bims-mascan 2025-12-07 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–12–07
twenty papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Evaluating the Robustness of Micro-Pillar Array Columns for Quantitative Proteomics Applications.
Opt-TMT: An optimized and scaled-down TMT labeling strategy for limited sample.
Untargeted Metabolomics for Diagnosis, Monitoring, and Understanding the Pathophysiology of Inherited Metabolic Disorders.
Metabolic Profiling of the EmDia Cohort by LC-MS Reveals Empagliflozin-Intake Associated Regulation of 1,5-anhydroglucitol and Urate.
High-Throughput Label-free Single-Cell Proteomics Enabled by Multicolumn NanoLC with a 5-min Cycle Time.
Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics.
Evaluating and Optimizing Mass Spectrometry Proteomics Data to Deconvolve Cell-Type-Specific Protein Expression in Tumors.
Integrating Ex Vivo Isotope Labeling with Mass Spectrometry Imaging to Spatially Trace Heterogeneous Metabolic Activity in Rat Kidney Tissue.
Analytical Considerations for the Development of Plate-Based Proteomics Platforms Using Isobaric Labeling.
Automated metabolite formula ranking using formula subset analysis for LC-MS/MS-based metabolomics.
CIeaD: A Complementary CID and EAD Mass Spectral Library for Phytochemicals.
Multiple Spectrum Alignment for Molecular Networking Exploration and Discovery.
Metabolic flux and resource balance in the oleaginous yeast Rhodotorula toruloides.
A comprehensive metabolomic dataset of human synovial fluid in osteoarthritis and healthy controls.
Digital twins for in vivo metabolic flux estimations in patients with brain cancer.
Recurrent Breast Cancer Cells Depend on De novo Pyrimidine Biosynthesis to Suppress Ferroptosis.
High-Resolution Ion Mobility as an Alternative to Quadrupole-Based Precursor Isolation for Reducing Chimeric Fragmentation Spectra in Bottom-Up Proteomics.
Cultivar to chemotype: characterizing complex botanicals with mass spectrometry metabolomics.
ProteoformDB: A Built-In Application to Generate Proteoform Database.
Charting the Undiscovered Metabolome with Synthetic Multiplexing.

J Proteome Res. 2025 Dec 04.

Evaluating the Robustness of Micro-Pillar Array Columns for Quantitative Proteomics Applications.

Christina B Schroeter, Ting-Yu Wei, Joao A Paulo.

  Nanoflow liquid chromatography capable of delivering consistent and reliable results across extensive sample sets is essential for the advancement of mass spectrometry-based proteomics. Micro-Pillar Array Columns (μPACs) represent a significant breakthrough, offering durability and performance stability. Here, we evaluate the robustness of μPACs by comparing a column used continuously for over 16 months with more than 7000 injections (μPAC1) to a nearly new column (μPAC2). Analysis of two TMT-labeled yeast TKO standards (TKOpro10u, a 10-plex unit-resolved standard; and TKOpro12, a 12-plex isotopolog-inclusive standard) showed that μPAC1 and μPAC2 yielded comparable numbers of unique peptides and proteins, exhibited similar reproducibility, and delivered equivalent chromatographic and spectral quality. Notably, these data showed that μPAC1 maintained high performance with minimal degradation of data quality, highlighting the exceptional durability of the μPAC technology. These findings underscore that μPAC can contribute to reducing workflow disruptions in high-throughput analytical workflows, particularly in proteomics workflows that utilize mass spectrometry.

Keywords:  LC-MS/MS; TMTpro; chromatography; column durability; μPAC

DOI:  https://doi.org/10.1021/acs.jproteome.5c00892
J Proteomics. 2025 Nov 27. pii: S1874-3919(25)00203-9. [Epub ahead of print]324 105576

Opt-TMT: An optimized and scaled-down TMT labeling strategy for limited sample.

Michele Martins, Fábio César Sousa Nogueira, Magno Junqueira, Guillaume Nugue.

