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.

Abstract Chemical cross-linking mass spectrometry (XL-MS) is a powerful technique for elucidating protein structures and interactions, with NHS ester-based cross-linkers being the most widely used. Traditionally, NHS esters are considered highly specific for primary amines; although their reactivity toward cysteine thiols has been reported, it has not been systematically characterized in XL-MS due to the extreme lability of the resulting thioester bonds. Here, we demonstrate that NHS esters efficiently label cysteine residues and form stable lysine-cysteine (K-C) cross-links--species previously deemed labile in XL-MS. To preserve these thioester-based cross-linked sites , we optimized the standard XL-MS workflow and applied it to four model proteins. K-C cross-links constitute 23% to 59% of total detected cross-links, with C-C distances predominantly ranging from 12 to 40 [A], and 60% satisfying theoretical linker constraints -- confirming that K-C cross-links are as reliable as canonical lysine-lysine (K-K) cross-link sites. Cross-software validation and benchmarking against the heterobifunctional K-C cross-linker GMBS further demonstrated that our optimized workflow captures ~ 40% of GMBS-identified K-C cross-linked sites. Collectively, these findings expand the practical utility of NHS esters and enhance protein structural characterization by enabling simultaneous acquisition of K-K and K-C distance constraints.

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.

Multimodal imaging workflows increasingly require multiple analytical techniques to be applied to the same tissue section to improve spatial co-registration and molecular interpretation. Polyethylene naphthalate (PEN) membrane substrates are attractive for this purpose because of their low thickness and compatibility with downstream modalities, but their use for matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI MSI) remains challenging due to low conductivity, reduced ion yield and susceptibility to laser-induced damage. In this study, MALDI MSI conditions for small-molecule analysis on PEN substrates were systematically optimised using bovine brain homogenate and mouse brain tissue using a TimsTOF flex MALDI-2 platform. Matrix selection, laser energy, pixel size (5 and 20 m), ionisation mode and PEN type were evaluated to identify acquisition conditions that balanced signal intensity with spectral quality and preservation of sample and membrane integrity. Among the matrices tested, 1,5-diaminonaphthalene (DAN) resulted in the best overall performance, providing higher ion intensities, less damage to the PEN substrates and a broader usable laser energy range than the other matrices tested. Different PEN substrate formats were also compared, showing distinct trade-offs between analytical performance and practical suitability for multimodal analysis. Under the tested conditions, glass-supported PEN (gPEN) provided the most practical overall compromise, while frame-supported PEN was not suitable for routine analysis on the instrument configuration used. Application of the optimised method to mouse brain tissue confirmed that spatially resolved metabolite imaging on PEN substrates is feasible and can support same-section multimodal workflows.

Our environment has become progressively contaminated with non-biological materials, especially synthetic carbon polymers, plastics. Unsurprisingly some of these make it into the human body as detected by chemical analyses, yet it is unknown whether they cause harm or are innocent bystanders. Building on our observations of glossy deposits and unusual non-biological fluorescent particles in blood vessels, we aimed to determine whether these objects represented plastics. To do this we prepared plastics-enriched pellets from brain and subjected them to pyrolysis gas-chromatography/mass spectroscopy (py-GC/MS), electron microscopy and confocal laser scanning microscopy. Py-GC/MS confirmed the presence of 10 different plastics. Contents of pellets were examined by thin-section EM. We then used laser scanning confocal microscopy to acquire hyperspectral profiles of each type of plastic in suspensions. We found fluorescent particles in all pellets from brains. All 12 plastics in the calibration standard fluoresced. We obtained hyperspectral profiles of single species plastics from industry: Polyethylene, polypropylene and polystyrene. Controls included chemical analysis of brain storage buffer, which had no detectable plastics; and imaging water only and areas on slides lacking tissue. Abundant particles with emission profiles similar to polyethylene and polypropylene decorated the walls of both arterioles and venules. These particles were coincident with glossy deposits we first observed in white matter, suggesting that both features represent plastics. In summary our results demonstrate that synthetic polymers exhibit detectable fluorescence, and particles with similar spectral properties are visible in histologic sections. This opens the door to investigating correlations between plastics and pathology.

