Recent Advances in Software Tools for More Generic and Precise Intact Glycopeptide Analysis

Clinical glycoproteomics: methods and diseases

Glycoproteins, representing a significant proportion of posttranslational products, play pivotal roles in various biological processes, such as signal transduction and immune response. Abnormal glycosylation may lead to structural and functional changes of glycoprotein, which is closely related to the occurrence and development of various diseases. Consequently, exploring protein glycosylation can shed light on the mechanisms behind disease manifestation and pave the way for innovative diagnostic and therapeutic strategies. Nonetheless, the study of clinical glycoproteomics is fraught with challenges due to the low abundance and intricate structures of glycosylation. Recent advancements in mass spectrometry-based clinical glycoproteomics have improved our ability to identify abnormal glycoproteins in clinical samples. In this review, we aim to provide a comprehensive overview of the foundational principles and recent advancements in clinical glycoproteomic methodologies and applications. Furthermore, we discussed the typical characteristics, underlying functions, and mechanisms of glycoproteins in various diseases, such as brain diseases, cardiovascular diseases, cancers, kidney diseases, and metabolic diseases. Additionally, we highlighted potential avenues for future development in clinical glycoproteomics. These insights provided in this review will enhance the comprehension of clinical glycoproteomic methods and diseases and promote the elucidation of pathogenesis and the discovery of novel diagnostic biomarkers and therapeutic targets.

Uncovering missing glycans and unexpected fragments with pGlycoNovo for site-specific glycosylation analysis across species

Precision mapping of site-specific glycans using mass spectrometry is vital in glycoproteomics. However, the diversity of glycan compositions across species often exceeds database capacity, hindering the identification of rare glycans. Here, we introduce pGlycoNovo, a software within the pGlyco3 software environment, which employs a glycan first-based full-range Y-ion dynamic searching strategy. pGlycoNovo enables de novo identification of intact glycopeptides with rare glycans by considering all possible monosaccharide combinations, expanding the glycan search space to 16~1000 times compared to non-open search methods, while maintaining accuracy, sensitivity and speed. Reanalysis of SARS Covid-2 spike protein glycosylation data revealed 230 additional site-specific N-glycans and 30 previously unreported O-glycans. pGlycoNovo demonstrated high complementarity to six other tools and superior search speed. It enables characterization of site-specific N-glycosylation across five evolutionarily distant species, contributing to a dataset of 32,549 site-specific glycans on 4602 proteins, including 2409 site-specific rare glycans, and uncovering unexpected glycan fragments.

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).

Expanding N-glycopeptide identifications by modeling fragmentation, elution, and glycome connectivity

Accurate glycopeptide identification in mass spectrometry-based glycoproteomics is a challenging problem at scale. Recent innovation has been made in increasing the scope and accuracy of glycopeptide identifications, with more precise uncertainty estimates for each part of the structure. We present a dynamically adapting relative retention time model for detecting and correcting ambiguous glycan assignments that are difficult to detect from fragmentation alone, a layered approach to glycopeptide fragmentation modeling that improves N-glycopeptide identification in samples without compromising identification quality, and a site-specific method to increase the depth of the glycoproteome confidently identifiable even further. We demonstrate our techniques on a set of previously published datasets, showing the performance gains at each stage of optimization. These techniques are provided in the open-source glycomics and glycoproteomics platform GlycReSoft available at https://github.com/mobiusklein/glycresoft .

Experimentally Determined Diagnostic Ions for Identification of Peptide Glycotopes

The analysis of the structures of glycans present on glycoproteins is an essential component for determining glycoprotein function; however, detailed glycan structural assignment on glycopeptides from proteomics mass spectrometric data remains challenging. Glycoproteomic analysis by mass spectrometry currently can provide significant, yet incomplete, information about the glycans present, including the glycan monosaccharide composition and in some circumstances the site(s) of glycosylation. Advancements in mass spectrometric resolution, using high-mass accuracy instrumentation and tailored MS/MS fragmentation parameters, coupled with a dedicated definition of diagnostic fragmentation ions have enabled the determination of some glycan structural features, or glycotopes, expressed on glycopeptides. Here we present a collation of diagnostic glycan fragments produced by traditional positive-ion-mode reversed-phase LC-ESI MS/MS proteomic workflows and describe the specific fragmentation energy settings required to identify specific glycotopes presented on N- or O-linked glycopeptides in a typical proteomics MS/MS experiment.

Glyco-signatures in patients with advanced lung cancer during anti-PD-1/PD-L1 immunotherapy

Immune checkpoint inhibitors (ICIs) targeting programmed cell death 1/programmed cell death ligand-1 (PD-1/PD-L1) have significantly prolonged the survival of advanced/metastatic patients with lung cancer. However, only a small proportion of patients can benefit from ICIs, and clinical management of the treatment process remains challenging. Glycosylation has added a new dimension to advance our understanding of tumor immunity and immunotherapy. To systematically characterize anti-PD-1/PD-L1 immunotherapy-related changes in serum glycoproteins, a series of serum samples from 12 patients with metastatic lung squamous cell carcinoma (SCC) and lung adenocarcinoma (ADC), collected before and during ICIs treatment, are firstly analyzed with mass-spectrometry-based label-free quantification method. Second, a stratification analysis is performed among anti-PD-1/PD-L1 responders and non-responders, with serum levels of glycopeptides correlated with treatment response. In addition, in an independent validation cohort, a large-scale site-specific profiling strategy based on chemical labeling is employed to confirm the unusual characteristics of IgG N-glycosylation associated with anti-PD-1/PD-L1 treatment. Unbiased label-free quantitative glycoproteomics reveals serum levels' alterations related to anti-PD-1/PD-L1 treatment in 27 out of 337 quantified glycopeptides. The intact glycopeptide EEQFN 177STYR (H3N4) corresponding to IgG4 is significantly increased during anti-PD-1/PD-L1 treatment (FC=2.65, P=0.0083) and has the highest increase in anti-PD-1/PD-L1 responders (FC=5.84, P=0.0190). Quantitative glycoproteomics based on protein purification and chemical labeling confirms this observation. Furthermore, obvious associations between the two intact glycopeptides (EEQFN 177STYR (H3N4) of IgG4, EEQYN 227STFR (H3N4F1) of IgG3) and response to treatment are observed, which may play a guiding role in cancer immunotherapy. Our findings could benefit future clinical disease management.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

Advancing mass spectrometry-based glycoproteomic software tools for comprehensive site-specific glycoproteome analysis

Glycoproteome analysis at a site-specific level and proteome scale stands out as a highly promising approach for gaining insights into the intricate roles of glycans in biological systems. Recent years have witnessed an upsurge in the development of innovative methodologies tailored for precisely this purpose. Breakthroughs in mass spectrometry-based glycoproteomic techniques, enabling the identification, quantification, and systematic exploration of site-specific glycans, have significantly enhanced our capacity to comprehensively and thoroughly characterize glycoproteins. In this short review, we delve into novel tools in advancing site-specific glycoproteomic analysis and summarize pertinent studies published in the past two years. Lastly, we discuss the ongoing challenges and outline future prospects in the field, considering both the analytical strategies of mass spectrometry and the tools employed for data interpretation.

