bims-mascan 2026-03-08 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–03–08
nineteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

A targeted metabolomic method to detect epigenetically relevant metabolites.
Optimization of the Identification Rate and Reproducibility of an Untargeted diaPASEF Method Applicable for Quantitative Peptide Profiling of Hydrolyzed Infant Formula.
Fine-Tuning of Label-Free Single-Cell Proteomics Workflows.
Ultrafast peptide preparation brings shotgun proteomics into the minute era.
Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform.
High Coverage Quantitative Lipidomic Analysis for Multiple Biological Matrices Using Ultrahigh-Performance Liquid-Chromatography and Tandem Mass Spectrometry.
Organelle-selective click chemistry for monitoring fatty acid metabolism at the subcellular level.
A systematically optimized two-dose differential strategy integrating stable isotope tracing and mass shift defect filtering for reducing false positives in high-resolution mass spectrometry-based drug metabolite profiling.
Modern LC-MS strategies for comprehensive metabolite identification of therapeutic oligonucleotides.
Non-Reducing Proteomics Reveals Disulfide-Dependent Proteoform Remodeling Under Oxidative Stress.
Need for speed: Advances in the era of high throughput interaction proteomics.
One-Pot O-Glycan Release and Permethylation (OPORP) Enables Rapid and Quantitative O-Glycan Analysis from Low-Input Samples.
Spatial Neurobiology in Brain Organoids Using Mass Spectrometry Imaging-Based Multiomics Approach.
Functional Lipid Analysis via Index-Based Lipidomics Profile: A New Computational Module in LipidOne.
Protocol for quantifying the oxidative cleavage of amyloid-β using oxygen-18 isotope labeling and LC-MS/MS.
Direct infusion nano-electrospray ionization mass spectrometry for untargeted metabolomics of trace-volume aqueous humor from prematurity infants.
DrPISA: Deep eutectic solvent-assisted reverse proteome-integrated solubility alteration for high-sensitivity drug target identification.
Metabolomics biomarkers for precision psychiatry.
Quantitative Succinylacetone Measurement by Gas Chromatography-Tandem Mass Spectrometry (GC-MS/MS) Facilitates Diagnosis, Monitoring, and Characterization of Tyrosinemia Type 1 and Other Hypersuccinylacetonemias.

Mol Metab. 2026 Feb 26. pii: S2212-8778(26)00026-8. [Epub ahead of print] 102342

A targeted metabolomic method to detect epigenetically relevant metabolites.

Joan Miro-Blanch, Alexandra Junza, Jordi Capellades, Aurelie Balvay, Claire Maudet, Marta Kovatcheva, Silvia Raineri, Sylvie Rabot, Jane Mellor, Manuel Serrano, Oscar Yanes.

   PURPOSE OF THE RESEARCH: To develop a sensitive, versatile analytical method capable of simultaneously detecting epigenetically relevant metabolites without chemical derivatization. We also aim to establish a stable isotope tracing methodology to track the biosynthesis of key epigenetic donors, S-adenosylmethionine (SAM) and acetyl-coenzyme A (acetyl-CoA), and demonstrate the method's reproducibility and quantitative accuracy through case-control studies that link metabolism to epigenetics.
BASIC PROCEDURES: After a comprehensive literature review, we selected 42 metabolites based on their roles in epigenetic processes such as methylation and acetylation, and devised a targeted metabolomics approach to extract, detect, and quantify these metabolites (Supplementary table 1 and Figure 1). We then optimized ionization parameters and scan rate using pure standards to maximize metabolite coverage in LC-MS/MS. We chose a biphasic extraction method adapted from Lotti et al., using phosphoric acid (15%) and methyl tert-butyl ether (MTBE) for efficient extraction of a wide range of metabolites, including short-chain fatty acids (SCFAs) and formate, without the need for chemical derivatization. The organic phase was analyzed by GC-MS/MS, while the aqueous phase was subjected to LC-MS/MS using a zwitterionic HILIC column with medronic acid to improve peak shape and retention of charged metabolites. To potentially link metabolism and epigenetic modifications, we implemented a stable isotope tracing methodology to track 13C-labeled glucose, glutamine, or serine into SAM and acetyl-CoA. Our method focuses on measuring isotopomers rather than isotopologues, offering a nuanced understanding of labeled carbon atom fate.
MAIN FINDINGS: Our method demonstrated high reproducibility and sensitivity, enabling the quantitative analysis of over 30 epigenetically relevant metabolites, including SCFAs, SAM, and acetyl-CoA, in various biological samples. We successfully quantified these metabolites in three case-control studies: (1) liver and gut content from germ-free and conventional mice, revealing significant differences in SCFA levels and other metabolites linked to one-carbon metabolism and energy production. (2) During OSKM reprogramming of mouse embryonic fibroblasts vitamin B12 supplementation enhances cellular reprogramming. Using 13C-serine as a tracer, we observed a time-dependent increase in SAM enrichment, with additive effects from vitamin B12, primarily due to heightened labeling of the +1 isotopomers formate and methyl group. (3) In an isogenic human glioma cell line with the IDH1 R132H mutation, both wild-type and mutant cells predominantly used glucose carbons for acetyl-CoA synthesis. However, while no significant differences were observed in glucose metabolism between WT and mutant cells, we noted increased glutamine consumption in IDH1-R132H cells, evidenced by higher enrichment of the acetyl group in acetyl-CoA.
NEW AND IMPORTANT ASPECTS OF OUR STUDY: We present an innovative analytical methodology for the simultaneous detection and quantification of over 30 epigenetically relevant metabolites, including short chain fatty acids. Using stable isotope tracing to track the synthesis of S-adenosylmethionine (SAM) and acetyl-Coenzyme A (acetyl-CoA), our method reveals new insights into metabolism linked to epigenetic modifications, including glycolysis, the pentose phosphate pathway, de novo glycine synthesis, and the folate and methionine cycle. Demonstrating practical utility in case-control studies, this approach supports integrative multi-omics strategies to explore the interplay between metabolism and epigenetics across various biological systems and diseases.

