bims-mascan 2026-07-12 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–07–12
27 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Kinetic Lipidomics: Quantifying in vivo changes in lipid metabolism using metabolic labeling.
Analytical challenges in mapping the subcellular metabolome and lipidome.
Analyte-Class-Dependent Electrophoretic Organization Enables Single-Run Proteome-Metabolome Analysis by CE-ESI-MS.
Data-Independent Acquisition for Improved Compound Annotation in MALDI MS Imaging.
Orbitrap Collision Cross Section Measurements Enhance Isomer Annotations in Lipidomics.
LC-MS-Based Metabolomics and Proteomics Sample Preparation of Urine: Methods and Multi-Omics Integration.
LC-MS FADE: Leveraging Untargeted Lipidomics Data for Fatty Acid Profiling.
PSAQ+1: Absolute Protein Quantification Using a 13C1-Labeled Protein Standard, Coisolation of Peptide Pairs and LC-PRM.
Methods for the Investigation of Protein-Ligands Interactions.
Reverse Biotransformation-Guided Annotation of Untargeted MS/MS Features: A Computational Framework for Candidate Enzyme and Gene Hypothesis Generation.
Sample Preparation for Quantitative Proteome and Phosphoproteome Profiling of Maize Tissues.
Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.
Mass Spectrometry-Based Validation of Chimeric RNAs.
In Vivo Quantification of Histone Acetylation Turnover and Acetyl-CoA Sources Using ²H₂O Metabolic Labeling and High-Resolution Mass Spectrometry.
Emerging Trends in Mass Spectrometry-Based Quantitative Proteome and Phosphoproteome Profiling in Maize.
An Overview of Pterin Analysis in Biological Samples: From Occurrence and Properties to Sample Pretreatment Combined With Hyphenated Separation Techniques.
Improved Surfactant-Assisted One-Pot Sample Preparation for Robust Convenient Single-Cell Proteomics.
Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.
Sample Preparation Strategies for Microbial Cell Surface Proteomics: Integrating Shaving and Shotgun Approaches.
Clinical validation of an automated extraction protocol and liquid chromatography single quadrupole mass spectrometry (LC-MS) method for plasma vitamins A and E analysis.
Metabolomic data of melittin-intervened murine cervical cancer cells based on liquid chromatography-mass spectrometry.
An m/z- and Intensity-Based HRMS Clustering Algorithm Targeted toward Single-Cell Metabolomics.
Large-scale discovery and annotation of substructure patterns in mass spectrometry profiles.
Quantitative Profiling and Metabolic Study of Caffeoylquinic Acids and Their Metabolites by Liquid Chromatography-Tandem Mass Spectrometry in Rats and Laying Hens.
The clinical promise of mass spectrometry based single-cell proteomics: from bedside to bench.
The alternative NF-κB RelB subunit is a novel critical sensor of lipid metabolism to promote tumor development.
A guide to laser microdissection-based proteomics.

bioRxiv. 2026 Jul 01. pii: 2026.06.29.735310. [Epub ahead of print]

Kinetic Lipidomics: Quantifying in vivo changes in lipid metabolism using metabolic labeling.

Coleman O Nielsen, Russell L Denton, Chad R Quilling, Kenneth L Virgin, Kyle J Cutler, Spencer S Gates, Martin D Sorensen, Mitchel F Poulson, Tayler C Hilton, Benjamin K Driggs, Zabdi Hernandez, Parker M Snedaker, Bradley C Naylor, Mark K Transtrum, John C Price.

Lipid metabolism reflects the dynamic balance between metabolic turnover and concentration. Kinetic mass spectrometry (MS) enables direct quantification of molecular turnover in vivo. Previous work has shown that MS-based kinetic proteomics has provided powerful insights into proteome regulation. Analogous lipidome-wide kinetic measurements remain limited by challenges in defining molecule-specific labeling behavior. Here, we extend kinetic MS to untargeted lipidomics. Isotope labeling with deuterated water ( 2 H 2 O) is commonly used for monitoring turnover of palmitate and other select lipids by measuring labeling of stable C-H positions with deuterium ( 2 H). Here, we extend the deuterium-incorporation model underlying these targeted lipid turnover assays to support untargeted analysis of all detectable lipids. This allows us to empirically quantify the effective fraction of endogenous synthesis ( A syn ) and the turnover rate ( k ) across hundreds of lipid species simultaneously. One central barrier to lipidome-wide kinetic modeling is determining the endogenous number of deuterium-labeling sites for each molecule ( n L ) which is required to estimate A syn and k accurately. The n L value is an essential component of biological kinetic assays. In kinetic proteomics, curated amino acid n L libraries enable peptide-level modeling by summing sequence-specific labeling-site values, but comparable resources are lacking for lipids and may not generalize across metabolic states or non-mammalian systems. Yet, gaps remain for lipids and for amino acids in modified metabolic conditions or non-mammalian biologies. Here, we empirically determine lipid n L values and validate the process with peptides against an n L library. To evaluate this strategy in a biologically relevant setting, we applied it to brain tissue from transgenic mice expressing human ApoE isoforms, where altered lipid transport and metabolism are implicated in Alzheimer's disease risk. These data validate the method in a clinically relevant context and suggest that genotype-dependent metabolism can alter empirically determined lipid n L values.

DOI: https://doi.org/10.64898/2026.06.29.735310
Analyst. 2026 Jul 07.

Analytical challenges in mapping the subcellular metabolome and lipidome.

Marta Nobile, Simone Serrao, Eleonora Bossi, Francesca Re, Francesca Raimondo, Giuseppe Paglia.

