bims-mascan 2026-02-15 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–02–15
25 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

What's in a name? Metabolite identification: challenges and pitfalls in untargeted metabolomics.
Ultrasensitive Detection of Tissue Lipids at the Femtomole Scale Using Low-Microflow Targeted Lipidomics.
CellMate─A Deep Learning-Assisted Single-Cell Data Processing Platform.
Evaluation of LC/MS Methods for Hydrophilic Metabolites to Enable Integration of Human Blood Metabolome Data.
Advancing DIA-Based Limited Proteolysis Workflows: Introducing DIA-LiPA.
Deep-coverage single-cell metabolomics enabled by ion mobility-resolved mass cytometry.
Metabolic permissiveness: how tissue context shapes cancer.
Deep profiling reveals coordinated remodeling of ganglioside metabolism in MCF-7 breast cancer cell line.
RPLC- and HILIC-based non-targeted metabolomics workflow for blood microsamples.
Online supercritical fluid derivative extraction-pressure change focusing-supercritical fluid chromatography-mass spectrometry for identification of double bond positional isomers in unsaturated lipids.
ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings.
Rapid Generation of Tandem Mass Spectrometry Reference Libraries Using Immediate Drop-on-Demand Liquid Handling Coupled to an Open Port Sampling Interface.
Spatiotemporal metabolic mapping reveals diet-independent remodeling of the postnatal mouse brain.
Cross ionization mode chemical similarity prediction between tandem mass spectra in metabolomics.
Sensitive detection of cancer antigens enabled by user-defined peptide libraries.
LipidLocator: an open source Shiny web application for spatial lipidomics.
Novel high-resolution ion mobility mass spectrometry for site-specific quantification of the sirtuin-5 regulated kidney succinylome.
Community Curation of Microbial Metabolites Enables Biological Insights of Metabolomics Data.
Single Plaque Proteomics Reveals the Composition and Dynamics of the Amyloid Microenvironment in Alzheimer's Disease.
Analyzing Plant Low-Molecular-Weight Polar Metabolites: A GC-MS Approach.
Beyond lipid homochirality: Analytical strategies and biological implications.
MS/MS Mass Spectrometry Filtering Tree for Bile Acid Isomer Annotation.
A searchable metadata network graph for microbiome metabolomics.
Applications of mass spectrometry in the routine diagnostic medical laboratory - a status report 2025.
Derivatization agents for LC-MS analysis of amino acids: effects of core structure and functional groups.

Metabolomics. 2026 Feb 09. 22(2): 22

What's in a name? Metabolite identification: challenges and pitfalls in untargeted metabolomics.

Georgios Theodoridis, Oliver Fiehn, Michal Holčapek, Royston Goodacre, Daniel Raftery, Robert Plumb, Timothy M D Ebbels, Michael Witting, Helen Gika, Ian D Wilson.

   BACKGROUND: The aim of metabolic phenotyping (metabotyping) is to discover and identify metabolites (including lipids) that can be used to characterize biological samples and differentiate between different physiological states. The identification of the metabolites responsible for this differentiation is essential if mechanistic understanding is to be obtained. Confident metabolite identification arguably represents the most important outcome of untargeted metabolomics studies but currently the standards used for metabolite identification reported in many publications do not strictly follow the various published guidelines and thus these identifications lack sufficient proof.
AIM OF REVIEW: In this perspective we define problems that currently plague the field of metabolite identification using MS-based techniques, particularly LC-MS, in untargeted metabolic phenotyping. Despite considerable efforts by the community (researchers, instrument manufacturers, software, and database developers) this continues to be a contentious and error-prone step in the metabolomics workflow. The majority of publications provide only sparse data on the evidence for metabolic markers "identified" and we have observed an alarming increase in the frequency of erroneous metabolite identifications. Here, we describe the problem and provide several illustrative case studies. Our goal is to raise awareness and highlight the issue of poor metabolite identification, since it is also increasingly apparent that these errors are not always recognised during the reviewing process, such that papers with potentially erroneous metabolite identities reach publication.
KEY SCIENTIFIC CONCEPTS OF REVIEW: Poor metabolite identification potentially represents an existential threat to the credibility of untargeted "discovery" metabolomics and can pollute the literature. Here we describe the aetiology of the problem and explain how and why this issue affects the field. We argue that coordinated action is required by researchers, database managers, scientific societies and the reviewers, editors and publishers of scientific journals to both acknowledge and address this important problem.

Keywords:  Biomarkers; Lipidomics; Mass spectrometry; Metabolite annotation; Metabonomics; Research integrity

DOI:  https://doi.org/10.1007/s11306-025-02387-0
Anal Chem. 2026 Feb 09.

Ultrasensitive Detection of Tissue Lipids at the Femtomole Scale Using Low-Microflow Targeted Lipidomics.

Olivia L Murtagh, Payton E Lowrey, Ku-Lung Hsu.

Liquid chromatography-mass spectrometry (LC-MS)-based lipidomics is a widely adopted method for profiling and quantifying changes in cellular and tissue lipids in response to biological and pharmacological perturbations. Effective chromatographic separation is critical for lipid analysis, yet conventional LC flow rates often compromise either sensitivity (analytical flow) or throughput (nanoflow). Microflow chromatography has proven to be an effective intermediate option; however, few lipidomic methods have been developed on this scale. Here, we describe an optimized low-microflow (25 μL/min) LC method coupled to a triple-quadrupole mass spectrometer for sensitive targeted lipidomic analysis. A 0.5 mm inner diameter C12 column provides a stable and reproducible separation across 13 different lipid classes with femtomolar limits of detection and quantitation. Using four short (∼5 min) optimized gradients, we achieve detection of over 500 endogenous lipid molecular species across six mouse tissues, with a median CV of 13% across 36 biologically independent samples. The C12 stationary phase increases the coverage of monoacylglycerols and diacylglycerols (DAGs), the nonpolar lipid classes that are often underrepresented in existing workflows. We applied low-microflow targeted lipidomics to discover an unexpected decrease in polyunsaturated DAGs in tissues from DAG lipase-beta knockout compared with wild-type mice.

