Computational framework for identification of intact glycopeptides in complex samples

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

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

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).

Development of a novel lectin-based gold nanoparticle point-of-care immunoassay for rapid diagnosis of patients with severe Dengue infection

Rapid diagnosis of patients with severe Dengue infection can be useful for the efficient clinical management of cases caused by the Dengue virus. Lateral Flow Immunoassay (LFIA) have been broadly used for rapid Dengue diagnosis, because of their quick readouts with the human eye, simplicity of use, and affordability. Despite the availability of several commercial Dengue point-of-care assays, none has shown to be successful in discriminating between severe and nonsevere forms of Dengue infection. In the current study, for the first time, a novel lectin-based point-of-care assay for the early detection of patients with severe Dengue infection with gold-adorned sheets as detection labels is being reported. In this assay, Dengue severity was diagnosed by detecting the glycosylation profile of vitronectin, a known Dengue severity marker. Two lectins were employed namely DSA (Datura stramonium) and MAA (Maackia amurensis) that can recognize specific glycans like galactose Gal-(1-4) GlcNAc and sialic acid in an (α2-3) linkage, which displayed high sensitivity and high specificity, i.e. 90% and 85% for DSA and 90.91% and 95% for MAA. The new assay has a detection limit of 5 µg µl-1 and enables the quick (30 min) and sensitive detection of severe Dengue cases. The reported point-of-care immunoassay exhibits considerable promise for early identification of patients with Dengue severity.

Development and validation of a method for analyzing the sialylated glycopeptides of recombinant erythropoietin in urine using LC-HRMS

Erythropoietin (EPO) is a glycoprotein hormone that stimulates red blood cell production. It is produced naturally in the body and is used to treat patients with anemia. Recombinant EPO (rEPO) is used illicitly in sports to improve performance by increasing the blood's capacity to carry oxygen. The World Anti-Doping Agency has therefore prohibited the use of rEPO. In this study, we developed a bottom-up mass spectrometric method for profiling the site-specific N-glycosylation of rEPO. We revealed that intact glycopeptides have a site-specific tetra-sialic glycan structure. Using this structure as an exogenous marker, we developed a method for use in doping studies. The profiling of rEPO N-glycopeptides revealed the presence of tri- and tetra-sialylated N-glycopeptides. By selecting a peptide with a tetra-sialic acid structure as the target, its limit of detection (LOD) was estimated to be < 500 pg/mL. Furthermore, we confirmed the detection of the target rEPO glycopeptide using three other rEPO products. We additionally validated the linearity, carryover, selectivity, matrix effect, LOD, and intraday precision of this method. To the best of our knowledge, this is the first report of a doping analysis using liquid chromatography/mass spectrometry-based detection of the rEPO glycopeptide with a tetra-sialic acid structure in human urine samples.

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.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching

Liquid chromatography coupled with tandem mass spectrometry is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. The software for glycopeptide identification in such data (e.g., the commercial software Byonic) analyzes the individual data set and does not exploit the redundant spectra of glycopeptides presented in the related data sets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic data sets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic data sets showed that the concurrent approach can identify 105%-224% more spectra as glycopeptides compared to the glycopeptide identification on individual data sets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.

Glycoproteomic Analyses of Influenza A Viruses Using timsTOF Pro MS

N-Linked glycosylation in hemagglutinin and neuraminidase glycoproteins of influenza viruses affects antigenic and receptor binding properties, and precise analyses of site-specific glycoforms in these proteins are critical in understanding the antigenic and immunogenic properties of influenza viruses. In this study, we developed a glycoproteomic approach by using a timsTOF Pro mass spectrometer (MS) to determine the abundance and heterogeneity of site-specific glycosylation for influenza glycoproteins. Compared with a Q Exactive HF MS, the timsTOF Pro MS method without the hydrophilic interaction liquid chromatography column enrichment achieved similar glycopeptide coverage and quantities but was more effective in identifying low-abundance glycopeptides. We quantified the distributions of intact site-specific glycopeptides in hemagglutinin of A/chicken/Wuxi/0405005/2013 (H7N9) and A/mute swan/Rhode Island/A00325125/2008 (H7N3). Results showed that hemagglutinin for both viruses had complex N-glycans at N22, N38, N240, and N483 but only high-mannose glycans at N411 and, however, that the type and quantities of glycans were distinct between these viruses. Collisional cross section (CCS) provided by the ion mobility spectrometry from the timsTOF Pro MS data differentiated sialylation linkages of the glycopeptides. In summary, timsTOF Pro MS method can quantify intact site-specific glycans for influenza glycoproteins without enrichment and thus facilitate influenza vaccine development and production.

