bims-mascan 2026-05-03 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–05–03
nineteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Liquid Chromatography-Mass Spectrometry (LC-MS)-Based Analysis of Molecular Lipids in Algae Samples Using Data Dependent Acquisition (DDA).
DIA-NN EasyFilter Workflow for the Fast and User-Friendly Critical Assessment and Visualization of DIA-NN Proteomics Analysis Outcome.
Simultaneous In-Depth Single-Cell Proteomic and Metabolomic Analysis.
TIE-UP-SIN: A Method for Enhanced Identification of Protein-Protein Interactions.
A Tiered Approach to Human Synapse Proteomics: Optimized LC-MS/MS Analysis of Whole-Tissue Lysate and Synaptosome Preparations from Frozen Post-Mortem Brain Samples.
DDA-BERT: end-to-end training for data-dependent acquisition mass spectrometry-based proteomics.
Fully Integrated Sample Preparation-Based Spatial Proteomic Analysis.
Chromatographic resolution and reliable quantification of eleven endogenous and microbiota-derived tryptophan metabolites in human plasma by HPLC-MS/MS.
Characterization of blood serum and cell lines by an automated high-throughput well plate sampling system using laser ablation-rapid evaporative ionization mass spectrometry.
Lipidome Analysis of Cancer Cells and Their Extracellular Vesicles Reveals Cancer-Type-Specific Lipid Signatures and Enables the Design of EV-Mimetic Liposomes.
Mass Spectrometric Detected Cancer Proteins as Resources for Cancer Research.
Nitrogen metabolism profiling reveals cell state-specific pyrimidine synthesis pathway choice.
TCA cycle rewiring underpins histone acetylation sourcing and cell-fate transitions during exit from naive pluripotency.
A validated chiral LC-MS/MS method for enantioselective quantification of aromatic lactic acids in human faeces.
Significance of Lipid Metabolism Reprogramming in Tumor Microenvironment and Development of Novel Therapeutic Intervention.
A Multidimensional Workflow for Comprehensive Xenometabolome Profiling by Integrating TIMS-PASEF with LC-HRMS and Biotransformation-Informed Suspect and Nontarget Screening.
Protocol for integrating and interpreting multi-omics data combining unsupervised and supervised data integrating approaches.
Bimodal Peptide Collision Cross Section Distribution Reflects Two Stable Conformations in the Gas Phase.
Proteomics-based approaches to neutrophil biology.

Methods Mol Biol. 2026 ;2996 71-78

Liquid Chromatography-Mass Spectrometry (LC-MS)-Based Analysis of Molecular Lipids in Algae Samples Using Data Dependent Acquisition (DDA).

Niels van Boxtel, Heli Nygren, Tuulikki Seppänen-Laakso, Heiko Rischer.

  In this paper, an analytical method for the analysis of molecular lipids in algae samples is reported. The sample preparation is based on a modified Folch extraction, and the analysis is carried out with ultrahigh performance liquid chromatography combined with mass spectrometry (UPLC-MS). For the characterization of lipids, data dependent acquisition (DDA) analyses are carried out utilizing a high-resolution quadrupole-time-of-flight (Q-ToF) instrument. Throughput of the method is over 100 samples/day. The repeatability is good, and the relative standard deviation of spiked samples is <15%.

Keywords:  Data dependent acquisition; Lipid; Liquid chromatography; Mass spectrometry

DOI:  https://doi.org/10.1007/978-1-0716-5031-8_7
J Proteome Res. 2026 Apr 27.

DIA-NN EasyFilter Workflow for the Fast and User-Friendly Critical Assessment and Visualization of DIA-NN Proteomics Analysis Outcome.

Gontse Mabuse Moagi, Thatiana Ferraz Ferreira, Endre Kristof, Adzani Gaisani Arda, Rini Arianti, Peter Horvatovich, Éva Csösz.

  Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics, particularly data-independent acquisition (DIA), has become widely adopted across One Health approaches for biological and clinical research for quantitative protein characterization. Among the many computational tools available, DIA-NN has demonstrated superior performance; however, the primary output of the current versions is provided as a compact, compressed PARQUET file that can be difficult to interrogate without programming expertise. To address this limitation, we developed DIA-NN EasyFilter (DEF), a fast, user-friendly, KNIME-based workflow for comprehensive protein filtering and visualization. DEF integrates chromatographic peak-based filtering, curated contaminant libraries, and quantity-quality assessment along with interactive modules for qualitative and quantitative data exploration. The workflow is optimized for efficient execution within the KNIME local desktop environment and is designed to support end-users in improving accuracy and interpretability without requiring coding skills. We provide a detailed description on how to run DEF and demonstrate the utility and robustness of DEF using published large-scale proteomics data sets, showing high comparability across studies regardless of instrument platform or data set size.