DOI: https://doi.org/10.1021/acs.analchem.5c05829
Anal Chem. 2026 Feb 13.

CellMate─A Deep Learning-Assisted Single-Cell Data Processing Platform.

Felix Friedrich, Cátia Marques, Ingela Lanekoff.

Mass spectrometry-based single-cell metabolomics (SCM) reveals the inherent heterogeneity of individual cells among seemingly identical cell types. Fast-scanning and high-resolving mass analyzers provide the sensitivity and specificity required to probe minuscule amounts of biological material. However, acquiring data from hundreds of individual cells to achieve statistical power results in complex data sets. This challenge is compounded by the limited availability of specialized data analysis tools for single-cell metabolomics, as many techniques depend on the use of specialized sampling and ionization probes. This results in incompatibility with conventional metabolomics data processing tools. Here, we present CellMate, a MATLAB-based data processing platform designed for single-cell metabolomics using direct infusion techniques. CellMate comprises identification and peak alignment of detected metabolites in an intuitive graphical user interface. CellMate supports customizable quantitative, targeted, and nontargeted metabolomic workflows. The untargeted workflow is enabled by a novel deep learning-based image classification algorithm that effectively distinguishes endogenous metabolites from background species. The source code, along with a compiled installer, is available at github.com /LanekoffLab/CellMate. We believe that CellMate represents a significant advancement in the single-cell metabolomics toolbox, enabling comprehensive data extraction of precious metabolite information from single cells.

DOI: https://doi.org/10.1021/acs.analchem.5c07205
Mass Spectrom (Tokyo). 2026 ;15(1): A0188

Evaluation of LC/MS Methods for Hydrophilic Metabolites to Enable Integration of Human Blood Metabolome Data.

Yuri Imado, Masatomo Takahashi, Yuki Soma, Shunsuke Aburaya, Kohta Nakatani, Taizo Hanai, Takeshi Bamba, Yoshihiro Izumi.

  Information on candidate biomarker metabolites identified in recent disease biomarker discovery research is expected to play a key role in the future of personalized and precision medicine. Liquid chromatography mass spectrometry (LC/MS) is a powerful method for metabolomic analysis due to its comprehensive coverage and high detection sensitivity. However, the suitability of LC/MS methods for the identification and quantification of hydrophilic metabolites remains debatable. Here, we evaluated the performance of LC/MS methods combining four types of LC [hydrophilic interaction chromatography (HILIC), ion chromatography (IC) with an anion-exchange (AEX) column (AEX-IC), reversed-phase LC (RPLC) with a pentafluorophenylpropyl (PFPP) column (PFPP-RPLC), and unified-hydrophilic interaction AEX LC (unified-HILIC/AEX)], using the same Orbitrap mass spectrometer, with the aim of integrating future human plasma metabolome data. First, we conducted a qualitative performance evaluation of four LC/MS methods, HILIC/MS, AEX-IC/MS, PFPP-RPLC/MS, and unified-HILIC/AEX/MS, by analyzing 511 hydrophilic metabolite standards and NIST Standard Reference Material (SRM) 1950 (Metabolites in Frozen Human Plasma). The evaluation focused on metabolome coverage, peak width, sensitivity, and separation performance of isomers. Next, we thoroughly evaluated the quantitative performance of the four analytical methods for 63 hydrophilic metabolites in SRM 1950 using a stable isotope-labeled internal standard (SILIS) mixture derived from 13C-labeled Escherichia coli extracts. Furthermore, we successfully estimated new concentration values for 29 metabolites without certified values in SRM 1950 using quantitative data from the four LC/MS methods. We objectively evaluated the performance of the four LC/MS methods and demonstrated that absolute quantification using SILIS is effective for integrating hydrophilic metabolite data in metabolomics.

Keywords:  Escherichia coli; human plasma; intra-laboratory comparison; metabolomics; stable isotope-labeled internal standard mixture

DOI:  https://doi.org/10.5702/massspectrometry.A0188
Anal Chem. 2026 Feb 14.

Advancing DIA-Based Limited Proteolysis Workflows: Introducing DIA-LiPA.

Chloé Van Leene, Emin Araftpoor, An Staes, Andrea Argentini, Marcel Bühler, Lieven Clement, Kris Gevaert.

Limited proteolysis coupled to mass spectrometry (LiP-MS) probes protein conformational dynamics, but interpretation of LiP-MS data is complicated by heterogeneous proteolytic cleavage patterns and missing data. Recent advances in data-independent acquisition (DIA) and machine learning-based search engines promise improved sensitivity and reproducibility, yet their performance in LiP-MS workflows remains underexplored. We systematically evaluated selected library-free DIA workflows using a rapamycin-treated human cell lysate and a yeast heat shock data set, benchmarking DIA-NN and Spectronaut for identification depth, reproducibility, and false discovery rate control. Our results show that library-free approaches achieve high sensitivity, eliminating the experimental overhead and sample requirements associated with empirical libraries. Building on these advances, we introduce a DIA-based Limited Proteolysis data Analysis pipeline (DIA-LiPA), a data analysis workflow tailored for LiP-MS data that integrates semitryptic- and tryptic-level precursor data and accounts for missingness to enable structural interpretation. Validation across multiple data sets confirmed that DIA-LiPA reproduces known structural signatures and uncovers additional regulatory patterns, providing a robust framework for mechanistic insights into protein dynamics.

DOI: https://doi.org/10.1021/acs.analchem.5c07014
Nat Methods. 2026 Feb 09.

Deep-coverage single-cell metabolomics enabled by ion mobility-resolved mass cytometry.

Mingdu Luo, Tianzhang Kou, Yandong Yin, Shengyi Zhou, Xiaolan Zhu, Xinhao Zeng, Junhao Hu, Zheng-Jiang Zhu.