Keywords:  Epigenetics; Mass spectrometry; Metabolism; Metabolomics; Microbiota; Stable isotope labeling

DOI:  https://doi.org/10.1016/j.molmet.2026.102342
J Proteome Res. 2026 Mar 04.

Optimization of the Identification Rate and Reproducibility of an Untargeted diaPASEF Method Applicable for Quantitative Peptide Profiling of Hydrolyzed Infant Formula.

Alina Kupfer, Sabrina Gensberger-Reigl, Joachim Stützer, Monika Pischetsrieder.

  Quality control of hydrolyzed infant formula (HIF) requires comprehensive and precise quantification of its peptide components. Quantitative peptidome analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) with data-independent acquisition (DIA) and parallel accumulation-serial fragmentation (PASEF) is used for this application. Here, an optimization strategy was developed to increase the peptide identification rate and the qualitative and quantitative reproducibility of this approach. To expand the peptide identification rate, the originally assigned equidistant ion mobility (IM) windows were transferred to variable ion mobility windows with manually adjusted window placement. To improve the reproducibility, major acquisition parameters, such as the number of diaPASEF scans and ion mobility windows as well as the resulting cycle time, were systematically optimized. Thus, the approach was modified from 17 equidistant windows with a cycle time of 1.8 s to 30 variable windows with a cycle time of 1.7 s. The optimization process led to the identification of 628 peptides versus 522 peptides, increasing the identification rate by 20.3%. Concurrently, the coefficient of variation (CV) for peptide identification was reduced from 10.9 to 0.8%, and for quantitative reproducibility, it was reduced from 24.3 to 17.2%. Based on these results, an optimization workflow is presented to systematically improve the identification rate and reproducibility for other sample types and instruments.

Keywords:  LC−MS/MS; cycle time; data-dependent acquisition; data-independent acquisition; ion mobility; isolation window width; precursor coverage; spectral library; timsTOF Pro

DOI:  https://doi.org/10.1021/acs.jproteome.5c01162
J Proteome Res. 2026 Mar 04.

Fine-Tuning of Label-Free Single-Cell Proteomics Workflows.

Pauline Perdu-Alloy, Charline Keller, Anjali Seth, Christine Carapito.

  Mass spectrometry-based single-cell proteomics emerges as the most promising method for studying cellular heterogeneity at the global proteome level with unprecedented depth and coverage. Its widespread application remains limited due to robustness, reproducibility, and throughput requirements, still difficult to meet as analyzing large cohorts of single cells is necessary to ensure statistical confidence. In this context, we conducted method optimizations at three levels. First, we benchmarked three distinct workflows compatible with the nanoElute2 platform using different sample collection/preparation plate supports (EVO96 oil-free, LF48 oil-based, and LF48 oil-free, a streamlined automated sample resuspension, and direct injection protocol). Then, we compared the optimized EVO96 workflow on nanoElute2 with Evosep-based separations operating at two analytical throughputs (80 and 120 samples per day). Subsequently, we evaluated digestion efficiency using a range of enzyme/protein ratios (1:1; 10:1; 20:1; 50:1) to maximize peptide recovery. Finally, the chromatographic setup was refined to determine the best compromise between throughput and robustness. Altogether, these optimizations allowed to establish a robust workflow quantifying up to 5000 proteins in 10 min gradient time per single HeLa cell at a 55 samples-per-day throughput.

Keywords:  LC–MS/MS acquisition; SCP sample preparation; dia-PASEF; digestion efficiency; label-free quantification; single-cell proteomics (SCP)

DOI:  https://doi.org/10.1021/acs.jproteome.5c01075
Anal Chim Acta. 2026 Apr 15. pii: S0003-2670(26)00173-X. [Epub ahead of print]1395 345223

Ultrafast peptide preparation brings shotgun proteomics into the minute era.

Haowei Ma, Ming Bi, Lang Zhao, Zhixin Tian.

