Journal of the American Society for Mass Spectrometry

The pixel size of imaging mass spectrometry (IMS) is fundamentally limited by several factors, including the diameter of the incident probe and the raster step size of the sample stage. We have previously demonstrated that hydrogel-based tissue expansion, originally developed for microscopy (ExM), can also be adapted for imaging mass spectrometry to physically magnify the size of the tissue. Expansion imaging mass spectrometry (ExIMS) uses a superabsorbent hydrogel to isotropically expand thin tissue sections, which can then be sampled via imaging mass spectrometry, resulting in improved effective spatial resolution. Separately, multimodal image fusion has been used to computationally upsample the effective spatial resolution in imaging mass spectrometry by predictively mapping mass spectrometric intensity values to the smaller diameter pixel sizes of a microscopy image of the same tissue section. Here, we present ExFusion, a unified workflow that combines these two approaches by computationally fusing structurally detailed fluorescent ExM and chemically detailed lipid ExIMS data obtained from the same 9.4-fold expanded mouse brain tissue. Following a 10-fold upsampling from image fusion, multimodal expansion image fusion enabled prediction of MS images at a [~]106 nm pixel size on a commercial mass spectrometer using a 10 m raster step size. At this resolution, lipids in the Purkinje cells of the cerebellum are clearly defined with intracellular distributions.

Capillary electrophoresis-mass spectrometry (CE-MS) offers unique analytical advantages for polar metabolite profiling but has remained underutilized in metabolomics relative to liquid chromatography-MS (LC-MS), in part due to challenges in managing migration time drift during data analysis. Here we introduce the use of indexed migration time (iMT) for easily managing this aspect of CE-MS data for metabolomics. Migration time indexing using a panel of stable isotope-labeled (SIL) amino acid reference standards, stored as an iRT database in Skyline, outperformed both uncorrected migration time and relative migration time (RMT) correction across three independent analytical batches spanning 90 samples from four biological matrices. The indexed migration time approach achieved sub-1% relative standard deviation (RSD) in migration index across batches, compared to up to [~]15% RSD for uncorrected migration times. Additionally, we evaluate the use of single-point external calibration in Skyline for the purposes of metabolite quantification from complex matrices in order to ease the burden of translational metabolite quantification from metabolomics using high-resolution mass spectrometry (HRMS). Single-point external calibration using a biological matrix-based calibrator was benchmarked against a 13-point linear calibration curve across a panel of amino acids; above 1 M, greater than 95% of back-calculated concentrations fell within {+/-}20% of multi-point calibration. Application of the complete workflow to plasma, serum, urine, and NIST Standard Reference Material (SRM)-1950 demonstrated low inter-batch variability by principal components analysis, broad metabolite coverage across 126 quantifiable analytes, and strong quantitative concordance (Deming slope = 0.862, pseudo-R2 = 0.994, n = 64 analytes) with an independent comprehensive reference dataset for NIST SRM-1950. Together, these results establish a practical mCE-HRMS metabolomics workflow that bridges targeted and discovery metabolomics paradigms and lays the groundwork for single-point external calibration as a powerful tool for translational metabolomics.

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.

Glycosylation is a structurally diverse, non-template-driven modification whose analysis by liquid chromatography-mass spectrometry is constrained by discovery-mode acquisition rules developed for proteomics. Data-dependent acquisition filters, such as intensity-based precursor selection and charge-state exclusion, map poorly onto glycan analysis, which span wide ranges of charge state and abundance independent of their biological importance. Here we present glycosylation real-time mass spectrometry (GlycoRTMS), an instrument-API method that annotates observed precursor masses with glycan compositions in real time and uses this context to guide fragmentation. Composition-aware precursor prioritisation sampled deeper into the precursor space, expanding MS2 coverage of a hyaluronic acid hydrolysate from four to eight oligosaccharide subunits. Charge-state-specific collision energy equations tailored to oligosaccharides produced complete fragment ladders where fixed normalised collision energy did not. MS3 triggering gated by both diagnostic ions and glycan composition matching enabled efficient, chromatography-compatible characterisation of O-acetylated sialic acids and identified product ions specific to O-acetylation. Together, these strategies improve both the depth and quality of glycan detection and characterisation within a single injection.

