bims-mascan 2026-02-22 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–02–22
twenty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Scalable Incremental Clustering for Tandem Mass Spectra in Untargeted Metabolomics.
Alkyne lipids as tracers of lipid metabolism.
Profiling of Low-Abundance Branched-Chain Fatty Acids via Radical Directed Dissociation Tandem Mass Spectrometry.
Decoding cancer across scales with metabolomics.
An Open-Source Platform for Reference Data-Driven Analysis of Untargeted Metabolomics.
Data-Independent Acquisition Enhancement of a Competitive Activity-Based Protein Profiling Platform for Kinase Inhibitor Screening.
High-resolution sphingolipid analysis in vitro using LC-qToF MS.
High-resolution phosphoinositide analysis.
A Stable Isotope Tracing Primer for the Mass Spectrometrist.
Visualization of single-cell lipidomes with MALDI-MSI.
Metabolic labeling of glycerophospholipids.
Multi-structural profiling of neutral glycosphingolipids using selective enrichment and the Paternò-Büchi derivatization.
Strategies to minimize artificial lipid oxidation in mass spectrometry based epilipidomics analysis.
pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics.
Scout-Triggered Multiple Reaction Monitoring Enables Robust Quantification of Host Cell Proteins Across Bioprocess Matrices.
Proteomic profiling identifies prognostic signature for Krukenberg tumor of gastrointestinal origin.
Mass spectrometry-based lipid analysis in NPC1 disease: Methods for phosphoinositide quantification, lipid imaging, and myelin lipid profiling.
Metabolic crosstalk between cancer and stromal cells: Implications for precision oncology.
Protocol for quantitative analysis of adult zebrafish swimming behavior using DeepLabCut.
Lipidomic signatures reveal biomarkers of mild cognitive impairment.
Single-cell spatial proteomics maps human liver zonation patterns and their vulnerability to disruption in tissue architecture.
PNA-HPLC and Hybrid LC-MS/MS as Complementary Platforms for Bioanalysis of Next-Generation Nucleic Acid Therapeutics.

J Proteome Res. 2026 Feb 18.

Scalable Incremental Clustering for Tandem Mass Spectra in Untargeted Metabolomics.

Xianghu Wang, Deepa D Archarya, Christopher J Brown, Ken Clevenger, Jie Hu, Ashley Kretsch, Carla Menegatti, Quanbo Xiong, Wout Bittremieux, Mingxun Wang.

  Tandem mass spectrometry (MS/MS) has become the analytical backbone of large-scale untargeted metabolomics, routinely generating millions of spectra per study. However, existing clustering methods struggle to process this scale due to computational and memory bottlenecks, limiting the utility of clustering in downstream analysis. This bottleneck is especially acute in long-term studies and public repositories, where new data are continuously added over time. Here we present a scalable clustering framework for MS/MS metabolomics data. Our method incrementally incorporates new spectra batches while preserving clustering performance through a novel spectrum pooling strategy, which propagates local density structure across batches. Using both database-search-based evaluation on proteomics data sets and the MS1-retention time (MS-RT) method on metabolomics data sets, we show that incremental clustering achieves comparable performance to the state-of-the-art clustering methods in terms of cluster purity and completeness. Critically, our approach scales up to clustering tasks consisting of 368 million spectra clustering task and millions of clusters, completing in under 10,000 CPU hours, while traditional methods could not scale to this data volume and failed to complete due to excessive memory or time requirements. Our method offers a practical solution for large-scale, continuously growing MS/MS studies and is well suited for integration into public metabolomics platforms such as GNPS2.

Keywords:  MS-RT evaluation; falcon; incremental clustering; large-scale clustering; metabolomics; spectrum pooling; tandem mass spectrometry

DOI:  https://doi.org/10.1021/acs.jproteome.5c00998
Methods Enzymol. 2026 ;pii: S0076-6879(25)00486-0. [Epub ahead of print]726 85-104

Alkyne lipids as tracers of lipid metabolism.

Christoph Thiele.

  The high complexity of cellular lipidomes and of the underlying metabolic pathways requires powerful labeling and detection systems for systematic lipid tracing experiments. Alkyne fatty acids are tracers with favorable biological properties very similar to unlabeled natural counterparts. We have developed a labeling and detection system based on alkyne lipid tracers and specialized reporter molecules that confer high specificity and sensitivity to labeled metabolites. Tracers are added to living cells and metabolites are extracted in pulse-chase setups to achieve time resolution. Copper(I)-dependent click reaction between extracted lipids and the C171 or C175 reporter molecules is followed by mass spectrometry analysis. The reporter carries a positive charge leading to improved ionization and increased sensitivity. Uniform and predictable neutral loss-type fragmentation in tandem mass spectrometry leads to reliable identification and quantification of labeled metabolites. Parallel multi-labeling with several precursors, combined with multiplexed analysis enables efficient high-content tracing. This chapter introduces the basic concepts and a step-by-step protocol with detailed explanation of key procedures to obtain optimal results.

