Journal of the American Society for Mass Spectrometry

A previously overlooked class of protein entanglements, non-covalent lasso entanglements (NCLEs), has been found to play a role in widespread protein misfolding. However, understanding the influence NCLEs have on biological processes is hindered by the absence of dedicated algorithms and computational tools to detect and characterize these geometries in protein structures, molecular dynamics simulations, and in comparison to experimental data from limited proteolysis (LiP) and cross-linking (XL) mass spectrometry (MS). Here, we present EntDetect, a software tool designed to: (1) identify non-redundant NCLEs in protein structures, (2) detect misfolded states by comparing NCLE changes through pairwise comparisons of structures, (3) extract structural ensembles consistent with experimental signals from LiP-MS and XL-MS, and (4) investigate proteome-wide protein misfolding using high-throughput MS data. We demonstrate the utility of EntDetect on a simulated structural ensemble of phosphoglycerate kinase (PGK), alongside corresponding LiP- and XL-MS experimental data. Additionally, we detail the application of EntDetect to detect misfolding associated with native NCLEs on a proteome-wide MS dataset and select candidate proteins for further investigation. This protocol is intended for biophysicists, structural biologists, and molecular biologists with domain knowledge of protein structure, mass spectrometry proteomics data, and beginner experience with Python who want to interpret their experimental observations and computer simulations results through the presence and potential misfolding of NCLE topologies. EntDetect is open-source and freely available (https://github.com/obrien-lab-psu/EntDetect). NCLEweb is also available which is a webserver that identifies NCLEs within a given user-uploaded structure (https://www.ncleweb.org/).

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.

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.

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.

Native mass spectrometry is a central analytical method for characterizing intact proteins, antibody-drug conjugates, and non-covalent assemblies, and it is increasingly the deciding measurement in biotherapeutic development pipelines. A single screening attempt requires days of expression, purification, and buffer exchange into ammonium acetate, followed by 30 to 60 minutes of optimization on a Q-Exactive UHMR or comparable instrument. To our knowledge, no published sequence-based predictor currently estimates native MS suitability before experimental screening. We curated 634 unique proteins with documented native MS outcomes, drawn from a 232-protein hand-curated base set, 358 entries recovered from RCSB PDB by full-text searching for native MS terminology, and 44 evidence-based extractions from supplementary tables across 80 EuropePMC papers. We trained four model variants on this benchmark: a 36-feature BioPython physicochemical baseline, an ESM-2 linear probe, an ESM-2 PCA-256 random forest, and a combined model that concatenates ESM-2 PCA components with BioPython features. All variants were evaluated under cluster-aware 5-fold cross-validation (GroupKFold over ESM-2 embedding-similarity clusters) with isotonic calibration, and standard stratified 5-fold cross-validation is reported as a sensitivity analysis. Under cluster-aware 5-fold cross-validation (GroupKFold over ESM-2 embedding-similarity clusters, our defense against homology leakage), the combined model achieved an AUC of 0.869 plus or minus 0.036, robust against the original stratified-CV value (0.873) and the BioPython baseline (0.852). The ESM-2-only variants showed AUC drops of 0.024 to 0.046 between stratified and cluster-aware splits, indicating that some of the apparent ESM-2 contribution under standard CV reflects homology leakage. Negative recall was 9.4 percent under cluster-aware splitting versus 26.0 percent under stratified, confirming that the models apparent failure-detection capability was substantially inflated by within-fold homology. We report both numbers and treat the cluster-aware values as the primary results. We release the curated dataset, the trained model, and an interactive web tool at nativeready.netlify.app. In its current form, NativeReady should be interpreted primarily as a positive-suitability triage tool; failure prediction remains limited by the scarcity of experimentally documented negative cases. We propose a user-contribution mechanism to accumulate real failure data over time. To our knowledge, no published sequence-based predictor currently estimates native MS suitability before experimental screening, and NativeReady is the first open benchmark and triage model specifically designed for this task.

Thermal proteome profiling (TPP) and its higher-throughput derivative, the proteome integral solubility alteration (PISA) assay, measure changes in protein thermal stability upon ligand binding or other perturbations and have been widely adopted in drug discovery and biomedical research. Though the PISA workflow is straightforward, key parameters, including detergent concentration, methods for removing denatured aggregates, and temperature range selection, vary across studies and can markedly influence assay outcomes. Yet these factors have not been systematically evaluated, limiting rational experimental design and data interpretation. Here, through a combined use of TPP, PISA, tandem mass tag (TMT)-based multiplexing, and computational simulation, we systematically characterize these parameters based on the melting behavior of [~]9,000 proteins. We find that reducing detergent concentration elevates apparent Tm by 1.5-2{degrees}C proteome-wide, and aggregate removal by filtration versus centrifugation further alters measurements. We leverage these observations to optimize PISA then apply the optimized conditions to identify the aminopeptidase NPEPPS as a previously uncharacterized binding partner of angiotensin II, a key vasoactive peptide hormone in blood pressure regulation. Together, this work provides a general framework for assay design and data interpretation, and extends the utility of PISA beyond small molecules to dissecting peptide-protein interactions, an increasingly important modality in drug discovery.

