Algorithms and design strategies towards automated glycoproteomics analysis

Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. We found that search engines show a range of behavior between peptide-centric and glycan-centric, which manifests itself already in the dependence of optimal collision energy on m/z. Using classical statistical and machine learning methods, we revealed that peptide hydrophobicity, glycan and peptide masses, and the number of mobile protons also have significant and search-engine-dependent influence, as opposed to a series of other parameters we probed. We envisioned an MS/MS workflow making a smart collision energy choice based on online available features such as the hydrophobicity (described by retention time) and glycan mass (potentially available from a scout MS/MS). Our assessment suggests that this workflow can lead to a significant gain (up to 100%) in the identification confidence, particularly for low-scoring hits close to the filtering limit, which has the potential to enhance reproducibility of N-glycopeptide analyses. Data are available via MassIVE (MSV000093110).

MS-based glycomics and glycoproteomics methods enabling isomeric characterization

Glycosylation is one of the most significant and abundant posttranslational modifications in mammalian cells. It mediates a wide range of biofunctions, including cell adhesion, cell communication, immune cell trafficking, and protein stability. Also, aberrant glycosylation has been associated with various diseases such as diabetes, Alzheimer's disease, inflammation, immune deficiencies, congenital disorders, and cancers. The alterations in the distributions of glycan and glycopeptide isomers are involved in the development and progression of several human diseases. However, the microheterogeneity of glycosylation brings a great challenge to glycomic and glycoproteomic analysis, including the characterization of isomers. Over several decades, different methods and approaches have been developed to facilitate the characterization of glycan and glycopeptide isomers. Mass spectrometry (MS) has been a powerful tool utilized for glycomic and glycoproteomic isomeric analysis due to its high sensitivity and rich structural information using different fragmentation techniques. However, a comprehensive characterization of glycan and glycopeptide isomers remains a challenge when utilizing MS alone. Therefore, various separation methods, including liquid chromatography, capillary electrophoresis, and ion mobility, were developed to resolve glycan and glycopeptide isomers before MS. These separation techniques were coupled to MS for a better identification and quantitation of glycan and glycopeptide isomers. Additionally, bioinformatic tools are essential for the automated processing of glycan and glycopeptide isomeric data to facilitate isomeric studies in biological cohorts. Here in this review, we discuss commonly employed MS-based techniques, separation hyphenated MS methods, and software, facilitating the separation, identification, and quantitation of glycan and glycopeptide isomers.

Systematic characterization of extracellular glycoproteins using mass spectrometry

Surface and secreted glycoproteins are essential to cells and regulate many extracellular events. Because of the diversity of glycans, the low abundance of many glycoproteins, and the complexity of biological samples, a system-wide investigation of extracellular glycoproteins is a daunting task. With the development of modern mass spectrometry (MS)-based proteomics, comprehensive analysis of different protein modifications including glycosylation has advanced dramatically. This review focuses on the investigation of extracellular glycoproteins using MS-based proteomics. We first discuss the methods for selectively enriching surface glycoproteins and investigating protein interactions on the cell surface, followed by the application of MS-based proteomics for surface glycoprotein dynamics analysis and biomarker discovery. We then summarize the methods to comprehensively study secreted glycoproteins by integrating various enrichment approaches with MS-based proteomics and their applications for global analysis of secreted glycoproteins in different biological samples. Collectively, MS significantly expands our knowledge of extracellular glycoproteins and enables us to identify extracellular glycoproteins as potential biomarkers for disease detection and drug targets for disease treatment.

Defining the altered glycoproteomic space of the early secretory pathway by class I mannosidase pharmacological inhibition

N-glycosylation is a key process for various biological functions like protein folding, maturation and sorting for the conventional secretory compartment, cell-cell communication and immune response. This is usually accomplished by a complex system of mannosidases in which those from class I have an outstanding role, commonly involved in the early protein sorting associated to the Endoplasmic Reticulum (ER) in the N-glycan dependent quality control (ERQC) and ER-associated degradation (ERAD). Although these are vital processes in maintaining cellular homeostasis, large-scale analysis studies for this pool of molecules, further denoted as proteins from the early secretory pathway (ESP), were limited addressed. Here, using a custom workflow employing a combination of glycomics and deglycoproteomics analyses, using lectin affinity and selective Endoglycosidase H (Endo H) digestion, we scrutinize the steady-state oligomannosidic glycoprotein load and delineate ESP fraction in melanoma cells. All of these were assessed by applying our workflow for glycosite relative quantification of both the peptide chain and carbohydrate structure in cells with inhibited activity of class I mannosidases after kifunensine treatment. We found that most of the ESP are transient clients involved in cell communication via extracellular matrix, particularly integrin-mediated communication which adopt Man9 N-glycans in kifunensine-treated cells. Moreover, our results reveal that core-fucosylation is decreased subsequent inhibition of class I mannosidases and this could be explained by a general lower protein level of FUT8, the enzyme responsible for fucosylation. By comparing our data with results obtained following downregulation of a key mannosidase in misfolded protein degradation, we mapped both novel and previously suggested endogenous substrate candidates like PCDH2, HLA-B, LAMB2 or members of the integrin family of proteins such as ITGA1 and ITGA4, thus validating the findings obtained using our workflow regarding accumulation and characterization of ESP transitory members following mannosidase class I inhibition. This workflow and the associated dataset not only allowed us to investigate the oligomannosidic glycoprotein fraction but also to delineate differences mediated at glycosite-level upon kifunensine treatment and outline the potential associated cellular responses.