Keywords:  Click reaction; Lipid metabolism; Mass spectrometry; Reporter molecule

DOI:  https://doi.org/10.1016/bs.mie.2025.11.005
J Am Soc Mass Spectrom. 2026 Feb 18.

Profiling of Low-Abundance Branched-Chain Fatty Acids via Radical Directed Dissociation Tandem Mass Spectrometry.

Ruijun Jian, Shengzhuo Wang, Lipeng Qiao, Xue Zhao, Yu Xia.

  Branched-chain fatty acids (BCFAs) are key components of the bacterial lipidome, playing a role in regulating membrane fluidity and permeability. In mammals, BCFAs occur at much lower concentrations, and their functions remain largely unexplored. Conventional lipid analysis methods, employing collision-induced dissociation (CID)-tandem mass spectrometry (MS/MS), often fail to locate methyl branching, as fragmentation rarely occurs around the branching site. Here, we introduce a bifunctional derivatization reagent, 1-(8-methoxy-5-quinolinyl) methanamine (MeO-QN), for pinpointing methyl branching in BCFAs with high sensitivity. MeO-QN enhances ionization efficiency of derivatized BCFAs in positive ion mode due to its quinoline moiety and serves as a precursor for radical-directed dissociation (RDD). Upon CID, the quinoline-O radical (QN-O•) is generated, which subsequently induces RDD along the fatty acyl chain and forms a characteristic 28 Da spacing indicative of the branching point. By integrating this MS/MS method with reversed-phase liquid chromatography, we have developed a sensitive analytical workflow, detecting BCFAs at sub-nM levels in mammalian samples. We detected the rarely reported n-5 methyl branched fatty acid (FA 16:0;12Me) in pooled human plasma. We also observed significantly reduced even-chain isobranched fatty acids in breast cancer cells (MDA-MB-468) versus normal breast cells (MCF-10A), suggesting its potential in cancer biomarker discovery.

Keywords:  branched-chain fatty acids; liquid chromatography; quinoline-O radical; radical-directed dissociation; tandem mass spectrometry

DOI:  https://doi.org/10.1021/jasms.6c00001
Nat Rev Cancer. 2026 Feb 20.

Decoding cancer across scales with metabolomics.

Joe L Rowles, Gary J Patti.

It is well established that malignant cells alter their metabolism to support proliferation, but the nutrients required to meet the anabolic demands of different cancers located at various anatomical sites throughout the body remain largely unknown. Moreover, the extent to which nutrients are supplied by neighbouring stromal cells or distant tissues, possibly due to metabolic reprogramming, is poorly understood. Metabolomics provides a unique biochemical approach to address these gaps in our knowledge, but cancer studies require careful consideration because it is challenging to identify appropriately matched control samples for comparison. Here, we detail a collection of metabolomics workflows designed to interrogate cancer across three discrete scales. First, we describe experiments to define the nutrient demands of cancer cells themselves. Second, we focus on identifying metabolic relationships between neighbouring cells in the tumour microenvironment. Finally, we highlight strategies to explore the metabolic crosstalk between cancer cells and distant tissues in the tumour macroenvironment. The approaches outlined span cells in culture, animal models and human specimens from patients with cancer. Special emphasis is dedicated to the application of emerging technologies and computational pipelines in the field of mass spectrometry that enable global profiling of metabolites and lipids.

DOI: https://doi.org/10.1038/s41568-026-00908-0
J Am Soc Mass Spectrom. 2026 Feb 17.

An Open-Source Platform for Reference Data-Driven Analysis of Untargeted Metabolomics.

Alejandro Mendoza Cantu, Julia M Gauglitz, Wout Bittremieux.