Mass spectrometry imaging (MSI) records rich molecular spectra at each pixel, but pathology-oriented interpretation requires visualizations analogous to complementary histopathological stains. We present an expert-aligned framework for constructing multi-view MSI panels. Soft Landmark Contrast Edges (SoLaCE) extracts molecular boundaries directly from high-dimensional spectra. Because standard visualization metrics correlated poorly with rankings from a single expert pathologist, we combine luminance contrast and chromatic diversity with SpecEdge-Dice, a boundary-aware measure of agreement between visualization edges and SoLaCE boundaries. Parametric MiCS+LMC (pMiCS) uses a neural network trained on subsampled data to distill multiple MSI segmentations into a reusable spectral-to-RGB mapping, enabling rapid full-image inference, out-of-sample projection, and more consistent color semantics across aligned images. A concept-based interpretation procedure explains pMiCS outputs through sparse mixtures of spectral concepts. In a blinded benchmark, pMiCS ranked highest among the compared methods. We integrate these components into Virtual Pathology Panels, which use hyperparameter optimization to select high-performing or spatially complementary views. This framework supports future workflows that combine morphology-oriented tissue assessment and molecular analysis within a single MSI acquisition. TeaserVirtual pathology panels transform MSI spectra into complementary views for scalable, interpretable tissue analysis.

Detecting low-abundance bacterial peptides in samples dominated by human proteins using liquid chromatography tandem mass spectrometry (LC MS/MS) is challenging. Bacterial peptides are typically far less abundant than host-derived peptides, and sequence similarity between bacterial and human peptides can complicate confident attribution to a bacterial origin. To address this problem, we developed and benchmarked an in silico spectral library protein identification workflow for detecting Staphylococcus aureus USA300 peptides against a background of A375 cells. Peptide and protein identifications obtained at a 1% peptide-level false discovery rate were compared between the in silico spectral library search and a conventional sequence database search across a defined bacterial dilution series containing 0, 1%, 2%, 10%, 25%, 50%, and 100% bacterial protein. A human sequence similarity filter was applied to identifications from both search strategies to reduce the likelihood of retaining human derived or human like peptide sequences. Both search strategies showed concentration dependent bacterial detection and shared a practical detection limit of approximately 1 to 2% bacterial protein but exhibited distinct performance profiles. The in silico spectral library search produced fewer bacterial-like background identifications in the human-only control, whereas MS2Rescore substantially increased spectral library identifications at higher bacterial concentrations, exceeding database search at 100% bacterial protein. The partially overlapping peptide sets identified by the two strategies indicate that they provide complementary coverage of the bacterial proteome.

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.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) requires extraction of broad, time-dependent peptide isotope envelopes from noise and overlapping signals. The ExMS/ExMS2 lineage established a retention-time-aware framework for raw-data extraction and integrated HDX-MS analysis. Building on this foundation, msengine is an open-source Python engine that adapts mass-selection windows and retention-time boundaries to the observed data. Spectra are reconstructed as nonnegative combinations of the theoretical undeuterated (all-H) isotope distribution shifted across allowed deuteration states, without prescribing a population shape. The fitted weights, termed deuteration coefficients, describe each state's contribution and guide refinement of mass-selection windows. Spectral matches establish an initial retention interval, which is then expanded using chromatographic intensity to include weaker portions of the elution profile. Interference screening and final quality checks assess the recovered interval. In a 1:1 mixture of undeuterated and deuterated material, interval expansion changed the relative population intensities within the extracted envelope resulting in closer agreement to the 1:1 target, illustrating the importance of boundary selection. Across a four-timepoint experiment with the SET1/SWD2 complex collected in duplicate, msengine accepted an average of 901 peptide-charge targets per measurement versus 636 for ExMS2, a 41.7% increase, while improving replicate-centroid disagreement and retention-window widths between replicates. In a same-workstation benchmark, total wall time decreased from 71.9 to 7.3 min, with higher peak memory use. Together, these results demonstrate adaptive signal extraction across mass and retention time as a means to retain multimodal envelopes while increasing accepted-target yield, persistence, accuracy, and throughput.

