bims-mascan 2026-07-26 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–07–26
24 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Mass spectrometry-based metabolomics for studying gut microbiota-derived metabolites in biological samples.
Evaluation of Protein Preparation Methods for Quantitative Analysis of Pig Muscle Proteome.
Automated magnetic-bead-assisted extraction followed by chemical isotope derivatization with 4-(aminomethyl)-N,N-dimethylaniline-d0/d6 for determination of oxylipins derived from n-6 and n-3 polyunsaturated fatty acids by LC-MS/MS.
Recent advances in the bioanalysis of acylcarnitines: Methodologies, challenges, and clinical perspectives.
'If the shoe fits?' - Benchmarking plasma proteomic sample-preparation workflows across human and rat biofluids.
A systematic DoE approach for optimizing urinary LC-HRMS metabolomics: enhancing reliability in bladder cancer profiling.
Bridging complexity and accessibility in metabolomics with MetaboApps.
Identifying lipid-protein interactomes with functional lipid probes.
MSstatsQC-ML: A Supervised Machine Learning Approach to Monitor System Suitability and Quality Control in Mass Spectrometry-Based Proteomics.
Advanced technologies of single-cell metabolomics unveiling cellular metabolic heterogeneity for biological and biomedical research.
Comparative Metabolomics Reveals the Production of Sulfated Metabolites by Human Gut Bacteria.
Competitive catabolism drives hyperglycemia and hyperinsulinemia in obesity.
Clinical proteomics in inborn errors of metabolism: from biomarker discovery to implementation.
Formic Acid Enhanced Single-Pot Sample Preparation to Improve Identification Coverage of Membrane Proteome of Laser Capture Microdissected Tissue Sections.
Improved Protein Identification in Shotgun Proteomics with a Group-Level Extension of the LPGF Model.
The evolution of DESI-MSI: A comprehensive look at preprocessing and quantification strategies for metabolomics.
A One Health Framework for Proteomics Across the Tree of Life to Advance Food Security, Animal Health, and Ecosystem Resilience.
nh-PHIP Hyperpolarization for Biological Mixture Analysis.
Towards spatial lipid profiling by using mass spectrometry: analytical challenges and applications.
Plasma LC-HRMS metabolomics in parkinsonian syndromes: comparative evaluation of sample preparation protocols for exploratory metabolic signatures.
Development of a liquid chromatography-ion mobility-mass spectrometry method for the screening of d-amino acid alterations in plasma.
Proteomic and amino acid profiling of SK-MEL-30 melanoma reveals metabolic and structural changes.
LABEL-FREE TARGETED PROTEOMICS DATA ANALYSIS WORKFLOW SELECTION - BENCHMARKING AI-BASED AND DATA-DRIVEN APPROACHES.
Development and Validation of an LC-MS/MS Method for the Quantification of HY-078020 in Human Plasma Using Liquid-Liquid Extraction and Its Application to Human Pharmacokinetic Study.

J Food Drug Anal. 2026 Jun 01. 34(1): 2-19

Mass spectrometry-based metabolomics for studying gut microbiota-derived metabolites in biological samples.

Ya-Ting Lin, Huai-Hsuan Chiu, Chih-Ning Cheng, Ching-Hua Kuo.

Gut microbiota produces a wide range of metabolites and plays a critical role in maintaining host health. Dysregulation of these metabolites can influence host metabolism through systemic circulation, contributing to the development of various diseases, including immunological, neurological and cancer-related disorders. Chromatography coupled with mass spectrometry (MS) has emerged as a powerful analytical approach, offering high sensitivity and resolution for studying gut microbiota-related metabolites. This review provides a comprehensive overview of chromatographic MS-based methods applied to the study of the gut microbial metabolome. We summarize strategies for sample collection, storage, and preparation of commonly analyzed sample types, including feces, plasma/serum, urine, and tissue samples. In addition, we included the main chromatographic MS-based approaches, as well as data analysis techniques, for investigating the gut microbial metabolome. The characteristics and utility of liquid chromatographicmass spectrometry (LC-MS), gas chromatographic-mass spectrometry (GC-MS), and capillary electrophoresis-mass spectrometry (CE-MS) were discussed in the context of providing broader coverage of gut microbiota-derived metabolites with diverse physicochemical properties. Finally, we summarize recent studies that have employed chromatographic MS-based approaches to investigate gut microbiota-related disease. Through the integration of appropriate sample handling and advanced analytical strategies, a deeper understanding of host-microbiota interactions and their roles in health and disease can be achieved.

DOI: https://doi.org/10.38212/2224-6614.3568
OMICS. 2026 Jul 23. 15578100261464022

Evaluation of Protein Preparation Methods for Quantitative Analysis of Pig Muscle Proteome.

Didier Viala, Robin Maillet, Arnaud Delavaud, Christophe Chambon, Thierry Sayd, Michel Hébraud.

  High-throughput shotgun proteomics is often hindered by the incompatibility of detergents with mass spectrometry (MS), making sample preparation a critical bottleneck for accuracy and robustness. This challenge is amplified in muscle proteomics, where the high dynamic range of protein abundance requires highly efficient and reproducible workflows to capture low-abundance proteins. To address this, we performed a systematic benchmarking of five preparation methods-stacking-gel (SG), tube-gel (TG), solid-phase extraction (SPE), filter-aided sample preparation (FASP), and suspension traps (S-TRAP)-using the sarcoplasmic fraction of pig muscle. The protein extracts obtained were subjected to label-free semi-quantitative proteomic analysis using high-performance nano-liquid chromatography coupled to tandem MS. Qualitative and quantitative results were compared using bioinformatics and biostatistics tools. Our study identified 530 proteins with significant variations across methods. S-TRAP provided the highest identification depth, capturing the broadest proteome coverage. Conversely, the TG method demonstrated superior quantitative reproducibility, essential for detecting subtle physiological changes. For translational research, such as meat quality science or muscle-related clinical models, our findings imply that S-TRAP is the preferred choice for discovery-phase proteomics (biomarker hunting), while TG or SG should be prioritized for high-precision validation studies.

