bims-mascan 2026-08-02 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–08–02
25 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Fast untargeted and targeted high-resolution mass spectrometry workflows for large-scale lipidomics.
Implementation of Nonisobaric TMT Analogs for Accurate Precursor-Level Quantification by plexDIA.
Harmonized nucleoside mass spectrometry enables reproducible cross-platform RNA modification quantification.
Aziridination-Enabled Structural and Quantitative Lipidomics.
Electron-activated dissociation for structural resolution of lipid isomers: Principles, performance, and the path to routine workflows.
GlycoMsHelper: A Simple and Traceable Workflow for Glycomic Mass Spectrometry Data Interpretation.
T-ReXing Synthetic Cannabinoids─Unveiling Metabolite Features Suitable for Urine Screening by Trapped Ion Mobility HRMS.
AI proteomics: from protein identification to virtual cells.
Mass Spectrometry-Based Chromatographic and Computational Workflows for Biomarker and Therapeutic Target Discovery: A Comprehensive Review.
Generation of peptide detectability datasets from single DIA experiment for prediction model fine-tuning.
Acetic acid as mobile phase additive facilitated the identification and quantification of uridine modifications in negative ion mode by HPLC-MS/MS.
Comprehensive characterization of myopic guinea pigs' retina following PI3K/AKT inhibition using a data-independent acquisition dataset on an Orbitrap Exploris 480 mass spectrometer.
Scalable discovery of homomeric protein-protein interactions from cross-linking mass spectrometry data with CLAUDIO 2.0.
MALDI-MSI-Guided Laser Capture Microdissection Coupled with MS for Integrated Spatial Multi-Omics in Mouse Brain.
AAV-delivered TurboRFP enables streamlined tracing and analysis of corticospinal tract sprouting.
LipiDetective: a deep learning model for the identification of molecular lipid species in tandem mass spectra.
Human 28S rRNA analysed by state-of-the-art oligonucleotide mass spectrometry: benchmarking current capabilities and a call to action for MS-Seq.
Fatty acids metabolic reprogramming and tumor microenvironment in pancreatic cancer: targeting pathways.
Improving the Throughput and Specificity for Small-Molecule Analysis During First-Tier Mass Spectrometry-Based Newborn Screening.
Making Ozonolysis Mass Spectrometry More Accessible: A Simple LC-OzESI-MS Workflow for Carbon-Carbon Double Bond Position Elucidation in Lipids and Natural Products.
Improved comprehensive profiling of fecal bile acids through chemical derivatization combined with HPLC-MS/MS analysis.
Development of a liquid chromatography coupled to tandem mass spectrometry method for the quantification of Arginine Vasopressin in human plasma.
Transsulfuration links aspartate-asparagine metabolism and redox homeo-stasis to drive tumor growth.
Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis.
PEth in review: Phosphatidylethanol discovery and LC-MS/MS analysis.

Anal Chim Acta. 2026 10 01. pii: S0003-2670(26)00709-9. [Epub ahead of print]1417 345759

Fast untargeted and targeted high-resolution mass spectrometry workflows for large-scale lipidomics.

Yi Yun Pang, Guo Shou Teo, Wai Kin Tham, Woon Puay Koh, Chew Kiat Heng, Thusitha W T Rupasinghe, Paul R S Baker, Federico Torta, Hyungwon Choi.

   BACKGROUND: Lipidomes are highly complex and variable, and high-throughput analytical strategies are essential for comprehensive lipidome profiling in large-scale analysis. The use of hydrophilic interaction liquid chromatography (HILIC) coupled with conventional untargeted acquisition strategies remains unpopular due to the challenges in interpreting complex spectral data. To address this gap, we present a detailed lipidomic workflow based on a fast 6.7 min-long HILIC separation method coupled with data-dependent acquisition/sequential window acquisition of all theoretical fragment ions analysis (DDA/SWATH) and improve its applicability with a novel framework for untargeted data processing. In parallel, we also established a targeted high-resolution multiple reaction monitoring (MRMHR) method for comprehensive multi-class lipidomic profiling. To evaluate the applicability of these two approaches in large-scale clinical lipidomics, we used both workflows for the analysis of 240 case-control matched plasma samples from a population-based prospective cohort.
RESULTS: After method development and optimization, application of the two complementary workflows to 240 plasma samples generated lipid profiles of over 400 and 500 features for targeted and untargeted analysis, respectively. As expected, the coverage of lipids by the untargeted workflow was notably different from that of the targeted workflow at the lipid species level. In terms of quantitation of lipids detected in both approaches, we observed differences in the quantitation of lipids in individual samples, but statistical comparisons of sample groups remained largely concordant. Furthermore, we demonstrated that the choice of internal standards for normalization can influence downstream statistical outcomes.
SIGNIFICANCE: Two fast HILIC-based workflows were demonstrated to be applicable for large-scale clinical lipidomics and represent reliable high-throughput alternatives to existing longer separation methods for both untargeted and targeted analyses using high-resolution mass spectrometry. Furthermore, the influence of targeted and untargeted approaches, together with the assessment of internal standard selection on downstream statistical outcomes, provides important methodological considerations for large-scale lipidomic studies.

Keywords:  Data-dependent acquisition; Data-independent acquisition; High-resolution multiple reaction monitoring; Human plasma; Hydrophilic interaction liquid chromatography

DOI:  https://doi.org/10.1016/j.aca.2026.345759
Rapid Commun Mass Spectrom. 2026 Oct 30. 40(20): e70146

Implementation of Nonisobaric TMT Analogs for Accurate Precursor-Level Quantification by plexDIA.

Ting-Yu Wei, Joao A Paulo.

   RATIONALE: Multiplexed quantitative proteomics enables simultaneous analysis of multiple biological samples, increasing throughput while reducing instrument time and sample requirements. However, integrating sample multiplexing with data-independent acquisition (DIA) remains challenging. We present a TMTpro plexDIA strategy leveraging MS1-level mass differences between nonisobaric TMTpro reagent variants to enable multiplexed quantification without compromising DIA sensitivity.
METHODS: Three Saccharomyces cerevisiae deletion strains (Δmet6, Δpfk2, and Δura2) were labeled with TMTproZero (light), TMTpro16 (standard), and super-heavy TMTpro (heavy), mixed in three permutations, and analyzed by narrow-window DIA (2 m/z isolation, 300 scan events) on an Orbitrap Astral mass spectrometer. Database searching was performed using FragPipe/MSFragger with plex-DIA quantification enabled.
RESULTS: Over 2000 protein groups and over 20 000 precursors were identified per channel per mixture, with closely matched identification rates across all three channels. All nine expected deletion patterns were correctly identified, with channel-specific depletion reproduced consistently across precursor charge states (2+, 3+, and 4+).
CONCLUSIONS: TMTpro plex-DIA enables accurate, multiplexed quantitative proteomics through MS1-level mass separation of the nonisobaric TMTpro isotopologs. The characteristic deletion patterns observed for each knockout strain serve as intrinsic molecular barcodes, validating sample identity and demonstrating the broad utility of plex-DIA for high-throughput, multiplexed proteomics applications.

