Journal of the American Society for Mass Spectrometry

Molecular specificity remains a major challenge in mass spectrometry imaging (MSI), particularly when low-abundance species coexist with structurally related isomers that cannot be distinguished by accurate mass and exhibit similar fragmentation behavior. Bis(monoacylglycero)phosphates (BMPs), lysosomal lipids increasingly implicated in lipid homeostasis and disease, represent a particularly demanding example because they are structural isomers of phosphatidylglycerols (PGs) and display highly similar negative-ion fragmentation. Here, we developed an ion mobility-guided targeted MALDI-MS/MS imaging workflow for direct on-tissue discrimination of endogenous BMP/PG isomeric pairs. Orthogonal HILIC-DDA-PASEF analysis provided accurate-mass, retention-time, fragmentation, and ion-mobility information used to define mobility-constrained precursor coordinates for scheduled MALDI-iPRM-PASEF acquisition. Ion-mobility measurements showed high agreement across ESI-TIMS, MALDI-TIMS, and tissue-based MALDI-TIMS-MSI, while optimization of laser sampling minimized ion-load-dependent mobility shifts. Narrow mobility windows reduced reciprocal PG/BMP cross-talk to below 4% while preserving selective detection under strongly unbalanced abundance conditions. The workflow enabled distinct precursor- and product-ion imaging of endogenous PG 34:1 and BMP 34:1 in sagittal mouse brain, supporting their acyl-chain-level assignment as PG 16:0_18:1 and BMP 16:0_18:1. Application to a CLN3-knockout mouse model revealed BMP-specific reductions across brain, kidney, and lung that were not mirrored by the corresponding PG isomers, providing an orthogonal biological validation of the analytical discrimination. Mobility-constrained targeted MS/MS additionally resolved type-II isotopic interference that remained ambiguous at the MS1 level. Overall, this work provides a strategy for reciprocal spatial discrimination and structural confirmation of endogenous BMP and PG isomers directly in tissue and highlights the value of combining ion mobility with targeted product-ion imaging to increase molecular specificity in spatial lipidomics.

Same-tissue section multimodal imaging is a powerful strategy that spatially profiles tissue histology, cell populations and molecular composition while maximizing tissue economy, preserving spatial molecular relationships, and increasing co-registration capacity. However, performing multiple modalities on the same tissue section can destroy or chemically alter the tissue, compromising downstream data. Here, we systematically assess integration of picrosirius red staining, hematoxylin and eosin staining, and multiplexed immunofluorescence into N-glycan and extracellular matrix peptide matrix-assisted laser/desorption ionization imaging mass spectrometry (IMS) workflows. We evaluate alterations in tissue morphology, stain efficiency, IMS feature intensity as well as IMS feature localization after upstream modality integration. We propose an optimized multimodal sequence that maximizes data quality and follows a very specific order of: autofluorescence microscopy, multiplexed immunofluorescence, picrosirius red staining, N-glycan IMS, hematoxylin and eosin staining, and extracellular matrix peptide IMS. Overall, this work develops an optimized multimodal workflow that comprehensively images tissue morphology, collagen fibers, and cell populations at single-cell resolution as well as multiplexed N-glycan composition and multiplexed extracellular matrix peptides with post-translational modification status from a single tissue section.

