bims-mascan 2026-10-11 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–10–11
twenty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Unlocking the bile acid universe: advanced workflows and a multidimensional library of 280 unique species.
From Single Injection to Dual Insights: Integrating EAD and CID Acquisition for Spectral Library Generation and EAD-CID Spectral Matching.
Integrated assessment of fatty acid metabolism and cellular energy status using HILIC-MS/MS.
Reproducible Amyloid Typing by Data-Independent Acquisition Mass Spectrometry.
A Comprehensive Dual Platform, End-to-end Workflow for Metabolic Profiling of Dried Blood Microsamples.
Metabonaut: an open educational resource for learning reproducible, script-based untargeted LC-MS/MS metabolomics.
Correlating metabolites, lipids, and N-glycans in murine kidney microstructures via integrative mass spectrometry imaging.
Multi-reflecting time-of-flight mass spectrometry enables high-accuracy metabolite identification in a demonstration study using serum samples from COVID-19 patients.
ProFormer: generalizable classification of single-cell and plasma proteomes using deep learning.
Storage-dependent changes in the soluble urinary proteome: a pre-analytical stability study in inflammatory clinical urine samples.
Pretrained spectrum representations for diverse tandem mass spectrometry proteomics tasks.
Unified down-stream analysis of crosslinking mass spectrometry results with pyXLMS.
Non-coding RNAs at the intersection of epigenetics and cancer metabolism.
Benchmarking Spectral Library Prediction Platforms for Neuropeptidomics Applications.
How to Design 1000-Plex Mass Tags Using the Differential Mass Defect.
Stable Isotope Labeling to Track De Novo Fatty Acid Synthesis in Mouse Cartilage-Enriched Joint Tissue In Vivo: A Practical Workflow.
Advances in single-cell proteomics: label-free quantification of over 3500 protein groups from individual Arabidopsis thaliana seedling cells.
Pilot proteomics study of three different human femur sites and autologous serum samples.
Tumor organoids for testing metabolic flexibility in precision oncology.
Targeted quantification of 25 tryptophan metabolites across three metabolic pathways in serum by ultra-high-performance liquid chromatography coupled with quadrupole orbitrap high-resolution mass spectrometry.
Nanomatrix engineering for LDI-MS based biomedical metabolomics.
Spatial proteomics: technical evolution and applications in cancer research.

Sustain Microbiol. 2026 ;3(4): qvag039

Unlocking the bile acid universe: advanced workflows and a multidimensional library of 280 unique species.

Guozhi Zhang, Emily C Vincent, Sadie M Disselkoen, James N Dodds, Quentin DuVal-Smith, Abubaker Patan, Ipsita Mohanty, Victoria Deleray, Jian Zhang, Paul A Thiessen, Evan E Bolton, Emma L Schymanski, Pieter C Dorrestein, Casey M Theriot, Erin S Baker.

  Microbes and bile acids are tightly intertwined, especially in the gut. While the liver produces primary bile acids from cholesterol, gut bacteria transform these into diverse secondary forms which act as powerful signaling molecules. Since bile acid changes are increasingly linked to health and disease, their accurate measurement in the gut and circulation is essential. Bile acid analytical evaluations, however, are challenging as many co-elute in liquid chromatography (LC), share identical mass spectrometry (MS) precursor masses, and produce similar tandem mass spectrometry (MS/MS) spectra. As a result, conventional LC-MS/MS workflows struggle to differentiate bile acids and have motivated the addition of orthogonal separations such as ion mobility spectrometry (IMS). Here, we assess optimal bile acid extraction parameters for stool, serum, and plasma; compare LC conditions; and assess electrospray ionization performance across polarities. Additionally, we introduce a publicly available multidimensional reference library containing LC retention times, IMS collision cross section values, and accurate precursor masses for 280 unique bile acids (264 endogenous and 16 deuterium-labeled species) including unconjugated, host-conjugated, and microbially conjugated bile acids. This multidimensional library empowers bile acid identification in complex samples and enables a more comprehensive exploration of their biological roles and disease associations.

Keywords:  bile acid extraction; bile acids; collision cross section; ion mobility spectrometry; suspect screening

DOI:  https://doi.org/10.1093/sumbio/qvag039
Anal Chem. 2026 Oct 02.

From Single Injection to Dual Insights: Integrating EAD and CID Acquisition for Spectral Library Generation and EAD-CID Spectral Matching.

Lisa Panzenboeck, Christina Brenner, Veronika Fitz, Mingxun Wang, Patrick Mueller, Evelyn Rampler, Yasin El Abiead, Gunda Koellensperger.