Keywords:  TKO standard; nonisobaric multiplexing; plexDIA; quantitative proteomics

DOI:  https://doi.org/10.1002/rcm.70146
bioRxiv. 2026 Jul 21. pii: 2026.07.17.739095. [Epub ahead of print]

Harmonized nucleoside mass spectrometry enables reproducible cross-platform RNA modification quantification.

Jan Felix Dalwigk, Kira Kerkhoff, Oskar Knittelfelder, Robert L Ross, Maria Cristina Petrella, Anna Kuśnierczyk, Tulsi Bhandari, Aurore Attina, Alicia Burkard, Carolina Brás-Costa, Michael S DeMott, Kitty Johnson, Xenia Kerkhoff-Bernaciak, Jennifer Kist, Ken Kögel, Martina Krämer, Ricardo Moreno-Ballesteros, Ganna Podoprygorina, Kaley Simcox, Özge Simsir, Anton Skriba, Sam Wein, Hana Cahova, Benjamin A Garcia, Mark Helm, Alexandre David, Katharina Höfer, Sebastian Leidel, Patrick A Limbach, Eva Novoa, Eduard Sabidó, Sabine Schneider, Philippe Wolff, Peter Dedon, Vivian Cheung, Stefanie Kaiser.

RNA modification analysis by LC-MS/MS is central to epitranscriptomics, yet quantitative comparison across laboratories and instrument platforms remains poorly standardized. Here, we performed a community-driven benchmarking study during the first Human RNome Project workshop to systematically evaluate cross-platform reproducibility of ribonucleoside mass spectrometry workflows. Using the same analytical column and gradient, standardized RNA samples, and shared reagents, we compared nucleoside quantification across quadrupole, time-of-flight, and orbitrap-based LC-MS platforms employing distinct acquisition strategies. While chromatographic separation was highly reproducible across systems, nucleoside-specific MS response behavior differed substantially between platforms and limited direct comparability of relative signal intensities. These response differences varied across analytes and concentration ranges, demonstrating that harmonized chromatography alone is insufficient for transferable quantitative analysis. Stable isotope-labeled internal standard (SILIS) normalization substantially reduced platform-and method-dependent response and improved agreement for most evaluated modifications. External calibration improved agreement between qTOF and Orbitrap workflows for a subset of modifications but did not fully resolve residual intersystem differences. Based on these findings, we establish benchmark-derived recommendations for harmonized relative and absolute RNA modification quantification, including guidance for calibration design, quality control, and data reporting. Together, this work provides a methodological framework for reproducible nucleoside LC-MS/MS workflows and establishes a foundation for large-scale comparative epitranscriptomic studies.
Graphical abstract:

DOI: https://doi.org/10.64898/2026.07.17.739095
J Am Soc Mass Spectrom. 2026 Jul 26.

Aziridination-Enabled Structural and Quantitative Lipidomics.

Erin A Hirtzel, Gopal Reddy Ramidi, Xin Yan.

  Chemical derivatization has long been employed to enhance the structural characterization of lipids by mass spectrometry (MS). In recent years, olefin aziridination has emerged as a powerful and versatile strategy in lipidomics, driven by its ability to selectively target carbon-carbon double bonds (C═C bonds) and to introduce nitrogen-containing functionalities that facilitate both structural elucidation and quantitative analysis. Aziridination-enabled MS approaches provide reliable C═C bond localization through diagnostic fragmentation, while simultaneously improving ionization efficiency, particularly for nonpolar lipid classes. A diverse range of aziridination chemistry has been developed, each offering distinct advantages for lipid analysis. In this review, we summarize recent advances in aziridination-enabled MS methodologies, with an emphasis on reaction development and analytical performance. We further highlight applications across biological and complex sample systems. These developments highlight aziridination-assisted MS as a powerful strategy for precision lipidomics with isomer-resolved capability and accurate quantification.

Keywords:  aziridination; lipidomics; mass spectrometry

DOI:  https://doi.org/10.1021/jasms.6c00107
J Pharm Biomed Anal. 2026 Jul 27. pii: S0731-7085(26)00351-1. [Epub ahead of print]281 117683

Electron-activated dissociation for structural resolution of lipid isomers: Principles, performance, and the path to routine workflows.

Carolina Gonzalez-Riano.

  Lipids encode critical biological information not only through their class and sum composition, but also through fine structural features, including fatty acyl chain stereospecific numbering (sn) position in glycerolipids and glycerophospholipids, and carbon-carbon double-bond (C=C) location and geometry. These isomeric features are vital for modulating membrane organization and cell signaling and have been correlated with numerous disease phenotypes. However, conventional high-throughput lipidomics workflows, typically relying on collision-induced dissociation (CID), are often unable to resolve this isomer-level detail. Electron-activated dissociation (EAD) is an umbrella term for a family of electron-based fragmentation methods. Within this family, the low-energy regime, historically known as electron impact excitation of ions from organics (EIEIO), uses radical chemistry to induce precise cleavages along the lipid backbone and acyl chains. This mechanism generates direct, diagnostic fragment ions that enable the comprehensive resolution of sn- and C=C position isomers, branching, hydroxylation and, in selected cases, cis/trans geometry from intact complex lipids. Although EAD's powerful capability for structural elucidation has been demonstrated, its community-wide implementation in routine lipidomics remains an emerging transition. This systematic review thoroughly details EAD fragmentation principles across major lipid classes, including triacylglycerols, glycerophospholipids, sphingolipids, and cardiolipins, and surveys the current literature for its structural applications. Consequently, I evaluate the critical bottlenecks to broad adoption and discuss how recent advancements in instrument sensitivity, data-independent acquisition (DIA) workflows, and automated EAD-aware bioinformatics software are effectively addressing these challenges. Ultimately, I outline the necessary steps to fully integrate EAD into routine, high-throughput standard practice for isomer-resolved lipidomics.

