data.
xcms in Peak Form: Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Software Ecosystem.
In-depth analysis of data characteristics and comparative evaluation of dda and dia accuracy in label-free quantitative proteomics of biological samples.
A step-by-step guide to performing cancer metabolism research using custom-made media.
Optimization of Methods for the Quantitative Analysis of Global Cell Surface Proteome and Cell Surface Polarization.
Advancing Blood Proteome Analysis Past the Plasma Age: Mass Spectrometry of Whole Blood and Plasma Collected with Volumetric Absorptive Microsampling Devices.
Future of clinical proteomics: could targeted multi-protein panels supplant low-throughput methods?
Advances in LC-MS strategies for comprehensive characterization and quantification of antibody-drug conjugates in preclinical and clinical settings.
Data-Independent Acquisition (DIA)-Based Proteomics for the Identification of Biomarkers in Tissue Washings of Endometrial Cancer.
Mapping the unknown: an anchor-based molecular networking workflow for comprehensive identification of new psychoactive substances and analogues.
The sour regulation of metabolism: acidity challenges tumor cells while fueling their metabolic survival.
Mast cell metabolism in cancer: an underexplored frontier demanding more attention.

Anal Methods. 2025 Dec 10.

Data independent acquisition mass spectrometry (DIA-MS) in cancer studies: a concise review.

Bharath S Kumar.

Advances in proteomics are reshaping our understanding of cancer biology by enabling the direct quantification of proteins and their modifications in complex biological systems. Among emerging mass spectrometry techniques, Data-Independent Acquisition (DIA) has established itself as a transformative approach for cancer proteomics. DIA offers unprecedented depth, reproducibility, and scalability by systematically fragmenting all precursor ions across defined mass ranges, allowing comprehensive proteome coverage and retrospective data analysis. This review highlights the fundamental principles of DIA-MS, recent technological developments-including spectral library-free workflows, and their impact on cancer research. We discuss the application of DIA in tumor classification, biomarker discovery, therapeutic target identification, and treatment response monitoring. Particular attention is given to its compatibility with clinical samples such as formalin-fixed paraffin-embedded (FFPE) tissues and its integration into large-scale efforts like CPTAC. Current challenges with the technique will be explored, including data analysis complexity and standardization, and future directions that could bring DIA-MS closer to clinical utility. DIA-MS is rapidly evolving into a cornerstone technology for precision oncology, with the potential to bridge research and clinical practice through robust, high-resolution proteomic profiling.

DOI: https://doi.org/10.1039/d5ay01294e
EMBO J. 2025 Dec 11.

Integrating endogenous TurboID and data-independent acquisition mass spectrometry for in vivo proximity labeling.

David S Fay, Boopathi Balasubramaniam, Sean M Harrington, Philip T Edeen.

  Proximity labeling has emerged as a powerful approach for identifying protein-protein interaction networks within living systems, particularly those involving weak or transient associations. Here, we present a comprehensive revised proximity labeling workflow, integrating TurboID labeling of endogenously expressed fusion proteins and data-independent acquisition (DIA) mass spectrometry (MS). We benchmark this pipeline with a study of five conserved Caenorhabditis elegans proteins-NEKL-2, NEKL-3, MLT-2, MLT-3, and MLT-4- that form two NEKL-MLT kinase-scaffold subcomplexes involved in membrane trafficking and actin regulation. Profiling of NEKL-MLT interactomes across 23 experiments validated our approach through the identification of known NEKL-MLT binding partners and conserved nekl-mlt genetic interactors, including the discovery of several novel functional interactors. Importantly, inclusion of methodological variations, stringent controls, and filtering strategies enhanced sensitivity and reproducibility, defining a set of intuitive quantitative metrics for routine assessment of experimental quality. We show that DIA-based interactome workflows produce physiologically relevant findings, even in the presence of experimental noise and variability across biological replicates. Our study underscores the utility of DIA mass spectrometry in proximity labeling applications and highlights the value of incorporating internal controls, quantitative metrics, and biological validation to enhance confidence in candidate interactors.