AO_SCPLOWBSTRACTC_SCPLOWHigh-resolution mass spectrometry is routinely used for the analysis of complex samples in pharmaceutical, environmental, and omics related studies. Such applications require instrumentation to be capable of combining sub-ppm mass accuracy, high resolving power, rapid full m/z range acquisition, and a wide dynamic range. Achieving these requirements simultaneously places constraints on analyzer design and performance. Multi-reflecting time-of-flight (MRT) based analyzers have been previously reported as a means of extending effective flight path length in compact TOF designs. Here, further instrument and functionality advances in a compact MRT mass spectrometer design are described and the impact of these enhancements is demonstrated for omics applications.

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.

Despite advances in high-resolution mass spectrometry (HRMS), confident lipid annotation remains challenging due to the extensive chemical diversity of the lipidome and the prevalence of isomeric species. Ion mobility collision cross section (CCS) measurements provide structural information that complements HRMS; however, not all HRMS platforms can perform these measurements, necessitating a trade-off among mass resolution, accuracy, and robustness. Here, we introduce a method to infer lipid CCS values directly from liquid chromatography (LC)-Orbitrap MS experiments (Orbi). We show that Orbitrap mass analyzer pressure readings, and therefore CCS values, are influenced by the LC gradient solvent composition, requiring correction using isotopically labeled internal standards injected post-column. We also show that hundreds of lipid features can be assigned OrbiCCS values in a single LC run, with average precision better than 1% and an accuracy of 1-2% relative to reference DTCCS and TIMSCCS values. This excellent CCS accuracy not only enables more reliable annotation of lipid species in complex mixtures by matching OrbiCCS values to reference databases but also accelerates lipid structural elucidation based on the unknown's position in Orbi-retention time-m/z space.

Microbial libraries remain an important resource for natural product discovery; however, constructing taxonomically and chemically diverse collections remains a challenge. Advances in dereplication strategies, including molecular networking, have reduced the rediscovery of known bioactive molecules and facilitated the identification of novel chemical scaffolds, but these approaches are typically applied after library construction or to existing repositories. Furthermore, many dereplication workflows require scaled fermentation and extraction, increasing the time needed to assess a microbes metabolite profile. Here, we integrate matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI-MS/MS) into the bioinformatics platform IDBac, enabling streamlined characterization of microbial taxonomic identity, metabolite production potential, and preliminary metabolite annotation through GNPS2 molecular networking. This miniaturized high-content workflow facilitates strain prioritization by providing metabolite annotations directly from single microbial colonies prior to scale-up and extraction. Application of this approach to marine actinomycetes enabled the annotation of lavanducyanin and multiple napyradiomycin analogs. Subsequent investigation led to the discovery of napyradiomycin B8 from marine Streptomyces sp. CNZ-289, which was confirmed by 1D and 2D NMR spectroscopy and MALDI-MS/MS. Expanding this workflow to an untargeted analysis of 25 commensal marine vertebrate-derived bacterial isolates resulted in the annotation of several known bioactive natural products, including surugamides, antimycins, desferrioxamine siderophores, and the isolation and elucidation of harmane derivatives using NMR. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/733640v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@c92cfforg.highwire.dtl.DTLVardef@1a9522borg.highwire.dtl.DTLVardef@151b309org.highwire.dtl.DTLVardef@c1531f_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Peptide spectrum annotation is critical for the assignment of peptides and the localisation of modifications. While many existing tools provide spectrum annotation capacities, they often lack the flexibility required to allow bespoke spectral annotation of peptides containing multiple labile modifications or the accurate assignment of peptides in which fragmentation deviates from canonical patterns. In these cases, user-guided annotation is widely used to improve assignment completeness, however it typically does not integrate peptide scoring, making it challenging to assess the empirical improvement of the associated annotation and its impact on downstream false-discovery rate estimations. Here, we introduce an interactive annotation environment, the 'MassSpectrum Analyzer', which aims to streamline the exploration and analysis of modified peptides by enabling user-defined customisation with peptide scoring. Using (2-Aminoethyl)trimethylammonium carboxyl-derivatised peptides and glycopeptides as case studies we demonstrate the capacity of the MassSpectrum Analyzer to rapidly explore and allow the assessment of modified peptide datasets. By enabling direct assessment of the impact of user-guided choices on peptide scoring, we show how the detection of highly modified peptides can be improved through post-search integration of modification fragmentation information in a statistically robust manner. Similarly, by permitting comparisons of peptide ion intensities across spectra, we show that global fragmentation patterns can be quantified allowing the interrogation of trends that only become clear when spectra are assessed en masse. Combined, the MassSpectrum Analyzer streamlines the generation of publication-ready spectra and provides a means to assess how the inclusion of annotated features influences assignment scores.