Mass spectrometry-based metabolomics and lipidomics are central analytical tools for characterizing cellular chemical composition. However, most workflows still rely on the simplifying assumption of homogeneous intracellular pools, which is increasingly inadequate for spatially organized eukaryotic systems. Metabolites and lipids are distributed across subcellular compartments that differ in chemical environment, turnover, and accessibility, thereby affecting both measurement and interpretation. Recent advances in subcellular and spatial metabolomics have highlighted both the potential and the limitations of organelle-resolved analysis, particularly in terms of extraction chemistry, quantification, and data interpretation. In this review, we critically examine organelle-resolved metabolomics and lipidomics from a mass spectrometry-centric perspective, treating subcellular compartmentalization as an analytical variable rather than solely a biological feature. By comparing metabolomics and lipidomics studies on subcellular compartments, we evaluate fractionation-based, affinity-based, and spatial MS strategies, and we highlight current capabilities, common artefacts, and future opportunities, including the integration of stable isotope tracing and emerging single-organelle approaches such as Nanoscale Secondary Ion Mass Spectrometry (NanoSIMS) and Direct Organelle Mass Spectrometry (DOMS).

DOI: https://doi.org/10.1039/d6an00535g
bioRxiv. 2026 Jul 03. pii: 2026.07.03.736334. [Epub ahead of print]

Analyte-Class-Dependent Electrophoretic Organization Enables Single-Run Proteome-Metabolome Analysis by CE-ESI-MS.

Ramesh Kumar, Ryan O'Neal, Peter Nemes.

Dual proteome-metabolome measurements from limited samples typically require sample splitting or sequential analyses using electrospray ionization mass spectrometry (ESI-MS). Here we show that capillary electrophoresis (CE) can avoid that tradeoff by organizing predominantly singly charged small molecules and multiply charged peptides into partially resolved, analyte-class-dependent regions of migration time-m/z space. Leveraging this intrinsic electrophoretic organization together with charge- and m/z-resolved precursor selection, we developed a single-run CE-ESI-MS workflow that combines single-vial sample processing with class-resolved tandem MS acquisition. In a HeLa digest spiked with 17 amino acids, the integrated analysis detected all amino acids while preserving proteomic depth relative to a dedicated proteomics run, yielding 1,221 versus 1,227 cumulative protein groups. Applied to identified single Xenopus laevis blastomeres, the method provided matched readouts of 86 metabolite features together with 1,097 and 1,083 protein groups from D1.1 and V1.1 cells, respectively. The paired measurements resolved cell-type-dependent molecular differences and mapped protein and metabolite changes into shared pathway context. These results establish analyte-class-dependent electrophoretic organization coupled to class-resolved MS acquisition as an analytical basis for single-run proteome-metabolome analysis by CE-ESI-MS in material-limited samples.

DOI: https://doi.org/10.64898/2026.07.03.736334
J Am Soc Mass Spectrom. 2026 Jul 10.

Data-Independent Acquisition for Improved Compound Annotation in MALDI MS Imaging.

Carmen Paschke, Kerstin Strupat, Carolin M Morawietz, Karl-Christian Schaefer, Christoph Henrich, Bernhard Spengler.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) enables spatial mapping of biomolecules within biological tissues, where conventional MS1-based workflows often result in ambiguous compound annotations. Data-dependent acquisition (DDA) can improve annotation specificity but is biased toward high-abundant ions and lacks reproducibility. Here, we present a novel MALDI MSI workflow integrating data-independent acquisition (DIA) to obtain both spatial and fragmentation information. In this approach, MS1 and DIA MS2 spectra are acquired alternately across the sample without prior knowledge of compound localization. Each image pixel consists of one MS1 and several DIA MS2 subpixels, providing both high spatial resolution for MS1 data and broad m/z coverage. Using small, randomized m/z isolation windows reduces spectral overlap and improves fragment-ion specificity. Data were processed using an extended Compound Discoverer, integrating mzCloud and LipidSearch for compound annotation. Applied to tissue of the parasitic worm Fasciola hepatica, this workflow produced detailed lipid maps and improved annotation confidence by combining precursor-mass, DIA MS2, and spatial-correlation information. Our results demonstrate that DIA offers a flexible strategy to integrate fragmentation information into MALDI MSI, expanding its capabilities for spatial metabolomics.

DOI: https://doi.org/10.1021/jasms.6c00128
bioRxiv. 2026 Jul 03. pii: 2026.07.03.735735. [Epub ahead of print]

Orbitrap Collision Cross Section Measurements Enhance Isomer Annotations in Lipidomics.

Ziqin Ni, Konstantin Ayzikov, Alexander Makarov, Samuel Moore, David A Gaul, Kyle L Fort, Facundo M Fernández.

Despite advances in high-resolution mass spectrometry (HRMS), confident lipid annotation remains challenging due to the extensive chemical diversity of the lipidome and the prevalence of isomeric species. Ion mobility collision cross section (CCS) measurements provide structural information that complements HRMS; however, not all HRMS platforms can perform these measurements, necessitating a trade-off among mass resolution, accuracy, and robustness. Here, we introduce a method to infer lipid CCS values directly from liquid chromatography (LC)-Orbitrap MS experiments ( Orbi CCS ). We show that Orbitrap mass analyzer pressure readings, and therefore CCS values, are influenced by the LC gradient solvent composition, requiring correction using isotopically labeled internal standards injected post-column. We also show that hundreds of lipid features can be assigned Orbi CCS values in a single LC run, with average precision better than 1% and an accuracy of 1-2% relative to reference DT CCS and TIMS CCS values. This excellent CCS accuracy not only enables more reliable annotation of lipid species in complex mixtures by matching Orbi CCS values to reference databases but also accelerates lipid structural elucidation based on the unknown's position in Orbi CCS -retention time- m/z space.

DOI: https://doi.org/10.64898/2026.07.03.735735
Adv Exp Med Biol. 2026 ;1510 1-20

LC-MS-Based Metabolomics and Proteomics Sample Preparation of Urine: Methods and Multi-Omics Integration.

Muath Khairi Mousa, Nelson C Soares, Hugo M Santos.