Mass spectral MS/MS libraries are essential in nontargeted metabolomics as they facilitate metabolite annotation and provide the basis for in silico annotation tools and fragmentation rules. While significant progress has been made toward more comprehensive MS/MS libraries, those efforts have focused mainly on collision-induced dissociation (CID)- and higher-energy collisional dissociation (HCD)-based approaches. In this work, we introduce a flow injection-high-resolution targeted MS/MS-based acquisition strategy that acquires electron-activated dissociation (EAD) and CID across a wide range of fragmentation energies within a single injection. The workflow supports both nontargeted and targeted analysis, through efficient library generation and MS/MS parameter optimization, respectively. We apply the workflow via a set of 45 steroid standards, known for their important role in human health, disease, and doping, expanding the coverage of public libraries for both CID and EAD. Finally, we utilize public MS/MS libraries and raw data to reveal that EAD MS/MS spectra can, to some extent, be annotated via existing MS/MS libraries, governed by CID and HCD, and show that the acquired CID library enables novel annotations in publicly available clinical data, demonstrating its utility for biological studies.

DOI: https://doi.org/10.1021/acs.analchem.6c01204
bioRxiv. 2026 Aug 13. pii: 2026.08.12.744242. [Epub ahead of print]

Integrated assessment of fatty acid metabolism and cellular energy status using HILIC-MS/MS.

Mariana Lopes, Kyle D Roberts, Alexis E Heath, Peder J Lund.

Acetyl-CoA and other acyl-CoA thioesters are critical intermediates in the metabolic reactions that cells rely on to produce energy and carry out biosynthesis. Therefore, the analysis of acyl-CoA provides valuable information about the metabolic activity of cells, especially when combined with stable isotope tracing. Acyl-CoA species are routinely monitored by reversed-phase liquid chromatography coupled to tandem mass spectrometry (RPLC-MS/MS). However, drastic differences in the hydrophobicity of short-chain versus long-chain acyl-CoA species have been challenging to accommodate with a single set of RPLC conditions. Here, we describe a convenient method based on hydrophilic interaction liquid chromatography (HILIC-MS/MS) for the concurrent detection of both short-chain and long-chain acyl-CoA and their corresponding acyl-carnitine species. Using this strategy, we tracked the metabolism of isotope-labeled fatty acids in multiple cell lines, which revealed differences in their propensities for fatty acid oxidation and the extent to which isotope incorporation into acyl-CoA mirrored that of acyl-carnitine. We also applied the HILIC-MS/MS workflow to the analysis of NADH and ATP, making it a useful technique for gauging cellular bioenergetics as reflected by the acetyl-CoA/CoA, NADH/NAD + , and ATP/ADP ratios. Altogether, this HILIC-MS/MS platform enables a streamlined analysis of acyl-CoA species and other key intermediates in cell metabolism.

DOI: https://doi.org/10.64898/2026.08.12.744242
Clin Chem. 2026 Oct 07. pii: hvag132. [Epub ahead of print]

Reproducible Amyloid Typing by Data-Independent Acquisition Mass Spectrometry.

Alexandra Emmanuel, Kelly D Smith, William Catungal, Erinn Kuest, Han-Yin Yang, Deanna L Plubell, Songlin Yu, Jingxiang Ma, Niklas Krumm, Christine C Wu, William S Noble, Brian C Searle, Michael J MacCoss, Andrew N Hoofnagle, William S Phipps.

BACKGROUND: Mass spectrometry (MS) remains underutilized in the evaluation of formalin-fixed, paraffin-embedded (FFPE) tissue. Clinical deployment of MS in anatomic pathology is largely confined to data-dependent acquisition (DDA) work flows for amyloid typing. Alternative acquisition strategies such as data-independent acquisition (DIA) enable quantitative, peptide-centric tissue analysis and can expand MS use in biopsy evaluation.
METHODS: We performed DIA-based LC-MS/MS analyses of amyloid-positive tissue specimens alongside an established DDA clinical work flow. The DIA pipeline incorporated automated data processing to support standardization and transferability. Peptide peak areas were extracted using Skyline, and machine-learning classifiers and peptide scoring rules were developed to predict amyloid type.
RESULTS: DIA peptide measurements were evaluated across 304 amyloid-positive tissue specimens representing 16 discrete amyloid types. In a development group (n = 259), a random forest classifier enabled accurate classification of the five most common amyloid types from individual LC-DIA-MS/MS injections: λ and κ immunoglobulin light chain, transthyretin, leukocyte chemotactic factor 2, and serum amyloid A. In prospective evaluation (n = 45), the combined DIA classification strategy was fully concordant with the reference DDA clinical assignments. Amyloid types not represented in classifier training were resolved using peptide-level diagnostic signals evaluated against empirically derived intensity thresholds.
CONCLUSIONS: DIA-based mass spectrometry enables reproducible peptide-level classification of amyloid deposits in FFPE tissue. Combined with standardized data processing, machine-learning classification, and rule-based peptide scoring, this approach provides a scalable framework for proteomic amyloid typing. More broadly, it may support wider adoption of mass spectrometry for peptide biomarker quantification in anatomic pathology.

DOI: https://doi.org/10.1093/clinchem/hvag132
J Vis Exp. 2026 Oct 06.

A Comprehensive Dual Platform, End-to-end Workflow for Metabolic Profiling of Dried Blood Microsamples.