Keywords:  FASP; LC-MS/MS; S-TRAP; SPE; internal standard; label-free quantification; proteins; sample preparation techniques; sarcoplasmic fraction; stacking gel; tube-gel

DOI:  https://doi.org/10.1177/15578100261464022
J Pharm Biomed Anal. 2026 Jul 16. pii: S0731-7085(26)00327-4. [Epub ahead of print]281 117659

Automated magnetic-bead-assisted extraction followed by chemical isotope derivatization with 4-(aminomethyl)-N,N-dimethylaniline-d0/d6 for determination of oxylipins derived from n-6 and n-3 polyunsaturated fatty acids by LC-MS/MS.

Junhe Chen, Peng Cui, Chengtao Nie, Fan Wu, Qian Wang, Hongwei Zhou, Fangbo Xia.

  Oxylipins derived from n-6 and n-3 polyunsaturated fatty acids (PUFAs) are crucial signaling molecules involved in various physiological and pathological processes. However, the quantification of these metabolites remains challenging due to their low abundance, high structural similarity, and poor electrospray ionization efficiency. Furthermore, existing methods often suffer from complex sample preparation and the lack of cost-effective, reliable internal standards. Therefore, establishing a highly sensitive, automated, and standardized analytical platform for comprehensive oxylipin profiling is the prerequisite for elucidating their complex roles in disease pathogenesis. To address these challenges, we developed a highly sensitive and precise liquid chromatography-tandem mass spectrometry (LC-MS/MS) method, incorporating automated sample preparation through magnetic solid-phase extraction and chemical isotope derivatization using 4- (aminomethyl)-N,N-dimethylaniline-d0/d6 (4-AND-d0/d6) reagents. This approach enables the quantification of 69 oxylipins derived from n-6 and n-3 PUFAs, achieving a remarkable 55- to 1000- fold increase in MS detection sensitivity compared to non-derivatized analysis. Additionally, a retention index (RI)-based predictive model was established to facilitate the screening and identification of unknown regio-isomers. As a proof of concept, this method was applied to quantify oxylipins in serum from a neonatal hypoxic-ischemic encephalopathy (HIE) rat model and to evaluate its applicability in complex biological matrices. Our method offers a sensitive, automated, and reliable tool for oxylipin profiling, with potential applications in biomarker discovery and lipid mediator research. This work presents the first integrated LC-MS/MS strategy combining automated magnetic-bead-assisted extraction and chemical isotopic derivatization for oxylipin analysis, enabling the highly sensitive quantification of 69 target analytes. This study is significant for establishing a robust retention index-based predictive model that facilitates the screening and identification of unknown regio-isomers, offering a reliable and high-throughput analytical solution for clinical biomarker discovery and lipid mediator profiling.

Keywords:  Chemical Isotope Derivatization; HIE Model; LC-MS/MS; Oxylipins; Quantification; Retention Index

DOI:  https://doi.org/10.1016/j.jpba.2026.117659
Pharm Sci Adv. 2026 Dec;4 100130

Recent advances in the bioanalysis of acylcarnitines: Methodologies, challenges, and clinical perspectives.

Jiaxin Zheng, Wanting Chen, Fengxia Qin, Pei Zhang, Fengguo Xu.

  Acylcarnitines (ACs) are a diverse class of fatty acid esters of L-carnitine that serve as critical mediators in energy homeostasis and mitochondrial function. Beyond their classical role in newborn screening for inborn errors of metabolism, ACs have emerged as promising biomarkers for complex pathologies, including cardiovascular diseases, diabetes, and drug-induced toxicities. However, the accurate quantification and comprehensive profiling of ACs in biological matrices remain analytically challenging due to their broad polarity range, vast concentration disparities, and the presence of isomers. This review provides a comprehensive overview of the current bioanalytical strategies for ACs, covering sample preparation techniques and detection platforms, with a focus on liquid chromatography-mass spectrometry. Special attention is given to the differentiation of isomers. Finally, we discuss the clinical applications of ACs profiling and highlight future perspectives, including the integration of ion mobility spectrometry, automated high-throughput workflows, spatial and single-cell metabolomics, and AI-driven analytics, to pave the way for precision medicine.

Keywords:  Acylcarnitines; Clinical diagnosis; Mass spectrometry; Metabolic disease; Sample pretreatment

DOI:  https://doi.org/10.1016/j.pscia.2026.100130
Mol Cell Proteomics. 2026 Jul 18. pii: S1535-9476(26)00122-2. [Epub ahead of print] 101626

'If the shoe fits?' - Benchmarking plasma proteomic sample-preparation workflows across human and rat biofluids.

Samantha J Emery-Corbin, Joel R Steele, Dylan H Multari, Erwin Tanuwidjaya, Iresha Hanchapola, Komagal Kannan Sivaraman, Han-Chung Lee, Scott A Blundell, Idrish Ali, Terence J O'Brien, Pouya Faridi, Ralf B Schittenhelm.