Comparative Transcriptomics and Proteomics of Cancer Cell Lines Cultivated by Physiological and Commercial Media

Aiming to reduce the gap between in vitro and in vivo environment, a complex culture medium, Plasmax, was introduced recently, which includes nutrients and metabolites with concentrations normally found in human plasma. Herein, to study the influence of this medium on cellular behaviors, we utilized Plasmax to cultivate two cancer cell lines, including one breast cancer cell line, MDA-MB-231BR, and one brain cancer cell line, CRL-1620. Cancer cells were harvested and prepared for transcriptomics and proteomics analyses to assess the discrepancies caused by the different nutritional environments of Plasmax and two commercial media: DMEM, and EMEM. Total RNAs of cells were extracted using mammalian total RNA extract kits and analyzed by next-generation RNA sequencing; proteomics analyses were performed using LC-MS/MS. Gene oncology and pathway analysis were employed to study the affected functions. The cellular invasion and cell death were inhibited in MDA-MB-231BR cell line when cultured in Plasmax compared to DMEM and EMEM, whereas the invasion, migration and protein synthesis of CRL-1620 cell line were activated in Plasmax in relative to both commercial media. The expression changes of some proteins were more significant compared to their corresponding transcripts, indicating that Plasmax has more influence upon regulatory processes of proteins after translation. This work provides complementary information to the original study of Plasmax, aiming to facilitate the selection of appropriate media for in vitro cancer cell studies.

Comprehensive N- and O-Glycoproteomic Analysis of Multiple Chinese Hamster Ovary Host Cell Lines

Glycoproteomic analysis of three Chinese hamster ovary (CHO) suspension host cell lines (CHO-K1, CHO-S, and CHO-Pro5) commonly utilized in biopharmaceutical settings for recombinant protein production is reported. Intracellular and secreted glycoproteins were examined. We utilized an immobilization and chemoenzymatic strategy in our analysis. Glycoproteins or glycopeptides were first immobilized through reductive amination, and the sialyl moieties were amidated for protection. The desired N- or O-glycans and glycopeptides were released from the immobilization resin by enzymatic or chemical digestion. Glycopeptides were studied by Orbitrap Liquid chromatography-mass spectrometry (LC/MS), and the released glycans were analyzed by Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF). Differences were detected in the relative abundances of N- and O-glycopeptide types, their resident and released glycans, and their glycoprotein complexity. Ontogeny analysis revealed key differences in features, such as general metabolic and biosynthetic pathways, including glycosylation systems, as well as distributions in cellular compartments. Host cell lines and subfraction differences were observed in both N- and O-glycan and glycoprotein pools. Differences were observed in sialyl and fucosyl glycan distributions. Key differences were also observed among glycoproteins that are problematic contaminants in recombinant antibody production. The differences revealed in this study should inform the choice of cell lines best suited for a particular bioproduction application.

Mass spectrometry based biomarkers for early detection of HCC using a glycoproteomic approach

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide and 80%-90% of HCC develops in patients that have underlying cirrhosis. Better methods of surveillance are needed to increase early detection of HCC and the proportion of patients that can be offered curative therapies. Recent work in novel mass spec-based methods for glycomic and glycopeptide analysis for discovery and confirmation of markers for early detection of HCC versus cirrhosis is reviewed in this chapter. Results from recent work in these fields by several groups and the progress made in developing markers of early HCC which can outperform the current serum-based markers are described and discussed. Also, recent developments in isoform analysis of glycans and glycopeptides and in various mass spec fragmentation methods will be described and discussed.