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.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2017-2018

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization mass spectrometry (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2018. Also included are papers that describe methods appropriate to glycan and glycoprotein analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, new methods, matrices, derivatization, MALDI imaging, fragmentation and the use of arrays. The second part of the review is devoted to applications to various structural types such as oligo- and poly-saccharides, glycoproteins, glycolipids, glycosides, and biopharmaceuticals. Most of the applications are presented in tabular form. The third part of the review covers medical and industrial applications of the technique, studies of enzyme reactions, and applications to chemical synthesis. The reported work shows increasing use of combined new techniques such as ion mobility and highlights the impact that MALDI imaging is having across a range of diciplines. MALDI is still an ideal technique for carbohydrate analysis and advancements in the technique and the range of applications continue steady progress.

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.

Advancements in automation for plasma proteomics sample preparation

Automation is necessary to increase sample processing throughput for large-scale clinical analyses. Replacement of manual pipettes with robotic liquid handler systems is especially helpful in processing blood-based samples, such as plasma and serum. These samples are very heterogenous, and protein expression can vary greatly from sample-to-sample, even for healthy controls. Detection of true biological changes requires that variation from sample preparation steps and downstream analytical detection methods, such as mass spectrometry, remains low. In this mini-review, we discuss plasma proteomics protocols and the benefits of automation towards enabling detection of low abundant proteins and providing low sample error and increased sample throughput. This discussion includes considerations for automation of major sample depletion and/or enrichment strategies for plasma toward mass spectrometry detection.

Diversity Matters: Optimal Collision Energies for Tandem Mass Spectrometric Analysis of a Large Set of N-Glycopeptides

Identification and characterization of N-glycopeptides from complex samples are usually based on tandem mass spectrometric measurements. Experimental settings, especially the collision energy selection method, fundamentally influence the obtained fragmentation pattern and hence the confidence of the database search results ("score"). Using standards of naturally occurring glycoproteins, we mapped the Byonic and pGlyco search engine scores of almost 200 individual N-glycopeptides as a function of collision energy settings on a quadrupole time of flight instrument. The resulting unprecedented amount of peptide-level information on such a large and diverse set of N-glycopeptides revealed that the peptide sequence heavily influences the energy for the highest score on top of an expected general linear trend with m/z. Search engine dependence may also be noteworthy. Based on the trends, we designed an experimental method and tested it on HeLa, blood plasma, and monoclonal antibody samples. As compared to the literature, these notably lower collision energies in our workflow led to 10-50% more identified N-glycopeptides, with higher scores. We recommend a simple approach based on a small set of reference N-glycopeptides easily accessible from glycoprotein standards to ease the precise determination of optimal methods on other instruments. Data sets can be accessed via the MassIVE repository (MSV000089657 and MSV000090218).

Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins

Proteomics studies allow for the determination of the identity, amount, and interactions of proteins under specific conditions that allow the biological state of an organism to ultimately change. These conditions can be either beneficial or detrimental. Diseases are due to detrimental changes caused by either protein overexpression or underexpression caused by as a result of a mutation or posttranslational modifications (PTM), among other factors. Identification of disease biomarkers through proteomics can be potentially used as clinical information for diagnostics. Common biomarkers to look for include PTM. For example, aberrant glycosylation of proteins is a common marker and will be a focus of interest in this review. A common way to analyze glycoproteins is by glycoproteomics involving mass spectrometry. Due to factors such as micro- and macroheterogeneity which result in a lower abundance of each version of a glycoprotein, it is difficult to obtain meaningful results unless rigorous sample preparation procedures are in place. Microheterogeneity represents the diversity of glycans at a single site, whereas macroheterogeneity depicts glycosylation levels at each site of a protein. Enrichment and derivatization of glycopeptides help to overcome these limitations. Over the time range of 2016 to 2020, several methods have been proposed in the literature and have contributed to drastically improve the outcome of glycosylation analysis, as presented in the sampling surveyed in this review. As a current topic in 2020, glycoproteins carried by pathogens can also cause disease and this is seen with SARS CoV2, causing the COVID-19 pandemic. This review will discuss glycoproteomic studies of the spike glycoprotein and interacting proteins such as the ACE2 receptor.

Glycomic and Glycoproteomic Techniques in Neurodegenerative Disorders and Neurotrauma: Towards Personalized Markers

The proteome represents all the proteins expressed by a genome, a cell, a tissue, or an organism at any given time under defined physiological or pathological circumstances. Proteomic analysis has provided unparalleled opportunities for the discovery of expression patterns of proteins in a biological system, yielding precise and inclusive data about the system. Advances in the proteomics field opened the door to wider knowledge of the mechanisms underlying various post-translational modifications (PTMs) of proteins, including glycosylation. As of yet, the role of most of these PTMs remains unidentified. In this state-of-the-art review, we present a synopsis of glycosylation processes and the pathophysiological conditions that might ensue secondary to glycosylation shortcomings. The dynamics of protein glycosylation, a crucial mechanism that allows gene and pathway regulation, is described. We also explain how-at a biomolecular level-mutations in glycosylation-related genes may lead to neuropsychiatric manifestations and neurodegenerative disorders. We then analyze the shortcomings of glycoproteomic studies, putting into perspective their downfalls and the different advanced enrichment techniques that emanated to overcome some of these challenges. Furthermore, we summarize studies tackling the association between glycosylation and neuropsychiatric disorders and explore glycoproteomic changes in neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington disease, multiple sclerosis, and amyotrophic lateral sclerosis. We finally conclude with the role of glycomics in the area of traumatic brain injury (TBI) and provide perspectives on the clinical application of glycoproteomics as potential diagnostic tools and their application in personalized medicine.

