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PubMed2025

Single-cell glycome and transcriptome profiling enabled by a library of anti-glycan antibodies.

Glycans play critical roles in cellular processes and clinical applications, but they remain difficult to study due to a shortage of well-characterized anti-glycan reagents and high-throughput technologies for glycome profiling, especially ones capable of single-cell resolution. To meet these needs, we generated a database of 650 anti-glycan antibody sequences, recombinantly expressed a library of 154 antibodies, and extensively characterized their binding properties using glycan microarrays. In addition to providing valuable information and resources for the field, the sequence database and microarray data also enabled development of "Glycomic-seq" (Glycome profiling via multiplexed immunoglobulins combined with sequencing), a DNA-barcoded anti-glycan antibody platform that enables high-throughput, single-cell profiling of both RNA and cell-surface glycan expression. Using Glycomic-seq, we profiled two isogenic colorectal cancer cell lines. The results revealed various glycans associated with cancer stem cells and metastasis, demonstrating the power of integrating glycomic information with multi-omic efforts to discover biomarkers and therapeutic targets.

Polysaccharides↗

PubMed2026

NMR metabolomics and glycomics for cancer detection in patients with non-specific symptoms: a prospective observational cohort study.

BACKGROUND: Early cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1H NMR-based metabolomics can identify cancer with high accuracy. SCAN2 evaluated whether integrating metabolomics with glycomics provides complementary molecular information and improves discrimination in a clinically complex, real-world population. METHODS: Serum from 369 SCAN patients (59 cancers) was analysed using AXINON® System-derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. FINDINGS: In the full cohort, integration of glycomics with metabolomics achieved an AUC of 0.814 (95% CI 0.808-0.820). In a refined sub-cohort excluding major comorbidities and selected cancer types (32 cancers, 277 non-cancers), performance improved to an AUC of 0.884 (95% CI 0.879-0.890). Discriminatory features included cancer-associated biantennary fucosylated glycans alongside amino acid metabolites (glutamate, histidine) and lipoprotein-related measures. A classifier distinguishing metastatic from non-metastatic disease (n = 29 vs. 30) achieved an AUC of 0.80. Joint probability analysis in the full cohort preserved cancer-associated signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.2% (95% CI 85.7-92.6). INTERPRETATION: These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and indicate that integrating glycomics with metabolomics provides complementary biological information for cancer discrimination. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing. FUNDING: EPSRC, EU Horizon 2020, Wellcome/MLSTF, Novo Nordisk Foundation.

Humans↗

PubMed2025

Serum N-glycomics for non-invasive detection of significant liver pathology across clinical phases of treatment-naïve chronic hepatitis B.