The push for new clinical biomarkers has seen rapid innovation in biofluid analysis, particularly for plasma. For mass-spectrometry (MS)-based analysis, achieving depth and quantitative accuracy whilst ensuring throughput continues to shape plasma methods development. Numerous workflows have emerged that mitigate high-abundance suppression and expand dynamic range, especially when paired with next-generation MS instrumentation. Yet systematic evaluations that also consider biological variables (e.g., biofluid type, species) and technical parameters (e.g., MS methods) are limited. Here, we benchmarked eight sample-preparation workflows spanning neat approaches (SP3, STrap), depletion (perchloric acid, PerCA), and corona-enrichment strategies (MagNet HILIC/SAX, Enrich-iST, ProteoNano). We compared their performance across human plasma, human serum, and rat plasma, analysing all samples on an Orbitrap Astral (Thermo) using two plasma-optimised data-independent acquisition (DIA) methods: one discovery-maximised and one throughput-maximised. We identified 2,726 human and 3,767 rat proteins across workflows and methods, including ∼1,000 from neat plasma. Increasing throughput incurred a ∼20-30% reduction in depth, depending on workflow and species. EV-enrichment produced the deepest proteomes but with distinct compositions relative to neat, depleted, and secreted-protein-enriched samples, revealing a unique sub-proteome niche. Several workflows also performed markedly better in rat plasma, supporting improved sensitivity for preclinical analyses. Enrichment or depletion dramatically reshaped the balance of tissue- and cell-specific proteins detectable in plasma, suggesting that workflow choice should be guided by the organs, immune targets, or inflammatory signals most relevant to the study. In this vein, statistical analysis of differentially abundant proteins showed that >90% of detected proteins were significantly altered between workflows, with the largest numbers arising from the corona-enrichment strategies, underscoring how strongly workflow choice shapes the downstream proteome. Taken together, these findings emphasise a rapidly expanding plasma methodological landscape, where the most effective workflow is the one most precisely tailored to a cohort's biology.

DOI: https://doi.org/10.1016/j.mcpro.2026.101626
Anal Bioanal Chem. 2026 Jul 24.

A systematic DoE approach for optimizing urinary LC-HRMS metabolomics: enhancing reliability in bladder cancer profiling.

Anastasiia Frolova, Mikhail Vokuev, Yurii Ikhalainen, Daria Prosuntsova, Igor Rodin.

  Untargeted urinary metabolomics presents significant challenges in analytical reproducibility and biological interpretation, particularly in the context of clinical oncology. This study presents a systematically optimized liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflow for bladder cancer (BCa) biomarker discovery. To address variability in sample preparation, a two-stage design of experiments (DoE) approach was applied to systematically optimize key parameters affecting metabolite extraction efficiency, thereby improving the reproducibility of subsequent non-invasive profiling. The performance of the workflow was evaluated through the systematic assessment of instrumental stability and injection precision using pooled quality control (QC) samples. Following peak picking and alignment, a comprehensive raw dataset of 15,344 metabolic signals was generated, leading to the putative identification of 854 compounds. Unsupervised principal component analysis (PCA) demonstrated reproducible instrumental performance, indicated by tight QC sample clustering. From the total clinical cohort of 107 patients, a demographically matched sub-cohort of 50 individuals was evaluated to suppress confounding physiological noise. This comparative model revealed distinct disease-specific clustering and demonstrated significant perturbations in the tryptophan metabolic axis, membrane lipid remodeling, and enhanced proteolytic activity, characterized by an evident peptide overflow, associated with BCa progression. This systematically optimized methodology provides a reliable analytical approach for identifying non-invasive diagnostic panels, supporting the implementation of efficient laboratory workflows aligned with Analytics 5.0 principles.

Keywords:  Analytics 5.0; Bladder cancer biomarkers; Design of experiments (DoE); LC-HRMS; Untargeted metabolomics

DOI:  https://doi.org/10.1007/s00216-026-06678-w
Nat Methods. 2026 Jul 24.

Bridging complexity and accessibility in metabolomics with MetaboApps.

Helena Mannochio-Russo, Wilhan D Gonçalves Nunes, Haoqi Nina Zhao, Kine Eide Kvitne, Shipei Xing, Harsha Gouda, Julius Agongo, Ipsita Mohanty, Vincent Charron-Lamoureux, Prajit Rajkumar, Abzer K Pakkir Shah, Axel Walter, Rithi Krishnaraj, Yasin El Abiead, Patrick C Ferreira, Simone Zuffa, Abubaker Patan, Andrés Mauricio Caraballo-Rodríguez, Wout Bittremieux, Daniel Petras, Mingxun Wang, Pieter C Dorrestein.

DOI: https://doi.org/10.1038/s41592-026-03186-8
Nat Protoc. 2026 Jul 22.

Identifying lipid-protein interactomes with functional lipid probes.

Scotland E Farley, Gaelen Guzman, Berit Blume, Per Haberkant, Carsten Schultz, Fikadu G Tafesse.

Cellular lipids shape health and disease through specific protein interactions, yet lipid-protein networks remain poorly defined. Despite rapid advances in functional lipid probes, the field still lacks a practical, dedicated protocol for conducting lipid-protein interaction studies. We describe detailed methods for determining lipid interactomes within cells using multifunctionalized lipid derivatives. We provide a protocol that details how to (i) treat cells with lipid derivatives and perform photochemistry to obtain lipid-protein conjugates, (ii) extract cellular lysates for downstream analysis, (iii) perform click chemistry on lysates with a fluorophore and observe lipid-protein conjugates by in-gel fluorescence and (iv) perform click chemistry on lysates with azide beads and prepare lipid-protein conjugates for proteomic analysis. We provide context on important parameters for each step and include guidelines for controls, as well as suggestions for troubleshooting based on common problems encountered during the preparation of this protocol. This protocol enables identification of proteins that bind to specific lipids across diverse biological systems and cellular states. The entire workflow from cell treatment to complete proteomic sample preparation requires ~15 h over 4 d, depending on the type of experimental readout (in-gel fluorescence or proteomics) and the usage of pause points. Practitioners are expected to be familiar with standard biochemical techniques, such as sterile sample handling and tissue culture and gel electrophoresis. Additional skills are needed for mass spectrometric analysis, and collaboration with a proteomics core facility is recommended. The described procedures uniquely enable the identification of the protein interactors (the interactome) of select lipid species, providing for a major advance in the characterization of the biological roles of lipids in cellular systems.