   BACKGROUND: Mass spectrometry-based shotgun proteomics relies on efficient sample preparation, where proteins are reduced, alkylated, and digested into peptides for LC-MS/MS analysis. While LC-MS/MS and bioinformatic identification have advanced to minute-scale workflows, sample preparation remains a major bottleneck, typically requiring hours to days. Although methods such as high-pressure and droplet-based reactions have reduced processing times, achieving minute-scale preparation has remained challenging. There is a clear need for an integrated, rapid sample preparation strategy to match the speed of modern LC-MS/MS and data analysis platforms.
RESULTS: We developed OPPRAD (One-Pot Protein Reduction, Alkylation, and Digestion), an ultrafast sample preparation method conducted in water-in-oil nano/micro-droplets. This one-pot process completes all three key reactions within 5 min. When integrated with rapid LC-MS/MS on the Orbitrap Astral platform and MSFragger- or DIA-NN-based identification, the entire workflow achieved serum-to-peptide identification in 25.9 min and tissue-to-identification in 35.9 min-the fastest reported proteomic pipeline. The method showed high peptide recovery (∼68-71%), excellent cysteine alkylation (>99%), and miss cleavage rates comparable to conventional bulk digestion. It enabled deep proteome coverage across a wide dynamic range and was successfully applied to identify differentially expressed proteins in paired liver cancer tissues.
SIGNIFICANCE: OPPRAD drastically shortens proteomic sample preparation from hours to minutes, reducing steps, cost, and hands-on time. This innovation bridges a critical gap in high-throughput proteomics, enabling rapid translational applications and large-scale clinical studies. The method's speed and efficiency pave the way for real-time proteomic analysis and future integration with online desalting and database searching.

Keywords:  Alkylation; Digestion; Minute era; OPPRAD; Proteomics; Reduction

DOI:  https://doi.org/10.1016/j.aca.2026.345223
Nat Commun. 2026 Mar 02.

Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform.

Hui-Yin Chang, Yamei Deng, Ruohong Li, Dmitry Avtonomov, Bo Wen, Sarah E Haynes, Felipe da Veiga Leprevost, Bing Zhang, Fengchao Yu, Alexey I Nesvizhskii.

Isobaric mass tags, such as isobaric tags for relative and absolute quantitation (iTRAQ) and tandem mass tag (TMT), are widely utilized for peptide and protein quantification in multiplex quantitative proteomics. We present TMT-Integrator, a bioinformatics tool for processing quantitation results from TMT and iTRAQ experiments, offering integrative reports at the gene, protein, peptide, and post-translational modification site levels. We demonstrate the versatility of TMT-Integrator using five publicly available TMT datasets: clear cell renal cell carcinoma (ccRCC) whole proteome and phosphoproteome datasets from the Clinical Proteomic Tumor Analysis Consortium, an E. coli dataset with 13 spike-in proteins, and two human cell lysate datasets showcasing the latest advances with the Thermo Orbitrap Astral mass spectrometer and TMTpro 35-plex reagents. Integrated into the widely used FragPipe computational platform (https://fragpipe.nesvilab.org/), TMT-Integrator is a core component of TMT and iTRAQ data analysis workflows. We evaluated the performance of FragPipe coupled with TMT-Integrator analysis pipeline against MaxQuant and Proteome Discoverer with multiple benchmarks, facilitated by the bioinformatics tool OmicsEV. Our results show that FragPipe coupled with TMT-Integrator quantifies more proteins in the E. coli and ccRCC whole proteome datasets, quantifies more phosphorylated sites in the ccRCC phosphoproteome dataset, and overall delivers more robust quantification performance compared to other tools.

DOI: https://doi.org/10.1038/s41467-026-70118-7
Anal Chem. 2026 Mar 03.

High Coverage Quantitative Lipidomic Analysis for Multiple Biological Matrices Using Ultrahigh-Performance Liquid-Chromatography and Tandem Mass Spectrometry.

Qinsheng Chen, Chenhan Zhang, Xiaoli Zhang, Di Zhang, Shuaiyao Wang, Haoran Hu, Dandan Yang, Yushi Li, Pinghui Liu, Jingxian Zhang, Longlong Chen, Men Yu, Ting Liu, Yifei Xue, Qi Wang, Qingxia Huang, Hongyan Yao, Yulan Wang, Huiru Tang.

The lipid composition (lipidome) in biological samples is extremely complex, having diverse biofunctions. Quantifying lipidomes with high coverage is vital to understand such functions but challenging due to their levels spanning several orders of magnitude, limited available standards, and poor chromatographic performances for many acidic lipids such as sphingosine-1-phosphate, phosphatidylserines, and phosphatidic acids. Here, we report a reliable method for high-coverage quantitative lipidomics using ultrahigh-performance liquid chromatography and tandem mass spectrometry (UHPLC-MS/MS). By using both pH and ammonium gradients in elution, all lipids, especially acidic ones, had obviously improved LC separation. By using 267 lipid standards in 49 subclasses, we also established quantitative structure-retention relationship models to predict the retention time (tR) with good accuracy (ΔtR < 0.33 min, MRE ∼3.4%) for all lipid subclasses. With UHPLC-MS/MS in multiple-reaction monitoring mode, we subsequently developed a quantitative lipidomics method using three UHPLC conditions to enable coverage of over 21,700 lipids in 190 subclasses with good sensitivity, precision, accuracy and stability. We further confirmed its applicability by quantifying 2375 lipids in seven typical biological matrices including human plasma, urine, and non-small-cell lung cancer cells together with E. coli, Arabidopsis leaves, mouse liver tissue, and feces. This offers a high-coverage quantitative method for understanding molecular phenotypes associated with lipid functions in physiology and pathophysiology.