Analysis of N- and O-linked site-specific glycosylation by ion mobility mass spectrometry: State of the art and future directions

Glycosylation, the major post-translational modification of proteins, significantly increases the diversity of proteoforms. Glycans are involved in a variety of pivotal structural and functional roles of proteins, and changes in glycosylation are profoundly connected to the progression of numerous diseases. Mass spectrometry (MS) has emerged as the gold standard for glycan and glycopeptide analysis because of its high sensitivity and the wealth of fragmentation information that can be obtained. Various separation techniques have been employed to resolve glycan and glycopeptide isomers at the front end of the MS. However, differentiating structures of isobaric and isomeric glycopeptides constitutes a challenge in MS-based characterization. Many reports described the use of various ion mobility-mass spectrometry (IM-MS) techniques for glycomic analyses. Nevertheless, very few studies have focused on N- and O-linked site-specific glycopeptidomic analysis. Unlike glycomics, glycoproteomics presents a multitude of inherent challenges in microheterogeneity, which are further exacerbated by the lack of dedicated bioinformatics tools. In this review, we cover recent advances made towards the growing field of site-specific glycosylation analysis using IM-MS with a specific emphasis on the MS techniques and capabilities in resolving isomeric peptidoglycan structures. Furthermore, we discuss commonly used software that supports IM-MS data analysis of glycopeptides.

O-GlcNAcylation Facilitates the Interaction between Keratin 18 and Isocitrate Dehydrogenases and Potentially Influencing Cholangiocarcinoma Progression

Glycosylation plays a pivotal role in the intricate landscape of human cholangiocarcinoma (CCA), actively participating in key pathophysiological processes driving tumor progression. Among the various glycosylation modifications, O-linked β-N-acetyl-glucosamine modification (O-GlcNAcylation) emerges as a dynamic regulator influencing diverse tumor-associated biological activities. In this study, we employed a state-of-the-art chemical proteomic approach to analyze intact glycopeptides, unveiling the critical role of O-GlcNAcylation in orchestrating Keratin 18 (K18) and its interplay with tricarboxylic acid (TCA) cycle enzymes, specifically isocitrate dehydrogenases (IDHs), to propel CCA progression. Our findings shed light on the mechanistic intricacies of O-GlcNAcylation, revealing that site-specific modification of K18 on Ser 30 serves as a stabilizing factor, amplifying the expression of cell cycle checkpoints. This molecular event intricately fosters cell cycle progression and augments cellular growth in CCA. Notably, the interaction between O-GlcNAcylated K18 and IDHs orchestrates metabolic reprogramming by down-regulating citrate and isocitrate levels while elevating α-ketoglutarate (α-KG). These metabolic shifts further contribute to the overall tumorigenic potential of CCA. Our study thus expands the current understanding of protein O-GlcNAcylation and introduces a new layer of complexity to post-translational control over metabolism and tumorigenesis.

Prediction of glycopeptide fragment mass spectra by deep learning

Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.

Comparison of sialylated and fucosylated N-glycans attached to Asn 6 and Asn 41 with different roles in hyaluronan and proteoglycan link protein 1 (HAPLN1)

Hyaluronan and proteoglycan link protein 1 (HAPLN1) is an extracellular matrix protein stabilizing interactions between hyaluronan and proteoglycan. Although HAPLN1 is being investigated for various biological roles, its N-glycosylation is poorly understood. In this study, the structure of N-glycopeptides of trypsin-treated recombinant human HAPLN1 (rhHAPLN1) expressed from CHO cells were identified by nano-liquid chromatography-tandem mass spectrometry. A total of 66 N-glycopeptides were obtained, including 16 and 12 N-glycans at sites Asn 6 (located in the N-terminal region) and Asn 41 (located in the Ig-like domain, which interacts with proteoglycan), respectively. The quantities (%) of each N-glycan relative to the totals (100 %) at each site were calculated. Tri- and tetra-sialylation (to resist proteolysis and extend half-life) were more abundant at Asn 6, and di- (core- and terminal-) fucosylation (to increase binding affinity and stability) and sialyl-Lewis X/a epitope (a major ligand for E-selectin) were more abundant at Asn 41. These results indicate that N-glycans attached to Asn 6 (protecting HAPLN1) and Asn 41 (supporting molecular interactions) play different roles in HAPLN1. This is the first study of site-specific N-glycosylation in rhHAPLN1, which will be useful for understanding its molecular interactions in the extracellular matrix.

Mass Spectrometry Strategies for O-Glycoproteomics

Glycoproteomics has accelerated in recent decades owing to numerous innovations in the analytical workflow. In particular, new mass spectrometry strategies have contributed to inroads in O-glycoproteomics, a field that lags behind N-glycoproteomics due to several unique challenges associated with the complexity of O-glycosylation. This review will focus on progress in sample preparation, enrichment strategies, and MS/MS techniques for the identification and characterization of O-glycoproteins.

Glycosort: A Computational Solution to Post-process Quantitative Large-Scale Intact Glycopeptide Analyses

Protein glycosylation is a post-translational modification involving the addition of carbohydrates to proteins and plays a crucial role in protein folding and various biological processes such as cell recognition, differentiation, and immune response. The vast array of natural sugars available allows the generation of plenty of unique glycan structures in proteins, adding complexity to the regulation and biological functions of glycans. The diversity is further increased by enzymatic site preferences and stereochemical conjugation, leading to an immense amount of different glycan structures. Understanding glycosylation heterogeneity is vital for unraveling the impact of glycans on different biological functions. Evaluating site occupancies and structural heterogeneity aids in comprehending glycan-related alterations in biological processes. Several software tools are available for large-scale glycoproteomics studies; however, integrating identification and quantitative data to assess heterogeneity complexity often requires extensive manual data processing. To address this challenge, we present a python script that automates the integration of Byonic and MaxQuant outputs for glycoproteomic data analysis. The script enables the calculation of site occupancy percentages by glycans and facilitates the comparison of glycan structures and site occupancies between two groups. This automated tool offers researchers a means to organize and interpret their high-throughput quantitative glycoproteomic data effectively.

