Journal of the American Society for Mass Spectrometry

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.

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.

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.

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

Non-canonical HLA-presented peptides are promising therapeutic targets, but their low abundance makes them difficult to reproducibly identify and quantify, particularly in multiplexed immunopeptidomics workflows. Here we present MIRA-MS (Model-Informed Real-time Acquisition for Mass Spectrometry), a real-time acquisition strategy that combines fragment ion-indexed database searching with artificial intelligence-based prediction of peptide fragmentation and retention time to guide quantitative scan acquisition. In a clear cell renal cell carcinoma model, MIRA-MS increased the number of quantified non-canonical immunopeptides by 97-107% relative to standard acquisition methods while also improving recovery of canonical peptides by 45-89%. These results establish real-time AI-guided acquisition as a powerful approach for deeper and more reproducible immunopeptidome profiling.

Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics is an important technology for unbiased discovery of small molecules in biomedical (e.g., drug discovery to diagnostics), animal, plant, environmental, and microbial research. Over the past decade, ion mobility has added an additional dimension to the triplet of MS1, MS2, and retention time, helping resolve co-eluting or isomeric features in an LC-MS/MS that aid in compound identification. Here, we focused on evaluating the current trapped ion mobility spectrometry (TIMS)-amenable feature-finding tools (MZmine 4.9, MS-DIAL 5.5, and MetaboScape 2025 14.0.3) for pre-processing of metabolomics data generated using a popular ion mobility mass spectrometry (IM-MS) technique, TIMS. We leveraged ten public and three benchmark TIMS datasets to evaluate these tools for their strengths and weaknesses. Our results show that MZmine consistently identified the highest number of features and confidently annotated features; however, this performance was accompanied by an increased number of false positives, due to peak splitting, as well as reduced accuracy in collision cross section (CCS) measurements. In contrast, MetaboScape achieved the highest fraction of high-quality MS2 spectra, reflecting a more conservative feature detection strategy. MS-DIAL demonstrated balanced performance, identifying features that other tools missed. Finally, we publicly release the ground-truth datasets and code to support future developments in improving IMS data analysis.

Deep characterization of intact proteoforms remains an analytical challenge in functional proteomics, particularly for heterogenous multi-site post-translational modifications at distinct amino acid residues. Histones are among the most dynamically and diversely post-translationally modified proteins in eukaryote cells, carrying multiple, co-occurring and reversible modifications that can give rise to isomeric proteoform species. Tandem mass spectrometry with multimodal fragmentation capabilities is a promising approach for deep characterization of intact proteoforms, such as modified histones. We applied the novel timsOmni mass spectrometer, which incorporates the Omnitrap platform enabling multimodal MS workflows, for residue-level mapping of histone modifications, including acetylation and methylation. Recombinant histones H3.1 and H4 were in vitro acetylated by enzymes GCN5, PCAF and p300 to generate mono- and multi-acetylated proteoforms. Complementary MS2 electron- and collision-based dissociation (ECD, EID, RCID and ECciD), together with MS3 strategies, produced complete or near-complete backbone fragmentation of intact protein ions (>92% amino acid sequence coverage). For monoacetylated species generated by the more site-selective lysine acetyltransferases, the dominant proteoform matched the known catalytic preferences of the enzymes (H3.1K14ac for GCN5 and PCAF, and H4K8ac for PCAF), while minor positional isomers were also identified and their relative abundance estimated. In contrast, the broader substrate specificity of p300 produced a wide distribution of H4 proteoforms bearing up to seven acetylated lysine residues. Species carrying six and seven acetylations were characterized by multimodal MS2/MS3 experiments, enabling localization of individual acetylation sites and discrimination of positional isomers. Finally, endogenous histone proteoforms from liver extracts were analyzed, yielding sequence coverages of 92-93% for the most abundant species and enabling confident localization of multiple PTMs (acetylation and methylation). These results illustrate that multimodal MSn fragmentation of intact proteins supports residue-level assignment of combinatorial histone marks and coexisting positional isomers. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=165 HEIGHT=200 SRC="FIGDIR/small/722147v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@387ab5org.highwire.dtl.DTLVardef@2410org.highwire.dtl.DTLVardef@13fc392org.highwire.dtl.DTLVardef@140e054_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMultimodal MS{superscript 2}/MS3 maps histone PTMs on intact proteins. C_LIO_LIECD, EID, RCID, and ECciD provide complete or near-complete sequence coverage. C_LIO_LIMS3 localizes acetylation sites, distinguishes positional isomers. C_LIO_LIEndogenous H4 proteoforms are assigned with site-specific PTM mapping. C_LI