Keywords:  DEF; DIA; DIA-NN; KNIME; PARQUET; SGBS; XICs; data quality; visualization

DOI:  https://doi.org/10.1021/acs.jproteome.5c01278
Anal Chem. 2026 Apr 27.

Simultaneous In-Depth Single-Cell Proteomic and Metabolomic Analysis.

Jie Wu, Fang-Hua Lu, Li-Ye Tao, Ya-Li Wang, Yi Huang, Yu Wang, Yi Yang, Jian-Zhang Pan, Xiao Liang, Qun Fang.

Simultaneous characterization of proteomic and metabolomic profiles at the single-cell level is crucial for deciphering cellular heterogeneity and elucidating disease mechanisms. However, it is still a great challenge to achieve high-depth dual-omics analysis in the same single cell. Here, we propose a unified strategy called one-shot hybrid-mode single-cell proteome and metabolome analysis (hybrid-scPMA), in which the mass spectrometry (MS) detection mode of data-independent acquisition (DIA) is utilized for the analysis of peptides from protein digestion, and the data-dependent acquisition (DDA) mode is used for metabolite analysis in a single liquid chromatography-mass spectrometry (LC-MS) analysis run, enabling deep analysis of the proteome and metabolome in single-cell samples. Building upon the strategy, we established an improved single-cell multiomics analysis workflow that integrated automated single-cell capture, simplified sample pretreatment, LC injection and separation, and DIA-DDA hybrid-mode MS detection. With this approach, we achieved an average identification of 3510 protein groups and 255 metabolites from single HepG2 cells, representing a substantial increase in identification depth over previous approaches. We also performed time-resolved proteomic and metabolomic profiling of HepG2 single cells undergoing sorafenib drug intervention, resolving drug response characteristics at the single-cell level and providing multiomics insights into drug mechanisms from a proteo-metabolomic perspective.

DOI: https://doi.org/10.1021/acs.analchem.5c08090
Bio Protoc. 2026 Apr 20. 16(8): e5663

TIE-UP-SIN: A Method for Enhanced Identification of Protein-Protein Interactions.

Maximilian Schedlowski, Stephan Michalik, Tilly Hoffmüller, Marco Harms, Leif Steil, Kristin Surmann, Christian Hentschker, Manuela Gesell Salazar, Uwe Völker, Alexander Reder.

  Protein-protein interactions (PPIs) govern nearly all aspects of cellular physiology, yet identifying these interactions under native conditions remains challenging. Here, we present TIE-UP-SIN (targeted interactome experiment for unknown proteins by stable isotope normalization), a robust method for in vivo identification and quantification of PPIs in bacterial systems. The protocol combines metabolic labeling with 15N isotopes, reversible formaldehyde crosslinking, affinity purification, and quantitative mass spectrometry. TIE-UP-SIN preserves transient or weak interactions during purification and quantifies interaction partners using internal light/heavy peptide ratios, reducing experimental variability. The method employs a triple-sample design to distinguish specific from nonspecific interactors and can be adapted to various bacterial species and affinity tags. Data analysis is streamlined through a user-friendly web application (https://shiny-fungene.biologie.uni-greifswald.de/TIE_UP_SIN_app) that automates statistical analysis, normalization, and visualization, requiring no programming expertise. The entire workflow from cell culture to mass spectrometry data acquisition takes approximately 4-5 days, with data analysis completed in 1-2 days using the web application. Key features • Captures transient protein interactions in vivo through reversible formaldehyde crosslinking under native expression conditions. • Internal 15N metabolic labeling enables robust quantification and reduces experimental variability across biological replicates. • Triple-sample design (WT/WT, bait/WT, bait/bait) distinguishes specific from nonspecific interactors with high confidence. • Applicable to diverse bacterial systems with simple adaptation to any affinity-tagged bait protein.