GlycoTCFM: Glycoproteomics Based on Two Complementary Fragmentation Methods Reveals Distinctive O-Glycosylation in Human Sperm and Seminal Plasma

Human semen, consisting of spermatozoa (sperm) and seminal plasma, represents a special clinical sample type in human body fluid. Protein glycosylation in sperm and seminal plasma plays key roles in spermatogenesis, maturation, capacitation, sperm-egg recognition, motility of sperm, and fertilization. In this study, we profiled the most comprehensive O-glycoproteome map of human sperm and seminal plasma using our recently presented Glycoproteomics based on Two Complementary Fragmentation Methods (GlycoTCFM). We showed that sperm and seminal plasma contain many novel and distinctive O-glycoproteins, which are mostly located in the extracellular region (seminal plasma) and sperm membrane, enriched in the biological processes of cell adhesion and angiogenesis, and mainly involved in multiple biological functions including extracellular matrix structural constituents and binding. Based on GlycoTCFM, we created a comprehensive human sperm and seminal plasma O-glycoprotein database that contains 371 intact O-glycopeptides and 202 O-glycosites from 68 O-glycoproteins. Interestingly, 105 manually confirmed O-glycosites from 25 O-glycoproteins were reported for the first time, and they were mainly modified by core 1 O-glycans. We also found that three highly abundant, highly complex, and highly O-glycosylated proteins (semenogelin-1, semenogelin-2, and equatorin) may play important roles in sperm or seminal plasma composition and function. These data deepen our knowledge about O-glycosylation in sperm and seminal plasma and lay the foundation for the functional study of O-glycoproteins in male infertility.

An N-glycopeptide MS/MS data analysis workflow leveraging two complementary glycoproteomic software tools for more confident identification and assignments

Complete coverage of all N-glycosylation sites on the SARS-CoV2 spike protein would require the use of multiple proteases in addition to trypsin. Subsequent identification of the resulting glycopeptides by searching against database often introduces assignment errors due to similar mass differences between different permutations of amino acids and glycosyl residues. By manually interpreting the individual MS2 spectra, we report here the common sources of errors in assignment, especially those introduced by the use of chymotrypsin. We show that by applying a stringent threshold of acceptance, erroneous assignment by the commonly used Byonic software can be controlled within 15%, which can be reduced further if only those also confidently identified by a different search engine, pGlyco3, were considered. A representative site-specific N-glycosylation pattern could be constructed based on quantifying only the overlapping subset of N-glycopeptides identified at higher confidence. Applying the two complimentary glycoproteomic software in a concerted data analysis workflow, we found and confirmed that glycosylation at several sites of an unstable Omicron spike protein differed significantly from those of the stable trimeric product of the parental D614G variant.

Click-iG: Simultaneous Enrichment and Profiling of Intact N-linked, O-GalNAc, and O-GlcNAcylated Glycopeptides

Proteins are ubiquitously modified with glycans of varied chemical structures through distinct glycosidic linkages, making the landscape of protein glycosylation challenging to map. Profiling of intact glycopeptides with mass spectrometry (MS) has recently emerged as a powerful tool for revealing matched information of the glycosylation sites and attached glycans (i.e., intact glycosites), but is largely limited to individual glycosylation types. Herein, we describe Click-iG, which integrates metabolic labeling of glycans with clickable unnatural sugars, an optimized MS method, and a tailored version of pGlyco3 software to enable simultaneous enrichment and profiling of three types of intact glycopeptides: N-linked, mucin-type O-linked, and O-GlcNAcylated glycopeptides. We demonstrate the utility of Click-iG by the identification of thousands of intact glycosites in cell lines and living mice. From the mouse lung, heart, and spleen, a total of 2053 intact N-glycosites, 262 intact O-GalNAc glycosites, and 1947 O-GlcNAcylation sites were identified. Click-iG-enabled comprehensive coverage of the protein glycosylation landscape lays the foundation for interrogating crosstalk between different glycosylation pathways.

In-Depth Mass Spectrometry Analysis Reveals the Plasma Proteomic and N-Glycoproteomic Impact of an Amish-Enriched Cardioprotective Variant in B4GALT1

B4GALT1 encodes β-1,4-galactosyltransferase 1, an enzyme that plays a major role in glycan synthesis in the Golgi apparatus by catalyzing the addition of terminal galactose. Studies increasingly suggest that B4GALT1 may be involved in the regulation of lipid metabolism pathways. Recently, we discovered a single-site missense variant Asn352Ser (N352S) in the functional domain of B4GALT1 in an Amish population, which decreases the level of LDL-cholesterol (LDL-c) as well as the protein levels of ApoB, fibrinogen, and IgG in the blood. To systematically evaluate the effects of this missense variant on protein glycosylation, expression, and secretion, we developed a nano-LC-MS/MS-based platform combined with TMT-labeling for in-depth quantitative proteomic and glycoproteomic analyses in the plasma of individuals homozygous for the B4GALT1 missense variant N352S versus non-carriers (n = 5 per genotype). A total of 488 secreted proteins in the plasma were identified and quantified, 34 of which showed significant fold changes in protein levels between N352S homozygotes and non-carriers. We determined N-glycosylation profiles from 370 glycosylation sites in 151 glycoproteins and identified ten proteins most significantly associated with decreased galactosylation and sialyation in B4GALT1 N352S homozygotes. These results further support that B4GALT1 N352S alters the glycosylation profiles of a variety of critical target proteins, thus governing the functions of these proteins in multiple pathways, such as those involved in lipid metabolism, coagulation, and the immune response.