BACKGROUND: Early identification of significant liver pathology is crucial for timely antiviral intervention in individuals with chronic hepatitis B (CHB) infection. Current non-invasive methods show limited accuracy in detecting occult liver damage, particularly in those with normal ALT. This study evaluated serum N-glycan profiles for diagnosing significant liver pathology in treatment-naïve CHB patients across clinical phases. METHODS: This cross-sectional study analyzed 626 treatment-naïve CHB patients confirmed by liver biopsy, classified according to 2025 EASL guidelines. Serum N-glycan profiles were determined using DNA sequencer-assisted fluorophore-assisted carbohydrate electrophoresis. Significant liver pathology was defined as inflammation grade ≥ G2 and/or fibrosis stage ≥ S2 (per Scheuer scoring system). Multivariate logistic regression models were developed and compared with traditional non-invasive markers. RESULTS: Among 626 CHB patients, 66.0% had significant inflammation and 58.9% had significant fibrosis. Patients with significant pathology showed characteristic alterations, with elevated P1, P3, P6, P7, P11 peaks and decreased P0, P5, P8, P10 peaks (all p < 0.0001). Compared to respective infection phases, hepatitis phases showed P1 increases of 19.6% and 36% in HBeAg(+) and HBeAg(-) patients, with P11 increases of 82.4% and 73.4%, while P0 decreased by 20.3% and 27.6%, and P10 by 21.6% and 20.3%. Relative to mild pathology (G and S < 2), P1 increased by 27% in significant pathology (G and/or S ≥ 2), reaching 58.7%/48.7% in G4/S4 stages (vs. G0/S0). In ALT-normal HBeAg(+) infection phase, P1 increased by 80.2%/65.8% in G4/S4 stages (vs. G0/S0), with P2 also increasing by 54.1%/45.2%. Multivariate analysis identified P11 as strongest risk factor (OR = 3.84, 95%CI: 1.74-8.45, p = 0.0008), followed by P1 (OR = 2.04, 95%CI: 1.57-2.64, p < 0.0001) and P7 (OR = 1.75, 95%CI: 1.31-2.34, p = 0.0002), while P2 (OR = 0.07, 95%CI: 0.02-0.26, p < 0.0001) and P0 (OR = 0.30, 95%CI: 0.12-0.79, p = 0.0140) served as protective factors. The glycomics combined model (AUC = 0.876 (0.844-0.908)) achieved superior performance and outperformed the clinical model (AUC = 0.818 (0.779-0.857)), LSM (AUC = 0.817 (0.775-0.858)), APRI (AUC = 0.830 (0.792-0.867)), and FIB-4 (AUC = 0.672 (0.621-0.723)) (all p < 0.001), with 78.7% sensitivity and 83.2% specificity. The optimized model reached AUC = 0.917 (0.891-0.942) with accuracy 84.2%, with 78.7% sensitivity and 94.6% specificity. Both glycomics-based models maintained diagnostic capability in ALT-normal patients particularly in HBeAg(+) infection. CONCLUSIONS: Serum N-glycomics demonstrates promising potential for non-invasive identification of significant liver pathology in treatment-naïve CHB patients, providing an alternative approach for early treatment decisions, especially in ALT-normal patients with occult liver damage.

Humans↗

PubMed2026

Rapid glycomic analysis of serum EVs reveals altered N-glycosylation patterns in ASD.

Objective laboratory diagnostics for autism spectrum disorder (ASD) are lacking, necessitating rapid clinical screening tools. Because serum extracellular vesicle (EV) N-glycosylation captures critical neurodevelopmental signatures, we developed a fast, biologically interpretable diagnostic strategy. EVs from ASD patients with language impairment and neurotypical controls were isolated using a rapid extra-polyethylene glycol precipitation/filtration (EPF) workflow, benchmarked against ultracentrifugation. Following MALDI-TOF/MS profiling, machine learning was re-evaluated using repeated nested cross-validation to reduce optimistic bias and potential information leakage. Among five classifiers, Random Forest (RF) showed the best overall balance across discrimination, calibration, and classification metrics. RF-based SHAP analysis provided transparent interpretation, highlighting key discriminative glycans, including H4N3S1F1, H5N5S1F1, and H3N5F1. To elucidate molecular mechanisms, we integrated public EV transcriptomic data. This revealed significant dysregulation of N-glycosylation machinery genes (e.g., MAN1A1, NEU1, OSTC, RPN2), whose expression directionally aligned with observed glycan shifts in synaptic pathways. Collectively, this rapid serum EV N-glycomic workflow, combined with leakage-controlled RF-based interpretation, provides a promising foundation for non-invasive ASD biomarker discovery and future multicenter validation.

Humans↗

PubMed2026

Strategy for Simultaneous Multiomic Survey of N-Glycomic and Extracellular Matrix Proteome by Mass Spectrometry Imaging.

