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

Extracting informative glycan-specific ions from glycopeptide MS/MS spectra with GlyCounter

Glycopeptide tandem mass spectra typically contain numerous glycan-specific fragments that can inform several features of glycan modifications, including glycan class, composition, and structure. While these fragment ions are often straightforward to observe by eye, few tools exist to systemically explore these common glycopeptide spectral features or explore their relationships to each other. Instead, most studies rely on manual inspection to understand glycan-informative ion content in their data, or they are restricted to evaluating the presence of these ions only in the small fraction of spectra that are identified by glycopeptide search algorithms. Here we introduce GlyCounter as a freely available, open-source tool to rapidly extract oxonium, Y-type, and custom ion information from raw data files. We highlight GlyCounter's utility by evaluating glycan-specific fragments in a diverse selection of publicly available datasets to demonstrate how others in the field can make immediate use of this software. In several cases, we show how conclusions drawn in these publications are evident simply through GlyCounter's extracted ion information without requiring database searches or experiment-specific programs. Although one of our goals is to decouple spectral evaluation from glycopeptide identification, we also show that evaluating oxonium ion content with GlyCounter can supplement a database search as valuable spectral evidence to validate conclusions. In all, we present GlyCounter as a user-friendly platform that can be easily incorporated into most glycoproteomic workflows to refine sample preparation, data acquisition, and post-acquisition identification methods through straightforward evaluation of the glycan content of glycoproteomic data. Software and instructions are available at https://github.com/riley-research/GlyCounter.

Current issues of tandem mass spectrum (MS2)-based glycoproteomics and efforts to complement them

With the development of liquid chromatography/mass spectrometers that support proteomics and the associated development of analytical methods and data analysis software, the number of proteins that can be identified and quantified in a single analysis is approaching the level of transcriptomics. However, many problems remain to be solved in protein glycosylation analysis. This mini-review discusses the technical issues of MS2-based glycoprotein identification and efforts to complement them by the Human Glycome Atlas (HGA) Project in Japan.

Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics

Mass spectrometry-based proteomics is about 35 years old, and recent progress appears to be speeding up across all subfields. In this review, we focus on advances over the last two years in select areas within bottom-up proteomics, including approaches to high-throughput experiments, data analysis using machine learning, drug discovery, glycoproteomics, extracellular vesicle proteomics, and structural proteomics.

Quantitative proteome-wide O-glycoproteomics analysis with FragPipe

Identification of O-glycopeptides from tandem mass spectrometry data is complicated by the near complete dissociation of O-glycans from the peptide during collisional activation and by the combinatorial explosion of possible glycoforms when glycans are retained intact in electron-based activation. The recent O-Pair search method provides an elegant solution to these problems, using a collisional activation scan to identify the peptide sequence and total glycan mass, and a follow-up electron-based activation scan to localize the glycosite(s) using a graph-based algorithm in a reduced search space. Our previous O-glycoproteomics methods with MSFragger-Glyco allowed for extremely fast and sensitive identification of O-glycopeptides from collisional activation data but had limited support for site localization of glycans and quantification of glycopeptides. Here, we report an improved pipeline for O-glycoproteomics analysis that provides proteome-wide, site-specific, quantitative results by incorporating the O-Pair method as a module within FragPipe. In addition to improved search speed and sensitivity, we add flexible options for oxonium ion-based filtering of glycans and support for a variety of MS acquisition methods and provide a comparison between all software tools currently capable of O-glycosite localization in proteome-wide searches.

Integrative Quantitative Analysis of Platelet Proteome and Site-Specific Glycoproteome Reveals Diagnostic Potential of Platelet Glycoproteins for Liver Cancer

The role of peripheral blood platelets as indicators of cancer progression is increasingly recognized, and the significance of abnormal glycosylation in platelet function and related disorders is gaining attention. However, the potential of platelets as a source of protein site-specific glycosylation for cancer diagnosis remains underexplored. In this study, we proposed a general pipeline that integrates quantitative proteomics with site-specific glycoproteomics, allowing for an in-depth investigation of the platelet glycoproteome. With this pipeline, we generated a data set comprising 3,466 proteins with qualitative information, 3,199 proteins with quantitative information, 3,419 site-specific glycans with qualitative information and 3,377 site-specific glycans with quantitative information from peripheral blood platelets of hepatocellular carcinoma (HCC) patients, metastatic liver cancer (mLC) patients, and healthy controls. The integrated analysis revealed significant changes in platelet protein N-glycosylation in liver cancer patients. Further systems biology analysis and lectin pull-down-coupled ELISA assays in independent clinical samples confirmed two N-glycoproteins with specific glycan types, complement C3 (C3) with oligomannose modification and integrin β-3 (ITGB3) with sialylation, as potential biomarkers distinguishing liver cancer patients from healthy individuals, without differentiating between HCC and mLC patient group. These findings highlight the potential of platelet protein glycosylation as biomarkers.