  Untargeted tandem mass spectrometry (MS/MS)-based metabolomics enable broad characterization of small molecules in complex samples, yet the majority of spectra in a typical experiment remain unannotated, limiting biological interpretation. Reference data-driven (RDD) metabolomics addresses this gap by contextualizing spectra through comparison to curated, metadata-annotated reference data sets, allowing inference of spectrum origins without requiring exact structural identification. Here, we present an open-source RDD metabolomics platform comprising a user-friendly web application and a Python software package that performs RDD analyses directly from molecular networking outputs generated by GNPS. The tools support visualization and statistical analysis of RDD results, including interactive bar plots, heat maps, principal component analysis, and Sankey diagrams. We illustrate the approach using a hierarchical reference data set of 3500 food items to derive dietary patterns from stool metabolomics data of omnivore and vegan participants. The analysis reveals clear dietary group separation, demonstrating how RDD metabolomics can extract biologically meaningful patterns from otherwise unannotated spectra. Thus, the RDD metabolomics platform removes technical barriers for the metabolomics community to adopting RDD analysis, with the functionality freely available at https://github.com/bittremieuxlab/gnps-rdd and https://gnps-rdd.bittremieuxlab.org/.

Keywords:  dietary read-out; reference data-driven analysis; software package; untargeted metabolomics; web platform

DOI:  https://doi.org/10.1021/jasms.5c00372
J Mass Spectrom. 2026 ;61(3): e70038

Data-Independent Acquisition Enhancement of a Competitive Activity-Based Protein Profiling Platform for Kinase Inhibitor Screening.

Ravi Tharakan, Yanyan Qu, Michele Ceribelli, Patrick J Morris, Yuhong Fang, Craig J Thomas, Christopher A LeClair, Dingyin Tao.

  Kinase inhibitors represent a vital class of therapeutic agents widely used in cancer research, immunology, and other disease areas. Mass spectrometry (MS) employing specially designed small-molecule kinase-binding probes has become an essential strategy for identifying novel kinase drug targets. While traditional MS approaches often rely on targeted proteomics (e.g., multiple reaction monitoring [MRM]) or data-dependent acquisition (DDA), data-independent acquisition (DIA) offers broader and more reproducible quantification, especially for low-abundance peptides. In this study, we systematically developed an activity-based protein profiling (ABPP) platform leveraging DIA, through integrated in-house informatics tools for data filtering and motif analysis, to provide an effective kinase profiling workflow. Compared to DDA, the DIA approach yielded more than a 100% increase in identified biotinylated peptides and over 40% improvement in kinase peptide coverage, while reducing the analysis time by half (90 min vs. 180 min per sample). Additionally, there was a modest improvement to the coefficient of variation (CV) in kinase peptide quantification (decrease from 11.41% to 10.70%; mean CV). Shorter liquid chromatography (LC) gradient times (60, 45, and 30 min) were evaluated as a means for increasing sample analysis throughput. Notably, no significant loss in kinase peptide coverage was observed due to shorter gradients, highlighting the capability of DIA to significantly enhance the efficiency and scalability of kinase profiling workflows.

Keywords:  activity‐based protein profiling; data‐independent acquisition; kinase; mass spectrometry; proteomics

DOI:  https://doi.org/10.1002/jms.70038
Methods Enzymol. 2026 ;pii: S0076-6879(25)00489-6. [Epub ahead of print]726 157-180

High-resolution sphingolipid analysis in vitro using LC-qToF MS.

Shweta Chitkara, G Ekin Atilla-Gokcumen.

  The complexity of the sphingolipidome, characterized by variations in chain length, saturation, and headgroup composition, makes it essential to develop analytical strategies capable of high sensitivity and structural precision. Traditional biochemical methods lack the resolution to discriminate among closely related species, underscoring the transformative role of liquid chromatography-mass spectrometry (LC-MS) in sphingolipidomics. LC-MS provides unparalleled capabilities for sphingolipid analysis, combining chromatographic separation with high-resolution mass detection to achieve both qualitative and quantitative accuracy. In particular, liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-qToF MS) has emerged as a powerful platform, offering mass accuracy, broad dynamic range, and rapid acquisition rates. These features enable confident identification of isobaric and structurally related sphingolipids, which is essential for understanding their roles in cellular physiology and pathology. This chapter focuses on an optimized LC-qToF MS method tailored for sphingolipid profiling in cultured mammalian cells. By focusing on the analytical strengths of LC-MS, the approach provides a robust foundation for dissecting sphingolipid metabolism and its dysregulation in cellular processes.

Keywords:  Ceramides; LC-MS; LC-qToF MS; Sphingolipids

DOI:  https://doi.org/10.1016/bs.mie.2025.11.008
Methods Enzymol. 2026 ;pii: S0076-6879(25)00520-8. [Epub ahead of print]726 45-83

High-resolution phosphoinositide analysis.