DOI: https://doi.org/10.1038/s41596-026-01405-2
J Proteome Res. 2026 Jul 20.

MSstatsQC-ML: A Supervised Machine Learning Approach to Monitor System Suitability and Quality Control in Mass Spectrometry-Based Proteomics.

Eralp Dogu, Shantam Gupta, Roger Olivella, Eduard Sabido, Olga Vitek.

  Mass spectrometry offers numerous ways to analyze the composition, function, and interactions of complex proteomes. Unfortunately, it suffers from the variation introduced by technological artifacts, which reduces the reproducibility and reliability of the results. In order to detect and minimize deviations from optimal performance, researchers monitor standard mixtures of proteins or peptides and various associated metrics using statistical summaries. Although this approach to monitoring multiple analytes and metrics is often beneficial, most of these methods do not scale well to multivariate situations. In this paper, we present MSstatsQC-ML, a machine learning approach to quality control that optimizes decision-making from standard mixtures with many analytes and metrics. MSstatsQC-ML combines machine learning classifiers with experimental design strategies to simulate possible suboptimal MS runs that have not yet been observed. For training the classifiers, the proposed approach incorporates informative features from metrics. Analysis of longitudinal values of each feature allows us to interpret the root causes of suboptimal performance and helps to design preventive actions. In evaluations on quality control data from discovery and targeted proteomic experiments, MSstatsQC-ML reduced error rates of detecting suboptimal performance and outperformed traditional approaches. MSstatsQC-ML is available as part of the open-source MSstatsQC R/Bioconductor package.

Keywords:  mass spectrometry; proteomics; quality control; statistical process control; supervised machine learning

DOI:  https://doi.org/10.1021/acs.jproteome.6c00186
J Food Drug Anal. 2026 Jun 01. 34(1): 45-54

Advanced technologies of single-cell metabolomics unveiling cellular metabolic heterogeneity for biological and biomedical research.

Shang-Che Kuo, Ting-Wei Tsao, I-Fan Wang, Ying-Lan Chen.

Metabolomics provides direct insights into cellular physiology, yet it faces greater analytical challenges compared to genomics and transcriptomics due to the chemical diversity, instability, and environmental sensitivity of metabolites. Conventional bulk metabolomics averages signals across cell populations, thereby masking cellular heterogeneity critical for understanding disease mechanisms. Recent breakthroughs in single-cell metabolomics (SCM), driven by advances in mass spectrometry, microfluidics, isotope tracing, and spatial omics, have enabled the detection of metabolic diversity at unprecedented resolution. SCM has uncovered cell-type-specific biomarkers, revealed metabolic reprogramming in cancer and immunity and revealed disease progression. These studies highlight SCM's transformative potential in biomarker discovery, clinical diagnostics, and precision medicine. Despite rapid progress, SCM remains limited by low metabolite abundance, instability during cell handling, lack of standardized quantification methods, and challenges in integrative multi-omics analysis. Future developments will require innovations to improve sensitivity and spatial resolution, establish cross-laboratory quality control frameworks, and apply artificial intelligence for data interpretation. With continued technological convergence, SCM is poised to evolve from a niche research tool into a cornerstone platform for biological and biomedical research.

DOI: https://doi.org/10.38212/2224-6614.3590
J Am Chem Soc. 2026 Jul 20.

Comparative Metabolomics Reveals the Production of Sulfated Metabolites by Human Gut Bacteria.

Gabriel D D'Agostino, Cecilia H Kim, Jinseok Park, Yancong Zhang, Bashar Amer, Eric A Franzosa, Susan S Bird, Curtis Huttenhower, Jun R Huh, A Sloan Devlin.

The sulfated metabolome─the collection of sulfate-containing metabolites─is an emerging source of structurally unique bioactive compounds that influence metabolism, immune responses, and neurological function. Recent studies have shown that, in addition to host enzymes, gut bacteria also encode sulfotransferase enzymes (SULTs) that generate sulfated metabolites. However, the substrate scope of characterized gut bacterial SULTs remains narrow, and comprehensive discovery is limited by a lack of methods to detect and assign sulfated metabolites in complex samples. Here, we develop a comparative metabolomics workflow that leverages the universal SULT cofactor 3'-phosphoadenosine-5'-phosphosulfate (PAPS) to incorporate heavy (34S) or light (32S) sulfur into sulfated metabolites, enabling discovery of microbiome-dependent sulfated compounds. By applying this approach in both "bottom-up" bacterial culture and "top-down" in vivo studies, we find that gut bacteria sulfonate hydroxy fatty acids. We identify a gut commensal microbe, Eubacterium ramulus, that performs this transformation, as well as an enzyme in this bacterium that performs this sulfonation, ErSULT. Metagenomic analyses reveal that ErSULT is prevalent across diverse human gut microbiomes. Together, this workflow and its application demonstrate that sulfated metabolite production by gut bacteria is more widespread than previously appreciated and provide a platform for future studies investigating the biosynthesis and biological functions of microbiome-derived sulfated small molecules.

DOI: https://doi.org/10.1021/jacs.6c02487
Cell Metab. 2026 Jul 21. pii: S1550-4131(26)00274-3. [Epub ahead of print]

Competitive catabolism drives hyperglycemia and hyperinsulinemia in obesity.