Current single-cell metabolomics approaches are limited by insufficient sensitivity, robustness and metabolite coverage. We present an ion mobility-resolved mass cytometry technology that integrates high-throughput single-cell injection with ion mobility-mass spectrometry for multidimensional metabolomic profiling. Ion mobility-enabled selective ion accumulation and cell superposition-based amplification strategies substantially enhance sensitivity, robustness and overall analytical performance. Combined with our computational tool, MetCell, this technology allows high-throughput analysis while achieving exceptional profiling depth, detecting over 5,000 metabolic peaks and annotating approximately 800 metabolites per cell-representing a 3-fold to 10-fold improvement over existing methods. It offers attomole-level sensitivity and captures a broad dynamic range of metabolites within individual cells. Applied to 45,603 primary liver cells from aging mice, it enabled accurate cell-type and cell-subtype annotation and revealed distinct metabolic states and heterogeneity in hepatocytes during aging. This platform sets a new benchmark for high-throughput single-cell metabolomics, advancing our understanding of metabolic heterogeneity at single-cell resolution.

DOI: https://doi.org/10.1038/s41592-025-02970-2
Genes Dev. 2026 Feb 09.

Metabolic permissiveness: how tissue context shapes cancer.

Chrysanthi Moschandrea, Christian Frezza.

  An emerging paradox in cancer metabolism is that identical oncogenic mutations produce profoundly different metabolic phenotypes depending on tissue context, with many mutations exhibiting striking tissue-restricted distributions. Here we introduce metabolic permissiveness as the inherent capacity of a tissue to tolerate, adapt to, or exploit metabolic disruptions, providing a unifying framework for explaining this selectivity. We examine tissue-specific metabolic rewiring driven by canonical oncogenes (MYC and KRAS), tumor suppressors (p53, PTEN, and LKB1), and tricarboxylic acid (TCA) cycle enzymes (FH, SDH, and IDH), demonstrating that baseline metabolic architecture, nutrient microenvironment, redox buffering, and compensatory pathways determine whether mutations confer a selective advantage or metabolic crisis. We further discuss how the tumor microenvironment shapes metabolic adaptation and therapeutic vulnerability. This framework reveals shared principles of tissue-specific metabolic vulnerability in cancer and provides a mechanistic basis for precision metabolic therapies.

Keywords:  cancer; metabolism; permissiveness

DOI:  https://doi.org/10.1101/gad.353516.125
Chem Sci. 2026 Jan 26.

Deep profiling reveals coordinated remodeling of ganglioside metabolism in MCF-7 breast cancer cell line.

Yichun Wang, Gaoge Sun, Hang Yin, Yu Xia.

Gangliosides are vital cell membrane components whose metabolic dysregulation is implicated in various cancers. However, a systems-level understanding of their metabolism has been hindered by their structural complexity and low cellular abundance. Herein, we have developed a deep profiling workflow for gangliosides that integrates selective enrichment, liquid chromatography-ion mobility spectrometry, and isomer-resolved tandem mass spectrometry. This workflow enhances detection sensitivity 100-fold, enabling the identification of 391 ganglioside structures in a human breast adenocarcinoma cell line (MCF-7) at multiple structural levels. We further reveal coordinated remodeling of gangliosides in MCF-7 cancer cells, including shifts toward a-series glycans, increased incorporation of long-chain sphingosine bases, and altered C[double bond, length as m-dash]C location isomers. By integrating these lipidomic findings with targeted gene expression analysis and quantitative proteomics, we reconstruct a ganglioside biosynthetic network that delineates dysregulation across five key structural modules. This lipid-centric approach offers new insights into the metabolic reprogramming of gangliosides and holds potential for studying lipid metabolism in diverse diseases.

DOI: https://doi.org/10.1039/d5sc09445c
Metabolomics. 2026 Feb 09. 22(2): 23

RPLC- and HILIC-based non-targeted metabolomics workflow for blood microsamples.

Pauline Couacault, Michael Witting.

   INTRODUCTION: Blood microsampling (BμS) has emerged as an alternative to invasive sampling methods, including blood and plasma sampling. Several studies have shown that BμS are suitable alternatives for analyzing endogenous metabolites and for metabolomics applications. Dried blood spots (DBS) have long been used for clinical applications, particularly for newborn screening. New quantitative BμS have emerged, including volumetric absorptive microsampling (VAMS).
OBJECTIVES: We aimed to develop an extraction protocol from BµS for non-targeted metabolomics analysis using a reversed-phase liquid chromatography/mass spectrometry (RPLC-MS) method for the mid- to non-polar metabolome and a hydrophilic interaction chromatography/mass spectrometry (HILIC-MS) method for the polar metabolome, based on existing protocols from the literature. To improve coverage, two new HILIC-MS methods have been developed.
METHODS: We used an in-house RPLC-MS method for the analysis of mid- to non-polar metabolites. Two new HILIC-MS/MS methods were developed using 73 chemical reference standards of polar metabolites from various classes. To optimize extraction, five procedures were investigated and compared to identify the most appropriate protocol for extracting metabolites from BµS for non-targeted metabolomics analysis. The final workflow was optimized on both DBS and VAMS.
RESULTS AND CONCLUSION: We developed and optimized a 15-minute HILIC-MS method that included column re-equilibration. Our experiments showed that using a 20% H2O/80% MeOH (v/v) mixture for extraction, with sample rehydration, is a good compromise for detecting many metabolite features. Our extraction and LC-MS methodology covered metabolites from many pathways, including amino acids, acylcarnitines, and bile acids.

Keywords:  Blood microsampling; DBS; HILIC-MS; Non-targeted metabolomics; RPLC-MS; VAMS

DOI:  https://doi.org/10.1007/s11306-026-02402-y
Talanta. 2026 Feb 01. pii: S0039-9140(26)00150-5. [Epub ahead of print]303 129495

Online supercritical fluid derivative extraction-pressure change focusing-supercritical fluid chromatography-mass spectrometry for identification of double bond positional isomers in unsaturated lipids.