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.

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.

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.

C-SEQer: An Open-Source de Novo Glycan Identification Tool in C+

Glycans play an important role in many biochemical processes, including protein function and cell signaling. Mass spectrometry (MS) provides the potential for high-throughput, high-sensitivity analysis of glycans but relies heavily on computational interpretation of experimental results. Open-source, stand-alone algorithms for de novo glycan MS analysis are few. One such algorithm, Sweet-SEQer, is available in Python. Glycan analysis of mass spectra can easily involve high volumes of data where Python's performance in time and memory is a noticeable bottleneck. This manuscript describes C-SEQer, a new implementation of the Sweet-SEQer algorithm in C++, which produces the same output as the original algorithm in approximately 15-fold less time with substantially less memory usage. The implementation is freely available with an MIT license.

An integrated strategy for the construction of a species-specific glycan library for mass spectrometry-based intact glycopeptide analyses

Mass spectrometry (MS)-based strategies and related software tools using glycan mass lists have greatly facilitated the analysis of intact glycopeptides. Most glycan mass lists are derived from normal glycans of mammals and contain limited monosaccharides, which has significantly hindered high throughput studies of unusual glycosylation events observed in other species. In this work, an integrated strategy was developed for the construction of a species-specific glycan mass list from glycan structure databases and published papers. We developed a computational tool called LibGlycan, which could process the different formats of glycans. Then, the software tool generated a glycan library that contained the monoisotope mass, average mass, isotope distribution, and glycan mass list for input into Byonic software. This strategy was applied to analyze the N-glycosylation of rice roots and O-glycosylation of Acinetobacter baumannii ATCC17978, leading to the identification of 296 and 145 intact glycopeptides respectively. Combined, these results show that this strategy is a robust computational approach for the determination of glycan diversity within different complex biological systems.

Generating orthogonal glycosyltransferase and nucleotide sugar pairs as next-generation glycobiology tools

Protein glycosylation fundamentally impacts biological processes. Nontemplated biosynthesis introduces unparalleled complexity into glycans that needs tools to understand their roles in physiology. The era of quantitative biology is a great opportunity to unravel these roles, especially by mass spectrometry glycoproteomics. However, with high sensitivity come stringent requirements on tool specificity. Bioorthogonal metabolic labeling reagents have been fundamental to studying the cell surface glycoproteome but typically enter a range of different glycans and are thus of limited specificity. Here, we discuss the generation of metabolic 'precision tools' to study particular subtypes of the glycome. A chemical biology tactic termed bump-and-hole engineering generates mutant glycosyltransferases that specifically accommodate bioorthogonal monosaccharides as an enabling technique of glycobiology. We review the groundbreaking discoveries that have led to applying the tactic in the living cell and the implications in the context of current developments in mass spectrometry glycoproteomics.

Generating orthogonal glycosyltransferase and nucleotide sugar pairs as next-generation glycobiology tools

Protein glycosylation fundamentally impacts biological processes. Nontemplated biosynthesis introduces unparalleled complexity into glycans that needs tools to understand their roles in physiology. The era of quantitative biology is a great opportunity to unravel these roles, especially by mass spectrometry glycoproteomics. However, with high sensitivity come stringent requirements on tool specificity. Bioorthogonal metabolic labeling reagents have been fundamental to studying the cell surface glycoproteome but typically enter a range of different glycans and are thus of limited specificity. Here, we discuss the generation of metabolic 'precision tools' to study particular subtypes of the glycome. A chemical biology tactic termed bump-and-hole engineering generates mutant glycosyltransferases that specifically accommodate bioorthogonal monosaccharides as an enabling technique of glycobiology. We review the groundbreaking discoveries that have led to applying the tactic in the living cell and the implications in the context of current developments in mass spectrometry glycoproteomics.

The Role of Data-Independent Acquisition for Glycoproteomics

Data-independent acquisition (DIA) is now an emerging method in bottom–up proteomics and capable of achieving deep proteome coverage and accurate label-free quantification. However, for post-translational modifications, such as glycosylation, DIA methodology is still in the early stage of development. The full characterization of glycoproteins requires site-specific glycan identification as well as subsequent quantification of glycan structures at each site. The tremendous complexity of glycosylation represents a significant analytical challenge in glycoproteomics. This review focuses on the development and perspectives of DIA methodology for N- and O-linked glycoproteomics and posits that DIA-based glycoproteomics could be a method of choice to address some of the challenging aspects of glycoproteomics. First, the current challenges in glycoproteomics and the basic principles of DIA are briefly introduced. DIA-based glycoproteomics is then summarized and described into four aspects based on the actual samples. Finally, we discussed the important challenges and future perspectives in the field. We believe that DIA can significantly facilitate glycoproteomic studies and contribute to the development of future advanced tools and approaches in the field of glycoproteomics.