Mass spectrometry imaging (MSI) characterizes the spatial heterogeneity of molecular abundances in biological samples. Experiments with complex designs, involving multiple conditions and multiple samples, provide particularly useful insight into differential abundance of analytes. However, analyses of these experiments require attention to details such as signal processing, selection of regions of interest, and statistical methodology. This manuscript contributes a statistical analysis workflow for detecting differentially abundant analytes in MSI experiments with complex designs. Using a case study of histologic samples of human tibial plateaus from knees of osteoarthritis patients and cadaveric controls, as well as simulated datasets, we illustrate the impact of the analysis decisions. We illustrate the importance of signal processing and feature aggregation for preserving biological relevance and alleviating the stringency of multiple testing. We further demonstrate the importance of selecting regions of interest in ways that are compatible with differential analysis. Finally, we contrast several common statistical models for differential analysis, showcase the appropriate use of replication, and demonstrate model-based calculation of sample size for followup investigations. The discussion is accompanied by detailed recommendations and an open-source R-based implementation that can be followed by other investigations.

BackgroundRapid and accurate identification of urinary tract infection (UTI) pathogens is critical for effective treatment and combating antimicrobial resistance. Conventional culture-based diagnostics are slow, and standard tandem mass spectrometry workflows are resource-intensive. MethodsWe present a proof-of-concept workflow that integrates high-resolution data-independent acquisition (DIA) MS/MS on the Thermo Scientific Orbitrap Astral with MS1-only spectra from the Orbitrap Exploris 480. DIA data establish a reference panel of pathogen-specific peptides, which are then identified in MS1 spectra from urine samples. Machine learning models trained on these matched MS1 features were used to classify eight common uropathogens and non-infected controls across synthetic inoculations, pure cultures, and clinical patient samples. ResultsThe approach accurately distinguished bacterial species in both controlled inoculated samples and clinical patient samples, achieving a Matthews Correlation Coefficient (MCC) of 0.924 on held-out test data and 0.77 on patient samples. ConclusionsThis proof-of-concept demonstrates that pairing DIA-derived peptide panels with MS1-only data acquired on a cost-effective instrument suitable for routine analysis, enables rapid, culture-free identification of UTI pathogens. The method provides a scalable, high-throughput platform suitable for clinical applications and establishes a foundation for broader biomarker discovery and potential quantitative workflows.

IntroductionRetinoids are bioactive vitamin A derivatives that regulate cellular differentiation and gene expression, yet their reliable quantification remains challenging due to low abundance, structural isomerism, and sensitivity to ionization conditions while handling. ObjectivesIn this study, we performed a systematic optimization of liquid chromatography-mass spectrometry (LC-MS)-based detection of retinoids across tissues and biofluids. MethodsChromatographic separation, adduct formation, ionization parameters, fragmentation behavior, and extraction procedures were evaluated in an integrated workflow. ResultsChromatographic conditions influenced not only retention time but also the ionic species detected, affecting precursor selection for MS{superscript 2} analysis. Retinoids exhibited compound-dependent responses to electrospray ionization and collision energy, requiring tailored acquisition parameters. Extraction experiments demonstrated differential recovery among retinoid classes and revealed matrix-dependent behavior, indicating that protocols used for tissues cannot be directly transferred to low-abundance biofluids. Using optimized conditions, retinoids were detected in mouse cerebrospinal fluid (CSF) at concentrations approaching the analytical detection limit, where MS{superscript 2} confirmation was necessary for reliable identification. ConclusionTogether, our results provide a framework for reproducible retinoid profiling across biological matrices and enables comparative studies of retinoid biology in low-volume and low-abundance biofluids.