Capitalizing glycomic changes for improved biomarker-based cancer diagnostics

Cancer serum biomarkers are valuable or even indispensable for cancer diagnostics and/or monitoring and, currently, many cancer serum markers are routinely used in the clinic. Most of those markers are glycoproteins, carrying cancer-specific glycan structures that can provide extra-information for cancer monitoring. Nonetheless, in the majority of cases, this differential feature is not exploited and the corresponding analytical assays detect only the protein amount, disregarding the analysis of the aberrant glycoform. Two exceptions to this trend are the biomarkers α-fetoprotein (AFP) and cancer antigen 19-9 (CA19-9), which are clinically monitored for their cancer-related glycan changes, and only the AFP assay includes quantification of both the protein amount and the altered glycoform. This narrative review demonstrates, through several examples, the advantages of the combined quantification of protein cancer biomarkers and the respective glycoform analysis, which enable to yield the maximum information and overcome the weaknesses of each individual analysis. This strategy allows to achieve higher sensitivity and specificity in the detection of cancer, enhancing the diagnostic power of biomarker-based cancer detection tests.

Identification, characterization, and engineering of glycosylation in thrombolyticsa

Cardiovascular diseases, such as myocardial infarction, ischemic stroke, and pulmonary embolism, are the most common causes of disability and death worldwide. Blood clot hydrolysis by thrombolytic enzymes and thrombectomy are key clinical interventions. The most widely used thrombolytic enzyme is alteplase, which has been used in clinical practice since 1986. Another clinically used thrombolytic protein is tenecteplase, which has modified epitopes and engineered glycosylation sites, suggesting that carbohydrate modification in thrombolytic enzymes is a viable strategy for their improvement. This comprehensive review summarizes current knowledge on computational and experimental identification of glycosylation sites and glycan identity, together with methods used for their reengineering. Practical examples from previous studies focus on modification of glycosylations in thrombolytics, e.g., alteplase, tenecteplase, reteplase, urokinase, saruplase, and desmoteplase. Collected clinical data on these glycoproteins demonstrate the great potential of this engineering strategy. Outstanding combinatorics originating from multiple glycosylation sites and the vast variety of covalently attached glycan species can be addressed by directed evolution or rational design. Directed evolution pipelines would benefit from more efficient cell-free expression and high-throughput screening assays, while rational design must employ structure prediction by machine learning and in silico characterization by supercomputing. Perspectives on challenges and opportunities for improvement of thrombolytic enzymes by engineering and evolution of protein glycosylation are provided.

Roles of glycosylation at the cancer cell surface: opportunities for large scale glycoproteomics

Cell surface glycosylation has a variety of functions, and its dysregulation in cancer contributes to impaired signaling, metastasis and the evasion of the immune responses. Recently, a number of glycosyltransferases that lead to altered glycosylation have been linked to reduced anti-tumor immune responses: B3GNT3, which is implicated in PD-L1 glycosylation in triple negative breast cancer, FUT8, through fucosylation of B7H3, and B3GNT2, which confers cancer resistance to T cell cytotoxicity. Given the increased appreciation of the relevance of protein glycosylation, there is a critical need for the development of methods that allow for an unbiased interrogation of cell surface glycosylation status. Here we provide an overview of the broad changes in glycosylation at the surface of cancer cell and describe selected examples of receptors with aberrant glycosylation leading to functional changes, with emphasis on immune checkpoint inhibitors, growth-promoting and growth-arresting receptors. Finally, we posit that the field of glycoproteomics has matured to an extent where large-scale profiling of intact glycopeptides from the cell surface is feasible and is poised for discovery of new actionable targets against cancer.

New hydrophilic material based on hydrogel polymer for the selective enrichment of intact glycopeptides from serum protein digests

The paper describes the preparation and characterization of a new HILIC material for the enrichment of N-linked glycopeptides. The material was prepared using 2-acrylamido-2-methyl-1-propanesulfonic acid as the monomer and ethylene glycol dimethacrylate as the cross-linker. The material was developed by a Box-Behnken experimental design, taking into consideration the amount of monomer-to-crosslinker ratio, the composition, and the amount of porogen mixture. By this approach, the property of the resulting polymer could be fine-tuned to modulate the hydrophilicity and porosity. As HILIC enrichment is mostly dependent on hydrophilic interactions, including H-bonding, the amount of swelling was expected to have an important function, therefore the optimization considered a monomer percent in the range of 20-80%, which implied very different water swelling capacities. After assessing the potential of this new polymer family on fetuin digests, the 17 materials resulting from the Box-Behnken experimental design were used for the enrichment of glycopeptides from serum protein digests. The materials displayed a superior performance over cotton HILIC enrichment, both in terms of the number of enriched N-linked glycopeptides and selectivity, providing up to 762 N-linked glycopeptides with 77% selectivity. The optimization indicated that a high amount of monomer significantly affected the number of enriched glycopeptides, which is also closely connected with the hydrogel nature of the resulting polymers. The results not only provide one additional HILIC material for the enrichment of glycopeptides but also pave the way for the use and development of hydrogel materials for the enrichment of N-linked glycopeptides.

Recent advances in mass spectrometry analysis of neuropeptides

Due to their involvement in numerous biochemical pathways, neuropeptides have been the focus of many recent research studies. Unfortunately, classic analytical methods, such as western blots and enzyme-linked immunosorbent assays, are extremely limited in terms of global investigations, leading researchers to search for more advanced techniques capable of probing the entire neuropeptidome of an organism. With recent technological advances, mass spectrometry (MS) has provided methodology to gain global knowledge of a neuropeptidome on a spatial, temporal, and quantitative level. This review will cover key considerations for the analysis of neuropeptides by MS, including sample preparation strategies, instrumental advances for identification, structural characterization, and imaging; insightful functional studies; and newly developed absolute and relative quantitation strategies. While many discoveries have been made with MS, the methodology is still in its infancy. Many of the current challenges and areas that need development will also be highlighted in this review.

Methods for quantification of glycopeptides by liquid separation and mass spectrometry

Recent advances in analytical techniques provide the opportunity to quantify even low-abundance glycopeptides derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Herein, we discuss the sample preparation procedures and the mass spectrometry (MS) strategies that have facilitated glycopeptide quantification, as well as the standards used for glycopeptide quantification. For sample preparation, various glycopeptide enrichment methods are summarized including the columns used for glycopeptide separation in liquid chromatography separation. For MS analysis strategies, MS1 level-based quantification and MS2 level-based quantification are described, either with or without labeling, where we have covered isotope labeling, TMT/iTRAQ labeling, data dependent acquisition, data independent acquisition, multiple reaction monitoring, and parallel reaction monitoring. The strengths and weaknesses of these methods are compared, particularly those associated with the figures of merit that are important for clinical biomarker studies and the pathological and functional studies of glycoproteins in various diseases. Possible future developments for glycopeptide quantification are discussed.