Keywords:  Electron Impact Excitation of Ions from Organics (EIEIO); Electron-Activated Dissociation (EAD); Isomer resolution; Lipidomics; Mass spectrometry; Structural elucidation

DOI:  https://doi.org/10.1016/j.jpba.2026.117683
Glycobiology. 2026 Jul 28. pii: cwag057. [Epub ahead of print]

GlycoMsHelper: A Simple and Traceable Workflow for Glycomic Mass Spectrometry Data Interpretation.

Wei-Ze Kong, Yann Guerardel, Morihisa Fujita.

  Glycosylation is a fundamental post-translational and lipid modification that plays critical roles in diverse cellular processes. Although mass spectrometry (MS) is the primary platform for glycomic analysis, large-scale interpretation of glycomic MS data remains heavily dependent on expert manual annotation, creating a major bottleneck. Key challenges include effective spectral denoising and reliable identification of glycan-derived tandem MS (MS2) spectra. Here, we present GlycoMsHelper, a simple and flexible R-based workflow for positive-ion mode glycomic MS composition analysis. GlycoMsHelper integrates multiple denoising strategies with logical expression-based recognition of diagnostic fragment ion patterns to identify glycan-derived MS2 spectra. Candidate spectra are matched against glycan libraries constructed using curated biosynthetic constraints, generating traceable outputs for downstream validation. By defining appropriate parameters, the workflow can be applied to diverse glycan classes, including N-glycans, O-glycans, and glycosphingolipids (GSLs). Because each module operates independently, produces standardized outputs, and is supported by a graphical user interface, GlycoMsHelper can be readily integrated into existing glycomic analysis workflows. In an N-glycan MS dataset containing 43 expert-curated compositions, GlycoMsHelper correctly identified 42 compositions. Application to GSL and O-glycan datasets further demonstrated its versatility and enabled identification of glycan compositions overlooked during manual annotation. GlycoMsHelper is particularly useful for exploratory analysis of newly acquired or previously uncharacterized datasets lacking established reference libraries, providing an accessible and practical solution for semi-automated composition-level interpretation of glycomic MS data.

Keywords:  Glycan annotation; Glycoinformatics; Glycomics; Mass spectrum; Spectral denoising

DOI:  https://doi.org/10.1093/glycob/cwag057
J Am Soc Mass Spectrom. 2026 Jul 27.

T-ReXing Synthetic Cannabinoids─Unveiling Metabolite Features Suitable for Urine Screening by Trapped Ion Mobility HRMS.

Annette Zschiesche, Ilona Nordhorn, Birgit Schneider, Carsten Baessmann, Jürgen Kempf, Laura M Huppertz.

  Targeted and untargeted mass spectrometric analyses of urine, a preferred matrix for toxicological screening, require comprehensive spectral libraries of drug metabolites. To detect new synthetic cannabinoids (SCs), biomarkers for newly emerging compounds must be incorporated rapidly. Unfortunately, reference standards for metabolites of new psychoactive substances (NPS) such as SCs are scarce. Since parent SCs are rarely detectable in urine, elucidating their metabolism poses a significant challenge for forensic toxicology laboratories. Untargeted high-resolution mass spectrometry (HRMS) approaches, often complemented by in silico methods, are increasingly vital to forensic toxicology and metabolomics. A pooled human liver microsome (pHLM) assay was used for in vitro generation of phase I metabolites of three prevalent SCs (ADB-BUTINACA, MDMB-BUTINACA, and MDMB-4en-PINACA). Analysis was performed by trapped ion mobility spectrometry-time-of-flight mass spectrometry (timsTOF-MS) using parallel accumulation serial fragmentation (PASEF) acquisition and MetaboScape software with the T-ReX (time-aligned region complete extraction) 4D workflow including in silico metabolite prediction, fragmentation, and collision cross section (CCS) forecasting. In addition, hydrolyzed and nonhydrolyzed SC-positive urine samples were reanalyzed with this workflow. Results showed successful annotation of predicted metabolites for all three SCs, including monohydroxylations and ester hydrolysis. Qualitative pHLM and urine findings were in good agreement. Compound-specific and most abundant in vivo metabolites were incorporated into a targeted UHPLC-QTOF-MS method to analyze 42 urine samples from the casework. Although complex multistep biotransformation reactions (e.g., ADB-BUTINACA dihydrodiol formation) continue to pose a challenge for in silico prediction, this approach is significantly less time-consuming and less labor-intensive than manual evaluation of known metabolic patterns.

Keywords:  MetaboScape; T-ReX-4D annotation; collusion cross section (CCS); in silico metabolite prediction; new psychoactive substances; nontargeted workflow; synthetic cannabinoid receptor agonists; tims HRMS

DOI:  https://doi.org/10.1021/jasms.6c00160
Nat Methods. 2026 Jul 28.

AI proteomics: from protein identification to virtual cells.

Yingying Sun, Jun A, Zhiwei Liu, Rui Sun, Liujia Qian, Samuel H Payne, Wout Bittremieux, Markus Ralser, Chen Li, Yi Chen, Zhen Dong, Yasset Perez-Riverol, Asif Khan, Chris Sander, Ruedi Aebersold, Juan Antonio Vizcaíno, Jonathan R Krieger, Jianhua Yao, Wen Han, Linfeng Zhang, Yunping Zhu, Yue Xuan, Benjamin Boyang Sun, Liang Qiao, Henning Hermjakob, Haixu Tang, Huanhuan Gao, Yamin Deng, Qing Zhong, Cheng Chang, Nuno Bandeira, Ming Li, Weinan E, Siqi Sun, Yuedong Yang, Gilbert S Omenn, Yue Zhang, Ping Xu, Yan Fu, Xiaowen Liu, Christopher M Overall, Yu Wang, Eric W Deutsch, Luonan Chen, Jürgen Cox, Vadim Demichev, Fuchu He, Jiaxing Huang, Huilin Jin, Chao Liu, Nan Li, Zhongzhi Luan, Jiangning Song, Kaicheng Yu, Wanggen Wan, Tai Wang, Kang Zhang, Le Zhang, Peter A Bell, Matthias Mann, Bing Zhang, Tiannan Guo.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

DOI: https://doi.org/10.1038/s41592-026-03085-y
Biomed Chromatogr. 2026 Sep;40(9): e70575

Mass Spectrometry-Based Chromatographic and Computational Workflows for Biomarker and Therapeutic Target Discovery: A Comprehensive Review.

