bims-mascan 2025-06-01 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–06–01
eighteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Stable 13C-glutamine Tracing Resolved Metabolomics for Cancer Metabolism Study.
Mass-spectrometry based metabolomics: an overview of workflows, strategies, data analysis and applications.
Development of an Adaptive, Economical, and Easy-to-Use SP3-TMT Automated Sample Preparation Workflow for Quantitative Proteomics.
Analysis of Methylated Quaternary Ammonium Compounds Using Hydrophilic Interaction Liquid Chromatography Combined With Mass Spectrometry.
Enabling pan-repository reanalysis for big data science of public metabolomics data.
Comprehensive profiling of folates across polyglutamylation and one-carbon states.
Enhanced Structure-guided Molecular Networking Annotation Method for Untargeted Metabolomics Data from Orbitrap Astral Mass Spectrometer.
A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy.
Standard Addition as a Method for Quantitative Mass Spectrometry Imaging.
TopLib: Building and Searching Top-Down Mass Spectral Libraries for Proteoform Identification.
MSstatsTMT improves accuracy of thermal proteome profiling by trading off temperature treatments and biological replicates.
The pan-cancer proteome atlas, a mass spectrometry-based landscape for discovering tumor biology, biomarkers, and therapeutic targets.
Improvements in Glycoproteomics through Architecture Changes to the Orbitrap Tribrid MS Platform.
Data-independent profiling of phenolic constituents in shea using comprehensive two-dimensional liquid chromatography (RPLC × HILIC) hyphenated to cyclic ion mobility-quadrupole-time-of-flight mass spectrometry.
Tegavivint triggers TECR-dependent nonapoptotic cancer cell death.
Differential Protein Precipitation-Based GalNAc-siRNA Sample Preparation with LC/MS Method Development Workflow in Plasma.
Gut lactate increases circulating L-Lac-Phe and key metabolites linked to GLP-1 and human health.
xOmicsShiny: an R Shiny application for cross-omics data analysis and pathway mapping.

Bio Protoc. 2025 May 20. 15(10): e5322

Stable 13C-glutamine Tracing Resolved Metabolomics for Cancer Metabolism Study.

Yaogang Zhong, Liqing He, Xinmin Yin, Logan Mazik, Xiang Zhang, Deliang Guo.

  Stable isotopes have frequently been used to study metabolic processes in live cells both in vitro and in vivo. Glutamine, the most abundant amino acid in human blood, plays multiple roles in cellular metabolism by contributing to the production of nucleotides, lipids, glutathione, and other amino acids. It also supports energy production via anaplerosis of tricarboxylic acid cycle intermediates. While 13C-glutamine has been extensively employed to study glutamine metabolism in various cell types, detailed analyses of specific lipids derived from 13C-glutamine via the reductive carboxylation pathway are limited. In this protocol, we present a detailed procedure to investigate glutamine metabolism in human glioblastoma (GBM) cells by conducting 13C-glutamine tracing coupled with untargeted metabolomics analysis using liquid chromatography-mass spectrometry (LC-MS/MS). The method includes step-by-step instructions for the extraction and detection of polar metabolites and long-chain fatty acids (LCFAs) derived from 13C-glutamine in GBM cells. Notably, this approach enables the distinction between isomers of two monounsaturated FAs with identical masses: palmitoleic acid (16:1n-7) (cis-9-hexadecenoic acid) and palmitelaidic acid (16:1n-7) (trans-9-hexadecenoic acid) derived from 13C-glutamine through the reductive carboxylation process. In addition, using this protocol, we also unveil previously unknown metabolic alterations in GBM cells following lysosome inhibition by the antipsychotic drug pimozide. Key features • Methods for analyzing the flux of the stable isotope 13C-glutamine in cancer cells and identifying its derived polar metabolites and long-chain fatty acids (LCFAs). • Distinguishes isomers of long-chain fatty acids, such as palmitoleic acid (16:1n-7) (cis-9-Hexadecenoic acid) and palmitelaidic acid (16:1n-7) (trans-9-Hexadecenoic acid), which share the exact same mass. • The method is utilized to investigate glutamine metabolism reprogramming in cancer cells following lysosome inhibition.