Recent trends in glycoproteomics by characterization of intact glycopeptides

This trends article provides an overview of the state of the art in the analysis of intact glycopeptides by proteomics technologies based on LC-MS analysis. A brief description of the main techniques used at the different steps of the analytical workflow is provided, giving special attention to the most recent developments. The topics discussed include the need for dedicated sample preparation for intact glycopeptide purification from complex biological matrices. This section covers the common approaches with a special description of new materials and innovative reversible chemical derivatization strategies, specifically devised for intact glycopeptide analysis or dual enrichment of glycosylation and other post-translational modifications. The approaches are described for the characterization of intact glycopeptide structures by LC-MS and data analysis by bioinformatics for spectra annotation. The last section covers the open challenges in the field of intact glycopeptide analysis. These challenges include the need of a detailed description of the glycopeptide isomerism, the issues with quantitative analysis, and the lack of analytical methods for the large-scale characterization of glycosylation types that remain poorly characterized, such as C-mannosylation and tyrosine O-glycosylation. This bird's-eye view article provides both a state of the art in the field of intact glycopeptide analysis and open challenges to prompt future research on the topic.

Profiling Serum Intact N-Glycopeptides Using Data-Independent Acquisition Mass Spectrometry

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data-independent acquisition (DIA) mass spectrometry is an emerging technology with deep proteome coverage as well as accurate quantitative capability for large-scale proteomics studies and has also been applied to the field of glycoproteomics. In this protocol, we describe how to analyze data from a DIA experiment for profiling serum intact N-glycopeptides. We present a comprehensive data analysis workflow using GproDIA, including glycopeptide spectral library building, chromatographic feature extraction from the DIA data, and feature scoring with appropriate statistical control of error rates. We anticipate that this method could provide a powerful tool to explore the serum glycoproteome.

Recent advances in computational algorithms and software for large-scale glycoproteomics

Glycoproteomics, or characterizing glycosylation events at a proteome scale, has seen rapid advances in methods for analyzing glycopeptides by tandem mass spectrometry in recent years. These advances have enabled acquisition of far more comprehensive and large-scale datasets, precipitating an urgent need for improved informatics methods to analyze the resulting data. A new generation of glycoproteomics search methods has recently emerged, using glycan fragmentation to split the identification of a glycopeptide into peptide and glycan components and solve each component separately. In this review, we discuss these new methods and their implications for large-scale glycoproteomics, as well as several outstanding challenges in glycoproteomics data analysis, including validation of glycan assignments and quantitation. Finally, we provide an outlook on the future of glycoproteomics from an informatics perspective, noting the key challenges to achieving widespread and reproducible glycopeptide annotation and quantitation.

Glycomics-Assisted Glycoproteomics Enables Deep and Unbiased N-Glycoproteome Profiling of Complex Biological Specimens

Mass spectrometry-driven glycomics and glycoproteomics, the system-wide profiling of detached glycans and intact glycopeptides from biological samples, respectively, are powerful approaches to interrogate the heterogenous glycoproteome. Efforts to develop integrated workflows employing both glycomics and glycoproteomics have been invested since the concerted application of these complementary approaches enables a deeper exploration of the glycoproteome. This protocol paper outlines, step-by-step, an integrated -omics technology, the "glycomics-assisted glycoproteomics" method, that first establishes the N-glycan fine structures and their quantitative distribution pattern of protein extracts via porous graphitized carbon-LC-MS/MS. The N-glycome information is then used to augment and guide the challenging reversed-phase LC-MS/MS-based profiling of intact N-glycopeptides from the same protein samples. Experimental details and considerations relating to the sample preparation and the N-glycomics and N-glycoproteomics data collection, analysis, and integration are discussed. Benefits of the glycomics-assisted glycoproteomics method, which can be readily applied to both simple and complex biological specimens such as protein extracts from cells, tissues, and bodily fluids (e.g., serum), include quantitative information of the protein carriers and site(s) of glycosylation, site occupancy, and the site-specific glycan structures directly from biological samples. The glycomics-assisted glycoproteomics method therefore facilitates a comprehensive view of the complexity and dynamics of the heterogenous glycoproteome.

In-depth characterization of protein N-glycosylation for a COVID-19 variant-design vaccine spike protein

COVID-19 is caused by SARS-CoV-2 infection and remains one of the biggest pandemics around the world since 2019. Vaccination has proved to be an effective way of preventing SARS-CoV-2 infection and alleviating the hospitalization burden. Among different forms of COVID-19 vaccine design, the spike protein of SARS-CoV-2 virus is widely used as a candidate vaccine antigen. As a surface protein on the virus envelop, the spike was reported to be heavily N-glycosylated and glycosylation had a great impact on its immunogenicity and efficacy. Besides, N-glycosylation might vary greatly on different expression systems and sequence variant designs. Therefore, comprehensive analysis of spike N-glycosylation is of great significance for better vaccine understanding and quality control. In this study, full characterization of N-glycosylation was performed for a Chinese Hamster Ovary (CHO) cell expressed variant-designed spike protein. The spike protein featured the latest six-proline substitution design together with the incorporation of a combination of mutation sites. Trypsin and Glu-C digestion coupled with PNGase F strategies were adopted, and effective LC-MS/MS methods were applied to analyze samples. As a result, a total of 19 N-glycosites were identified in the recombinant pike protein at intact N-glycopeptide level. Quantitative analysis of released glycan by LC-MS/MS was also performed, and 31 high-abundance N-glycans were identified. Sequencing analysis of glycan was further provided to assist glycan structure confirmation. Moreover, all of the analyses were performed on three consecutive manufactured batches and the glycosylation results on both glycosite and glycans showed good batch-to-batch consistency. Thus, the reported analytical strategy and N-glycosylation information may well facilitate studies on SARS-CoV-2 spike protein analysis and quality studies.

Systems-Wide Site-Specific Analysis of Glycoproteins

Glycosylation is one of the most common and complex post-translation modifications that influence the structural and functional properties of proteins. Glycoproteins are highly heterogeneous and exhibit site- and protein-specific expression differences. Mass spectrometry in combination with liquid chromatography has emerged as the most powerful tool for the comprehensive characterization of glycosylation. The analysis of intact glycopeptides has emerged as a promising strategy to analyze glycoproteins for their glycan heterogeneity at both protein- and site-specific levels. Nevertheless, intact glycopeptide characterization is challenging as elucidation of the glycan and peptide moieties requires specific sample preparation workflows that, combined with the tandem mass spectrometry approach, enable the identification of single glycopeptide species. In this chapter, we provide a detailed description of the methods that include procedures for (i) proteolytic digestion using specific proteases, (ii) optional glycopeptide enrichment using hydrophilic interaction liquid chromatography, (iii) nano-LC-MS/MS analysis of glycopeptides, and (iv) data analysis for identification of glycopeptides. Together, our workflow provides a framework for the system-wide site-specific analysis of N- and O-glycopeptides derived from complex biological or clinical samples.

pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19-89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies.