Keywords:   C. elegans ; NIMA-related Kinases; Proximity Labeling; TurboID

DOI:  https://doi.org/10.1038/s44318-025-00660-5
Mol Cell Proteomics. 2025 Dec 09. pii: S1535-9476(25)00579-1. [Epub ahead of print] 101480

Enhanced Identifications and Quantification through Retention Time Down-Sampling in Fast-Cycling diagonal-PASEF Methods.

Christopher R Below, Oliver M Bernhardt, Stephanie Kaspar-Schönefeld, Sander Willems, Edoardo Coronado, Ino D Karemaker, Bettina Streckenbach, Monika Pepelnjak, Luca Räss, Sandra Schär, Dennis Trede, Jonathan R Krieger, Tejas Gandhi, Roland Bruderer, Lukas Reiter.

  Data-independent acquisition (DIA) mass spectrometry is essential for comprehensive quantification of proteomes, enabling deeper insights into cellular processes and disease mechanisms. On the timsTOF platform, diagonal-PASEF acquisition methods have recently been introduced to directly and continuously cover the observed diagonal shape of the peptide precursor ion distributions. Diagonal-PASEF has already shown great promise and its adaptation as a routine workflow can be further pushed with improved method development as well as enhanced algorithmic solutions. Here, we conducted a systematic and comprehensive optimization of diagonal-PASEF for 17-minute gradients on the timsTOF HT in conjunction to Spectronaut. We demonstrate that Spectronaut fully supports all tested diagonal-PASEF methods independent of the number of slices or overlaps and with minimal user intervention required. We derive an optimized analysis strategy where we coupled diagonal-PASEF acquisitions to retention time down-sampling by summation (RTsum) and thereby exploit the fast-cycling nature of diagonal-PASEF methods. Through the combination of RTsum with diagonal-PASEF, we demonstrate that this strategy yields higher signal-to-noise ratios while retaining the peak shape for analytes of interest ultimately improving overall number of peptide and protein identifications of diagonal-PASEF. Importantly, combining RTsum with diagonal-PASEF improved overall identifications and quantitative precision when compared to dia-PASEF with variable quadrupole isolation widths and across different input amounts for cell line injections. We also tested the performance of diagonal-PASEF in controlled quantitative experiments where diagonal-PASEF outperformed dia-PASEF in the overall number of retained candidates below 1% or 5% error-rate, quantitative precision and identifications on peptide level and protein level. These data indicate that RTsum demonstrates a positive use case of the high sampling rate of diagonal-PASEF and might therefore be a valuable addition to existing analysis pipelines. Collectively, our findings imply that diagonal-PASEF is developing into a competitive alternative to dia-PASEF and that the data analysis options are making fast progress.

Keywords:  dia-PASEF; diagonal-PASEF; discovery proteomics

DOI:  https://doi.org/10.1016/j.mcpro.2025.101480
Anal Chim Acta. 2026 Jan 15. pii: S0003-2670(25)01303-0. [Epub ahead of print]1383 344909

From identification to quantification: A step-by-step workflow for intact glycopeptide analysis by mass spectrometry.

Zhenyu Sun, Ding Chiao Lin, Yingwei Hu, Hui Zhang.

Protein glycosylation is a universal post-translational modification that plays vital roles in regulating protein function, stability, and cell-cell communication. Consequently, aberrant glycosylation is implicated in various diseases. Characterizing glycoproteomic changes holds significant potential for elucidating their underlying mechanisms and advancing clinical applications. Mass spectrometry (MS) enables in-depth profiling of intact glycopeptides, providing information on both site-specific glycan compositions alongside corresponding peptide backbones. In this tutorial, we present an optimized workflow for the qualitative and quantitative analysis of intact glycopeptides using MS, building upon our recent work. We detail practical protocols for sample preparation and intact glycopeptide enrichment, compare label-free and label-based quantification methods, describe fragmentation strategies with optimized parameters, and review available tools for data analysis. Our goal is to support reproducible, high-quality MS-based glycoproteomic studies across diverse biological and clinical applications.

DOI: https://doi.org/10.1016/j.aca.2025.344909
Bio Protoc. 2025 Dec 05. 15(23): e5535

Quantitative Proteomics of Nitrosylated Proteins in Melanoma Using the Biotin-Switch Technique Combined With Tandem Mass Tag Labeling.

Vipin K Yadav, Bin Fang, John M Koomen, Jyoti Srivastava, Sanjay Premi.