Tandem mass spectrometry has become central to untargeted metabolomics. The translation of unknown spectra into biological insight depends on assigning chemical identities to detected metabolites. Structural characterization typically begins with mass spectral library matching, in which experimental spectra are compared against reference libraries and candidate annotations are ranked by their spectral similarity to the query. As spectral libraries and experimental datasets grow, however, more candidates achieve comparable similarity scores for a single query, and similarity scores give no indication of how reproducible a candidate match is or how sensitive it is to the underlying fragment evidence. Existing false-discovery-rate approaches can indicate annotation error at the dataset level but do not provide a per-match estimate of reliability. Here, we introduce a SpecReBoot-inspired query-focused bootstrapping approach that resamples the fragment evidence of each query spectrum. This approach relies on recomputing query similarity to candidate library spectra across bootstrap replicates, which provides a statistical distribution of scores rather than a single value. From this distribution we define the match support, a per-match reliability estimate quantifying the reproducibility of a match under spectral perturbation, together with measures of ranking stability that describe how often a candidate remains among the top-ranked matches across replicates. Applied to a forensic drug-of-abuse case, match support distinguished previously identified annotations from high-scoring false positives: a distinction cosine similarity failed to make. Furthermore, match support values remained stable as the reference library was expanded, whereas ranking stability metrics shifted significantly. In a cross-instrument endogenous metabolite library search, match support further revealed metric-specific annotation behavior, identifying metabolites consistently supported across different similarity metrics, while flagging annotations whose reliability depended strongly on the chosen scoring metric. Benchmarking against a natural-product reference library demonstrated that ranking based on match support values promoted true matches by four ranks on average compared with cosine-based ranking, without promoting analogs. Under controlled spectral perturbation experiments, match support flagged incorrect annotations with an AUROC of 0.75, whereas the cosine similarity score alone of the same match reached only 0.56. Query-focused bootstrapping thus provides a practical, per-match measure of annotation reliability, bringing the field a step toward reliable annotations at scale. We anticipate that incorporation of our annotation reliability scoring into computational metabolomics workflows will further promote the growth of spectral libraries and enhance their applicability across scientific disciplines.

Gangliosides are glycosphingolipids implicated in the neurodegenerative pathology of Niemann-Pick disease type C2 (NPC2 disease), where accumulation of GM2 and GM3 is a hallmark of disrupted lipid trafficking and disease progression. Some gangliosides are structural isomers with identical molecular composition and mass, making them difficult to distinguish by mass spectrometry imaging (MSI) alone and leaving their spatial distributions unresolved. To address this challenge, we combined ion mobility spectrometry with mass spectrometry imaging for isomer-resolved ganglioside analysis. We assessed ganglioside complexity in wild-type (Npc2+/+) and NPC2-deficient mice with or without AAV-BR1-mediated NPC2 gene therapy (Npc2-/- AAV-BR1-NPC2 and Npc2-/- vehicle) using matrix-assisted laser desorption ionization (MALDI) MSI, and imaging-parallel reaction monitoring-parallel accumulation serial fragmentation (iPRM-PASEF-MS/MS) for in situ brain tissue analysis. Trapped ion mobility spectrometry (TIMS) enabled gas-phase separation of isomeric gangliosides, while iPRM-PASEF-MS/MS facilitated structural discrimination based on diagnostic fragment ions and identification of molecular modifications. Brain sections were analyzed by MALDI-MSI at 20m spatial resolution, with selected regions examined using post-ionization imaging by MALDI-2 at 5m. 59 mobility-resolved ganglioside features representing 23 putative annotations were identified, including separation and spatial mapping of GM1a/GM1b and GD1a/GD1b isomers. Notably, unreported modifications of gangliosides were identified, including GM1 fucosylation in Npc2-/- mice and GD1 O-acetylation exclusively detected in Npc2+/+ mice. Spatial ganglioside profiling by MSI demonstrated that gene therapy of Npc2-/- mice partially restored normal ganglioside localization in brain, indicating modulation of lipid storage. This study establishes isomer-resolved lipid imaging for investigating ganglioside alterations in disease models and the effects of therapeutic intervention.