Keywords:  13C-glutamine; GBM cells; LC–MS/MS; Long-chain fatty acids; Lysosome; Pimozide; Polar metabolites

DOI:  https://doi.org/10.21769/BioProtoc.5322
Proteome Sci. 2025 May 26. 23(1): 5

Mass-spectrometry based metabolomics: an overview of workflows, strategies, data analysis and applications.

Kosar Hajnajafi, Mohammad Askandar Iqbal.

   BACKGROUND: Metabolomics, a burgeoning field within systems biology, focuses on the comprehensive study of small molecules present in biological systems. Mass spectrometry (MS) has emerged as a powerful tool for metabolomic analysis due to its high sensitivity, resolution, and ability to characterize a wide range of metabolites thus offering deep insights into the metabolic profiles of living systems.
AIM OF REVIEW: This review provides an overview of the methodologies, workflows, strategies, data analysis techniques, and applications associated with mass spectrometry-based metabolomics.
KEY SCIENTIFIC CONCEPTS OF REVIEW: We discuss workflows, key strategies, experimental procedures, data analysis techniques, and diverse applications of metabolomics in various research domains. Nuances of sample preparation, metabolite extraction, separation using chromatographic techniques, mass spectrometry analysis, and data processing are elaborated. Moreover, standards, quality controls, metabolite annotation, software for statistical and pathway analysis are also covered. In conclusion, this review aims to facilitate the understanding and adoption of mass spectrometry-based metabolomics by newcomers and researchers alike by providing a foundational understanding and insights into the current state and future directions of this dynamic field.

Keywords:  Analytes; LC–MS; Mass spectrometry; Metabolic fingerprinting; Metabolites; Metabolomics

DOI:  https://doi.org/10.1186/s12953-025-00241-8
J Proteome Res. 2025 May 27.

Development of an Adaptive, Economical, and Easy-to-Use SP3-TMT Automated Sample Preparation Workflow for Quantitative Proteomics.

Jake N Hermanson, Lea A Barny, Lars Plate.

  Liquid handling robots have been developed to automate various steps of the bottom-up proteomics workflow, however, protocols for the generation of isobarically labeled peptides remain limited. Existing methods often require costly specialty devices and are constrained by fixed workflows. To address this, we developed a cost-effective, flexible, automated sample preparation protocol for TMT-labeled peptides using the Biomek i5 liquid handler (Beckman Coulter). Our approach leverages single-pot solid-phase-enhanced sample preparation with paramagnetic beads to streamline protein cleanup and digestion. The protocol also allows for adjustment of trypsin concentration and peptide-to-TMT ratio to increase throughput and reduce costs, respectively. We compared our automated and manual 18-plex TMT-Pro labeling workflows by monitoring select protein markers of the unfolded protein response in pharmacologically activatable, engineered cell lines. Overall, the automated protocol demonstrated equivalent performance in peptide and protein identifications, digestion and labeling efficiency, and an enhancement in the dynamic range of TMT quantifications. Compared to the manual method, the Biomek protocol significantly reduces hands-on time and minimizes sample handling errors. The 96-well format additionally allows for the number of TMT reactions to be scaled up quickly without a significant increase in user interaction. Our optimized automated workflow enhances throughput, reproducibility, and cost-effectiveness, making it a valuable tool for high-throughput proteomics studies.

Keywords:  Biomek i5; automation; bottom-up approach; high throughput; mass spectrometry; proteomics; robotics; sample preparation; tandem mass tag; unfolded protein response

DOI:  https://doi.org/10.1021/acs.jproteome.5c00124
Mass Spectrom Rev. 2025 May 29.

Analysis of Methylated Quaternary Ammonium Compounds Using Hydrophilic Interaction Liquid Chromatography Combined With Mass Spectrometry.

Iman Zarei, Ambrin Farizah Babu, Ville M Koistinen, Marjo Tuomainen, Anna Kårlund, Retu Haikonen, Marko Lehtonen, Kati Hanhineva.

  Liquid chromatography-mass spectrometry (LC-MS) is a powerful technique for the detection and quantification of methylated quaternary ammonium compounds (mQACs), such as acylcarnitines and methylated amino-acid-derived (betainized) compounds, in biological matrices. Due to their high polarity and permanent charge, mQACs present analytical challenges, particularly in achieving efficient chromatographic retention and resolution. Here, we focus on the application of hydrophilic interaction liquid chromatography combined with mass spectrometric (HILIC-MS), for the analysis of these compound classes in biological samples. We highlight practical considerations in their analysis, including their MS/MS fragmentation patterns and identification in positive electrospray mode (ESI)+, to support researchers working with mQACs in targeted or untargeted metabolomics studies.

