Recent Advances in Analytical Approaches for Glycan and Glycopeptide Quantitation

Tools and techniques for quantitative glycoproteomic analysis

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

Recent Advances in Labeling-Based Quantitative Glycomics: From High-Throughput Quantification to Structural Elucidation

Glycosylation, a crucial posttranslational modification (PTM), plays important roles in numerous biological processes and is linked to various diseases. Despite its significance, the structural complexity and diversity of glycans present significant challenges for mass spectrometry (MS)-based quantitative analysis. This review aims to provide an in-depth overview of recent advancements in labeling strategies for N-glycomics and O-glycomics, with a specific focus on enhancing the sensitivity, specificity, and throughput of MS analyses. We categorize these advancements into three major areas: (1) the development of isotopic/isobaric labeling techniques that significantly improve multiplexing capacity and throughput for glycan quantification; (2) novel methods that aid in the structural elucidation of complex glycans, particularly sialylated and fucosylated glycans; and (3) labeling techniques that enhance detection ionization efficiency, separation, and sensitivity for matrix-assisted laser desorption/ionization (MALDI)-MS and capillary electrophoresis (CE)-based glycan analysis. In addition, we highlight emerging trends in single-cell glycomics and bioinformatics tools that have the potential to revolutionize glycan quantification. These developments not only expand our understanding of glycan structures and functions but also open new avenues for biomarker discovery and therapeutic applications. Through detailed discussions of methodological advancements, this review underscores the critical role of derivatization methods in advancing glycan identification and quantification.

Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers

INTRODUCTION

The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.

AREAS COVERED

This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.

EXPERT OPINION

The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.

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

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

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

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

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

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

High-throughput N-glycan analysis in aging and inflammaging: State of the art and future directions

As the global population ages at an unprecedented rate, the prevalence of age-related diseases is increasing, making inflammaging - a phenomenon characterized by a chronic, low-grade inflammatory state that follows aging - a significant concern. Understanding the mechanisms of inflammaging and its impact on health is critical for developing strategies to improve the quality of life and manage health in the aging population. Despite their crucial roles in various biological processes, including immune response modulation, N-glycans, oligosaccharides covalently attached to many proteins, are often overlooked in clinical and research studies. This repeated oversight is largely due to their inherent complexity and the complexity of the analysis methods. High-throughput N-glycan analysis has emerged as a transformative tool in N-glycosylation research, enabling cost- and time-effective, detailed, and large-scale examination of N-glycan profiles. This paper is the first to explore the application of high-throughput N-glycomics techniques to investigate the complex interplay between N-glycosylation and the immune system in aging. Technological advancements have significantly improved Nglycan detection and characterization, providing insights into age-related changes in Nglycosylation. Key findings highlight consistent shifts in immunoglobulin G (IgG) and plasma/serum glycoprotein glycosylation with age, with a pronounced rise in agalactosylated structures bound to IgG that also affect the composition of the total plasma N-glycome. These N-glycan modifications seem to be strongly associated with inflammaging and have been identified as valuable biomarkers for biological age, predictors of disease risk, and proxy biomarkers for monitoring intervention efficacy at the individual level. Despite current challenges related to data complexity and methodological limitations, ongoing technological innovations and interdisciplinary research are expected tofurther advance our knowledge of glycan biology, improve diagnostic and therapeutic strategies, and promote healthier aging. The integration of glycomics with other omics approaches holds promise for a more comprehensive understanding of the aging immune system, paving the way for personalized medicine and targeted interventions to mitigate inflammaging. In conclusion, this paper underscores the transformative impact of high-throughput Nglycan analysis in aging and inflammaging.