Longhui Huang, Zaixin Wu, Jiaqi Liu, Dianbao Yu, Ting Zhou.

  Precise localization of carbon-carbon double bonds in unsaturated lipids is essential for elucidating lipid functions and disease mechanisms, yet conventional liquid chromatography-mass spectrometry workflows are hampered by labor-intensive pretreatment, solvent consumption, and insufficient sensitivity. Here, we presented an online supercritical fluid derivative extraction-pressure change focusing-supercritical fluid chromatography-mass spectrometry platform that integrated derivatization, extraction, purification, separation, and detection into a single automated workflow. The supercritical fluid derivative extraction strategy enabled simultaneous in situ epoxidation and cleanup, while the pressure change focusing strategy effectively mitigated chromatographic band broadening, yielding sharper peaks and enhanced sensitivity. Systematic optimization established robust operating conditions, enabling comprehensive lipid analysis to be accomplished within 24 min using only 2.5 μL of sample. The validated method achieved excellent linearity, trueness, and recovery, showing coefficients of determination (R2) > 0.9930, recoveries of 73.8-111.8%, and trueness of 82.1-116.4% with precision better than 13.7% (RSD). The method was applied to plasma samples from schizophrenia mouse models. A total of 56 unsaturated fatty acids were identified with fully resolved positions of carbon-carbon double bonds, of which eight species exhibited significant abundance changes. Moreover, isomer ratio analysis revealed disease-associated remodeling of desaturation patterns, providing new insights into lipid metabolic dysregulation in schizophrenia. Overall, the established online platform represents a rapid, sensitive, and environmentally friendly strategy for structural lipidomics, offering strong potential for biomarker discovery and broader applications in biomedical and clinical research.

Keywords:  Pressure change focusing; Schizophrenia; Structural lipidomics; Supercritical fluid chromatography; Supercritical fluid derivative extraction

DOI:  https://doi.org/10.1016/j.talanta.2026.129495
Brief Bioinform. 2026 Jan 07. pii: bbag054. [Epub ahead of print]27(1):

ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings.

Muhammad Faizan-Khan, Roger Giné, Josep M Badia, Maribel Pérez-Ribera, Manuel Ruiz-Botella, Alexandra Junza, Jordi Capellades, Iván Pérez-López, Shipei Xing, Abubaker Patan, Laura Brugnara, Anna Novials, Joan-Marc Servitja, Maria Vinaixa, Pieter C Dorrestein, Marta Sales-Pardo, Roger Guimerà, Oscar Yanes.

  Machine learning offers a promising path to annotating the large number of unidentified MS/MS spectra in metabolomics, addressing the limited coverage of current reference spectral libraries. However, existing methods often struggle with the high dimensionality and sparsity of MS/MS spectra and metabolite structures. ChemEmbed tackles these challenges by integrating multidimensional, continuous vector representations of chemical structures with enhanced MS/MS spectra. This enhancement is achieved by merging spectra across multiple collision energies and incorporating calculated neutral losses from 38 472 distinct compounds, providing richer input for a convolutional neural network (CNN). ChemEmbed ranks the correct candidate first in over 42% of cases and within the top five in more than 76% of cases. In external benchmarks such as CASMI 2016 and 2022, ChemEmbed outperforms SIRIUS 6, the current state-of-the-art in computational metabolomics. We applied ChemEmbed to predict structures in the Annotated Recurrent Unidentified Spectra (ARUS) dataset and confirmed 25 previously unidentified compounds. These findings demonstrate ChemEmbed's potential as a robust, scalable tool for accelerating metabolite identification in untargeted mass spectrometry workflows.

Keywords:  deep learning; mass spectrometry; metabolite identification; molecular embeddings; untargeted metabolomics

DOI:  https://doi.org/10.1093/bib/bbag054
J Am Soc Mass Spectrom. 2026 Feb 10.

Rapid Generation of Tandem Mass Spectrometry Reference Libraries Using Immediate Drop-on-Demand Liquid Handling Coupled to an Open Port Sampling Interface.

Emily A Kurfman, Vilmos Kertesz, John F Cahill.

  In metabolomics, tandem MS (MS2) fragmentation libraries are important for the identification of unknown features, but generating these libraries takes many valuable hours of instrument and operator time. Here, an immediate droplet-on-demand/open port sampling interface was used to rapidly acquire tandem MS of standards arrayed in a 96-well plate format. A workflow was developed for automated, high-throughput control of MS2 library generation. Pure standard mass spectral libraries were collected on Orbitrap and Q-TOF mass spectrometers for 192 compounds using 6 different collision energies with a throughput of 4 and 7.8 s/spectrum, respectively. Libraries were acquired using different solvent additives, precursor adducts, and ion polarities.

Keywords:  MS2; libraries; metabolomics; tandem MS; throughput

DOI:  https://doi.org/10.1021/jasms.5c00401
NPJ Metab Health Dis. 2026 Feb 10. 4(1): 7

Spatiotemporal metabolic mapping reveals diet-independent remodeling of the postnatal mouse brain.

Elisa M York, Anne Miller, Sylwia A Stopka, Nathalie Y R Agar, Gary Yellen.

Developing cells undergo extensive metabolic adaptations to support growth and differentiation. Here, using spatially resolved mass spectrometry imaging and stable isotope tracing, we systematically investigate metabolic remodeling in mouse brains at postnatal day 14 and day 28, a period coinciding with the transition from a maternal milk diet to solid food. Untargeted metabolomics reveals global shifts in lipid composition, and region-specific remodeling of central energy metabolism, including increased glycolytic intermediates in grey matter-enriched regions and a global decrease in tricarboxylic acid (TCA) cycle metabolites after weaning. Despite these marked changes in metabolite levels, the glucose incorporation rate remains constant across these developmental stages. Notably, weaning mice onto a milk-replacement diet demonstrates that the observed metabolic adaptations are largely diet-independent. Together, our data suggest that postnatal brain metabolic remodeling is an intrinsically programmed feature of maturation providing region-specific metabolic reorganization to support developmental demands.