Keywords:  Affinity purification–mass spectrometry; Formaldehyde crosslinking; Heavy nitrogen metabolic labeling; In vivo crosslinking; Mass spectrometry; Protein–protein interactions; Quantitative proteomics

DOI:  https://doi.org/10.21769/BioProtoc.5663
Cells. 2026 Apr 21. pii: 736. [Epub ahead of print]15(8):

A Tiered Approach to Human Synapse Proteomics: Optimized LC-MS/MS Analysis of Whole-Tissue Lysate and Synaptosome Preparations from Frozen Post-Mortem Brain Samples.

Femke C Roig-Kuhn, Remco V Klaassen, Frank T W Koopmans, Tiara S Z Koolman, August B Smit, Sabine Spijker.

  Recent advancements in neuroproteomics have enabled detailed analysis of protein expression in the human brain, yet resolving synaptic dysfunction-a central feature of many neurological and psychiatric disorders-requires careful methodological consideration. Leveraging the high sensitivity of modern liquid chromatography-tandem mass spectrometry (LC-MS/MS), we evaluated the utility of whole-tissue lysates versus enriched synaptosome preparations for detecting synaptic protein signatures. First, we optimized and standardized a sample preparation protocol for frozen human gray matter (GM) by refining the suspension trapping (sTRAP) digestion method using thin human tissue sections. We accomplished low technical variation by minimizing sample handling and achieved a highly reproducible sample preparation workflow by rigorously applying standardization and randomization across dissection, processing, and LC-MS/MS runs. Second, comparative LC-MS/MS analysis showed that while whole-tissue lysates provide a high-throughput survey of the synaptic proteome, synaptosome isolation is required to investigate synapse-specific proteins to detect alterations at the terminal that are obscured in the soma. Because these methods offer distinct but synergistic levels of information, we recommend a tiered neuroproteomics strategy. This approach utilizes whole-tissue lysates for broad disease-associated screening and consistent quantification in large cohorts, followed by targeted synaptosome proteomics to provide a unique window of insight into synaptic composition and stability. This integrated workflow respects the biological necessity of spatial resolution while maintaining the reproducibility required for robust human brain proteomics. Furthermore, initial tissue-level analysis provides the necessary context to correctly interpret synaptosome data in cases of global synapse loss or gain.

Keywords:  frozen tissue; human; sTRAP; shotgun neuroproteomics; synapse proteomics

DOI:  https://doi.org/10.3390/cells15080736
Nat Commun. 2026 Apr 27.

DDA-BERT: end-to-end training for data-dependent acquisition mass spectrometry-based proteomics.

Jun A, Pu Liu, Yingying Sun, Jiaying Lin, Xiaofan Zhang, Zongxiang Nie, Jingnan Liu, Zhiguo Yu, Yuqi Zhang, Ziyuan Xing, Yi Chen, Tiannan Guo.

Peptide-spectrum match (PSM) rescoring is critical for accurate peptide identification in data-dependent acquisition (DDA)-based proteomics. Existing rescoring frameworks typically combine search-engine scores with heuristic or learned auxiliary features to refine PSM ranking and confidence estimation. Although recent approaches incorporate deep learning-derived representations of spectra, retention time, or ion mobility, the final decision stage still commonly relies on separately trained shallow classifiers, constraining the expressive capacity of the overall scoring framework. Here, we introduce DDA-BERT, a transformer-based end-to-end deep learning model trained with ~271 million PSMs from 11 species. DDA-BERT consistently outperforms existing tools across species-specific benchmarks, achieving 2.24%-269.35%, 3.73%-141.46%, 5.53%-45.64%, and 3.68%-62.77% increases in peptide identifications on human, yeast, Drosophila, and Arabidopsis datasets, respectively. The model retains high sensitivity in trace-level proteomics samples. On HLA immunopeptidomics data, DDA-BERT further increases peptide identifications by 4.14%-87.47%. The main limitations of DDA-BERT include the requirement for GPU-based computing and the need for substantial, diverse training datasets to achieve optimal model performance. This study introduces an alternative DDA rescoring approach and establishes a methodological foundation for scalable, AI-driven peptide identification in DDA proteomics.

DOI: https://doi.org/10.1038/s41467-026-72246-6
Methods Mol Biol. 2026 ;2995 67-76

Fully Integrated Sample Preparation-Based Spatial Proteomic Analysis.

Qian Kong, Xi Wang, Ruijun Tian.