A complement C4-derived glycopeptide is a biomarker for PMM2-CDG

BACKGROUNDDiagnosis of PMM2-CDG, the most common congenital disorder of glycosylation (CDG), relies on measuring carbohydrate-deficient transferrin (CDT) and genetic testing. CDT tests have false negatives and may normalize with age. Site-specific changes in protein N-glycosylation have not been reported in sera in PMM2-CDG.METHODSUsing multistep mass spectrometry-based N-glycoproteomics, we analyzed sera from 72 individuals to discover and validate glycopeptide alterations. We performed comprehensive tandem mass tag-based discovery experiments in well-characterized patients and controls. Next, we developed a method for rapid profiling of additional samples. Finally, targeted mass spectrometry was used for validation in an independent set of samples in a blinded fashion.RESULTSOf the 3,342 N-glycopeptides identified, patients exhibited decrease in complex-type N-glycans and increase in truncated, mannose-rich, and hybrid species. We identified a glycopeptide from complement C4 carrying the glycan Man5GlcNAc2, which was not detected in controls, in 5 patients with normal CDT results, including 1 after liver transplant and 2 with a known genetic variant associated with mild disease, indicating greater sensitivity than CDT. It was detected by targeted analysis in 2 individuals with variants of uncertain significance in PMM2.CONCLUSIONComplement C4-derived Man5GlcNAc2 glycopeptide could be a biomarker for accurate diagnosis and therapeutic monitoring of patients with PMM2-CDG and other CDGs.FUNDINGU54NS115198 (Frontiers in Congenital Disorders of Glycosylation: NINDS; NCATS; Eunice Kennedy Shriver NICHD; Rare Disorders Consortium Disease Network); K08NS118119 (NINDS); Minnesota Partnership for Biotechnology and Medical Genomics; Rocket Fund; R01DK099551 (NIDDK); Mayo Clinic DERIVE Office; Mayo Clinic Center for Biomedical Discovery; IA/CRC/20/1/600002 (Center for Rare Disease Diagnosis, Research and Training; DBT/Wellcome Trust India Alliance).

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Development and application of GlycanDIA workflow for glycomic analysis

Glycans modify protein, lipid, and even RNA molecules to form the regulatory outer coat on cells called the glycocalyx. The changes in glycosylation have been linked to the initiation and progression of many diseases. Thus, while the significance of glycosylation is well established, a lack of accessible methods to characterize glycans has hindered the ability to understand their biological functions. Mass spectrometry (MS)-based methods have generally been at the core of most glycan profiling efforts; however, modern data-independent acquisition (DIA), which could increase sensitivity and simplify workflows, has not been benchmarked for analyzing glycans. Herein, we developed a DIA-based glycomic workflow, termed GlycanDIA, to identify and quantify glycans with high sensitivity and accuracy. The GlycanDIA workflow combined higher energy collisional dissociation (HCD)-MS/MS and staggered windows for glycomic analysis, which facilitates the sensitivity in identification and the accuracy in quantification compared to conventional data-dependent acquisition (DDA)-based glycomics. To facilitate its use, we also developed a generic search engine, GlycanDIA Finder, incorporating an iterative decoy searching for confident glycan identification and quantification from DIA data. The results showed that GlycanDIA can distinguish glycan composition and isomers from N-glycans, O-glycans, and human milk oligosaccharides (HMOs), while it also reveals information on low-abundant modified glycans. With the improved sensitivity, we performed experiments to profile N-glycans from RNA samples, which have been underrepresented due to their low abundance. Using this integrative workflow to unravel the N-glycan profile in cellular and tissue glycoRNA samples, we found that RNA-glycans have specific forms as compared to protein-glycans and are also tissue-specific differences, suggesting distinct functions in biological processes. Overall, GlycanDIA can provide comprehensive information for glycan identification and quantification, enabling researchers to obtain in-depth and refined details on the biological roles of glycosylation.

12-Plex Isobaric Multiplex Labeling Reagents for Carbonyl-Containing Compound (SUGAR) Tag-Enabled High-Throughput Quantitative Glycomics

Glycans, which are ubiquitously distributed on most proteins and cell surfaces, are a class of important biomolecules playing crucial roles in various biological processes such as molecular recognition and cellular communication. Modern mass spectrometry (MS) coupled with novel chemical probe labeling strategies has greatly advanced analysis of glycans. However, the requirement of high-throughput and robust quantitative analysis still calls for the development of more advanced tools. Recently, we devised isobaric multiplex reagents for carbonyl-containing compound (SUGAR) tags for 4-plex N-glycan analysis. To further improve the throughput, we utilized the mass-defect strategy and expanded the multiplexing capacity to 12 channels without changing the chemical structure of the SUGAR tag, achieving a threefold enhancement in throughput compared with the original design and managing to perform high-throughput N-glycan analysis in a single LC - MS/MS injection. Herein, we present detailed methods for the synthesis of 12-plex SUGAR isobaric tags, the procedure to release and label the N-glycans from proteins, and the analysis by high-resolution LC-MS/MS, as well as data processing to achieve multiplexed quantitative glycomics.

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

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

Targeted Glycoproteomics Analysis Using MRM/PRM Approaches

MS-target analyses are frequently utilized to analyze and validate structural changes of biomolecules across diverse fields of study such as proteomics, glycoproteomics, glycomics, lipidomics, and metabolomics. Targeted studies are commonly conducted using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) techniques. A reliable glycoproteomics analysis in intricate biological matrices is possible with these techniques, which streamline the analytical workflow, lower background interference, and enhance selectivity and specificity.