Mass Spectrometry Metabolomics Identification Connection Algorithm (MSMICA) is an algorithm for automated metabolite identification in untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analyses. Limitations in metabolite identification can occur due to the availability and cost of standards and prevent recognition of metabolic factors impacting human health and disease. MSMICA performs mass-to-charge-ratio matching with chemical structures and clusters of LC-HRMS features for adduct and isotope forms. A local optimization is then used to integrate retention time prediction, metabolite precursor-product and transporter correlations, and biospecimen-specific abundance information for metabolite identification. Applying MSMICA to various internal and external mammalian datasets, validation results showed a 96.2 +- 5.1% correct rate of metabolite identification. When multiple LC-HRMS datasets were used, MSMICA enabled greater metabolite identifications, expanded metabolic pathway coverage, and data harmonization. Thus, MSMICA applies multiple pieces of evidence to substantially improve metabolite identification coverage and accuracy for known metabolites.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables the spatial mapping of endogenous biomolecules within native biological specimens; however, it remains limited in achieving single-cell resolution. While advances in instrument modifications, computational processing methods, and tissue-based sample preparation have facilitated high lateral resolutions and cellular level imaging, resolving metabolic heterogeneity at the single-cell level remains challenging for users without specific expertise or custom instrumentation. Here, we present tensile expansion mass spectrometry (TExMS), a cost-effective approach for single-cell MALDI-MSI that is compatible with commercial MSI instrumentation. TExMS utilizes highly stretchable hydrogels as a substrate for live-cell seeding, attachment, and desiccation, avoiding the need for chemical fixation and enabling the retention of both intracellular and extracellular metabolites, including media-derived components that are lost during fixation and washing. We used TExMS to expand individual cells of a human high-grade serous ovarian cancer (HGSOC) cell line and spatially map their small molecule (<800 Da) production. TExMS enabled [~]4-fold linear expansion of the hydrogel, translating to a [~]1.7-fold increase in average cell area and [~]1.3-fold increase in nuclear area and resulting in improved lateral resolution of metabolite distributions. Benchmarking against other platforms for high resolution MALDI-MSI, TExMS offered comparable spatial resolution to microgrid-enabled MALDI-MSI with 15 to 20-fold shorter acquisition times. We then used TExMS to map numerous intermediates from glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid biosynthesis and probe the effects of serum starvation conditions on metabolic flux through these pathways, demonstrating a powerful use case for single-cell MALDI-MSI through TExMS. Table of Contents (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/745024v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@18f8c09org.highwire.dtl.DTLVardef@132bc4aorg.highwire.dtl.DTLVardef@1e7c2caorg.highwire.dtl.DTLVardef@a586cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

Mass spectrometry (MS) is the leading technology for identifying proteins in complex biological samples. It relies on the use of tandem MS alongside a reference database of canonical protein sequences to computationally identify peptides and their parent proteins. The canonical sequences represent the most widely expressed and functionally validated forms of proteins. Consequently, disease-induced or disease-supportive variants, such as those associated with cancer, will evade detection if they are absent from the database. To address this challenge, this study introduces a revised release of the Unkown Mutation Analysis (XMAn) database by incorporating coding missense and nonsense mutations from the latest versions (v103) of the COSMIC Genome Screen Mutants (GSM) and Cancer Gene Census (CGC) datasets in two distinct FASTA-formatted peptide databases comprising 3,848,499 and 312,658 variants, respectively. The mutated peptides were matched to reviewed, non-redundant UniProt Homo sapiens protein entries (18,362 and 746), and characterized in terms of nucleotide- and amino acid mutation frequencies, peptide length distributions, and associations between specific single-nucleotide (SNV) and single amino acid (SAAVs) variants. Applied to the analysis of MDA-MB-231 breast cancer cell-membrane protein fractions, the database enabled the identification of 300+ high-quality variant peptides - several localized to functional protein-binding and catalytic domains - and 23 aberrant protein products mapped to the CGC dataset. The database is hosted and available for download on Zenodo (XMAn/gsm doi: 10.5281/zenodo.21781023; XMAn/cgc doi: 10.5281/zenodo.21781514) or can be accessed through https://sites.google.com/vt.edu/xman-db/home.