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Protein glycosylation and challenges in glycoproteomics.

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Data-independent acquisition for deglycosylated and intact N-linked glycopeptides.

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Unbiased screening of oxonium ions from all glycopeptide precursors.

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Glyco–data-independent acquisition on mucin-type O-glycopeptides.

Calculating Glycoprotein Similarities From Mass Spectrometric Data

Complex protein glycosylation occurs through biosynthetic steps in the secretory pathway that create macro- and microheterogeneity of structure and function. Required for all life forms, glycosylation diversifies and adapts protein interactions with binding partners that underpin interactions at cell surfaces and pericellular and extracellular environments. Because these biological effects arise from heterogeneity of structure and function, it is necessary to measure their changes as part of the quest to understand nature. Quite often, however, the assumption behind proteomics that posttranslational modifications are discrete additions that can be modeled using the genome as a template does not apply to protein glycosylation. Rather, it is necessary to quantify the glycosylation distribution at each glycosite and to aggregate this information into a population of mature glycoproteins that exist in a given biological system. To date, mass spectrometric methods for assigning singly glycosylated peptides are well-established. But it is necessary to quantify glycosylation heterogeneity accurately in order to gauge the alterations that occur during biological processes. The task is to quantify the glycosylated peptide forms as accurately as possible and then apply appropriate bioinformatics algorithms to the calculation of micro- and macro-similarities. In this review, we summarize current approaches for protein quantification as they apply to this glycoprotein similarity problem.

Bioinformatics in Immunoglobulin Glycosylation Analysis

Analytical methods developed for studying immunoglobulin glycosylation rely heavily on software tailored for this purpose. Many of these tools are now used in high-throughput settings, especially for the glycomic characterization of IgG. A collection of these tools, and the databases they rely on, are presented in this chapter. Specific applications are detailed in examples of immunoglobulin glycomics and glycoproteomics data processing workflows. The results obtained in the glycoproteomics workflow are emphasized with the use of dedicated visualizing tools. These tools enable the user to highlight glycan properties and their differential expression.

Glycan Compositions with GlyConnect Compozitor to Enhance Glycopeptide Identification

Glycoproteomics is unquestionably on the rise and its current development benefits from past experience in proteomics, in particular when attending to bioinformatics needs. An extensive range of software solutions is available, but the reproducibility of mass spectrometry data processing remains challenging. One of the key issues in running automated glycopeptide identification software is the selection of a reference glycan composition file. The default choices are often too broad, and a fastidious literature search to properly target this selection can be avoided. This chapter suggests the use of GlyConnect Compozitor to collect relevant information on glycosylation in a given tissue or cell line and shape an appropriate glycan composition set that can be input in the majority of search engines accommodating user-defined compositions.

Relative Quantification of Phosphorylated and Glycosylated Peptides from the Same Sample Using Isobaric Chemical Labelling with a Two-Step Enrichment Strategy

Post-translational modifications (PTMs) are essential for the regulation of all cellular processes. The interplay of various PTMs on a single protein or different proteins comprises a complexity that we are far from understanding in its entirety. Reliable strategies for the enrichment and accurate quantification of PTMs are needed to study as many PTMs on proteins as possible. In this protocol we present a liquid chromatography-tandem mass spectrometry (LC/MS/MS)-based workflow that enables the enrichment and quantification of phosphorylated and N-glycosylated peptides from the same sample. After extraction and digestion of proteins, we label the peptides with stable isotope-coded tandem mass tags (TMTs) and enrich N-glycopeptides and phosphopeptides by using zwitterionic hydrophilic interaction chromatography (ZIC-HILIC) and titanium dioxide (TiO₂) beads, respectively. Labelled and enriched N-glycopeptides and phosphopeptides are further separated by high pH (basic) reversed-phase chromatography and analyzed by LC/MS/MS. The enrichment strategies, together with quantification of two different PTM types from the same sample, allow investigation of the interplay of those two PTMs, which are important for signal transduction inside the cell (phosphorylation), as well as for messaging between cells through decoration of the cellular surface (glycosylation).

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.

A Pragmatic Guide to Enrichment Strategies for Mass Spectrometry-Based Glycoproteomics

Glycosylation is a prevalent, yet heterogeneous modification with a broad range of implications in molecular biology. This heterogeneity precludes enrichment strategies that can be universally beneficial for all glycan classes. Thus, choice of enrichment strategy has profound implications on experimental outcomes. Here we review common enrichment strategies used in modern mass spectrometry-based glycoproteomic experiments, including lectins and other affinity chromatographies, hydrophilic interaction chromatography and its derivatives, porous graphitic carbon, reversible and irreversible chemical coupling strategies, and chemical biology tools that often leverage bioorthogonal handles. Interest in glycoproteomics continues to surge as mass spectrometry instrumentation and software improve, so this review aims to help equip researchers with the necessary information to choose appropriate enrichment strategies that best complement these efforts.

SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

MOTIVATION

Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes.

RESULTS

Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae.