We present an ultrahigh-field magic-angle spinning (MAS) solid-state NMR (ssNMR) study to characterize intact nontuberculous mycobacteria (NTM) at the molecular level. Hydrated and dried whole-cell Mycobacterium abscessus samples were investigated by combining conventional high-field ssNMR at 750 MHz with ultrahigh-field ssNMR at 1.2 GHz and ultrafast MAS at 100 kHz. To improve sensitivity and enable multidimensional experiments, 13C/15N isotope labeling was performed after growth in synthetic cystic fibrosis medium (SCFM). We utilized 1D 13C and multidimensional 1H-13C and 13C-13C ssNMR experiments to characterize the chemical composition, dynamics, and structural organization of the M. abscessus cell envelope. The isotope-labeling efficiency was found to be non-uniform across different molecular classes, with high incorporation into polysaccharides and lower incorporation into lipid and peptide-associated signals. INEPT- and CP-based experiments selectively probed flexible and rigid fractions of the samples, revealing substantial differences in linewidth, dynamics, and sensitivity between hydrated and dried preparations. Conventional 750 MHz experiments provided high-resolution multidimensional spectra and enabled identification of distinct chemical environments associated with peptidoglycan, arabinogalactan, mycolic acids, lipids, and peptide-associated components. Ultrahigh-field ssNMR at 1.2 GHz combined with ultrafast MAS and 1H detection substantially improved spectral resolution and sensitivity in particular per mg of sample amount, allowing detection of weak and previously unresolved resonances, including polysaccharide and possible nucleic-acid-associated signals. Together, these results demonstrate that ultra-high-field and ultrafast-MAS ssNMR enables detailed characterization of intact NTM cell envelopes under near-native conditions and provides a framework for future molecular investigations of antimicrobial interactions.

Open modification search (OMS) strategies have gained popularity in mass spectrometry-based proteomics for identification of peptides carrying unknown or unexpected post-translational modifications. However, most OMS search engines report only the overall mass difference between the precursor and the matched peptide and do not explicitly identify or score combinations of multiple modifications at the peptide-spectrum match (PSM) level, leaving the interpretation of mass shifts up to the end user and to using downstream analysis tools. Here, we introduce MS Andrea, a novel OMS search engine developed to directly identify and score combinations of up to four variable modifications per peptide without having to predefine them. MS Andrea uses a sequence tag-based strategy to efficiently filter candidate peptides prior to scoring. Remaining candidates are evaluated using the MS Amanda scoring function, first considering fixed modifications only, followed by a second scoring stage in which combinations of modifications from the Unimod database are considered based on the observed mass difference and matched to the spectrum. We evaluated MS Andrea using phosphopeptide datasets from HeLa cells and Arabidopsis thaliana and compared its performance with the widely used OMS engines MSFragger and Sage. Across datasets, MS Andrea identified the highest number of PSMs at 1% false discovery rate while achieving comparable peptide-level identifications. Importantly, MS Andrea directly reports modification identities and sites at the PSM level and enables the identification of peptides having up to four variable modifications. Together, these results demonstrate that MS Andrea facilitates more detailed and interpretable characterization of peptide modifications while maintaining competitive identification performance in OMS-based proteomic analyses. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/714851v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@52f65forg.highwire.dtl.DTLVardef@acf4e3org.highwire.dtl.DTLVardef@10171caorg.highwire.dtl.DTLVardef@1d594ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

The structural study of membrane proteins has traditionally relied on detergent-based extraction from cellular membranes. Although native-like reconstitution approaches have advanced, fully understanding membrane protein dynamics requires examining them within their native membrane environment. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful method for probing structural dynamics in reconstituted systems, but the presence of the lipid bilayer introduces considerable complexity, limiting broader adoption under physiological conditions. Here, we present the first fully automated HDX-MS platform incorporating a two-stage delipidation workflow. We applied this approach to monitor the dynamics of the ABC transporter MsbA in isolated inner membrane vesicles (IIMVs) from Escherichia coli through its ATPase cycle. IIMVs revealed distinct dynamic features within the nucleotide binding domains and substrate binding cavity, highlighting physiologically relevant motions not observed with detergent solubilised MsbA. This platform significantly advances HDX-MS and underscores the importance of studying membrane proteins in native lipid environments.