HexNAcQuest: A Tool to Distinguish O-GlcNAc and O-GalNAc

Protein glycosylation plays crucial roles in the regulation of diverse biological processes. As a critical step, mass spectrometry-based site-specific analysis of protein glycosylation is important to better understand these events. Despite the great progress, characterization of structural isomers of glycans and glycopeptides remains challenging. In typical glycoproteomic analysis, collision-induced dissociation (CID) or higher-energy collisional dissociation (HCD) fragmentation produces abundant saccharide oxonium ions containing N-acetylhexosamine (HexNAc) residues. However, it has been difficult to distinguish isobaric GalNAc and GlcNAc modifications by using mass spectrometry only. By using intensities of oxonium ions of standard O-GlcNAc/O-GalNAc peptides, we systematically investigated the fragmentation patterns of different ions. Then a binary logistic regression model was established by training comprehensive data sets from glycoproteomics studies reported. The model was then tested with independent O-glycoproteomics data sets, with reliable classification achieved (>87% accuracy). In comparison to empirical observations and criteria used previously, our model is accurate and generalized. Based on this model, a corresponding Web server HexNAcQuest has been constructed, which is freely accessible to users. The model can also be easily integrated in MS-based glycoproteomics workflows to distinguish the isobaric HexNAc modifications.

Charting the Proteoform Landscape of Serum Proteins in Individual Donors by High-Resolution Native Mass Spectrometry

Most proteins in serum are glycosylated, with several annotated as biomarkers and thus diagnostically important and of interest for their role in disease. Most methods for analyzing serum glycoproteins employ either glycan release or glycopeptide centric mass spectrometry-based approaches, which provide excellent tools for analyzing known glycans but neglect previously undefined or unknown glycosylation and/or other co-occurring modifications. High-resolution native mass spectrometry is a relatively new technique for the analysis of intact glycoproteins, providing a “what you see is what you get” mass profile of a protein, allowing the qualitative and quantitative observation of all modifications present. So far, a disadvantage of this approach has been that it centers mostly on just one specific serum glycoprotein at the time. To address this issue, we introduce an ion-exchange chromatography-based fractionation method capable of isolating and analyzing, in parallel, over 20 serum (glyco)proteins, covering a mass range between 30 and 190 kDa, from 150 μL of serum. Although generating data in parallel for all these 20 proteins, we focus the discussion on the very complex proteoform profiles of four selected proteins, i.e., α-1-antitrypsin, ceruloplasmin, hemopexin, and complement protein C3. Our analyses provide an insight into the extensive proteoform landscape of serum proteins in individual donors, caused by the occurrence of various N- and O-glycans, protein cysteinylation, and co-occurring genetic variants. Moreover, native mass intact mass profiling also provided an edge over alternative approaches revealing the presence of apo- and holo-forms of ceruloplasmin and the endogenous proteolytic processing in plasma of among others complement protein C3. We also applied our approach to a small cohort of serum samples from healthy and diseased individuals. In these, we qualitatively and quantitatively monitored the changes in proteoform profiles of ceruloplasmin and revealed a substantial increase in fucosylation and glycan occupancy in patients with late-stage hepatocellular carcinoma and pancreatic cancer as compared to healthy donor samples.

Advances in glycopeptide enrichment methods for the analysis of protein glycosylation over the past decade

Advances in bioanalytical technology have accelerated the analysis of complex protein glycosylation, which is beneficial to understanding glycosylation in drug discovery and disease diagnosis. Due to its biological uniqueness in the course of disease occurrence and development, disease-specific glycosylation requires quantitative characterization of protein glycosylation. We provide a comprehensive review of recent advances in glycosylation analysis, including workflows for glycoprotein digestion, glycopeptide separation and enrichment, and mass-spectrometry sequencing. We specifically focus on different strategies for glycopeptide enrichment through physical interaction, chemical oxidation, or metabolic labeling of intact glycopeptides. The recent advances and challenges of O-glycosylation analysis are presented, and the development of improved enrichment methods combining different proteases to analyze O-glycosylation is also proposed. This article is protected by copyright. All rights reserved.

Integrated N-glycoproteomics Analysis of Human Saliva for Lung Cancer

Aberrant protein N-glycosylation is a cancer hallmark, which has great potential for cancer detection. However, large-scale and in-depth analysis of N-glycosylation remains challenging because of its high heterogeneity, complexity, and low abundance. Human saliva is an attractive diagnostic body fluid, while few efforts explored its N-glycoproteome for lung cancer. Here, we utilized a zwitterionic-hydrophilic interaction chromatography-based strategy to specifically enrich salivary glycopeptides. Through quantitative proteomics analysis, 1492 and 1234 intact N-glycopeptides were confidently identified from pooled saliva samples of 10 subjects in the nonsmall-cell lung cancer group and 10 subjects in the normal control group. Accordingly, 575 and 404 N-glycosites were revealed for the lung cancer group and normal control group. In particular, 154 N-glycosites and 259 site-specific glycoforms were significantly dysregulated in the lung cancer group. Several N-glycosites located at the same glycoprotein and glycans attached to the same N-glycosites were observed with differential expressions, including haptoglobin, Mucin-5B, lactotransferrin, and α-1-acid glycoprotein 1. These N-glycoproteins were mainly related to inflammatory responses, infectious diseases, and cancers. Our study achieved comprehensive characterization of salivary N-glycoproteome, and dysregulated site-specific glycoforms hold promise for noninvasive detection of lung cancer.

Progress of proteomics-driven precision medicine: From a glycosylation view

Currently, cancer is one of the leading causes of death worldwide, partially owing to the lack of early diagnosis methods and effective therapies. With the rapid development of various omics, the precision medicine strategy becomes a promising way to increase the survival rates by considering individual differences. Glycosylation is one of the most essential protein post-translational modifications and plays important roles in a variety of biological processes. Therefore, it is highly possible to acquire understanding of the molecular mechanisms as well as discover novel potential markers for diagnosis and prognosis based on glycoproteomics research. This review summarizes the recent glycoproteomics studies about N-glycosylation of several cancer types, mainly in the past 5 years. We also highlight corresponding mass spectrometry-based analytical methods to give a brief overview on the main techniques applied in glycoproteomics.