  To decipher the complex tissue microenvironment for comprehensively elucidating the biological machinery, spatial proteomics is becoming popular. Laser microdissection (LMD) technology combined with integrated sample preparation and high-sensitive liquid chromatography-mass spectrometry (LC-MS) is the most powerful strategy. In this chapter, we describe the protocols for tissue slice preparation and staining, LMD-based histological regions collection, simple and integrated spintip-based proteomics technology (SISPROT) based sample preparation, and highly sensitive LC-MS analysis.

Keywords:  LC-MS; LMD; SISPROT; Spatial proteomics

DOI:  https://doi.org/10.1007/978-1-0716-5027-1_5
J Chromatogr A. 2026 Apr 24. pii: S0021-9673(26)00365-1. [Epub ahead of print]1779 467035

Chromatographic resolution and reliable quantification of eleven endogenous and microbiota-derived tryptophan metabolites in human plasma by HPLC-MS/MS.

Kateryna Tkachenko, Alice Laroni, Marco Ponassi, Davide Maioli, Camillo Rosano, Aldo Profumo.

  Tryptophan metabolism plays a central role in host-microbiota interactions and immune regulation, yet the simultaneous quantification of metabolites from the kynurenine, serotonin, and indole pathways remains analytically challenging due to their diverse physicochemical properties and wide dynamic concentration ranges. Here, we report an efficient, derivatization-free HPLC-MS/MS workflow for the targeted quantification of eleven key tryptophan-derived metabolites in human plasma. The method employs a biphenyl stationary phase to achieve robust chromatographic resolution of structurally similar analytes, including compounds known to co-elute in C18 stationary phase, improving chromatographic selectivity and minimizing co-elution of structurally related analytes. Sample preparation requires only 50 µL of plasma and relies on a simple protein-precipitation step, allowing high throughput. The method demonstrated strong performance in terms of sensitivity, accuracy, and reproducibility, supported by extensive internal standardization. The combination of multi-pathway coverage, analytical cycle, and suitability for human plasma positions this workflow as a valuable tool for monitoring gut-related metabolic signatures in both research studies and potential clinical applications.

Keywords:  Analytical methods; LC-MS/MS; Microbiota; Sample preparation; Tryptophan metabolites

DOI:  https://doi.org/10.1016/j.chroma.2026.467035
Anal Bioanal Chem. 2026 Apr 28.

Characterization of blood serum and cell lines by an automated high-throughput well plate sampling system using laser ablation-rapid evaporative ionization mass spectrometry.

Gabriel S Horkovics-Kováts, Daniel Simon, Richárd Schäffer, István Pap, Zahra Nozari, Alexander König, Sonja Decking-Paede, Roland Szand, Adrienn Molnár, Gitta Schlosser, Nóra Kucsma, Gergely Szakács, Zoltán Takáts, Júlia Balog.

  We introduce a novel automated well plate sampling system (AmbiSampler) using laser ablation-rapid evaporative ionization mass spectrometry (LA-REIMS) for label-free high-throughput biochemical screening for small volume biological fluid samples and cell line panels from multiwell plates. The human blood serum samples featured intensive signals in the low m/z range including fatty acids and metabolites and in the higher (m/z > 600) range associated with complex lipids, peptides, and proteins. We demonstrate the current quantification performance of the system through blood serum samples spiked with labelled amino acid mix. Cell line analysis was demonstrated by analyzing the NCI60 human cell lines panel grown in multiwell plates. Without any sample preparation, these experiments yielded rich metabolic and lipidomic mass spectrometry profiles, with classification results showing a 98.7% correct classification rate. We demonstrated that the new high-throughput setup using LA-REIMS technology provides an efficient and versatile approach for ambient ionization mass spectrometric analysis of various biological samples in a high-throughput manner.

Keywords:  Ambient ionization; High-throughput; Laser ablation; Mass spectrometry

DOI:  https://doi.org/10.1007/s00216-026-06484-4
J Extracell Vesicles. 2026 May;15(5): e70265

Lipidome Analysis of Cancer Cells and Their Extracellular Vesicles Reveals Cancer-Type-Specific Lipid Signatures and Enables the Design of EV-Mimetic Liposomes.

Noélie Douanne, Yousra Benslimane, Rubén R López, Prisca Bustamante, Thupten Tsering, Yunxi Chen, Laura Kienzle, Masoud Ghasemi, Lorne Taylor, Yuning Chen, Anaïs Toreja Boutin, David Juncker, Catherine Mounier, Vahé Nerguizian, Julia V Burnier.