Tools and techniques for quantitative glycoproteomic analysis

Recent advances in mass spectrometry (MS)-based methods have significantly expanded the capabilities for quantitative glycoproteomics, enabling highly sensitive and accurate quantitation of glycosylation at intact glycopeptide level. These developments have provided valuable insights into the roles of glycoproteins in various biological processes and diseases. In this short review, we summarize pertinent studies on quantitative techniques and tools for site-specific glycoproteomic analysis published over the past decade. We also highlight state-of-the-art MS-based software that facilitate multi-dimension quantification of the glycoproteome, targeted quantification of specific glycopeptides, and the analysis of glycopeptide isomers. Additionally, we discuss the potential applications of these technologies in clinical biomarker discovery and the functional characterization of glycoproteins in health and disease. The review concludes with a discussion of current challenges and future perspectives in the field, emphasizing the need for more precise, high-throughput and efficient methods to further advance quantitative glycoproteomics and its applications.

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.

α-Mannosylated HLA-II glycopeptide antigens dominate the immunopeptidome of immortalised cells and tumour tissues

Immunopeptides are cell surface-located protein fragments that aid our immune system to recognise and respond to pathogenic insult and malignant transformation. In this two-part communication, we firstly summarise and reflect on our recent discovery documenting that MHC-II-bound immunopeptides from immortalised cell lines prevalently carry N-glycans that differ from the cellular glycoproteome (Goodson, Front Immunol, 2023). These findings are important as immunopeptide glycosylation remains poorly understood in immunosurveillance. The study also opened up new technical and biological questions that we address in the second part of this communication. Our study highlighted that the performance of the search engines used to detect glycosylated immunopeptides from LC-MS/MS data remains untested and, importantly, that little biochemical in vivo evidence is available to document the nature of glycopeptide antigens in tumour tissues. To this end, we compared the N-glycosylated MHC-II-bound immunopeptides that were reported from tumour tissues of 14 meningioma patients in the MSFragger-HLA-Glyco database (Bedran, Nat Commun, 2023) to those we identified with the commercial Byonic software. Encouragingly, the search engines produced similar outputs supporting that N-glycosylated MHC-II-bound immunopeptides are prevalent in meningioma tumour tissues. Consistent also with in vitro findings, the tissue-derived MHC-II-bound immunopeptides were found to predominantly carry hyper-processed (paucimannosidic- and chitobiose core-type) and hypo-processed (oligomannosidic-type) N-glycans that varied in prevalence and distribution between patients. Taken together, evidence is emerging suggesting that α-mannosidic glycoepitopes abundantly decorate MHC-II-bound immunopeptides presented in both immortalised cells and tumour tissues warranting further research into their functional roles in immunosurveillance.

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.

Mass spectrometry-based structure-specific N-glycoproteomics and biomedical applications

N-linked glycosylation is a common posttranslational modification of proteins that results in macroheterogeneity of the modification site. However, unlike simpler modifications, N-glycosylation introduces an additional layer of complexity with tens of thousands of possible structures arising from various dimensions, including different monosaccharide compositions, sequence structures, linking structures, isomerism, and three-dimensional conformations. This results in additional microheterogeneity of the modification site of N-glycosylation, i.e., the same N-glycosylation site can be modified with different glycans with a certain stoichiometric ratio. N-glycosylation regulates the structure and function of N-glycoproteins in a site- and structure-specific manner, and differential expression of N-glycosylation under disease conditions needs to be characterized through site- and structure-specific quantitative analysis. Numerous advanced methods ranging from sample preparation to mass spectrum analysis have been developed to distinguish N-glycan structures. Chemical derivatization of monosaccharides, online liquid chromatography separation and ion mobility spectrometry enable the physical differentiation of samples. Tandem mass spectrometry further analyzes the macro/microheterogeneity of intact N-glycopeptides through the analysis of fragment ions. Moreover, the development of search engines and AI-based software has enhanced our understanding of the dissociation patterns of intact N-glycopeptides and the clinical significance of differentially expressed intact N-glycopeptides. With the help of these modern methods, structure-specific N-glycoproteomics has become an important tool with extensive applications in the biomedical field.

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.