DOI: https://doi.org/10.1021/acs.analchem.5c06487
Methods Enzymol. 2026 ;pii: S0076-6879(26)00024-8. [Epub ahead of print]727 355-371

Organelle-selective click chemistry for monitoring fatty acid metabolism at the subcellular level.

Tomonori Tamura, Seita Kawamoto, Itaru Hamachi.

  Fatty acids are vital cellular components, serving as energy sources and building blocks of membranes. Their metabolism involves multiple enzymatic processes localized to specific organelles, suggesting organelle-dependent distribution of fatty acid-containing lipids. Conventional lipidomics methods, while powerful, often lack spatiotemporal resolution due to reliance on bulk extracts or fractionation. To overcome this, we developed an organelle-selective labeling strategy combining metabolic incorporation of azide-modified fatty acids (AFAs) with organelle-directed copper-free click chemistry. Following the metabolic incorporation of azide analogs of palmitate or oleate into mammalian cells, azide-modified lipids in the endoplasmic reticulum (ER)/Golgi apparatus, mitochondria, lysosomes, and plasma membrane could be visualized and profiled through labeling with organelle-targeting clickable dyes. Distinct lipid distributions were observed among organelles, consistent with known metabolic pathways, such as enrichment of polyunsaturated lipids in mitochondria. Pulse-chase experiments enabled the tracking of interorganelle transport, particularly ER-to-mitochondria trafficking of phosphatidylcholine and phosphatidylethanolamine, and they further revealed a transient accumulation of diacylglycerol within mitochondria. Overall, this methodology enables fractionation-free, organelle-level lipidomics with high spatial and temporal resolution, providing unprecedented insights into fatty acid metabolism and offering a versatile platform for future studies of subcellular lipid dynamics. Here we describe detailed protocols for sample preparation and subsequent analyses by thin-layer chromatography and mass spectrometry.

Keywords:  Fatty acids; Interorganelle lipid transport; Lipids; Organelle-selective click reaction; Organelles; Pulse chase analysis

DOI:  https://doi.org/10.1016/bs.mie.2026.01.016
Talanta. 2026 Feb 23. pii: S0039-9140(26)00237-7. [Epub ahead of print]305 129582

A systematically optimized two-dose differential strategy integrating stable isotope tracing and mass shift defect filtering for reducing false positives in high-resolution mass spectrometry-based drug metabolite profiling.

Chang-Te Lin, Yi-Shiou Chiou, Ji-Rui Yang, Jie-Huei Wang, Meng-Chi Yen, Cai-Rong Li, Chia-Lung Shih.

  Drug metabolite detection using metabolomics-based approaches is often challenged by high false-positive rates and the limited availability of authentic reference standards. In this study, we systematically optimized a data processing workflow that integrates a two-dose differential strategy with stable isotope tracing (SIT) and mass shift defect filter (MSDF) to improve the detection and confirmation of drug-related metabolites. Using isotopically labeled (D0/D3) compounds, metabolite features were confirmed based on MS/MS fragmentation profiles and characteristic isotopic mass shifts, providing indirect yet robust evidence for metabolite assignment. A total of 56 sildenafil-related metabolite features were putatively detected following MS/MS-based confirmation. Comparative analysis of three incubation setups revealed that the separated incubation setup consistently yielded the largest number of metabolite features, despite showing a relatively modest improvement in detection rate after MSDF incorporation. Notably, mixing D0- and D3-labeled compounds within the same incubation tube resulted in a marked reduction in metabolite detection, consistent with previous findings and underscoring the importance of experimental design in isotope-assisted metabolomics studies. The effects of key analytical parameters, including MSDF threshold, sample size, and retention time tolerance, were systematically evaluated. An MSDF window with an absolute deviation of <0.12 Da and a retention time tolerance of 0.2 min, with a sample size of three paired samples, were identified as optimal settings that balance detection rate and metabolite coverage. Overall, this work demonstrates a robust and scalable workflow for comprehensive drug metabolite profiling and provides practical guidance for optimizing metabolomics-based metabolite identification strategies in the absence of authentic reference standards.

Keywords:  Mass defect filtering; Stable isotope tracing; Systematic workflow optimization

DOI:  https://doi.org/10.1016/j.talanta.2026.129582
J Chromatogr B Analyt Technol Biomed Life Sci. 2026 Mar 03. pii: S1570-0232(26)00090-5. [Epub ahead of print]1275 125001

Modern LC-MS strategies for comprehensive metabolite identification of therapeutic oligonucleotides.