AVAILABILITY

The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Semiautomated glycoproteomics data analysis workflow for maximized glycopeptide identification and reliable quantification

Glycoproteomic data are often very complex, reflecting the high structural diversity of peptide and glycan portions. The use of glycopeptide-centered glycoproteomics by mass spectrometry is rapidly evolving in many research areas, leading to a demand in reliable data analysis tools. In recent years, several bioinformatic tools were developed to facilitate and improve both the identification and quantification of glycopeptides. Here, a selection of these tools was combined and evaluated with the aim of establishing a robust glycopeptide detection and quantification workflow targeting enriched glycoproteins. For this purpose, a tryptic digest from affinity-purified immunoglobulins G and A was analyzed on a nano-reversed-phase liquid chromatography-tandem mass spectrometry platform with a high-resolution mass analyzer and higher-energy collisional dissociation fragmentation. Initial glycopeptide identification based on MS/MS data was aided by the Byonic software. Additional MS1-based glycopeptide identification relying on accurate mass and retention time differences using GlycopeptideGraphMS considerably expanded the set of confidently annotated glycopeptides. For glycopeptide quantification, the performance of LaCyTools was compared to Skyline, and GlycopeptideGraphMS. All quantification packages resulted in comparable glycosylation profiles but featured differences in terms of robustness and data quality control. Partial cysteine oxidation was identified as an unexpectedly abundant peptide modification and impaired the automated processing of several IgA glycopeptides. Finally, this study presents a semiautomated workflow for reliable glycoproteomic data analysis by the combination of software packages for MS/MS- and MS1-based glycopeptide identification as well as the integration of analyte quality control and quantification.

Meta-heterogeneity: Evaluating and Describing the Diversity in Glycosylation Between Sites on the Same Glycoprotein

Mass spectrometry-based glycoproteomics has gone through some incredible developments over the last few years. Technological advances in glycopeptide enrichment, fragmentation methods, and data analysis workflows have enabled the transition of glycoproteomics from a niche application, mainly focused on the characterization of isolated glycoproteins, to a mature technology capable of profiling thousands of intact glycopeptides at once. In addition to numerous biological discoveries catalyzed by the technology, we are also observing an increase in studies focusing on global protein glycosylation and the relationship between multiple glycosylation sites on the same protein. It has become apparent that just describing protein glycosylation in terms of micro- and macro-heterogeneity, respectively, the variation and occupancy of glycans at a given site, is not sufficient to describe the observed interactions between sites. In this perspective we propose a new term, meta-heterogeneity, to describe a higher level of glycan regulation: the variation in glycosylation across multiple sites of a given protein. We provide literature examples of extensive meta-heterogeneity on relevant proteins such as antibodies, erythropoietin, myeloperoxidase, and a number of serum and plasma proteins. Furthermore, we postulate on the possible biological reasons and causes behind the intriguing meta-heterogeneity observed in glycoproteins.

Glycoproteomics: growing up fast

Glycoproteomics is a rapidly growing field which seeks to identify and characterise glycosylation events at a proteome scale. Over the last few years considerable effort has been made in developing new technologies, enrichment systems, and analysis strategies to enhance the quality of glycoproteomic studies. Within this review we discuss the recent developments in glycoproteomics and the current state of the art approaches for analysing glycosylated substrates. We highlight key improvements in mass spectrometry instrumentation coupled with the advancements in enrichment approaches for key classes of glycosylation including mucin-O-glycosylation, O-GlcNAc glycosylation and N-linked glycosylation which now allow the identification/quantification of hundreds to thousands of glycosylation sites within individual experiments. Finally, we summarise the emerging trends within glycoproteomics to illustrate how the field is moving away from studies simply focused on identifying glycosylated substrates to studying specific mechanisms and disease states.

Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics

Regulation of protein N-glycosylation is essential in human cells. However, large-scale, accurate, and site-specific quantification of glycosylation is still technically challenging. We here introduce SugarQuant, an integrated mass spectrometry-based pipeline comprising protein aggregation capture (PAC)-based sample preparation, multi-notch MS3 acquisition (Glyco-SPS-MS3) and a data-processing tool (GlycoBinder) that enables confident identification and quantification of intact glycopeptides in complex biological samples. PAC significantly reduces sample-handling time without compromising sensitivity. Glyco-SPS-MS3 combines high-resolution MS2 and MS3 scans, resulting in enhanced reporter signals of isobaric mass tags, improved detection of N-glycopeptide fragments, and lowered interference in multiplexed quantification. GlycoBinder enables streamlined processing of Glyco-SPS-MS3 data, followed by a two-step database search, which increases the identification rates of glycopeptides by 22% compared with conventional strategies. We apply SugarQuant to identify and quantify more than 5,000 unique glycoforms in Burkitt’s lymphoma cells, and determine site-specific glycosylation changes that occurred upon inhibition of fucosylation at high confidence.

Comprehensive quantitative profiling of intact glycopeptides remains technically challenging. To address this, the authors here develop an integrated quantitative glycoproteomic workflow, including optimized sample preparation, multiplexed quantification and a dedicated data processing tool.

Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting this data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide-spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search and yielded 4–6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

Examining and Fine-tuning the Selection of Glycan Compositions with GlyConnect Compozitor

A key point in achieving accurate intact glycopeptide identification is the definition of the glycan composition file that is used to match experimental with theoretical masses by a glycoproteomics search engine. At present, these files are mainly built from searching the literature and/or querying data sources focused on posttranslational modifications. Most glycoproteomics search engines include a default composition file that is readily used when processing MS data. We introduce here a glycan composition visualizing and comparative tool associated with the GlyConnect database and called GlyConnect Compozitor. It offers a web interface through which the database can be queried to bring out contextual information relative to a set of glycan compositions. The tool takes advantage of compositions being related to one another through shared monosaccharide counts and outputs interactive graphs summarizing information searched in the database. These results provide a guide for selecting or deselecting compositions in a file in order to reflect the context of a study as closely as possible. They also confirm the consistency of a set of compositions based on the content of the GlyConnect database. As part of the tool collection of the Glycomics@ExPASy initiative, Compozitor is hosted at https://glyconnect.expasy.org/compozitor/ where it can be run as a web application. It is also directly accessible from the GlyConnect database.

GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

Mass spectrometry glycoproteomics is rapidly maturing, allowing unprecedented insights into the diversity and functions of protein glycosylation. However, quantitative glycoproteomics remains challenging. We developed GlypNirO, an automated software pipeline which integrates the complementary outputs of Byonic and Proteome Discoverer to allow high-throughput automated quantitative glycoproteomic data analysis. The output of GlypNirO is clearly structured, allowing manual interrogation, and is also appropriate for input into diverse statistical workflows. We used GlypNirO to analyse a published plasma glycoproteome dataset and identified changes in site-specific N- and O-glycosylation occupancy and structure associated with hepatocellular carcinoma as putative biomarkers of disease.

Optimal Dissociation Methods Differ for N- and O-Glycopeptides

Site-specific characterization of glycosylation requires intact glycopeptide analysis, and recent efforts have focused on how to best interrogate glycopeptides using tandem mass spectrometry (MS/MS). Beam-type collisional activation, i.e., higher-energy collisional dissociation (HCD), has been a valuable approach, but stepped collision energy HCD (sceHCD) and electron transfer dissociation with HCD supplemental activation (EThcD) have emerged as potentially more suitable alternatives. Both sceHCD and EThcD have been used with success in large-scale glycoproteomic experiments, but they each incur some degree of compromise. Most progress has occurred in the area of N-glycoproteomics. There is growing interest in extending this progress to O-glycoproteomics, which necessitates comparisons of method performance for the two classes of glycopeptides. Here, we systematically explore the advantages and disadvantages of conventional HCD, sceHCD, ETD, and EThcD for intact glycopeptide analysis and determine their suitability for both N- and O-glycoproteomic applications. For N-glycopeptides, HCD and sceHCD generate similar numbers of identifications, although sceHCD generally provides higher quality spectra. Both significantly outperform EThcD methods in terms of identifications, indicating that ETD-based methods are not required for routine N-glycoproteomics even if they can generate higher quality spectra. Conversely, ETD-based methods, especially EThcD, are indispensable for site-specific analyses of O-glycopeptides. Our data show that O-glycopeptides cannot be robustly characterized with HCD-centric methods that are sufficient for N-glycopeptides, and glycoproteomic methods aiming to characterize O-glycopeptides must be constructed accordingly.

A Perspective on the Confident Comparison of Glycoprotein Site-Specific Glycosylation in Sample Cohorts

Protein glycosylation, resulting from glycosyl transferase reactions under complex control in the secretory pathway, consists of a distribution of related glycoforms at each glycosylation site. Because the biosynthetic substrate concentration and transport rates depend on architecture and other aspects of cellular phenotypes, site-specific glycosylation cannot be predicted accurately from genomic, transcriptomic, or proteomic information. Rather, it is necessary to quantify glycosylation at each protein site and how this changes among a sample cohort to provide information about disease mechanisms. At present, mature mass spectrometry-based methods allow for qualitative assignment of the glycan composition and glycosylation site of singly glycosylated proteolytic peptides. To make such quantitative comparisons, it is necessary to sample the glycosylation distribution with sufficient coverage and accuracy for confident assessment of the glycosylation changes that occur in the biological cohort. In this Perspective, we discuss the unmet needs for mass spectrometry acquisition methods and bioinformatics for the confident comparison of protein site-specific glycosylation among sample cohorts.

Recent advances in glycoinformatic platforms for glycomics and glycoproteomics

Protein glycosylation is the most complex and prevalent post-translation modification in terms of the number of proteins modified and the diversity generated. To understand the functional roles of glycoproteins it is important to gain an insight into the repertoire of oligosaccharides present. The comparison and relative quantitation of glycoforms combined with site-specific identification and occupancy are necessary steps in this direction. Computational platforms have continued to mature assisting researchers with the interpretation of such glycomics and glycoproteomics data sets, but frequently support dedicated workflows and users rely on the manual interpretation of data to gain insights into the glycoproteome. The growth of site-specific knowledge has also led to the implementation of machine-learning algorithms to predict glycosylation which is now being integrated into glycoproteomics pipelines. This short review describes commercial and open-access databases and software with an emphasis on those that are actively maintained and designed to support current analytical workflows.