Plasma/Serum Proteomics based on Mass Spectrometry

Human blood is a window of physiology and disease. Examination of biomarkers in blood is a common clinical procedure, which can be informative in diagnosis and prognosis of diseases, and in evaluating treatment effectiveness. There is still a huge demand on new blood biomarkers and assays for precision medicine nowadays, therefore plasma/serum proteomics has attracted increasing attention in recent years. How to effectively proceed with the biomarker discovery and clinical diagnostic assay development is a question raised to researchers who are interested in this area. In this review, we comprehensively introduce the background and advancement of technologies for blood proteomics, with a focus on mass spectrometry (MS). Analyzing existing blood biomarkers and newly-built diagnostic assays based on MS can shed light on developing new biomarkers and analytical methods. We summarize various protein analytes in plasma/serum which include total proteome, protein post-translational modifications, and extracellular vesicles, focusing on their corresponding sample preparation methods for MS analysis. We propose screening multiple protein analytes in the same set of blood samples in order to increase success rate for biomarker discovery. We also review the trends of MS techniques for blood tests including sample preparation automation, and further provide our perspectives on their future directions.

6-Plex mdSUGAR Isobaric-Labeling Guide Fingerprint Embedding for Glycomics Analysis

Glycans are vital biomolecules with diverse functions in biological processes. Mass spectrometry (MS) has become the most widely employed technology for glycomics studies. However, in the traditional data-dependent acquisition mode, only a subset of the abundant ions during MS1 scans are isolated and fragmented in subsequent MS2 events, which reduces reproducibility and prevents the measurement of low-abundance glycan species. Here, we reported a new method termed 6-plex mdSUGAR isobaric-labeling guide fingerprint embedding (MAGNI), to achieve multiplexed, quantitative, and targeted glycan analysis. The glycan peak signature was embedded by a triplicate-labeling strategy with a 6-plex mdSUGAR tag, and using ultrahigh-resolution mass spectrometers, the low-abundance glycans that carry the mass fingerprints can be recognized on the MS1 spectra through an in-house developed software tool, MAGNIFinder. These embedded unique fingerprints can guide the selection and fragmentation of targeted precursor ions and further provide rich information on glycan structures. Quantitative analysis of two standard glycoproteins demonstrated the accuracy and precision of MAGNI. Using this approach, we identified 304 N-glycans in two ovarian cancer cell lines. Among them, 65 unique N-glycans were found differentially expressed, which indicates a distinct glycosylation pattern for each cell line. Remarkably, 31 N-glycans can be quantified in only 1 × 103 cells, demonstrating the high sensitivity of our method. Taken together, our MAGNI method offers a useful tool for low-abundance N-glycan characterization and is capable of determining small quantitative differences in N-glycan profiling. Therefore, it will be beneficial to the field of glycobiology and will expand our understanding of glycosylation.

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

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

Glycan/Protein-Stable Isotope Labeling in Cell Culture for Enabling Concurrent Quantitative Glycomics/Proteomics/Glycoproteomics

The complexity and heterogeneity of protein glycosylation present an analytical challenge to the studies of characterization and quantitation. Various LC-MS-based quantitation strategies have emerged in recent decades. Metabolic stable isotope labeling has been developed to enhance the accurate LC/MS-based quantitation between different cell lines. Stable isotope labeling by amino acids in a cell culture (SILAC) is the most widely used metabolic labeling method in proteomic analysis. However, it can only label the peptide backbone and is thus limited in glycomic studies. Here, we present a metabolic isotope labeling strategy, named GlyProSILC (Glycan Protein Stable Isotope Labeling in Cell Culture), that can label both the glycan motif and peptide backbone from the same batch of cells. It was performed by feeding cells with a heavy medium containing amide-15N-glutamine, 13C6-arginine (Arg6), and 13C6-15N2-lysine (Lys8). No significant change of cell line metabolism after GlyProSILC labeling was observed based on transcriptomic, glycomic, and proteomic data. The labeling conditions, labeling efficiency, and quantitation accuracy were investigated. After quantitation correction, we simultaneously quantified 62 N-glycans, 574 proteins, and 344 glycopeptides using the same batch of mixed 231BR/231 cell lines. So far, GlyProSILC provides an accurate and effective quantitation approach for glycomics, proteomics, and glycoproteomics in a cell culture system.