Daniel R Weilandt, Won Dong Lee, Michael R MacArthur, Lingfan Liang, Corey D Holman, Qingwei Chu, Alexis J Cowan, Joseph A Baur, Ned S Wingreen, Joshua D Rabinowitz.

  Systemic metabolic homeostasis maintains circulating nutrient concentrations within physiological ranges. Insulin is central to this process, lowering circulating levels of glucose, lactate, free fatty acids, and ketones. Yet how the simultaneous homeostasis of these nutrients is achieved remains unclear. Here, we develop a differential equation model of fasting metabolic homeostasis. Grounded in mass action kinetics, this multi-nutrient model reveals how a fixed energy demand naturally leads to competition between major circulating nutrients for oxidation ("competitive catabolism"). Perturbative nutrient infusions confirm this emergent behavior. The multi-nutrient model predicts that insulin promotes fasting glucose homeostasis primarily indirectly by slowing lipolysis. It further identifies a physiological circuit by which obesity causes insulin resistance: increased fat mass promotes lipolysis, releasing fatty acids into circulation that compete with glucose for oxidation, elevating glucose and thus insulin, which acts to restore proper lipid catabolic flux. Thus, quantitative modeling reveals a physiological homeostatic circuit through which obesity causes type 2 diabetes.

Keywords:  competitive catabolism; differential equation modeling; hyperinsulinemia; insulin regulation; insulin resistance; mass action kinetics; metabolic homeostasis; nutrient competition; obesity; type 2 diabetes

DOI:  https://doi.org/10.1016/j.cmet.2026.07.001
Expert Rev Proteomics. 2026 Jul 23. 1-14

Clinical proteomics in inborn errors of metabolism: from biomarker discovery to implementation.

Michela Geminiani, Alfonso Trezza, Anna Visibelli, Rebecca Finetti, Pierfrancesco Mastroeni, Bianca Roncaglia, Ottavia Spiga, Annalisa Santucci.

   INTRODUCTION: Inborn errors of metabolism (IEMs) are rare, heterogeneous disorders traditionally diagnosed through genetic testing, enzyme assays, and metabolite measurements. However, these tools often do not fully explain phenotypic variability, organ involvement, disease progression, or treatment response. Clinical proteomics provides a complementary functional layer by capturing changes in protein abundance, proteoforms, post-translational modifications (PTM), and biological pathways, offering insights beyond genotype- and metabolite-based approaches.
AREAS COVERED: This review examines the role of high-resolution mass spectrometry and computational proteomics in biomarker discovery and clinical decision-making for IEMs. It focuses on their contribution to diagnosis, variant interpretation, patient stratification, and treatment monitoring. Disease-specific applications are discussed, with the strongest evidence in lysosomal storage disorders, mitochondrial diseases, congenital disorders of glycosylation, and selected neurodegenerative or renal metabolic conditions. The literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar, covering peer-reviewed articles available up to 2026, with emphasis on methodological advances and translational applications in clinical proteomics for IEMs.
EXPERT OPINION: Proteomics will not replace established diagnostic tools, but it can help address clinically actionable questions in selected contexts. Translation into clinical practice will require standardized workflows, multicenter validation, clinically anchored endpoints, and integration with other omics approaches.

Keywords:  Clinical proteomics; bioinformatics; biomarker discovery; inborn errors of metabolism; mass spectrometry; multi-omics; rare diseases

DOI:  https://doi.org/10.1080/14789450.2026.2709148
Anal Chem. 2026 Jul 23.

Formic Acid Enhanced Single-Pot Sample Preparation to Improve Identification Coverage of Membrane Proteome of Laser Capture Microdissected Tissue Sections.

Yingyun He, Xinyu He, Dan Liu, Yinfang Xie, Jiahang Tang, Baofeng Zhao, Jianhui Liu, Zhen Liang, Yukui Zhang, Xin-An Liu, Lihua Zhang, Huiming Yuan.

To interpret the heterogeneous multicellular microenvironment in biological tissues, cell type-resolved global proteome profiling has attracted great attention. Due to its high compatibility with mass spectrometry (MS), n-Dodecyl-d-maltoside (DDM) is commonly used for protein extraction from small tissue sections in spatially resolved proteomics workflows, improving low-input sample recovery without surfactant removal prior to MS analysis. However, extracting and solubilizing highly hydrophobic membrane proteins using mild, nondenaturing detergents remains challenging. Herein, we propose a formic acid (FA)-enhanced single-pot sample preparation (FAESP) method to improve protein solubility and extraction from tissue samples, enhancing membrane proteome identification coverage in laser capture microdissected tissue sections. Due to its high volatility, FA is rapidly removed by vacuum drying without cleaning steps. Combination with DDM ensures compatibility with subsequent digestion and LC-MS analysis. Compared with the FA-free DDM-based method, FAESP achieves a 38% increase in proteome coverage, with 40% of identified proteins annotated as membrane proteins. The FAESP workflow was applied to neuron type-specific proteome profiling in the medial habenula (MHb) of mouse brain, revealing regional specificity of synaptic proteins and solute carrier proteins in aging MHb subregions. These results demonstrate that FAESP provides a superior tool for discovering membrane protein candidates that distinguish neuronal types, facilitating biological and clinical applications.

DOI: https://doi.org/10.1021/acs.analchem.6c01074
J Proteome Res. 2026 Jul 19.

Improved Protein Identification in Shotgun Proteomics with a Group-Level Extension of the LPGF Model.

Gorka Prieto, Jesús Vázquez.