  Mass spectrometry-based proteomics has evolved and currently requires minimal sample quantities. However, manufacturers' isobaric labeling protocols, such as Tandem Mass Tag (TMT), are still designed for larger sample amounts, leading to significant costs and limiting research possibilities. Here, we present an optimized isobaric labeling protocol (Opt-TMT) that maintains high labeling efficiency while substantially reducing reagent consumption and sample requirements. We achieved consistent labeling efficiency even with peptide quantities as low as 6-15 μg per channel by adjusting reaction conditions, including volume reduction and increased peptide concentration. Importantly, our protocol reduces reagent costs by up to 90 % compared to the commercial protocol, while maintaining labeling efficiency above 99 %. This cost-effective approach addresses key challenges in proteomic research, especially for studies involving limited biological material or post-translational modification analyses. The Opt-TMT method provides a practical solution for researchers seeking to maximize the utility of isobaric labeling while minimizing resource expenditure, without compromising analytical quality. SIGNIFICANCE: The Opt-TMT protocol provides a major advancement for proteomics by making isobaric labeling both cost-effective and scalable to very limited sample amounts. By reducing reagent costs up to 90 % without compromising labeling efficiency (>99 %), this approach enables high-quality quantitative proteomics for studies where material is scarce, such as patient-derived samples, biopsies, or post-translational modification analyses. Importantly, Opt-TMT broadens access to TMT-based workflows for laboratories with limited resources, while preserving analytical robustness. This work contributes a practical and impactful methodological improvement that directly benefits both fundamental and applied proteomics research.

Keywords:  Isobaric labeling; Labeling efficiency; Quantitative proteomics; TMT

DOI:  https://doi.org/10.1016/j.jprot.2025.105576
J Inherit Metab Dis. 2026 Jan;49(1): e70120

Untargeted Metabolomics for Diagnosis, Monitoring, and Understanding the Pathophysiology of Inherited Metabolic Disorders.

Jonathan Martens, Udo F H Engelke, Ron A Wevers, Dirk J Lefeber, Purva Kulkarni.

  Inherited metabolic disorders (IMDs) encompass a diverse and expanding group of rare diseases caused by genetic disruptions mainly in metabolic enzymes and transporters. Clinical diagnosis of IMDs presents significant challenges due to phenotypic heterogeneity, nonspecific symptoms, and the limited scope of current targeted biochemical assays typically available. Recent advances in mass spectrometry-based untargeted metabolomics offer promising solutions to several of these challenges by simultaneous detection and relative quantification of thousands of metabolites, not relying on any prior hypotheses. With the expansion of genetic diagnostics via whole-exome and whole-genome sequencing, metabolic insights are often crucial for understanding the pathogenicity of genetic variants of unknown significance, often enabling a clear diagnosis for patients. This review details current applications of untargeted metabolomics in IMDs, including biomarker discovery and elucidation of previously unknown pathophysiological mechanisms. Successful examples of biomarker identification in well-studied IMDs, such as pyridoxine-dependent epilepsy and phenylketonuria, are highlighted to provide novel disease insights. Additionally, we address technical and interpretation challenges inherent to this methodology, particularly concerning metabolite identification, high-dimensional data complexity, and limited patient numbers. Emerging analytical technologies and data analysis approaches are highlighted that are poised to mitigate these challenges in the upcoming years. Finally, we provide an outlook on future directions, emphasizing the complementary roles of targeted and untargeted metabolomics and the prospects for the identification of new therapeutic targets as well as therapy monitoring for the clinical management of IMDs.

Keywords:  biomarkers; dark matter of the metabolome; de‐VUSing; diagnostics; inborn errors of metabolism; mass spectrometry; metabolic disease discovery; untargeted metabolomics

DOI:  https://doi.org/10.1002/jimd.70120
Proteomics. 2025 Dec 03. e70075

Metabolic Profiling of the EmDia Cohort by LC-MS Reveals Empagliflozin-Intake Associated Regulation of 1,5-anhydroglucitol and Urate.

Fabian Schmitt, Vincent Ten Cate, Zlatka Fischer, Mathias Hagen, Barbara A Steigenberger, Stefan Tenzer, Philipp S Wild, Thierry Schmidlin.

  The EmDia trial, designed to study the effects of the sodium glucose cotransporter-2 (SGLT2) inhibitor empagliflozin on cardiovascular comorbidities in type 2 diabetes mellitus (T2DM) patients, has been investigated for short-term metabolic alterations by a limited set of clinical assays. To expand on this data, we report on the development of a liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach employing an optimized metabolite separation by pentafluorophenyl chromatography. High-confidence metabolite annotation based on reference standards allows for fast and robust metabolic characterization of large plasma cohorts due to scalability. Applied to EmDia, we show the high predictive power of our methodology for several clinical parameters, including a near-perfect prediction of fasting blood glucose (R2 = 0.97), and demonstrate how empagliflozin leads to reduced plasma levels of deoxyhexoses, such as 1,5-anhydroglucitol, a short-term biomarker for glycemic control. SUMMARY: Clinical metabolomics studies continue to gain interest due to their comprehensive metabolite coverage, offering insights into metabolic alterations in health and disease. In this study, we present a robust data-independent acquisition liquid chromatography-mass spectrometry-based metabolomics workflow employing an optimized metabolite separation by pentafluorophenyl chromatography that showcases a comprehensive coverage of plasma metabolites. Applied to characterize plasma metabolite profiles in samples of EmDia, a placebo controlled study investigating the effect of the SGLT2 inhibitor empagliflozin, we assess the predictive power of metabolite signals for clinical parameters describing organ physiologies and pathophysiologies. Descriptive statistics are applied to the metabolite profiles to identify empagliflozin intake-associated metabolite markers.