Previous research has shown that mammalian fatty acids (FAs) can influence antibiotic tolerance of Staphylococcus aureus, yet many of these studies overlook the sources of these FAs, which are primarily esterified into glycero- and phospholipids, and the impact S. aureus lipase activity has on host lipids. Here we attempt to gain insight into the complex interplay between the S. aureus lipidome and its environment using culture media supplemented tissue-specific phospholipid mixtures. Phospholipid profiles of heart, liver, and brain-derived lipids revealed distinct distributions of headgroup and fatty acyl tail structures within the phospholipids. Following the growth of S. aureus in lipid-enriched broth, PG species containing mono- and poly-unsaturated acyl tails were detected with abundances that correlated strongly with the FA profile of the tissue extract. We found that S. aureus cultured with liver-derived lipid extract, which yielded the most unsaturated PGs, promoted growth in high concentrations of the membrane-targeting antimicrobial daptomycin. To explore the influence of lipase activity on the extracellular lipids, comparative analysis of fresh versus spent media revealed that the lipase-mediated degradation of complex phospholipid mixtures was influenced by both head group structure and acyl tail linkage. Concurrently, the spent media contained elevated levels of mono- and polyunsaturated lysophospholipids that were predominantly of the 2-acyl form rather than the 1-acyl form observed in the fresh media. Together, these results demonstrate the extent to which the lipase activity of S. aureus remodels both its own lipidome as well as the structures of the phospholipids in the surrounding environment. IMPORTANCES. aureus releases a secreted glycerol ester hydrolase, Geh, into the extracellular environment, which enables the bacterium to generate free FA from glycerolipids, phospholipids, and cholesterol esters that are present in surrounding tissue of an infection. The liberated FAs can be incorporated into the phospholipids of S. aureus, thereby altering its membrane physiology with mono- and poly-unsaturated FAs it cannot otherwise synthesize. Simultaneously, the action of Geh on lipids in the host environment leads to higher levels of bioactive lysophospholipids that participate in mammalian signaling pathways. This work reveals the preferences of S. aureus Geh across phospholipids with different head group and acyl tail structures found within tissue-derived lipid extracts, as well as the fate of the liberated FAs within the staphylococcal membrane lipids. The impacts of these processes on both the host and bacterium have implications for the immune response to and antibiotic treatment of S. aureus infections.

Variable-temperature electrospray ionization native mass spectrometry (vT-ESI nMS) provides a means to determine how temperature-dependent changes in protein and solution dynamics influence individual ligand-binding reactions within multiligand protein complexes. Here, the GroEL single-ring mutant (SR1), which binds up to seven nucleotides, serves as a model system due to its sensitivity to solution conditions. Native MS resolves individual ligand-bound populations, while vT-ESI extends these measurements across temperature (3-43 {degrees}C), enabling determination of binding affinities and the associated Gibbs free energy, enthalpy, entropy, heat capacity changes ({Delta}Cp), and enthalpy-entropy compensation (EEC) for each binding reaction. Temperature-dependent changes in van,t Hoff curvature produce distinct {Delta}Cp profiles, indicating changes in the dynamics governing ligand binding that may reflect contributions from protein conformational and protonation microstates. Temperature-dependent shifts in average charge state, consistent with changes in solvent-accessible surface area, indicate corresponding changes in protein conformation. Binding is predominantly enthalpy-driven below ~23 {degrees}C, with increasing entropic contributions at higher temperatures, while the seventh ADP binding reaction exhibits a distinct EEC profile. Comparison of the seventh ADP binding reaction in H2O and D2O reveals pronounced differences in van,t Hoff curvature,{Delta}Cp, and EEC, demonstrating that changes in the hydration environment alter the thermodynamic response. Collectively, these measurements show that temperature-dependent changes in protein and solution dynamics alter the distributions and thermodynamics of individual ligand-bound states and demonstrate the utility of vT-ESI nMS for resolving these effects in complex multiligand systems.

Bruker timsTOF acquisitions produce dense native .d files whose storage, transfer, and archival become substantial at high throughput. We present dnoise, an open-source Rust tool that removes points directly from timsTOF frames and writes a native-compatible .d directory. dnoise retains ions that form coherent streaks across the ion-mobility dimension and applies acquisition-aware gates to signal that cannot be selected for fragmentation. On a three-species benchmark spanning ddaPASEF and diaPASEF at 5- and 15-minute gradients, default MS1-only denoising reduced the frame binary by 35 to 53%. Label-free quantification accuracy was preserved in both modes. ddaPASEF peptide-spectrum-match, peptide, and protein-group counts were unchanged, as expected with the searched MS/MS spectra untouched, and diaPASEF precursor and protein-group counts changed only slightly. Every tested processing run completed in 69 seconds or less on the benchmark workstation. Optional MS/MS denoising produced greater reduction but sacrificed several percent of identifications. Thus, a substantial fraction of native timsTOF frame data can be removed with little analytical change.