  Urine is an ideal biological fluid due to the highly metabolomic and proteomic information it provides and its easy collection in large amounts. Urinary biomarkers reported for different types of diseases included the urological tract and systemic diseases. Although the most common approach in omics is single-omics studies, combining multi-omics biomarkers such as metabolomics, proteomics, transcriptomics, and genomics can improve diagnostic accuracy and provide deeper insights into disease mechanisms than single biomarkers. The gold standard technique for bioanalysis is liquid chromatography coupled to mass spectrometry (LC-MS/MS) due to its high sensitivity, specificity, and selectivity for the analysis of metabolites and proteins in complex biological samples. One of the most important aspects in metabolomics and proteomics is sample extraction and preparation before the analysis. Different types of metabolites and protein extraction methods can be used effectively for urine samples, including protein precipitation, liquid-liquid extraction, and solid-phase extraction. However, in the multi-omics approach integrating metabolomics and proteomics, sample preparation could be either individual for each or simultaneous for both from a single sample. In this chapter, we discuss aspects of LC-MS-based metabolomics and proteomics sample preparation, as well as their integration for a multi-omics approach. In clinical practice, the reported sample preparation methods for bladder cancer metabolomics and proteomics were also discussed.

Keywords:  Bladder cancer; LC-MS; Metabolomics; Multi-omics; Proteomics; Sample preparation; Urinary biomarkers

DOI:  https://doi.org/10.1007/978-3-032-21638-0_1
Anal Chem. 2026 Jul 09.

LC-MS FADE: Leveraging Untargeted Lipidomics Data for Fatty Acid Profiling.

Jocelyn A Menard, Joshua A Roberts, Jacob H Clarke, Karlye E A Rudd, Christian A Rosales, Jeffrey C Smith.

Fatty acid (FA) profiling has historically been accomplished via gas chromatography-mass spectrometry (GC-MS) and can be used in tandem with liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomics workflows to comprehensively investigate global FA and lipid dynamics. This approach requires both GC-MS and LC-MS platforms, adding expense, acquisition time, and sample consumption, yet lacks the ability to directly correlate fatty acid profiles to intact lipid molecules. To address this issue, we present a workflow to determine FA profiles from lipidomics data sets termed LC-MS fatty acid data extraction (LC-MS FADE). FA data were extrapolated from intact lipid peak areas and expressed as percent fractions. A total of eight disparate lipid extracts, including fetal bovine serum, nutritional yeast, beef liver, canola oil, Viral Producing Cells 1.0, Arabidopsis thaliana, Acheta domesticus, and Escherichia coli K12 were analyzed to assess the accuracy of this strategy. By comparing the LC-MS FADE profile to the GC-MS FAME profile, an average R2 value of 0.89 was obtained, demonstrating that LC-MS FADE can successfully and accurately extract FA profiles. LC-MS FADE data sets displayed improved detectivity by identifying more FAs with improved reproducibility compared to GC-MS FAME data. Significantly, lipid class-specific FA insights were obtained using LC-MS FADE that were not accessible with traditional GC-MS methods. LC-MS FADE can also be applied retrospectively to existing lipidomics data sets, enabling extraction of FA profiles from data not originally intended for FA analysis. This method provides additional insight into FA dynamics in complex untargeted lipidomics data sets and can be readily implemented into lipidomic workflows.

DOI: https://doi.org/10.1021/acs.analchem.6c01325
Anal Chem. 2026 Jul 04.

PSAQ+1: Absolute Protein Quantification Using a 13C1-Labeled Protein Standard, Coisolation of Peptide Pairs and LC-PRM.

David Pérez Compte, Anne-Marie Hesse, Benoit Gilquin, Marie Courçon, Yohann Couté, Michel Jaquinod, Christophe Bruley, Virginie Brun.

Absolute quantification of proteins by mass spectrometry (MS)-based methods is useful in analytical medicine and systems biology. To push the boundaries of protein quantification performance, we developed the PSAQ+1 method. PSAQ+1 uses a protein standard that is minimally distinct from the analyte, a recombinant analogue of the protein target, labeled with 13C1 on arginine and lysine residues, inducing a single unit mass difference for tryptic peptides. It can therefore be introduced early during the analytical workflow to ensure consistent behavior throughout biochemical preparation, liquid chromatography and MS analysis. In this article, we present a proof of concept with an optimized parallel reaction monitoring (PRM) analytical workflow targeting a single composite isotopic peak for each pair of signature peptides. To demultiplex signals originating from the coanalysis of the PSAQ+1 and the endogenous peptides, we developed a dedicated precise quantification model, made available as an R-package (rPSAQ). Our results demonstrate that coanalysis allows robust, reproducible, accurate, and specific quantification. Demultiplexing of the MS/MS signal and the accuracy of quantification were validated using the dedicated mathematical model. Compared to conventional isotope dilution approaches, in which the endogenous and labeled peptides are isolated and fragmented independently, PSAQ+1 enables coisolation and cofragmentation of both peptides as well as codetection of fragments. This halves the cycle time required per peptide target, while coisolation in a narrow mass window reduces matrix interferences, improving specificity and sensitivity. The PSAQ+1 method thus represents a promising strategy for accurate protein quantification in highly complex biological matrices.

DOI: https://doi.org/10.1021/acs.analchem.6c02932
Adv Exp Med Biol. 2026 ;1510 163-211

Methods for the Investigation of Protein-Ligands Interactions.

Ilaria Iacobucci, Flora Cozzolino, Irene Cipollone, Maria Monti.

  Biomolecular interactions involving proteins, nucleic acids, and small molecules constitute the molecular foundation of cellular regulation, signaling, and therapeutic intervention. Advances in mass spectrometry-based proteomics have enabled the systematic characterization of these interactions at unprecedented depth, sensitivity, and structural resolution. This chapter provides a comprehensive overview of state-of-the-art proteomics methodologies developed to investigate protein-protein, protein-nucleic acid, and protein-drug interactions, with particular emphasis on experimental design, sample preparation, and data quality control. Targeted and untargeted strategies are discussed, including affinity purification-mass spectrometry, proximity-dependent labeling, cross-linking mass spectrometry, blue native electrophoresis, and size-exclusion chromatography-mass spectrometry for protein-protein interactions; affinity capture, EMSA-MS, chromatin immunoprecipitation-mass spectrometry, CRISPR-based locus-specific enrichment, and CLIP-based approaches for protein-nucleic acid complexes; and chemoproteomics, thermal proteome profiling, and label-free structural proteomics for protein-drug interaction analysis. The chapter further highlights recent technological innovations, computational tools, and integrative multi-omics strategies that enhance interaction mapping across biological scales. By critically evaluating the strengths, limitations, and appropriate applications of each methodology, this work aims to provide practical guidance for researchers seeking to design robust interactomics experiments and to interpret complex molecular networks in both basic and translational research contexts.