Native mass spectrometry (nMS) is a powerful tool for analyzing biomolecules and their complexes under near native conditions. The preservation of the native state depends strongly on the ionization methods used to transfer intact molecules from solution to gas phase. In this work, capillary vibrating sharp-edge spray ionization (cVSSI)- based nMS and in-droplet hydrogen deuterium exchange mass spectrometry (HDX-MS) were used to evaluate calcium-dependent interactions between calmodulin and calmidazolium (CDZ). We found that cVSSI produced a narrow charge-state-distribution (CSD) with low average charge states indicating that this method preserved the native-like state. cVSSI was also able to resolve stepwise Ca2+-binding containing one to four Ca2+-bound species of the protein. In absence of Ca2+, no detectable CDZ-binding was observed. However, CDZ-binding was observed when calmodulin was fully loaded with Ca2+. CDZ-binding to the protein caused marked redistribution of the CSD toward lower charge states, consistent with ligand-induced stabilization of the protein into a more compact conformation. The apparent dissociation constant (Kd) of the interaction was determined to be 261 {+/-} 29 nM and 126 {+/-} 17 nM from Langmuir and quadratic binding models, respectively. Complementary in-droplet HDX-MS showed an approximately 23% reduction in deuterium uptake upon ligand binding indicating reduced solvent accessibility and increased structural stabilization supporting nMS findings. Together, these results demonstrate that cVSSI-based nMS coupled with in-droplet HDX-MS provides an integrated platform for simultaneously resolving metal loading, ligand binding, binding affinity, and ligand-induced conformational changes. This approach complements traditional structural methods by enabling direct interrogation of dynamic, metal-dependent protein-ligand interactions in their native states.

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.

Individual Ion Mass Spectrometry (I2MS) using Direct Mass Technology mode on an Orbitrap mass spectrometer (DMTm) increases sensitivity, resolution, and mass range for protein analysis. Here, we present an end-to-end workflow for deep proteoform sequencing using top-down mass spectrometry with DMTm. By assigning the charge of individual fragment ions and converting spectra from the m/z to the mass domain, DMTm resolves overlapping isotopic distributions that have limited conventional top-down mass spectrometry. Across different fragmentation modes on Orbitrap mass spectrometers, top-down DMTm significantly outperformed conventional top-down mass spectrometry methods. For a glycosylated 50.8 kDa antibody heavy chain, sequence coverage was greatly increased, from 27.5% to 83.3%, in 10 minutes of acquisition using a single fragmentation mode. Coverage of the middle 350 residues improved from 0% to >95%, demonstrating near-complete coverage of the difficult-to-characterize internal region of a large protein. The fragmentation patterns of DMTm were found to be complementary to conventional top-down, with higher internal coverage for DMTm and higher terminal coverage for conventional. Accordingly, aggregation of the data from the two modes further increased heavy chain sequence coverage to 90.2%. A new software platform, Proteoform Studio, provided optimized ion processing for improved sequence coverage and enabled real-time experimental monitoring as individual ions were accumulated. The platform automatically integrates conventional and DMTm data to provide the most comprehensive sequence coverage possible. Together, these advances enable substantially deeper proteoform sequencing and establish a straightforward, complete top-down DMTm workflow to confidently define proteoforms in biological systems and biotherapeutic development.