Chronic obstructive pulmonary disease (COPD), a chronic lung disease, involves complex metabolic disturbances that remain poorly characterized using non-invasive matrices. The metabolic alterations associated with cigarette smoke (CS), one of the major drivers of disease progression in COPD patients, have not been explored in detail. This study primarily aimed to investigate the metabolic signatures in COPD patients categorized into smoker (n=15), ex-smoker (n=11), and non-smoker (n=3) subgroups. Utilizing saliva as a noninvasive sample, we identified 26 metabolites with differential expression in smokers and 31 in ex-smokers. However, no such significant alteration was observed in the non-smokers subgroup. The multivariate analysis distinctly separated the COPD subgroups from healthy controls. Additionally, pathway enrichment analysis revealed perturbations in key metabolic pathways, including unsaturated fatty acid biosynthesis, arginine biosynthesis, the tricarboxylic acid (TCA) cycle, and pyruvate metabolism. Moreover, univariate Random forest analysis identified four metabolites (cyclopentanone, tetradecane 4-methyl, acetophenone, and scyllo-inositol) as potential biomarkers distinguishing COPD subgroups from healthy controls. This study offers novel molecular insights into the association of smoking with disease progression and provides a mechanistic understanding of COPD in different subgroups for better management of the disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/717654v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1c3be84org.highwire.dtl.DTLVardef@10ce0aorg.highwire.dtl.DTLVardef@1470712org.highwire.dtl.DTLVardef@2163b6_HPS_FORMAT_FIGEXP M_FIG C_FIG

Mass spectrometry (MS) is a powerful technique for characterizing modified RNA as it directly sequences and quantifies all mass-altering modifications simultaneously. However, the physicochemical properties of RNA result in poor ionization efficiencies during electrospray ionization, presenting a major barrier to sensitive MS measurements necessary for low abundance RNA samples and RNAs with low modification stoichiometries. Here, we report a ligation-based approach to increase ionization efficiencies of RNA oligonucleotides. We show that short ([~]5 nt), chemically modified DNA oligonucleotides can be enzymatically ligated to RNA to serve as MS signal enhancers. Among a series of signal enhancers appended with various alkyl and alkylimidazolium functional groups, we found that decyl-functionalized derivatives improved MS sensitivity by [~]15-fold compared to unlabeled oligonucleotide. When ligated to RNA standards, the decyl-modified signal enhancer increased MS signals 2-4-fold with the additional benefit of improved retention during liquid chromatography (LC) separations without ion pairing agents. To apply the ligation-based approach to RNase T1 digests of longer RNAs, a multi-step enzymatic approach was optimized to maximize ligation efficiencies. We then ligated signal enhancers to a yeast transfer RNA (tRNA) digest and observed increased MS signals for numerous sequence-informative digestion products. Importantly, the sequences of RNA oligonucleotides ligated to signal enhancers were readily determined by tandem mass spectrometry with collision-induced dissociation. This ligation-based strategy for enhancing LC-MS/MS characterization of RNA creates opportunities to measure low abundance RNA samples and their modifications.

Ambient metabolomics techniques such as laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) enable fast, preparation-free fingerprinting of biological samples but are inherently limited by spectral congestion in the absence of chromatographic separation. While ion mobility spectrometry provides additional gas-phase separation, maintaining ion transmission under the transient signals characteristic of laser desorption, remains analytically challenging. Here, we define operating conditions for cyclic traveling-wave ion mobility spectrometry (cIMS) that preserve transmission under LA-REIMS duty-cycle constraints and systematically evaluate how cIMS integration reshapes biofluid fingerprints and enhances chemical specificity in chromatography-free metabolomics analysis. Under optimized single-pass conditions, cIMS separation reorganized LA-REIMS spectra into structured mass/mobility feature domains, enabling selective mobility-based filtering of matrix-derived salt cluster ions. This reduced non-biological background contributions by up to 35% of total spectral intensity while preserving over 90% of detected untargeted features. Although cIMS operation introduced a sensitivity penalty relative to time-of-flight-only acquisition, approximately 80% of the total ion current was recovered under optimized conditions. Mobility-resolved data revealed coherent homologous series and class-specific structural trends, particularly for lipids, supporting class-level annotation. Analysis of 101 metabolite and lipid standards covering a broad physicochemical range (logP -5.30 to 19.40) demonstrated comprehensive molecular coverage, high mass accuracy (mean 2.4 ppm), and good agreement with reference CCS values (mean deviation 4.0%), with isomer separation observed for biologically important secondary bile acids in extended separation cycles. Collectively, these results establish LA-REIMS-cIMS as a practical analytical strategy for enhancing chemical specificity and spectral interpretability in support of high-throughput large-scale metabolic fingerprinting. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/709786v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@18a2dfdorg.highwire.dtl.DTLVardef@d165d6org.highwire.dtl.DTLVardef@1750291org.highwire.dtl.DTLVardef@fbbce9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Ion mobility spectrometry adds an orthogonal gas-phase separation to LA-REIMS, reorganizing complex biofluid spectra into distinct mass-mobility feature bands and improving molecular resolution in rapid ambient ionization metabolomics. C_FIG

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

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.