Immunoglobulin G N-glycan Biomarkers for Autoimmune Diseases: Current State and a Glycoinformatics Perspective

The effective treatment of autoimmune disorders can greatly benefit from disease-specific biomarkers that are functionally involved in immune system regulation and can be collected through minimally invasive procedures. In this regard, human serum IgG N-glycans are promising for uncovering disease predisposition and monitoring progression, and for the identification of specific molecular targets for advanced therapies. In particular, the IgG N-glycome in diseased tissues is considered to be disease-dependent; thus, specific glycan structures may be involved in the pathophysiology of autoimmune diseases. This study provides a critical overview of the literature on human IgG N-glycomics, with a focus on the identification of disease-specific glycan alterations. In order to expedite the establishment of clinically-relevant N-glycan biomarkers, the employment of advanced computational tools for the interpretation of clinical data and their relationship with the underlying molecular mechanisms may be critical. Glycoinformatics tools, including artificial intelligence and systems glycobiology approaches, are reviewed for their potential to provide insight into patient stratification and disease etiology. Challenges in the integration of such glycoinformatics approaches in N-glycan biomarker research are critically discussed.

Measuring change in glycoprotein structure

Biosynthetic enzymes in the secretory pathway create distributions of glycans at each glycosite that elaborate the biophysical properties and biological functions of glycoproteins. Because the biosynthetic glycosylation reactions do not go to completion, each protein glycosite is heterogeneous with respect to glycosylation. This heterogeneity means that it is not sufficient to measure protein abundance in omics experiments. Rather, it is necessary to sample the distribution of glycosylation at each glycosite to quantify the changes that occur during biological processes. On the one hand, the use of data-dependent acquisition methods to sample glycopeptides is limited by the instrument duty cycle and the missing value problem. On the other, stepped window data-independent acquisition samples all precursors, but ion abundances are limited by duty cycle. Therefore, the ability to quantify accurately the flux in glycoprotein glycosylation that occurs during biological processes requires the exploitation of emerging mass spectrometry technologies capable of deep, comprehensive sampling and selective high confidence assignment of the complex glycopeptide mixtures. This review summarizes recent technical advances and mass spectral glycoproteomics analysis strategies and how these developments impact our ability to quantify the changes in glycosylation that occur during biological processes. We highlight specific improvements to glycopeptide characterization through activated electron dissociation, ion mobility trends and instrumentation, and efficient algorithmic approaches for glycopeptide assignment. We also discuss the emerging need for unified standards to enable interlaboratory collaborations and effective monitoring of structural changes in glycoproteins.

Application of Educational Psychology-Based Dance Therapy in College Students' Life Education

The purpose is to strengthen the life education of contemporary college students and give better play to the vital role of life education in preventing college students' mental diseases. Specifically, it discusses the role of dance therapy (DT) in College Students' Life Education (CSLE). Firstly, based on educational psychology (EP), this manuscript analyzes the relevant theoretical concepts of EP and life education and discusses the importance of life education to contemporary college students. Secondly, following a Questionnaire Survey (QS) and using deep learning (DL) Convolutional Neural Network (CNN) and Facial Emotion Recognition (FER), this manuscript reviews and examines the CSLE's current situation and the DT effect. Research findings are summarized combined with the QS results and scores of 20 subjects before and after five activities in 3 months. (I) After DT intervention, the positive dimensions of college students' life values have improved, especially self-development and dedication, and their quality of life is refined. Thus, DT group counseling proves the positive role of DT in CSLE. (II) After DT intervention, 96.5% of the members think DT is effective. Therefore, EP-based DT is more effective and scientific in CSLE. The research findings provide a DT-based teaching concept for CSLE, explore the feasibility and effectiveness of life education, and enrich the DT scheme of CSLE. The research provides a practical reference for further applying DT in college students' psychological education.

MS-Decipher: a user-friendly proteome database search software with an emphasis on deciphering the spectra of O-linked glycopeptides

MOTIVATION

The interpretation of mass spectrometry (MS) data is a crucial step in proteomics analysis, and the identification of post-translational modifications (PTMs) is vital for the understanding of the regulation mechanism of the living system. Among various PTMs, glycosylation is one of the most diverse ones. Though many search engines have been developed to decipher proteomic data, some of them are difficult to operate and have poor performance on glycoproteomic datasets compared to advanced glycoproteomic software.

RESULTS

To simplify the analysis of proteomic datasets, especially O-glycoproteomic datasets, here, we present a user-friendly proteomic database search platform, MS-Decipher, for the identification of peptides from MS data. Two scoring schemes can be chosen for peptide-spectra matching. It was found that MS-Decipher had the same sensitivity and confidence in peptide identification compared to traditional database searching software. In addition, a special search mode, O-Search, is integrated into MS-Decipher to identify O-glycopeptides for O-glycoproteomic analysis. Compared with Mascot, MetaMorpheus and MSFragger, MS-Decipher can obtain about 139.9%, 48.8% and 6.9% more O-glycopeptide-spectrum matches. A useful tool is provided in MS-Decipher for the visualization of O-glycopeptide-spectra matches. MS-Decipher has a user-friendly graphical user interface, making it easier to operate. Several file formats are available in the searching and validation steps. MS-Decipher is implemented with Java, and can be used cross-platform.

AVAILABILITY AND IMPLEMENTATION

MS-Decipher is freely available at https://github.com/DICP-1809/MS-Decipher for academic use. For detailed implementation steps, please see the user guide.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Strategies for Proteome-Wide Quantification of Glycosylation Macro- and Micro-Heterogeneity

Protein glycosylation governs key physiological and pathological processes in human cells. Aberrant glycosylation is thus closely associated with disease progression. Mass spectrometry (MS)-based glycoproteomics has emerged as an indispensable tool for investigating glycosylation changes in biological samples with high sensitivity. Following rapid improvements in methodologies for reliable intact glycopeptide identification, site-specific quantification of glycopeptide macro- and micro-heterogeneity at the proteome scale has become an urgent need for exploring glycosylation regulations. Here, we summarize recent advances in N- and O-linked glycoproteomic quantification strategies and discuss their limitations. We further describe a strategy to propagate MS data for multilayered glycopeptide quantification, enabling a more comprehensive examination of global and site-specific glycosylation changes. Altogether, we show how quantitative glycoproteomics methods explore glycosylation regulation in human diseases and promote the discovery of biomarkers and therapeutic targets.