Keywords:  Affinity purification; Chemoproteomics; Cross-linking ms; Interactomics; Mass spectrometry; Protein–ligand interactions; Protein–nucleic acid interactions; Protein–protein interactions; Proximity labeling; Structural proteomics; Systems biology; Thermal proteome profiling

DOI:  https://doi.org/10.1007/978-3-032-21638-0_8
J Proteome Res. 2026 Jul 10.

Reverse Biotransformation-Guided Annotation of Untargeted MS/MS Features: A Computational Framework for Candidate Enzyme and Gene Hypothesis Generation.

Smaroki Smruti Rekha, Palok Aich.

  Untargeted LC-MS/MS experiments detect thousands of metabolic features, yet most remain unannotated due to limited spectral and structural database coverage. To address this limitation, we present a modular workflow that identifies closest structural analogs from MS/MS-derived features, applies rule-based reverse biotransformation to infer plausible precursor reactions, and maps these reactions to candidate enzymes and genes. Confidence-aware parameter selection and rule scoring are incorporated to balance annotation coverage with biological plausibility. Evaluation on reference data sets using systematic sensitivity analyses justified analog retrieval and biotransformation confidence thresholds. Pipeline-derived gene sets consistently recapitulated pathways reported in prior metabolite-gene association studies and exhibited stronger pathway enrichment than baseline associations. Application to independent experimental metabolite lists produced biologically coherent pathway enrichments across heterogeneous data sets. For a lesser-characterized metabolite, inferred genes were highly enriched in glutathione metabolism and oxidative stress pathways (adjusted p < 2 × 10-4). This confidence-aware integration of spectral annotation and reverse biotransformation provides a reproducible and interpretable framework for generating candidate enzyme and gene hypotheses from poorly annotated MS/MS features, enhancing the biological interpretation of metabolomics-driven discovery.

Keywords:  MS/MS spectral annotation; enzyme-gene mapping; pathway enrichment; reverse biotransformation; untargeted metabolomics

DOI:  https://doi.org/10.1021/acs.jproteome.6c00138
Cold Spring Harb Protoc. 2026 Jul 07.

Sample Preparation for Quantitative Proteome and Phosphoproteome Profiling of Maize Tissues.

Shikha Malik, Gaoyuan Song, Christian Montes, Justin W Walley.

Zea mays (maize) is a globally important cereal crop and a key system for studying plant development and stress responses. Proteome profiling and phosphoproteome profiling provide direct, quantitative readouts of protein abundance and phosphorylation states, which offer insights into aspects of regulation and cellular function that transcript-level measurements alone cannot provide. Robust and reproducible methods are essential for generating accurate and biologically relevant data in proteomics studies. The complexity of plant tissues, however, poses challenges for developing reliable sample preparation workflows. Here, we describe a detailed sample preparation protocol for quantitative proteome and phosphoproteome profiling in maize. The protocol encompasses protein extraction, filter-aided sample preparation (FASP), peptide desalting, tandem mass tag (TMT)-based labeling for quantitative multiplexing, and complementary TiO2 and Fe-NTA enrichment steps, yielding peptides suitable for analysis by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). This approach enables the quantitative profiling of protein abundance and phosphorylation dynamics in maize tissues.

DOI: https://doi.org/10.1101/pdb.prot108733
Bioinformatics. 2026 Jul 01. pii: btag297. [Epub ahead of print]42(Supplement_1):

Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.

Tom Altenburg, Thilo Muth, Patrick van Zalm, Hanno Steen, Bernhard Y Renard.

MOTIVATION: Mass spectrometry-based proteomics allows studying all proteins of a sample on a molecular level. However, mass spectra are noisy and contain complex patterns, making them inherently challenging to analyze with algorithmic approaches. In terms of the protein sequence landscape, most recent bottom-up MS-based proteomics studies consider either a diverse pool of post-translational modifications, employ large databases-as in metaproteomics or proteogenomics, study multiple isoforms of proteins, include unspecific cleavage sites or even combinations thereof. All this makes peptide and protein identifications challenging.
RESULTS: Here, we present a foundation model, called yHydra, that jointly embeds spectra and peptides. This allows us to implement various downstream tasks and search modes in Euclidean space. We implement an open search which allows querying multiple ten-thousands of spectra against millions of peptides. Furthermore, we implement an error-tolerant search for identifying additional proteoforms that are not included in off-the-shelf reference proteomes. Our foundation model provides meaningful embeddings, as we interpret learned peptide embeddings in comparison to the peptide's physico-chemical properties. Hydra's open search, assigns delta masses to each identification which allows to unrestrictedly characterize post-translational modifications. The error-tolerant mode of yHydra can be used as post-processing to existing search engines or as a standalone. yHydra is evaluated on several real life data sets for the identification of modified peptide sequences and shows up to 25% increase in peptide identification at constant false discovery rate compared to the current state-of-the-art.
AVAILABILITY AND IMPLEMENTATION: Code is available on Gitlab: https://gitlab.com/dacs-hpi/yHydra, and https://gitlab.com/dacs-hpi/yHydra_train.

DOI: https://doi.org/10.1093/bioinformatics/btag297
Methods Mol Biol. 2026 ;3024 83-91

Mass Spectrometry-Based Validation of Chimeric RNAs.

Shafaque Zahra, Justin Elfman, Hui Li.