  Lipid metabolism reprogramming is a hallmark of cancer, yet the global lipidome of cancer cells and their extracellular vesicles (EVs) remains poorly understood. Using mass spectrometry, we analyzed the lipid profiles of a panel of human cancer and non-cancer cell lines along with their secreted EVs. Cancer cells exhibited distinct lipid signatures, including elevated lipid raft components. Cancer-derived EVs displayed unique lipid compositions that clustered separately from cell lipid profiles, suggesting active lipid sorting during EV biogenesis. Comparative analysis of primary and metastatic cells and their EVs, highlighted phospholipid alterations during metastasis. These findings suggest that EV lipid profiles could serve as cancer biomarkers, and the data can inform synthetic EV-based nanoparticle design for drug delivery. Our study provides one of the most comprehensive characterizations of the cancer EV lipidome to date, offering novel insights into lipid metabolism in cancer progression and potential therapeutic applications.

Keywords:  biomarkers; cancer; cells and extracellular vesicles (EVs); colorectal cancer; lipid classes/families; lipidomics; liver metastasis; saturation levels; tumor microenvironment; uveal melanoma

DOI:  https://doi.org/10.1002/jev2.70265
J Proteome Res. 2026 May 01.

Mass Spectrometric Detected Cancer Proteins as Resources for Cancer Research.

Yuanyu Huang, Lijun Chen, Peter I-Fan Wu, Poorva Juneja, Li Chen, Yvonne A Evrard, Liyuan Jiao, Yingwei Hu, Xu Zhang, James H Doroshow, Ratna R Thangudu, Alexander Pilozzi, Hui Zhang.

  Protein evidence derived from mass spectrometry (MS) across cancer cohorts and model systems is extensive but remains fragmented across individual studies and repositories, limiting rapid retrieval and evidence-based benchmarking of cancer-context protein detection. Here we present the Mass Spectrometric Detected Cancer Proteins (MSCP) resource, an integrated database assembled from 27 large-scale cancer proteomics sources spanning human tumor cohorts, cancer cell lines, and patient-derived xenograft (PDX) models. Protein identifications were harmonized to UniProtKB-Swiss-Prot (release 2025_01) and integrated under FDR-controlled identification outputs to generate a unified catalog of 15,964 MS-supported human proteins. Benchmarking against neXtProt PE1 identified 525 proteins newly supported by MS evidence in the integrated cancer context, including proteins previously associated with chromosome-level evidence inconsistencies. Functional interpretation of the newly identified set using GO and Reactome enrichment highlighted immune- and barrier-associated processes and chromatin- and genome-regulatory pathways, including DNA methylation and histone deacetylation. Orthogonal verification using synthetic unique peptides confirmed representative newly identified proteins by concordant precursor m/z and fragment-ion patterns. MSCP provides a provenance-aware, UniProtKB-aligned resource for cancer proteomics that supports both cohort- and model-specific querying and coverage-oriented evidence aggregation, enabling standardized comparisons to reference proteomes and facilitating downstream assay planning and translational studies.

Keywords:  NeXtProt protein existence (PE) metrics; cancer proteins; mass spectrometry; missing proteins; peptide identification; proteomics database

DOI:  https://doi.org/10.1021/acs.jproteome.5c01259
Nat Metab. 2026 Apr 29.

Nitrogen metabolism profiling reveals cell state-specific pyrimidine synthesis pathway choice.

Milan R Savani, Bingbing Li, Bailey C Smith, Wen Gu, Yi Xiao, Gerard Baquer, Tracey Shipman, Skyler S Oken, Namya Manoj, Lauren G Zacharias, Vinesh T Puliyappadamba, Sylwia A Stopka, Michael S Regan, Michael M Levitt, Charles K Edgar, William H Hicks, Soummitra Anand, Misty S Martin-Sandoval, Rainah Winston, João S Patrício, Xandria Johnson, Trevor S Tippetts, Diana D Shi, Andrew Lemoff, Timothy E Richardson, Pascal O Zinn, Ashley Solmonson, Thomas P Mathews, Nathalie Y R Agar, Ralph J DeBerardinis, Kalil G Abdullah, Samuel K McBrayer.