Daniel Marques de Sá E Silva, Marlene Thaitumu, Georgios Theodoridis, Helen Gika.

Blood microsampling (BµS) has emerged as a robust, minimally invasive alternative to traditional venipuncture, enabling the collection of low-volume specimens outside conventional clinical settings. By supporting decentralized sampling and improving patient adherence, these devices can be integrated with advanced omics technologies to facilitate large-scale and longitudinal study designs. However, successful implementation requires optimization of multiple critical steps, from sample collection and storage to extraction and analytical preparation. In this work, we propose a comprehensive end-to-end untargeted metabolomics workflow for BµS, with an emphasis on key parameters governing sample preparation and analytical performance. Volumetric absorptive microsampling (VAMS) devices were evaluated using two complementary platforms: reversed-phase liquid chromatography coupled to mass spectrometry and gas chromatography coupled to mass spectrometry. Application of the optimized workflow demonstrated broad metabolomic coverage across diverse chemical classes, highlighting the potential of VAMS-based sampling to support scalable, decentralized metabolomics studies while maintaining analytical robustness, reproducibility, and biological relevance.

DOI: https://doi.org/10.3791/72533
Metabolomics. 2026 Oct 03. pii: 165. [Epub ahead of print]22(5):

Metabonaut: an open educational resource for learning reproducible, script-based untargeted LC-MS/MS metabolomics.

Philippine Louail, Marilyn De Graeve, Alexandru Mahmoud, Daniel Marques de Sá E Silva, Kozo Nishida, Vilhelm Suksi, Anna Tagliaferri, Gabriele Tomè, Vinicius Verri Hernandes, Johannes Rainer.

   BACKGROUND: Two decades after the Metabolomics Standards Initiative, public deposition of raw LC-MS/MS data into archives such as MetaboLights, Metabolomics Workbench, GNPS and MassBank has matured, but the analyses that turn raw files into reported feature tables remain difficult to share, audit and reuse. The FAIR principles, originally formulated for data, later extended to research software and to workflows, make a strong case that this gap matters; we adopt that view and ask how an educational resource can help researchers cross it.
AIM OF REVIEW: This is an educational review where we describe the principles of FAIR and reproducible analysis as they apply to untargeted LC-MS metabolomics, and we show how those principles are realised in Metabonaut, an open educational resource built on top of the R for Mass Spectrometry/Bioconductor ecosystem. Metabonaut provides executable example workflows from raw mzML files to annotated feature tables, demonstrates interoperability across programming languages, and is built with and for the community.
KEY SCIENTIFIC CONCEPTS OF REVIEW: After a brief tour of the underlying software ecosystem and of literate programming, the review discusses three topics, each through one or more Metabonaut vignettes: visual inspection and quality control, reuse of public archives at scale, and cross-language interoperability. Together, they teach how to carry FAIR practice from data to analysis: making the analysis itself, and not only the data it consumes, shareable, auditable and reusable. The source code of Metabonaut and links to pre-rendered workflow documents are available at https://github.com/RforMassSpectrometry/Metabonaut .

Keywords:   Metabonaut ; FAIR principles; Literate programming; Open educational resource; Reproducible research; Untargeted metabolomics

DOI:  https://doi.org/10.1007/s11306-026-02538-x
Nat Commun. 2026 09 04. pii: 10544. [Epub ahead of print]17(1):

Correlating metabolites, lipids, and N-glycans in murine kidney microstructures via integrative mass spectrometry imaging.

Nina Ogrinc, Rick Ursem, Hans Dalebout, Jesper Kers, Martin Giera, Manfred Wuhrer, Noortje de Haan.

Understanding how metabolites, lipids and glycans interact within tissues at the same cellular layer is key to uncovering their roles in health and disease. Herein, we present an integrative multi-omic mass spectrometry imaging (MSI) approach correlating metabolites, lipids and N-glycans, including their linkage-specific sialic acid information, in a single renal tissue section. Our protocol enables the sequential detection of hundreds of metabolites and lipids species alongside diverse glycosylation traits in small cell clusters across the kidney. Importantly, our pixel-to-pixel co-registration and clustering correlates glycans (recorded in positive mode MS), metabolites, and lipids (both in negative mode MS) across kidney microstructures. This analysis reveals strong association between taurine, TCA metabolites, sulfatides, and polyunsaturated phospholipids, and specific sialic acid linkages on N-glycans in the thick ascending limb and collecting ducts. The robust strategy maximizes molecular insights from scarcely available samples and offers a powerful tool for exploring tissue complexity in both normal and disease states.

DOI: https://doi.org/10.1038/s41467-026-77127-6
Metabolomics. 2026 Oct 03. pii: 168. [Epub ahead of print]22(5):

Multi-reflecting time-of-flight mass spectrometry enables high-accuracy metabolite identification in a demonstration study using serum samples from COVID-19 patients.