Recent advances in spatially resolved molecular profiling have positioned matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) as a powerful platform for multiomic tissue analyses. However, conventional workflows that sequentially target distinct molecular classes are time- and resource-intensive, requiring repeated sequential sample preparation, imaging, and data integration. Here, we evaluate streamlined strategies for simultaneous or combined acquisition of N-glycan and collagen-derived peptide information using PNGase F and collagenase. In-solution studies demonstrate that simultaneous enzymatic digestion yields comparable peptide identifications and glycan profiles relative to traditional sequential workflows, with minimal impact on enzymatic specificity. On the basis of these findings, we developed and optimized MALDI-MSI protocols enabling either simultaneous enzyme application or sequential enzyme treatment with unified matrix deposition and single-pass imaging. While direct coapplication reduced image uniformity, a hybrid approach that used sequential enzyme deposition with combined imaging preserved spatial fidelity and spectral quality while significantly reducing processing and computational demands. Application to human tissues, including vertebral bone and ocular samples, highlights the utility of this workflow for fragile specimens and exploratory multiomic surveys. Collectively, these results establish a framework for integrated glycomic and proteomic imaging targeting the extracellular microenvironment, expanding multiomic MALDI-MSI analyses.

Spectrometry, Mass, Matrix-Assisted Laser Desorpti↗

PubMed2026

Dysregulation of the serum and IgG N-glycome in decompensated cirrhosis and its association with Model for End-Stage Liver Disease-Sodium (MELD-Na).

BACKGROUND AND AIMS: N-glycans modulate glycoprotein structure and function and are altered during chronic inflammation. We sought to define the extent of serum and IgG N-glycan disruption in patients with decompensated liver cirrhosis from alcohol-related liver disease (ALD), primary sclerosing cholangitis (PSC), and ALD-related hepatocellular carcinoma (HCC). Finally, we aimed to examine whether serum and IgG glycosylation is associated with changes in Model for End-stage Liver Disease-Sodium (MELD-Na) scores, a clinical marker used to prioritise liver transplantation. METHODS: Serum samples were obtained from patients with ALD (n = 17), PSC (n = 7), ALD-related HCC (n = 4), and healthy controls (n = 10). N-glycans were released, fluorescently labelled, and profiled by hydrophilic interaction ultra performance liquid chromatography (HILIC-UPLC). Chromatograms were integrated into 46 and 23 glycan peaks for serum and IgG respectively. These peaks and their associated glycosylation traits were statistically compared with healthy controls using age- and sex-adjusted linear regression models. RESULTS: In serum, decompensated cirrhosis shows statistically significant shifts toward less complex, agalactosylated and asialylated biantennary glycans, accompanied by significant losses of highly branched, galactosylated and sialylated structures. IgG mirrored this pattern, which is characteristic of a pro-inflammatory signature, with increased agalactosylation and bisected glycan levels, along with reduced levels of digalactosylated and sialylated species. N-glycan profiles showed significant associations with MELD-Na scores, indicating that inflammatory processes in decompensated liver cirrhosis continue to reshape serum glycoproteins. CONCLUSION: Decompensated liver cirrhosis shows profound remodelling of serum and IgG N-glycans. These data establish a reference framework for terminal glycomic disruption in liver disease and highlight the potential value of incorporating glycosylation analysis into broader assessments of liver disease progression.

Humans↗

PubMed2025

Integrative glycomic analysis reveals the crucial role of protein glycosylation in fungal pathogenesis.

Protein glycosylation, a co- and post-translational modification that enhances the functional diversity of the proteome, contributes to various molecular and cellular functions by transferring different polysaccharides onto proteins. During the last decade, the role of glycosylation in plant pathogenic fungi has received significant attention, and glycoproteins are expected to play essential roles in various biological processes including pathogenicity. However, the comprehensive functional genetic analyses for protein glycosylation pathways and glycan structures of phytopathogenic fungi are still largely unknown. Here, we investigated the role of protein glycosylation in Fusarium graminearum by identifying 65 putative genes involved in protein glycosylation and characterizing their functions. Through cell wall component profiling and HPLC analysis, we characterized the overall N- and O-glycan structures in F. graminearum and found that deletion of ALG3 and ALG12 led to truncated core N-glycan structures. Quantitative proteomics analysis revealed that the truncated core N-glycans, generated by the loss of two key enzymes in the initial core N-glycosylation pathway, Alg3 and Alg12, affected a wide range of glycoproteins-including transcription factors, phosphatases, kinases, peroxidases, and other proteins involved in various biological processes-ultimately impacting the virulence of F. graminearum. This study elucidates the complex roles of glycosylation, highlighting the connections among genes involved in the protein glycosylation pathway, glycans, and glycoproteins in regulating the general biology and pathogenicity of F. graminearum. It also would be the fungal glycobiology study initiative.