Site-Specific Intact N-Linked Glycopeptide Characterization of Prostate-Specific Membrane Antigen from Metastatic Prostate Cancer Cells

The composition of N-linked glycans that are conjugated to the prostate-specific membrane antigen (PSMA) and their functional significance in prostate cancer progression have not been fully characterized. PSMA was isolated from two metastatic prostate cancer cell lines, LNCaP and MDAPCa2b, which have different tissue tropism and localization. Isolated PSMA was trypsin-digested, and intact glycopeptides were subjected to LC-HCD-EThcD-MS/MS analysis on a Tribrid Orbitrap Fusion Lumos mass spectrometer. Differential qualitative and quantitative analysis of site-specific N-glycopeptides was performed using Byonic and Byologic software. Comparative quantitative analysis demonstrates that multiple glycopeptides at asparagine residues 51, 76, 121, 195, 336, 459, 476, and 638 were in significantly different abundance in the two cell lines (p < 0.05). Biochemical analysis using endoglycosidase treatment and lectin capture confirm the MS and site occupancy data. The data demonstrate the effectiveness of the strategy for comprehensive analysis of PSMA glycopeptides. This approach will form the basis of ongoing experiments to identify site-specific glycan changes in PSMA isolated from disease-stratified clinical samples to uncover targets that may be associated with disease progression and metastatic phenotypes.

GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control

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

GlycoHybridSeq: Automated Identification of N-Linked Glycopeptides Using Electron Transfer/High-Energy Collision Dissociation (EThcD)

Glycosylation is one of the most common post-translational modifications (PTM) occurring in a large variety of proteins with important biological functions in human and other higher organisms. Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been routinely used to characterize site-specific protein glycosylation at high throughput in complex glycoproteomic samples. Recently, electron transfer/high-energy collision dissociation (EThcD) was introduced for glycopeptide identification, which offers rich structural information on glycopepides with the fragment ions from the cleavages of both the glycan and the peptide backbone. Herein, we present the software GlycoHybridSeq for automated interpretation of EThcD-MS/MS spectra from glycoproteomic data using a customized scoring function, which enables the functionalities of identifying glycopeptides, characterizing glycosylation sites, and distinguishing some isomeric glycans. We evaluate GlycoHybridSeq on glycoproteomic data collected for cancer biomarker discovery. The results showed that it achieved comparable or better performance than that of Byonic and MSFragger. GlycoHybridSeq is released as an open source software and is ready to be used in large-scale glycoproteomic data analyses.

Recent Advances in Analytical Approaches for Glycan and Glycopeptide Quantitation

Growing implications of glycosylation in physiological occurrences and human disease have prompted intensive focus on revealing glycomic perturbations through absolute and relative quantification. Empowered by seminal methodologies and increasing capacity for detection, identification, and characterization, the past decade has provided a significant increase in the number of suitable strategies for glycan and glycopeptide quantification. Mass-spectrometry-based strategies for glycomic quantitation have grown to include metabolic incorporation of stable isotopes, deposition of mass difference and mass defect isotopic labels, and isobaric chemical labeling, providing researchers with ample tools for accurate and robust quantitation. Beyond this, workflows have been designed to harness instrument capability for label-free quantification, and numerous software packages have been developed to facilitate reliable spectrum scoring. In this review, we present and highlight the most recent advances in chemical labeling and associated techniques for glycan and glycopeptide quantification.

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

Intact glycopeptide identification has long been known as a key and challenging barrier to the comprehensive and accurate understanding the role of glycosylation in an organism. Intact glycopeptide analysis is a blossoming field that has received increasing attention in recent years. MS-based strategies and relative software tools are major drivers that have greatly facilitated the analysis of intact glycopeptides, particularly intact N-glycopeptides. This article provides a systematic review of the intact glycopeptide-identification process using MS data generated in shotgun proteomic experiments, which typically focus on N-glycopeptide analysis. Particular attention is paid to the software tools that have been recently developed in the last decade for the interpretation and quality control of glycopeptide spectra acquired using different MS strategies. The review also provides information about the characteristics and applications of these software tools, discusses their advantages and disadvantages, and concludes with a discussion of outstanding tools.

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.

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.

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.

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.

Databases and Bioinformatic Tools for Glycobiology and Glycoproteomics

Glycosylation plays critical roles in various biological processes and is closely related to diseases. Deciphering the glycocode in diverse cells and tissues offers opportunities to develop new disease biomarkers and more effective recombinant therapeutics. In the past few decades, with the development of glycobiology, glycomics, and glycoproteomics technologies, a large amount of glycoscience data has been generated. Subsequently, a number of glycobiology databases covering glycan structure, the glycosylation sites, the protein scaffolds, and related glycogenes have been developed to store, analyze, and integrate these data. However, these databases and tools are not well known or widely used by the public, including clinicians and other researchers who are not in the field of glycobiology, but are interested in glycoproteins. In this study, the representative databases of glycan structure, glycoprotein, glycan-protein interactions, glycogenes, and the newly developed bioinformatic tools and integrated portal for glycoproteomics are reviewed. We hope this overview could assist readers in searching for information on glycoproteins of interest, and promote further clinical application of glycobiology.