Helene Jahn, Berit Blume, Alexis E Traynor-Kaplan, Fikadu G Tafesse, Carsten Schultz.

  Cells derived from diseased tissue and their related cell lines exhibit numerous metabolic changes, including variations in lipid composition and metabolism. Indeed, lipids are important biomarkers of various diseases and exhibit crucial signaling roles during disease states. However, lipids, especially low-abundant and transient lipids like phosphoinositides, are difficult to study due to a lack of sophisticated tools. Here, we describe a unique targeted lipidomics method that allows us to define and compare the phosphoinositide composition of diseased and healthy tissues as well as related cell lines.

Keywords:  ESI-MS/MS; Lipid analysis; Lipidomics; Phosphatidylinositol; Phosphatidylinositol phosphates; Phosphatidylinositol trisphosphate; Phosphoinositides; Targeted mass spectrometry

DOI:  https://doi.org/10.1016/bs.mie.2025.11.016
Annu Rev Anal Chem (Palo Alto Calif). 2026 Feb 17.

A Stable Isotope Tracing Primer for the Mass Spectrometrist.

Ashley Solmonson, Brandon Faubert, Thomas P Mathews.

Metabolic function plays a key role in our understanding of both biological and pathophysiological processes. Metabolism is a complex combination of intrinsic processes and environmental cues across a heterogeneous mix of cell types. To investigate metabolism, stable isotope tracing is a versatile approach to assess metabolism across scales, including in cultured cells, animal models, and humans. From the first tracing studies over a century ago, the development and utility of these studies have gone hand-in-hand with technological advances in detecting these labeled atoms, particularly with mass spectrometry. In this review, we describe the instrumentation used to measure isotopically labeled metabolites and approaches to analyze and interpret stable isotope tracing data, and discuss current challenges and opportunities for discovery with these methods.

DOI: https://doi.org/10.1146/annurev-anchem-080524-014717
Methods Enzymol. 2026 ;pii: S0076-6879(25)00530-0. [Epub ahead of print]726 193-215

Visualization of single-cell lipidomes with MALDI-MSI.

Jean Andrea Maillat, Nika Goršek, Pavel Barahtjan, Giovanni D'Angelo.

  Lipids are central to cellular structure, metabolism, and signaling, and yet their distribution within and across cells is highly heterogeneous. Traditional bulk mass spectrometry masks this diversity, whereas single-cell lipidomics can uncover distinct lipid configurations that define cellular states and organize tissues. Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) has emerged as a powerful tool to probe lipid heterogeneity beyond bulk analyses. Here, we describe a workflow for single-cell lipidomics that integrates optimized matrix deposition, high-resolution acquisition, and optical co-registration. This approach enables label-free, spatially resolved detection of endogenous lipid species with cellular precision and minimal sample perturbation. Applied to cultured cells, the method uncovers pronounced cell-to-cell variability and reveals coherent lipid domains across neighboring cells in tissues. By providing a robust and scalable strategy for visualizing lipidomes at single-cell resolution, MALDI-MSI bridges the gap between lipid localization and metabolic diversity, advancing lipidomics toward the study of cellular identity, tissue organization, and disease mechanisms.

Keywords:  MALDI-MSI; lipid heterogeneity; single-cell lipidomics; spatial metabolomics

DOI:  https://doi.org/10.1016/bs.mie.2025.12.003
Methods Enzymol. 2026 ;pii: S0076-6879(25)00491-4. [Epub ahead of print]726 289-319

Metabolic labeling of glycerophospholipids.

Robert J Maraski, Christelle F Ancajas, Jinchao Lou, Michael D Best.

  The approach of metabolic labeling provides an invaluable tool for elucidating previously unknown and poorly understood metabolic processes within cells. By introducing clickable versions of substrates into cells, the products of these biomolecule mimics can be conveniently tracked via post-derivatization of the clickable tag with a variety of reporter groups. Here, we will describe lipid metabolic labeling as an invaluable approach for interrogating lipid metabolic pathways, which can yield crucial information regarding complex lipid biosynthesis and trafficking networks that can open new therapeutic targets involving downstream natural products. In this chapter, we present detailed experimental procedures for the development of clickable serine probes for the labeling of phosphatidylserine (PS) and other lipids, including probe design and synthesis as well as analysis of biological incorporation via confocal microscopy, thin-layer chromatography (TLC), and liquid chromatography mass spectrometry (LCMS). This strategy provides a powerful approach for interrogating lipid biosynthetic pathways centered around PS.