Devendra Kumar, Nilesh Dahibhate, Neerja Trivedi.

  Therapeutic oligonucleotides (ONs), including antisense-oligonucleotides, small interfering RNA, aptamers, and conjugated modalities, have emerged as an important class of drugs with increasing clinical impact. Unlike small molecules, ONs undergo metabolism primarily through nuclease-mediated cleavage, generating complex profiles of shortened metabolites that often differ by a single nucleotide and may retain pharmacological or toxicological relevance. Comprehensive metabolite identification is therefore essential for understanding ONs pharmacokinetics (PK), tissue exposure, and safety. Liquid chromatography coupled to mass spectrometry (LC-MS) has become the principal analytical platform for ONs metabolite identification. Recent advances in chromatographic separation, high-resolution mass spectrometry, fragmentation strategies, and data processing tools have substantially improved the depth, confidence, and throughput of metabolite characterization. This review provides an overview of ONs biotransformation pathways and critically examines modern LC-MS strategies used for metabolite separation, detection, and structural elucidation. Emphasis is placed on high-resolution MS acquisition approaches, charge-state management, complementary fragmentation techniques, and software-assisted metabolite annotation. Emerging trends and future directions in ONs metabolite analysis are also discussed, with a focus on supporting translational PK and regulatory decision making.

Keywords:  LC-MS; Metabolite identification; Oligonucleotides; Pharmacokinetics (PK)

DOI:  https://doi.org/10.1016/j.jchromb.2026.125001
Mol Cell Proteomics. 2026 Mar 02. pii: S1535-9476(26)00044-7. [Epub ahead of print] 101548

Non-Reducing Proteomics Reveals Disulfide-Dependent Proteoform Remodeling Under Oxidative Stress.

Yeonjoo Lee, Tae-Kyung Kim, Seungjin Na, Kong-Joo Lee, Jaeho Jeong, Eun Joo Song.

Oxidative stress triggers redox-sensitive post-translational modifications, notably disulfide bond formation involving cysteine residues. However, these bonds are often overlooked in proteomics due to the routine use of reducing agents. Here, we employed liquid chromatography-mass spectrometry (LC-MS) based metabolomics and non-reducing tandem mass tag (TMT) proteomics to investigate the effects of H2O2 on MDA-MB-231 cells. Metabolomic analysis revealed pathway-specific inhibition of major metabolic pathways including glycolysis, the tricarboxylic acid (TCA) cycle, and nucleotide biosynthesis. Proteomic analysis using the DBond algorithm revealed extensive and isoform-specific disulfide crosslinks across more than 1,000 proteins. These linkages were enriched at redox-sensitive cysteines near basic residues and displayed high isoform specificity. Our findings demonstrate that disulfide bond formation serves as a selective mechanism of redox regulation. This study highlights the utility of non-reducing proteomics in elucidating redox-controlled protein networks and structural dynamics under oxidative stress.

DOI: https://doi.org/10.1016/j.mcpro.2026.101548
Mol Omics. 2026 Mar 07. pii: aaiag012. [Epub ahead of print]

Need for speed: Advances in the era of high throughput interaction proteomics.

Fabian Frommelt, Uliana Federico, Ruedi Aebersold, Andrea Fossati.

  Protein-protein interactions are central to virtually all biological processes, forming intricate networks that operate in a highly regulated manner. These interactions are not permanent but rather continuously adapt to environmental changes, developmental cues, or disease-related stress. Understanding which protein interactions are present in a specific cellular state and how they adapt to specific stimuli is one of the long-standing goals of modern systems biology. Mass spectrometry-based proteomics has emerged as the primary tool for charting these networks. Over the past two decades, continuous advances in instrumentation, sample preparation, and data analysis have enabled researchers to explore the protein interaction landscape with increasing depth and accuracy. This has led to important discoveries in areas ranging from fundamental cell signaling to the identification of new therapeutic targets. We present the current state of MS-based protein interaction analysis, focusing on the three most widely utilized approaches: affinity purification, proximity labeling and co-fractionation mass spectrometry. For each we discuss the fundamental approach, technical considerations, limitations and highlight the potential integration with future technologies and datasets. Recent innovations such as short-gradient chromatography and faster data acquisition have further improved sensitivity and throughput. Together, these developments are bringing researchers closer to mapping the dynamic, context-dependent architecture of protein networks in unprecedented detail.

Keywords:  co-fractionation; protein complex profiling; protein complexes; protein-protein interactions

DOI:  https://doi.org/10.1093/molecular-omics/aaiag012
Anal Chem. 2026 Mar 02.

One-Pot O-Glycan Release and Permethylation (OPORP) Enables Rapid and Quantitative O-Glycan Analysis from Low-Input Samples.

Shuhong Guo, Nafisa Tursumamat, Shengyang Liu, Qiannan Liu, Zhenyu Wang, Huili Lu, Ying Han, Piliang Hao, Jianwei Zhu, Juan Wei.