In conventional native mass spectrometry (MS), one faces severe limitations when challenged with heterogenous, high mass samples, commonly failing to resolve clear peak distributions and thus mass determination. Charge detection MS (CDMS) has emerged as a premier method to analyze these samples by determining mass-to-charge ratio (m/z) and charge (z) simultaneously. Here, the two currently available commercialized CDMS systems, the Orbitrap-based Direct Mass Technology (DMT) and the electrostatic linear ion trap (ELIT)-based Xevo CDMS are applied to human norovirus capsids from two different strains, GI.1 Norwalk and GII.17 Kawasaki. The norovirus capsid is highly heterogenous due to N-terminal processing on the repeating subunits that it is built from and commonly forms T = 3 and sometimes T = 4 particles. Both CDMS approaches were able to determine similar masses in both strains. GII.17 Kawasaki exhibits both T = 3 and T = 4 particles, though the Xevo CDMS measurements were closer to the theoretical mass than the DMT instrument. Interestingly, GII.17 Kawasaki also displayed non-classical mass distributions with high abundance in-between T = 3 and T = 4 which was then confirmed by cryogenic electron microscopy (cryo-EM), demonstrating an oval capsid shape. GI.1 Norwalk displays a wide mass distribution in both instruments that exceeds the theoretical T = 3 mass by 8-10 %. Proteomics and native MS experiments suggest possible interactions with a protein from the expression system. This study demonstrates the capabilities of two distinct CDMS methodologies on two viral capsids and presents the first non-classical capsid assembly in a GII.17 noroviral capsid.

Accurate determination of drug-to-antibody ratios (DARs) is essential for the development, quality control, and performance evaluation of antibody-drug conjugates (ADCs); yet conventional analytical approaches often require extensive sample preparation, long analysis time, and substantial sample consumption. The peak distribution of intact ADCs is highly complex due to inherent glycosylation heterogeneity and variable drug conjugation. By applying enzymatic digestion, ADC can be converted into smaller subunits or deglycosylated species, thereby significantly simplifying the mass spectral profile. This reduction in structural heterogeneity facilitates clearer peak assignment and enables more accurate and reliable DAR quantification. Herein, we report an ultrafast microdroplet digestion-mass spectrometry strategy for rapid DAR characterization of ADCs. Microdroplet enzymatic digestion of antibodies and ADCs occurs within microsecond-time scales during spray ionization, enabling direct online subunit analysis with minimal sample preparation. The method was validated using NIST monoclonal antibody (mAb) conjugated to ADC mimics spanning low to high DAR (0-14) ranges, Cetuximab-derived ADC mimics (DAR[~]5) with complex glycosylation, and the commercial ADC Kadcyla (DAR[~]3.5). Consistent DAR values were obtained across multiple enzymatic workflows (IdeS, EndoS2, and EndoF3) with good reproducibility (%CV typically <5%). This approach substantially reduces analysis time while maintaining analytical accuracy and structural specificity, providing a rapid, sensitive platform for high-throughput ADC characterization and process monitoring. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/729562v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@23145corg.highwire.dtl.DTLVardef@10ddd88org.highwire.dtl.DTLVardef@14b1370org.highwire.dtl.DTLVardef@1e94ad6_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Glycosaminoglycans (GAGs) are linear polysaccharides with essential roles in a myriad of biological processes. Despite their biological importance, methods to determine both spatial and compositional information is limited. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides spatially resolved compositional information of biological molecules without enzymatic digestion or label incorporation, enabling unbiased analysis independent of enzyme or label selectivity, overcoming many current limitations in GAG analysis. Here, we present the identification and validation of GAG discriminatory ions from biological samples by comparison of spectra from purified GAGs and cells with genetically modified GAG biosynthetic pathways. Ions discriminatory of specific GAG sub-families are identified and related to GAG structural components. The analysis is applied to human induced pluripotent stem cells engineered to lack heparan sulphate (HS), where compensatory changes in GAG display that link to function were observed. Furthermore, the broad applicability and spatial resolution of the technique is highlighted through detection of a disease-induced reduction in HS within the individual glomeruli of diabetic mice.