Adam King, Eleanor Matthews, Garth Maker, Robert Trengove, Shaufa Shareef, Stephen Fowler, Timothy Felton, Angela Simpson, Martin Palmer, Lee A Gethings, Robert S Plumb, E N Clare Mills.

   INTRODUCTION: Accurate metabolite identification remains a major challenge in untargeted metabolomics due to overlapping signals, isomeric compounds, and insufficient mass resolution. The multi-reflecting time-of-flight (MRT) platform provides mass resolution exceeding 200,000 Full Width at Half Maximum and sub-ppm mass accuracy, enabling detailed isotopic fine structure analysis.
OBJECTIVES: The performance of the MRT platforms in identifying lipids and metabolites was evaluated using selected serum samples from a group of hospitalized COVID-19 patients.
METHODS: Lipids and polar metabolites were extracted from human serum samples from the Manchester Asthma, Allergy and Thoracic Surgery Biobank and analysed using the MRT platform with data-independent acquisition.
RESULTS: The MRT platform facilitated the resolution of overlapping lipid species and improved annotation confidence. In a pilot cohort (n = 8), metabolomic differences associated with disease severity were observed, alongside annotation of clinically relevant metabolites including sulphated bile acids and retained iohexol associated with renal dysfunction.
CONCLUSIONS: These findings demonstrate that MRT platform enhances confidence in metabolite identification and supports high-throughput metabolomics, offering significant potential for biomarker discovery in clinical studies.

Keywords:  Biomarkers; COVID-19; Lipidomics; Mass spectrometry; Metabolomics; Time-of-flight

DOI:  https://doi.org/10.1007/s11306-026-02539-w
Nat Commun. 2026 10 05. pii: 10493. [Epub ahead of print]17(1):

ProFormer: generalizable classification of single-cell and plasma proteomes using deep learning.

Karl K Krull, Arlene Kühn, Julia Höhn, Syed Azmal Ali, Martin H Otterbein, Titus J Brinker, Jeroen Krijgsveld.

Proteins are the main drivers of cell function and disease, making proteomics a powerful technique for biomarker discovery and defining cell identity. While current technologies can profile thousands of proteins and reach single-cell sensitivity, limited throughput restricts applications such as robust classification of large biological or clinical cohorts. To close this gap, we present a deep-learning (DL) approach for the direct analysis of mass spectrometric (MS) data, assigning proteomic profiles to sample identity. Specifically, we introduce the ProFormer, a transformer pipeline that classifies samples by tabular MS1-level features derived from peptide ions, eliminating the need for time-consuming data interpretation. The ProFormer outperforms traditional machine-learning and image-based convolutional neural networks (CNNs), demonstrating enhanced classification and generalization over 14 evaluated architectures. We further provide detailed insights into how the ProFormer dynamically aggregates MS1 data, while preserving signal contribution and enabling explainable AI at single-peptide resolution. Applied to diverse LC-MS data sets, the ProFormer accurately classified single-cell proteomes by cell type, cycle stage or differentiation trajectory, as well as patient disease status from plasma proteomes. Thereby, the ProFormer provides a versatile and rapid framework for the classification of proteomic data, with important implications for patient stratification, early detection and single-cell analysis.

DOI: https://doi.org/10.1038/s41467-026-78143-2
J Liq Biopsy. 2026 Dec;14 100505

Storage-dependent changes in the soluble urinary proteome: a pre-analytical stability study in inflammatory clinical urine samples.

Angelo Novak, Stephanie Tautges, Lisa Geissler, Florian Wagenlehner, Daniel R Engel, Hannah Voss, Olga Shevchuk.

  Urine is a highly accessible biofluid for clinical proteomics, but pre-analytical variability during collection, storage, and processing can compromise reproducibility. Mass spectrometry-based proteomics enables broad measurement of urinary proteins, making it suitable for evaluating how storage conditions affect proteome composition. However, clinical translation of urinary proteomics is limited by insufficient standardization of pre-analytical workflows and limited knowledge of proteome stability during storage, compromising reproducibility. This limitation is particularly relevant for inflammatory urine samples, where immune-cell-derived and complement-associated proteins may continue to change ex vivo during delayed processing. Here, we systematically evaluated the stability of the soluble urinary proteome under clinically relevant storage conditions. Urine samples from patients with pyelonephritis were used as a clinically relevant inflammatory urine model and processed under six pre-analytical storage conditions, including immediate freezing at -80 °C, repeated freeze-thaw cycles, and incubation for 30 min or 3 h at either 4 °C or room temperature prior to freezing. High-resolution proteomics using data-independent acquisition coupled with parallel accumulation-serial fragmentation (DIA-PASEF) identified more than 4,300 protein groups across four pyelonephritis patients. Global proteome profiles were highly reproducible across all conditions (Pearson r > 0.9). In contrast, specific immune and metabolic proteins exhibited storage-dependent abundance changes, particularly after prolonged room temperature exposure, consistent with ex vivo immune activation. These results support standardized urine handling, particularly avoidance of prolonged room-temperature storage, to reduce storage-induced changes in immune-associated urinary proteins.