DOI: https://doi.org/10.1038/s44324-025-00098-7
Nat Commun. 2026 Feb 07.

Cross ionization mode chemical similarity prediction between tandem mass spectra in metabolomics.

Niek F de Jonge, Elena Chekmeneva, Robin Schmid, David Joas, Lem-Joe Truong, Justin J J van der Hooft, Florian Huber.

Mass spectrometry is a cornerstone of untargeted metabolomics, enabling the characterization of metabolites in both positive and negative ionization modes. However, comparisons across ionization modes have remained a substantial challenge due to the distinct fragmentation patterns produced by each polarity. To overcome this barrier, we present MS2DeepScore 2.0, a machine learning-based model to predict chemical similarity between mass fragmentation spectra, which works both between different and the same ionization modes. We demonstrate the utility of MS2DeepScore 2.0 in three case studies, where MS2DeepScore enabled cross-ionization mode molecular networking, enhancing data exploration and metabolite annotation. To ensure robustness, we have implemented a quality estimation method that flags spectra with low information content or those dissimilar to the training data, thereby minimizing false predictions. Altogether, MS2DeepScore 2.0 extends our current capabilities in organizing, exploring, and annotating untargeted metabolomics profiles.

DOI: https://doi.org/10.1038/s41467-026-69083-y
Nat Biotechnol. 2026 Feb 09.

Sensitive detection of cancer antigens enabled by user-defined peptide libraries.

Kasidet Manakongtreecheep, Claudia Ctortecka, Luis O Correa-Medero, Timothy Zhu, Isabelle Lippincott, Griffin M Lawrence, Alexis Howard, Gabrielle M Hernandez, Cleo Forman, Emma C Duggan, Marta A Wilbrink, Eva K Verzani, Alexander B Afeyan, Jiaxun Li, Alexey I Nesvizhskii, Giacomo Oliveira, Derin B Keskin, Patrick A Ott, Karl R Clauser, Matthew Bakalar, Siranush Sarkizova, Nir Hacohen, Steven A Carr, Jennifer G Abelin, Catherine J Wu.

Human leukocyte antigen (HLA)-bound tumor peptides can be routinely isolated from cancer samples and identified using mass spectrometry (MS). However, MS approaches can be stochastic or rely on spectral libraries, which are not customarily available for individual-specific peptides, thus limiting the ability to discover novel peptides. Here, we introduce Pepyrus, which generates user-defined, individual-specific or disease-specific peptide libraries in Escherichia coli to improve the sensitivity and confidence of MS peptide identification, including lowly abundant neoantigens. Using Pepyrus-generated peptide libraries paired with an HLA-specific data-independent acquisition strategy, we recover >75% of the expected sequences per single injection for libraries of >10,000 peptides and identify 0.1 fmol of spiked-in peptides in a complex background. We apply Pepyrus to create personalized libraries, facilitating identification of clinically relevant HLA peptides, including several novel peptides from cell lines derived from persons with melanoma and renal cell carcinoma. Pepyrus enables identification of rare HLA-bound peptides and provides the ability to generate large training datasets to improve spectra, retention time and ion mobility prediction tools.

DOI: https://doi.org/10.1038/s41587-026-03003-9
Bioinform Adv. 2026 ;6(1): vbag012

LipidLocator: an open source Shiny web application for spatial lipidomics.

Prateek Arora, Simon Isfort, Nick Kirschke, Mojgan Masoodi, Nadia Mercader.

Motivation: Spatial lipidomics enables the study of how lipids are distributed within tissues, providing insights into tissue structure and function. However, analyzing complex mass spectrometry (MS) imaging (MSI) data remains challenging due to limited tools for high-confidence annotation, especially for integrating MSI, MS, and MS/MS pipelines.
Results: We developed LipidLocator, an open-source, interactive Shiny web application as a unified spatial lipidomics pipeline. LipidLocator integrates MSI data analysis from normalization, spatial clustering, and differential abundance analysis to MS and MS/MS-based lipid annotation. We utilized LipidLocator to analyze DESI-MSI and AP-SMALDI data from adult zebrafish sections, human renal carcinoma, and mouse whole brain sections, to demonstrate its ability to segment distinct anatomical structures and tissue sub-regions and to generate high-confidence lipid profiles using integrated MS and MS/MS annotation. LipidLocator is an end-to-end open-source spatial lipidomics pipeline, facilitating lipid imaging studies in various organisms and covering different lipid detection technologies, providing a valuable and user-friendly resource for investigating lipid metabolism.
Availability and implementation: The LipidLocator application is freely available as a Docker image on Docker Hub at pratarora/lipidlocator. Installation instructions and code are available at https://github.com/MercaderLabAnatomy/LipidLocator.

DOI: https://doi.org/10.1093/bioadv/vbag012
J Proteomics. 2026 Feb 11. pii: S1874-3919(26)00028-X. [Epub ahead of print] 105625

Novel high-resolution ion mobility mass spectrometry for site-specific quantification of the sirtuin-5 regulated kidney succinylome.

Birgit Schilling, Leonard Rorrer, Lauren Royer, Anne Silva-Barbosa, Katherine Pfister, Eric Goetzman, Sunder Sims-Lucas, Daniel DeBord, Joanna Bons.