K Priya, R Rajesh, T Abhishek, M Ankitha, Ramesh Kumar.

  The identification of biomarkers and therapeutic targets is essential to the success of precision medicine. However, to reliably identify them, one must employ a combination of analytical and computational methods. This article describes the importance of high-quality study design, strict adherence to preanalytical and quality assurance practices and high levels of quality assurance as well as the performance characteristics of multiple types of chromatography/mass spectrometry to achieve the best possible results for sensitive, selective biomarker profiling in various biological fluids such as plasma, serum, urine, cerebrospinal fluid (CSF), and tissues. In addition, it explores the key workflows used in preparing biological samples (i.e., solid-phase extraction/specialised physical or chemical extractions/derivatisation), software/data processing pipelines for peak analysis (i.e., XCMS/MZmine/OpenMS/MS-DIAL) and processes for the identification of compounds by combining spectral libraries (e.g., Human Metabolome Database [HMDB], National Institute of Standards and Technology [NIST] and FiehnLib) with in silico tools (e.g., SIRIUS, CSI: FingerID, CANOPUS). Finally, it discusses several ways in which artificial intelligence/machine learning can be applied to the field of mass spectrometry for peak detection and provides a comprehensive review of the requirements for regulatory-grade validation and the workflow for targeted/multiplexed/quantitative liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC-MS) using stable isotope-labelled internal standards.

Keywords:  biomarker discovery; chromatography–mass spectrometry; computational workflows; data processing; regulatory‐grade method validation

DOI:  https://doi.org/10.1002/bmc.70575
Bioinform Adv. 2026 ;6(1): vbag180

Generation of peptide detectability datasets from single DIA experiment for prediction model fine-tuning.

Léo Schneider, Julie Flecheux, Zied Bouyahia, Stéphane Derrode, Jérome Lemoine.

Motivation: Accurate prediction of peptide detectability in mass spectrometry-based proteomics is critical for improving both protein identification and quantification. Current models generally estimate detectability from amino acid sequences; however, peptide detectability is influenced by the instruments, acquisition methods, and experimental conditions, limiting the applicability of sequence-based models. State-of-the-art approaches mitigate this issue by fine-tuning models for each experimental setup, yet this strategy demands extensive training datasets-often comprising up to 300 000 peptides-incurring substantial experimental and computational costs.
Results: In this study, we present a complementary approach for generating peptide detectability datasets directly from a single DIA experiment. These datasets enable fine-tuning of prediction models with minimal raw data, while improving adaptation to specific experimental conditions. This strategy substantially reduces both the data and cost requirements typically associated with model training. Furthermore, we show that filtering search libraries based on predicted detectability increases peptide identification rates and decreases computational time.
Availability and implementation: Code is available on https://github.com/leoschn/Detectability. Data are available via ProteomeXchange with identifier https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD076276PXD076276.

DOI: https://doi.org/10.1093/bioadv/vbag180
Anal Chim Acta. 2026 10 01. pii: S0003-2670(26)00762-2. [Epub ahead of print]1417 345812

Acetic acid as mobile phase additive facilitated the identification and quantification of uridine modifications in negative ion mode by HPLC-MS/MS.

Keqiang Shi, Jiancong Liao, Cuirong Sun, Hongru Feng, Yuanjiang Pan.

  RNA modifications are vital for all living organisms. A total of at least 63 types of uridine modification have been discovered, accounting for the largest proportion of all reported RNA modifications. Uridine modifications have also been shown to be dysregulated in various human diseases. However, it's well known that mass spectrometry analysis of uridine modifications is limited by their low ionization efficiencies. Here, we developed a simple and robust method for the simultaneous identification and quantification of 22 uridine modifications in biological samples. In fact, uridine and lots of its modifications are more easily ionized in negative ion mode, compared with positive ion mode commonly used. Besides, the fragmentation patterns of uridine modifications in negative ion mode could provide more structural information, compared with well-known glycosidic bond cleavage in positive ion mode. Furthermore, simply by adding 0.2 mM acetic acid into the mobile phase, the peak areas of uridine and all 22 uridine modifications in negative ion mode could be improved by 4.1-55.4 folds. With this novel method, we quantified 16, 14, 10, 12 and 16 uridine modifications in 293T total RNA, 293T small RNA, E. coli tRNA, S. cerevisiae tRNA and wheat germ tRNA, accordingly. This study provides a general good method for researchers interested in the identification and quantification of uridine modifications in various biological samples.

Keywords:  Mass spectrometry; Modified ribonucleosides; Negative ion mode; RNA modification; Uridine modification

DOI:  https://doi.org/10.1016/j.aca.2026.345812
Data Brief. 2026 Aug;67 113045

Comprehensive characterization of myopic guinea pigs' retina following PI3K/AKT inhibition using a data-independent acquisition dataset on an Orbitrap Exploris 480 mass spectrometer.

Hu Xiao, Wen Xin, Oi Ying Wong, Man Hin Leung, Jimmy Ka-Wai Cheung, Da Qian Lu, Lei Zhou, Rachel Ka Man Chun, Jing Fang Bian, Thomas Chuen Lam.

  Myopia has emerged as a global ocular health concern. The progression of myopia is predominantly driven by abnormal signaling pathways, with the PI3K/AKT pathway playing a crucial role. By utilizing the lens-induced myopia model in guinea pigs, we intravitreally injected the PI3K/AKT inhibitor LY294002 and carried out quantitative proteomic analysis using data-independent acquisition mass spectrometry. In this research, proteomic data of the retinas from guinea pigs in the normal, myopic, and drug-treated groups were provided. Moreover, a comprehensive retinal proteome database for guinea pig myopia was established, providing a resource for future investigations into myopia-related signaling pathways. All mass spectrometry data were deposited in the ProteomeXchange Consortium via the PRIDE partner (https://www.ebi.ac.uk/pride) with the dataset identifier PRIDE: PXD074201.

Keywords:  Guinea Pigs; Mass spectrometer; Myopia; PI3K/AKT pathway; Retina

DOI:  https://doi.org/10.1016/j.dib.2026.113045
Protein Sci. 2026 Aug;35(8): e70732

Scalable discovery of homomeric protein-protein interactions from cross-linking mass spectrometry data with CLAUDIO 2.0.

Tobias Löser, Alexander Röhl, Markus Baier, Andrei Lupas, Oliver Kohlbacher, Hadeer Elhabashy.