Keywords:  Mass spectrometry; Pre-analytical variability; Proteome stability; Pyelonephritis; Storage conditions; Urine proteomics

DOI:  https://doi.org/10.1016/j.jlb.2026.100505
Mol Cell Proteomics. 2026 Oct 07. pii: S1535-9476(26)00169-6. [Epub ahead of print] 101673

Pretrained spectrum representations for diverse tandem mass spectrometry proteomics tasks.

Justin Sanders, Melih Yilmaz, Jacob H Russell, Wout Bittremieux, William E Fondrie, Nicholas M Riley, Sewoong Oh, William S Noble.

Mass spectrometry is the dominant technology in the field of proteomics, enabling high-throughput analysis of the protein content of complex biological samples. Due to the complexity of the instrumentation and resulting data, sophisticated computational methods are required for the processing and interpretation of acquired mass spectra. Machine learning has shown great promise to improve the analysis of mass spectrometry data, with numerous purpose-built methods for improving specific steps in the data acquisition and analysis pipeline reaching widespread adoption. Each of these tasks rely on a shared fundamental understanding of the information present in a mass spectrum. Here, we propose a transfer learning framework, CasanovoTL, which utilizes pretrained spectrum representations, derived from a de novo sequencing model trained on a massive proteomics dataset, rather than training a spectrum encoder from scratch for each task. We show that using these pre-trained spectrum representations significantly improves our performance on the four downstream tasks of spectrum quality prediction, chimericity prediction, phosphorylation prediction, and glycosylation status prediction. Finally, we perform end-to-end finetuning, as well as multi-task training, and find that finetuned spectrum representations further improve performance on each individual task. Overall, our work demonstrates that a pretrained spectrum encoder for tandem mass spectrometry proteomics trained on de novo sequencing learns generalizable representations of spectra, improves performance on downstream tasks where training data is limited, and can ultimately enhance data acquisition and analysis in proteomics experiments.

DOI: https://doi.org/10.1016/j.mcpro.2026.101673
Nat Commun. 2026 09 04. pii: 10508. [Epub ahead of print]17(1):

Unified down-stream analysis of crosslinking mass spectrometry results with pyXLMS.

Micha J Birklbauer, Louise M Buur, Sabrina Kaser, Fränze Müller, Manuel Matzinger, Karl Mechtler, Stephan Winkler, Viktoria Dorfer.

Crosslinking mass spectrometry has become the method of choice for the identification of protein-protein interactions and for gaining insight into the structures of proteins in vivo. However, connecting crosslink search engine results with down-stream analysis tools, and therefore gaining biological insight from crosslink identifications, has remained a manual and cumbersome step in the analysis that often requires expert bioinformatics knowledge. Here we introduce pyXLMS, a python package and public web application which aims to simplify and streamline this intermediate step, enabling researchers even without bioinformatics knowledge to conduct in-depth crosslink analyses. In its current state pyXLMS supports input from more than seven different crosslink search engines, as well as the mzIdentML format of the HUPO Proteomics Standards Initiative. Data processing and quality control is facilitated by functionality that is directly available within pyXLMS such as aggregation, validation, annotation, filtering, and visualization. In addition, the data can easily be exported to more than ten supported down-stream analysis tools and formats. We demonstrate the applicability and benefits of pyXLMS by re-analyzing a publicly available crosslink dataset with a variety of different search engines and show how the same data analysis workflow can be applied using pyXLMS.

DOI: https://doi.org/10.1038/s41467-026-77407-1
Front Epigenet Epigenom. 2025 ;3 1699969

Non-coding RNAs at the intersection of epigenetics and cancer metabolism.

Nicolò Mangraviti, Serena Castelli.

  Cancer cells are primarily characterized by uncontrolled proliferation, which is closely linked to an increased energy demand. According to this, cancer cells preferentially utilize the glycolytic pathway for energy production. Metabolic regulation in cancer occurs at multiple levels, including post-translational modifications, which are rapid and energy-efficient, as they do not require enzyme degradation. In this context, non-coding RNA-mediated control plays a crucial role, as these small RNAs can directly modulate the mRNAs of key metabolic enzymes or induce epigenetic modulations, ultimately inducing alterations in cancer metabolism. In this mini-review, we summarize how non-coding RNAs (ncRNAs), including microRNAs, long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), modulate core metabolic pathways, including glycolysis, mitochondrial respiration, lipid metabolism, and amino acid biosynthesis, in cancer. We also highlight recent findings that uncover novel regulatory mechanisms and propose that ncRNAs serve as dynamic integrators of metabolic and oncogenic signals. Understanding these interactions may open new avenues for biomarker identification and ncRNAs-targeted therapies development.

Keywords:  circRNAs; lncRNA; metabolic reprogramming; microRNA; ncRNA

DOI:  https://doi.org/10.3389/freae.2025.1699969
bioRxiv. 2026 Aug 13. pii: 2026.08.07.743122. [Epub ahead of print]

Benchmarking Spectral Library Prediction Platforms for Neuropeptidomics Applications.