Glycosylation↗

PubMed2026

Genetic Association of the Transcriptome and Immunoglobulin G N-glycome with Cognitive Function.

OBJECTIVE: Immunoglobulin G (IgG) N-glycosylation is associated with mild cognitive impairment through the regulation of inflammatory balance; however, the underlying mechanisms remain unclear. METHODS: Our study utilized a post-genome-wide association studies (GWAS) method that integrated GWAS data for cognitive function with gene expression quantitative trait loci (eQTL), protein QTL (pQTL), and IgG N-glycan-QTL data. RESULTS: Mendelian randomization (MR) analyses suggested bidirectional causalities between glycan peaks (GPs) and cognitive function, with GP7, GP12, and GP19 showing a causal effect on cognitive function, while cognitive function conversely showed a causal effect on GP1 and GP8. Two proteins and 10 genes were implicated in the regulation of IgG N-glycosylation. Furthermore, multivariable MR results suggested complex causalities between genes/proteins and IgG N-glycans, which jointly promote or independently affect cognitive function. CONCLUSION: Our study reveals a novel mechanism by which genes, proteins, and modified IgG N-glycans converge to pathologically affect cognitive function.

Immunoglobulin G↗

PubMed2022

Cystic Fibrosis Airway Mucus Hyperconcentration Produces a Vicious Cycle of Mucin, Pathogen, and Inflammatory Interactions that Promotes Disease Persistence.

The dynamics describing the vicious cycle characteristic of cystic fibrosis (CF) lung disease, initiated by stagnant mucus and perpetuated by infection and inflammation, remain unclear. Here we determine the effect of the CF airway milieu, with persistent mucoobstruction, resident pathogens, and inflammation, on the mucin quantity and quality that govern lung disease pathogenesis and progression. The concentrations of MUC5AC and MUC5B were measured and characterized in sputum samples from subjects with CF (N = 44) and healthy subjects (N = 29) with respect to their macromolecular properties, degree of proteolysis, and glycomics diversity. These parameters were related to quantitative microbiome and clinical data. MUC5AC and MUC5B concentrations were elevated, 30- and 8-fold, respectively, in CF as compared with control sputum. Mucin parameters did not correlate with hypertonic saline, inhaled corticosteroids, or antibiotics use. No differences in mucin parameters were detected at baseline versus during exacerbations. Mucin concentrations significantly correlated with the age and sputum human neutrophil elastase activity. Although significantly more proteolytic cleavages were detected in CF mucins, their macromolecular properties (e.g., size and molecular weight) were not significantly different than control mucins, likely reflecting the role of S-S bonds in maintaining multimeric structures. No evidence of giant mucin macromolecule reflecting oxidative stress-induced cross-linking was found. Mucin glycomic analysis revealed significantly more sialylated glycans in CF, and the total abundance of nonsulfated O-glycans correlated with the relative abundance of pathogens. Collectively, the interaction of mucins, pathogens, epithelium, and inflammatory cells promotes proteomic and glycomic changes that reflect a persistent mucoobstructive, infectious, and inflammatory state.

Cystic Fibrosis↗

PubMed2024

Insights Into Glycobiology and the Protein-Glycan Interactome Using Glycan Microarray Technologies.