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.

The chorioallantoic membrane (CAM) model represents a promising three-dimensional in vivo platform for preclinical drug testing in human tissues. In this study, we investigated whether the tissue penetration and distribution of benzbromarone, a known inhibitor of the Ca2+ activated chloride channel TMEM16A and potential therapeutic agent for autosomal dominant polycystic kidney disease (ADPKD), can be successfully visualized in human renal cyst tissue cultured on the CAM. To this end, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) combined with an ultrahigh-resolution time-of-flight mass spectrometer was employed. We achieved spatially resolved molecular mapping of endogenous metabolites and lipids as well as the applied compound. MSI enabled clear differentiation between CAM and cystic tissue based on their distinct lipid profiles. Benzbromarone was reproducibly detected in the cyst specimens and exhibited selective accumulation along the cyst epithelium, which is considered the principal site of action. These observations were complemented by multivariate analyses including Uniform Manifold Approximation and Projection (UMAP), and sparse multinomial logistic zero-sum classification. The data-driven approach confirmed molecular differences between tissue types and allowed accurate classification of drug-treated and untreated regions. This study demonstrates that topically applied benzbromarone penetrates human renal cyst tissue in the CAM model and localizes to pharmacologically relevant tissue regions, notably the location of the Ca2+ activated chloride channel TMEM16A in the epithelial lining. The integration of high-resolution DESI-MSI with advanced statistical analysis provides a robust and label-free method to study drug distribution in human tissue grafts. Our findings contribute to the advancement of translational research in analytical chemistry and pharmacology.

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.

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.

Metabolite detection with mass spectrometry (MS) in untargeted metabolomics is limited by the wide concentration range of metabolites, where high-abundance signals dominate MS1 scans and suppress detection of low-abundance features. This reduces metabolite coverage and obscures biologically relevant signals, particularly in complex cellular systems. Full Scan enhanced Dynamic Range (eDR) MS addresses these limitations by partitioning the MS1 mass range into multiple subscans and mass windows, reducing saturation effects from dominant ions. Here, we systematically evaluate different eDR acquisition strategies for untargeted metabolomics. Across four hepatocellular carcinoma cell lines, Full Scan eDR MS increased detectable features up to [~]3.5-fold compared to Full Scan MS. Among equidistant window configurations, 12 windows yielded the highest feature count and broadest dynamic range, while custom window distributions further improved detection in ion-dense regions. In particular, allocating smaller window sizes to the low m/z region selectively increased detection of low-mass features while preserving performance for higher mass ions. Full Scan eDR MS also improved data quality, reducing variation and increasing signal-to-noise ratios, especially for low-abundance metabolites. MS2 coverage and metabolite identifications increased substantially, resulting in unique detection of cancer-relevant metabolites. Importantly, the increased depth of metabolite detection enabled improved discrimination between cancer cell lines, supporting deeper interrogation of metabolic heterogeneity. Overall, these results establish Full Scan eDR MS as a flexible strategy to improve sensitivity and metabolome coverage in untargeted metabolomics. Customization of window size and distribution enable targeted expansion of dynamic range within predefined mass regions, allowing MS acquisition to be tailored to sample complexity and metabolites of interest.

Attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy of proteins in aqueous solution is often limited by water absorption and other optical artifacts. To overcome these limitations, we evaluated the structural features and hydrogen-deuterium exchange (HDX) kinetics of the -helical protein GCN4 in both hydrated (wet) and vacuum-dried (dry) states. While solvent heavily mask the second-derivative spectra of wet samples, vacuum drying yielded a thin, protein-rich film on the ATR crystal, significantly enhancing the signal-to-noise ratio and resolving the protein features without altering the native structure. Dry-state analysis clearly resolved the Amide I, Amide II, and deuterium-shifted Amide II' (1450 cm-1) bands. Notably, second-derivative analysis of the dry spectra of the HDX samples revealed a bimodal Amide I distribution consisting of a stationary band at 1653 cm-1 from the solvent-inaccessible regions and an isotopically sensitive band shifting from 1648 cm-1 to 1644 cm-1 from solvent-accessible regions. These results demonstrate that vacuum-dried ATR-FTIR spectroscopy effectively eliminates solvent masking, providing the spectral clarity required to resolve discrete -helical sub-populations after deuteration.

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.

Ribonucleic acid (RNA) modifications, with over 170 identified types, play diverse roles in cellular processes. The past decade has witnessed surging demand for accurate identification and localization of RNA modifications in both endogenous and synthetic therapeutic RNAs. With accurate spectral annotation for RNA, tandem mass spectrometry (MS/MS) can meet this demand. Here we present RNabel, a user-friendly software tool for in-depth annotation of MS/MS spectra of RNA oligonucleotides. RNabel considers a full set of backbone-cleavage ions (a, b, c, d, a-B, w, x, y, z) in which the ribonucleotide unit could be A, U, C, G, Y (pseudouridine), or I (Inosine). Additionally, RNabel considers 196 modifications on the base, the phosphoribose linkage, the 5' or the 3' terminus, or detachment of a sub-nucleotide fragment as a neutral or charged group. Users can create new components if needed, including ribonucleotides, modifications, neutral or charged groups that could detach from a ribonucleotide. RNabel efficiently processes large datasets in four acceptable formats including .mgf, .raw, .txt from msConvert, and RNabel batch files. Multiple statistical metrics are provided for quality assessment of spectral annotation. To accelerate RNA modification analysis, RNabel is made freely available for Mac and Windows users at https://github.com/songge1111/RNabel/releases. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/733900v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8ccae5org.highwire.dtl.DTLVardef@15c8cfaorg.highwire.dtl.DTLVardef@12b93a2org.highwire.dtl.DTLVardef@1e9aab9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background chemical ions are a pervasive but often underappreciated limitation in LC-MS metabolomics, where they can suppress analyte signal, obscure endogenous metabolites, increase spectral complexity, and consume MS/MS acquisition events. Tributylamine (TBA) ion-pairing reversed-phase LC-MS provides stable retention and broad coverage of polar anionic metabolites, including central carbon intermediates, nucleotides, cofactors, and bile acids, but the back-ground burden introduced by the ion-pairing reagent itself has not been systematically addressed. Here, we identify commercial TBA as a major source of nonbiological contaminant ions and develop a practical strategy to reduce background burden while preserving metabolite coverage. Serial solid-phase extraction of TBA using orthogonal reversed-phase, strong anion-exchange, and strong cation-exchange sorbents removed chemically diverse contaminants, including isobaric background ions that interfered with endogenous hydroxybutyrate isomers. We further optimized the workflow by reducing medronic acid concentration, restricting medronic acid to the organic mobile phase, replacing phosphoric-acid column conditioning with metal-passivated column hardware, and adding EDTA to the sample reconstitution solvent to improve citrate detection. In mouse liver extracts, the optimized method increased signal intensity for most annotated metabolites and improved the fraction of full-scan ion current attributable to target analytes. Method optimization also altered compound-specific retention behavior, resolving some co-elution-based interferences while introducing new suppression relationships for selected analytes. Across mouse liver, human B lymphocytes, and NIST SRM 1950 plasma, the optimized workflow increased total feature detection by 45%, 72%, and 42%, respectively, and improved the number of low-variance features, precursors with data-dependent MS/MS spectra, and MS/MS library matches. These findings establish background-ion mitigation as a central design principle for LC-MS method development. More broadly, this work provides a generalizable framework for identifying, reducing, and validating reagent- and additive-derived background to improve targeted and untargeted LC-MS data quality.

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