Glycan heterogeneity is a fundamental property of glycoproteins. A holistic understanding of glycan modification states is critical to translating glycoproteome regulation to biological function, but the high degree of glycosite-level heterogeneity leads to technical challenges in measuring glycoproteoforms. Common bottom-up glycoproteomics provide some insights but cannot recapitulate the full ensemble of glycoproteoforms from glycopeptide measurements alone. Promising efforts to profile masses of intact glycoproteins have recently explored data-independent acquisition (DIA) coupled with proton-transfer charge reduction (PTCR) or electron-capture-induced charge reduction mass spectrometry (MS). While valuable for generating broad glycoproteoform mass distributions, these approaches have remained limited in their ability to generate discrete glycoproteoform mass measurements, largely because they rely on low-resolving power measurements and deconvolution that does not account for isotopic information. Here, we develop a DIA-PTCR workflow that couples high-resolving power (Rp ~240,000 at m/z 200) tandem mass spectra with an open-source processing suite to define glycoproteoform populations within 20 ppm mass accuracy thresholds. We demonstrate the glycoproteoform characterization capabilities of this platform using a collection of glycoproteins with well-described translational interests (EpCAM, TIGIT, CD40, PDL1, and CD24). With a focus on EpCAM, we showcase how intact glycoproteoform masses acquired using our high-resolving power DIA-PTCR (hRp-DIA-PTCR) approach can be integrated with bottom-up intact glycoproteomics and Direct-Mass Technology (i.e., Orbitrap-based charge-detection MS) acquisitions to inform structural and biological insights. Altogether, our hRp-DIA-PTCR method extends the current capabilities of intact glycoprotein analyses by enabling robust characterization of isotopically resolved proteoforms and facilitating deep biological interpretation of glycosylation heterogeneity. Our open-source informatics platform includes a GUI-based tool called PTsliCR to clean PTCR spectra directly from DIA-PTCR raw files and a deconvolution R package called IsoTrac, both of which are freely available on GitHub at https://github.com/riley-research.

Metabolomics studies are increasingly being applied with hundreds to thousands, even tens of thousands of samples that demand rapid, automated data processing while maintaining analytical sensitivity or quantitative accuracy. A major computational bottleneck is feature finding, which is the transformation of LC-MS and LC-MS/MS data into a set of analyte signals aligned and quantified across samples. Feature finding can be computationally intensive and often requires manual iterative parameter optimization. To accelerate this process, we present the Everything Bagel (EB) feature finder, an ultra-fast automated feature finding tool that integrates feature detection, retention-time alignment, and gap filling designed for run-time and memory efficiency. We benchmarked EB against two automated feature finding methods on eight benchmarking datasets. Specifically, we evaluated these three feature finding methods by measuring spike-in standard detection coverage, dilution series quantification accuracy, and yeast 12C/13C credentialed features. In this evaluation, the EB feature finder achieved performance comparable to, and often exceeding, existing methods while requiring up to 150-fold lower CPU hours and up to 113-fold lower wall time. We further demonstrated the bioanalytical validity of EB by reanalyzing published datasets used for biomarker discovery and reproduced biologically significant features that matched the published findings using manually tuned feature finding settings. Taken along with the speed improvements, we anticipate EB will enhance the ability to automatically analyze datasets with thousands to tens of thousands of samples for the community.

Simultaneous quantification of monoglycosphingolipid stereoisomers is required to monitor changes in defective enzymatic pathways linked to diseases such as Gaucher Disease, Parkinson's Disease, and Krabbe Disease. Resolution of beta-glucosyl and beta-galactosyl epimers cannot be achieved by standard liquid chromatography, electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS). Separation becomes possible when field asymmetric ion mobility spectrometry (FAIMS), also known as differential mobility mass spectrometry (DMS), is added as an orthogonal separation technique to LC. FAIMS/DMS separates epimeric ion clusters in a high versus low electric field (separation voltage, SV) then redirects the target epimeric ions to the mass spectrometer through the application of a direct current (compensation voltage, CoV). Resolving SVs and CoVs must be manually determined for each lipid. Manual derivation is a labour-intensive process that requires pure synthetic standards, limiting the number of stereoisomers a user can include in an assay. To address this problem, we introduce here intelligent DMS (iDMS). iDMS is an in silico supervised neural network model that learns the ion mobility relationships between SV and CoV and the monoglycosphingolipid structural features of sugar headgroup, N-acyl chain length, and N-acyl degree of unsaturation. iDMS predicts the SV and CoV combinations capable of resolving any stereoisomer pair from a training dataset of composed of measured signal intensities across a range of SVs and CoVs of 12 lipids. This machine learning alternative to manual DMS optimization promises to accelerate the deployment of multiple-reaction-monitoring mode (MRM) RPLC-ESI-DMS-MS/MS assays for the routine and rapid quantification of biologically relevant monoglycosphingolipid stereoisomers.