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.

Multiplexed Comparative Analysis of Intact Glycopeptides Using Electron-Transfer Dissociation and Synchronous Precursor Selection Based Triple-Stage Mass Spectrometry

A recently developed synchronous precursor selection (SPS) mass spectrometry to the third (MS3) protocol enables more accurate multiplexed quantification of proteins/peptides using tandem mass tags (TMT) through comparison of reporter ion intensities at the MS3 level. However, challenges still exist for TMT-based simultaneous quantification and identification of intact glycopeptides due to inefficient peptide backbone fragmentation when using collision-induced dissociation (CID). To overcome this limitation, here we report an improved SPS/ETD workflow for TMT-based intact glycopeptide quantification and identification. The SPS/ETD approach was implemented on an Orbitrap Tribrid mass spectrometer and begins with selection of a parent ion in the MS scan, followed by tandem mass spectrometry (MS2) fragmentation by CID in the ion trap. Following MS2 fragmentation, SPS enables simultaneous isolation of the top 10 MS2 fragment ions for further higher energy collisional dissociation (HCD) fragmentation with the resulting MS3 fragments detected in an Orbitrap analyzer. Here, in addition to the standard SPS workflow, an electron-transfer dissociation (ETD) MS2 was performed and analyzed in the ion trap. The resultant ETD and CID spectra were used for the identification of the intact glycopeptides, while the quantitative comparison of site-specific glycans was achieved utilizing TMT reporter ions from HCD MS3 spectra. For intact glycopeptides, through systematic optimization and evaluation using a glycoprotein interference model, the SPS/ETD approach was demonstrated to offer improved accuracy, precision, and sensitivity compared to traditional data-dependent MS2 quantification, while maintaining the glycopeptide identification capability. Finally, this workflow was applied for the site-specific quantitative comparison of the glycoforms for two therapeutic enzymes (Cerezyme and VPRIV) and their different lots. The results demonstrate that this workflow is suitable for TMT-based intact glycopeptide characterization of glycoproteins.

New Energy Setup Strategy for Intact N-Glycopeptides Characterization Using Higher-Energy Collisional Dissociation

With the optional setting of multiple stepped collisional energies (NCEs), higher-energy collisional dissociation (HCD) as available on Orbitrap instruments is a widely adopted dissociation method for intact N-glycopeptides characterization, where peptide backbones and N-glycan moieties are selectively fragmented at high and low NCEs, respectively. Initially, a dependent setting of a central value plus minus a variation is available to the users to set up NCEs, and the combination of 30 ± 10% to give the energies 20%/30%/40% has been mostly adopted in the literature. With the recent availability of an independent NCEs setup, we found that the combination of 20%/30%/30% is better than 20%/30%/40%; in the analysis of complex intact N-glycopeptides enriched from gastric cancer tissues, total IDs with spectrum-level FDR ≤ 1%, site-specific IDs with site-determining fragment ions, and structure-specific IDs with structure-diagnostic fragment ions were increased by 42% (4,767 → 6,746), 57% (599 → 942), and 97% (1771 → 3495), respectively. This finding will benefit all the coming N-glycoproteomics studies using HCD as the dissociation method.

Novel methods in glycomics: a 2019 update

Introduction: Glycomics, which aims to define the glycome of a biological system to better assess the biological attributes of the glycans, has attracted increasing interest. However, the complexity and diversity of glycans present challenging barriers to glycome definition. Technological advances are major drivers in glycomics.Areas covered: This review summarizes the main methods and emphasizes the most recent advances in mass spectrometry-based methods regarding glycomics following the general workflow in glycomic analysis.Expert opinion: Recent mass spectrometry-based technological advances have significantly lowered the barriers in glycomics. The field of glycomics is moving toward both generic and precise analysis.

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 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.

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 .