O-glycosylation, an exceptionally complex and heterogeneous post-translational modification, plays pivotal roles in diverse biological and pathological processes, and is a key regulator of biopharmaceutical quality and efficacy. However, the vast structural diversity and the absence of a universal O-glycosidase make simple and reproducible O-glycan analysis a long-standing challenge, especially for low-input samples. Current O-glycan preparation workflows typically require microgram-level starting protein material and involve laborious derivatization and purification steps. Moreover, many O-glycan release methods are prone to "peeling" reactions, leading to glycan degradation and compromised quantitative accuracy. Here, we present a highly efficient One-Pot strategy for simultaneous O-glycan Release and Permethylation, termed OPORP, yielding derivatized glycans compatible with both matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and widely available reversed-phase liquid chromatography-mass spectrometry (RPLC-MS). Integrated MALDI-MS, OPORP enables comprehensive O-glycan profiling from nanogram-level protein samples within 2 h. Notably, major O-glycans could be detected from as low as 1 ng of fetuin input and as few as 1,000 MCF-7 cells using RPLC-MS. The method also provides low inter- and intra-assay variability (CV < 20%) and good quantitative linearity for low-input samples (R2 ≥ 0.95). With robust quantitative performance, we reveal markedly distinct O-glycan profiles between darbepoetin alfa and a higher-potency novel analog with accuracy. Overall, the simple yet powerful OPORP strategy combines exceptional sensitivity, throughput, and robust quantification, establishing a new methodological benchmark for O-glycan analysis with broad applications.

DOI: https://doi.org/10.1021/acs.analchem.5c07679
ACS Chem Neurosci. 2026 Mar 03.

Spatial Neurobiology in Brain Organoids Using Mass Spectrometry Imaging-Based Multiomics Approach.

Nav Raj Phulara, Kadia Lissit, Yuejia Huang, Jin-Chong Xu, Herana Kamal Seneviratne.

  Brain organoids are stem cell-derived, three-dimensional models that more accurately mimic the cellular complexity and architecture of human brain tissues compared to traditional two-dimensional (2D) cultures or animal models. Their physiological relevance and human-specific neurobiology enhance translational research while aligning with current regulatory shifts toward reducing animal testing in biomedical science. A thorough understanding of the molecular landscape of various biomolecules, such as lipids, metabolites, proteins, and glycans in physiologically relevant brain models, such as organoids, is essential to deciphering complex neurobiology. While mass spectrometry has long been used to understand such molecular landscape in tissues, a single-omics approach is insufficient to fully capture the complexity of brain biology. Therefore, multiomics strategies, such as high-resolution mass spectrometry imaging (MSI), mass spectrometry-based proteomics, and lipidomics, together can provide a holistic view of biomolecular interplay within tissue microenvironments. Moreover, since MSI retains spatial information within tissues, MSI-based multiomics approaches hold immense potential to uncover complex neurobiology within brain organoids. In this article, we present our perspectives on leveraging MSI-based multiomics in brain organoids to understand the complex molecular interplay underlying neurobiology.

Keywords:  MALDI mass spectrometry imaging; brain organoids; lipid metabolism; multiomics; spatial neurobiology

DOI:  https://doi.org/10.1021/acschemneuro.5c00971
Bioinformatics. 2026 Mar 01. pii: btag090. [Epub ahead of print]

Functional Lipid Analysis via Index-Based Lipidomics Profile: A New Computational Module in LipidOne.

Husam B R Alabed, Dorotea Frongia Mancini, Martina Pergola, Luigina Romani, Sabata Martino, Albert Koulman, Roberto Maria Pellegrino.

MOTIVATION: Understanding the functional roles of lipids is essential for interpreting metabolic phenotypes in health, disease, and dietary interventions. However, lipidomic analyses typically focus on individual lipid species, making it difficult to extract mechanistic and systems-level insights. We therefore asked how quantitative lipidomic data can be translated into biologically structured and function-oriented interpretations.
RESULTS: Here, we present a major update to LipidOne (lipidone.eu), introducing the novel analytical module: Functional Lipid Analysis (FLA). FLA computes 42 indices describing lipid functions related to membrane structure, energy storage, and signaling. Indices are derived from lipid classes and fatty acyl-, alkyl-, and alkenyl-chain composition, statistically compared across experimental groups, and explored using multivariate and visualization tools. Each index is semantically annotated and linked to predicted protein mediators, enabling pathway- and network-based interpretation. Application to published datasets confirmed previous conclusions while uncovering additional biologically coherent functional insights.
AVAILABILITY AND IMPLEMENTATION: New FLA module is freely available through LipidOne.eu web platform. The LipidOne FLA core R script (v1.0.0) is archived on Zenodo (DOI: 10.5281/zenodo.18468230). The LipidOne web platform is available at https://lipidone.eu.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btag090
STAR Protoc. 2026 Mar 05. pii: S2666-1667(26)00066-3. [Epub ahead of print]7(1): 104413

Protocol for quantifying the oxidative cleavage of amyloid-β using oxygen-18 isotope labeling and LC-MS/MS.