Stable isotope-tracing assays track few metabolites, yet cells use many nutrients to sustain nitrogen metabolism. Here we create a platform for tracing 30 nitrogen isotope-labelled metabolites in parallel to enable a system-level understanding of cellular nitrogen metabolism. This platform reveals that while primitive cells engage both de novo and salvage pyrimidine synthesis pathways, differentiated cells nearly exclusively salvage uridine. This link between cell state and pyrimidine synthesis pathway preference persists in murine and human tissues. Mechanistically, we find that S1900 phosphorylation of CAD, the first enzyme of the de novo pathway, is induced by uridine deprivation in differentiated cells and constitutively enriched in primitive cells. Mimicking CAD S1900 phosphorylation in differentiated cells constitutively activates de novo pyrimidine synthesis, while blocking this modification impairs the cellular response to uridine starvation. Collectively, we establish a method for nitrogen metabolism profiling and define a mechanism of cell state-specific pyrimidine synthesis pathway choice.

DOI: https://doi.org/10.1038/s42255-026-01520-0
Cell Stem Cell. 2026 Apr 24. pii: S1934-5909(26)00144-X. [Epub ahead of print]

TCA cycle rewiring underpins histone acetylation sourcing and cell-fate transitions during exit from naive pluripotency.

Eleni Kafkia, David Pladevall-Morera, Lidia Argemi-Muntadas, Gangqi Wang, Roberta Noberini, Arnau Casòliba-Melich, Sandra Bagés-Arnal, Matthias Anagho-Mattanovich, Rita Silvério-Alves, Johanna Gassler, Tiziana Bonaldi, Ton J Rabelink, Thomas Moritz, Jan Jakub Żylicz.

  Metabolism shapes stem cell differentiation and epigenome regulation, especially during the exit from naive pluripotency in vitro. Yet how metabolic networks reorganize at implantation remains unclear. Here, we map metabolite routing in pre- and post-implantation mouse embryos and across dynamic pluripotency transitions in stem cells, revealing that the tricarboxylic acid (TCA) cycle undergoes spatio-temporal rewiring rather than a simple shutdown. Pyruvate emerges as a central metabolic nexus, where pyruvate carboxylase and malic enzyme activities create a cyclical carbon flow essential for balanced metabolic and transcriptional states, timely exit from naive pluripotency, and differentiation. As cells leave naive pluripotency, glutamine increasingly fuels the TCA cycle; unexpectedly, it is also the dominant carbon source for histone acetylation. The necessary acetyl-CoA is generated via IDH1-mediated reductive glutamine carboxylation and is coupled to pyruvate cycling, sustaining histone acetylation. These findings uncover a metabolically rewired, route-specific nutrient utilization program that links metabolism to epigenomic regulation and pluripotency transitions at implantation.

Keywords:  13C isotope tracing; development; differentiation; embryo; epigenetics; histone acetylation; metabolism; pluripotency; spatial metabolomics; stem cells

DOI:  https://doi.org/10.1016/j.stem.2026.04.004
J Chromatogr A. 2026 Apr 15. pii: S0021-9673(26)00327-4. [Epub ahead of print]1779 466997

A validated chiral LC-MS/MS method for enantioselective quantification of aromatic lactic acids in human faeces.

Ahmed M Gaafar, Gladys Stolberg-Mathieu, Henrik M Roager, Giorgia La Barbera.

  Bifidobacterium, a key genus of the infant gut microbiome, produces d- and l- enantiomers of aromatic lactic acids that may influence early-life immune development through stereochemistry-dependent biological activity. No validated analytical methods currently enable their accurate enantio‑separation and quantification in human biological samples. We report the first validated, targeted, liquid chromatography-mass spectrometry method using a chiral column for the enantioselective separation and quantification of d- and l- forms of phenyllactic acid (PLA), 4-hydroxyphenyllactic acid (4OH-PLA), and indolelactic acid (ILA) in faecal samples. The method achieves baseline separation of all enantiomers within 10 min, with resolution values of 2.66 (PLA), 1.77 (4OH-PLA), and 2.42 (ILA). Solid-phase extraction reduces matrix effects (>80 %) and improves analyte recovery (>80 %). Limits of quantification range from 2.9 to 6.7 ng mL-1, and calibration curves show excellent linearity (R² > 0.99). Inter- and intra-day precision expressed as %RSD are < 15 % for most analytes. The method was successfully applied to infant faecal samples, enabling sensitive and stereospecific quantification of aromatic lactic acids, thus establishing a foundation for exploring the biological relevance of these enantiomers in infant health.