Keywords:  Click chemistry; Fluorescence microscopy; Lipids; Membranes; Metabolic labeling; Phosphatidylserine; Phospholipids

DOI:  https://doi.org/10.1016/bs.mie.2025.11.010
Methods Enzymol. 2026 ;pii: S0076-6879(25)00488-4. [Epub ahead of print]726 357-378

Multi-structural profiling of neutral glycosphingolipids using selective enrichment and the Paternò-Büchi derivatization.

Zidan Wang, Yichun Wang, Yu Xia.

  Neutral glycosphingolipids (GSLs) are important glycan scaffolds in mammal cell membranes, typically comprising one to four neutral monosaccharide units. They also serve as precursors for the biosynthesis of more complex acidic GSLs. Accumulating evidence links alterations in neutral GSL profiles to various physiological and pathological processes. However, the detailed structural characterization of GSL remains challenging owing to their low abundance and structural complexity. Herein, we present a deep profiling workflow for neutral GSLs which combines a selective enrichment strategy using magnetic titanium dioxide nanoparticles with off-line charge-tagging Paternò-Büchi derivatization and liquid chromatography-mass spectrometry analysis. This approach provides unique characterization capability on multiple-structural levels, including headgroup identity, chain composition, carbon-carbon double bond location, and hydroxylation site. This workflow allows deep profiling over 300 structural of neutral GSLs, with relative concentrations across three orders of magnitude from porcine brain total lipid extracts, human brain tissue, and human plasma.

Keywords:  Magnetic titanium dioxide nanoparticle; Mass spectrometry; Neutral glycosphingolipid; Paternò–Büchi reaction; Structural characterization

DOI:  https://doi.org/10.1016/bs.mie.2025.11.007
Methods Enzymol. 2026 ;pii: S0076-6879(25)00523-3. [Epub ahead of print]726 253-267

Strategies to minimize artificial lipid oxidation in mass spectrometry based epilipidomics analysis.

Sider Penkov, Gabriele Lombardi Bendoula, Maria Fedorova.

  (Per)oxidized lipids represent a well-studied component of the epilipidome, a subset of the native lipidome formed through both enzymatic and non-enzymatic lipid oxidation. Given their diverse biological roles, including cellular signalling, regulation of immune responses, and modulation of cell death pathways, accurate detection of lipid peroxidation products is essential. Mass spectrometry-based approaches have become the method of choice for the sensitive, multiplexed detection and structural characterization of oxidized lipids across a variety of biological samples. However, the structural features of lipids, particularly the presence of acyl chains containing multiple double bonds, render them susceptible not only to endogenous oxidation but also to artificial oxidation during sample preparation prior to analysis. Consequently, special care must be taken throughout sample collection, storage, and lipid extraction to minimize artefacts arising from lipid autoxidation. Here, we describe protocols developed in our laboratory over recent years aimed at preventing artificial lipid oxidation during sample preparation, with examples spanning biological materials derived from cell culture, animal and human tissue biopsies, and biofluids. Finally, we propose internal quality control procedures to assess the effectiveness of these measures in preventing lipid autoxidation.

Keywords:  Autoxidation; Epilipidomics; Lipid peroxidation; Quality assurance; Sample preparation

DOI:  https://doi.org/10.1016/bs.mie.2025.11.019
Mol Cell Proteomics. 2026 Feb 17. pii: S1535-9476(26)00026-5. [Epub ahead of print] 101530

pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics.

Qi-Xuan Yue, Chengxin Dai, Selvakumar Kamatchinathan, Chakradhar Bandla, Henry Webel, Asier Larrea, Wout Bittremieux, Julian Uszkoreit, Tom David Müller, Jinqiu Xiao, Juergen Cox, Fengchao Yu, Philip Ewels, Vadim Demichev, Oliver Kohlbacher, Timo Sachsenberg, Chris Bielow, Mingze Bai, Yasset Perez-Riverol.

The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, FragPipe, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the SDRF format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

DOI: https://doi.org/10.1016/j.mcpro.2026.101530
Proteomes. 2026 Feb 17. pii: 9. [Epub ahead of print]14(1):

Scout-Triggered Multiple Reaction Monitoring Enables Robust Quantification of Host Cell Proteins Across Bioprocess Matrices.

Julie Flecheux, Chloé Bardet, Laura Herment, Tanguy Fortin, Jérôme Lemoine.