Yiying Zhu.

  Here, we present a protocol for quantifying the oxidative cleavage of amyloid-β using oxygen-18 labeling coupled with high-resolution LC-MS/MS. We describe procedures for amyloid-β peptide preparation, copper(II)/ascorbate-mediated oxidative cleavage, oxygen-18 incorporation, LC-MS/MS acquisition, and isotopologue-based quantitative data analysis. By incorporating oxygen-18 into newly generated C-termini during metal-driven cleavage and applying defined isotope conditions, this protocol enables site-specific identification of true cleavage-derived fragments while discriminating them from background signals arising from natural isotopic overlap and pre-existing impurities. This strategy supports confident identification of oxidative cleavage products and can be adapted to studies of peptide degradation in related systems. For complete details on the use and execution of this protocol, please refer to Yang et al.1.

Keywords:  Chemistry; Neuroscience; Proteomics

DOI:  https://doi.org/10.1016/j.xpro.2026.104413
Talanta. 2026 Feb 23. pii: S0039-9140(26)00232-8. [Epub ahead of print]305 129577

Direct infusion nano-electrospray ionization mass spectrometry for untargeted metabolomics of trace-volume aqueous humor from prematurity infants.

Wen Fan, Nianfeng Tang, Le Tong, Weiqi Li, Miao Xu, Sichao Xie, Runmin Xie, Dechen Jiang, Songtao Yuan.

  Reactivation of retinopathy of prematurity (ROP) after anti-VEGF therapy poses a serious risk of retinal detachment. Metabolomics of aqueous humor (AH) holds promise for the discovery of early biomarkers, but the extremely limited sample volumes (as low as 2 μL) from prematurity infants hinders traditional liquid chromatography-mass spectrometry (LC-MS). Here, we applied direct infusion nano-electrospray ionization mass spectrometry (DI-nESI-MS) for metabolomic profiling of trace-volume AH samples without chromatographic separation. By comparing the aqueous humor of infants with and without ROP reactivation, as well as before and after treatment, we found significant metabolic changes, including abnormal fatty acid oxidation, inhibition of glycolysis and dysregulation of the serine-glycine pathway. Elevated ascorbic acid levels are regarded as a potential protective factor. Our research established DI-nESI-MS as a powerful tool for the analysis of extremely small amounts of biological fluids and revealed new metabolic insights into ROP reactivation.

Keywords:  Aqueous humor; Direct infusion nano-electrospray ionization mass spectrometry; Metabolomics; Reactivation; Retinopathy of prematurity

DOI:  https://doi.org/10.1016/j.talanta.2026.129577
Anal Chim Acta. 2026 Apr 22. pii: S0003-2670(26)00184-4. [Epub ahead of print]1396 345234

DrPISA: Deep eutectic solvent-assisted reverse proteome-integrated solubility alteration for high-sensitivity drug target identification.

Liu Yang, Tian-Bo Ma, Chen-Wan Guo, Run-Bo Yu, Ping Li, Hua Yang, Wen Gao.

   BACKGROUND: Thermal stability-based proteomic strategies, including thermal proteome profiling (TPP) and proteome integral solubility alteration (PISA), enable proteome-wide target identification but predominantly analyze soluble proteins, overlooking heat-induced aggregates that may contain informative targets. Conventional denaturants used for solubilization of these precipitates offer limited performance and compatibility. Here, we developed a deep eutectic solvent (DES)-assisted reverse PISA (DrPISA) strategy to enhance insoluble proteome accessibility and improve detection sensitivity for subtle thermal stability alterations.
RESULTS: A systematic evaluation of 65 DES formulations identified DES-48 (l-proline:glycerol:water, 1:1:4) as an optimal solubilization reagent for heat-aggregated proteomes, delivering up to 71.7% more identified proteins than GuHCl and 23.5% more than urea, together with 80.6% fully cleaved peptides and excellent quantitative reproducibility. Integrating DES-48 into a reverse PISA workflow enabled sensitive detection of early-stage aggregation events not captured in soluble-focused assays. Across five reference compounds, DrPISA reproducibly recovered known targets and increased kinase coverage, identifying 45 kinases among 1142 quantified proteins, including marginal kinase responses in staurosporine-treated samples undetectable by conventional PISA. A simplified six-temperature dimethyl labeling workflow was applied, pooling samples from six discrete heat treatments prior to isotopic labeling and quantitative mass spectrometry, reducing reagent consumption and MS acquisition time by more than 50%. Application to celastrol identified LULL1 as a previously under-recognized interacting protein, illustrating the biological insights gained by expanding target coverage into aggregated fractions.
SIGNIFICANCE: DrPISA extends thermal profiling to insoluble fractions with improved performance over conventional workflows, offering a scalable approach for high-sensitivity target deconvolution. The introduction of DES-48 enables enhanced recovery and quantification of heat-aggregated proteins, broadening the analytical scope of chemical proteomics. These advances establish DrPISA as a practical and complementary strategy for expanding the discoverable drug-protein interaction landscape.