Characterization of site-specific N-glycosylation signatures of isolated uromodulin from human urine

Uromodulin (Umod, Tamm-Horsfall protein) is the most abundant urinary N-glycoprotein produced exclusively by the kidney. It can form filaments to antagonize the adhesion of uropathogens. However, the site-specific N-glycosylation signatures of Umod in healthy individuals and patients with IgA nephropathy (IgAN) remain poorly understood due to the lack of suitable isolation and analytical methods. In this study, we first presented a simple and fast method based on diatomaceous earth adsorption to isolate Umod. These isolated glycoproteins were digested by trypsin and/or Glu-C. Intact N-glycopeptides with or without HILIC enrichment were analyzed using our developed EThcD-sceHCD-MS/MS. Based on the optimized workflow, we identified a total of 780 unique intact N-glycopeptides (7 N-glycosites and 152 N-glycan compositions) from healthy individuals. As anticipated, these glycosites exhibited glycoform heterogeneity. Almost all N-glycosites were modified completely by the complex type, except for one N-glycosite (N275), which was nearly entirely occupied by the high-mannose type for mediating Umod's antiadhesive activity. Then, we compared the N-glycosylation of Umod between healthy controls (n = 9) and IgAN patients (n = 9). The N-glycosylation of Umod in IgAN patients will drastically decrease and be lost. Finally, we profiled the most comprehensive site-specific N-glycosylation map of Umod and revealed its alterations in IgAN patients. Our method provides a high-throughput workflow for characterizing the N-glycosylation of Umod, which can aid in understanding its roles in physiology and pathology, as well as serving as a potential diagnostic tool for evolution of renal tubular function.

Coupling Isotopic Shifts with Collision Cross-Section Measurements for Carbohydrate Characterization in High-Resolution Ion Mobility Separations

Herein, we introduce a two-dimensional strategy to better characterize carbohydrate isomers. In a single experiment, we can derive cyclic ion mobility-mass spectrometry (cIMS-MS)-based collision cross-section (CCS) values in conjunction with measuring isotopic shifts through the relative arrival times of light and heavy isotopologues. These isotopic shifts were introduced by permethylating carbohydrates with either light, CH3, or heavy, CD3, labels at every available hydroxyl group to generate a light/heavy pair of isotopologues for every individual species analyzed. We observed that our calculated CCS values, which were exclusively measured for the light isotopologues, were orthogonal to our measured isotopic shifts (i.e., relative arrival time values between heavy and light permethylated isotopologues). Our permethylation-induced isotopic shifts scaled well with increasing molecular weight, up to ∼m/z 1300, expanding the analysis of isotopic shifts to molecules 3-4 times as large as those previously studied. Our presented use of coupling CCS values with the measurement of isotopic shifts in a single cIMS-MS experiment is a proof-of-concept demonstration that our two-dimensional approach can improve the characterization of challenging isomeric carbohydrates. We envision that our presented 2D approach will have broad utility for varying molecular classes as well as being amenable to many forms of derivatization.

The role of clinical glyco(proteo)mics in precision medicine

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.

Surface Glycan Profiling of Extracellular Vesicles by Lectin Microarray and Glycoengineering for Control of Cellular Interactions

BACKGROUND

Extracellular vesicles (EVs) are a group of cell-derived membrane vesicles that carry a variety of cargo such as protein, nucleic acids, and lipids, and are secreted by almost all cell types. Functionally, EVs play important roles in physiological and pathological processes such as immune responses and tumor growth through intercellular communication by transferring this molecular information between cells. Therefore, they have potential versatile clinical applications as disease biomarkers and drug delivery carriers.

PROBLEM

Notably, subpopulations of EVs exhibit distinct characteristics depending on their cell of origin, including the expression of surface glycans, which have been implicated in a variety of cellular processes such as field cancerization, cell recognition, and signal transduction. However, these are features have not been fully exploited because of the difficulty in analyzing these proteins.

APPROACH

In this paper, we summarize the advancements in glycoengineering and high-performance lectin microarray for high-throughput analysis of EV glycans to generate an index of heterogeneity to identify disease biomarkers, and describe how understanding the function of EVs in disease can enhance their potential application in the clinic.

Innovative Metrics for Reporting and Comparing the Glycan Structural Profile in Biotherapeutics

Glycosylation is a critical quality attribute in biotherapeutics, impacting properties such as protein stability, solubility, clearance rate, efficacy, immunogenicity, and safety. Due to the heterogenic and complex nature of protein glycosylation, comprehensive characterization is demanding. Moreover, the lack of standardized metrics for evaluating and comparing glycosylation profiles hinders comparability studies and the establishment of manufacturing control strategies. To address both challenges, we propose a standardized approach based on novel metrics for a comprehensive glycosylation fingerprint which greatly facilitates the reporting and objective comparison of glycosylation profiles. The analytical workflow is based on a liquid chromatography-mass spectrometry-based multi-attribute method. Based on the analytical data, a matrix of glycosylation-related quality attributes, both at site-specific and whole molecule level, are computed, which provide metrics for a comprehensive product glycosylation fingerprint. Two case studies illustrate the applicability of the proposed indices as a standardized and versatile approach for reporting all dimensions of the glycosylation profile. The proposed approach further facilitates the assessments of risks associated with changes in the glycosylation profile that may affect efficacy, clearance, and immunogenicity.