1.1Hair has applications in biomarker discovery and forensics, yet the influence of proteomics software tools on hair proteome characterisation remains underexplored. This study compares four bottom-up proteomics workflows (MaxQuant, FragPipe, MetaMorpheus, and SearchGUI/PeptideShaker). Publicly available hair proteomes were analysed following extraction with 1-dodecyl-3-methylimidazolium chloride (DMC), sodium dodecanoate (SDD), sodium dodecyl sulfate (SDS), and urea. Data were acquired on Orbitrap-based DDA platforms. Peptide identification, protein inference, functional annotation, physicochemical properties, and label-free quantification (LFQ) were evaluated. Peptide-level performance differed across tools. MS-GF+ and FragPipe identified the most unique peptides, while X!Tandem reported the fewest. Protein inference showed a dissociation from peptide-level results. MetaMorpheus reported the highest number of protein groups despite only the third highest peptide counts. FragPipe and MaxQuant followed, while PeptideShaker consistently inferred the fewest proteins. Protein-level concordance was low, with only 30.3% overlap across tools and extraction methods. These differences extended to downstream analyses. Functional enrichment showed moderate concordance (38.25% overlap). Physicochemical profiles varied, with MetaMorpheus identifying more hydrophobic proteomes and PeptideShaker more hydrophilic profiles. At the quantitative level, reproducibility depended on extraction buffer. SDS and urea showed lower variability (CV =< 0.025), while DMC and SDD showed higher variability (up to 0.10). Absolute LFQ intensities and differential expression outputs varied across tools despite moderate to strong correlation (r = 0.77 to 0.93). Overall, software choice influences proteome coverage, physicochemical profiles, and quantitative outcomes. Relative trends were partially conserved, but magnitude and significance varied. These findings support careful method selection and multi-tool validation in hair proteomics

Native mass spectrometry (nMS) is well established for measuring protein masses and stoichiometries using nano-electrospray ionization (nESI), yet salt adduction and source activation energies can limit routine measurements. In this study, we benchmark submicron quartz nanopipette nESI emitters (<50 nm internal diameter) across three mass spectrometry platforms (quadrupole-time-of-flight, quadrupole-Orbitrap, and tribrid-Orbitrap platforms) and a wide protein mass range (17-800 kDa). We analysed holo-myoglobin (17 kDa) over a range of concentrations (10 M-10 nM) and capillary voltages to determine limits of detection and define a gentle operating regime. We additionally observe reduced Na+ adduction and preservation of the Zn2+-bound metalloproteoform of carbonic anhydrase II (29 kDa). Proteins and protein complexes spanning the mid-to-high mass range including ovalbumin ([~]44 kDa), malate dehydrogenase ([~]70 kDa), glutamate dehydrogenase ([~]350 kDa), {beta}-galactosidase ([~]465 kDa), and GroEL ([~]800 kDa), were readily detected using nanopipette emitters. Compared with conventional 1-2 m internal diameter borosilicate emitters, quartz nanopipettes provided higher signal-to-noise ratios and fewer adducts. Finally, direct analysis of clarified bacterial lysate expressing -synuclein yielded a clear monomeric charge-state distribution, demonstrating compatibility with complex biological matrices. Collectively, these results establish quartz nanopipette nESI as an instrument-portable, salt-tolerant approach suitable for routine nMS analysis across a broad range of protein molecular weights and sample complexities.

Fresh-frozen tissues are considered the gold standard for proteomic analyses due to superior preservation of protein integrity; however, their use is limited by the logistical and financial requirements of long-term storage. Formaldehyde-fixed paraffin-embedded (FFPE) tissues provide a practical alternative owing to their stability and widespread availability in clinical settings. A critical step in FFPE proteomics is deparaffinization, which traditionally relies on organic solvents such as xylene, along with efficient reversal of formaldehyde-induced crosslinks. In this study, we evaluated multiple FFPE protein extraction and digestion workflows including chaotropic, surfactant-based, and detergent-free approaches in combination with xylene-free deparaffinization strategies, using label-free data-independent acquisition (DIA) LC-MS/MS. Among the tested methods, a chaotropic-, reductant-, and surfactant-free in-solution digestion workflow demonstrated robust protein and peptide recovery. A modified version of this protocol further improved peptide coverage while maintaining comparable protein depth. The applicability of the optimized workflow was assessed using FFPE needle biopsy samples from control, hepatic steatosis, and liver fibrosis groups. Distinct proteomic patterns were observed across conditions, with hepatic steatosis associated with early activation of stress-response pathways, while fibrosis showed evidence suggesting altered lipid metabolism. Overall, this study presents a simple, xylene-free, and MS-compatible workflow for FFPE proteomics that is suitable for low-input clinical samples and may support broader application of archival tissues in proteomic research.