  Protein S-nitrosylation is a critical post-translational modification that regulates diverse cellular functions and signaling pathways. Although various biochemical methods have been developed to detect S-nitrosylated proteins, many suffer from limited specificity and sensitivity. Here, we describe a robust protocol that combines a modified biotin-switch technique (BST) with streptavidin-based affinity enrichment and quantitative mass spectrometry to detect and profile nitrosylated proteins in cultured cells. The method involves blocking free thiols, selective reduction of nitrosothiols, biotin labeling, enrichment of biotinylated proteins, and identification by tandem mass tag (TMT)-based quantitative mass spectrometry. Additionally, site-directed mutagenesis is employed to generate "non-nitrosylable" mutants for functional validation of specific nitrosylation sites. This protocol provides high specificity, quantitative capability, and versatility for both targeted and global analysis of protein nitrosylation. Key features • Specific thiol blocking and labeling: Free thiols are blocked with N-ethylmaleimide, followed by selective reduction and biotinylation of S-nitrosothiols for precise nitrosylation detection. • Quantitative proteomics: TMT-labeling with high-resolution LC-MS/MS enables multiplexed, accurate quantification and comprehensive nitrosylome profiling with faster data acquisition and fewer missing values than label-free proteomics. • Functional mutagenesis: Site-directed mutagenesis of cysteine residues generates "non-nitrosylable" mutants to study nitrosylation's impact on protein function. • Versatile application: The protocol is adaptable for both targeted protein analysis and global nitrosylation profiling across diverse cell types and experimental conditions. This protocol is used in: Cancer Research (2025), DOI: 10.1158/0008-5472.CAN-24-0693.

Keywords:  Biotin-Switch Technique; Liquid chromatography–tandem mass spectrometry (LC–MS/MS); Protein S-nitrosylation; Quantitative proteomics; Site-directed mutagenesis; Streptavidin affinity enrichment; Tandem mass tag (TMT) labeling; Thiol blocking

DOI:  https://doi.org/10.21769/BioProtoc.5535
Mol Cell Proteomics. 2025 Dec 05. pii: S1535-9476(25)00580-8. [Epub ahead of print] 101481

In-cell Proteomics Enables High-Resolution Spatial and Temporal Mapping of Early Xenopus tropicalis Embryos.

Jian Sun, Xiaolu Xu, Shuo Wei, Yanbao Yu.

Early embryonic development requires tightly regulated molecular programs to coordinate cell division, fate specification, and spatial patterning. While transcriptomic profiling is widely performed, proteomic analyses of early vertebrate embryos remain limited due to technical challenges in embryonic sample preparation. In this study, we present an "in-cell proteomics" approach, which bypasses cell lysis and yolk depletion, processes individual embryos directly in functionalized filter devices, and generates liquid chromatography-mass spectrometry (LC-MS)-friendly samples in an extremely robust and streamlined manner. This single-vessel approach minimizes sample loss and technical variation, offering a highly sensitive and accurate alternative to low-input and low-cell quantitative proteomics. Coupled with field asymmetric ion mobility spectrometry (FAIMS) and single-shot data-independent acquisition (DIA) MS workflow, this approach enabled us to consistently quantify ∼6,200 proteins from a single Xenopus tropicalis embryo, representing the deepest proteomic coverage of early X. tropicalis developmental stages reported to date. Investigation of the temporal proteomes across five cleavage stages (from 1- to 16-cell) revealed a drastic proteomic shift between 2- and 4-cell stages, followed by more gradual transitions thereafter. Spatial analysis of dissected 8-cell blastomeres uncovered pronounced molecular asymmetry along the animal-vegetal axis, while dorsal-ventral differences were minimal. This study establishes a novel in-cell proteomics technology in conjunction with FAIMS and DIA-MS as a robust platform for high-resolution, low-input developmental proteomics analysis, and provides a comprehensive spatiotemporal protein atlas for early X. tropicalis embryos.

DOI: https://doi.org/10.1016/j.mcpro.2025.101481
Nat Commun. 2025 Dec 09. 16(1): 10600

A resource to empirically establish drug exposure records directly from untargeted metabolomics data.