   BACKGROUND: Host cell proteins (HCPs) are process-related impurities that must be monitored in biopharmaceutical products due to their potential impact on product quality and patient safety. Targeted LC-MS/MS approaches such as multiple reaction monitoring (MRM) enable protein-specific HCP quantification but are difficult to apply in highly multiplexed assays because of retention time (RT) variability across complex bioprocess matrices.
METHODS: Here, we show that conventional RT-scheduled MRM workflows lack transferability when applied to heterogeneous drug substances and process intermediates. Using a targeted assay comprising 240 peptides corresponding to 97 CHO-derived HCPs, RT shifts of several minutes resulted in truncated chromatographic peaks and peptide signal loss, even when wide scheduling windows were used. To overcome this limitation, a scout-triggered MRM (st-MRM) acquisition strategy based on event-driven monitoring was implemented.
RESULTS: This approach enabled robust peptide detection across diverse matrices within a single injection, without method re-optimization. Absolute quantification using stable isotope-labeled peptides spanned six orders of magnitude, with HCPs quantified down to 2.9 ppm in purified drug substances.
CONCLUSION: Overall, st-MRM improves the robustness and transferability of highly multiplexed targeted proteomics workflows for HCP analysis.

Keywords:  antibody(s) (mAbs); bioprocess development; host cell protein (HCP); liquid chromatography-mass spectrometry (LC-MS); multiple reaction monitoring (MRM); proteomic

DOI:  https://doi.org/10.3390/proteomes14010009
iScience. 2026 Feb 20. 29(2): 114682

Proteomic profiling identifies prognostic signature for Krukenberg tumor of gastrointestinal origin.

Xiaoling Wang, Xiao Yi, Zhangzhi Xue, Wei Liu, Xiaodong Teng, Han Zhang, Tiannan Guo, Yi Zhu, Bo Wang.

  Krukenberg tumor (KT) primarily originates from the stomach and colorectum, but reliable biomarkers for distinguishing KT from other tumors at the same sites and predicting ovarian metastasis remain lacking. Using pressure cycling technology (PCT) and data-independent acquisition (DIA) mass spectrometry, we analyzed 263 formalin-fixed paraffin-embedded (FFPE) samples, identifying 10,837 proteins. The results revealed distinct proteomic signatures from the primary gastrointestinal lesions of KT. Comparative analyses identified group-specific pathways, particularly mesenchymal-epithelial transition (MET) signaling pathways and extracellular matrix (ECM) pathways, that were enriched in the primary lesions of KT. We developed protein-based classifiers with promising diagnostic value in distinguishing the primary gastrointestinal lesions of KT from those without ovarian metastases. We depicted distinct proteomic signatures in the primary gastrointestinal lesions of KT and identified potential biomarkers for prediction and early intervention of gastrointestinal cancer patients at risk of ovarian metastases.

Keywords:  Cancer; Proteomics

DOI:  https://doi.org/10.1016/j.isci.2026.114682
Methods Enzymol. 2026 ;pii: S0076-6879(25)00484-7. [Epub ahead of print]726 333-355

Mass spectrometry-based lipid analysis in NPC1 disease: Methods for phosphoinositide quantification, lipid imaging, and myelin lipid profiling.

Koralege C Pathmasiri, Stephanie M Cologna.

  Loss of NPC cholesterol transporter 1 protein function results in severe lipid dysregulation in multiple vital organs, including the brain, in Niemann-Pick Type C1 (NPC1) disease. Investigation of lipid changes and lipid metabolism disruptions in NPC1 is critical to elucidating the disease mechanisms driving the pathophysiology, identifying potential biomarkers, and guiding therapeutic strategies. One such example is phosphoinositides, which are key lipids involved in multiple signaling pathways relevant to NPC1 that are challenging to study due to their low abundance and detection difficulty. In this chapter, we present a detailed phosphoinositide analysis protocol using mass spectrometry. When studying lipids, spatial information is also important because it reveals distribution within the tissue, which can provide insights into functional roles and disease-related alterations. MALDI-MS lipid imaging is a powerful tool for investigating the spatial distribution of lipids. Herein, we also discuss a protocol for lipid imaging using MALDI-MSI, along with key precautions and troubleshooting tips. Finally, we present a myelin isolation protocol integrated with LC-MS lipidomics to investigate the myelin lipidome in tissues such as the brain, as myelin lipid composition is crucial for maintaining neuronal function and is often disrupted in neurodegenerative diseases like NPC1, including the investigation of phosphoinositides.