  Chimeric RNAs, formed by the fusion of exons from two or more distinct genes, represent a significant class of noncanonical transcripts with increasing implications in cancer biology, development, and other biological processes. Their inherent novelty and the potential for sequence similarity with parental transcripts pose significant challenges for accurate detection and validation. While next-generation sequencing (NGS) has become the primary tool for chimeric RNA discovery, orthogonal validation methods are crucial to confirm their existence, delineate their precise structure, and quantify their abundance. Mass spectrometry (MS)-based approaches offer a powerful and complementary strategy for the robust validation of chimeric RNAs. This chapter will delve into the principles and applications of MS-based techniques for the definitive characterization of these fusion transcripts, highlighting their strengths in providing direct evidence of the chimeric junction at the peptide level, confirming the reading frame, and offering quantitative insights. We will explore various MS workflows, including targeted and untargeted peptidomics, and discuss the critical considerations for sample preparation, data acquisition, and bioinformatic analysis to ensure reliable and high-confidence validation of chimeric RNAs.

Keywords:  Chimeric RNA; Fusion junction; Fusion transcript; Mass spectrometry (MS); Peptide validation; Protein validation; RNA sequencing (RNA-Seq)

DOI:  https://doi.org/10.1007/978-1-0716-5202-2_8
bioRxiv. 2026 Jun 29. pii: 2026.06.26.734905. [Epub ahead of print]

In Vivo Quantification of Histone Acetylation Turnover and Acetyl-CoA Sources Using ²H₂O Metabolic Labeling and High-Resolution Mass Spectrometry.

Andrea Arias-Alvarado, Usman Sabir, Serguei Ilchenko, Spencer Parrish, Mirjavid Aghayev, Wentao He, Tsung-Heng Tsai, Guo-Fang Zhang, Takhar Kasumov.

Dysregulated histone acetylation links cellular metabolism to gene expression, but measuring its in vivo turnover remains technically challenging. Here, we introduce a 2 H2O -based metabolic labeling method coupled with high-resolution Orbitrap mass spectrometry to quantify in vivo histone acetylation dynamics. The approach leverages differing deuterium incorporation rates between fast-labeling acetyl groups and slow-labeling peptide backbones. A two-tier analytical workflow uses full-scan mass spectrometry for mono-acetylated peptides, combined with parallel reaction monitoring (PRM) to resolve site-specific turnover and stoichiometry. Furthermore, monitoring acetyl-group plateau 2 H enrichment enables the evaluation of specific substrate contributions to the acetyl-CoA pool supporting histone acetylation. To demonstrate biological utility, we applied this approach to mice maintained on a high-carbohydrate diet or subjected to 48-h fasting to assess nutrient-dependent histone acetylation dynamics. Acetyl-group labeling reflected the metabolic origin of acetyl-CoA, showing greater 2 H enrichment in the fed state and reduced enrichment during fasting due to increased utilization of unlabeled fatty acid-derived acetyl-CoA. Fasting accelerated acetylation turnover across multiple histone sites and reduced overall acetylation stoichiometry. Quantitative tracing revealed that fatty acid oxidation becomes an important contributor to histone acetylation during fasting, whereas glucose remains the predominant source of nucleo-cytosolic acetyl-CoA (supplying > 60% of acetylation used carbon). This approach enables simultaneous in vivo assessment of histone acetylation turnover, site occupancy, and acetyl-CoA substrate utilization, offering a robust platform to investigate metabolic-epigenetic crosstalk in health and disease.

DOI: https://doi.org/10.64898/2026.06.26.734905
Cold Spring Harb Protoc. 2026 Jul 07.

Emerging Trends in Mass Spectrometry-Based Quantitative Proteome and Phosphoproteome Profiling in Maize.

Shikha Malik, Justin W Walley.

Maize (Zea mays) is both an agronomically important crop and a reference model organism that has enabled the dissection of the molecular basis of plant development and environmental responses. Mass spectrometry-based proteomics provides a powerful approach to identify and quantify proteins and their post-translational modifications, facilitating the discovery of molecular mechanisms underlying complex biological processes. Unlike the study of gene expression using transcriptomics, analysis of the proteome and phosphoproteome provides direct measurement of proteins, which are responsible for driving or regulating nearly all cellular processes, thus offering a more complete picture of the cell's functional state. Over the past two decades, advancements in mass spectrometry have enabled large-scale profiling of protein abundance and phosphorylation sites in maize, improving our understanding of various biological phenomena. Here, we briefly summarize some of the major biological insights gained from maize proteome and phosphoproteome studies, and provide an overview of mass spectrometry sample preparation and acquisition/analysis workflows for the quantitative and reproducible analysis of protein abundance and phosphorylation dynamics in maize.

DOI: https://doi.org/10.1101/pdb.top108452
J Sep Sci. 2026 Jul;49(7): e70483

An Overview of Pterin Analysis in Biological Samples: From Occurrence and Properties to Sample Pretreatment Combined With Hyphenated Separation Techniques.

Jindřich Brejcha, Zuzana Bosakova.

  Pterins are a structurally diverse group of biologically active compounds within the pteridine family, with key roles in pigmentation, redox metabolism, light sensing, and cellular signaling across a wide range of organisms. Their quantification in biological samples is analytically demanding due to their high polarity, chemical instability, and the presence of multiple oxidation states. This review presents an integrated overview of pterin occurrence, structure, and physicochemical properties, followed by a detailed discussion of sample preparation strategies designed to ensure compound stability and analytical accuracy. Methods such as chemical oxidation, photochemical derivatization, and antioxidant stabilization are evaluated in the context of various biological matrices. We further examine state-of-the-art analytical techniques that combine separation with detection, including capillary electrophoresis, gas and liquid chromatography coupled with fluorescence, UV, electrochemical, or mass spectrometric detection. Particular attention is given to recent advances in LC-MS techniques, including both tandem mass spectrometry (LC-MS/MS) and high-resolution approaches (e.g., HPLC-Q/TOF-MS), which have greatly improved the sensitivity, selectivity, and throughput of pterin analysis, especially in combination with HILIC separation mode. These developments support the growing use of pterins as biomarkers in clinical diagnostics and physiological research, and underscore the importance of robust, matrix-appropriate analytical protocols tailored to the specific challenges posed by this compound class.

Keywords:  biological samples; pretreatment; pterins; separation techniques

DOI:  https://doi.org/10.1002/jssc.70483
J Proteome Res. 2026 Jul 10.

Improved Surfactant-Assisted One-Pot Sample Preparation for Robust Convenient Single-Cell Proteomics.