Haoqi Nina Zhao, Kine Eide Kvitne, Corinna Brungs, Siddharth Mohan, Vincent Charron-Lamoureux, Wout Bittremieux, Runbang Tang, Robin Schmid, Santosh Lamichhane, Shipei Xing, Yasin El Abiead, Mohammadsobhan S Andalibi, Helena Mannochio-Russo, Madison Ambre, Nicole E Avalon, MacKenzie Bryant, Lindsey A Burnett, Andrés Mauricio Caraballo-Rodríguez, Martin Casas Maya, Loryn Chin, Lluís Corominas, Ronald J Ellis, Donald Franklin, Sagan Girod, Paulo Wender P Gomes, Lauren Hansen, Robert K Heaton, Jennifer E Iudicello, Alan K Jarmusch, Lora Khatib, Scott Letendre, Sarolt Magyari, Daniel McDonald, Ipsita Mohanty, Andrés Cumsille, David J Moore, Prajit Rajkumar, Dylan H Ross, Harshada Sapre, Mohammad Reza Zare Shahneh, Ruben Gil-Solsona, Sydney P Thomas, Caitlin Tribelhorn, Helena M Tubb, Corinn Walker, Crystal X Wang, Jasmine Zemlin, Simone Zuffa, David S Wishart, Pablo Gago-Ferrero, Rima Kaddurah-Daouk, Mingxun Wang, Manuela Raffatellu, Karsten Zengler, Tomáš Pluskal, Libin Xu, Rob Knight, Shirley M Tsunoda, Pieter C Dorrestein.

Despite extensive efforts, extracting medication exposure information from clinical records remains challenging. To complement this approach, here we show the Global Natural Product Social Molecular Networking (GNPS) Drug Library, a tandem mass spectrometry (MS/MS) based resource designed for drug screening with untargeted metabolomics. This resource integrates MS/MS references of drugs and their metabolites/analogs with standardized vocabularies on their exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data, supporting flexible summarization at multiple ontology levels to align with different research goals. We demonstrate its application by stratifying participants in a human immunodeficiency virus (HIV) cohort based on detected drug exposures. We uncover drug-associated alterations in microbiota-derived N-acyl lipids that are not captured when stratifying by self-reported medication use. Overall, GNPS Drug Library provides a scalable resource for empirical drug screening in clinical, nutritional, environmental, and other research disciplines, facilitating insights into the ecological and health consequences of drug exposures. While not intended for immediate clinical decision-making, it supports data-driven exploration of drug exposures where traditional records are limited or unreliable.

DOI: https://doi.org/10.1038/s41467-025-65993-5
Anal Chem. 2025 Dec 08.

xcms in Peak Form: Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Software Ecosystem.

Philippine Louail, Carl Brunius, Mar Garcia-Aloy, William Kumler, Norman Storz, Jan Stanstrup, Hendrik Treutler, Pablo Vangeenderhuysen, Michael Witting, Steffen Neumann, Johannes Rainer.

High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing data set scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms maintains long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.

DOI: https://doi.org/10.1021/acs.analchem.5c04338
Clin Proteomics. 2025 Dec 11.

In-depth analysis of data characteristics and comparative evaluation of dda and dia accuracy in label-free quantitative proteomics of biological samples.

Xun Zou, Lulu Wang, Yulu Chen, Hang Fu, Yuan Gao, Bin Liu, Minjia Tan, Linhui Zhai.

  Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) are widely used in MS-based proteomics. However, a comprehensive evaluation of their data characteristics-including protein and peptide identification, differential expression analysis, and the performance in revealing biological insights-remains lacking. In this study, we conducted a systematic comparison of DDA and DIA across three model sample types: one disease model, two drug-treated models, and their respective controls. Our analysis extended beyond conventional metrics such as total protein and peptide counts, precision, and accuracy, to include data completeness, detection of positive control markers, reproducibility, functional annotation reliability, and sources of methodological variation. The results demonstrated that DIA outperformed DDA in terms of protein identification (disease group: 7,735 vs. 5,067; drug-treated group 1: 7,987 vs. 4,605), quantitative coverage (average quantifiable protein ratio: DIA 98-99% vs. DDA 95-96%), and reproducibility (intragroup correlation coefficients: DIA > 0.98 vs. DDA 0.93-0.98). We also found DIA exhibited lower variability (intragroup CV < 10% vs. > 15% for DDA) and improved accuracy for low-abundance and housekeeping proteins. Additionally, the functional enrichment analyses further revealed DIA's superior capability in detecting pathway activation. Finally, discrepancies between DIA and DDA were primarily attributed to proteins identified with ≤ 5 peptides, the exclusion of single-peptide proteins enhanced overall data quality. Overall, this study systematically assess the overall capabilities of DDA and DIA approaches in uncovering biologically relevant findings and driving mechanistic insights within authentic pharmacological and disease models, thereby offering practical guidance for methodological choices in future research.