Critical considerations in N-glycoproteomics

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

Recent advances in mass spectrometry analysis of neuropeptides

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

Applications and continued evolution of glycan imaging mass spectrometry

Glycosylation is an important posttranslational modifier of proteins and lipid conjugates critical for the stability and function of these macromolecules. Particularly important are N-linked glycans attached to asparagine residues in proteins. N-glycans have well-defined roles in protein folding, cellular trafficking and signal transduction, and alterations to them are implicated in a variety of diseases. However, the non-template driven biosynthesis of these N-glycans leads to significant structural diversity, making it challenging to identify the most biologically and clinically relevant species using conventional analyses. Advances in mass spectrometry instrumentation and data acquisition, as well as in enzymatic and chemical sample preparation strategies, have positioned mass spectrometry approaches as powerful analytical tools for the characterization of glycosylation in health and disease. Imaging mass spectrometry expands upon these strategies by capturing the spatial component of a glycan's distribution in-situ, lending additional insight into the organization and function of these molecules. Herein we review the ongoing evolution of glycan imaging mass spectrometry beginning with widely adopted tissue imaging approaches and expanding to other matrices and sample types with potential research and clinical implications. Adaptations of these techniques, along with their applications to various states of disease, are discussed. Collectively, glycan imaging mass spectrometry analyses broaden our understanding of the biological and clinical relevance of N-glycosylation to human disease.

Methods for quantification of glycopeptides by liquid separation and mass spectrometry

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

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

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

High-Throughput Quantitative Glycomics Enabled by 12-plex Isobaric Multiplex Labeling Reagents for Carbonyl-Containing Compound (SUGAR) Tags

Glycans, which are widely distributed on most proteins and cell surfaces, are a class of important biomolecules playing crucial roles in various biological processes such as immune response and cellular communication. Modern mass spectrometry (MS) coupled with novel chemical probes greatly facilitates routine analysis of glycans. However, the requirement of high-throughput analysis still calls for advanced tools to be developed. Recently, we devised isobaric multiplex reagents for carbonyl-containing compound (SUGAR) tags for 4-plex N-glycan analysis. To further improve the throughput, we utilized the subtle mass differences among different isotopologues and expanded the multiplexing capacity to 12 channels, a 3-fold throughput improvement for the original SUGAR tag design and achieved high-throughput N-glycan analysis in a single LC-MS/MS injection. We then applied 12-plex SUGAR tags to profile the N-glycans in four subtypes of human Immunoglobulin G (IgG) and to investigate the N-glycan changes in the endometrial cancer cells (ECC1) treated with Atovaquone, a quinone antimicrobial medication, and a dihydroorotate dehydrogenase (DHODH) inhibitor. Data are available via ProteomeXchange with the identifier PXD038501.

Higher Temperature Porous Graphitic Carbon Separations Differentially Impact Distinct Glycopeptide Classes

Mass spectrometry-based discovery glycoproteomics is highly dependent on the use of chromatography paradigms amenable to analyte retention and separation. When compared against established stationary phases such as reversed-phase and hydrophilic interaction liquid chromatography, reports utilizing porous graphitic carbon have detailed its numerous advantages. Recent efforts have highlighted the utility in porous graphitic carbon in high-throughput glycoproteomics, principally through enhanced profiling depth and liquid-phase resolution at higher column temperatures. However, increasing column temperature has been shown to impart disparaging effects in glycopeptide identification. Herein we further elucidate this trend, describing qualitative and semiquantitative effects of increased column temperature on glycopeptide identification rates, signal intensity, resolution, and spectral count linear response. Through analysis of enriched bovine and human glycopeptides, species with high mannose and sialylated glycans were shown to most significantly benefit and suffer from high column temperatures, respectively. These results provide insight as to how porous graphitic carbon separations may be appropriately leveraged for glycopeptide identification while raising concerns over quantitative and semiquantitative label-free comparisons as the temperature changes. RAW MS glycoproteomic data are available via ProteomeXchange with identifier PXD034354.

Mass Spectrometry-Based Glycoproteomic Workflows for Cancer Biomarker Discovery

Glycosylation has a clear role in cancer initiation and progression, with numerous studies identifying distinct glycan features or specific glycoproteoforms associated with cancer. Common findings include that aggressive cancers tend to have higher expression levels of enzymes that regulate glycosylation as well as glycoproteins with greater levels of complexity, increased branching, and enhanced chain length1. Research in cancer glycoproteomics over the last 50-plus years has mainly focused on technology development used to observe global changes in glycosylation. Efforts have also been made to connect glycans to their protein carriers as well as to delineate the role of these modifications in intracellular signaling and subsequent cell function. This review discusses currently available techniques utilizing mass spectrometry-based technologies used to study glycosylation and highlights areas for future advancement.