Glycans linked to proteins and lipids and also occurring in free forms have many functions, and these are partly elicited through specific interactions with glycan-binding proteins (GBPs). These include lectins, adhesins, toxins, hemagglutinins, growth factors, and enzymes, but antibodies can also bind glycans. While humans and other animals generate a vast repertoire of GBPs and different glycans in their glycomes, other organisms, including phage, microbes, protozoans, fungi, and plants also express glycans and GBPs, and these can also interact with their host glycans. This can be termed the protein-glycan interactome, and in nature is likely to be vast, but is so far very poorly described. Understanding the breadth of the protein-glycan interactome is also a key to unlocking our understanding of infectious diseases involving glycans, and immunology associated with antibodies binding to glycans. A key technological advance in this area has been the development of glycan microarrays. This is a display technology in which minute quantities of glycans are attached to the surfaces of slides or beads. This allows the arrayed glycans to be interrogated by GBPs and antibodies in a relatively high throughput approach, in which a protein may bind to one or more distinct glycans. Such binding can lead to novel insights and hypotheses regarding both the function of the GBP, the specificity of an antibody and the function of the glycan within the context of the protein-glycan interactome. This article focuses on the types of glycan microarray technologies currently available to study animal glycobiology and examples of breakthroughs aided by these technologies.

Polysaccharides↗

PubMed2025

Comparisons of Methods for Mucus Sampling and Mucin Semi-Quantification on Barramundi (Lates calcarifer) and Atlantic Salmon (Salmo salar) Epithelial Sites.

Fish epithelial surfaces are covered by a mucus layer. The highly glycosylated proteins called mucins are a main component of the mucus, which also contains a range of antibacterial enzymes, proteins, and peptides of importance for its protective properties. Here, we compared the practicality and yield of mucus harvesting from barramundi and Atlantic salmon epithelial sites using glass slide, swab, Super·SAL™ and whole tissue extract. We also compared the feasibility of using the orcinol assay, a glycan-on-membrane assay, and absorbance at 230 nm in combination with standard curves of pig gastric mucin to estimate the mucin concentration. Glycomics demonstrated that non-amine hexose content differed more between fish and tissues than terminal monosaccharides with cis-hydroxy groups, and that non-mucin molecules had a major impact on the A230-based results, making the glycan-on-membrane assay the most versatile method for estimating mucin concentration. We conclude that the most versatile tool for mucus harvesting was swabs, allowing for sufficient amounts of sample to be harvested with relative ease and low levels of contamination from the oral cavity, gill, skin, and intestine. Furthermore, the glycan-on-membrane assay was useful for measuring mucus concentration, and it was beneficial to estimate both sample concentration and purity by comparing samples at relatively similar concentrations.

Animals↗

PubMed2025

Boosting Quantification of N-Glycans by an Enhanced Isobaric Multiplex Reagents for Carbonyl-Containing Compound (SUGAR) Tagging Strategy.

Glycans are complex molecules composed of various monosaccharides and exhibit diverse, branched polymer structures. Extensive research has been conducted on mass spectrometry (MS)-based qualitative and quantitative glycan analysis due to their critical biological functions. However, traditional data-dependent acquisition (DDA) in MS analysis primarily selects a limited subset of abundant ions during MS1 scans for fragmentation in subsequent MS2 stages. In this study, we introduce an advanced isobaric labeling strategy that incorporates a large amount of content-relevant sample labeled with one isobaric tag channel as an additional boosting channel. This innovation enhances the efficiency of isobaric multiplex reagents for carbonyl-containing compound (SUGAR) tagging in quantitative glycomics. Notably, this approach significantly improves the characterization of low-abundance N-glycans and enables the detection of subtle quantitative differences in N-glycan profiling.

Polysaccharides↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed2026

Glycoinformatic Profiling of Label-Free Intact Heparan Sulfate Oligosaccharides.

Heparan sulfates (HSs) are a group of heterogenous linear, sulfated polysaccharides that play a role in health and many diseases, including cancer, cardiovascular, and kidney diseases. The structural variety of HS has greatly challenged the development and utility of HS analytics, particularly for native (nondepolymerized) structures, leaving a significant gap in HS technologies for clinical application. Mass spectrometry-based profiling with bioinformatics offers an approach that can retain variety in large datasets. Using healthy human plasmas, we developed a mass spectrometry glycoprofiling approach for native HS oligosaccharides, which retains the structural complexity of each individual HS chain and generates an HS "index" (or Heparan-ome) for each patient. As a proof of concept, analysis of 53 plasma samples ranging from four groups of kidney disease patients revealed a new subset cluster (21%, 4/19) of membranous glomerulopathy patients with distinct HS profiles, highlighting the potential of HS glycoprofiling as a powerful new approach to clinical practice, which warrants future development into quantitative oligosaccharide glycosaminoglycanomics and clinical diagnostics of kidney and other diseases.