Mass spectrometry (MS)-based nucleic acid analysis provides direct chemical evidence for oligonucleotide sequence, composition, and modifications. However, oligonucleotide LC-MS analysis commonly relies on ion-pairing reversed-phase liquid chromatography (IP-RPLC). Although IP-RPLC provides strong retention and high-resolution separation of highly charged nucleic acids, ion-pairing reagents can contaminate LC-MS systems, suppress electrospray ionization, require extensive system cleaning, and limit the use of high-end MS platforms that are primarily dedicated to proteomics or metabolomics. Here, we developed and evaluated an ion-pair-free capillary hydrophilic interaction liquid chromatography mass spectrometry (capillary HILIC-MS) workflow for RNA modification mapping. To enable robust analysis of biologically relevant samples, we optimized sample preparation, high-organic loading conditions, chromatographic parameters, and MS source settings to overcome key challenges associated with capillary HILIC, including limited sample volume, solvent compatibility, and solvent breakthrough during injection. The optimized capillary HILIC-MS method provided effective separation of oligonucleotides below 30 nt and enabled sensitive detection of RNA modifications in the populations of tRNAs and rRNAs in biological samples. Importantly, the ion-pair-free workflow also allowed switching between nucleic acid analysis and proteomics on the same LC-MS platform without the need for extensive system decontamination. Together, this workflow provides a sensitive, robust, and MS-compatible approach for nucleic acid analysis, expanding the utility of high-end LC-MS systems for both therapeutic oligonucleotide characterization and biological RNA modification profiling.

ShortThe SwissProt database contains a stable 20,418 human protein-coding genes and 42,541 human protein sequences. Ribo-Seq suggests about 7,000 additional, non-canonical Open Reading Frames (ORFs) are present in humans, though only a few of them are confirmed by Mass Spectrometry (MS). Detecting these proteins requires extensive database searches, increasing computational load and inflating False Discovery Rates (FDR). Using the ionbot search engine with the OpenProt database allows for reliable detection of non-canonical proteins while controlling FDR. Ionbot surpasses the Trans-Proteomics Pipeline (TPP) in reproducibility, identifying more peptides and proteins supported by multiple spectra. In addition, open modification searches yield better PSMs compared to closed searches. This work highlights the importance of employing cutting-edge search engines in non-canonical protein research, as well as the value of open modification search in correcting errors in non-canonical protein detection. LongO_ST_ABSBackgroundC_ST_ABSThe SwissProt database reports a quite stable 20,418 human protein-coding genes and 42,541 human protein sequences, figures that have remained stable. New techniques like Ribo-Seq indicate that approximately 7,000 additional, non-canonical Open Reading Frames (ORFs) are translated in humans, few of which have been confirmed by Mass Spectrometry (MS). Detecting these non-canonical proteins requires comprehensive database searches, which increase computational load and False Discovery Rate (FDR). Here, we use the open search engine ionbot in combination with the OpenProt proteogenomics database to reproducibly detect non-canonical proteins while maintaining a well-controlled FDR. ResultsCompared to the current gold standard, the Trans-Proteomics Pipeline (TPP), ionbot shows higher reproducibility, with a higher number of peptides and proteins supported by multiple spectra, and across multiple samples. We observe that PSMs from the open modification search against OpenProt have higher fragment ion intensity correlation compared to PSMs obtained from the closed search, or by only searching canonical proteins. ConclusionsIn this work, we show the potential for open modification searching to correct potential mistakes in non-canonical proteins detection by preventing modified canonical peptides or variants from being incorrectly identified as non-canonical peptides. We also highlight the importance of assessing the FDR of non-canonical identifications separately from canonical ones, as global FDR calculations are biased by the scarcity of non-canonical identifications in each dataset.