Keywords:  SGLT2 inhibitor; data‐independent acquisition; human plasma; metabolomics; type 2 diabetes mellitus

DOI:  https://doi.org/10.1002/pmic.70075
Res Sq. 2025 Nov 18. pii: rs.3.rs-8042176. [Epub ahead of print]

High-Throughput Label-free Single-Cell Proteomics Enabled by Multicolumn NanoLC with a 5-min Cycle Time.

Ryan Kelly, Chao Wang, Hsien-Jung Lin, Siqi Huang, Garrett Haynie, Kenneth Triggs, YenJou Chang, David Hansen, Thy Truong, Xiaofeng Xie.

Mass spectrometry (MS)-based single-cell proteomics (SCP) enables proteome-wide analysis at single-cell resolution, offering insights into cellular heterogeneity, biological processes, and disease mechanisms. However, conventional nanoLC-MS workflows are constrained by long gradients and low throughput, limiting their application to large cohorts. Here, we present a multicolumn nanoLC-MS platform that achieves 5-minute separation windows with 100% duty cycles at ~ 100 nL/min, enabling the analysis of up to 288 single cells per day with minimal additional hardware. The system provides stable peptide separation, negligible carryover, and robust retention-time reproducibility across 2,000 consecutive injections. Over the course of this study, we successfully analyzed more than 4,000 samples at nearly 288 samples per day (SPD) throughput. The platform identified ~ 4,400 proteins per 250 pg digest injection and an average of ~ 3,200 proteins per single HeLa cell with maxima exceeding 4,300, which is comparable to state-of-the-art longer-gradient workflows. Quantitative benchmarking with mixed-species standards confirmed accurate measurements across ~ 6,000 proteins. The workflow distinguished proteome profiles across hundreds of single cells, and profiling of RAW264.7 macrophages revealed LPS-induced markers and macrophage activation pathways. Together, these results establish a robust and scalable platform for high-throughput SCP, demonstrating the feasibility of thousands of single-cell analyses within a single study while maintaining deep proteome coverage and biological interpretability.

DOI: https://doi.org/10.21203/rs.3.rs-8042176/v1
EMBO Mol Med. 2025 Dec 05.

Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics.

Huanhuan Gao, Yuecheng Zhan, Yuanqi Liu, Zhiyi Zhu, Yuxiu Zheng, Liqin Qian, Zhangzhi Xue, Honghan Cheng, Zongxiang Nie, Weigang Ge, Senlin Ruan, Jiaxu Liu, Jikai Zhang, Yingying Sun, Lei Zhou, Dongyue Xun, Yingrui Wang, Heyun Xu, Huiwen Miao, Yi Zhu, Tiannan Guo.

  Circulating blood proteomics enables minimally invasive biomarker discovery. Nanoparticle-based circulating plasma proteomics studies have reported varying number of proteins (ca 2000-7000), but it remains unclear whether a higher protein number is more informative. Here, we first develop OmniProt-a silica-nanoparticle workflow optimized through a systematic evaluation of nanoparticle types and protein corona formation parameters. Next, we present an Astral spectral library for 10,109 protein groups. Using the Astral with 60 sample-per-day throughput, OmniProt identifies ca 3000 to 6000 protein groups from human plasma. Platelet/erythrocyte/coagulation-related contamination artificially inflates protein identifications and compromises quantification accuracy in nanoparticle-enriched samples. Through controlled contamination experiments, we identified biomarkers for platelet/erythrocyte/coagulation-related contamination in nanoparticle-based plasma proteomics. We developed open-access software Baize for contamination assessment. We validated the pipeline in 193 patients with CT-indistinct benign nodules or early-stage lung cancers, flagging five contaminated samples. This study reveals that contamination alters protein identification/quantification in nanoparticle-based plasma proteomics and presents Baize software to evaluate it.