Recent advances in mass spectrometry (MS)-based glycoproteomics in complex biological samples

Protein glycosylation plays a key role in various biological processes and disease-related pathological progression. Mass spectrometry (MS)-based glycoproteomics is a powerful approach that provides a system-wide profiling of the glycoproteome in a high-throughput manner. There have been numerous significant technological advances in this field, including improved glycopeptide enrichment, hybrid fragmentation techniques, emerging specialized software packages, and effective quantitation strategies, as well as more dedicated workflows. With increasingly sophisticated glycoproteomics tools on hand, researchers have extensively adapted this approach to explore different biological systems both in terms of in-depth glycoproteome profiling and comparative glycoproteome analysis. Quantitative glycoproteomics enables researchers to discover novel glycosylation-based biomarkers in various diseases with potential to offer better sensitivity and specificity for disease diagnosis. In this review, we present recent methodological developments in MS-based glycoproteomics and highlight its utility and applications in answering various questions in complex biological systems.

Recent advances in mass spectrometry (MS)-based glycoproteomics in complex biological samples

Protein glycosylation plays a key role in various biological processes and disease-related pathological progression. Mass spectrometry (MS)-based glycoproteomics is a powerful approach that provides a system-wide profiling of the glycoproteome in a high-throughput manner. There have been numerous significant technological advances in this field, including improved glycopeptide enrichment, hybrid fragmentation techniques, emerging specialized software packages, and effective quantitation strategies, as well as more dedicated workflows. With increasingly sophisticated glycoproteomics tools on hand, researchers have extensively adapted this approach to explore different biological systems both in terms of in-depth glycoproteome profiling and comparative glycoproteome analysis. Quantitative glycoproteomics enables researchers to discover novel glycosylation-based biomarkers in various diseases with potential to offer better sensitivity and specificity for disease diagnosis. In this review, we present recent methodological developments in MS-based glycoproteomics and highlight its utility and applications in answering various questions in complex biological systems.

Global and site-specific analysis of protein glycosylation in complex biological systems with Mass Spectrometry

Protein glycosylation is ubiquitous in biological systems and plays essential roles in many cellular events. Global and site-specific analysis of glycoproteins in complex biological samples can advance our understanding of glycoprotein functions and cellular activities. However, it is extraordinarily challenging because of the low abundance of many glycoproteins and the heterogeneity of glycan structures. The emergence of mass spectrometry (MS)-based proteomics has provided us an excellent opportunity to comprehensively study proteins and their modifications, including glycosylation. In this review, we first summarize major methods for glycopeptide/glycoprotein enrichment, followed by the chemical and enzymatic methods to generate a mass tag for glycosylation site identification. We next discuss the systematic and quantitative analysis of glycoprotein dynamics. Reversible protein glycosylation is dynamic, and systematic study of glycoprotein dynamics helps us gain insight into glycoprotein functions. The last part of this review focuses on the applications of MS-based proteomics to study glycoproteins in different biological systems, including yeasts, plants, mice, human cells, and clinical samples. Intact glycopeptide analysis is also included in this section. Because of the importance of glycoproteins in complex biological systems, the field of glycoproteomics will continue to grow in the next decade. Innovative and effective MS-based methods will exponentially advance glycoscience, and enable us to identify glycoproteins as effective biomarkers for disease detection and drug targets for disease treatment. © 2019 Wiley Periodicals, Inc. Mass Spec Rev 9999: XX-XX, 2019.

DecoyDeveloper: An On-Demand, De Novo Decoy Glycopeptide Generator

Glycopeptide analysis is a growing field that is struggling to adopt effective, automated tools. Many creative workflows and software apps have emerged recently that offer promising capabilities for assigning glycopeptides to MS data in an automated fashion. The effectiveness of these tools is best measured and improved by determining how often they would select a glycopeptide decoy as a spectral match, instead of its correct assignment; yet generating the appropriate number and type of glycopeptide decoys can be challenging. To address this need, we have designed DecoyDeveloper, an on-demand decoy glycopeptide generator that can produce a high volume of decoys with low mass differences. DecoyDeveloper has a simple user interface and is capable of producing large sets of decoys containing complete, biologically relevant glycan and peptide sequences. We demonstrate the tool's efficiency by applying it to a set of 80 glycopeptide targets. This tool is freely available and can be found at http://glycopro.chem.ku.edu/J1.php .

GlycopeptideGraphMS: Improved Glycopeptide Detection and Identification by Exploiting Graph Theoretical Patterns in Mass and Retention Time

The leading proteomic method for identifying N-glycosylated peptides is liquid chromatography coupled with tandem fragmentation mass spectrometry (LCMS/MS) followed by spectral matching of MS/MS fragment masses to a database of possible glycan and peptide combinations. Such database-dependent approaches come with challenges such as needing high-quality informative MS/MS spectra, ignoring unexpected glycan or peptide sequences, and making incorrect assignments because some glycan combinations are equivalent in mass to amino acids. To address these challenges, we present GlycopeptideGraphMS, a graph theoretical bioinformatic approach complementary to the database-dependent method. Using the AXL receptor tyrosine kinase (AXL) as a model glycoprotein with multiple N-glycosylation sites, we show that those LCMS features that could be grouped into graph networks on the basis of glycan mass and retention time differences were actually N-glycopeptides with the same peptide backbone but different N-glycan compositions. Conversely, unglycosylated peptides did not exhibit this grouping behavior. Furthermore, MS/MS sequencing of the glycan and peptide composition of just one N-glycopeptide in the graph was sufficient to identify the rest of the N-glycopeptides in the graph. By validating the identifications with exoglycosidase cocktails and MS/MS fragmentation, we determined the experimental false discovery rate of identifications to be 2.21%. GlycopeptideGraphMS detected more than 500 unique N-glycopeptides from AXL, triple the number found by a database search with Byonic software, and detected incorrect assignments due to a nonspecific protease cleavage. This method overcomes some limitations of the database approach and is a step closer to comprehensive automated glycoproteomics.