Characterization of intact glycopeptides reveals the impact of culture media on site-specific glycosylation of EPO-Fc fusion protein generated by CHO-GS cells

With the increasing demand to provide more detailed quality attributes, more sophisticated glycan analysis tools are highly desirable for biopharmaceutical manufacturing. Here, we performed an intact glycopeptide analysis method to simultaneously analyze the site-specific N- and O-glycan profiles of the recombinant erythropoietin Fc (EPO-Fc) protein secreted from a Chinese hamster ovary glutamine synthetase stable cell line and compared the effects of two commercial culture media, EX-CELL (EX) and immediate advantage (IA) media, on the glycosylation profile of the target protein. EPO-Fc, containing the Fc region of immunoglobulin G1 (IgG1) fused to EPO, was harvested at Day 5 and 8 of a batch cell culture process followed by purification and N- and O-glycopeptide profiling. A mixed anion exchange chromatographic column was implemented to capture and enrich N-linked glycopeptides. Using intact glycopeptide characterization, the EPO-Fc was observed to maintain their individual EPO and Fc N-glycan characteristics in which the EPO region presented bi-, tri-, and tetra-branched N-glycan structures, while the Fc N-glycan displayed mostly biantennary glycans. EPO-Fc protein generated in EX medium produced more complex tetra-antennary N-glycans at each of the three EPO N-sites while IA medium resulted in a greater fraction of bi- and tri-antennary N-glycans at these same sites. Interestingly, the sialylation content decreased from sites 1-4 in both media while the fucosylation progressively increased with a maximum at the final IgG Fc site. Moreover, we observed that low amounts of Neu5Gc were detected and the content increased at the later sampling time in both EX and IA media. For O-glycopeptides, both media produced predominantly three structures, N1F1F0SOG0, N1H1F0S1G0, and N1H1F0S2G0, with lesser amounts of other structures. This intact glycopeptide method can decipher site-specific glycosylation profile and provide a more detailed characterization of N- and O-glycans present for enhanced understanding of the key product quality attributes such as media on recombinant proteins of biotechnology interest.

An Improved Approach for N-Linked Glycan Structure Identification from HCD MS/MS Spectra

Glycosylation is a frequently observed post-translational modification on proteins. Currently, tandem mass spectrometry (MS/MS) serves as an efficient analytical technique for characterizing structures of oligosaccharides. However, developing effective computational approaches for identifying glycan structures from mass spectra is still a great challenge in glycoproteomics research. In this study, we proposed an approach for matching the input spectra with glycan structures acquired from a glycan structure database by incorporating a de novo sequencing assisted ranking scheme. The proposed approach is implemented as a software tool, GlycoNovoDB, for automated glycan structure identification from HCD MS/MS of glycopeptides. Experimental results showed that GlycoNovoDB can identify glycans effectively and has better performance than our previously proposed de novo sequencing algorithm as well as another software GlycoMaster DB.

N-Linked Glycopeptide Identification Based on Open Mass Spectral Library Search

Confident characterization of intact glycopeptides is a challenging task in mass spectrometry-based glycoproteomics due to microheterogeneity of glycosylation, complexity of glycans, and insufficient fragmentation of peptide bones. Open mass spectral library search is a promising computational approach to peptide identification, but its potential in the identification of glycopeptides has not been fully explored. Here we present pMatchGlyco, a new spectral library search tool for intact N-linked glycopeptide identification using high-energy collisional dissociation (HCD) tandem mass spectrometry (MS/MS) data. In pMatchGlyco, (1) MS/MS spectra of deglycopeptides are used to create spectral library, (2) MS/MS spectra of glycopeptides are matched to the spectra in library in an open (precursor tolerant) manner and the glycans are inferred, and (3) a false discovery rate is estimated for top-scored matches above a threshold. The efficiency and reliability of pMatchGlyco were demonstrated on a data set of mixture sample of six standard glycoproteins and a complex glycoprotein data set generated from human cancer cell line OVCAR3.

Enhanced Quantitative LC-MS/MS Analysis of N-linked Glycans Derived from Glycoproteins Using Sodium Deoxycholate Detergent