  Protein post-translational modifications (PTMs) dynamically regulate essential biological and cellular processes. Lysine succinylation changes the amino acid charge, potentially affecting protein structures and functions, and dysregulation of protein succinylation may lead to metabolic disorders. Proteome-wide succinylation quantification using proteomic tools remains challenging, especially due to the low abundance of succinylated peptides and the frequent presence of isomeric PTM forms. Ion mobility spectrometry workflows that can differentiate peptidoforms with different PTM distributions represent a powerful strategy to alleviate these challenges. Recently, a new Parallel Accumulation with Mobility Aligned Fragmentation (PAMAF™) operating mode for high-resolution ion mobility-mass spectrometry (HRIM-MS) analysis based on the structures for lossless ion manipulation (SLIM) technology was introduced. Here, we first assessed the performance of PAMAF mode for protein succinylation analysis using synthetic succinylated peptides, demonstrating residue-level differentiation of co-eluting isomers and isobars and precise PTM site localization. We leveraged this novel approach to investigate succinylome remodeling in kidney tissues from wild-type and Sirtuin-5 (Sirt5) knock-out mice, a NAD+-dependent lysine de-succinylase. PAMAF acquisitions yielded ~1000 confidently identified and accurately quantified succinylated peptides and sites from mouse kidney. Sirt5 regulated succinylation of mitochondrial proteins involved in metabolic processes, including fatty acid oxidation, the tricarboxylic acid cycle, and propionate metabolism. SIGNIFICANCE: Understanding the dynamic remodeling of the protein post-translational modification landscape is critical to gain insights into the underlying molecular mechanisms of biological systems. Lysine succinylation is a recently discovered reversible post-translational modification (PTM), that regulates various biological processes and associates with diverse diseases. However, this PTM is poorly characterized, partly due to analytical barriers. Here, we present a novel mass spectrometry (MS) methodology leveraging high-resolution ion mobility (HRIM) spectrometry and Parallel Accumulation with Mobility Aided Fragmentation (PAMAF) technology to profile and quantify succinylated peptides. The unique combination of liquid chromatography, ion mobility in a very long ion path (13 m), and alternate acquisition of MS and MS/MS spectra for all ions entering the mass spectrometer provided comprehensive profiling and accurate quantification of succinylated peptides in complex matrices. This technology enabled confident resolution of succinylated isomeric peptides, that could not be differentiated without high-resolution ion mobility separation and subsequent MS/MS PTM site identification. We investigated the kidney succinylome of Sirtuin-5 (desuccinylase) knockout mice compared to wild-type mice, with over 1000 succinylated peptides identified and quantified. We analyzed the hypersuccinylation of proteins upon Sirtuin-5 deletion, especially of mitochondrial proteins involved in diverse metabolic processes.

Keywords:  High-resolution ion mobility-mass spectrometry; PTM site localization; Parallel accumulation with mobility aligned fragmentation; Post-translational modifications; Sirtuin 5; Succinylation

DOI:  https://doi.org/10.1016/j.jprot.2026.105625
bioRxiv. 2026 Jan 26. pii: 2026.01.24.701521. [Epub ahead of print]

Community Curation of Microbial Metabolites Enables Biological Insights of Metabolomics Data.

Helena Mannochio-Russo, Wilhan D Goncalves Nunes, Shipei Xing, Fernanda de Oliveira, Andres Mauricio Caraballo-Rodriguez, Paulo Wender Portal Gomes, Vincent Charron-Lamoureux, Julius Agongo, Nicole E Avalon, Tammy Bui, Lucia Cancelada, Marc G Chevrette, Andres Cumsille, Moyses B de Araujo, Marilyn De Graeve, Victoria Deleray, Mohamed S Donia, Mutsawashe B Dzveta, Yasin El Abiead, Ronald J Ellis, Donald Franklin, Neha Garg, Harsha Gouda, Claude Y Hamany Djande, Anastasia Hiskia, Benjamin N Ho, Chambers C Hughes, Sunghoon Hwang, Sofia Iliakopoulou, Jennifer E Iudicello, Alan K Jarmusch, Triantafyllos Kaloudis, Irina Koester, Robert Konkel, Hector H F Koolen, Kine Eide Kvitne, Sabina Leanti La Rosa, Anny Lam, Santosh Lamichhane, Motseoa Lephatsi, Scott Letendre, Sarolt Magyari, Hanna Mazur-Marzec, Daniel McDonald, Ipsita Mohanty, Monica Monge-Loria, David J Moore, Thiago Andre Moura Veiga, Musiwalo S Mulaudzi, Lerato Nephali, Griffith Nguyen, Martin Orsagh, Abubaker Patan, Tomas Pluskal, Phillip B Pope, Livia Soman de Medeiros, Paolo Stincone, Andrej Tekel, Sydney Thomas, Ralph R Torres, Shirley M Tsunoda, Fidele Tugizimana, Martijn van Faassen, Felipe Vasquez-Castro, Giovanni A Vitale, Berenike C Wagner, Crystal X Wang, Sevasti-Kiriaki Zervou, Haoqi Nina Zhao, Simone Zuffa, Daniel Petras, Laura-Isobel McCall, Rob Knight, Mingxun Wang, Pieter C Dorrestein.

Microbial metabolites play a critical role in regulating ecosystems, including the human body and its microbiota. However, understanding the physiologically relevant role of these molecules, especially through liquid chromatography tandem mass spectrometry (LC-MS/MS)-based untargeted metabolomics, poses significant challenges and often requires manual parsing of a large amount of literature, databases, and webpages. To address this gap, we established the Collaborative Microbial Metabolite Center knowledgebase (CMMC-KB), a platform that fosters collaborative efforts within the scientific community to curate knowledge about microbial metabolites. The CMMC-KB aims to collect comprehensive information about microbial molecules originating from microbial biosynthesis, drug metabolism, exposure-related molecules, food, host-derived molecules, and, whenever available, their known activities. Molecules from other sources, including host-produced, dietary, and pharmaceutical compounds, are also included. By enabling direct integration of this knowledgebase with downstream analytical tools, including molecular networking, we can deepen insights into microbiota and their metabolites, ultimately advancing our understanding of microbial ecosystems.

DOI: https://doi.org/10.64898/2026.01.24.701521
bioRxiv. 2026 Feb 04. pii: 2026.02.02.703320. [Epub ahead of print]

Single Plaque Proteomics Reveals the Composition and Dynamics of the Amyloid Microenvironment in Alzheimer's Disease.