Background: Low-dose computed tomography (LDCT) screening reduces lung cancer mortality but is limited by low uptake and associated with high rates of false-positives and indeterminate-nodules. Breath volatile organic compound (VOC) analysis is a non-invasive candidate biomarker approach that could complement LDCT, but prior work has relied on laboratory-based high-resolution mass spectrometry (HRMS), limiting point-of-care deployment. Methods: In this pilot study, breath samples were collected from 40 patients with treatment-naive, pathologically confirmed non-small cell lung cancer (NSCLC) and 25 lung-cancer-screening-eligible healthy controls. Paired samples were analyzed via a compact point-of-care GC-MS platform (CLARION) and a laboratory HRMS reference. Diagnostic classification models were built independently for each platform using elastic net logistic regression with leave-one-out cross-validation, and performance was evaluated by area under the receiver operating characteristic curve (AUC). Results: CLARION identified 103 VOCs across breath specimens, compared to over 900 identified by HRMS. Despite this difference in panel size, CLARION achieved diagnostic performance nearly identical to HRMS for distinguishing NSCLC cases from controls (AUC 0.864 vs. 0.863). Compared to controls, performance statistics were similar for early-stage NSCLC (AUC 0.854 vs. 0.841) and adenocarcinoma (AUC 0.770 vs. 0.787). VOCs of interest include p-cymene, phenol, propylbenzene, tetradecane, {beta}-ocimene, 2,3-dihydro-indole, and 1-methylthio-(Z)-1-propene. Conclusion: A compact, point-of-care breath GC-MS platform achieved diagnostic performance for NSCLC detection comparable to a laboratory HRMS reference despite a substantially smaller detected VOC panel. These findings support continued development of point-of-care breath VOC testing as a non-invasive, field-deployable complement to LDCT-based lung cancer screening.

Quantitative membrane proteomics remains fundamentally limited by sample preparation because detergent extraction can perturb membrane protein interactions, ligand-responsive conformations, and higher-order assemblies before mass spectrometric analysis. Here, we demonstrate that peptide-based surfactants (Peptergents) enable a complete detergent-free workflow for native membrane proteomics. Membrane proteins are extracted directly from biological membranes while preserving their structural and functional integrity and remaining fully compatible with downstream LC-MS/MS workflows. Functional preservation is evidenced by maintenance of ligand-responsive conformations in the ABC transporter MsbA and the endogenous GPCR P2RY12, together with stabilization of the detergent-sensitive nine-subunit holo-translocon HTL, indicating that fragile membrane protein assemblies remain intact. At the proteome level, despite recovering fewer membrane proteins than conventional detergent extraction, Peptergent consistently generates higher peptide signal intensities, retains tissue-specific membrane proteome signatures, and preferentially enriches endoplasmic reticulum-associated metabolic networks, including cytochrome P450 enzymes and their interaction network. Together, these findings establish Peptergents as a broadly applicable membrane extraction technology for LC-MS/MS-based membrane proteomics, preserving native membrane organization and expanding the proteomics toolbox for biochemical, structural, and systems-level analyses of membrane proteins. In Brief StatementThis study establishes Peptergents as a detergent-free membrane extraction technology for LC-MS/MS-based membrane proteomics. Peptergent extraction preserves ligand-responsive membrane proteins, fragile membrane protein assemblies, and tissue-specific membrane proteome signatures while remaining fully compatible with quantitative proteomic workflows. These findings provide a broadly applicable strategy for preserving native membrane organization for biochemical, structural, and systems-level analyses of membrane proteins. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/744532v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@1fe34b0org.highwire.dtl.DTLVardef@35400corg.highwire.dtl.DTLVardef@1ffe97aorg.highwire.dtl.DTLVardef@394fc4_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPeptergents preserve ligand-responsive membrane proteins. C_LIO_LISupport chemoproteomics in thermal proteome profiling assays. C_LIO_LISimplify membrane proteomics workflow. C_LIO_LIMaintain native tissue-specific membrane biology. C_LIO_LIPreserve fragile membrane protein assemblies. C_LI