  Cross-linking mass spectrometry (XL-MS) is a powerful biochemical approach for residue-level characterization of protein structures and interactions under near-native conditions. The growing scale of XL-MS datasets demands scalable analysis pipelines that capture signals often overlooked in conventional workflows, including homomeric interactions. Here, we present CLAUDIO 2.0, a next-generation framework for structural analysis of large-scale XL-MS data. CLAUDIO 2.0 identifies homomeric interactions using overlapping peptide sequences and structural evaluation. Our optimized workflow improves computational efficiency, enabling scalable analysis and expanding structural coverage. Applied to a human mitochondrial XL-MS dataset, CLAUDIO 2.0 evaluates over 75% of cross-links using available high-confidence structural models, reduces runtime by over 95% (averaging 5 s per cross-link) compared to its predecessor, and identifies 205 proteins with homomeric interaction signals. CLAUDIO 2.0 is freely available under the MIT License at (https://github.com/ElhabashyLab/CLAUDIO) and as a web server at (https://elhabashylab.org/claudio), providing an accessible platform for scalable structural proteomics.

Keywords:  cross‐linking mass spectrometry; homomeric interaction

DOI:  https://doi.org/10.1002/pro.70732
Life (Basel). 2026 Jul 16. pii: 1177. [Epub ahead of print]16(7):

MALDI-MSI-Guided Laser Capture Microdissection Coupled with MS for Integrated Spatial Multi-Omics in Mouse Brain.

Byoung-Kyu Cho, Jessica K Lukowski, Minsoo Son, Antonia Zamacona Calderon, Moshe Levi, Katherine Stumpo, Savannah Snyder, Michael Esterling, Young Ah Goo.

  Spatial multi-omics analyzes biomolecules such as the proteome, metabolome, and lipidome within their native spatial context in tissues or cells. Mass spectrometry imaging (MSI) has emerged as a powerful technique for mapping the region-specific molecular distribution in regions of interest (ROIs). Laser capture microdissection coupled with mass spectrometry (LCM-MS) is another well-established workflow, enabling the accurate characterization of biomolecules in ROIs. To advance the current analytical application, we expanded a matrix-assisted laser desorption/ionization (MALDI)-MSI-guided LCM-MS workflow for integrated multi-omics analysis and applied it to mouse brain tissue as a proof-of-principle validation. MALDI-MSI annotated 387 putative metabolites and lipids, revealing distinct molecular distributions between the cortex and hippocampus. Both regions were subsequently isolated as ROIs using LCM and analyzed by LC-MS/MS metabolomics, lipidomics, and proteomics to achieve accurate biomolecular profiling. LC-MS/MS metabolomics and lipidomics annotated 249 compounds, several of which exhibited distinct abundance patterns between the two regions. LC-MS/MS proteomics matched to over 3500 protein groups across the two regions. Biological network analysis revealed strong associations between molecular pathways and known region-specific phenotypes. Overall, this MALDI-MSI-guided LCM-MS workflow enables comprehensive spatial multi-omics profiling and quantitative biomolecular analysis, providing valuable insights into complex biological systems and spatial molecular organization.

Keywords:  LC-MS/MS; laser capture microdissection; mass spectrometry imaging; matrix-assisted laser desorption/ionization; spatial multi-omics

DOI:  https://doi.org/10.3390/life16071177
Cell Rep Methods. 2026 Jul 31. pii: S2667-2375(26)00241-9. [Epub ahead of print] 101540

AAV-delivered TurboRFP enables streamlined tracing and analysis of corticospinal tract sprouting.

Marc Hernaiz-Llorens, Camilo J Londoño, Ellen Wang, Felicia Cai, Junmi M Saikia, Carmine L Chavez-Martinez, Hugo J Kim, Chimuanya K Agba, Binhai Zheng.

  Quantitative analysis of corticospinal tract (CST) sprouting after injury requires reliable labeling of long-range axons and fine collateral branches. Conventional biotinylated dextran amine (BDA) tracing has limited sensitivity and requires additional surgeries, while some viral-based approaches, although robust, rely on extensive tissue processing and signal amplification. Here, we describe a streamlined adeno-associated virus (AAV)-based workflow for CST sprouting analysis using TurboRFP that enables robust labeling of descending CST axons, including sprouting fibers after unilateral pyramidotomy. This approach allows direct visualization of fine CST axons without immunostaining or signal amplification, simplifying tissue processing and reducing experimental variability. Using a standardized workflow, we enable consistent CST labeling and reproducible quantification of CST remodeling. In addition, compatibility with co-delivery of other AAVs enables simultaneous circuit tracing and genetic manipulation within the same neuronal population. This workflow provides a practical platform that lowers technical barriers to axon repair research and potentially improves reproducibility across laboratories.

Keywords:  AAV vectors; CNS injury; CP: imaging; CP: neuroscience; CST; axon sprouting; axon tracing; corticospinal tract; fluorescent labeling; neural repair; pyramidotomy

DOI:  https://doi.org/10.1016/j.crmeth.2026.101540
Brief Bioinform. 2026 Jul 03. pii: bbag378. [Epub ahead of print]27(4):

LipiDetective: a deep learning model for the identification of molecular lipid species in tandem mass spectra.

Vivian Würf, Nikolai Köhler, Florian Molnar, Lisa Hahnefeld, Robert Gurke, Michael Witting, Josch K Pauling.

  Confidently identifying lipids in samples is a prerequisite for understanding their many functions in health and disease. However, accurate molecular lipid species identification via tandem mass spectrometry remains challenging. Most current approaches match measured spectra against an in-house reference library, which hinders the comparability of results. To address this challenge, the transformer model LipiDetective was developed and trained on a dataset of spectra from lipid standards, databases, and publications. Learning the characteristic lipid fragmentation patterns, LipiDetective can accurately annotate molecular lipid species in tandem mass spectra independently of the experimental setup. Integrated gradients reveals that LipiDetective focuses on peaks matching known fragments, making its predictions humanly interpretable. Therefore, LipiDetective offers a data-driven approach for molecular lipid species identification that may improve the comparability of annotations across different laboratories and analysis workflows.

Keywords:  deep learning; lipid identification; lipidomics; mass spectrometry; transformer neural network

DOI:  https://doi.org/10.1093/bib/bbag378
bioRxiv. 2026 Jul 17. pii: 2026.07.17.739151. [Epub ahead of print]

Human 28S rRNA analysed by state-of-the-art oligonucleotide mass spectrometry: benchmarking current capabilities and a call to action for MS-Seq.