Keywords:  DDA; DIA; Interferon pathway; Mass spectrometry quantification; Proteomics; Reproducibility

DOI:  https://doi.org/10.1186/s12014-025-09572-2
Life Sci Alliance. 2026 Feb;pii: e202503529. [Epub ahead of print]9(2):

A step-by-step guide to performing cancer metabolism research using custom-made media.

Sophie Seifert, Francisco Yanqui-Rivera, Tim Kühn, Lena Elise Høyland, Toman Borteçen, Bella Agranovich, Lara Elea Eckhardt, Martin Herdt, Alessa Henneberg, Verena Panitz, Faisal Hayat, Marie Migaud, Gernot Poschet, Ifat Abramovich, Eyal Gottlieb, Jeroen Krijgsveld, Mathias Ziegler, Mirja Tamara Prentzell, Christiane A Opitz.

The preparation of custom-made media offers precise control over nutrient composition, enabling detailed studies of cellular metabolism. We demonstrate how self-made media formulations enable diverse assay designs and readouts to assess cancer metabolism. Self-made media can be used in Seahorse assays to measure mitochondrial respiration under defined conditions. In nutrient deprivation experiments, amino acid or vitamin removal can uncover how cancer cells adapt to metabolic stress. Using labeled amino acids enables analysis of nascent protein synthesis and translational regulation, while stable-isotope tracing reveals metabolic fluxes through key pathways. This guide presents a suite of metabolic assays using custom-made media, covering experimental design, the selection of controls, sample preparation, data acquisition, and interpretation. The accompanying online media calculator "Media Minds" streamlines the creation of custom media formulations, ensuring accuracy and reproducibility.

DOI: https://doi.org/10.26508/lsa.202503529
Int J Mol Sci. 2025 Nov 28. pii: 11570. [Epub ahead of print]26(23):

Optimization of Methods for the Quantitative Analysis of Global Cell Surface Proteome and Cell Surface Polarization.

Katalin Kuffa, Tamás Langó, András Czirók, Júlia Tárnoki-Zách, Szilvia Bősze, Gábor E Tusnády, Zoltán Szabó.

  The cell surface proteome of polarized epithelial cells plays a central role in barrier function, signaling, and vectorial transport, yet the quantitative characterization of their surface proteins remains technically challenging. We developed an optimized chemoproteomic strategy specifically tailored to studying the surface proteins of polarized cells while keeping membrane integrity intact. By applying a disulfide-linked membrane-impermeable biotin reagent, labeling was restricted to extracellular regions of transmembrane proteins (TMPs) and secreted proteins, thereby minimizing contributions from intracellular contaminants. Following biotinylated peptide-level or protein-level enrichment and mass spectrometric analysis, we systematically compared data-dependent (DDA) and data-independent acquisition (DIA) approaches, showing that while DIA increases proteome coverage, DDA more reliably identifies biotinylated peptides in our studies. To ensure robustness, we established replicate-based normalization and contaminant-aware quality control metrics that minimize biases from proteins in cell culture medium and damaged cells. The application of the workflow to Madin-Darby canine kidney (MDCK) II epithelial monolayers enabled the large-scale quantification of apical versus basolateral domains, yielding over 2100 proteins, with 235 showing significant polarized distribution, in agreement with known biology. This method offers high specificity for the extracellular labeling and quantitative resolution of cell surface protein (CSP) polarization, providing a powerful platform for studying epithelial biology and identifying extracellular epitopes relevant to diagnostics and therapeutic targeting.

Keywords:  cell surface; polarization; proteomics

DOI:  https://doi.org/10.3390/ijms262311570
J Proteome Res. 2025 Dec 08.