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

Metabolomics uses tandem mass spectrometry (MS/MS) data to gain structural insights of small molecules that play biological roles, generating datasets whose size and complexity demand systematic organisation. Molecular networking addresses this by representing MS/MS spectra as nodes and their pairwise similarity as edges, but its output is critically sensitive to user-defined parameters: similarity score cut-off, maximum component size, maximum links and minimum matching peaks. These parameters are routinely left at default values, which can either collapse interpretable molecular families into entangled "hairballs" or fragment them into disconnected singletons. In the absence of ground truth, no standardised framework exists to evaluate molecular networks or to assess whether their connections are robust to run-to-run variability present in metabolomic experiments. Here, we introduce arteMIS (Accelerated Ranking and Tuning using Multi-metric Interpretability across Scores), a framework for systematic parameter optimisation that uses Latin Hypercube Sampling to efficiently cover the four-dimensional parameter space and ranks candidate networks through a user-tuneable composite Z-score, combining topology- and chemistry-based metrics. This framework supports three complementary modes: global, seed, and target-class, adapting optimisation to fully unannotated datasets, curated subset of reference features or class-focused discovery, respectively. Benchmarking across four spectral libraries (~600 to ~13,000 spectra) and four scoring methods (Cosine, Modified Cosine, Spec2Vec, MS2DeepScore), we provide practical guidance for parameter selection as a function of scoring method and dataset size and show that optimal settings do not transfer between them. Top-ranked arteMIS configurations outperformed GNPS defaults in chemistry and topology metrics and produced networks with higher edge-stability under subsampling. Applied to actinobacteria and fungal samples, arteMIS rescued structurally meaningful families that remained fragmented under default settings. We conclude that arteMIS reframes molecular network construction from a default-driven step into a task-customisable optimisation.

1A-adrenoceptor (1A-AR) is a class A G-protein coupled receptor (GPCR) that stimulates smooth muscle contraction in response to adrenaline and noradrenaline. GPCRs exist in a dynamic equilibrium between multiple conformational states. Ligand binding induces structural rearrangements via conserved microswitches, which are thought to shift the equilibrium and trigger signalling. For structural and biochemical studies, GPCRs must be solubilised from the membrane, typically using detergent micelles. However, detergents can disrupt native dynamics of membrane proteins, potentially confounding experimental results. To address this, phospholipid bilayer mimetics such as nanodiscs and saposin nanoparticles (SNPs) have been developed to provide a more native-like environment. Thermostabilised 1A-AR serves as a GPCR prototype and can be expressed and isotopically labelled for NMR purposes. To investigate how membrane mimetics influence the conformational diversity of 1A-AR, we compared 1H 13C3-HMQC NMR experiments of 13CH3-Met labelled 1A-AR incorporated into either DDM, LMNG, or SNPs, in presence of ligands with varying efficacies. Several methionine residues are positioned near key microswitches, including M2035.57, located closed to the G protein binding site. Its resonance has been proposed as a readout of receptor conformational state, shifting with ligand efficacy. Spectra of 13CH3-Met labelled 1A-AR in LMNG closely resembled those in DDM with some temperature-dependent dynamic variation. In contrast, incorporation into SNPs led to a complete loss of M2035.57 signal, consistent with an intermediate exchange rate. These findings demonstrate that the membrane environment can profoundly influence conformational dynamics in GPCR NMR studies. Our results highlight the need to carefully consider membrane environment when interpreting NMR data and underscore the value of benchmarking against biologically relevant controls.