Reta Birhanu Kitata, Zhangyang Xu, Nadia Bayou, Rui Zhao, William B Chrisler, Matthew J Gaffrey, Karl K Weitz, Mara Serena Serafini, Elisabetta Molteni, Vladislav A Petyuk, Massimo Cristofanilli, Tao Liu, Carolina Reduzzi, Tujin Shi.

  Recent advances in mass spectrometry-based single-cell proteomics (SCP) technologies have revolutionized the SCP field. However, current SCP approaches generally employ sub-μL to 1 μL processing volumes for effective single-cell sample preparation using either ultralow-volume specialized devices or a 384-well plate by frequently adding water to compensate for evaporation, which limits their broad accessibility and analytical robustness. Here, we report a robust and convenient SCP method termed iSOP (improved surfactant-assisted one-pot processing) for the processing of single cells at a low μL processing volume using a 384-well plate with tight sealing to avoid sample drying loss. After systematic optimization, 3 μL was selected for iSOP as the processing volume, with a mixture of trypsin and Lys-C enzymes (2 ng of each enzyme) in terms of robustness, sensitivity, and operation convenience. With a commonly accessible LC-MS platform, iSOP-MS can detect and quantify ∼1200-1800 protein groups from single HeLa or MCF7 cells. Application of iSOP-MS to two neuroblastoma cell lines enabled reliable identification of an average of ∼1700 and ∼2050 protein groups from single BE2-C and SK-N-SH cells, respectively, and precise characterization of cellular heterogeneity. When compared to other available SCP methods, iSOP-MS is more robust and convenient for routine, cost-effective, quantitative SCP analysis.

Keywords:  384-well plate; cellular heterogeneity; iSOP-MS; low μL processing volume; single-cell proteomics (SCP)

DOI:  https://doi.org/10.1021/acs.jproteome.6c00063
Bioinformatics. 2026 Jul 01. pii: btag224. [Epub ahead of print]42(Supplement_1):

Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.

Chen Qian, Kaifei Wang, Pengzhi Mao, Ranfei Chen, Hao Chi.

MOTIVATION: Protein search engines are essential for interpreting mass spectrometry data into biological insight. Current tools often face limitations in sensitivity when analyzing complex modern datasets, and lack a unified framework that effectively integrates deep learning features for both restricted and open searches, especially for scenarios aimed at discovering unknown modifications.
RESULTS: We present pFind+, a high-performance search engine for data-dependent acquisition (DDA) proteomics, extending pFind. It introduces an enhanced raw scoring that delivers substantially improved pre-filtering ability, while recovering most of the computational overhead through a tailored acceleration strategy. Coupled with an enhanced rescoring framework that effectively integrates deep learning features, pFind+ uniquely supports high-sensitivity, DL-enhanced open search, enabling comprehensive PTM discovery while incorporating hardware-aware inference optimizations for practical deployment. Evaluations across diverse datasets demonstrate its superior sensitivity, with gains of 12.7%-29.3% (average 17.9%) in restricted search and 8.0%-38.4% (average 25.8%) in open search over the best existing tools.

DOI: https://doi.org/10.1093/bioinformatics/btag224
Adv Exp Med Biol. 2026 ;1510 147-162

Sample Preparation Strategies for Microbial Cell Surface Proteomics: Integrating Shaving and Shotgun Approaches.

Dorota Satala, Katarzyna Kowalik, Justyna Karkowska-Kuleta.

  A range of mass spectrometry-based proteomic approaches is now employed to characterize the surface proteome-surfaceome-of pathogenic microorganisms, including fungi capable of causing infections in humans. Nevertheless, the preparation of surface-enriched samples remains technically challenging because of the persistent risk of intracellular protein contamination. Cell surface shaving represents a focused sample treatment approach in which proteolytic enzymes act on intact cells to release surface-exposed peptides, thereby enriching this fraction for high-resolution liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analysis. Optimizing sample preparation for fungal surface proteomics is critical, as reducing sample complexity, enriching surface-exposed components, and ensuring LC-MS/MS compatibility must be achieved while avoiding matrix interferences, partial lysis, and biases toward only trypsin-accessible epitopes. These methodological challenges, as well as strategies for validating sample-treatment selectivity with appropriate controls, are discussed in this chapter.

Keywords:  Fungi; Sample preparation; Shaving; Surface; Surfaceome; Trypsin

DOI:  https://doi.org/10.1007/978-3-032-21638-0_7
Clin Chem Lab Med. 2026 Jul 10.

Clinical validation of an automated extraction protocol and liquid chromatography single quadrupole mass spectrometry (LC-MS) method for plasma vitamins A and E analysis.

Rowan Hellier, Lesley Heirene, Stuart J Moat.

   OBJECTIVES: Quantitation of plasma vitamins A and E is essential for assessing nutritional status. Traditional methods typically involve liquid-liquid extraction followed by high-performance liquid chromatography with UV detection (HPLC-UV). In this study, we evaluated an automated sample extraction-method paired with liquid chromatography-single quadrupole mass spectrometry (LC-MS) as an alternative analytical approach.
METHODS: Plasma samples were extracted using Oasis PRiME HLB µElution Plates automated on a Tecan Liquid-Handling Platform and analysed using LC-MS. Injection-to-injection time was 5-min. Method performance was assessed by comparison with the Chromsystems™ Vitamin A and E HPLC-UV Kit, using patient specimens (n=70), ClinChek® Controls, and external quality assessment (EQA) materials.
RESULTS: The developed method demonstrated acceptable inter-assay imprecision (CV≤5 %). The bias compared to the HPLC-UV method for vitamin A was 5.67 % and -0.15 % for vitamin E. Good agreement was observed for both vitamins (concordance correlation coefficients (CCC) >0.950). The assay was linear up to 12 μmol/L for vitamin A and 125 μmol/L for vitamin E. Using EQA materials, the mean bias for both analytes was <4 %. The lower limit of quantification (LLOQ) was 0.16 μmol/L for vitamin A and 0.47 μmol/L for vitamin E. Extracts stored at 4 °C and -20 °C were stable for 5 and 14 days, respectively. The analytical column demonstrated good retention time stability (<3.0 % change for both analytes) and was suitable for >1,500 injections.
CONCLUSIONS: This method demonstrated robust analytical performance and good agreement with HPLC-UV. Our approach confers practical advantages over a manual based extraction and HPLC-UV analysis including analytical selectivity and workflow time efficiency.