Keywords:  4-hydroxyphenyllactic acid; Aromatic lactic acids; Chiral chromatography; Gut microbiome; Indolelactic acid; Infants; LC-MS/MS; Method validation; Phenyllactic acid; Targeted metabolomics

DOI:  https://doi.org/10.1016/j.chroma.2026.466997
Cell Biochem Funct. 2026 Apr;44(4): e70215

Significance of Lipid Metabolism Reprogramming in Tumor Microenvironment and Development of Novel Therapeutic Intervention.

Pallabi Mondal, Sankar Bhattacharyya.

  The metabolic reprogramming of cancer cell has recently gained heightened attention in the field of tumor metastasis. This metabolic reprogramming helps the cancer cells to meet increased energy and biosynthetic requirements. Beyond their structural role in membrane integrity, fatty aids are also crucial for the energy requirement of cancer cell which ultimately helps epithelial to mesenchymal transition and metastatic progression. There is urgent need for identifying the varied role of fatty acid metabolism in the tumor microenvironment (TME), that includes tumor cell, immune cells and stromal cells. Understanding how the tumor cells alter their lipid metabolism after their interaction with other cells in the TME can present a promising therapeutic strategy against cancer. This metabolic interaction between cancer cells and other cells of the TME (like immune cells and stromal cells) which supply fatty acids that helps in the formation of metastatic niche. In this review, we discussed in detail the role of exogenous fatty acid uptake and endogenous fatty acid synthesis in tumor cells and the mechanism through which cancer cells regulate lipid metabolism. Also, the involvement of immune and stromal cell in the metabolic reprogramming and the molecules or drugs that can affect the receptor or enzymes involved in lipid metabolism are identified. This review underscores the importance of further research focusing on targeting fatty acid metabolism to identify susceptibilities and enhance cancer therapy.

Keywords:  fatty acid synthesis; fatty acid uptake; lipid metabolism; metastasis; tumor; tumor microenvironment

DOI:  https://doi.org/10.1002/cbf.70215
Anal Chem. 2026 May 01.

A Multidimensional Workflow for Comprehensive Xenometabolome Profiling by Integrating TIMS-PASEF with LC-HRMS and Biotransformation-Informed Suspect and Nontarget Screening.

Dimitrios E Damalas, Nikolaos S Thomaidis.

Comprehensive xenometabolome characterization is essential for understanding the effects of xenobiotics in biological systems. This study presents a multidimensional analytical workflow integrating orthogonal chromatographic separations, trapped ion mobility spectrometry (TIMS), high-resolution mass spectrometry and biotransformation-informed data processing to address xenometabolome assessment challenges. Zebrafish larvae exposed to 4-Methylbenzotriazole (4-MeBT) were used as a challenging case study. TIMS dimension provided orthogonal experimental evidence for isomer annotation, with inverse reduced mobility (1/K0) supporting conjugation site assignment for the dominant O-S-4MeBT and O-G-4MeBT isomers. The combination of TIMS with the Parallel Accumulation Serial Fragmentation (PASEF) acquisition further reduced spectral complexity, enhanced signal-to-noise ratio, and improved MS/MS coverage (70%), generating high-quality analytical evidence crucial for structural elucidation. To leverage these analytical dimensions, we developed a data processing strategy that leverages in-silico-based suspect screening and biotransformation-informed nontarget screening. In this regard, we introduce two novel frameworks; the "Building Blocks" (BB) concept which interprets unknown bio-TPs as modular assemblies of parent- and pathway-derived substructures, and the "Spectral Characteristics Knowledgebase" (SCKB), which use known biotransformation MS/MS motifs to provide structural insights and facilitate unknown identification. Our results demonstrated the identification of all previously known 4-MeBT bio-TPs with enhanced confidence (O-Sulfate- and O-Glucuronide-4MeBT) and the discovery of 29 new bio-TP features across 12 bio-TP classes, highlighting its efficacy in unraveling complex xenobiotic metabolism. Among these, a putative dimerization product (4-MeBT-263) was reported for the first time in zebrafish. Overall, this workflow has the potential to advance the understanding of bio-TP formation and detoxification processes in xenometabolome studies.