Oxylipins are potent signaling lipids that affect inflammation, vascular tone, and metabolism, making them relevant in many diseases. Oxylipins are measured with liquid chromatography-mass spectrometry (LC-MS), but challenges in quantification arise due to low abundance and rapid degradation. In this study, we optimize LC-MS methods to improve the quantification of oxylipins in human plasma given growing interest in oxylipins and their impact on clinical research. Plasma samples were obtained from healthy participants and extracted by solid-phase extraction to concentrate the oxylipins. We then utilized a reversed phase targeted LC-MS/MS method using an Agilent 6495D triple quadrupole with transitions for 248 oxylipin species. Ion funnel voltages were set at 50 or 100 volts. Given the rapid degradation of oxylipins with bio-reactive surfaces, we compared both standard and Altura (bio-inert) columns, as well as standard and bio- inert LC setups. We observed that ion funnel parameters significantly alter detectable levels of oxylipins within LC-MS/MS analysis. By decreasing voltages applied to ions inside the ion funnel, signal was increased for most oxylipin species while peak quality was maintained. We also demonstrated that fully bio-inert setups quantify more compounds and show increased levels of some compounds, but fewer epoxyoctadecadienoic acid (EpODE) species. To explore this further, we injected analytical grade alpha-linolenic acid (ALA), the direct precursor of EpODEs, and observed formation of EpODEs within the instrumentation when using stainless steel columns. Our data shows that oxylipins benefit from fully bio-inert systems and optimized pre-mass analyzer parameters. The stainless-steel components of the column may also be contributing to epoxidation reactions of polyunsaturated fatty acids (PUFAs), generating oxylipin species during analysis. Finally, we utilized this method to perform oxylipin analysis in other human tissues including granulocytes, mononuclear cells, erythrocytes, skeletal muscle, and THP-1 cells, a human derived monocyte cell line.

Summary: Mass spectrometry resolution of glycosphingolipids requires advanced separation methods. The virtual-ly identical structures of glycolipid epimers cannot be discriminated by routine multiple reaction monitoring-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (MRM-LC-ESI-MS/MS). Sep-aration can be achieved using field asymmetric (differential) ion mobility mass spectrometry (FAIMS/DMS). A major bottleneck to MRM-LC-ESI-DMS-MS/MS method development lies in manually optimizing the compound-dependent parameters of separation voltage (SV) and compensation voltage (CoV). To address this challenge, we developed intelligent Differential Mobility Spectrometry (iDMS). iDMS is a supervised deep neural network that evaluates and predicts the optimal ion mobility SV and CoV parameters for any user-requested monoglycosphingolipid from a training set of measured signal intensities composed of 12 lipids (6 stereoisomer pairs). We initially introduced iDMS as a series of python scripts. Here, we share desktop applications of both iDMS and its pre-processing input data assembly modules (DMS Data Extraction Toolkit). Both software assemblies enable users to rapidly deploy a validated machine learning alternative to manual DMS optimization. The applications are user-friendly, operate with-out need for any technical operating environment, make training dataset assembly quick and simple, and return measured, normalized, and predicted ionograms at every user-interrogated SV, evaluation of separability at each SV and CoV combination, and prediction of optimal resolution parameters to accelerate users MRM-LC-ESI-DMS-MS/MS deployment. Availability and implementation: Desktop iDMS and DMS Data Extraction Toolkit are native desktop applications for macOS (Apple Silicon), freely available for download at https://www.neurolipidomics.com/platforms.html under the GNU v3.0 license. The source code is available from GitHub and Zenodo at https://github.com/Neurolipidomics/iDMS, https://zenodo.org/records/22214408, https://github.com/Neurolipidomics/DMSDataExtractionToolkit, and https://zenodo.org/records/22777403. Bench-marking metrics are available at https://www.biorxiv.org/content/10.64898/2026.08.26.747394v1.

Polyethylene glycol (PEG) is a synthetic polymer ubiquitous in pharmaceuticals, personal care products, food additives, and industrial manufacturing. Despite its widespread use and potential importance as an exposure chemical, the prevalence of PEG exposure and its excretion in human populations remain largely uncharacterized. Moreover, PEG detected in human biofluids is frequently assumed to arise from analytical contamination during sample preparation, potentially obscuring its contribution to the human exposome and confounding metabolic phenotyping studies. Here, we show that PEG is as component of the human xenobiotic exposome and is further metabolized into PEG hydoxy acid and diacid metabolites in humans. We further find that PEG exposure is associated with alterations in microbiome composition and short-chain fatty acid metabolism, suggesting its biological impact of its exposure. We identify PEG exposure in approximately 2.3% of publicly available metabolomics data files and provide a reusable 85,484 candidate PEG and PEGylated MS/MS spectral library for future use for the metabolomic community. Together, these findings establish PEG signal in human biofluids can reflect genuine exposure, and that PEG exposure is neither metabolically inert nor biologically silent.