  Shotgun proteomics relies on robust statistical methods to increase the confidence of the protein identifications. Previously, we introduced LPGF, a protein probability model based on unique peptides. In this study, we extend LPGF to protein groups that share peptides by considering only peptides that are unique to each group. This strategy preserves the principle of unique evidence while enabling the probability estimation at the protein-group level. Applying our method to three tissues from the Human Proteome Map (HPM), we evaluated the gain in protein identifications obtained with group-unique peptides compared to those obtained using only protein-unique peptides and different scores. The accuracy of false discovery rate (FDR) estimation was further validated using a standard data set for protein inference based on human Protein Epitope Signature Tags (PrESTs), as well as through an entrapment experiment using the substantially larger HPM data set. To facilitate adoption, we developed an R package (b10prot) that integrates the full workflow, including protein-level and group-level LPGF score computation and protein grouping via an R port of our previous tool PAnalyzer. Our results demonstrate that extending LPGF to protein groups increases sensitivity while maintaining robust FDR control, thus providing the proteomics community with a practical framework for more comprehensive protein identification.

Keywords:  FDR; protein identification; proteomics; target-decoy approach

DOI:  https://doi.org/10.1021/acs.jproteome.5c01253
J Food Drug Anal. 2026 Jun 01. 34(1): 55-67

The evolution of DESI-MSI: A comprehensive look at preprocessing and quantification strategies for metabolomics.

Cheng-Hung Yang, Hung-Yao Ho, Chen-Yu Chang, Mei-Ling Cheng.

Mass spectrometry imaging (MSI) has become an indispensable tool in metabolomics for visualizing the spatial distribution of biomolecules within tissues. Due to its direct, ambient analysis capability, desorption electrospray ionization (DESI-MSI) has evolved from a rapid screening tool into a precise and reliable quantitative platform. We delve into its technical principles and particularly review the criticality of sample preprocessing, highlighting the paradigm shift from a "no-preprocessing" approach to one of "optimized preprocessing." The paper details how factors like sample preservation, choice of embedding agents, washing, drying, and on-tissue chemical derivatization all impact data quality. We also discuss how the addition of standards and internal standards can mitigate matrix effects and signal variability, enabling accurate quantitative analysis. Finally, this article looks ahead to the future of DESI-MSI, including its combination with emerging technologies such as tissue expansion mass spectrometry imaging to achieve single-cell-level spatial resolution and open up new possibilities for metabolomics research.

DOI: https://doi.org/10.38212/2224-6614.3583
Proteomes. 2026 Jun 24. pii: 32. [Epub ahead of print]14(3):

A One Health Framework for Proteomics Across the Tree of Life to Advance Food Security, Animal Health, and Ecosystem Resilience.

Tarun Mishra, Ritudhwaj Tiwari, Tuyelee Das, Maneesh Lingwan.

  As global ecosystems and food systems face unprecedented anthropogenic and climatic challenges, there is a demand for an integrated understanding of biological systems. Proteomics has emerged as a definitive approach offering a direct view of the molecular phenotype, yet it is traditionally separated into plant and animal disciplines. With recent advances in mass spectrometry (MS) and bioinformatics tools, this prospective review proposes that combining a One Health proteomics approach with deep-learning data analysis can revolutionize global food security, animal productivity, and ecosystem health by uncovering proteoform signatures that drive resilience across life. The potential of a unified One Health proteomic framework, highlighting major developments, including 4D proteomics, Data-Independent Acquisition (DIA), and single-cell resolution, and emphasizes their capacity to resolve the complex proteoform landscape across kingdoms. Review emphasizes the applications of proteogenomics as a cross-disciplinary tool to improve genome annotations, explain evolutionary differences, discover biomarkers in animals and resolve complex signaling networks in plants under stress. Nevertheless, contemporary proteogenomics methods still show limitations in their ability to comprehensively resolve proteoforms due to the fact that the use of peptide-based approaches makes it difficult to fully appreciate the post-translational modifications specific to each protein isoform. We show that One Health proteomics will provide a transformative roadmap for deciphering the functional proteoform signatures that underpin resilience across the tree of life.

Keywords:  animal health; crop biofortification; mass spectrometry; metaproteomics; one health proteomics; proteogenomic

DOI:  https://doi.org/10.3390/proteomes14030032
Magn Reson Chem. 2026 Jul 19.

nh-PHIP Hyperpolarization for Biological Mixture Analysis.

Kerti Ausmees, Indrek Reile.

  Non-hydrogenative parahydrogen-induced polarization (nh-PHIP) is an NMR sensitivity enhancement technique that is particularly suitable for studying biological samples. It can provide up to three orders of magnitude signal enhancements for a continuously expanding range of valuable analyte classes like drugs, vitamins, nucleobases, nucleosides and nucleotides, amino acids, oligopeptides, etc. The strengths of nh-PHIP lay in its modest instrumental requirements over a standard commercial NMR spectrometer, its simple operating workflow that is similar to traditional NMR spectroscopy, and its proven capability to offer quantitative information from below the sensitivity barrier of regular NMR. This tutorial will give an overview of the background, the principles, and the present application scope of nh-PHIP hyperpolarization. We provide guidelines for setting up an nh-PHIP experiment, from sample preparation to optimizing the hyperpolarization for a particular analyte class. This includes the description of the basic experimental setup and outlining the considerations for method development. An overview of different available nh-PHIP pulse sequences will be given to assist the reader in implementing nh-PHIP.

Keywords:  NMR; hyperpolarization; nh‐PHIP; parahydrogen; signal enhancement

DOI:  https://doi.org/10.1002/mrc.70133
Anal Bioanal Chem. 2026 Jul 23.

Towards spatial lipid profiling by using mass spectrometry: analytical challenges and applications.

Yaowei Liu, Yihua Ou, Hemi Luan, Tiangang Luan.