Keywords:  Blood Proteomics; Mass Spectrometry; Nanoparticle; Platelet/Erythrocyte Contamination; Protein Corona

DOI:  https://doi.org/10.1038/s44321-025-00346-9
J Proteome Res. 2025 Dec 02.

Evaluating and Optimizing Mass Spectrometry Proteomics Data to Deconvolve Cell-Type-Specific Protein Expression in Tumors.

Yingnan Song, Qi Zhou, Chen Huang.

  Understanding intratumoral heterogeneity is essential for elucidating tumor biology. Compared to RNA expression, omics-level characterization of cell-type-specific protein expression remains a technical challenge. Bulk mass spectrometry (MS) provides abundant proteomics resources to infer cell-type specificity via data deconvolution; however, it is unclear which proteomic quantification formats are optimal, as they differ from the data types for which most deconvolution methods were designed. Here, leveraging recently generated large-cohort proteogenomics data, we systematically evaluated different MS proteomics quantification formats and preprocessing strategies to resolve cell-type-specific protein expression. Our results indicate that while label-free spectral counts can be used directly, TMT MS1 intensities and MS2 ratios are less suitable and require appropriate data transformation. We demonstrate that a 'min-score' transformation significantly improves MS1 intensity-based deconvolution, providing useful insights for subtyping pancreatic cancer. Moreover, we identified the coefficient of variation (CV) as a robust statistical indicator of deconvolution suitability. Finally, we developed "ProTransDeconv", an R package integrating data transformation, deconvolution, and quality checks for major MS proteomics data formats. This work provides practical guidance for deconvolving bulk proteomics to study cell-type-specific protein-level dysregulation.

Keywords:  cancer protein markers; deconvolution; intratumor heterogeneity; proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.5c00980
Anal Chem. 2025 Dec 02.

Integrating Ex Vivo Isotope Labeling with Mass Spectrometry Imaging to Spatially Trace Heterogeneous Metabolic Activity in Rat Kidney Tissue.

Xinyi Tu, Xin Li, Ting Li, Xinzhu Li, Shiyu Zhu, Qingce Zang, Hongxian Zhang, Ruiping Zhang.

Spatially resolved isotope tracing provides a powerful means for achieving deeper and more accurate characterization of metabolic activities in biological tissues. However, its application in mammals remains limited by several key technical challenges, including complex surgical procedures, the need for high tracer doses, and poor suitability by clinical tissue samples. In this study, we developed a novel spatially resolved isotope tracing method by integrating ex vivo tissue labeling with airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI). Using liquid chromatography-mass spectrometry, we identified 263 labeled metabolites in ex vivo U-13C glucose-labeled rat kidney tissue, including amino acids, nucleotides, organic acids, and lipids. Moreover, AFADESI-MSI enabled the in situ characterization of 27 labeled metabolites. This method allowed the simultaneous visualization of complex metabolic networks and their spatially dynamic activities without the need for in vivo tracer injection. Our results revealed that the kidney cortex exhibited significantly higher glucose uptake efficiency and gluconeogenic activity compared with the medulla, whereas the medulla demonstrated greater activity in reducing pyruvate to lactate. We anticipate that this method will provide new strategies and analytical tools for investigating metabolic heterogeneity in clinical tumors and other complex tissues.

DOI: https://doi.org/10.1021/acs.analchem.5c03723
Anal Chem. 2025 Dec 03.

Analytical Considerations for the Development of Plate-Based Proteomics Platforms Using Isobaric Labeling.

Jose Navarrete-Perea, Shideh Mirhadi, Jiaming Li, Dylan Charles Mitchell, Shubhlakhan Dhillon, Ryan Hastings Gerkman, Smaranda Bodea, An Chi.

Mass spectrometer-based proteomics platforms have great potential to rapidly advance our systematic understanding of complex biological problems, enable drug discovery, decipher drug mechanisms of action, and discover novel biomarkers. As the demand for processing large sets of samples in an automatic manner is constantly increasing, the integration of automation platforms (nanoliter dispensers, liquid handlers, etc.) has become a routinary configuration paired with liquid chromatography-mass spectrometers. The functional integration of all of those instruments into a single unit is what we call a plate-based high-throughput proteomics platform (HT proteomics). The readout of the platform is the quantitative proteome data at the protein or peptide level. In this work, we developed a plate-based HT proteomics standard that we called the HT-sKO. The HT-sKO allows the evaluation of accuracy and the estimation of the relative limit of quantification when the target proteins vary up to 60-fold in abundance. The HT-sKO utilizes nonhuman recombinant proteins that can be spiked into the samples, allowing for sample acquisition and HT proteomics platform evaluation at the same time. We also showed the foundational role of a robust acquisition strategy for developing a stable HT proteomics platform and the value of using a tube-based method as an informant assay on data quality expectations for the platform. Using this new standard, we demonstrated that the intra- and inter-plate variance is around 4-6% for the protein level or around 10% for the peptide-level readout. We also showed that the HT-sKO standard is compatible with whole-proteome, phospho-proteome, and reactive cysteine profiling platforms.