Keywords:  acylcarnitines; betainized compounds; electrospray ionization (ESI); hydrophilic interaction liquid chromatography (HILIC); liquid chromatography‐mass spectrometry (LC–MS); metabolomics; methylated quaternary ammonium compound (mQAC)

DOI:  https://doi.org/10.1002/mas.21942
Nat Commun. 2025 May 24. 16(1): 4838

Enabling pan-repository reanalysis for big data science of public metabolomics data.

Yasin El Abiead, Michael Strobel, Thomas Payne, Eoin Fahy, Claire O'Donovan, Shankar Subramamiam, Juan Antonio Vizcaíno, Ozgur Yurekten, Victoria Deleray, Simone Zuffa, Shipei Xing, Helena Mannochio-Russo, Ipsita Mohanty, Haoqi Nina Zhao, Andres M Caraballo-Rodriguez, Paulo Wender P Gomes, Nicole E Avalon, Trent R Northen, Benjamin P Bowen, Katherine B Louie, Pieter C Dorrestein, Mingxun Wang.

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.

DOI: https://doi.org/10.1038/s41467-025-60067-y
Metabolomics. 2025 May 27. 21(3): 71

Comprehensive profiling of folates across polyglutamylation and one-carbon states.

Sevcan Erşan, Yu Chen, Junyoung O Park.

   INTRODUCTION: One-carbon metabolism is central to carbon fixation, methylation, and biosynthesis of amino acids, lipids, and nucleotides. Folates are organic cofactors that harbor one-carbon units and shunt them across these metabolic pathways. Despite its essentiality to all life forms, the diverse nature of folate species with various polyglutamylation and one-carbon states makes their measurement challenging.
OBJECTIVES: We aim to illuminate one-carbon metabolism by streamlining comprehensive profiling of folate polyglutamates.
METHODS: We analyze folate standards and cellular extracts containing diverse folates species by liquid chromatography-mass spectrometry (LC-MS).
RESULTS: We observe that Escherichia coli cells possess diverse folate polyglutamates with one to ten terminal glutamates. Interestingly, most folate polyglutamates form doubly charged ions as well as singly charged ions in LC-MS. Folates also undergo in-source fragmentation. The disparate fates of folates in MS make their quantitation prone to underestimation. Fragmentation by in-source collision-induced dissociation (CID) and LC separation circumvent this issue and facilitate robust and sensitive quantification of folates. In-source CID of folates generates reporter fragment ions that yield higher signals in the mass-to-charge ratio (m/z) range near the maximal mass resolution of Orbitrap MS. Our LC methods complement MS by effectively separating folates based on their polyglutamylation and one-carbon states.
CONCLUSION: Our metabolomics approach tailored to folate polyglutamates reveals multiple layers of one-carbon metabolism organized by the lengths of polyglutamate tails in folates. Our analytical workflow is broadly applicable to folate profiling across various cell types to advance our knowledge of one-carbon metabolism as well as biotechnology and medicine.

Keywords:  Folate; LC-MS; Metabolomics; One-carbon metabolism; Polyglutamylation

DOI:  https://doi.org/10.1007/s11306-025-02269-5
Anal Chem. 2025 May 29.

Enhanced Structure-guided Molecular Networking Annotation Method for Untargeted Metabolomics Data from Orbitrap Astral Mass Spectrometer.

Xinxin Wang, Yao Chen, Zaifang Li, Ziquan Fan, Rui Zhong, Tian Liu, Xiangjun Li, Xin Lu, Guowang Xu.