Protein glycosylation is a common protein post-translational modification (PTM) in living organisms and has been shown to associate with multiple diseases, and thus may potentially be a biomarker of such diseases. Efficient protein/glycoprotein extraction is a crucial step in the preparation of N-glycans derived from glycoproteins prior to LC-MS analysis. Convenient, efficient and unbiased sample preparation protocols are needed. Herein, we evaluated the use of sodium deoxycholate (SDC) acidic labile detergent to release N-glycans of glycoproteins derived from biological samples such as cancer cell lines. Compared to the filter-aided sample preparation approach, the sodium deoxycholate (SDC) assisted approach was determined to be more efficient and unbiased. SDC removal was determined to be more efficient when using acidic precipitation rather than ethyl acetate phase transfer. Efficient extraction of proteins/glycoproteins from biological samples was achieved by combining SDC lysis buffer and beads beating cell disruption. This was suggested by a significant overall increase in the intensities of N-glycans released from cancer cell lines. Additionally, the use of SDC approach was also shown to be more reproducible than those methods that do not use SDC.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2013-2014

This review is the eighth 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 2014. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, and 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. Much of this material is 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. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 37:353-491, 2018.

Status Report on the High-Throughput Characterization of Complex Intact O-Glycopeptide Mixtures

A very complex mixture of intact, human N- and O-glycopeptides, enriched from the tryptic digest of urinary proteins of three healthy donors using a two-step lectin affinity enrichment, was analyzed by LC-MS/MS, leading to approximately 45,000 glycopeptide EThcD spectra. Two search engines, Byonic and Protein Prospector, were used for the interpretation of the data, and N- and O-linked glycopeptides were assigned from separate searches. The identification rate was very low in all searches, even when results were combined. Thus, we investigated the reasons why was it so, to help to improve the identification success rate. Focusing on O-linked glycopeptides, we noticed that in EThcD, larger glycan oxonium ions better survive the activation than those in HCD. These fragments, combined with reducing terminal Y ions, provide important information about the glycan(s) present, so we investigated whether filtering the peaklists for glycan oxonium ions indicating the presence of a tetra- or hexasaccharide structure would help to reveal all molecules containing such glycans. Our study showed that intact glycans frequently do not survive even mild supplemental activation, meaning one cannot rely on these oxonium ions exclusively. We found that ETD efficiency is still a limiting factor, and for highly glycosylated peptides, the only information revealed in EThcD was related to the glycan structures. The limited overlap of results delivered by the two search engines draws attention to the fact that automated data interpretation of O-linked glycopeptides is not even close to being solved. Graphical abstract ᅟ.

Recent advances in mass spectrometry-based glycoproteomics

Protein glycosylation plays fundamental roles in many biological processes as one of the most common, and the most complex, posttranslational modification. Alterations in glycosylation profile are now known to be associated with many diseases. As a result, the discovery and detailed characterization of glycoprotein disease biomarkers is a primary interest of biomedical research. Advances in mass spectrometry (MS)-based glycoproteomics and glycomics are increasingly enabling qualitative and quantitative approaches for site-specific structural analysis of protein glycosylation. While the complexity presented by glycan heterogeneity and the wide dynamic range of clinically relevant samples like plasma, serum, cerebrospinal fluid, and tissue make comprehensive analyses of the glycoproteome a challenging task, the ongoing efforts into the development of glycoprotein enrichment, enzymatic digestion, and separation strategies combined with novel quantitative MS methodologies have greatly improved analytical sensitivity, specificity, and throughput. This review summarizes current MS-based glycoproteomics approaches and highlights recent advances in its application to cancer biomarker and neurodegenerative disease research.

An approach for N-linked glycan identification from MS/MS spectra by target-decoy strategy

Glycan structure determination serves as an essential step for the thorough investigation of the structure and function of protein. Currently, appropriate sample preparation followed by tandem mass spectrometry has emerged as the dominant technique for the characterization of glycans and glycopeptides. Although extensive efforts have been made to the development of computational approaches for the automated interpretation of glycopeptide spectra, the previously appeared methods lack a reasonable quality control strategy for the statistical validation of reported results. In this manuscript, we introduced a novel method that constructed a decoy glycan database based on the glycan structures in the target database, and searched the experimental spectra against both the target and decoy databases to find the best matched glycans. Specifically, a two-layer scoring scheme for calculating a normalized matching score is applied in the search procedure which enables the unbiased ranking of the matched glycans. Experimental analysis showed that our proposed method can report more structures with high confidence compared with previous approaches.