DOI: https://doi.org/10.1021/acs.analchem.5c04756
Metabolomics. 2025 Dec 01. 22(1): 7

Automated metabolite formula ranking using formula subset analysis for LC-MS/MS-based metabolomics.

Ke-Shiuan Lynn.

   INTRODUCTION: Metabolite identification remains a bottleneck in untargeted liquid chromatography-tandem mass spectrometry (LC-MS) metabolomics studies, particularly when the underlying metabolite is absent in the tandem mass spectrometry (MS/MS) databases.
OBJECTIVE: A new approach, formula subset analysis (FSA), was developed to effectively prescreen and rank the chemical formula candidates for an MS/MS spectrum.
METHODS: This approach first computes mother-daughter relationships (MDRs) among possible formulas of fragments and the precursor under a given mass tolerance and then determines the characteristic fragments (CFs) that only present one MDR with the precursor and other fragments. Subsequently, the precursor formula candidates are ranked by the scores derived from the number of MDRs.
RESULTS: A numerical study using eight large datasets totaling 30,690 MS/MS spectra from 6792 metabolites consisting of C, H, O, N, S, and P showed that FSA ranked the correct chemical formula as the top-1 candidate for a metabolite in 85.28% of the cases and in the top-5 candidates in 97.35% of the cases. The average processing time for each spectrum was 0.024 s. Moreover, FSA does not require training data, not rely on MS/MS databases, can be applied to a wide mass range, and can be quickly expanded with more chemical elements and formulas to identify different chemical species.
CONCLUSIONS: FSA has not utilized structural information yet and therefore its accuracy may not be competitive with some of the state-of-the-art identification tools. However, its advantages in speed, expandability, and applicability, make it suitable for prescreening candidates in untargeted LC-MS metabolomics studies.

Keywords:  Formula ranking; LC-MS/MS; Metabolomics; Mother-daughter relationship

DOI:  https://doi.org/10.1007/s11306-025-02379-0
J Am Soc Mass Spectrom. 2025 Dec 01.

CIeaD: A Complementary CID and EAD Mass Spectral Library for Phytochemicals.

Yatendra Singh, Paul C Norris, Sibesh Maharjan, Jase Gillespie, Caroline Ferrante, Zeyad Ibrahim, Sixue Chen.

  Plant metabolomics faces major challenges in metabolite identification due to the structural diversity of plant metabolites and limited coverage in existing spectral libraries. To address this, we developed CIeaD (Collision-Induced and Electron-Activated Dissociation), an open-access plant metabolite spectral library containing complementary CID and EAD spectra. The library includes curated high-resolution spectra for 2,305 phytochemicals across major metabolite classes, acquired in both positive and negative modes with a dual fragmentation mechanism to capture a wide range of diagnostic ions. CIeaD library is provided in multiple formats and can be accessed at https://www.moleculardetective.org/Links.html.

Keywords:  collision-induced dissociation; electron-activated dissociation; mass spectrometry; metabolomics; phytochemicals

DOI:  https://doi.org/10.1021/jasms.5c00329
J Am Soc Mass Spectrom. 2025 Dec 02.

Multiple Spectrum Alignment for Molecular Networking Exploration and Discovery.

Amy Lau, Xianghu Wang, Tao Xu, Tilman Schramm, Yasin El Abiead, Daniel Petras, Vanessa V Phelan, Mingxun Wang.

Molecular networking is a computational mass spectrometry technique used to visualize and connect tandem mass spectra from putatively related molecules to reveal structural relationships. Despite their utility, existing tools for interpreting molecular networks are limited in the ability to easily organize fragmentation patterns within molecular families. We developed an interactive web-based tool, the Multiple Mass Spectral Alignment (MMSA) approach, that enhances the visualization of molecular networks by displaying detailed spectral alignment information among all the spectra in a network component in one visualization. MMSA identifies sets of consensus peaks that contribute to the alignment of multiple tandem mass spectra, offering insights into how structural moieties captured by specific fragments influence the construction of molecular networks. We demonstrate that MMSA facilitates insightful understanding of molecular networks and improves the interpretability of the tandem mass spectra, capturing the chemical modifications or core structures within a molecular family. We envision that the MMSA tool will significantly enhance the ability to interpret molecular networks, with implications for more rapid identification and prioritization of new metabolites for full characterization.