A Novel Integrated Pipeline for Site-Specific Quantification of N-glycosylation

The site-specific N-glycosylation changes of human plasma immunoglobulin gamma molecules (IgGs) have been shown to modulate the immune response and could serve as potential biomarkers for the accurate diagnosis of various diseases. However, quantifying intact N-glycopeptides accurately in large-scale clinical samples remains a challenge, and the quantitative N-glycosylation of plasma IgGs in patients with chronic kidney diseases (CKDs) has not yet been studied. In this study, we present a novel integrated intact N-glycopeptide quantitative pipeline (termed GlycoQuant), which combines our recently developed mass spectrometry fragmentation method (EThcD-sceHCD) and an intact N-glycopeptide batch quantification software tool (the upgraded PANDA v.1.2.5). We purified and digested human plasma IgGs from 58 healthy controls (HCs), 48 patients with membranous nephropathy (MN), and 35 patients with IgA nephropathy (IgAN) within an hour. Then, we analyzed the digested peptides without enrichment using EThcD-sceHCD-MS/MS, which provided higher spectral quality and greater identified depth. Using upgraded PANDA, we performed site-specific N-glycosylation quantification of IgGs. Several quantified intact N-glycopeptides not only distinguished CKDs from HCs, but also different types of CKD (MN and IgAN) and may serve as accurate diagnostic tools for renal tubular function. In addition, we proved the applicability of this pipeline to complex samples by reanalyzing the intact N-glycopeptides from cell, urine, plasma, and tissue samples that we had previously identified. We believe that this pipeline can be applied to large-scale clinical N-glycoproteomic studies, facilitating the discovery of novel glycosylated biomarkers.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00150-w.

ppmFixer: a mass error adjustment for pGlyco3.0 to correct near-isobaric mismatches

Modern glycoproteomics experiments require the use of search engines due to the generation of countless spectra. While these tools are valuable, manual validation of search engine results is often required for detailed analysis of glycopeptides as false-discovery rates are often not reliable for glycopeptide data. Near-isobaric mismatches are a common source of misidentifications for the popular glycopeptide-focused search engine pGlyco3.0, and in this technical note we share a strategy and script that improves the accuracy of the search utilizing two manually validated datasets of the glycoproteins CD16a and HIV-1 Env as proof-of-principle.

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.

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.

High Density of N- and O-Glycosylation Shields and Defines the Structural Dynamics of the Intrinsically Disordered Ectodomain of Receptor-type Protein Tyrosine Phosphatase Alpha

The intracellular phosphatase domain of the receptor-type protein tyrosine phosphatase alpha (PTPRA) is known to regulate various signaling pathways related to cell adhesion through c-Src kinase activation. In contrast, the functional significance of its relatively short, intrinsically disordered, and heavily glycosylated ectodomain remains unclear. Through detailed mass spectrometry analyses of a combination of protease and glycosidase digests, we now provide the first experimental evidence for its site-specific glycosylation pattern. This includes the occurrence of O-glycan at the N-glycosylation sequon among the more than 30 O-glycosylation sites confidently identified beside the 7 N-glycosylation sites. The closely spaced N- and O-glycans appear to have mutually limited the extent of further galactosylation and sialylation. An immature smaller form of full-length PTPRA was found to be deficient in O-glycosylation, most likely due to failure to transit the Golgi. N-glycosylation, on the other hand, is dispensable for cell surface expression and contributes less than the extensive O-glycosylation to the overall solution structure of the ectodomain. The glycosylation information is combined with the overall structural features of the ectodomain derived from small-angle X-ray scattering and high-speed atomic force microscopy monitoring to establish a dynamic structural model of the densely glycosylated PTPRA ectodomain. The observed high structural flexibility, as manifested by continuous transitioning from fully to partially extended and fold-back conformations, suggests that the receptor-type phosphatase is anchored to the membrane and kept mostly at a monomeric state through an ectodomain shaped and fully shielded by glycosylation.

Critical considerations in N-glycoproteomics

N-Glycoproteomics, the system-wide study of glycans asparagine-linked to protein carriers, holds a unique and still largely untapped potential to provide deep insights into the complexity and dynamics of the heterogeneous N-glycoproteome. Despite the advent of innovative analytical and informatics tools aiding the analysis, N-glycoproteomics remains challenging and consequently largely restricted to specialised laboratories. Aiming to stimulate discussions of method harmonisation, data standardisation and reporting guidelines to make N-glycoproteomics more reproducible and accessible to the community, we here discuss critical considerations related to the design and execution of N-glycoproteomics experiments and highlight good practices in N-glycopeptide data collection, analysis, interpretation and sharing. Giving the rapid maturation and, expectedly, a wide-spread implementation of N-glycoproteomics capabilities across the community in future years, this piece aims to point out common pitfalls, to encourage good data sharing and documentation practices, and to highlight practical solutions and strategies to enhance the insight into the N-glycoproteome.