Keywords:  Chromatography; Imaging; Lipidomics; Mass spectrometry; Niemann-Pick Type C

DOI:  https://doi.org/10.1016/bs.mie.2025.11.003
Surg Oncol. 2026 Feb 12. pii: S0960-7404(26)00017-4. [Epub ahead of print]65 102366

Metabolic crosstalk between cancer and stromal cells: Implications for precision oncology.

Wanwan Yang, Yuwen Ding, Hongzhan Tian.

  Metabolic reprogramming is a hallmark of cancer that extends beyond the boundaries of individual tumor cells to encompass a complex metabolic network within the tumor microenvironment (TME). Cancer cells engage in dynamic metabolic crosstalk with stromal components including fibroblasts, immune cells, endothelial cells, and adipocytes through the exchange of metabolites, signaling molecules, and extracellular vesicles. These interactions coordinate energy production, redox homeostasis, and biosynthetic pathways that sustain tumor growth, angiogenesis, immune evasion, and therapeutic resistance. Cancer-associated fibroblasts (CAFs) supply lactate, amino acids, and lipids that fuel tumor anabolism; immune cells undergo metabolic suppression under nutrient competition and acidic stress; endothelial and adipose cells contribute to angiogenesis and metastatic adaptation through glycolysis and lipid transfer. This metabolic dialogue is governed by key signaling pathways (HIF-1α, mTOR, AMPK, c-Myc, PPAR, NRF2) and modulated by epigenetic mechanisms linking metabolic flux to gene expression. Understanding these multilayered communications provides novel insights into the cooperative and competitive nature of tumor metabolism. Emerging technologies such as spatial metabolomics and single-cell multi-omics are now enabling the identification of patient-specific metabolic dependencies. Targeting metabolic symbiosis rather than isolated pathways represents a promising direction for precision oncology, offering opportunities to disrupt tumor stroma cooperation, overcome therapeutic resistance, and personalize metabolism-based interventions.

Keywords:  Cancer-associated fibroblasts; Metabolic crosstalk; Tumor microenvironment

DOI:  https://doi.org/10.1016/j.suronc.2026.102366
STAR Protoc. 2026 Feb 17. pii: S2666-1667(26)00027-4. [Epub ahead of print]7(1): 104374

Protocol for quantitative analysis of adult zebrafish swimming behavior using DeepLabCut.

Pramuk Keerthisinghe, Anwarul Karim, Aditya Basak, James P Orengo.

  Here, we present a protocol for quantifying adult zebrafish swimming behavior using open-source software. We describe steps for recording and preprocessing swimming behavior videos, labeling and training a DeepLabCut network, and applying the trained model to analyze behavior. We then detail procedures for processing and visualizing behavioral data. This protocol is optimized for single-fish tracking under standardized recording conditions and can be completed using beginner-level computational skills with basic laboratory hardware.

Keywords:  Behavior; Computer sciences; Model Organisms; Neuroscience

DOI:  https://doi.org/10.1016/j.xpro.2026.104374
Transl Psychiatry. 2026 Feb 16.

Lipidomic signatures reveal biomarkers of mild cognitive impairment.

MiaGB Consortium

Mild cognitive impairment (MCI) is an early stage in the progression toward dementia. Lipids are central to neurodegeneration, yet the biomarker potential of lipidomics from saliva, plasma, and feces remains underexplored. As part of the Microbiome in Aging Gut and Brain (MiaGB) consortium, saliva, plasma, and fecal samples were collected from older adults with MCI and healthy controls. Samples were analyzed by high-performance liquid chromatography coupled with high-resolution mass spectrometry (LC/MS), to profile lipidomic alterations and identify candidate biomarkers. Lipidomic profiling annotated over 200 molecular species spanning five major lipid classes. Compared with controls, MCI patients exhibited increased oxidized triacylglycerols (oxTGs) in saliva, reduced cholesteryl linoleate (CE 18:2) in plasma, and decreased fatty acid esters of hydroxy fatty acids (FAHFAs) in feces. Receiver operating characteristic (ROC) analysis identified α-linolenic acid (FA 18:3), docosapentaenoic acid (FA 22:5), and CE 18:2 as discriminatory metabolites with notable diagnostic performance. Moreover, elevated fecal triacylglycerols containing medium-chain fatty acids (TG-MCFAs) were observed in MCI, suggesting impaired lipid absorption or altered metabolism. This multi-sample lipidomics strategy highlights TG-MCFAs as fecal biomarkers for MCI detection, supporting further mechanistic and longitudinal validation.

DOI: https://doi.org/10.1038/s41398-026-03893-y
Nat Metab. 2026 Feb 20.