DOI: https://doi.org/10.1021/jasms.5c00237
Metab Eng. 2025 Dec 01. pii: S1096-7176(25)00185-5. [Epub ahead of print]

Metabolic flux and resource balance in the oleaginous yeast Rhodotorula toruloides.

Eric J Mooney, Patrick F Suthers, Wheaton L Schroeder, Hoang V Dinh, Xi Li, Yihui Shen, Tianxia Xiao, Catherine M Call, Heide Baron, Arjuna M Subramanian, Daniel R Weilandt, Felix C Keber, Martin Wühr, Joshua D Rabinowitz, Costas D Maranas.

The yeast Rhodotorula toruloides is a promising bioproduction organism due to its high lipid yields and ability to grow on cheap and abundant substrates. Quantitative, systems-level assessment of its metabolic activity is accordingly merited. Resource-balance analysis (RBA) models capture not only reaction stoichiometry but also enzyme requirements for catalysis, providing valuable tools for understanding metabolic trade-offs and optimizing metabolic engineering strategies. Here, we present systems-level measurements of R. toruloides metabolic flux based on isotope tracing and metabolic flux analysis. In combination with new proteomic measurements, these flux data are used to parameterize a genome-scale resource balance model rtRBA. We find that S. cerevisiae and R. toruloides grow at nearly indistinguishable rates using similar biosynthetic but dramatically different central metabolic programs. R. toruloides consumes one-fifth as much glucose, which it metabolizes primarily via the pentose phosphate pathway and TCA cycle unlike primarily glycolysis in S. cerevisiae. Overall, across these two divergent yeasts, protein abundances aligned more closely than metabolic flux. Resource balance modeling of these metabolic programs predicts superior theoretical yields but lower productivities in R. toruloides than S. cerevisiae for industrial chemicals, highlighting the value of rapid glucose uptake for productivity but respiratory metabolism for yields.

DOI: https://doi.org/10.1016/j.ymben.2025.11.012
Data Brief. 2025 Dec;63 112224

A comprehensive metabolomic dataset of human synovial fluid in osteoarthritis and healthy controls.

Ping Chen, Shuai Zhang, Long Wu, Zhiyao Yong, Qunhua Jin, Kening Sun.

  Osteoarthritis (OA) is a prevalent degenerative joint disorder that causes chronic pain and disability. The synovial fluid (SF), which bathes the joint, reflects the pathological state and is a valuable source for biomarker discovery. However, a comprehensive metabolomic profile of SF in OA remains to be fully established. Here, we present a comprehensive metabolomic dataset derived from SF samples of 10 OA patients and 10 matched healthy controls. Using non-targeted metabolomics based on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS), we identified 23,232 features, with 1431 metabolites annotated. Comparative analysis revealed 163 significantly differential metabolites (118 upregulated, 45 downregulated). Functional enrichment and pathway analyses highlighted disruptions in amino acid metabolism, energy metabolism, and lipid pathways. Several metabolites showed high diagnostic potential in ROC analysis. This dataset provides a valuable resource for understanding OA-associated metabolic alterations and offers robust candidate biomarkers for OA diagnosis and therapeutic monitoring.

Keywords:  Biomarkers; Osteoarthritis; Synovial fluid; UHPLC-MS; Untargeted metabolomics

DOI:  https://doi.org/10.1016/j.dib.2025.112224
Cell Metab. 2025 Dec 01. pii: S1550-4131(25)00482-6. [Epub ahead of print]

Digital twins for in vivo metabolic flux estimations in patients with brain cancer.

Baharan Meghdadi, Wajd N Al-Holou, Andrew J Scott, Anjali Mittal, Ningning Liang, Palavalasa Sravya, Abhinav Achreja, Alexandra O'Brien, Kathy Do, Zhe Wu, Jiane Feng, Nathan R Qi, Vijay Tarnal, Sriram Venneti, C Ryan Miller, Jann N Sarkaria, Weihua Zhou, Theodore S Lawrence, Costas A Lyssiotis, Daniel R Wahl, Deepak Nagrath.