Keywords:  automation; liquid chromatography; mass spectrometry; vitamins A and E

DOI:  https://doi.org/10.1515/cclm-2026-0108
Data Brief. 2026 Aug;67 113051

Metabolomic data of melittin-intervened murine cervical cancer cells based on liquid chromatography-mass spectrometry.

Jianrong Jiang, Yaqin Gao, Mengyi Wang, Ronghua Zhang, Jiwu Lou, Yueliang Li, Jianfeng Qiu, Dafu Chen, Tizhen Yan, Rui Guo, Yanhui Liu.

  Melittin-treated murine cervical cancer U14 cells have been widely recognized as a classic cellular model for anti-tumor research in cervical cancer. This article contains metabolomic data of U14 cell lysates from both melittin-treated and control groups. Untargeted metabolomic profiling was carried out by liquid chromatography-mass spectrometry (LC-MS) to systematically elucidate the global metabolic disturbances in cervical cancer cells upon melittin intervention. LC-MS raw data were processed for peak extraction and alignment using XCMS software, followed by quality control normalization with metaX software. Metabolite annotation was performed against the HMDB and KEGG databases as well as an in-house MS/MS spectral library, yielding metabolite feature data including mass-to-charge ratio (m/z), retention time (RT), and MS/MS-identified metabolites (MS2). A total of 22,976 metabolic ions were detected in this study, among which 16,176 were assigned Level 1 annotations and 1114 were identified with high confidence at Level 2. All raw and processed data are publicly accessible at NGDC (accession number PRJCA065444). This untargeted LC-MS-based metabolomic dataset not only provides a comprehensive resource for elucidating metabolism-related anticancer mechanisms of melittin in murine U14 cervical cancer cells but also supports the development of targeted therapeutic strategies against cervical cancer.

Keywords:  Cervical cancer; Melittin; Metabolites; Metabolomics; U14 cells

DOI:  https://doi.org/10.1016/j.dib.2026.113051
J Am Soc Mass Spectrom. 2026 Jul 10.

An m/z- and Intensity-Based HRMS Clustering Algorithm Targeted toward Single-Cell Metabolomics.

Dirk Wevers, Laura Koldenhof, Laura Castaneda, Anouschka van der Zeeuw, Hellen Fass, Thomas Hankemeier, Charlie Clark, Ahmed Ali.

  Single-cell (SC) metabolomics holds great potential in the development of novel diagnostic tools and mechanistic insights into cell biology. Using high-resolution mass spectrometry (HRMS), the masses of a single cell's constituents can be determined with an accuracy high enough to derive their respective elemental compositions. Using a molecule's mass and its MS fragmentation pattern, in many cases a molecular structure can be assigned or looked up in databases. Due to the small measurement cell volume of an Orbitrap HRMS instrument, samples need to be scanned multiple times, which necessitates across-scan clustering per sample, and across-sample alignment of m/z values. However, existing HRMS data processing software is not designed to process SC HRMS data, as it typically requires liquid chromatography retention times or reference spectra for m/z clustering and alignment. Herein, a novel, robust SC HRMS m/z clustering and alignment algorithm is presented and compared with two commercially available and industry standard algorithms used by Sciex MarkerView and Thermo FreeStyle. Furthermore, output is compared with clustering results from DBSCAN and MaldiQuant binning. Our algorithm, Global Clustering unTargeted Analysis (GCTA), enforces a strict maximum on the cluster size, thereby reducing the chance of peak aggregation. Furthermore, by design, GCTA enables noise filtering based on intensity and number of peaks. Across-scan clustering and across-sample alignment were contrasted for accuracy in finding peaks identified by commercial software output and peaks with known m/z values corresponding to standards and HMDB and LipidMaps database hits. Comparisons are made based on data recorded for quality control samples containing standard mixes as well as SC HRMS data recorded for two different cell lines. This work shows that the presented algorithm is comparable in accuracy with respect to MarkerView and FreeStyle, reliably identifies compounds, is less prone to peak splitting than MaldiQuant binning while providing similar levels of error in clustering peaks, and successfully filters noise. Furthermore, it is shown to be competitive with DBSCAN, MaldiQuant binning and MarkerView when compared to theoretical m/z values based on database hits. GCTA encompasses both m/z clustering and reference-free alignment, which makes it pivotal to further development of untargeted SC HRMS metabolomics.

Keywords:  HRMS data processing; aggregation; noise filtering; peak splitting; reference-free clustering and alignment; single-cell metabolomics

DOI:  https://doi.org/10.1021/jasms.6c00150
Nat Commun. 2026 Jul 06.

Large-scale discovery and annotation of substructure patterns in mass spectrometry profiles.

Laura Rosina Torres Ortega, Jonas Dietrich, Joe Wandy, Hans Mol, Justin J J van der Hooft.

Untargeted mass spectrometry can detect thousands of molecules at once, potentially offering powerful insights into complex samples. However, the increasing scale of experimental datasets and spectral libraries limits our ability to extract and annotate structural information to allow for interpretation. Here, we present the software tool MS2LDA 2.0 that helps to address this gap by identifying recurring fragmentation patterns (Mass2Motifs) that can reflect shared chemical substructures. We introduce automated annotation support through Mass2Motif Annotation Guidance (MAG) that provides suggestions to interpret detected patterns. Our unsupervised pattern mining tool enables the study of much larger datasets with up to 14 times faster analysis than its predecessor. We demonstrate the utility of MS2LDA 2.0 and MAG in applications such as detecting pesticide-related substructures and exploring unknown fungal compounds. Together, these advances make it easier to uncover meaningful chemical patterns in complex data.