Informatics is essential in metabolomics to analyze and interpret complex data for the advancement of biological insights. However, many current data-processing tools are time-consuming, require careful parameter selection, and depend heavily on user expertise, making reproducibility a challenge. To address these challenges, we developed maxiM/Ze, a Python-based application that utilizes image recognition algorithms to process liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics data prior to statistical analysis. The software implements an automated sequential pipeline that includes mass detection, extracted ion chromatogram (EIC) generation, peak alignment, and data visualization. By converting extracted ion chromatograms into PNG images, maxiM/Ze applies image processing techniques from OpenCV, including Canny edge detection, watershed segmentation, and Pearson correlation-based clustering, to align peaks across samples with minimal user input. Validation against Compound Discoverer 3.4 and mzmine 4.8.30 using eight replicate pooled plasma samples demonstrated competitive feature detection (12,067 features), annotation (219 unique compounds), and reproducibility (median CV of 35.8%) across platforms. The application is prepared for release on both Mac OS and Windows platforms, with the goal of improving reproducibility in metabolomics data analysis.

The extracellular matrix (ECM) and cell surface glycocalyx are key components of biology and play crucial roles in development and tissue function, as well as disease. Proteoglycans, and their glycosaminoglycan (GAG) side chains, are critical components of the ECM and the glycocalyx. GAGs can bind to many different proteins, such as chemokines, and form hydrated barriers around cells. Existing and new methods are helping us to uncover more about the roles of GAGs in biology. Here, we expand on existing technologies and provide streamlined, standardised and well-documented methods that can be easily adopted in standard analytical facilities. We provide extensive detailed step-by-step guides describing sample disruption, GAG disaccharide preparation from biological tissues and their analysis by HILIC-MS/MS. In addition, we demonstrate utility of this method when using a range of different samples as biological sources. This method will sit alongside existing and new techniques to help improve access to GAG analysis, and thereby further the field of understanding GAG function in complex biological contexts.

Spatial metabolomics, measured through mass spectrometry imaging (MSI), provides high-throughput, spatially resolved information on metabolite distributions within tissues, including endogenous metabolites and exogenous compounds. This offers a direct readout of cellular biochemical activity and phenotypes, not fully captured by transcriptomics or proteomic profiling. However, inferring biologically meaningful patterns from noisy, high-dimensional MSI data, particularly across multiple samples and complex experimental designs, remains challenging, and often requires substantial programming expertise. Here we introduce SMEW (Spatial Metabolomics Enhanced Workflow), a flexible, interactive and shareable Shiny-based platform designed to enable code-free downstream analysis of spatial metabolomics MSI data. SMEW provides a unified environment for hierarchical analysis across bulk-, region- and pixel-level resolutions, allowing comparisons between experimental conditions like disease or treatment groups while highlighting coherent metabolic patterns and linking these patterns to biological pathways. The workflow leverages local spatial covariation to robustly summarise MSI data through dimensionality reduction, clustering and identification of spatially variable metabolites. In addition, metabolite co-localisation and covariation network analysis, together with spatially resolved pathway enrichment facilitate the biological interpretation of cross-condition datasets within a single integrated interface. SMEW is applicable across MSI technologies and mass resolutions, as illustrated through case studies on DESI and MALDI-ToF datasets from lung, liver, and kidney. By complementing existing MSI processing and visualisation tools with an accessible, multi-sample, and biologically interpretable analysis framework, SMEW enables functional, flexible, rigorous and intuitive exploration of spatial metabolomics datasets. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/721059v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1e2abaeorg.highwire.dtl.DTLVardef@753ee9org.highwire.dtl.DTLVardef@1756fc1org.highwire.dtl.DTLVardef@fbedc7_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LISMEW provides a flexible, interactive and shareable Shiny-based platform designed to enable code-free downstream analysis of spatial metabolomics MSI data C_LIO_LIThe SMEW framework enables hierarchical analysis at bulk-, region- and pixel levels within a unified framework without relying on extensive programming expertise C_LIO_LIThe pipeline integrates spatially aware clustering, pathway analysis and identification of metabolite co-localisation modules C_LIO_LIThe workflow facilitates flexible comparison of multi-sample experimental conditions through multivariate modelling, differential testing and covariation networks to study treatment- and disease-associated metabolite dynamics C_LIO_LISMEW has been applied to interrogate diverse biological questions, including characterising disease-associated remodelling in a mouse bleomycin model of pulmonary fibrosis, exploring the therapeutic index of antisense oligonucleotides in the liver and assessing metabolic heterogeneity in a small molecule-treated mouse renal tumour model C_LI