Large-scale intact glycopeptide identification by Mascot database search

Workflows capable of determining glycopeptides in large-scale are missing in the field of glycoproteomics. We present an approach for automated annotation of intact glycopeptide mass spectra. The steps in adopting the Mascot search engine for intact glycopeptide analysis included: (i) assigning one letter codes for monosaccharides, (ii) linearizing glycan sequences and (iii) preparing custom glycoprotein databases. Automated annotation of both N- and O-linked glycopeptides was proven using standard glycoproteins. In a large-scale study, a total of 257 glycoproteins containing 970 unique glycosylation sites and 3447 non-redundant N-linked glycopeptide variants were identified in 24 serum samples. Thus, a single tool was developed that collectively allows the (i) elucidation of N- and O-linked glycopeptide spectra, (ii) matching glycopeptides to known protein sequences, and (iii) high-throughput, batch-wise analysis of large-scale glycoproteomics data sets.

Intact glycopeptide characterization using mass spectrometry

Glycosylation is one of the most prominent and extensively studied protein post-translational modifications. However, traditional proteomic studies at the peptide level (bottom-up) rarely characterize intact glycopeptides (glycosylated peptides without removing glycans), so no glycoprotein heterogeneity information is retained. Intact glycopeptide characterization, on the other hand, provides opportunities to simultaneously elucidate the glycan structure and the glycosylation site needed to reveal the actual biological function of protein glycosylation. Recently, significant improvements have been made in the characterization of intact glycopeptides, ranging from enrichment and separation, mass spectroscopy (MS) detection, to bioinformatics analysis. In this review, we recapitulated currently available intact glycopeptide characterization methods with respect to their advantages and limitations as well as their potential applications.

A Comprehensive, Open-source Platform for Mass Spectrometry-based Glycoproteomics Data Analysis

Glycosylation is among the most abundant and diverse protein post-translational modifications (PTMs) identified to date. The structural analysis of this PTM is challenging because of the diverse monosaccharides which are not conserved among organisms, the branched nature of glycans, their isomeric structures, and heterogeneity in the glycan distribution at a given site. Glycoproteomics experiments have adopted the traditional high-throughput LC-MSⁿ proteomics workflow to analyze site-specific glycosylation. However, comprehensive computational platforms for data analyses are scarce. To address this limitation, we present a comprehensive, open-source, modular software for glycoproteomics data analysis called GlycoPAT (GlycoProteomics Analysis Toolbox; freely available from www.VirtualGlycome.org/glycopat). The program includes three major advances: (1) "SmallGlyPep," a minimal linear representation of glycopeptides for MSⁿ data analysis. This format allows facile serial fragmentation of both the peptide backbone and PTM at one or more locations. (2) A novel scoring scheme based on calculation of the "Ensemble Score (ES)," a measure that scores and rank-orders MS/MS spectrum for N- and O-linked glycopeptides using cross-correlation and probability based analyses. (3) A false discovery rate (FDR) calculation scheme where decoy glycopeptides are created by simultaneously scrambling the amino acid sequence and by introducing artificial monosaccharides by perturbing the original sugar mass. Parallel computing facilities and user-friendly GUIs (Graphical User Interfaces) are also provided. GlycoPAT is used to catalogue site-specific glycosylation on simple glycoproteins, standard protein mixtures and human plasma cryoprecipitate samples in three common MS/MS fragmentation modes: CID, HCD and ETD. It is also used to identify 960 unique glycopeptides in cell lysates from prostate cancer cells. The results show that the simultaneous consideration of peptide and glycan fragmentation is necessary for high quality MSⁿ spectrum annotation in CID and HCD fragmentation modes. Additionally, they confirm the suitability of GlycoPAT to analyze shotgun glycoproteomics data.

pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification

The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with ¹⁵N/¹³C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.

Protein glycosylation is a heterogeneous post-translational modification that generates greater proteomic diversity that is difficult to analyze. Here the authors describe pGlyco 2.0, a workflow for the precise one step identification of intact N-glycopeptides at the proteome scale.

Two New Tools for Glycopeptide Analysis Researchers: A Glycopeptide Decoy Generator and a Large Data Set of Assigned CID Spectra of Glycopeptides