Advancing Blood Proteome Analysis Past the Plasma Age: Mass Spectrometry of Whole Blood and Plasma Collected with Volumetric Absorptive Microsampling Devices.

Elisabeth Karsten, Natasha Lucas, Cameron Hill, Rosalee McMahon, Ben Herbert.

  Mass spectrometry-based proteomics of blood faces significant challenges due to the high dynamic range of proteins, with abundant proteins masking detection of low-abundance biomarkers. This study evaluated a novel volumetric absorptive microsampling (VAMS) method for blood proteome analysis compared with conventional sample processing. Plasma and whole blood samples were processed using three different methods: liquid sample analysis, VAMS-direct processing, and our VAMS-optimized protocol involving selective washing steps. All samples were analyzed using shotgun LC-MS/MS. The VAMS-optimized method demonstrated superior performance, increasing protein identifications 4-fold for plasma (745 vs 3024 proteins) and 2.1-fold for whole blood compared to liquid samples, while improving quantitative reproducibility with mean coefficients of variation below 11%. Further, the VAMS-optimized approach produced more protein identifications and lower %CVs for single-spun plasma and whole blood when compared with a commercially available protein corona bead assay. Whole blood samples showed greater robustness than plasma, with reduced variability from preanalytical processing steps and improved storage stability at room temperature for up to 14 days. The VAMS-optimized approach addresses key limitations in current blood proteomics workflows, enabling patient-centric sample collection for longitudinal studies while maintaining analytical depth and reproducibility, essential for biomarker discovery applications.

Keywords:  VAMS; plasma; protein corona beads; proteomics; whole blood

DOI:  https://doi.org/10.1021/acs.jproteome.5c00687
Expert Rev Proteomics. 2025 Dec 12.

Future of clinical proteomics: could targeted multi-protein panels supplant low-throughput methods?

Poornima Ramesh, Thottethodi Subrahmanya Keshava Prasad.

   INTRODUCTION: Mass spectrometry (MS)-based proteomics, especially the targeted applications, hold great potential as Laboratory Developed Tests (LDTs) for clinical applications. They are suitable for widespread clinical use due to their impressive sample/protein multiplexing capabilities, analytical sensitivity and replicability, adaptability to diverse clinical samples, and highly evolved sample processing protocols. Although multiple LDTs have been developed and approved by regulatory agencies, various areas still need improvement.
AREAS COVERED: This article focuses on introducing MS-based LDT as a potential clinical technology, its superiority over low-throughput or antibody-based methods, existing hurdles in the adoption of such LDTs in clinics, what they can adopt to, and regulatory and analytical considerations that need to be addressed to develop a robust MS-based LDT.
EXPERT OPINION: Recent efforts to optimize instrumentation and sample preparation for MS-based applications have made these LDTs promising contenders for clinical utilization. With focused research to answer quality assessment requirements, data interpretability, method scalability, and ease of use, MS-based LDTs can revolutionize clinical diagnostics. Drawing parallels to other omics technologies, these LDTs can address the long-standing multiplexing hinge and further establish multi-protein diagnostics as next-generation diagnostics of low-throughput methods.

Keywords:  Mass spectrometry; analytical accuracy and precision; biomarker; clinical proteomics; clinical sensitivity and specificity

DOI:  https://doi.org/10.1080/14789450.2025.2604161
J Chromatogr A. 2025 Dec 04. pii: S0021-9673(25)00950-1. [Epub ahead of print]1766 466606

Advances in LC-MS strategies for comprehensive characterization and quantification of antibody-drug conjugates in preclinical and clinical settings.

Devendra Kumar, Neerja Trivedi.