The rapid, efficient, and accurate annotation of compounds in complex samples remains a significant challenge in metabolomics. The recently developed Orbitrap Astral mass spectrometer (MS) integrates a traditional quadrupole Orbitrap with a novel Astral mass analyzer, providing fast MS/MS scanning speed and high sensitivity. However, existing metabolomics annotation methods have not fully exploited the advanced capabilities of Astral MS. In this study, an enhanced structure-guided molecular networking (E-SGMN) method was developed, which is specifically tailored for the Orbitrap Astral mass spectrometer (MS). Unlike previous network annotation methods, E-SGMN extracted both previously detected metabolites and those potentially detected by Astral from the metabolome database, enabling more efficient and accurate network construction through structural similarity. E-SGMN expands annotation coverage by accurately improving network size, while minimizing the inclusion of irrelevant compounds, achieving a balance between annotation scale and accuracy. Validation results revealed that Astral-E-SGMN achieved an annotation coverage and accuracy of 76.84% and 78.08%, respectively, for a spiked plasma, significantly outperforming E-SGMN-Q Exactive HF (E-SGMN-QE HF). Notably, 5440 metabolite features from NIST SRM 1950 human plasma were annotated by Astral-E-SGMN, a 3.6-fold increase over QE HF-SGMN. Comparative analyses for six types of typical biological samples demonstrate that E-SGMN-Astral enhanced metabolite annotations by 3.7-44.2 times compared to conventional annotation methods, highlighting E-SGMN's wider metabolite annotation coverage. This method not only enhances annotation coverage, but also provides a transformative tool for understanding complex biological systems, holding significant potential for life science and clinical medicine.

DOI: https://doi.org/10.1021/acs.analchem.5c00314
Nat Commun. 2025 May 30. 16(1): 5034

A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy.

Barbora Salovska, Wenxue Li, Oliver M Bernhardt, Pierre-Luc Germain, Qinyue Wang, Tejas Gandhi, Lukas Reiter, Yansheng Liu.

Quantifying protein turnover is fundamental to understanding cellular processes and advancing drug discovery. Multiplex-DIA mass spectrometry (MS), combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC) reliably measures protein turnover and degradation kinetics. Previous multiplex-DIA-MS workflows have employed various strategies including leveraging the highest isotopic labeling channels to enhance the detection of isotopic signal pairs. Here we present a robust workflow that integrates a machine learning algorithm and channel-specific statistical filtering, enabling dynamic adaptation to channel ratio changes across multiplexed experiments and enhancing both coverage and accuracy of protein turnover profiling. We also introduce KdeggeR, a data analysis tool optimized for pSILAC-DIA experiments, which determines and visualizes peptide and protein degradation profiles. Our workflow is broadly applicable, as demonstrated on 2-channel and 3-channel DIA datasets and across two MS platforms. Applying this framework to an aneuploid cancer cell model before and after cisplatin resistance, we uncover strong proteome buffering of key protein complex subunits encoded by the aneuploid genome mediated by protein degradation. We identify resistance-associated turnover signatures, including mitochondrial metabolic adaptation via accelerated degradation of respiratory complexes I and IV. Our approach provides a powerful platform for high-throughput, quantitative analysis of proteome dynamics and stability in health and disease.

DOI: https://doi.org/10.1038/s41467-025-60319-x
Anal Chem. 2025 May 27.

Standard Addition as a Method for Quantitative Mass Spectrometry Imaging.

Lucie Davidová, Ingela Lanekoff.

In mass spectrometry imaging (MSI), analytes are desorbed and ionized directly from a complex and unique chemical microenvironment in each pixel, which makes their quantification challenging. Matrix effects have been addressed by the use of isotopically labeled internal standards (IS), either included in the solvent or sprayed over the tissue section, for pixel-by-pixel relative quantification. However, in addition to requiring preselection, isotopically labeled IS may be costly or unavailable. Here, we introduce a novel approach for quantification in MSI, based on the standard addition method. We report a workflow for both acquiring and processing quantitative data. Furthermore, we compare the detected concentrations obtained by standard addition to the detected concentrations obtained using both IS quantification and external calibration. Finally, we show the applicability of using molecules extracted from tissue as an easily accessible standard mixture for standard addition quantification in MSI. The possibility of using analytical standards and readily available endogenous analytes as a source of calibration standards makes our standard addition-based quantitative approach cost-effective, accessible, and versatile.

DOI: https://doi.org/10.1021/acs.analchem.5c00549
Anal Chem. 2025 May 29.

TopLib: Building and Searching Top-Down Mass Spectral Libraries for Proteoform Identification.

Kun Li, Haixu Tang, Xiaowen Liu.