Multi-attribute Glycan Identification and FDR Control for Glycoproteomics

Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward; however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collisional or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B-type (oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates. Combined with false discovery rate estimation for the glycan assignment, we show that this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of N-glycopeptide spectra with false discovery rate control of both peptide and glycan components that is both sensitive and robust against false identifications.

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Identifying the glycan on intact glycopeptides remains difficult in glycoproteomics.

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We developed a method to assign glycan compositions in N-glycoproteomics searches.

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We demonstrate well-controlled glycan FDR in multiple sample types.

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The method annotates more glycopeptide spectra than competing tools.

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The method is included PTM-Shepherd for a full glycoproteomics workflow in FragPipe.

Glycopeptide-Centric Approaches for the Characterization of Microbial Glycoproteomes

Protein glycosylation is increasingly recognized as a common class of modifications within microbial species that can shape protein functions and the proteome at large. Due to this, there is an increasing need for robust analytical methods, which allow for the identification and characterization of microbial glycopeptides from proteome samples in a high-throughput manner. Using affinity-based enrichment (either hydrophilicity or antibody-based approaches) glycopeptides can easily be separated from non-glycosylated peptides and analyzed using mass spectrometry. By utilizing multiple mass spectrometry fragmentation approaches and open searching-based bioinformatic techniques, novel glycopeptides can be identified and characterized without prior knowledge of the glycans used for glycosylation. Using these approaches, glycopeptides within samples can rapidly be identified as well as quantified to understand how glycosylation changes in response to stimuli or how changes in glycosylation systems impact the glycoproteome. This chapter outlines a set of robust protocols for the initial preparation, enrichment, and analysis of microbial glycopeptides for both qualitative and quantitative glycoproteomic studies. Using these approaches, glycosylation events can be easily identified by researchers without the need for extensive manual analysis of proteomic datasets.

Breast cancer glycan biomarkers: their link to tumour cell metabolism and their perspectives in clinical practice

INTRODUCTION

Breast cancer (BCa) is the most common cancer type diagnosed in women and 5^th most common cause of deaths among all cancer deaths despite the fact that screening program is at place. This is why novel diagnostics approaches are needed in order to decrease number of BCa cases and disease mortality.

AREAS COVERED

In this review paper, we aim to cover some basic aspects regarding cellular metabolism and signalling in BCa behind altered glycosylation. We also discuss novel exciting discoveries regarding glycan-based analysis, which can provide useful information for better understanding of the disease. The final part deals with clinical usefulness of glycan-based biomarkers and the clinical performance of such biomarkers is compared to already approved BCa biomarkers and diagnostic tools based on imaging.

EXPERT OPINION

Recent discoveries suggest that glycan-based biomarkers offer high accuracy for possible BCa diagnostics in blood, but also for better monitoring and management of BCa patients. The review article was written using Web of Science search engine to include articles published between 2019 and 2021.

Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.

Advances in mass spectrometry-based glycoproteomics: An update covering the period 2017-2021

Protein glycosylation is one of the most common posttranslational modifications, and plays an essential role in a wide range of biological processes such as immune response, intercellular signaling, inflammation, host-pathogen interaction, and protein stability. Glycoproteomics is a proteomics subfield dedicated to identifying and characterizing the glycans and glycoproteins in a given cell or tissue. Aberrant glycosylation has been associated with various diseases such as Alzheimer's disease, viral infections, inflammation, immune deficiencies, congenital disorders, and cancers. However, glycoproteomic analysis remains challenging because of the low abundance, site-specific heterogeneity, and poor ionization efficiency of glycopeptides during LC-MS analyses. Therefore, the development of sensitive and accurate approaches to efficiently characterize protein glycosylation is crucial. Methods such as metabolic labeling, enrichment, and derivatization of glycopeptides, coupled with different mass spectrometry techniques and bioinformatics tools, have been developed to achieve sophisticated levels of quantitative and qualitative analyses of glycoproteins. This review attempts to update the recent developments in the field of glycoproteomics reported between 2017 and 2021.

Post-synthesis of boric acid-functionalized magnetic covalent organic framework as an affinity probe for the enrichment of N-glycopeptides

A novel type of boric acid-functionalized magnetic covalent organic framework (mCOF) with polyethyleneimine (PEI) as a linker (denoted as mCOF@PEI@B(OH)₂) has been prepared through a post-synthesis strategy, which points out an achievable path for the construction of boronic acid-functionalized COFs. Based on the boric acid chemistry, the obtained core-shell structured mCOF@PEI@B(OH)₂ can selectively isolate glycopeptides through the modified boronic acid groups. The mCOF@PEI@B(OH)₂ exhibits excellent performance with good reusability (ten cycles), low detection limit (0.5 fmol·μL^-1), size-exclusion effect, and relatively high loading capacity (80 μg·mg^-1), recovery yield (94.9 ± 2.8%), and selectivity (HRP digests:BSA digests = 1:500). Detection is done by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). In addition, 37 endogenous glycopeptides are captured from human saliva with mCOF@PEI@B(OH)₂, providing effective proofs for its capability to capture low-abundance glycopeptides from actual biological samples.

The Hitchhiker's guide to glycoproteomics

Protein glycosylation is one of the most common post-translational modifications that are essential for cell function across all domains of life. Changes in glycosylation are considered a hallmark of many diseases, thus making glycoproteins important diagnostic and prognostic biomarker candidates and therapeutic targets. Glycoproteomics, the study of glycans and their carrier proteins in a system-wide context, is becoming a powerful tool in glycobiology that enables the functional analysis of protein glycosylation. This 'Hitchhiker's guide to glycoproteomics' is intended as a starting point for anyone who wants to explore the emerging world of glycoproteomics. The review moves from the techniques that have been developed for the characterisation of single glycoproteins to technologies that may be used for a successful complex glycoproteome characterisation. Examples of the variety of approaches, methodologies, and technologies currently used in the field are given. This review introduces the common strategies to capture glycoprotein-specific and system-wide glycoproteome data from tissues, body fluids, or cells, and a perspective on how integration into a multi-omics workflow enables a deep identification and characterisation of glycoproteins - a class of biomolecules essential in regulating cell function.