  Antibody-Drug Conjugates (ADCs) represent a rapidly growing class of targeted therapeutics, combining the specificity of monoclonal antibodies with the potency of cytotoxic payloads. The structural complexity and heterogeneity of ADCs arising from variations in drug to antibody ratio (DAR), conjugation sites, and post translational modifications demand advanced analytical strategies for comprehensive characterization and quantification throughout development. Liquid chromatography coupled with mass spectrometry (LC-MS) has emerged as an indispensable platform for ADC analysis, offering high sensitivity, selectivity, and structural resolution across multiple levels. This review highlights recent advances in LC-MS workflows, including intact mass analysis, subunit/middle-down profiling, peptide mapping, and bioanalytical assays for free payloads and catabolites. We discuss emerging technologies such as multi-attribute methods (MAM), native MS, ion mobility, and hybrid ligand-binding assay (LBA)-LC-MS platforms that enhance throughput and analytical depth. Special focus is given to quantification strategies in biological matrices and regulatory expectations, including International Council for Harmonization (ICH) M10 and Food and Drug Administration (FDA) guidance on method validation. As ADC pipelines expand into new therapeutic areas, the integration of automation and AI-driven data processing is poised to transform LC-MS into a high throughput, intelligent tool for both product characterization and clinical monitoring. These innovations collectively support safer, more effective ADC development from discovery through approval.

Keywords:  Antibody drug conjugates; Drug to antibody ratio; Immunocapture; LC–MS; Multi-attribute method; Peptide mapping

DOI:  https://doi.org/10.1016/j.chroma.2025.466606
Int J Mol Sci. 2025 Nov 27. pii: 11498. [Epub ahead of print]26(23):

Data-Independent Acquisition (DIA)-Based Proteomics for the Identification of Biomarkers in Tissue Washings of Endometrial Cancer.

Lorenzo Monasta, Valeria Capaci, Feras Kharrat, Milena Ciampechini, Nour Balasan, Andrea Conti, Valentina Golino, Pietro Campiglia, Michelangelo Aloisio, Danilo Licastro, Giovanni Di Lorenzo, Federico Romano, Giuseppe Ricci, Blendi Ura.

  Endometrial cancers (ECs) are mainly adenocarcinomas arising from the uterine endometrium. In this work, we employed data-independent acquisition (DIA) mass spectrometry (MS)-based label-free quantification (LFQ-MS) proteomics to analyze the proteome of tissue washings collected from 25 control (CTRL) subjects, 25 patients with low-grade type 1 endometrial cancer (EC), and 24 patients with high-grade type 1 EC. Following quantification and statistical analysis, we identified 42 proteins able to discriminate CTRL from EC patients, and 151 proteins differentiating high-grade EC cases from low-grade EC cases. Notably, PRRC2A and SYDE2 effectively distinguished both EC patients from controls and advanced EC cases from low-grade EC cases. Validation by Western blot analysis in an independent cohort comprising 19 CTRL patients, 19 patients with low-grade EC, and 19 patients with high-grade EC confirmed the upregulation of PRRC2A and SYDE2. These proteins are implicated in the translocation of SLC2A4, the regulation of MECP2, and extracellular matrix (ECM) proteoglycan pathways, all of which are associated with tumor growth. Our results demonstrate that DIA-based proteomic analysis of tissue washings enables the identification of potential biomarkers for endometrial cancer (EC). Moreover, this study highlights tissue washings as a promising biological fluid for biomarker discovery in EC.

Keywords:  biomarker; endometrial cancer; mass spectrometry; proteomics; tissue washings

DOI:  https://doi.org/10.3390/ijms262311498
Anal Bioanal Chem. 2025 Dec 11.

Mapping the unknown: an anchor-based molecular networking workflow for comprehensive identification of new psychoactive substances and analogues.

Carla Fournié, Laurence Labat, Mathieu Le Seigle, Bruno Mégarbane, Pascal Houzé, Romain Magny.

  The rapid emergence and structural diversity of New Psychoactive Substances (NPS) challenge toxicological screening, which usually relies on targeted detection of known compounds. To address this limitation, we developed a novel, database-independent analytical workflow capable of anticipating unknown or emerging NPS in complex biological matrices. A comprehensive workflow combining sample preparation, orthogonal liquid chromatography, and high-resolution tandem mass spectrometry (LC-HRMS/MS) was developed for the identification of new structures and their metabolites. Three extraction protocols were benchmarked in both reversed-phase (RP-LC) and hydrophilic interaction (HILIC) chromatographic modes to maximize analyte coverage. The method was optimized using 77 drug standards and extended to a mixture of 122 compounds (Mix122) to build an Anchor-Based Molecular Network (ABMN). Biological samples from presumed NPS consumers were integrated into the reference network to connect patient-derived features with anchor compounds. Data were processed with MZmine for feature extraction, MetGem for molecular networking, and SIRIUS for in silico structure prediction. Protocol P1 (protein precipitation and analyte concentration) provided the best extraction, recovering 90% of analytes with high chromatographic quality. P1 achieved the lowest limit of identification, enabling MS/MS acquisition for 100% of compounds at 50 ng/mL, 97% at 5 ng/mL, and 42% at 0.5 ng/mL. RP-LC and HILIC proved complementary, improving analyte coverage. The Mix122 dataset yielded chemically coherent clusters, supporting integration of clinical samples. Several structurally related analogues such as bromazolam, fluoromethamphetamine, or MDPHP were identified. The ABMN-based LC-HRMS/MS strategy provides a robust and transferable analytical platform for comprehensive and sensitive screening of unknown NPS, even at trace levels down to 1 ng/mL.