  Recent advancements in metabolic flux estimations in vivo are limited to preclinical models, primarily due to challenges in tissue sampling, tumor microenvironment (TME) heterogeneity, and non-steady-state conditions. To address these limitations and enable flux estimation in human patients, we developed two machine learning-based frameworks. First, the digital twin framework (DTF) integrates first-principles stoichiometric and isotopic simulations with convolutional neural networks to estimate fluxes in patient bulk samples. Second, the single-cell metabolic flux analysis (13C-scMFA) framework combines patient single-cell RNA sequencing (scRNA-seq) data with 13C-isotope tracing, allowing single-cell-level flux quantification. These studies allow quantification of metabolic activity in neoplastic glioma cells, revealing frequently elevated purine synthesis and serine uptake, compared with non-malignant cells. Our models also identify metabolic heterogeneity among patients and mice with brain cancer, in turn predicting treatment responses to metabolic inhibitors. Our frameworks advance in vivo metabolic flux analysis, may lead to novel metabolic therapies, and identify biomarkers for metabolism-directed therapies in patients.

Keywords:  (13)C-single-cell metabolic flux analysis; cancer metabolism; glioblastoma; in vivo isotope tracing; in vivo metabolism; machine learning; purine metabolism; scRNA-seq; serine metabolism; tumor microenvironment

DOI:  https://doi.org/10.1016/j.cmet.2025.10.022
bioRxiv. 2025 Nov 17. pii: 2025.11.17.688927. [Epub ahead of print]

Recurrent Breast Cancer Cells Depend on De novo Pyrimidine Biosynthesis to Suppress Ferroptosis.

Kelley R McCutcheon, Josh Wu, Yasemin Ceyhan Ozdemir, Brock J McKinney, Sharan Srinivasan, Ayaha Itokawa, Oliver J Newsom, Anna Vigil, Douglas B Fox, Lucas B Sullivan, James V Alvarez.

Breast cancer recurrence remains a major clinical challenge, often associated with therapy resistance and altered metabolic states. To define metabolic vulnerabilities of recurrent disease, we performed a CRISPR knockout screen targeting 421 metabolic genes in paired primary and recurrent HER2-driven breast cancer cell lines. While both primary and recurrent tumors shared dependencies on core metabolic pathways, recurrent tumors exhibited selective essentiality for the de novo pyrimidine synthesis pathway, including Cad , Dhodh , and Ctps . Pharmacologic inhibition of the rate-limiting enzyme DHODH with BAY-2402234 selectively impaired the growth of recurrent tumor cells, while primary tumor cells were relatively resistant. BAY treatment robustly inhibited pyrimidine synthesis in all lines, but only recurrent cells underwent iron-dependent lipid peroxidation and ferroptotic cell death. Lipidomic profiling revealed enrichment of polyunsaturated ether phospholipids in recurrent cells, which may predispose them to ferroptosis. A sensitizer CRISPR screen in primary cells further identified nucleotide salvage and lipid metabolic pathways as modifiers of DHODH inhibitor sensitivity. Stable isotope tracing and nutrient depletion experiments showed that primary cells can compensate for DHODH inhibition through nucleotide salvage, whereas recurrent cells exhibit impaired salvage capacity, likely due to reduced expression of Slc28 / Slc29 nucleoside transporters. Together, these findings reveal that breast cancer recurrence is associated with increased dependence on de novo pyrimidine synthesis to suppress ferroptosis, highlighting a therapeutically actionable metabolic vulnerability in recurrent disease.

DOI: https://doi.org/10.1101/2025.11.17.688927
Proteomics. 2025 Dec 05. e70084

High-Resolution Ion Mobility as an Alternative to Quadrupole-Based Precursor Isolation for Reducing Chimeric Fragmentation Spectra in Bottom-Up Proteomics.

Isabel R Uribe, Liulin Deng, Leonard Rorrer, Lauren Royer, Miriam Fico, Daniel DeBord.

To assess the potential for high resolution ion mobility (HRIM) as an alternative means of precursor isolation for mass spectrometry fragmentation analysis we performed a meta-analysis of predicted tryptic peptide features from the human proteome to measure the rate of chimeric spectrum generation relative to traditional quadrupole-based isolation. Results indicate that for proteomic mixtures, HRIM separation with a peak capacity of 100 produces chimeric spectra at a rate commensurate with a ∼5 Th quadrupole isolation window, while providing the additional benefit of generating non-chimeric spectra for many isobaric and isomeric peptides unresolvable with a quadrupole filter. This behavior was verified experimentally using a HRIM-QTOF mass spectrometry system. The ability to combine HRIM and MS isolation resulted in >10× increase in precursor isolation specificity as compared to either of the techniques independently.

DOI: https://doi.org/10.1002/pmic.70084
Nat Prod Rep. 2025 Dec 03.

Cultivar to chemotype: characterizing complex botanicals with mass spectrometry metabolomics.