Lauren Fields, Emily M Hubecky, Kendra G Selby, Lingjun Li.

Data-independent acquisition (DIA) mass spectrometry has emerged as a powerful tool for neuropeptidomics, but its success relies heavily on the quality of spectral libraries used for peptide identification. There are inherent challenges to mass spectrometry analysis of crustacean neuropeptides, including the endogenous nature in which they are analyzed, extensive post-translational modification (PTM), and atypical fragmentation patterns. Thus, general-purpose proteomic spectral prediction tools may not perform optimally in the endogenous peptide domain. In this study, we benchmark four widely used spectral prediction platforms, Prosit, MS2PIP, AlphaPeptDeep, and UniSpec, to evaluate their performance in predicting the fragmentation of neuropeptides. Using an empirically derived spectral library from crustacean tissues as reference, we assess model compatibility, dot-product similarity, Pearson correlation, and DIA-based identifications across brain, sinus gland, and pericardial organ samples. Our results reveal that no single model comprehensively captures neuropeptide fragmentation characteristics. While UniSpec showed unexpected strengths due to its inclusion of neutral loss ions, AlphaPeptDeep demonstrated the highest spectral similarity, and MS2PIP and Prosit outperformed in DIA-NN identifications. We further highlight the critical impact of neutral loss fragments, present in over 50% of empirical spectra, and emphasize the need for hybrid spectral libraries that integrate complementary strengths across models. This work provides a foundational framework for optimizing spectral library selection in neuropeptidomics and underscores the importance of model-specific biases when analyzing structurally diverse endogenous peptides.

DOI: https://doi.org/10.64898/2026.08.07.743122
J Proteome Res. 2026 Oct 07.

How to Design 1000-Plex Mass Tags Using the Differential Mass Defect.

Harrison Specht, Kevin McDonnell, Daniel J Geiszler, Michael P Agius.

  Multiplexing samples in mass-spectrometry-based proteomics has long been accomplished by isotopologues of small molecules. These chemically identical "mass tags" conjugate to peptides to encode samples with different mass offsets for parallel analysis. The current state of the art for multiplexing with nonisobaric mass tags was recently improved from 3-plex to 9-plex, but what is the largest plex size that can be reasonably achieved with current technology? A full answer to this question requires evaluating current mass spectrometry hardware, facets of which have been well investigated by others. However, it may be underappreciated that multiplexing 1000s of samples with mass tags does not actually require 1000s of isotopes or 1000s of synthesis steps to create. Nonintuitively, high-plex mass tags can require relatively few different isotopes. The focus of this exposition is to characterize the potential of the differential mass defect to create tens to over a thousand isotopologues of small molecules and how careful combinations of these small molecules can combinatorially scale the plex size to minimize the number of synthetic steps. Importantly, we show using formulas from publicly reported compounds that plex sizes in the hundreds, an order of magnitude greater than the state of the art, are achievable using molecules comparable in size to existing commercial tags and that going beyond hundreds may require larger molecules. Additionally, we show how plexing with the differential mass defect allows significantly higher-purity yields during reagent creation for a given plex size. In summary, we investigated and present here how the differential mass defect must be utilized to create high-plex mass tags with limited dopable sites.

Keywords:  barcodes; bottom-up proteomics; combinatorial; isotopes; isotopologues; mass defect; multiplexing

DOI:  https://doi.org/10.1021/acs.jproteome.6c00166
J Orthop Res. 2026 Oct;44(10): e70290

Stable Isotope Labeling to Track De Novo Fatty Acid Synthesis in Mouse Cartilage-Enriched Joint Tissue In Vivo: A Practical Workflow.

Shadi Moradi, Anupama Binoy, Shouan Zhu.

  Cartilage is traditionally viewed as a metabolically quiescent tissue that relies primarily on exogenous lipid uptake to meet its energetic and structural demands. However, increasing evidence suggests that chondrocytes exhibit metabolically active behaviors, particularly under aging and pathological conditions. Here, we developed and applied an in vivo deuterium oxide (D2O)-based stable isotope labeling approach to directly quantify de novo fatty-acid synthesis in cartilage. Using gas chromatography-mass spectrometry (GC-MS), we tracked deuterium incorporation into palmitate in serum and articular cartilage from wild-type (WT) and cartilage-specific acetyl-CoA carboxylase 1 (ACC1) knockout mice. Progressive incorporation of deuterium into palmitate confirmed active fatty-acid synthesis in cartilage, with labeling kinetics distinct from those observed in serum, consistent with slower lipid turnover in the matrix-dense cartilage environment. Although fractional new synthesis of palmitate was comparable between genotypes during the labeling window, ACC1-deficient cartilage exhibited reduced palmitate abundance and decreased higher-order isotopomer enrichment at early labeling time points, indicating attenuated lipogenic flux. These findings provide direct in vivo evidence that chondrocytes possess intrinsic lipogenic capacity and that ACC1 contributes to the maintenance of the cartilage lipid pool. Together, this study establishes D2O-based isotope tracing as a robust and quantitative framework for assessing cartilage lipid metabolism in vivo and reveals de novo lipogenesis as an active metabolic process in cartilage. CLINICAL SIGNIFICANCE: This approach offers a valuable platform for investigating lipid dysregulation in osteoarthritis and other joint diseases.