Keywords:  Analytical workflow; Liquid chromatography-high-resolution tandem mass spectrometry; New psychoactive substances; Orthogonal chromatography; Sample preparation; Untargeted metabolomics

DOI:  https://doi.org/10.1007/s00216-025-06262-8
Cancer Res. 2025 Dec 11.

The sour regulation of metabolism: acidity challenges tumor cells while fueling their metabolic survival.

Bruna Martins Garcia, Patricia Altea-Manzano.

The tumor microenvironment imposes diverse metabolic challenges to cancer cells. Overcoming these challenges is essential for survival, proliferation, and dissemination. However, how cancer cells cope with the harsh environment and how the different coexisting stresses affect the tumor in vivo is unknown. Recently, Groessl, Kalis and colleagues published their findings in Science showing that acidosis outweighs all other stresses and plays a major role in the adaptation to them. Mechanistically, acidosis inhibits the ERK-DRP1 pathway, resulting in mitochondria elongation, which triggers a metabolic shift from glycolysis to oxidative phosphorylation. These findings highlight the plasticity of cancer cell mitochondria and refute the previous belief that cancer mitochondria are inherently dysfunctional. Indeed, inhibition of mitochondrial fusion or oxidative phosphorylation in acidic tumors is sufficient to promote cell death. Thus, enhancing respiration under acidosis comes to light as an essential metabolic adaptation to cancer survival and proliferation and targeting how cancer cells adapt to acidosis emerges as a new avenue for therapy.

DOI: https://doi.org/10.1158/0008-5472.CAN-25-5633
Front Immunol. 2025 ;16 1693954

Mast cell metabolism in cancer: an underexplored frontier demanding more attention.

Barbara Frossi, Giuseppina Beatrice Scialpi, Silvia Tonon, Elena Jachetti.

  Cancer metabolism is gaining considerable attention. Tumor cells are characterized by a peculiar metabolic state to sustain the continuous demand of energy and metabolites needed for their proliferation and long-term survival. Such metabolic alterations extend beyond cancer cells, affecting multiple components of the tumor microenvironment (TME), including immune cells, stromal cells, and endothelial structures, and are influenced by both local and systemic conditions. Mast cells (MCs) are innate immune cells capable of both pro- and anti- tumorigenic functions and with the potential to modulate the activity of bystander immune cells. Nevertheless, despite their established importance in the TME, the impact of MCs in modulating cancer metabolism remains largely unexplored. This review outlines current findings regarding the metabolic conditions in the TME that modulate MC function, and, vice versa, how MC-derived metabolites can influence tumor progression, acting both on cancer and stromal cells. We focus on four main altered conditions in the TME: glucose metabolism, amino acid availability, lipid composition, and hypoxia. As studies investigating MC metabolism in cancer are limited, we also discuss relevant literature addressing how metabolic stimuli influence MC activity, as well as the effects of MC-derived metabolites on target cells, in non-cancer physiological or pathological conditions, to highlight possible mechanisms that deserve further investigation in cancer settings. Deeper investigation of MC-related metabolic networks in the TME is needed, not only to elucidate their functional modulation in response to current metabolic interventions, but also to explore their potential as therapeutic targets in the context of cancer metabolism.

Keywords:  immunometabolism; mast cells; metabolic interventions; tumor metabolism; tumor microenvironment; tumor-stroma crosstalk

DOI:  https://doi.org/10.3389/fimmu.2025.1693954