Mengqi Chu, Ju Wang, Jay M Yarbro, Ping-Chung Chen, Him K Shrestha, Huan Sun, Mingming Niu, Zhen Wang, Sarah Harvey, Zhiping Wu, Yingxue Fu, Zuo-Fei Yuan, Haiyan Tan, Anthony A High, Aijun Zhang, Xusheng Wang, Meifen Lu, Heather Sheppard, Geidy E Serrano, Thomas G Beach, Gang Yu, Yun Jiao, Junmin Peng.

Alzheimer's disease (AD) is characterized by amyloid plaques that form complex microenvironments in the brain. However, the molecular composition of these plaques and their temporal regulation are not well defined. Here, we developed a sensitive workflow for quantitative proteomic profiling of single plaques using refined laser capture microdissection and data-independent acquisition mass spectrometry (LCM-DIA-MS). From >200 plaques and control regions in AD mouse models (5xFAD and APP-KI) and human brains, we quantified >7,000 proteins, revealing stage-dependent, cell-type-related remodeling of the amyloid proteome (amyloidome). Temporal profiling uncovered early immune and lysosomal activation followed by engagement of RNA processing and synaptic pathways. Cross-model and cross-species analyses determined a conserved amyloidome including APOE, MDK, PTN, and HTRA1, validated by co-localization in imaging analysis. Network analysis highlighted modules in lipid transport, vesicle organization, and autophagy. These findings establish amyloid plaques as conserved, dynamic multicellular hubs that link amyloid accumulation to downstream cellular events.

DOI: https://doi.org/10.64898/2026.02.02.703320
Plants (Basel). 2026 Jan 31. pii: 445. [Epub ahead of print]15(3):

Analyzing Plant Low-Molecular-Weight Polar Metabolites: A GC-MS Approach.

Tatiana Bilova, Nadezhda Frolova, Anastasia Orlova, Svetlana Silinskaia, Akif Mailov, Veronika Popova, Andrej Frolov.

  Decades ago, the introduction of GC-MS marked a significant advancement in primary plant metabolite studies. Here, in our review, we will delve into critical aspects of the workflow, spanning the selection of an analytical platform, sample preparation, analytical acquisition, and data processing and interpretation. The exceptional separation capabilities of GC, characterized by remarkable chromatographic resolution, render it ideal for analysis of the complex plant metabolome, including the separation of isomeric compounds. The diversity of analytical platforms allows the investigation of plant metabolomes using targeted and non-targeted approaches. GC-MS, equipped with efficient extraction methods and reliable derivatization protocols for semi- and non-volatile compounds, enables qualitative and quantitative analysis of these molecules. The stability of derivatives forms the foundation for the robustness and reproducibility of GC-MS methods, and their mass spectra provide characteristic fragments for confident identification and sensitive quantification of individual metabolites. There has been key progress in the advancement of GC-MS approaches to studying plant metabolism. However, the presence of artifacts during GC-MS analysis, particularly during derivatization, is a challenge that requires careful validations, which frequently necessitate additional investigations. The feasible solutions that were achieved to overcome the limitations in GC-MS-based studies are a particular focus of the present discussion.

Keywords:  GC-MS-based profiling; plant metabolomics; primary metabolites

DOI:  https://doi.org/10.3390/plants15030445
Talanta. 2026 Jan 27. pii: S0039-9140(26)00119-0. [Epub ahead of print]303 129464

Beyond lipid homochirality: Analytical strategies and biological implications.

Vijaya Shukla, Tatiana A Guaraca-Medina, Alessia Lodi, Stefano Tiziani.

  Nucleotides, carbohydrates, amino acids, and lipids have long been considered homochiral within mammalian systems. However, an increasing number of studies have reported a variety of chiral metabolites across various living organisms, some biologically active and others identified as potential disease biomarkers. Enantiomers of the same compound may have distinct biological activities, chemical reactivities, and metabolism, highlighting the increasing attention to molecular chirality in biomedical research. Like peptides, amino acids, and organic acids, lipids also possess chirality and are essential components of biological membranes, influencing both structure and functionality. Studies using simple model systems, like liposomes and vesicles, challenge the assumption that only homochiral membranes are stable, demonstrating comparable stability in racemic heterochiral membranes. Nevertheless, chirality within eukaryotic cells remains largely overlooked, resulting in limited understanding of its impact on lipid membrane organization, lipid-lipid and lipid-protein interactions, and the overall lipid metabolism. This gap primarily reflects the lack of robust experimental methods for chiral lipidomics profiling. This review provides a comprehensive overview of analytical techniques used for the separation and analysis of chiral lipids in complex biological samples, emphasizing advances in chromatographic and mass spectrometric techniques, and their application in disease biomarker discovery. We also discuss the structural and functional impact of chirality on phospholipid membranes and highlight future directions in chiral lipidomics research.

Keywords:  Chiral derivatization; Chiral lipidomics; Chromatographic and mass spectrometric techniques; Direct separation; Heterochirality; Homochirality

DOI:  https://doi.org/10.1016/j.talanta.2026.129464
bioRxiv. 2025 Mar 09. pii: 2025.03.04.641505. [Epub ahead of print]

MS/MS Mass Spectrometry Filtering Tree for Bile Acid Isomer Annotation.

Ipsita Mohanty, Shipei Xing, Vanessa Castillo, Julius Agongo, Abubaker Patan, Yasin El Abiead, Helena Mannochio-Russo, Simone Zuffa, Jasmine Zemlin, Alexandre Tronel, Audrey Le Gouellec, Thomas Soranzo, Crystal X Wang, Jennifer E Iudicello, Mohammadsobhan S Andalibi, Ronald J Ellis, David J Moore, Donald R Franklin, Marta Sala-Climent, Monica Guma, Robin M Voigt, Rima Kaddaruh-Dauok, Dionicio Siegel, Mingxun Wang, Lee R Hagey, Pieter C Dorrestein.