DOI: https://doi.org/10.1038/s41467-026-75038-0
J Sep Sci. 2026 Jul;49(7): e70485

Quantitative Profiling and Metabolic Study of Caffeoylquinic Acids and Their Metabolites by Liquid Chromatography-Tandem Mass Spectrometry in Rats and Laying Hens.

Sichao Wang, Wenjie Tian, Xiaohui Feng, Huiyan Zhang, Youyou Yang, Junmin Zhang.

  Caffeoylquinic acids are natural polyphenolic compounds with notable antioxidant and anti-inflammatory activities. This study developed and validated a sensitive high-performance liquid chromatography-tandem mass spectrometry method for the simultaneous quantification of isochlorogenic acid A (ICQA) and 22 metabolites in serum. The method exhibited excellent sensitivity with limits of quantification of 0.625-1.25 µg/L (signal-to-noise ratio = 10), inter/intra-day precision below 15%, and comprehensive coverage of phase I/II and microbial metabolites. Application of the method in rats and laying hens demonstrated its robustness for pharmacokinetic profiling and cross-species validation, revealing a triphasic metabolic pattern of ICQA, the consistent metabolic endpoints of the accumulation of microbial metabolites, and a critical role of gut microbiota in ICQA metabolism. The developed platform provides a reliable analytical tool for comprehensive metabolism studies of phenolic compounds, with high throughput and reproducibility.

Keywords:  caffeoylquinic acid; isochlorogenic acid A; liquid chromatography‐tandem mass spectrometry; metabolism; quantification

DOI:  https://doi.org/10.1002/jssc.70485
Expert Rev Proteomics. 2026 Jul 08.

The clinical promise of mass spectrometry based single-cell proteomics: from bedside to bench.

Heloísa Monteiro do Amaral-Prado, Jackson Gabriel Miyamoto, Ariane Fidelis Busso-Lopes, Larissa Bueno Tofani, Nilson Antônio da Rocha Coimbra, Fábio Malta de Sá Patroni, Daniella de Figueiredo, Gabriela Fonseca Rocha, Bianca Alves Pauletti, Carolina Moretto Carnielli, Daniela Campos Granato, Adriana Franco Paes Leme.

   INTRODUCTION: Single-cell proteomics (SCP) is entering into a transformative phase, moving beyond technically demanding benchmarking studies toward robust and reproducible workflows capable of quantifying thousands of proteins per cell. These advances highlight SCP's potential to address clinically relevant questions by resolving cellular and pathological heterogeneity that remains obscured in bulk proteomics.
AREAS COVERED: This review discusses current advances, challenges, and clinical applications of SCP based on literature identified through searches in major scientific databases. Many clinically relevant samples remain underexplored in SCP studies, in part because their application requires careful evaluation of pre-analytical variables that can strongly influence proteomic readouts. Current SCP methodologies vary according to sample type, experimental conditions, and available resources. Compared with single-cell RNA sequencing, SCP remains limited in cellular throughput, making it challenging to define optimal sample sizes and to reliably detect both abundant and rare cell populations. These limitations also make dataset integration difficult, as reduced cellular coverage and sampling depth increase data sparsity. Moreover, implementing quality control strategies across sequential SCP experiments is essential to ensure data robustness, comparability, and accurate biological interpretation.
EXPERT OPINION: Applying SCP to clinical samples advances our understanding of biological complexity and holds potential to drive progress in translational and precision medicine.

Keywords:  Mass spectrometry; biofluids; biopsy; cellular biology; medicine; tissue

DOI:  https://doi.org/10.1080/14789450.2026.2701811
Cell Death Dis. 2026 Jul 04.

The alternative NF-κB RelB subunit is a novel critical sensor of lipid metabolism to promote tumor development.

T Becquard, C Benatar, C Bretot, B Eluard, D Bordereaux, L Thirouard, A Hammoutene, A Montagne, F Dingli, D Loew, C Ransy, M-C Alves-Guerra, C Caradeuc, N Cagnard, M Boissan, N Giraud, F Bouillaud, G Bertho, V Paradis, D Chiron, R Dentin, C Prip-Buus, V Baud.

Cancer cells reprogram their metabolism to fulfill their high energetic demand. Lipid metabolism is most often reprogrammed for cancer cell survival and tumor development. The role of alternative oncogenic NF-κB/RelB subunit in the reprogramming of lipid metabolism in cancer is unknown. Here we report that RelB plays a central role at the crossroads of lipid storage and liberation of fatty acids from the lipid droplets to feed the fatty acid oxidation (FAO) and mitochondrial energetic metabolism. High RelB expression defines a subset of hepatocellular carcinoma (HCC) patients and cell lines with a peculiar gene expression profile enriched in lipid catabolic-related genes, including lipases. Functional studies revealed that high RelB activation controls the expression of major lipolytic lipases, including adipose triglyceride lipase (ATGL) and monoglyceride lipase (MAGL), and impacts on HCC cell survival, migration, and tumor development in vivo. Altogether, we uncovered that RelB is a central regulator of the lipid metabolism plasticity and an energy homeostasis sensor in cancer cells.

DOI: https://doi.org/10.1038/s41419-026-09064-7
FEBS J. 2026 Jul 10.

A guide to laser microdissection-based proteomics.

Cheng Shen, Kael Schoffer, Charlotte Williams, Maria C Tanzer.

  Laser microdissection-based proteomics enables spatially resolved profiling of the proteome within intact tissue. Recent advances in histology, image analysis, laser microdissection and mass spectrometry have markedly increased the capability of this technology and driven the development of a range of powerful workflows. These now extend from regional tissue proteomics to emerging single-cell applications, with subcellular proteome mapping on the horizon. In this review, we examine the current state of laser microdissection (LMD)-based proteomics, outline what these approaches can offer, describe the standard workflow, and discuss which applications and instrumentation are best suited to address specific biological questions. We also highlight the strengths and limitations of current workflows and direct readers to key publications that describe and exemplify their successful implementation.

Keywords:  Deep Visual Proteomics; FFPE tissue; laser microdissection; mass spectrometry; single‐cell proteomics; spatial proteomics

DOI:  https://doi.org/10.1111/febs.70653