Mass spectrometry has become a central technology for lipidomics, with data-independent acquisition (DIA) enabling broad and reproducible sampling of lipid signals. However, the multiplexed fragment-ion spectra in DIA data complicate lipid identification. Here, we introduce OpenLipid, a large language model (LLM)-based workflow for targeted DIA lipidomics. Using assay libraries built from data-dependent acquisition (DDA) results, OpenLipid directly evaluates extracted ion chromatograms (XICs) from DIA data in a zero-shot setting to identify target lipid peaks and generate human-readable rationales for individual lipid identification decisions. We benchmarked OpenLipid against manual annotations across four datasets comprising human plasma and mouse feces analyzed in positive and negative ionization modes. The plasma assay libraries contained 199 target lipids in positive mode and 147 in negative mode. The fecal assay libraries contained 181 target lipids in positive mode and 264 in negative mode. At a 5% false discovery rate (FDR) threshold, OpenLipid identified 110 (55.3%) and 28 (19.0%) library targets in plasma and 130 (71.8%) and 84 (31.8%) in feces, in positive and negative ionization modes, respectively. OpenLipid achieved an overall identification rate comparable to that of DIAMetAlyzer (57.8%, 19.7%, 89.0%, and 17.8% across the corresponding datasets) and substantially higher than that of untargeted MS-DIAL DIA analysis (12.6%, 0.0%, 47.5%, and 0.0%) on the same assay-library targets. LLM-derived chromatographic features also enabled supervised discrimination between correct and incorrect candidate peak groups for target lipids, with median cross-validation average precision values of 0.838-0.912. Together, these results demonstrate that OpenLipid is an effective LLM-based workflow for FDR-controlled targeted analysis of DIA lipidomics data.

Comprehensive analysis of intact sialylated N-glycopeptides remains challenging because of their low abundance, extensive structural heterogeneity, and limited peptide backbone fragmentation during tandem mass spectrometry. Here, we present an integrated workflow for high-confidence identification of intact sialylated N-glycopeptides that combines selective TiO2 enrichment, dual LC-MS/MS analysis of intact and deglycosylated glycopeptides, and the GPMAW glyco-search platform based on high-accuracy mass mapping. Unlike most conventional glycoproteomics search engines, GPMAW uses experimentally identified deglycopeptides to constrain glycan assignment before matching intact glycopeptide precursor masses to candidate glycan compositions. Identifications were validated using diagnostic oxonium ions, glycopeptide-associated Y-ion fragments, and an experimentally derived glycopeptide score. In addition, GPMAW integrates an interactive spectrum annotation interface that enables rapid manual validation of candidate identifications through visualization of annotated Y-ion series, oxonium ions, and peptide fragments, allowing individual assignments to be readily accepted or rejected. The workflow was optimized using bovine fetuin, validated on standard glycoproteins, and applied to depleted human plasma, where more than 2800 unique intact sialylated N-glycopeptides were identified across hundreds of glycosites and glycoproteins. Moreover, more than 1000 unique N-glycopeptides were identified from only 1 L of plasma. Comparative analysis demonstrated that GPMAW glyco-search identified more confidently assigned intact sialylated N-glycopeptides than three widely used N-glycoproteomics search engines while maintaining high reproducibility and low false-positive rates following manual validation. Together, this workflow provides a robust, flexible, and accessible platform for large-scale, high-confidence characterization of intact N-glycopeptides and establishes experimentally constrained glycan composition assignment combined with interactive spectrum validation as an effective strategy for reducing ambiguity in N-glycoproteomics. HighlightsO_LIThe program "GPMAW glyco-search" enables high-accuracy mass mapping for confident identification of intact N-glycopeptides. C_LIO_LIIntegrated workflow combining TiO2 enrichment, dual LC-MS/MS of intact and deglycosylated glycopeptides and GPMAW glyco-search for intact sialylated N-glycopeptides. C_LIO_LIOptimized TiO2 enrichment provides >95% selective enrichment of sialylated N-glycopeptides from complex biological samples. C_LIO_LIInteractive spectrum annotation and Y-ion-based scoring enable rapid manual validation and high-confidence glycopeptide identification. C_LIO_LIGPMAW glyco-search confidently identified more intact sialylated N-linked glycopeptides compared to three established glycoproteomics search engines. C_LI

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