The glycopeptide analysis field is tightly constrained by a lack of effective tools that translate mass spectrometry data into meaningful chemical information, and perhaps the most challenging aspect of building effective glycopeptide analysis software is designing an accurate scoring algorithm for MS/MS data. We provide the glycoproteomics community with two tools to address this challenge. The first tool, a curated set of 100 expert-assigned CID spectra of glycopeptides, contains a diverse set of spectra from a variety of glycan types; the second tool, Glycopeptide Decoy Generator, is a new software application that generates glycopeptide decoys de novo. We developed these tools so that emerging methods of assigning glycopeptides' CID spectra could be rigorously tested. Software developers or those interested in developing skills in expert (manual) analysis can use these tools to facilitate their work. We demonstrate the tools' utility in assessing the quality of one particular glycopeptide software package, GlycoPep Grader, which assigns glycopeptides to CID spectra. We first acquired the set of 100 expert assigned CID spectra; then, we used the Decoy Generator (described herein) to generate 20 decoys per target glycopeptide. The assigned spectra and decoys were used to test the accuracy of GlycoPep Grader's scoring algorithm; new strengths and weaknesses were identified in the algorithm using this approach. Both newly developed tools are freely available. The software can be downloaded at http://glycopro.chem.ku.edu/GPJ.jar.

Algorithms and design strategies towards automated glycoproteomics analysis

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.

Multi-Lectin Affinity Chromatography for Separation, Identification, and Quantitation of Intact Protein Glycoforms in Complex Biological Mixtures

Protein glycosylation is considered to be one of the most abundant post-translational modifications and is recognized for playing key roles in cellular functions. Aberrant N-linked glycosylation has been associated with several human diseases and has prompted the development and constant improvement of analytical tools to separate, characterize, and quantify glycoproteins in complex mixtures extracted from various biological samples (such as blood and tissue). Lectins, or carbohydrate-binding proteins, have been used as valuable tools for enriching for glycoproteins and selecting for specific types of glycosylation. Herein a method using multidimensional intact protein fractionation and LC-MS/MS analysis is described. Immunodepletion is used to remove highly abundant proteins from human plasma, followed by glycoform separation using multi-lectin affinity chromatography, in which specific lectins are chosen to capture and elute specific types of glycosylation. Reversed-phase chromatography prior to digestion is used for further fractionation, allowing for an increased number of protein identifications of moderate- to low-abundant proteins detectable in plasma. This method also incorporates isotopic labeling during alkylation for relative quantitation between two samples (such as a case and control). A bottom-up, tandem mass spectrometry-based proteomics approach is used for protein identification and quantitation, and allows for screening glycoform-specific changes across hundreds of plasma proteins.

Glycans and glycoproteins as specific biomarkers for cancer

Protein glycosylation and other post-translational modifications are involved in potentially all aspects of human growth and development. Defective glycosylation has adverse effects on human physiological conditions and accompanies many chronic and infectious diseases. Altered glycosylation can occur at the onset and/or during tumor progression. Identifying these changes at early disease stages may aid in making decisions regarding treatments, as early intervention can greatly enhance survival. This review highlights some of the efforts being made to identify N- and O-glycosylation profile shifts in cancer using mass spectrometry. The analysis of single or panels of potential glycoprotein cancer markers are covered. Other emerging technologies such as global glycan release and site-specific glycosylation analysis and quantitation are also discussed. Graphical Abstract Steps involved in the biomarker discovery.

Glycoproteins Enrichment and LC-MS/MS Glycoproteomics in Central Nervous System Applications

Proteins and glycoproteins play important biological roles in central nervous systems (CNS). Qualitative and quantitative evaluation of proteins and glycoproteins expression in CNS is critical to reveal the inherent biomolecular mechanism of CNS diseases. This chapter describes proteomic and glycoproteomic approaches based on liquid chromatography/tandem mass spectrometry (LC-MS or LC-MS/MS) for the qualitative and quantitative assessment of proteins and glycoproteins expressed in CNS. Proteins and glycoproteins, extracted by a mass spectrometry friendly surfactant from CNS samples, were subjected to enzymatic (tryptic) digestion and three down-stream analyses: (1) a nano LC system coupled with a high-resolution MS instrument to achieve qualitative proteomic profile, (2) a nano LC system combined with a triple quadrupole MS to quantify identified proteins, and (3) glycoprotein enrichment prior to LC-MS/MS analysis. Enrichment techniques can be applied to improve coverage of low abundant glycopeptides/glycoproteins. An example described in this chapter is hydrophilic interaction liquid chromatographic (HILIC) enrichment to capture glycopeptides, allowing efficient removal of peptides. The combination of three LC-MS/MS-based approaches is capable of the investigation of large-scale proteins and glycoproteins from CNS with an in-depth coverage, thus offering a full view of proteins and glycoproteins changes in CNS.