  Lipidomics, as a crucial branch of metabolomics, is dedicated to systematically analyzing the composition, structure, function, and dynamic changes of lipids in organisms, playing a pivotal role in elucidating disease mechanisms and discovering biomarkers. Conventional lipidomics methods based on liquid chromatography-mass spectrometry (LC-MS) require tissue homogenization, which obscures the spatial distribution of lipids and precludes the analysis of their heterogeneity within complex tissue microenvironments. In recent years, the development of spatial omics technologies such as mass spectrometry imaging (MSI) and laser capture microdissection (LCM) has provided powerful tools for the in situ and visual investigation of lipid spatial distribution. This paper systematically reviews the main analytical strategies in lipidomics, focusing on the technical principles, advances, and recent applications of spatial multi-omics integration. It further discusses the challenges faced by spatial lipidomics in terms of quantitative accuracy, isomer identification, and spatial localization precision, and provides an outlook on future technological developments. Spatial lipidomics breaks through the bottleneck of losing spatial information in traditional methods, and opens up a new path for further exploration of disease mechanisms and the discovery of new biomarkers in the spatial dimension.

Keywords:  Laser capture microdissection; Lipid metabolism; Mass spectrometry imaging; Spatial lipidomics; Spatial multi-omics

DOI:  https://doi.org/10.1007/s00216-026-06684-y
Anal Bioanal Chem. 2026 Jul 24.

Plasma LC-HRMS metabolomics in parkinsonian syndromes: comparative evaluation of sample preparation protocols for exploratory metabolic signatures.

Erika Esposito, Alessandro Perrone, Giovanna Lopane, Rosalba Vitagliano, Nicolò Interino, Alice Caravelli, Chiara Cancellerini, Giovanna Calandra-Buonaura, Jessica Fiori, Manuela Contin.

  Untargeted plasma metabolomics by LC-HRMS is increasingly used to explore candidate biomarkers in neurodegenerative disease, yet the analytical output and downstream biological interpretability remain highly dependent on end-to-end workflow choices. This limits reproducibility and cross-study comparability in parkinsonian syndromes where objective fluid biomarkers for differential diagnosis are still lacking. Here, we implemented a QC-anchored, untargeted LC-HRMS plasma platform to compare sample preparation strategies and to explore disease-associated metabolic patterns across Parkinson's disease (PD), multiple system atrophy-parkinsonian subtype (MSA-P), and progressive supranuclear palsy-parkinsonism (PSP-P) in a prospective single-center cohort (n = 102; 57 PD, 19 MSA-P, 26 PSP-P). Three protein precipitation-based workflows were compared within a harmonized analytical framework: tube precipitation, 96-well precipitation-filtration, and 96-well phospholipid removal. Integrated preparation QCs, system QCs, and blanks supported performance monitoring, while MS2 annotation enabled interpretation of disease-associated signals. Phospholipid removal provided the most favorable trade-off for discovery and quantitation, delivering reduced phospholipid-related chromatographic background, improved quantitative behavior, and a higher density of MS2-supported annotations despite a modest reduction in raw feature counts. Differential abundance was assessed using empirical Bayes moderated linear models implemented in limma. Pairwise contrasts were defined for PD vs. MSA-P, PD vs. PSP-P, and MSA-P vs. PSP-P, and P values were adjusted using the Benjamini-Hochberg false-discovery rate (FDR). Across workflows, the most robust exploratory disease-associated pattern was observed for PD versus PSP-P, whereas MSA-P comparisons yielded limited FDR-significant features. Because of the absence of MSI Level 1 annotation, the hypothesis-generating pathway-level was based on exploratory metabolomics interpretation, requiring targeted confirmation.

Keywords:  LC-HRMS; Metabolomics; Parkinsonian syndromes; Sample preparation

DOI:  https://doi.org/10.1007/s00216-026-06669-x
J Chromatogr A. 2026 Jul 16. pii: S0021-9673(26)00606-0. [Epub ahead of print]1785 467278

Development of a liquid chromatography-ion mobility-mass spectrometry method for the screening of d-amino acid alterations in plasma.

Luisa-Gabriela Bogos, Ioana-Ecaterina Pralea, Alina-Ramona Potra, Ina Maria Kacso, Radu-Cristian Moldovan, Cristina-Adela Iuga.

  Probing chirality, one of the fundamental properties of all biological systems, remains analytically challenging and often requires specialized analytical setups or reagents, limited to few laboratories. This study aimed to develop an efficient liquid chromatography coupled to ion-mobility mass spectrometry (LC-IM-MS) method for the simultaneous analysis of all proteinogenic d-amino acids (d-AAs) in human plasma, using exclusively commercially available reagents. An indirect enantioseparation strategy was developed based on derivatization with (S)-N-(4-nitrophenoxycarbonyl) phenylalanine methoxyethyl ester ((S)-NIFE), with five stationary phases evaluated for diastereomer separation capability. A design of experiments approach was employed for further optimization, achieving baseline separation of all proteinogenic AA diastereomers within 20 min on a phenyl-based stationary phase. A straightforward sample preparation protocol was characterized, then the entire workflow was used for the assessment of d-AAs alterations across 60 plasma samples obtained from patients with chronic kidney disease (CKD, stages 4 and 5) and healthy controls. The analysis revealed increased levels of d-Pro, d-Arg, d-Ser, d-Asn, d-Gln, and d-Ala in CKD samples, highlighting the effectiveness of the developed analytical workflow to detect disease-related changes in plasmatic d-AAs.

Keywords:  Chiral analysis; Chiral derivatizing reagent; Chronic kidney disease; Indirect enantioseparations; Metabolomics; d-amino acids

DOI:  https://doi.org/10.1016/j.chroma.2026.467278
Anal Biochem. 2026 Jul 18. pii: S0003-2697(26)00161-2. [Epub ahead of print]718 116205

Proteomic and amino acid profiling of SK-MEL-30 melanoma reveals metabolic and structural changes.