Jannick Schicktanz, Yuyang Qi, Junzhou Wu, Bibek Hamal, Antony Lechner, Sam Wein, Oskar Knittelfelder, Nur Yesiltac-Tosun, Felix Dalwigk, Katharina Hoja, Leona Rusling, Sofia Obersteiner, Ella Bellenberg, Kira Kerkhoff, Michael S DeMott, Robert Ross, Philippe Wolff, Kathrin Breuker, Patrick A Limbach, Peter Dedon, Stefanie Kaiser.

Oligonucleotide mass spectrometry (MS-Seq) is emerging as a powerful approach for sequence-resolved RNA modification analysis, yet the field lacks standards for experimental workflows, data analysis and reporting. To assess current capabilities, the Human RNome Project Consortium conducted a cross-platform benchmarking study using a common RNA sample. A partial RNase T1 digest of human 28S rRNA was distributed to participating laboratories and analysed using existing LC-MS/MS workflows spanning different chromatographic strategies and mass spectrometers. To enable direct comparison, datasets were analysed using a harmonized NucleicAcidSearchEngine (NASE) workflow. Despite substantial methodological differences, laboratories recovered highly overlapping oligonucleotide sets and generated similar sequence coverage maps with a global coverage of 54.16%, demonstrating reproducible sequence information across platforms under standardized sample and analysis conditions. The benchmark further revealed incomplete sequence coverage, platform-specific differences in data architecture and increased assignment ambiguity during dynamic modification searches. Together with the community consensus developed during the HRPC workshop, these findings define priorities for the field, including improved sensitivity, standardized data analysis and reporting, community repositories, and robust bioinformatic workflows for confident de novo RNA modification discovery. This study provides an experimental benchmark and roadmap toward routine MS-based mapping of the human RNome.

DOI: https://doi.org/10.64898/2026.07.17.739151
Clin Exp Med. 2026 Jul 29. pii: 282. [Epub ahead of print]26(1):

Fatty acids metabolic reprogramming and tumor microenvironment in pancreatic cancer: targeting pathways.

Bassant Ehab, Ekram Saleh.

  Pancreatic ductal adenocarcinoma (PDAC) represents a highly aggressive form of pancreatic cancer. It is distinguished by a profound metabolic plasticity that supports its survival in a hypoxic and nutrient deprived microenvironment. Among the metabolic pathways altered in PDAC, fatty acid metabolism including synthesis, uptake, storage and β-oxidation serves as a key metabolic process that supports tumor growth, progression, and therapeutic resistance. This review discusses emerging evidence on fatty acid metabolic reprogramming and its relevance to the development and progression of pancreatic ductal adenocarcinoma. Key dysregulated enzymes as well as critical metabolic regulators are highlighted. Also, we discuss how genetic alterations drive lipogenic and oxidative pathways to sustain proliferation, redox balance, and adaptation to metabolic stress. Additionally, this review integrates current evidence to illustrate how the tumor microenvironment influences enhancing lipid availability and promoting metabolic symbiosis. Finally, this review outlines current pharmacological approaches that interfere with fatty acid metabolism, emphasizing lipid metabolism as a promising potential intervention strategy to inhibit tumor growth and sensitize cancer cells to existing treatments.

Keywords:  Fatty acids; Lipogenesis; Metabolism; Pancreatic cancer; Reprogramming; Targeted therapy; Tumor microenvironment

DOI:  https://doi.org/10.1007/s10238-026-02235-y
Metabolites. 2026 Jun 25. pii: 443. [Epub ahead of print]16(7):

Improving the Throughput and Specificity for Small-Molecule Analysis During First-Tier Mass Spectrometry-Based Newborn Screening.

Samantha L Isenberg, Charles A Pickens, Rachel Lee, Carla Cuthbert, Konstantinos Petritis.

  Background/Objectives: Mass spectrometry-based newborn screening for small-molecule biomarkers typically employs a rapid first-tier screen that omits chromatographic separations before mass spectrometric analysis, followed, only for a subset of samples and disorders, by a longer, more specific second-tier assay that includes liquid chromatographic separation prior to mass spectrometry. The second-tier screen is used when the primary biomarker lacks sufficient specificity and may result in higher false-positive rates. The throughput and specificity of first-tier newborn screening assays have been relatively stagnant over the past two decades despite significant improvements in mass spectrometry instrumentation. With the continuous expansion of disorders added to the Recommended Uniform Screening Panel in the United States, newborn screening laboratories have a need for higher-throughput assays and improved specificity. Methods: We developed and evaluated two first-tier tandem mass spectrometry approaches using a modern dual-needle, dual-loop LC-MS/MS platform: (1) a 30-s flow injection analysis tandem mass spectrometry (FIA-MS/MS) assay and (2) a rapid first-tier liquid chromatography tandem mass spectrometry (LC-MS/MS) assay using a hydrophilic interaction chromatography (HILIC) guard column (1TH). Analytical performance was assessed using dried blood spot quality control and linearity materials, including evaluations of recovery, precision, linearity, and matrix effects. Results: The 30-s FIA-MS/MS assay quadrupled the throughput of current 2-min FIA-MS/MS assays used routinely in newborn screening laboratories. The throughput improvement was achievable due to increased scan speeds of the mass spectrometer as well as the dual needle/loop design of the autosampler. In addition, these instrumentation improvements made it possible to employ liquid chromatographic separations prior to MS/MS analysis without sacrificing the approximately 2-min sample-to-sample throughput of conventional FIA-MS/MS workflows. The 1TH LC-MS/MS method separated critical isobaric and isomeric biomarkers, reduced matrix effects, improved specificity and quantification accuracy, and demonstrated acceptable recovery, precision, and linearity for newborn screening applications. Conclusions: Recent advances in LC-MS/MS instrumentation can be leveraged to either substantially increase first-tier newborn screening throughput or improve analytical specificity while maintaining current workflow timelines. First-tier LC-MS/MS using a HILIC guard column provides improved specificity that can reduce the need for second-tier testing, thereby improving overall throughput and turnaround time of the newborn screening workflow. These approaches provide flexible solutions for newborn screening laboratories seeking to accommodate expanding screening panels without compromising analytical quality or efficiency.

Keywords:  acylcarnitines; amino acids; dried blood spots; high-throughput LC-MS/MS; newborn screening

DOI:  https://doi.org/10.3390/metabo16070443
J Am Soc Mass Spectrom. 2026 Jul 26.