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

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

High sensitivity glycomics in biomedicine

Many analytical challenges in biomedicine arise from the generally high heterogeneity and complexity of glycan- and glycoconjugate-containing samples, which are often only available in minute amounts. Therefore, highly sensitive workflows and detection methods are required. In this review mass spectrometric workflows and detection methods are evaluated for glycans and glycoproteins. Furthermore, glycomic methodologies and innovations that are tailored for enzymatic treatments, chemical derivatization, purification, separation, and detection at high sensitivity are highlighted. The discussion is focused on the analysis of mammalian N-linked and GalNAc-type O-linked glycans.

Methods to improve quantitative glycoprotein coverage from bottom-up LC-MS data

Advances in mass spectrometry instrumentation, methods development, and bioinformatics have greatly improved the ease and accuracy of site-specific, quantitative glycoproteomics analysis. Data-dependent acquisition is the most popular method for identification and quantification of glycopeptides; however, complete coverage of glycosylation site glycoforms remains elusive with this method. Targeted acquisition methods improve the precision and accuracy of quantification, but at the cost of throughput and discoverability. Data-independent acquisition (DIA) holds great promise for more complete and highly quantitative site-specific glycoproteomics analysis, while maintaining the ability to discover novel glycopeptides without prior knowledge. We review additional features that can be used to increase selectivity and coverage to the DIA workflow: retention time modeling, which would simplify the interpretation of complex tandem mass spectra, and ion mobility separation, which would maximize the sampling of all precursors at a giving chromatographic retention time. The instrumentation and bioinformatics to incorporate these features into glycoproteomics analysis exist. These improvements in quantitative, site-specific analysis will enable researchers to assess glycosylation similarity in related biological systems, answering new questions about the interplay between glycosylation state and biological function.

High-Throughput Glycomic Methods

Glycomics aims to identify the structure and function of the glycome, the complete set of oligosaccharides (glycans), produced in a given cell or organism, as well as to identify genes and other factors that govern glycosylation. This challenging endeavor requires highly robust, sensitive, and potentially automatable analytical technologies for the analysis of hundreds or thousands of glycomes in a timely manner (termed high-throughput glycomics). This review provides a historic overview as well as highlights recent developments and challenges of glycomic profiling by the most prominent high-throughput glycomic approaches, with N-glycosylation analysis as the focal point. It describes the current state-of-the-art regarding levels of characterization and most widely used technologies, selected applications of high-throughput glycomics in deciphering glycosylation process in healthy and disease states, as well as future perspectives.

On-tissue amidation of sialic acid with aniline for sensitive imaging of sialylated N-glycans from FFPE tissue sections via MALDI mass spectrometry

Spatial visualization of glycans within clinical tissue samples is critical for discovery of disease-relevant glycan dysregulations. Herein, we develop an on-tissue derivatization strategy for sensitive spatial visualization of N-glycans from formalin-fixed paraffin-embedded (FFPE) tissue sections, based on amidation of sialic acid residues with aniline. The sialylated N-glycans were stabilized and given enhanced signal intensity owing to selective capping of a phenyl group to the sialic acid residue after aniline labeling. Proof-of-concept experiments, including determinations of sialylglycopeptide and N-glycans enzymatically released from glycoproteins, were performed. Further, mass spectrometry (MS) imaging of N-glycans on human laryngeal cancer FFPE tissue sections was conducted via matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), based on our strategy for on-tissue amidation of sialylated N-glycans. We obtained higher sialylated N-glycan coverages for both the glycoproteins and cancer tissue samples, demonstrating that the detection sensitivity for sialylated N-glycans is notably improved by amidation derivatization. We also characterized N-glycan heterogeneity across the human laryngeal cancer tissue section, showing N-glycan dysregulation in the tumor region.