Bile acids are essential steroids regulating immunity, nutrient absorption, insulin, appetite, and body temperature. Their structural diversity is vast, but due to spectral similarities, MS/MS spectral matching often fails to resolve isomers. This study introduces a proof-of-concept workflow using a mass spectrometry query language filtering tree that distinguishes isomeric bile acids in untargeted LC-MS/MS data. Its application revealed a deoxycholyl-2-aminophenol amidate linked to whole grain consumption.

DOI: https://doi.org/10.1101/2025.03.04.641505
bioRxiv. 2026 Feb 05. pii: 2026.02.04.703849. [Epub ahead of print]

A searchable metadata network graph for microbiome metabolomics.

Vincent Charron-Lamoureux, Shipei Xing, Abubaker Patan, Corinn Walker, Ricardo-Almada Monter, Yasin El Abiead, Haoqi Nina Zhao, Lucas Patel, Yuhan Weng, Antonio Gonzalez, Gail Ackermann, Victoria Deleray, Vishant Gandhi, Ipsita Mohanty, Andres Mauricio Caraballo-Rodriguez, Kine Eide Kvitne, Simone Zuffa, Annabelle Norman, Anthony Martin, Loryn Chin, Rocio Paz-Gonzalez, Marta Sala-Climent, Nadia Suryawinata, Jasmine Zemlin, Harsha Gouda, Zhewen Hu, Grant Norton, Prajit Rajkumar, Anthony Ja Molina, Jaclyn Bergstrom, Monae Pinner, Sadie Giddings, Allegra T Aron, Liang Liang, Samira Dahesh, Santosh Lamichhane, Erin R Reilly, Victor Nizet, Anna Skrip, April L Lukowski, Selene F H Shore, Subhomitra Ghoshal, Melinda A Engevik, Thomas D Horvath, Simone Renwick, Julius Agongo, Maria L Marco, Sarkis K Mazmanian, Mingxun Wang, Heejung Yang, Daniel McDonald, Monica Guma, Evi Stegmann, Naybel Hernandez Perez, Paolo Stincone, Eric Kemen, Abzer Kelminal Pakkir Shah, Lars Bode, Daniel Petras, Dionicio Siegel, Manuela Raffatellu, Andrew D Patterson, Suzanne Devkota, Adrian Jinich, Rob Knight, Karsten Zengler, Pieter C Dorrestein.

Establishing the biological context of microbial metabolites remains a major challenge. We present microbiomeMASST, a metadata-driven network graph that maps metabolites across 467 available datasets with 144,424 mass spectrometry files from humans, animals, and microbial culture systems. MicrobiomeMASST integrates monocultures, synthetic communities, and host-associated samples across multiple body sites and plants. MS/MS spectra can be queried to trace occurrence across hosts, experimental conditions, and interventions, enabling cross-study integration. We demonstrate this framework by contextualizing microbial-conjugated bile acids and interrogating microbiome-mediated drug metabolism. Screening gut bacteria revealed deprolylation of the angiotensin-converting enzyme (ACE) inhibitor prodrug enalapril. Using microbiomeMASST, we traced this metabolite across human cohorts, microbial isolates, environmental samples, and in Gorilla gorilla . Structural modeling and enzymatic assays showed that microbial deprolylation abolishes ACE inhibition, thereby inactivating its therapeutic effect. Together, microbiomeMASST links MS/MS spectra to biological context, converting isolated observations into an interpretable microbiome map for cross-study analysis.

DOI: https://doi.org/10.64898/2026.02.04.703849
J Chromatogr B Analyt Technol Biomed Life Sci. 2026 Feb 05. pii: S1570-0232(26)00047-4. [Epub ahead of print]1273 124958

Applications of mass spectrometry in the routine diagnostic medical laboratory - a status report 2025.

Michael Vogeser, Katharina Habler.

Mass spectrometry methods have become an essential part of the methodological portfolio of laboratory medicine over the past three decades. At present, however, their application is still largely limited to highly specialized laboratories in relatively few countries. Nevertheless, the technology provides important impulses for laboratory diagnostics overall-for example, in clinical pharmacology through innovative applications in therapeutic drug monitoring and precision dosing. After relatively slow progress in the area of automation, the first fully automated, closed MS-based analytical systems have recently been introduced for routine medical laboratories. In terms of usability, these systems are comparable to standard platforms based on photometry or ligand-binding techniques. The aim of this article is to describe the current medical, analytical, and organizational aspects of MS applications in diagnostics.

DOI: https://doi.org/10.1016/j.jchromb.2026.124958
Anal Bioanal Chem. 2026 Feb 10.

Derivatization agents for LC-MS analysis of amino acids: effects of core structure and functional groups.

Tereza Hofmanova, Rudolf Andrys, Miroslav Lisa.

  The analysis of amino acids in biological samples is challenged by their high polarity, low ionization efficiency, and lack of chromophores, which limit detection by LC-UV or LC-MS. Derivatization is widely used to improve retention, sensitivity, and detection. In this study, derivatization agents based on pyridine, quinoline, and isoquinoline positional isomers scaffolds with COCl, SO₂Cl, and NHS ester reactive groups were systematically evaluated using deuterium-labeled amino acids to assess stability, derivatization efficiency, chromatographic behavior, and MS response. NHS ester-based agents were found to exhibit superior stability, maintaining activity for over 1 year, whereas carbonyl chlorides and sulfonyl chloride-based agents were highly reactive but less stable. NHS ester of isoquinoline-6-carboxylic acid (6-CiQ-NHS) was identified as the most effective agent, combining rapid derivatization, strong MS signal, and stability. LC-MS analysis demonstrated excellent linearity (R2 ≥ 0.995), low nanomolar detection limits (0.23-6.33 nM), and separation of isomeric amino acids (e.g., isoleucine/leucine). 6-CiQ-NHS is proposed as a practical derivatization approach for amino acid quantification and provides a framework for the rational design of future agents.

Keywords:  Amino acids; Derivatization; Isoquinoline; LC-MS; NHS ester

DOI:  https://doi.org/10.1007/s00216-026-06366-9