Mass spectral library search is a widely used approach for spectral identification in mass spectrometry (MS)-based proteomics. While numerous methods exist for building and searching bottom-up mass spectral libraries, there is a lack of software tools for top-down mass spectral libraries. To fill the gap, we introduce TopLib, a new software package designed for building and searching top-down spectral libraries. TopLib utilizes an efficient spectral representation technique to reduce database size and improve query speed and performance. We systematically evaluated various spectral representation techniques and scoring functions for top-down spectral clustering and search. Our results demonstrate that TopLib is significantly faster and yields higher reproducibility in proteoform identification compared to conventional database search methods in top-down MS.

DOI: https://doi.org/10.1021/acs.analchem.4c06627
Mol Cell Proteomics. 2025 May 27. pii: S1535-9476(25)00098-2. [Epub ahead of print] 100999

MSstatsTMT improves accuracy of thermal proteome profiling by trading off temperature treatments and biological replicates.

Amanda M Figueroa-Navedo, Rohan Kapre, Tushita Gupta, Yingrong Xu, Clifford G Phaneuf, Pierre M Jean Beltran, Liang Xue, Alexander R Ivanov, Olga Vitek.

Thermal proteome profiling investigates protein-protein, protein-nucleic acid, or protein-drug interactions, and the impact of metabolite binding and post-translational modifications on these interactions. The experiments quantitatively characterize biological samples treated with small molecules versus controls, and subjected to timed exposures to multiple temperatures. Typically, each enzymatically digested sample is labeled with a tandem mass tag (TMT), where each TMT channel corresponds to a specific temperature treatment, and profiled using liquid chromatography coupled with mass spectrometry in data-dependent data acquisition mode. The resulting mass spectra are processed with computational tools to identify and quantify proteins, and filter out noise. Protein-drug interactions are detected by fitting curves to the protein-level reporter ion abundances across the temperatures. Interacting proteins are identified by shifts in the fitted curves between treated samples and controls. In this manuscript, we focus on data processing and curve fitting in thermal proteome profiling. We review the statistical methods currently used for thermal proteome profiling, and demonstrate that such methods can yield substantially different results. We advocate for the statistical analysis strategy implemented in the open-source R package MSstatsTMT, as it does not require subjective pre-filtering of the data or curve fitting, and appropriately represents all the sources of variation. It supports experimental designs that trade-off temperatures for a larger number of biological replicates, and handles multiple drug concentrations or pools of samples treated with multiple temperatures, thus increasing the sensitivity of the results. We demonstrate these advantages of MSstatsTMT as compared to the currently used alternatives in a series of simulated and experimental datasets, which include conventional thermal proteome profiling and its OnePot counterpart that pools the samples treated at multiple temperatures into one sample, and incorporates multiple doses of a drug. The suggested MSstatsTMT-based workflow is documented in publicly available and fully reproducible R vignettes.

DOI: https://doi.org/10.1016/j.mcpro.2025.100999
Cancer Cell. 2025 May 27. pii: S1535-6108(25)00212-0. [Epub ahead of print]

The pan-cancer proteome atlas, a mass spectrometry-based landscape for discovering tumor biology, biomarkers, and therapeutic targets.

Jaco C Knol, Mengge Lyu, Franziska Böttger, Madalena Nunes Monteiro, Thang V Pham, Frank Rolfs, Andrea Vallés-Martí, Tim Schelfhorst, Richard R de Goeij-de Haas, Irene V Bijnsdorp, Shuaiyao Wang, Fangfei Zhang, Jun A, Bart A Westerman, Barbara Sitek, Janne Lehtiö, Jan Koster, Jan N M IJzermans, Hanneke W M van Laarhoven, Maarten F Bijlsma, Jan Paul Medema, Alex A Henneman, Sander R Piersma, Ruud H Brakenhoff, Jacqueline Cloos, Valentina Cordo', Daphne de Jong, Geert Kazemier, Danijela Koppers-Lalic, Mariette Labots, Tessa Y S Le Large, John W M Martens, Jules P P Meijerink, Madiha Mumtaz, Chantal Scheepbouwer, Robby E Kibbelaar, David P Noske, Renske D M Steenbergen, Nicole C T van Grieken, Winan van Houdt, Elisa Giovannetti, Geert J L H van Leenders, Jos Jonkers, Tong Liu, Meisi Yan, Xiaolu Zhan, Tiannan Guo, Connie R Jimenez.