Single-cell spatial proteomics maps human liver zonation patterns and their vulnerability to disruption in tissue architecture.

Caroline A M Weiss, Lauryn A Brown, Lucas Miranda, Paolo Pellizzoni, Sophia Steigerwald, Kirsten Remmert, Jonathan M Hernandez, Karsten Borgwardt, David E Kleiner, Shani Ben-Moshe, Florian A Rosenberger, Natalie Porat-Shliom, Matthias Mann.

Understanding protein distribution patterns across tissue architecture is crucial for deciphering organ function in health and disease. Here we show the application of single-cell Deep Visual Proteomics to perform spatially resolved proteome analysis of individual cells in native liver tissue. We built a robust framework comprising strategic cell selection and continuous protein gradient mapping, allowing the investigation of larger clinical cohorts. We generated a comprehensive spatial map of the human hepatic proteome by analysing hundreds of isolated hepatocytes from 18 individuals. Among the 2,500 proteins identified per cell, about half exhibited zonated expression patterns. Cross-species comparison with male mice revealed conserved metabolic functions and human-specific features of liver zonation. Analysis of samples with disrupted liver architecture demonstrated widespread loss of protein zonation, with pericentral proteins being particularly susceptible. Our study provides a comprehensive and open-access resource of human liver organization while establishing a broadly applicable framework for spatial proteomics analyses along tissue gradients.

DOI: https://doi.org/10.1038/s42255-026-01459-2
Curr Protoc. 2026 Feb;6(2): e70322

PNA-HPLC and Hybrid LC-MS/MS as Complementary Platforms for Bioanalysis of Next-Generation Nucleic Acid Therapeutics.

Christian Heinlein, Soham Mandal, Thessa Jacob, Steve Paulus, Lukas Sauermann, Markus Schuster, Timo Schierling, Satish Chavre, Ingo Roehl.

  Nucleic acid therapeutics (NATs), including antisense oligonucleotides and small interfering RNAs, represent an expanding class of therapeutic modalities with distinctive physicochemical, pharmacokinetic, pharmacodynamic, and biodistribution properties. Naturally, their bioanalysis requires platforms that can accurately quantify intact analytes of interest and metabolites across diverse biological matrices. Modifications ranging from 2'-modifications, alterations of the phosphate backbones, and varied ligands conjugated for targeted delivery, influence extraction recovery, matrix effects, and assay selectivity and sensitivity. Historically, ligand-binding assays and PCR-based methods were adopted due to exceptional sensitivity. However, these approaches often lacked structural resolution and overestimated intact analyte when "sequence-similar" metabolites prevailed. Conversely, two complementary methods emerged, providing higher structural resolution, i.e., peptide-nucleic acid (PNA)-based hybridization in conjunction with anion exchange high-performance liquid chromatography (PNA-HPLC assay), and liquid chromatography-tandem mass spectrometry (LC-MS/MS), enabling separation of "sequence-similar" metabolites from the parent, and additionally, metabolite identification by LC-MS and LC-MS/MS. Recent methodological advances in LC-MS/MS workflows combining sequence-specific enrichment have substantially bridged the previously observed sensitivity gap. The introduction of high-affinity capture probes has improved assay robustness and recovery for challenging analytes and enhanced signal response while minimizing matrix-effect and ion suppression. Comparative evaluation demonstrates that both the PNA-HPLC and the hybrid LC-MS/MS assays are comparably superior for metabolite profiling and tissue distribution studies. This article integrates the analytical principles, strengths, and limitations of those two assays with exemplary case studies for NATs. Practical guidance is provided for method selection, probe selection, sample preparation, assay validation, and cross-platform harmonization. Emerging trends include PNA probe engineering and high-resolution MS for structural elucidation. The integration of capture probe-based hybridization enrichment with modern LC-MS/MS detection now enables combined sensitivity and specificity. Together, these developments support increasingly robust, convergent, regulatory-compliant bioanalytical strategies for next-generation oligonucleotide therapeutics. © 2026 Wiley Periodicals LLC. Basic Protocol 1: PNA hybridization-based HPLC assay for the detection and quantification of therapeutic oligonucleotide in biological tissue samples Basic Protocol 2: Hybrid LC-MS/MS quantitative assay for identification and evaluation of NAT in biological tissue samples.

Keywords:  LC–MS/MS assays; low “lower limit of quantification” (LLOQ); method validation and metabolite profiling; oligonucleotide bioanalysis; peptide nucleic acid (PNA)‐HPLC assay

DOI:  https://doi.org/10.1002/cpz1.70322