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.

One critical step in any targeted mass spectrometry experiment is selecting, from each protein of interest, a small number of peptides that respond well in the mass spectrometer and can serve as reliable proxies for protein quantification. Existing methods select target peptides either by relying on prior empirical measurements, limiting their applicability to previously observed peptides, or using machine learning to predict peptide behavior from sequence alone. However, current machine learning tools suffer from various limitations, including using detectability as an indirect proxy for intensity, relying on small training sets, or ignoring the precursor charge state. In this study, we introduce Bromo, a transformer-based deep learning model that ranks peptide precursors from a given protein by their relative response, taking charge state into account. Trained on millions of annotated peptide pairs derived from large-scale, publicly available data-independent acquisition mass spectrometry data, Bromo consistently outperforms existing sequence-based methods across diverse, independent datasets. Furthermore, we show that fine-tuning Bromo on experiment-specific data can account for differences in sample preparation, sample matrix, and instrument platform, all of which influence which peptides serve as optimal targets. This adaptability makes Bromo a practical tool for selecting target peptides for selected reaction monitoring and parallel reaction monitoring assay development across a wide range of experimental conditions.

Plasma proteomics is used for a variety of applications including biomarker discovery, disease monitoring, and drug development. Data-independent acquisition (DIA) has vastly improved the breadth of proteins that are identified from samples; however, given challenges in reproducibility and translation, it is critical that the quantitative performance of these methods is reliable. Analysis of global proteomics data typically incorporates information from all detected peptides. However, some peptides do not reflect their parent protein amount, due to irreproducible digestion, modification, analytical interferences or instability. We hypothesise that including these peptides impacts protein relative quantification, and thus, a refined spectral library containing only quantitatively representative peptides provides superior protein quantification. By analysing a defined multi-species spike-in model, we show that refining a plasma spectral library by removing precursors that fail to meet quality control metrics (25.4% of all identified precursors) reduces noise and variability, improving precision, accuracy and differential abundance analysis by up to [~]11%, with minimal identification losses and substantial reduction in computational demand. This demonstrates proof-of-concept that refining spectral libraries produces results that prioritize quantification quality over quantity. This approach could enable development of universal tissue-specific refined spectral libraries able to improve quantification quality with easy implementation and minimal processing time. Significance of the StudyAs DIA mass spectrometry proteome depth increases, the quality of the associated protein quantifications must be considered alongside identification breadth, particularly in complex matrices such as plasma, which presents additional technical challenges. The spectral library used for protein identification and quantification is a critical determinant of DIA performance, and its composition requires considerable consideration. This work illustrates an initial step toward improving protein quantification starting at the spectral library level by filtering precursors which are poor quantitative representatives of their parent proteins. In doing so, the resulting data is more reliable for downstream and biological interpretation, with fewer false differential abundance assignments and reduced quantitative noise. As such, this work represents a broader shift away from the habitual focus of MS workflows on maximising the number of protein and differential abundance identifications and instead prioritises the quality of quantification over quantity. These initial findings lay the groundwork for further development of spectral library refinement strategies, with the potential to continue improving the accuracy and precision of protein quantification in DIA-based proteomics.