Keywords:  cartilage; de novo lipogenesis; fatty acid synthesis; stable Isotope

DOI:  https://doi.org/10.1002/jor.70290
Plant Cell Physiol. 2026 Oct 06. pii: pcag138. [Epub ahead of print]

Advances in single-cell proteomics: label-free quantification of over 3500 protein groups from individual Arabidopsis thaliana seedling cells.

Sara Christina Stolze, Anne Harzen, Christoph Krisp, Hirofumi Nakagami.



Keywords:  Arabidopsis thaliana; Data-independent acquisition; Label free; Single cell proteomics

DOI:  https://doi.org/10.1093/pcp/pcag138
Bone Rep. 2026 Dec;31 101949

Pilot proteomics study of three different human femur sites and autologous serum samples.

Fangtang Yu, Sjur Reppe, Vigdis T Gautvik, Keller McKowen, Wei-Zhi Lin, Tor P Utheim, Kaare M Gautvik, Yi-Hsiang Hsu.

  Tissue proteomics will help discover the molecular pathophysiology of bone diseases. So far, most human studies have been performed on serum/plasma samples. We present an initial pilot proteomics analysis comparing three clinically important anatomical femur sites (caput, collum, and trochanter). The specimens were obtained from osteoarthritic patients undergoing hip replacement surgery and represented predominantly cortical bone from collum and mostly trabecular bone from caput, together with a mixture from trochanter. The bone and the corresponding serum samples were analyzed by high-throughput data-independent acquisition (DIA) liquid chromatography-mass spectrometry (LC-MS/MS). The results showed that the protein composition of bone/bone marrow was substantially more complex than serum, presenting a total of 3085 and 597 detected proteins, respectively. A major part of the detected osteoblast/osteocyte, adipocyte, mesenchymal stem cell (MSC), and hematological/immunological characteristic proteins were similar between the corresponding femur sites in all donors and different from serum. Although the majority of the bone proteome was conserved across anatomical sites, exploratory analyses of the global proteomic landscape revealed site-specific protein signatures and associated functional pathways that were highly consistent across individuals. These findings suggest that anatomically adjacent femoral sites share a conserved core bone proteome while maintaining distinct molecular characteristics of potential functional relevance. With validation in larger cohorts, the results will contribute to the development of a human bone tissue proteomics atlas providing novel insight in the molecular microarchitecture and functionality relevant for bone health.

Keywords:  Bone metabolism; Human bone sample; Quantitative proteomics

DOI:  https://doi.org/10.1016/j.bonr.2026.101949
Front Oncol. 2026 ;16 1902688

Tumor organoids for testing metabolic flexibility in precision oncology.

Di Hao, Yijie Ren, Hui Zhang.

  Tumors adapt their metabolism to fluctuating nutrients, hypoxia, stromal interactions, immune pressure, and treatment. Tumor organoids are attractive systems in which to test this flexibility, but existing reviews have largely emphasized organoid applications, physiologic culture conditions or metabolomics workflows rather than the strength of evidence behind individual metabolic claims. Here, we address a narrower problem: which metabolic properties of a patient tumor are directly retained or recreated in organoid culture? We evaluate organoid evidence using three levels of support: direct metabolic measurements and perturbation in organoids, biological mechanisms established in other models that require organoid validation, and translational associations that require prospective clinical testing. Recent studies provide direct advances in pancreatic cancer organoids, including subtype-specific oxidative and glycolytic states, KRAS-associated cholesterol dependence and hypoxia-driven invasion, and in gastric and colorectal models where engineered co-cultures expose niche-dependent lipid or immune responses. These findings also expose a central gap: matched benchmarking of organoid metabolic flux and spatial heterogeneity against native tumors remains uncommon. We propose a validation framework based on physiologically specified environments, spatial and isotope-resolved measurements, multi-compartment co-culture and prospective linkage of functional readouts to patient outcomes. Tumor organoids can therefore advance cancer metabolism most credibly as tested, context-specific models rather than assumed replicas of metabolic flexibility in vivo.

Keywords:  cancer metabolism; metabolic flexibility; metabolic heterogeneity; patient-derived models; precision oncology; tumor microenvironment; tumor organoids

DOI:  https://doi.org/10.3389/fonc.2026.1902688
J Pharm Biomed Anal. 2026 Sep 16. pii: S0731-7085(26)00397-3. [Epub ahead of print]283 117729

Targeted quantification of 25 tryptophan metabolites across three metabolic pathways in serum by ultra-high-performance liquid chromatography coupled with quadrupole orbitrap high-resolution mass spectrometry.