Background: Acute phase response is an early immunometabolic response to brain injuries, primarily coordinated by the liver via the activation of acute phase proteins. These immune responses can be both beneficial, promoting tissue repair, and detrimental, exacerbating neurological deficits, if not properly controlled. Despite the central role of the liver in immunometabolism, how hepatic metabolism dynamically adapts to traumatic brain injury remains under explored, primarily due to limited liver-specific modalities that can assess metabolic pathways in vivo. 13C MRI utilizing hyperpolarized 13C-pyruvate can assess key regulatory enzyme activities in hepatic metabolism. Methods: Rats with controlled cortical impact were studied in vivo using hyperpolarized [1-13C]pyruvate and [2-13C]pyruvate under fed and fasted conditions 3-4 days after injury. Hyperpolarized 13C products, including [13C]bicarbonate from [1-13C]pyruvate and [5-13C]glutamate, [1-13C]acetyl-L-carnitine, and [2-13C]phosphoenolpyruvate from [2-13C]pyruvate, were evaluated to assess mitochondrial and gluconeogenic metabolism. In parallel, liver tissues were collected following [U-13C3]pyruvate injection for NMR isotopomer analysis of phosphoenolpyruvate, glucose, and glutamate. Results: While no metabolic differences were detected under fed condition, [13C]bicarbonate and [2-13C]phosphoenolpyruvate increased after brain injury under fasted condition, indicating an upregulation of the hepatic gluconeogenic pathway after injury. 13C NMR of liver tissue extracts from injured rats showed an elevated [2,3-13C2]glutamate-to-[4,5-13C2]glutamate ratio and increased 13C-labeling in phosphoenolpyruvate than controls, confirming enhanced hepatic gluconeogenic pathway. Conclusion: This study demonstrates that hepatic acute phase response to brain injuries can be monitored in vivo by hyperpolarized pyruvate, which may be further utilized for longitudinal immunometabolic evaluation of the liver during pathogenesis and therapeutic interventions.

The majority of metabolic pathways rely on the production of activated electrophilic intermediates, e.g., coenzyme-A esters, and their chemical structures and abundances are central to understanding enzyme function and biochemical mechanisms. However, most electrophilic metabolites are lost in traditional metabolomic analysis and thus remain poorly characterized. Here we introduce a biochemical probe, O-(trimethylammoniobutyl)-hydroxylamine (TAMOHA), that enables comprehensive profiling of electrophilic species such as coenzyme-A esters, ketones, and aldehydes. TAMOHA incorporates a highly nucleophilic hydroxyl amine that reacts quickly with electrophilic species upon tissue lysis, trapping them as stable derivatives that feature a tetraalkylammonium moiety whose constitutive charge and characteristic MS2 fragmentation fingerprint enable their highly sensitive detection. Using TAMOHA to survey the electrophilomes of E. coli, C. elegans, and mouse revealed several thousand electrophilic metabolites, most of which have not been characterized. We then demonstrate trapping of electrophilic metabolites with TAMOHA in the context of specific biochemical pathways, confirming previously proposed functions of two fatty acid metabolism enzymes, detecting formaldehyde production in mice, and providing new insights into the biosynthesis of ascaroside pheromones in C. elegans. We anticipate that use of TAMOHA for the profiling of electrophilic species will help clarify enzyme function and uncover previously elusive biochemical mechanisms in a wide range of biological systems. Table of Contents artwork O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/743530v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@199c060org.highwire.dtl.DTLVardef@12516d7org.highwire.dtl.DTLVardef@1fe81f1org.highwire.dtl.DTLVardef@5277a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Mass spectrometry-based chemical proteomics is a powerful method to analyze proteins labelled by small molecules to identify protein targets of active compounds and to profile protein post-translational modifications. The throughput and high protein input for chemical proteomics workflows has been often a limiting factor for application of the technology for specialized and difficult to culture cell lines. The high protein input was necessary to gain significant difference of noise to signal ratio in proteomics readout. Here, we describe a general chemical proteomics workflow, which is performed in 96-well plate and necessitate only 25 g of protein input to profile post-translationally modified proteins including abundant O-GlcNAcylated proteins as well as low abundant AMPylated proteins. The workflow integrates advances in Cu(I)-catalyzed azide-alkyne cycloaddition to minimize chemical side-reactivity of the click reaction and data-independent acquisition mode during LC-MS/MS measurement. An iterative optimization of protein clean-up on carboxylate-coated paramagnetic beads led to significant saving of the beads usage and lowers the unspecific protein background that resulted in sensitivity gain.