Keywords:  Celastrol; Deep eutectic solvent; Drug target discovery; Protein solubilization agent; Proteome integral solubility alteration

DOI:  https://doi.org/10.1016/j.aca.2026.345234
Front Psychiatry. 2026 ;17 1736206

Metabolomics biomarkers for precision psychiatry.

Daniele Cavaleri, Carlo Bassetti, Giorgio Cucchi, Pasquale De Fazio, Renato de Filippis, Umberto Albert, Luca Pellegrini, Giuseppe Carrà, Francesco Bartoli.

  Mental disorders remain diagnosed primarily through symptom-based classification systems that overlook biological heterogeneity, preventing the identification of mechanistically distinct patient subgroups and precluding pathophysiology-guided treatment selection. Metabolomics offers a promising pathway towards precision psychiatry by capturing dynamic biochemical readouts at the functional endpoint of the omics cascade, integrating genetic, environmental, and pharmacological influences on cellular metabolism. Over the past 15 years, untargeted and targeted metabolomics studies using nuclear magnetic resonance spectroscopy and mass spectrometry have identified consistent patterns of metabolic dysregulation across psychiatric disorders, particularly involving amino acid metabolism, lipid signaling, energy homeostasis, and oxidative stress pathways. Schizophrenia presents disruptions in arginine and proline metabolism, glutathione metabolism, and energy-related processes. Bipolar disorder shows perturbations in branched-chain and aromatic amino acids, kynurenine pathway, and tricarboxylic acid cycle dysfunction with phase-specific metabolic signatures. Major depressive disorder exhibits widespread alterations in amino acid turnover, bioenergetic processes, membrane lipid homeostasis, and glutamate-GABA cycling, with treatment-responsive metabolic changes. Despite these advances, substantial challenges remain: heterogeneous findings with disorder overlap, limited replication cohorts, predominance of cross-sectional designs, confounding by medication and lifestyle factors, pre-analytical variability, and high-dimensional data complexity. Future research requires harmonized multi-site protocols, longitudinal validation studies, multi-platform analytical approaches, integration with genomics, proteomics, and digital phenotyping, and implementation of artificial intelligence frameworks to enhance phenotype discrimination and predictive accuracy. In this mini-review, we provide an overview of current methodologies, major findings, strengths, challenges, and emerging directions in psychiatric metabolomics, with the goal of facilitating the translation of metabolomic insights into clinically applicable, personalized psychiatric treatment.

Keywords:  anxiety disorders; biomarkers; bipolar disorder; major depressive disorder; metabolomics; obsessive-compulsive disorder; post-traumatic stress disorder; schizophrenia

DOI:  https://doi.org/10.3389/fpsyt.2026.1736206
JIMD Rep. 2026 Mar;67(2): e70078

Quantitative Succinylacetone Measurement by Gas Chromatography-Tandem Mass Spectrometry (GC-MS/MS) Facilitates Diagnosis, Monitoring, and Characterization of Tyrosinemia Type 1 and Other Hypersuccinylacetonemias.

Denis Cyr, Bruno Maranda, Paula J Waters.

  Tyrosinemia type 1 (HT1), due to deficient activity of fumarylacetoacetate hydrolase, causes accumulation of succinylacetone (SA). SA concentrations in urine and plasma of untreated HT1 patients are typically several thousand-fold higher than normal, hence are readily recognized by traditional diagnostic methods in most cases. However, quantitation of SA in the nanomolar range is important for monitoring patients treated with nitisinone, for identifying attenuated or atypical forms of HT1, and for confirmation or refutation of the diagnosis of HT1 following a positive newborn screen. Our laboratory, a reference centre for diagnosis and monitoring of HT1, previously assayed SA by gas chromatography-mass spectrometry (GC-MS). Three years ago, we upgraded this method by transferring it to a new triple quadrupole technology (GC-MS/MS). A stable isotope dilution process is used, with sample treatment consisting of an oximation step followed by a single liquid-liquid extraction then trimethylsilyl derivatization. Quantitation is based on intensities of the ion transitions m/z 620 → 181 for SA and 625 → 186 for the internal standard. Method validation demonstrated enhanced analytical specificity and sensitivity, with good precision and accuracy. Using GC-MS/MS instead of GC-MS allowed a limit of quantitation of 1 nmol/L while decreasing the required specimen volumes, as well as reducing the number of sample processing steps, chromatographic run time, and instrument maintenance. This assay facilitates laboratory diagnosis and monitoring of HT1, permits identification and characterization of other hypersuccinylacetonemias including maleylacetoacetate isomerase deficiency, and is also a valuable tool for research studies using animal models and cellular models of HT1.

Keywords:  gas chromatography‐tandem mass spectrometry; hypersuccinylacetonemia; maleylacetoacetate isomerase; succinylacetone; tyrosinemia

DOI:  https://doi.org/10.1002/jmd2.70078