Min Guo, Zhihua Zhang, Liping Cao, Shunhe Wang, Yue Xiao, Linlin Wang, Jianxin Zhao.

  Tryptophan metabolism encompasses three interconnected pathways, namely the kynurenine, serotonin, and gut microbiota-mediated indole pathways, which contribute to immune regulation, neural signalling, and host-microbiota interactions. Quantitative analysis of metabolites spanning these pathways remains challenging because of their diverse physicochemical properties, broad concentration ranges, and matrix interference in biological samples. In this study, a targeted analytical method based on ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry (UPLC-Q-Orbitrap HRMS) was developed for the quantification of 25 representative tryptophan metabolites in serum. Chromatographic and mass spectrometric conditions were systematically optimized, and the target metabolites were analysed using parallel reaction monitoring. All analytes showed linear responses within their respective calibration ranges, with coefficients of determination (R²) of at least 0.9990. The limits of detection and quantification ranged from 0.1 to 300 ng/mL and from 0.3 to 500 ng/mL, respectively. Intra-day and inter-day precision ranged from 0.2% to 6.9% and from 0.4% to 14.0%, respectively, while accuracy ranged from 85.2% to 117.5%. Extraction recoveries ranged from 80.6% to 108.2%, and matrix-effect values ranged from 72.9% to 119.9%, indicating analyte-dependent ion suppression or enhancement. The method was applied to serum samples from humans, rats, specific-pathogen-free mice, and germ-free mice. Comparative profiling revealed exploratory differences in the relative distributions of tryptophan metabolites among the four serum sources. Overall, the method provides broad analytical coverage of the three major tryptophan metabolic pathways and a useful approach for fit-for-purpose quantification and exploratory comparison of serum tryptophan metabolites.

Keywords:  Parallel Reaction Monitoring; Serum; Simultaneous quantitative analysis; Tryptophan metabolites

DOI:  https://doi.org/10.1016/j.jpba.2026.117729
Nanoscale. 2026 Oct 06.

Nanomatrix engineering for LDI-MS based biomedical metabolomics.

Yi Gu, Yanxi Yang, Jiao Wu, Kun Qian.

Metabolomics provides functional molecular readouts of disease phenotypes, therapeutic responses, and tissue microenvironments, but its extension to low-volume, single-cell, and spatially resolved samples remains analytically challenging. Laser desorption/ionization mass spectrometry (LDI-MS) offers a rapid, direct, and high-throughput route for small-molecule analysis. However, its performance in biomedical metabolomics is largely governed by the matrix-analyte interface. Nanomaterials have emerged as attractive LDI-MS matrices because of their low background in the low-mass region, large surface area, tunable photothermal and electronic properties, and designable surface chemistry. In this review, we summarize recent advances in nanomatrix-assisted LDI-MS for biomedical metabolomics, emphasizing design principles. We discuss three key design dimensions of nanomatrix and interface engineering: structural engineering for energy absorption and desorption efficiency, surface functionalization for metabolite enrichment and low-interference ionization, and interface engineering for reproducibility, throughput, and low-volume sample compatibility. Representative biomedical applications are highlighted, including biofluid metabolic fingerprinting, extracellular vesicle and single-cell metabolomics, and spatial metabolic imaging. Finally, we discuss current challenges in matrix reproducibility, metabolite coverage, workflow standardization, and clinical validation, and outline future opportunities for integrated nanomatrix platforms in multiscale biomedical metabolomics.

DOI: https://doi.org/10.1039/d6nr02874h
Front Cell Dev Biol. 2026 ;14 1914317

Spatial proteomics: technical evolution and applications in cancer research.

Xiaoxiao Tian, Di Huang, Jiaxiang Hu.

  Spatial proteomics is reshaping tissue-based cancer research by linking protein expression to cellular identity, tissue architecture and clinical behavior. Conventional proteomics has provided powerful tools for protein identification and quantification, but most bulk workflows erase the spatial relationships that define tumor ecology. In contrast, spatial proteomics combines multiplexed imaging, mass spectrometry-based tissue analysis, region-selected profiling and computational spatial statistics to measure proteins in their native tissue context. This review summarizes the development of proteomics from early separation and mass spectrometry technologies to current spatial platforms. It then focuses on cancer applications, including tumor heterogeneity, tumor microenvironment, antitumor immunity, immunotherapy response, spatial biomarkers and treatment resistance. Across these areas, spatial proteomics is most valuable when it converts descriptive tissue patterns into testable hypotheses about cell states, neighborhood interactions and therapeutic vulnerabilities. The field now needs standardized workflows, clinically anchored cohorts and functional validation to move from high-dimensional tissue maps to robust translational tools.

Keywords:  cancer; mass spectrometry imaging; spatial biomarkers; spatial proteomics; tumor microenvironment

DOI:  https://doi.org/10.3389/fcell.2026.1914317