Making Ozonolysis Mass Spectrometry More Accessible: A Simple LC-OzESI-MS Workflow for Carbon-Carbon Double Bond Position Elucidation in Lipids and Natural Products.

Fayaj A Mulani, Ashraf M Omar, Qibin Zhang.

Elucidation of C═C unsaturation sites in biomolecules remains challenging using conventional tandem mass spectrometry techniques. Online ozonolysis inside a mass spectrometer offers a very specific C═C fragmentation strategy; however, adoption of the precursor-selective ozone-induced dissociation has been limited by its low reaction yields and the need for specialized mass spectrometers that can provide higher partial pressure of ozone and/or modifications to enable effective ion trapping. To make the online ozonolysis mass spectrometry more accessible, we present a setup in which ozone is introduced directly into the sheath gas line of a standard heated electrospray ionization source. This minimally modified online ozonolysis setup enables efficient interaction of ozone with electrospray droplets at atmospheric pressure while maintaining normal instrument operation and safety. In combination with reversed-phase liquid chromatography separation, this setup is very effective in annotating C═C positions in unsaturated biomolecules with just MS1 scanning by relying on the coelution of precursors and the ozonolysis product ions. This approach was validated across multiple lipid classes─including fatty acids, glycerophospholipids, sphingolipids, glycerolipids, and cholesteryl esters─as well as selected natural products, in complex mixtures and under both positive and negative ion modes. Reaction yields varied among analytes yet remained sufficient for unambiguous C═C position assignment. This LC-OzESI-MS strategy provides an efficient and easily adoptable platform for high-throughput structural characterization of unsaturated biomolecules, even in complex sample matrices.

DOI: https://doi.org/10.1021/jasms.6c00205
J Chromatogr B Analyt Technol Biomed Life Sci. 2026 Jul 24. pii: S1570-0232(26)00312-0. [Epub ahead of print]1282 125223

Improved comprehensive profiling of fecal bile acids through chemical derivatization combined with HPLC-MS/MS analysis.

Yanan Wang, Yongbo Chen, Qixian Ling, Libo Zhao, Xin Xiong.

  Bile acids (BAs) facilitate the digestion and absorption of fats and influence lipid and glucose homeostasis, making them potential therapeutic targets for obesity and related metabolic disorders. The liver and intestinal microbiota modify BAs structurally, generating diverse chemical forms and isomers. Comprehensive profiling of the BA pool is critical for understanding their key biological functions and as a therapeutic approach for related diseases. High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) is usually chosen as the preferred method for BA detection due to the complex chemical structures, the wide range of actual concentrations and the complexity of fecal sample matrices. However, free BAs are difficult to ionize, resulting in low detection signals and a lack of characteristic structural fragments to assist in structural identification. In this method, the labeling reagent (2-aminoethyl) trimethylammonium (AETMA) is employed to label the carboxyl group of BAs. Compared with underivatized BAs, the detection sensitivity of unconjugated BAs was enhanced by 25-180 fold, while that of conjugated BAs increased by 6-160 fold. It also generates unique fragment ions and enhances MS response, facilitating the discovery of potential BAs. Methodological parameters were validated using 38 BAs as representatives. Through methodological validation, it was verified that the precision, recovery, matrix effect and stability parameters of the method met acceptable criteria. We also identified 61 confirmed BAs and 55 additional candidate BAs in human pooled fecal samples. It has been successfully applied to fecal BA analysis in obese populations, providing valuable insights into potential therapeutic strategies for obesity.

Keywords:  Bile acid; Derivatization; Fecal; LC-MS/MS

DOI:  https://doi.org/10.1016/j.jchromb.2026.125223
Clin Chim Acta. 2026 Jul 25. pii: S0009-8981(26)00429-8. [Epub ahead of print]593 121247

Development of a liquid chromatography coupled to tandem mass spectrometry method for the quantification of Arginine Vasopressin in human plasma.

E Grifnée, E Cavalier, C Le Goff.

  Arginine Vasopressin (AVP) is a nonapeptide synthesized in the hypothalamus and released by the neurohypophysis. It plays a central role in water and electrolyte homeostasis, urine concentration and blood pressure regulation. Dysregulation of AVP secretion is associated with several pathological conditions, including central diabetes insipidus and syndrome of inappropriate antidiuretic hormone secretion. Currently, AVP quantification mainly relies on immunoassays which suffers from limitations such as cross reactivity, limited sensitivity and complex sample handling. Given the very low endogenous concentrations of AVP in human plasma (low ng/L range), a highly sensitive LC-MS/MS method was developed and validated for plasma AVP quantification. AVP was extracted using Oasis WCX solid-phase extraction prior to LC-MS/MS analysis. PBS containing 0.1% BSA was used as surrogate matrix for calibration and validation samples to avoid endogenous interferences. Method validation was performed according to the Clinical Laboratory Standards Institute guidelines. Precision, accuracy, and measurement uncertainty were evaluated using single-nested analysis of variation and e.noval software. Stability in whole blood and plasma samples was also investigated. The method was validated over a concentration range from 0.2 ng/L to 60 ng/L, with precision (coefficient of variation) below 10%, accuracy ranging from 96% and 109%, measurement uncertainty below 15%, and a lower limit of quantification of 0.2 ng/L. EDTA blood samples remained stable up to 3 h at room temperature before centrifugation. In conclusion, we have developed and validated a highly sensitive LC-MS/MS method for the quantification of AVP in plasma. Our novel methodology is well-suited for clinical applications.

Keywords:  Arginine-vasopressin; Liquid chromatography; Stability; Tandem mass spectrometry

DOI:  https://doi.org/10.1016/j.cca.2026.121247
bioRxiv. 2026 Jul 13. pii: 2026.07.10.737826. [Epub ahead of print]

Transsulfuration links aspartate-asparagine metabolism and redox homeo-stasis to drive tumor growth.

Mirko Milosevic, Kristina Dmytruk, Ahmad Alghadi, Pavel Jakoube, Julian Wong Soon, Petra Hyrossova, Muhammad Bin Munim, Sara Isabel Fernandes, Anna Shevzov-Zebrun, Robert Stanko, Isidora Mitric, Zuzana Cockova, Lukas Kucera, Juan Fernández-García, Ales Benda, Bryan Marzullo, Radislav Sedlacek, Jiri Neuzil, Sarah-Maria Fendt, Daniel A Tennant, Matthew G Vander Heiden, Katerina Rohlenova, Jakub Rohlena.