Comprehensive characterization of protein abundance and multiple post-translational modifications (PTMs) from the same biological samples is essential for understanding cellular regulation and PTM crosstalk but remains analytically challenging. Here, we present STEP-PTM (Sequential Tag-based Enrichment of Post-Translational Modifications), a modular TMT-multiplexed workflow that enables integrated quantitative analysis of the proteome, metabolome and multiple PTM classes from a single peptide preparation. Proteins are digested, isobarically labeled using tandem mass tags (TMT), and combined into a single multiplexed peptide pool prior to sequential PTM enrichment, thereby minimizing technical variability, reducing sample requirements and facilitating direct quantitative integration across datasets. STEP-PTM supports flexible sequential enrichment of phosphopeptides, peptides containing free and reversibly modified cysteines, sialylated N-linked glycopeptides, lysine-acetylated peptides and S-palmitoylated peptides, while preserving non-modified peptides for global proteome analysis. PTM-specific database searches further improve identification confidence and quantitative accuracy, and the modular workflow can readily be adapted by incorporating or omitting enrichment modules according to the biological question. Application of STEP-PTM to TMT16-plex cerebral brain organoids enabled the quantification of 10,413 proteins, 2,969 metabolites, 19,655 phosphopeptides, 28,876 peptides containing reversibly modified cysteines, 9,723 peptides containing free cysteines, 1,716 intact sialylated N-linked glycopeptides and 771 lysine-acetylated peptides from the same biological samples. We further demonstrate the applicability of the workflow to multiple mouse tissues, highlighting its broad utility for integrated systems-level characterization of protein expression and PTM regulation across diverse biological models.

Type 2 diabetes (T2D) is a prevalent metabolic disorder affecting millions worldwide, characterized by insulin resistance and impaired glucose homeostasis. While mammalian models are widely used, Drosophila melanogaster provides a powerful alternative due to its conserved insulin signaling pathways, genetic tractability, and suitability for high throughput studies. In addition to glucose dysregulation, lipid metabolism plays a crucial role in T2D pathophysiology, as alterations in lipid composition contribute to insulin resistance and metabolic dysfunction. Lipidomic studies have emerged as an essential approach to identify metabolic signatures and potential biomarkers for disease progression and therapeutic targeting. In this study, T2D like model was established by inducing insulin resistance through knockdown of the insulin receptor in brain insulin-producing cells using the dilp2-Gal4>UAS-InRRNAi system. This genetic manipulation resulted in significant metabolic dysregulation, including elevated glucose, trehalose, and triacylglyceride levels, along with increased oxidative stress indicators. Additionally, mRNA expression analysis of key insulin signaling components, including insulin receptor substrate 1, dilp2, dilp3, dilp5, and phosphorylated Akt, further validated the model. To further investigate metabolic alterations, Lipid profiling was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) in non targeted LC-MS-based metabolomics approach to identify lipid biomarkers associated with T2D. Multivariate statistical analyses, including PCA and PLS-DA, revealed distinct lipid signatures between wild-type and T2D flies. Notably, specific phosphatidylglycerol species PG 34:0, PG 34:4, PA 38:3, PIP 38:1, PIP2 38:6, and LPS 24:0 demonstrated an area under the curve (AUC) of 1, indicating their strong reliability as lipid biomarkers for T2D diagnosis.