Recent advances and future perspectives on carbohydrate-based cancer vaccines and therapeutics

Carbohydrates are abundantly expressed on the surface of both eukaryotic and prokaryotic cells, often as post translational modifications of proteins. Glycoproteins are recognized by the immune system and can trigger both innate and humoral responses. This feature has been harnessed to generate vaccines against polysaccharide-encapsulated bacteria such as Streptococcus pneumoniae, Hemophilus influenzae type b and Neisseria meningitidis. In cancer, glycosylation plays a pivotal role in malignancy development and progression. Since glycans are specifically expressed on the surface of tumor cells, they have been targeted for the discovery of anticancer preventive and therapeutic treatments, such as vaccines and monoclonal antibodies. Despite the various efforts made over the last years, resulting in a series of clinical studies, attempts of vaccination with carbohydrate-based candidates have proven unsuccessful, primarily due to the immune tolerance often associated with these glycans. New strategies are thus deployed to enhance carbohydrate-based cancer vaccines. Moreover, lessons learned from glycan immunobiology paved the way to the development of new monoclonal antibodies specifically designed to recognize cancer-bound carbohydrates and induce tumor cell killing. Herein we provide an overview of the immunological principles behind the immune response towards glycans and glycoconjugates and the approaches exploited at both preclinical and clinical level to target cancer-associated glycans for the development of vaccines and therapeutic monoclonal antibodies. We also discuss gaps and opportunities to successfully advance glycan-directed cancer therapies, which could provide patients with innovative and effective treatments.

Current strategies for characterization of mucin-domain glycoproteins

Glycosylation, and especially O-linked glycosylation, remains a critical blind spot in the understanding of post-translational modifications. Due to their nature as proteins defined by a large density and abundance of O-glycosylation, mucins present extra challenges in the analysis of their structure and function. However, recent breakthroughs in multiple areas of research have rendered mucin-domain glycoproteins more accessible to current characterization techniques. In particular, the adaptation of mucinases to glycoproteomic workflows, the manipulation of cellular glycosylation pathways, and the advances in synthetic methods to more closely mimic mucin domains have introduced new and exciting avenues to study mucin glycoproteins. Here, we summarize recent developments in understanding the structure and biological function of mucin domains and their associated glycans, from glycoproteomic tools and visualization methods to synthetic glycopeptide mimetics.

Online Hydrophilic Interaction Chromatography (HILIC) Enhanced Top-Down Mass Spectrometry Characterization of the SARS-CoV-2 Spike Receptor-Binding Domain

SARS-CoV-2 cellular infection is mediated by the heavily glycosylated spike protein. Recombinant versions of the spike protein and the receptor-binding domain (RBD) are necessary for seropositivity assays and can potentially serve as vaccines against viral infection. RBD plays key roles in the spike protein's structure and function, and thus, comprehensive characterization of recombinant RBD is critically important for biopharmaceutical applications. Liquid chromatography coupled to mass spectrometry has been widely used to characterize post-translational modifications in proteins, including glycosylation. Most studies of RBDs were performed at the proteolytic peptide (bottom-up proteomics) or released glycan level because of the technical challenges in resolving highly heterogeneous glycans at the intact protein level. Herein, we evaluated several online separation techniques: (1) C2 reverse-phase liquid chromatography (RPLC), (2) capillary zone electrophoresis (CZE), and (3) acrylamide-based monolithic hydrophilic interaction chromatography (HILIC) to separate intact recombinant RBDs with varying combinations of glycosylations (glycoforms) for top-down mass spectrometry (MS). Within the conditions we explored, the HILIC method was superior to RPLC and CZE at separating RBD glycoforms, which differ significantly in neutral glycan groups. In addition, our top-down analysis readily captured unexpected modifications (e.g., cysteinylation and N-terminal sequence variation) and low abundance, heavily glycosylated proteoforms that may be missed by using glycopeptide data alone. The HILIC top-down MS platform holds great potential in resolving heterogeneous glycoproteins for facile comparison of biosimilars in quality control applications.

Analysis of Viral Spike Protein N-Glycosylation Using Ultraviolet Photodissociation Mass Spectrometry

Characterization of protein glycosylation by tandem mass spectrometry remains challenging owing to the vast diversity of oligosaccharides bound to proteins, the variation in monosaccharide linkage patterns, and the lability of the linkage between the glycan and protein. Here, we have adapted an HCD-triggered-ultraviolet photodissociation (UVPD) approach for the simultaneous localization of glycosites and full characterization of both glycan compositions and intersaccharide linkages, the latter provided by extensive cross-ring cleavages enabled by UVPD. The method is applied to study glycan compositions based on analysis of glycopeptides from proteolytic digestion of recombinant human coronaviruse spike proteins from SARS-CoV-2 and HKU1. UVPD reveals unique intersaccharide linkage information and is leveraged to localize N-linked glycoforms with confidence.