DOI: https://doi.org/10.1021/acs.analchem.5c08213
STAR Protoc. 2026 Apr 30. pii: S2666-1667(26)00187-5. [Epub ahead of print]7(2): 104534

Protocol for integrating and interpreting multi-omics data combining unsupervised and supervised data integrating approaches.

Matthias Anagho-Mattanovich, Holda Awah Anagho-Mattanovich, Qian Gao, Thomas Moritz.

  Multi-omics integration combines data from transcriptomics, proteomics, and metabolomics to provide insights into biological systems. Here, we present a protocol for integrating and interpreting multi-omics data using unsupervised multi-omics factor analysis (MOFA), supervised projection-based integration (data integration analysis for biomarker discovery using latent components, DIABLO), along with general single-omics analysis techniques and visualizations. We describe steps for data preparation, model construction, and biological interpretation of multi-omics datasets. These approaches identify coordinated molecular changes across biological layers and reveal regulatory mechanisms that drive biological processes. For complete details on the use and execution of this protocol, please refer to Anagho-Mattanovich et al.1.

Keywords:  Bioinformatics; Metabolism; Metabolomics; Proteomics; Systems biology

DOI:  https://doi.org/10.1016/j.xpro.2026.104534
J Proteome Res. 2026 Apr 29.

Bimodal Peptide Collision Cross Section Distribution Reflects Two Stable Conformations in the Gas Phase.

Juan Restrepo, Daniel Szoelloesi, Tobias Kiermeyer, Christoph Wichmann, Helmut Grubmüller, Jürgen Cox.

  Recent high-throughput applications to shotgun proteomics have shown great benefits of coupling ion mobility spectrometry (IMS) to mass spectrometry. IMS adds a separation dimension by differentiating biomolecules from their size and shape. We (and others) find that the distribution of the peptide collision cross section (CCS) is often bimodal, which limits the utility of current machine learning predictions for peptide identification. Molecular dynamics simulations indicate that the peptides in the drift tube can adopt multiple stable conformations and that the two modes correspond to predominantly extended (mostly helical) and more compact (globular and less ordered) conformations. Most peptides have a charge-dependent strong preference for one of the two conformations, while some can adapt to both, as evidenced by a simple geometric model of the CCS data. We suggest a novel two-valued CCS predictor that allows for multiple peptide conformations. Its integration into data-independent acquisition proteomics increases identification rates of peptides compared with single-value predictors.

Keywords:  DIA; LC-IMS-MS/MS; bimodal distribution; collision cross section; ion mobility spectrometry; machine learning; molecular dynamics; peptide conformations

DOI:  https://doi.org/10.1021/acs.jproteome.5c01159
Expert Rev Proteomics. 2026 Apr 30.

Proteomics-based approaches to neutrophil biology.

Jose R Pittaluga-Villarreal, Aleksandra Nita-Lazar.

   INTRODUCTION: Neutrophils are central effectors of innate immunity and key contributors to inflammation, host defense, and tissue injury across a wide range of physiological and pathological contexts. Due to their short lifespan, rapid activation, and extensive post-translational regulation, comprehensive molecular characterization of neutrophil function requires approaches that go beyond transcriptomics or marker-based analyses.
AREAS COVERED: This review summarizes how proteomic technologies have advanced the understanding of neutrophil biology by enabling unbiased, system-wide profiling of protein abundance, subcellular organization, post-translational modifications, and functional heterogeneity. We discuss global and subcellular proteomics, PTM-centric analyses, and emerging low-input and single-cell proteomic strategies, highlighting recent studies of infection, cancer, metabolic disorders, aging, autoimmune disease, and inflammation. The literature covered includes current large-scale quantitative proteomics, targeted PTMs, and integrative multi-omics studies in both human samples and relevant experimental models.
EXPERT OPINION: Proteomics has established neutrophils as highly plastic and context-dependent cells whose functions are governed by coordinated remodeling of signaling, metabolism, and effector pathways. Future progress will depend on expanding neutrophil-specific PTM maps, improving low-input workflows, and integrating single-cell and spatial proteomics. Together, these advances are expected to redefine neutrophil functional states and accelerate translation toward clinically meaningful biomarkers and therapeutic strategies.

Keywords:  Mass spectrometry; Neutrophils; Phosphoproteomics; Post-translational modifications; Single-cell proteomics; innate immunity; neutrophil extracellular traps (NETs); polymorphonuclear leukocytes; proteomics

DOI:  https://doi.org/10.1080/14789450.2026.2667887