  Most cancer proteomics studies to date have focused on a single cancer type. We report The Pan-Cancer Proteome Atlas (TPCPA) based on data-independent acquisition mass spectrometry, to better understand cancer biology and identify therapeutic targets and biomarkers. TPCPA includes 9,670 proteins derived from 999 primary tumors representing 22 cancer types. We describe pan-cancer and cancer type-enriched proteins with extensive external annotation, prioritizing candidate drug targets and biomarkers. Relevant for proteolysis-targeting chimeras, we identify E3-ubiquitin ligases highly expressed in specific tumor types, including HERC5 (esophageal cancer) and RNF5 (liver cancer). Co-expression analysis reveals 13 modules, including unexpected hub proteins as potential drug targets (e.g., GFPT1, LRPPRC, PINK1, DOCK2, and PTPN6). Analysis of 195 colorectal cancers identifies protein markers for RNA-based consensus molecular subtypes (CMSs) and two immune subtypes with prognostic value. We report a cancer type classifier for identification of cancers of unknown primary origin. All TPCPA data can be queried in a dedicated web resource.

Keywords:  bioinformatics; biomarker/ target; colorectal cancer subtypes; mass spectrometry; multi-cancer (sub)type classification; pan-cancer; proteome

DOI:  https://doi.org/10.1016/j.ccell.2025.05.003
Anal Chem. 2025 May 29.

Improvements in Glycoproteomics through Architecture Changes to the Orbitrap Tribrid MS Platform.

Tim S Veth, Emmajay Sutherland, Kayla A Markuson, Ruby Zhang, Anna G Duboff, Jingjing Huang, David Bergen, Amanda E Lee, Rafael D Melani, Jesse D Canterbury, Vlad Zabrouskov, Graeme C McAlister, Christopher Mullen, Nicholas M Riley.

Hardware changes introduced on the Orbitrap Ascend Tribrid MS include dual ion routing multipoles (IRMs) that enable parallelized accumulation, dissociation, and Orbitrap mass analysis of three separate ion populations. The balance between these instrument functions is especially important in glycoproteomics, where complexities of glycopeptide fragmentation necessitate large precursor ion populations and long ion accumulation times for quality MS/MS spectra. To compound matters further, dissociation methods like electron transfer dissociation (ETD) that benefit glycopeptide characterization come with overhead times that slow down scan acquisition. Here we explored how the Orbitrap Ascend's dual IRM architecture can improve glycopeptide analysis, with a focus on O-glycopeptide characterization using ETD with supplemental collisional activation (EThcD). We found that parallelization of ion accumulation and EThcD fragmentation increased scan acquisition speed without sacrificing spectral quality, subsequently increasing the number of O-glycopeptides identified relative to analyses on the Orbitrap Eclipse (i.e., the previous generation Tribrid MS). Additionally, we systematically evaluated ion-ion reaction times and supplemental activation energies used for EThcD to understand how best to utilize acquisition time. We observed that shorter-than-expected ion-ion reaction times minimized scan overhead time without sacrificing c/z•-fragment ion generation and that higher supplemental collision energies can generate combinations of glycan-retaining and glycan-neutral-loss peptide backbone fragments that benefit O-glycopeptide identification. We also saw improvements in N-glycopeptide analysis using collision-based dissociation, especially with methods using faster scan speeds. Overall, these data show how architectural changes to the Tribrid MS platform benefit glycoproteomic experiments by parallelizing scan functions to minimize overhead time and improve sensitivity.

DOI: https://doi.org/10.1021/acs.analchem.4c06370
J Chromatogr A. 2025 May 24. pii: S0021-9673(25)00439-X. [Epub ahead of print]1756 466092

Data-independent profiling of phenolic constituents in shea using comprehensive two-dimensional liquid chromatography (RPLC × HILIC) hyphenated to cyclic ion mobility-quadrupole-time-of-flight mass spectrometry.

Nikoline J Nielsen, Oskar M Kronik, Romina A Forte Neran, Jan H Christensen, Tore K Ravn, André de Villiers.