Glycomics@ExPASy: Bridging the Gap

Glycomics@ExPASy (https://www.expasy.org/glycomics) is the glycomics tab of ExPASy, the server of SIB Swiss Institute of Bioinformatics. It was created in 2016 to centralize web-based glycoinformatics resources developed within an international network of glycoscientists. The hosted collection currently includes mainly databases and tools created and maintained at SIB but also links to a range of reference resources popular in the glycomics community. The philosophy of our toolbox is that it should be {glycoscientist AND protein scientist}-friendly with the aim of (1) popularizing the use of bioinformatics in glycobiology and (2) emphasizing the relationship between glycobiology and protein-oriented bioinformatics resources. The scarcity of data bridging these two disciplines led us to design tools as interactive as possible based on database connectivity to facilitate data exploration and support hypothesis building. Glycomics@ExPASy was designed, and is developed, with a long-term vision in close collaboration with glycoscientists to meet as closely as possible the growing needs of the community for glycoinformatics.

Quantitative glycomics using liquid phase separations coupled to mass spectrometry

Post-translational modification of proteins by the attachment of glycans is governed by a variety of highly specific enzymes and is associated with fundamental impacts on the parent protein's physical, chemical and biological properties. The inherent connection between cellular physiology and specific glycosylation patterns has been shown to offer potential for diagnostic and prognostic monitoring of altered glycosylation in the disease state. Conversely, glycoprotein based biopharmaceuticals have emerged as dominant therapeutic strategies in the treatment of intricate diseases. Glycosylation present on these biopharmaceuticals represents a major critical quality attribute with impacts on both pharmacokinetics and pharmacodynamics. The structural variety of glycans, based upon their non-template driven assembly, poses a significant analytical challenge for both qualitative and quantitative analysis. Labile monosaccharide constituents, isomeric species and often low sample availability from biological sources necessitates meticulous sample handling, ultra-high-resolution analytical separation and sensitive detection techniques, respectively. In this article a critical review of analytical quantitation approaches using liquid phase separations coupled to mass spectrometry for released glycans of biopharmaceutical and biomedical significance is presented. Considerations associated with sample derivatisation strategies, ionisation, relative quantitation through isotopic as well as isobaric labelling, metabolic/enzymatic incorporation and targeted analysis are all thoroughly discussed.

Advances in mass spectrometry-based glycoproteomics

Protein glycosylation, an important PTM, plays an essential role in a wide range of biological processes such as immune response, intercellular signaling, inflammation, and host-pathogen interaction. Aberrant glycosylation has been correlated with various diseases. However, studying protein glycosylation remains challenging because of low abundance, microheterogeneities of glycosylation sites, and poor ionization efficiency of glycopeptides. Therefore, the development of sensitive and accurate approaches to characterize protein glycosylation is crucial. The identification and characterization of protein glycosylation by MS is referred to as the field of glycoproteomics. Methods such as enrichment, metabolic labeling, and derivatization of glycopeptides in conjunction with different MS techniques and bioinformatics tools, have been developed to achieve an unequivocal quantitative and qualitative characterization of glycoproteins. This review summarizes the recent developments in the field of glycoproteomics over the past 6 years (2012 to 2018).

Distinctive and Complementary MS2 Fragmentation Characteristics for Identification of Sulfated Sialylated N-Glycopeptides by nanoLC-MS/MS Workflow

High sensitivity identification of sulfated glycans carried on specific sites of glycoproteins is an important requisite for investigation of molecular recognition events involved in diverse biological processes. However, aiming for resolving site-specific glycosylation of sulfated glycopeptides by direct LC-MS² sequencing is technically most challenging. Other than the usual limiting factors such as lower abundance and ionization efficiency compared to analysis of non-glycosylated peptides, confident identification of sulfated glycopeptides among the more abundant non-sulfated glycopeptides requires additional considerations in the selective enrichment and detection strategies. Metal oxide has been applied to enrich phosphopeptides and sialylated glycopeptides, but its use to capture sulfated glycopeptides has not been investigated. Likewise, various complementary MS² fragmentation modes have yet to be tested against sialylated and non-sialylated sulfoglycopeptides due to limited appropriate sample availability. In this study, we have investigated the feasibility of sequencing tryptic sulfated N-glycopeptide and its MS² fragmentation characteristics by first optimizing the enrichment methods to allow efficient LC-MS detection and MS² analysis by a combination of CID, HCD, ETD, and EThcD on hybrid and tribrid Orbitrap instruments. Characteristic sulfated glyco-oxonium ions and direct loss of sulfite from precursors were detected as evidences of sulfate modification. It is anticipated that the technical advances demonstrated in this study would allow a feasible extension of our sulfoglycomic analysis to sulfoglycoproteomics. Graphical Abstract ᅟ.