Cytosolic redox balance is tightly coupled to aspartate synthesis through the malate-aspartate shuttle, and limiting the malate-aspartate shuttle has been proposed to constrain tumor growth by restricting aspartate availability. Here we show that tumors derived from cancer cells lacking GOT1 and GOT2, the cytosolic and mitochondrial aspartate aminotransferases essential for as-partate production and malate-aspartate shuttle function, grow despite impaired canonical as-partate synthesis. This is because cytosolic redox state, not aspartate supply, is the primary metabolic bottleneck in GOT1/GOT2 knockout cells. Using single-cell transcriptomics, metabo-lite tracing, and a loss-of-function CRISPR screen, we find that these tumors engage an adaptive bypass in which availability of asparagine, a product of aspartate, enables serine- and methio-nine-dependent transsulfuration to generate α-ketobutyrate, whose reduction regenerates cy-tosolic NAD⁺ and restores redox homeostasis. Pharmacological inhibition or genetic ablation of transsulfuration abrogates this asparagine-driven rescue. These findings define asparagine as a regulator of cytosolic NAD⁺/NADH balance and reveal a link between amino acid metabolism and redox control that suggests transsulfuration as a targetable vulnerability in tumor redox maintenance.
Significance statement: Aspartate synthesis and cytosolic redox balance are both coupled through the malate-aspartate shuttle. We show that the cytosolic NAD⁺/NADH ratio, not aspartate supply, is a critical output of the malate-aspartate shuttle for tumor growth. Availability of asparagine, a product of aspar-tate, enables serine- and methionine-dependent transsulfuration to restore cytosolic NAD⁺/NADH balance, proliferation and tumor growth independently of canonical aspartate pro-duction by the malate-aspartate shuttle. This defines asparagine as a regulator of cytosolic re-dox and identifies transsulfuration as a targetable vulnerability in tumor redox maintenance.

DOI: https://doi.org/10.64898/2026.07.10.737826
Metabolites. 2026 Jun 25. pii: 445. [Epub ahead of print]16(7):

Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis.

Janvière Mutamuliza, Elizabeth Gori, Léon Mutesa, François-Guillaume Debray.

  Background: Inborn errors of metabolism (IEMs) represent a diverse group of genetic disorders affecting biochemical pathways. Despite advances in diagnostic technologies, comprehensive understanding of their historical evolution, classification systems, and diagnostic approaches remains fragmented. Objectives: This systematic review and meta-analysis aimed to synthesize evidence on the historical development, classification frameworks, and diagnostic modalities for IEMs, diagnostic accuracy, and prevalence estimates, providing a comprehensive resource for clinicians and researchers. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of seven electronic databases (PubMed/MEDLINE, Embase, Scopus, Web of Science, Google Scholar, SciSpace and ArXiv) from January 2000 to March 2026. Studies addressing historical perspectives, classification systems, or diagnostic approaches for IEMs were included. Two independent reviewers performed screening, data extraction, and quality assessment. Meta-analyses were conducted using random-effects models for diagnostic accuracy and prevalence estimates. Results: From 1342 identified records, 54 studies met the inclusion criteria, encompassing 8,234,567 individuals across 35 countries. Historical analysis revealed 16 major milestones from Garrod's 1902 "chemical individuality" concept to the current AI-powered diagnostics. Four major classification systems were identified: pathophysiological (intoxication, energy deficiency, complex molecule disorders), biochemical pathway (amino acid, organic acid, urea cycle, carbohydrate, fatty acid oxidation, mitochondrial, peroxisomal, lysosomal disorders), organelle-based, and the integrated Society for the Study of Inborn Errors of Metabolism (SSIEM) nosology. Meta-analysis demonstrated high diagnostic performance of tandem mass spectrometry (MS/MS) with a pooled sensitivity of 99.1% (95% CI: 98.6-99.5) and specificity of 99.8% (95% CI: 99.7-99.9%). The pooled global prevalence of IEMs was 50.9 per 100,000 live births (95% CI 45.2-56.8). Next-generation sequencing achieved a diagnostic yield of 42.8% (95% CI: 38.2-47.5%) in suspected cases. Emerging AI-powered diagnostic tools demonstrated high discrimination performance with area under the curve (AUC) values exceeding 0.95 for specific IEM, though external validation remains limited. Newborn screening expanded from single-disease to comprehensive panels detecting over 50 disorders. Conclusions: This comprehensive review demonstrates that IEMs have evolved from rare curiosities to systematically diagnosable conditions through technological advances. Integration of metabolomics, genomics, proteomics and artificial intelligence promises further diagnostic improvements. Standardized classification systems and evidence-based diagnostic algorithms are essential for optimal patient care. Future directions include artificial intelligence-enhanced diagnostics, expanded screening, and personalized medicine approaches.

Keywords:  artificial intelligence; classification systems; diagnosis accuracy; historical perspectives; inborn errors of metabolism; metabolomics; newborn screening; next-generation sequencing; systematic review; tandem mass spectrometry

DOI:  https://doi.org/10.3390/metabo16070445
J Chromatogr B Analyt Technol Biomed Life Sci. 2026 Jul 26. pii: S1570-0232(26)00307-7. [Epub ahead of print]1282 125218

PEth in review: Phosphatidylethanol discovery and LC-MS/MS analysis.

Grace R Williams, Justin L Poklis, Carrol Nanco, Carl E Wolf.

  The objective of this manuscript is to provide contextual information about the clinical ethanol biomarker phosphatidylethanol and evaluate contemporary analysis methods, mainly analysis by LC-MS/MS. The first part of the manuscript includes information regarding discovery, characterization, and result interpretation guidance relevant to the use of phosphatidylethanol as a clinical biomarker of ethanol ingestion. After this foundation information is discussed, various published analysis methods are reviewed and compared. Particular attention is paid to considerations for whole blood sample preparation and LC-MS/MS analysis conditions, including information regarding stability of PEth and a brief overview of dried-blood spot analysis. Lastly, special considerations relevant to the analysis of an endogenous, abnormal phospholipid are highlighted.

Keywords:  Biomarkers; Clinical biomarkers; Ethanol; Ethanol exposure; LCMS; Mass spectrometry; PEth; PEth 16:0/18:1; Phosphatidylethanol; Phospholipids; Transplant

DOI:  https://doi.org/10.1016/j.jchromb.2026.125218