Joshua J Kellogg, R Teal Jordan, Madhusha M Ranaweera, Kelsey Custer, Savannah G Anez, Julia Bendlin, Francisco T Chacon, Xiaoling Chen.

Covering up to 2025Plant products, including botanical dietary supplements, nutraceuticals, and herbal medicines, remain central to supporting human health and wellness. Their usage has been steadily increasing over the last few decades, which has also led to raised concerns about proper identification and characterization of plant materials. This information is crucial to evaluate the safety and efficacy of these botanical products and prevent misidentification or adulteration. While there are multiple analytical approaches to characterize botanicals, this review provides insight into how untargeted mass spectrometry metabolomics can profile these commonly complex mixtures and provide detailed datasets that are capable of taxonomically classifying samples, detecting adulteration, and providing insight into variation between plant materials and their nutritional, medicinal, or toxicological effects. We describe data analysis approaches for untargeted metabolomics, case studies on the various applications of this method for characterizing botanicals, and challenges that the growing field of mass spectrometry-based metabolomics is facing. The chosen topics reflect the current state of metabolomics analyses for complex systems with a look to the future of how to conceptualize botanical characterization.

DOI: https://doi.org/10.1039/d5np00040h
J Proteome Res. 2025 Dec 03.

ProteoformDB: A Built-In Application to Generate Proteoform Database.

Trung Hoàng, Yingwei Hu, Hui Zhang.

  Proteins play essential functions through their complex regulations on cell-type-specific expression, localization, and molecular complexes. Protein complexity is further enhanced by proteoforms, which are the diverse molecular forms that each gene can produce through genomic alterations, transcriptional variations, translational regulations, and protein modifications. Profiling of proteoforms is a promising method for gaining a deeper understanding of the role of proteins in biological pathways and disease mechanisms. Here, we developed ProteoformDB, an application tool for generating proteoform databases, and we cataloged a total of over one million unique single-site human proteoforms. We showed that ProteoformDB can serve as a valuable resource to document the experimentally identified proteoforms in a database, supporting protein characterization in quantitative proteomics for both total protein abundances and modified protein forms.

Keywords:  MS database search strategies; combinatorial PTMs; joint quantification; mass spectrometry-based proteomics; multisite proteoforms; proteoform annotation; proteoform biology; proteoform characterization; proteoformDB; quantitative proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.5c00461
bioRxiv. 2025 Nov 19. pii: 2025.11.18.689170. [Epub ahead of print]

Charting the Undiscovered Metabolome with Synthetic Multiplexing.

Abubaker Patan, Shipei Xing, Vincent Charron-Lamoureux, Zhewen Hu, Victoria Deleray, Julius Agongo, Yasin El Abiead, Helena Mannochio-Russo, Ipsita Mohanty, Harsha Gouda, Jasmine Zemlin, Prajit Rajkumar, Carlynda Lee, Daniel Leanos, Noah Weimann, Wataru Tsuda, Sadie Giddings, Tammy Bui, Kine Eide Kvitne, Haoqi Nina Zhao, Simone Zuffa, Vivian Nguyen, Aileen Andrade, Wilhan Donizete Gonçalves Nunes, Andrés M Caraballo-Rodríguez, Lurian Caetano David, Jeremy Carver, Nuno Bandeira, Mingxun Wang, Lindsey A Burnett, Dionicio Siegel, Pieter C Dorrestein.

In untargeted metabolomics, reference MS/MS libraries are essential for structural annotation, yet currently explain only 6.9% of the more than 1.7 billion MS/MS spectra in public repositories. We hypothesized that many unannotated features arise from simple, biologically plausible transformations of endogenous and exposure-derived compounds. To test this, we created a reference resource by synthesizing over 100,000 compounds using multiplexed reactions that mimic such biochemical transformations. 91% of the compounds synthesized are absent from existing structural databases. Through improvements in the construction of the computational infrastructure that enables pan repository-scale MS/MS comparisons, searching this biologically inspired MS/MS library increased the overall reference-based match rate by 17.4%, yielding over 60 million new matches and raising the global pan-repository MS/MS annotation rate to 8.1%. By facilitating structural hypotheses for previously uncharacterized MS/MS data, this framework expands the accessible detectable biochemical landscape across human, animal, plant, and microbial systems, revealing previously undescribed metabolites such as ibuprofen-carnitine and 5-ASA-phenylpropionic acid conjugates arising from drug-host and host-microbiome co-metabolism.

DOI: https://doi.org/10.1101/2025.11.18.689170