Systems analysis of plasma IgG intact N-glycopeptides from patients with chronic kidney diseases via EThcD-sceHCD-MS/MS

Immunoglobulin G (IgG) molecules modulate an immune response. However, site-specific N-glycosylation signatures of plasma IgG in patients with chronic kidney disease (CKD) remain unclear. This study aimed to propose a novel method to explore the N-glycosylation pattern of IgG and to compare it with reported methods. We separated human plasma IgG from 58 healthy controls (HC) and 111 patients with CKD. Purified IgG molecules were digested by trypsin. Tryptic peptides without enrichment of intact N-glycopeptides were analyzed using a combination of electron-transfer/higher-energy collisional dissociation (EThcD) and stepped collision energy/higher-energy collisional dissociation (sceHCD) mass spectrometry (EThcD-sceHCD-MS/MS). This resulted in higher spectral quality, more informative fragment ions, higher Byonic score, and nearly twice the depth of intact N-glycopeptide identification than sceHCD or EThcD alone. Site-specific N-glycosylation mapping revealed that intact N-glycopeptides were differentially expressed in HC and CKD patients; thus, it can be a diagnostic tool. This study provides a method for the determination of glycosylation patterns in CKD and a framework for understanding the role of IgG in the pathophysiology of CKD. Data are available via ProteomeXchange with identifier PXD027174.

Deuterium Oxide Labeling for Global Omics Relative Quantification (DOLGOReQ): Application to Glycomics

A new relative quantification strategy for glycomics, named deuterium oxide (D₂O) labeling for global omics relative quantification (DOLGOReQ), has been developed based on the partial metabolic D₂O labeling, which induces a subtle change in the isotopic distribution of glycan ions. The relative abundance of unlabeled to D-labeled glycans was extracted from the overlapped isotopic envelope obtained from a mixture containing equal amounts of unlabeled and D-labeled glycans. The glycan quantification accuracy of DOLGOReQ was examined with mixtures of unlabeled and D-labeled HeLa glycans combined in varying ratios according to the number of cells present in the samples. The relative quantification of the glycans mixed in an equimolar ratio revealed that 92.4 and 97.8% of the DOLGOReQ results were within a 1.5- and 2-fold range of the predicted mixing ratio, respectively. Furthermore, the dynamic quantification range of DOLGOReQ was investigated with unlabeled and D-labeled HeLa glycans mixed in different ratios from 20:1 to 1:20. A good correlation (Pearson's r > 0.90) between the expected and measured quantification ratios over 2 orders of magnitude was observed for 87% of the quantified glycans. DOLGOReQ was also applied in the measurement of quantitative HeLa cell glycan changes that occur under normoxic and hypoxic conditions. Given that metabolic D₂O labeling can incorporate D into all types of glycans, DOLGOReQ has the potential as a universal quantification platform for large-scale comparative glycomic experiments.

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

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

Integrated mass spectrometry-based multi-omics for elucidating mechanisms of bacterial virulence

Despite being considered the simplest form of life, bacteria remain enigmatic, particularly in light of pathogenesis and evolving antimicrobial resistance. After three decades of genomics, we remain some way from understanding these organisms, and a substantial proportion of genes remain functionally unknown. Methodological advances, principally mass spectrometry (MS), are paving the way for parallel analysis of the proteome, metabolome and lipidome. Each provides a global, complementary assay, in addition to genomics, and the ability to better comprehend how pathogens respond to changes in their internal (e.g. mutation) and external environments consistent with infection-like conditions. Such responses include accessing necessary nutrients for survival in a hostile environment where co-colonizing bacteria and normal flora are acclimated to the prevailing conditions. Multi-omics can be harnessed across temporal and spatial (sub-cellular) dimensions to understand adaptation at the molecular level. Gene deletion libraries, in conjunction with large-scale approaches and evolving bioinformatics integration, will greatly facilitate next-generation vaccines and antimicrobial interventions by highlighting novel targets and pathogen-specific pathways. MS is also central in phenotypic characterization of surface biomolecules such as lipid A, as well as aiding in the determination of protein interactions and complexes. There is increasing evidence that bacteria are capable of widespread post-translational modification, including phosphorylation, glycosylation and acetylation; with each contributing to virulence. This review focuses on the bacterial genotype to phenotype transition and surveys the recent literature showing how the genome can be validated at the proteome, metabolome and lipidome levels to provide an integrated view of organism response to host conditions.

Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research

Innovations in medical technology and dedicated focus from the scientific community have inspired numerous treatment strategies for benign and invasive cancers. While these improvements often lend themselves to more positive prognoses and greater patient longevity, means for early detection and severity stratification have failed to keep pace. Detection and validation of cancer-specific biomarkers hinges on the ability to identify subtype-specific phenotypic and proteomic alterations and the systematic screening of diverse patient groups. For this reason, clinical and scientific research settings rely on high throughput and high sensitivity mass spectrometry methods to discover and quantify unique molecular perturbations in cancer patients. Discussed within is an overview of quantitative proteomics strategies and a summary of recent applications that enable revealing potential biomarkers and treatment targets in prostate, ovarian, breast, and pancreatic cancer in a high throughput manner.