  A total of 50 secondary metabolites were profiled in shea kernels and shells using two-dimensional liquid chromatography (RPLC × HILIC) coupled with electrospray cyclic ion mobility quadrupole time-of-flight mass spectrometry (ESI-Q-cIMS-qTOF) and data-independent acquisition (DIA). The 2D chromatograms revealed distinct regions occupied by specific chemical classes, facilitating compound annotation based on high-resolution mass spectra and collision cross-section (CCS) data. CCS values showed maximum deviation of 2.9 %, and equivalent to the inherent CCS calibration error, from experimental values from the AllCCS database, and deviation of 0.1-6.6 % (mean 3.4 %) from predicted values, confirming compound identities. Key findings included the first-time identification of epitaxifolin, procyanidins B1-B4, prodelphinidins B1-B4, isoorientin and isoquercitrin in shea. The flavone, flavanones and flavanonols were 1.3-9 times more abundant in shells, while flavone glycosides, flavan-3-ols and their dimers, including mono- and di-galloylated forms, were 1.9-50 times higher in kernels. Notably, quercetin-species levels in shells were 4-15 times higher than in kernels. The analytical platform demonstrated high spectral purity in DIA mode due to the arrival time separation of precursor ions prior to fragmentation. The single pass cIMS had, in certain cases, the potential to separate isobaric (isoorientin and quercitrin) and near-isobaric ions (ellagic acid and quercetin). However, accessing the fully resolved and structured 4D data required customized computing tools due to a lack of fit-for-purpose software, highlighting a barrier to broader adoption. These findings support the further valorization of both shea kernels and shells for nutraceutical and cosmetic applications.

Keywords:  2D-LC; Arrival time separation; Flavonoids; Kernels; Proanthocyanidins; Secondary metabolites; Shells

DOI:  https://doi.org/10.1016/j.chroma.2025.466092
Nat Chem Biol. 2025 May 26.

Tegavivint triggers TECR-dependent nonapoptotic cancer cell death.

Logan Leak, Ziwei Wang, Alby J Joseph, Brianna Johnson, Alyssa A Chan, Cassandra M Decosto, Leslie Magtanong, Pin-Joe Ko, Weaverly Colleen Lee, Joan Ritho, Sophia Manukian, Alec Millner, Shweta Chitkara, Jennifer J Salinas, Rachid Skouta, Matthew G Rees, Melissa M Ronan, Jennifer A Roth, Chad L Myers, Jason Moffat, Charles Boone, Steven J Bensinger, David A Nathanson, G Ekin Atilla-Gokcumen, Everett J Moding, Scott J Dixon.

Small molecules that induce nonapoptotic cell death are of fundamental mechanistic interest and may be useful to treat certain cancers. Here we report that tegavivint, a drug candidate undergoing human clinical trials, can activate a unique mechanism of nonapoptotic cell death in sarcomas and other cancer cells. This lethal mechanism is distinct from ferroptosis, necroptosis and pyroptosis and requires the lipid metabolic enzyme trans-2,3-enoyl-CoA reductase (TECR). TECR is canonically involved in the synthesis of very-long-chain fatty acids but appears to promote nonapoptotic cell death in response to CIL56 and tegavivint via the synthesis of the saturated long-chain fatty acid palmitate. These findings outline a lipid-dependent nonapoptotic cell death mechanism that can be induced by a drug candidate currently being tested in humans.

DOI: https://doi.org/10.1038/s41589-025-01913-4
Anal Chem. 2025 May 26.

Differential Protein Precipitation-Based GalNAc-siRNA Sample Preparation with LC/MS Method Development Workflow in Plasma.

Youngjae Kim, Ting Ting Zhang, Hiroshi Sugimoto, Mark G Qian, Linlin Dong.