Humans↗

PubMed2025

When glycobiology meets inflammasome activation: Insights and implications.

BACKGROUND: Glycobiology focuses mainly on the study of glycan structures and their biological functions. Glycans not only provide a basic energy supply through the tricarboxylic acid cycle and glycolysis but also serve as important immune regulators during pathogen invasion and homeostasis maintenance. Inflammasomes are critical multiprotein complexes of the immune system that detect both exogenous pathogenic threats and endogenous danger signals to mediate inflammatory responses. Glycobiology has revealed significant insights into the mechanisms of immune responses, particularly in the context of inflammasome activation. AIM OF REVIEW: This review summarizes the multifaceted relationships between glycobiology and inflammasome activation, highlighting how glycan structures, glycosylation patterns, and glycan-binding proteins influence inflammasome pathways. This review sheds light on novel targets for drug development aimed at modulating inflammatory pathways through the targeting of specific glycan structures. KEY SCIENTIFIC CONCEPTS OF REVIEW: Glycans directly or indirectly provide prime and activation signals for inflammasomes, glycosylation of inflammasome-related proteins by glycan structures modulates inflammasome activation and downstream inflammation, and the interaction between glycans and lectins also provides regulatory signals for inflammasome activation. This intersection of glycobiology and inflammasome activation presents a unique opportunity to elucidate the molecular mechanisms underlying inflammatory responses and their potential therapeutic implications.

Inflammasomes↗

PubMed2024

GRable Version 1.0: A Software Tool for Site-Specific Glycoform Analysis With Improved MS1-Based Glycopeptide Detection With Parallel Clustering and Confidence Evaluation With MS2 Information.

High-throughput intact glycopeptide analysis is crucial for elucidating the physiological and pathological status of the glycans attached to each glycoprotein. Mass spectrometry-based glycoproteomic methods are challenging because of the diversity and heterogeneity of glycan structures. Therefore, we developed an MS1-based site-specific glycoform analysis method named "Glycan heterogeneity-based Relational IDentification of Glycopeptide signals on Elution profile (Glyco-RIDGE)" for a more comprehensive analysis. This method detects glycopeptide signals as a cluster based on the mass and chromatographic properties of glycopeptides and then searches for each combination of core peptides and glycan compositions by matching their mass and retention time differences. Here, we developed a novel browser-based software named GRable for semi-automated Glyco-RIDGE analysis with significant improvements in glycopeptide detection algorithms, including "parallel clustering." This unique function improved the comprehensiveness of glycopeptide detection and allowed the analysis to focus on specific glycan structures, such as pauci-mannose. The other notable improvement is evaluating the "confidence level" of the GRable results, especially using MS2 information. This function facilitated reduced misassignment of the core peptide and glycan composition and improved the interpretation of the results. Additional improved points of the algorithms are "correction function" for accurate monoisotopic peak picking; one-to-one correspondence of clusters and core peptides even for multiply sialylated glycopeptides; and "inter-cluster analysis" function for understanding the reason for detected but unmatched clusters. The significance of these improvements was demonstrated using purified and crude glycoprotein samples, showing that GRable allowed site-specific glycoform analysis of intact sialylated glycoproteins on a large-scale and in-depth. Therefore, this software will help us analyze the status and changes in glycans to obtain biological and clinical insights into protein glycosylation by complementing the comprehensiveness of MS2-based glycoproteomics. GRable can be freely run online using a web browser via the GlyCosmos Portal (https://glycosmos.org/grable).

Glycopeptides↗