Nebiye Pelin Turker.

  Melanoma exhibits high metastatic potential and therapy resistance, driven by metabolic flexibility and structural remodeling. In this study, we performed an integrated analysis of the SK-MEL-30 melanoma cell line using function-focused quantitative proteomics and amino acid profiling. Proteins were quantified using a label-free normalized spectral abundance factor (NSAF) approach, while intracellular amino acids were measured by LC-MS/MS. Functional enrichment analyses based on KEGG and Gene Ontology were used to associate protein expression patterns with metabolic pathways. A total of 148 proteins were identified, predominantly representing high-abundance and functionally relevant components of metabolic and structural pathways. Key cytoskeletal proteins, including vimentin and S100A11, were among the most abundant, consistent with a mesenchymal-like and potentially invasive phenotype. Metabolic profiling revealed elevated expression of glycolytic enzymes such as PKM and LDHA, consistent with a glycolytic shift. Increased levels of l-glutamine and l-glutamic acid, together with GOT2 expression, suggest an active glutamine aspartate axis supporting tricarboxylic acid cycle activity and nitrogen metabolism. In addition, elevated levels of stress-response proteins, including HSP90 and SOD2, indicate a proteostatic network adapted to metabolic stress. Although the proteome coverage is lower than that reported in large-scale deep proteomic studies, the NSAF-based workflow was designed to capture the most abundant and functionally relevant proteins, providing a focused overview of the major metabolic and structural characteristics of SK-MEL-30 melanoma cells. Overall, this integrative analysis highlights the coordination between metabolic reprogramming and cytoskeletal organization in SK-MEL-30 cells and suggests that glutamine-dependent metabolic pathways warrant further investigation as potential therapeutic targets in melanoma.

Keywords:  Amino acid metabolism; GOT2; Melanoma; NSAF; Quantitative proteomics; SK-MEL-30; Vimentin; Warburg effect

DOI:  https://doi.org/10.1016/j.ab.2026.116205
Mol Cell Proteomics. 2026 Jul 23. pii: S1535-9476(26)00127-1. [Epub ahead of print] 101631

LABEL-FREE TARGETED PROTEOMICS DATA ANALYSIS WORKFLOW SELECTION - BENCHMARKING AI-BASED AND DATA-DRIVEN APPROACHES.

Daniel Fochtman, Łukasz Marczak, Monika Pietrowska, Joanna Polańska, Anna Wojakowska.

As global proteomics continues to advance, the number of identifiable proteins has increased substantially. However, this does not inherently ensure optimal quantitative performance. While targeted assays using isotope-labeled peptides can be developed, label-free strategies remain an attractive and cost-efficient option for methodological validation. Yet, systematic evaluations of data analysis workflows for label-free targeted proteomics, particularly those incorporating AI-based tools, are still limited. Therefore, this study aimed to benchmark multiple data analysis approaches for label-free targeted proteomics as a validation framework for results obtained from global analyses. Missing-data imputation (MDI) strategies, including no MDI, k-nearest neighbors, data-driven, MSstats and AI-based methods, were evaluated, alongside consolidation and testing frameworks such as mathematical summation, best-peak selection, AI-based scaling with univariate statistics, p-value integration, MSstats TMP or linear models and multivariate testing. Data-driven MDI combined with p-value integration consistently showed the strongest performance across accuracy, precision, specificity, and false-discovery rate, outperforming all other strategies. These findings demonstrate that careful selection of data analysis workflows can yield substantially improved quantitative outcomes compared with commonly used approaches such as simple mathematical summation. Although our conclusions are based on a controlled benchmarking dataset comprising three yeast proteins spiked into a constant human background using only one targeted approach, they can be generally applied as a default workflow for biomarker validation using the label-free approach.

DOI: https://doi.org/10.1016/j.mcpro.2026.101631
Biomed Chromatogr. 2026 Sep;40(9): e70549

Development and Validation of an LC-MS/MS Method for the Quantification of HY-078020 in Human Plasma Using Liquid-Liquid Extraction and Its Application to Human Pharmacokinetic Study.

Yaqi Wang, Meiling Zhang, Yazhu Cui, Li Shao, Linying Meng, Mengqin Chen, Mengjie An, Zhijie Shen, Zhaoxing Chu.

  A sensitive and practical liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated for the quantification of HY-078020 in human plasma. Plasma samples (100 μL) were processed by liquid-liquid extraction with methyl tert-butyl ether using HY-078020-d6 as the internal standard. Chromatographic separation was achieved on a Phenomenex Luna C8(2) column with gradient elution, and detection was performed in positive electrospray ionization mode using multiple reaction monitoring. The monitored transitions were m/z 515.2 → 282.3 for HY-078020 and m/z 521.1 → 282.3 for the internal standard. The assay was validated over the concentration range of 4.0-2400 ng/mL with a lower limit of quantification of 4.0 ng/mL. The method showed acceptable selectivity, accuracy, precision, matrix effect, extraction recovery, carryover, and stability in accordance with current bioanalytical validation criteria. Intra- and interbatch precision was within 9.03% and 5.46%, respectively, and the analyte remained stable under benchtop, freeze-thaw, processed-sample, reinjection, and long-term storage conditions. With its small plasma volume requirement, broad calibration range, low carryover, and robust stability, this method is suitable for routine quantification of HY-078020 in human plasma and may support future pharmacokinetic studies.

Keywords:  HY‐078020; LC–MS/MS; bioanalytical method validation; carryover; human plasma; liquid–liquid extraction; stability

DOI:  https://doi.org/10.1002/bmc.70549