Liquid chromatography-mass spectrometry (LC/MS) plays a crucial role in the quantification of small interfering RNAs (siRNAs) in biological matrices. However, the recovery of siRNA from complex biological matrices remains a significant challenge. Focusing on liver-targeted N-acetylgalactosamine (GalNAc)-siRNA conjugates, the primary extraction methods currently used are solid-phase extraction (SPE) and hybridization. While both methods have advantages, SPE recovery can vary depending on the analyte and is costly, whereas the hybridization method requires specific reagents, limiting its applicability. To address these challenges, we developed a novel extraction method for LC/MS bioanalysis of GalNAc-siRNA. Our innovative approach uses a differential protein precipitation method with an optimized organic solvent mix to remove large, high-abundance plasma proteins as precipitates while preserving the GalNAc-siRNAs in the liquid phase. The workflow was optimized to identify the most intense MS/MS transitions, and LC-MS/MS parameters were fine-tuned using high-resolution Orbitrap and QTRAP hybrid mass spectrometers for the highly sensitive detection of targeted siRNA molecules. This approach achieved a lower limit of quantification in the single-digit ng/mL range for four FDA-approved GalNAc-siRNAs (Givosiran, Lumasiran, Inclisiran, and Vutrisiran) and a major Givosiran metabolite, AS(N-1)3'. The applicability of this approach was successfully demonstrated by analyzing plasma samples from an in vivo rat study involving three molecules (Givosiran, Givosiran AS(N-1)3', and Inclisiran). This method is straightforward, robust, highly sensitive, and cost-effective and should be readily adaptable for the bioanalysis of diverse GalNAc-siRNAs and, potentially, for late-stage sample analyses.

DOI: https://doi.org/10.1021/acs.analchem.5c01587
Am J Physiol Endocrinol Metab. 2025 May 26.

Gut lactate increases circulating L-Lac-Phe and key metabolites linked to GLP-1 and human health.

Emaan Ghias, Mette Glavind Bülow Pedersen, Camilla Bak Nielsen, Louise Vase Bech, Ida Marie Modvig, Natasa Brkovic Zubanovic, Jacob Marthinsen Seefeldt, Roni Nielsen, Esben Søndergaard, Kim Frisch, Jakob Hansen, Jens Juul Holst, Niels Møller, Nikolaj Rittig, Mogens Johannsen.

  Lactate, a small organic acid related to short-chain fatty acids (SFCAs), is emerging as a key energy metabolite, although much remains unknown about its actions in the gut. In the current study, we specifically tested how oral (PO) and parenteral (IV) lactate affects lactoylation of amino acids (Lac-AA) in humans and whether these clinical results could be reproduced in a perfused rat intestine model. Furthermore, using targeted and untargeted metabolomics we globally investigated how PO and IV lactate impact the circulating metabolome to delineate potential circulating messengers and obtain additional mechanistic insight into how oral lactate may potentially induce GLP-1 secretion as well as alternative metabolites correlated to human health. Our findings provide a better understanding of the general effects of lactate in the gut and how it potentially signals to increase satiety in humans.

Keywords:  Bile acid modulation; Gut metabolism; L-Lac-Phe; Lacate; Metabolomics

DOI:  https://doi.org/10.1152/ajpendo.00037.2025
Bioinform Adv. 2025 ;5(1): vbaf097

xOmicsShiny: an R Shiny application for cross-omics data analysis and pathway mapping.

Benbo Gao, Yu H Sun, Xinmin Zhang, Tinchi Lin, Wei Li, Romi Admanit, Baohong Zhang.

Summary: We developed xOmicsShiny, a feature-rich R Shiny-powered application that enables biologists to fully explore omics datasets across experiments and data types, with an emphasis on uncovering biological insights at the pathway level. The data merging feature ensures flexible exploration of cross-omics data, such as transcriptomics, proteomics, metabolomics, and lipidomics. The pathway mapping function covers a broad range of databases, including WikiPathways, Reactome, and KEGG pathways. In addition, xOmicsShiny offers several visualization options and analytical tasks for everyday omics data analysis, namely, PCA, Volcano plot, Venn Diagram, Heatmap, WGCNA, and advanced clustering analyses. The application employs customizable modules to perform various tasks, generating both interactive plots and publication-ready figures. This dynamic, modular design overcomes the issue of slow loading in R Shiny tools and allows it to be readily expanded by the research and developer community.
Availability and implementation: The R Shiny application is publicly available at: http://xOmicsShiny.bxgenomics.com. Researchers can upload their own data to the server or use the preloaded demo dataset. The source code, under MIT license, is provided at https://github.com/interactivereport/xOmicsShiny for local installation. A full tutorial of the application is available at https://interactivereport.github.io/xOmicsShiny/tutorial/docs/index.html.

DOI: https://doi.org/10.1093/bioadv/vbaf097