The Art of Destruction: Optimizing Collision Energies in Quadrupole-Time of Flight (Q-TOF) Instruments for Glycopeptide-Based Glycoproteomics

IgE glycosylation and impact on structure and function: A systematic review

The impact of human IgE glycosylation on structure, function and disease mechanisms is not fully elucidated, and heterogeneity in different studies renders drawing conclusions challenging. Previous reviews discussed IgE glycosylation focusing on specific topics such as health versus disease, FcεR binding or impact on function. We present the first systematic review of human IgE glycosylation conducted utilizing the PRISMA guidelines. We sought to define the current consensus concerning the roles of glycosylation on structure, biology and disease. Despite diverse analytical methodologies, source, expression systems and the sparsity of data on IgE antibodies from non-allergic individuals, collectively evidence suggests differential glycosylation profiles, particularly in allergic diseases compared with healthy states, and indicates functional impact, and contributions to IgE-mediated hypersensitivities and atopic diseases. Beyond allergic diseases, dysregulated terminal glycan structures, including sialic acid, may regulate IgE metabolism. Glycan sites such as N394 may contribute to stabilizing IgE structure, with alterations in these glycans likely influencing both structure and IgE-FcεR interactions. This systematic review therefore highlights critical IgE glycosylation attributes in health and disease that may be exploitable for therapeutic intervention, and the need for novel analytics to explore pertinent research avenues.

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

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

Experimentally Determined Diagnostic Ions for Identification of Peptide Glycotopes

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

Plasma glycoproteomics delivers high-specificity disease biomarkers by detecting site-specific glycosylation abnormalities

INTRODUCTION

The human plasma glycoproteome holds enormous potential to identify personalized biomarkers for diagnostics. Glycoproteomics has matured into a technology for plasma N-glycoproteome analysis but further evolution towards clinical applications depends on the clinical validity and understanding of protein- and site-specific glycosylation changes in disease.

OBJECTIVES

Here, we exploited the uniqueness of a patient cohort of genetic defects in well-defined glycosylation pathways to assess the clinical applicability of plasma N-glycoproteomics.

METHODS

Comparative glycoproteomics was performed of blood plasma from 40 controls and 74 patients with 13 different genetic diseases that impact the protein N-glycosylation pathway. Baseline glycosylation in healthy individuals was compared to reference glycome and intact transferrin protein mass spectrometry data. Use of glycoproteomics data for biomarker discovery and sample stratification was evaluated by multivariate chemometrics and supervised machine learning. Clinical relevance of site-specific glycosylation changes were evaluated in the context of genetic defects that lead to distinct accumulation or loss of specific glycans. Integrated analysis of site-specific glycoproteome changes in disease was performed using chord diagrams and correlated with intact transferrin protein mass spectrometry data.

RESULTS

Glycoproteomics identified 191 unique glycoforms from 58 unique peptide sequences of 34 plasma glycoproteins that span over 3 magnitudes of abundance in plasma. Chemometrics identified high-specificity biomarker signatures for each of the individual genetic defects with better stratification performance than the current diagnostic standard method. Bioinformatic analyses revealed site-specific glycosylation differences that could be explained by underlying glycobiology and protein-intrinsic factors.

CONCLUSION

Our work illustrates the strong potential of plasma glycoproteomics to significantly increase specificity of glycoprotein biomarkers with direct insights in site-specific glycosylation changes to better understand the glycobiological mechanisms underlying human disease.

Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow

Glycoproteins play important roles in numerous physiological processes and are often implicated in disease. Analysis of site-specific protein glycobiology through glycoproteomics has evolved rapidly in recent years thanks to hardware and software innovations. Particularly, the introduction of parallel accumulation serial fragmentation (PASEF) on hybrid trapped ion mobility time-of-flight mass spectrometry instruments combined deep proteome sequencing with separation of (near-)isobaric precursor ions or converging isotope envelopes through ion mobility separation. However, the reported use of PASEF in integrated glycoproteomics workflows to comprehensively capture the glycoproteome is still limited. To this end, we developed an integrated methodology using timsTOF Pro 2 to enhance N-glycopeptide identifications in complex mixtures. We systematically optimized the ion optics tuning, collision energies, mobility isolation width, and the use of dopant-enriched nitrogen gas (DEN). Thus, we obtained a marked increase in unique glycopeptide identification rates compared to standard proteomics settings, showcasing our results on a large set of glycopeptides. With short liquid chromatography gradients of 30 min, we increased the number of unique N-glycopeptide identifications in human plasma samples from around 100 identifications under standard proteomics conditions to up to 1500 with our optimized glycoproteomics approach, highlighting the need for tailored optimizations to obtain comprehensive data.

Variability of human Alpha-1-acid glycoprotein N-glycome in a Caucasian population

AIM

Alpha-1-acid glycoprotein (AGP) is a highly glycosylated protein in human plasma and one of the most abundant acute phase proteins in humans. Glycosylation plays a crucial role in its biological functions, and alterations in AGP N-glycome have been associated with various diseases and inflammatory conditions. However, large-scale studies of AGP N-glycosylation in the general population are lacking.

METHODS

Using recently developed high-throughput glycoproteomic workflow for site-specific AGP N-glycosylation analysis, 803 individuals from the Croatian island of Korcula were analyzed and their AGP N-glycome data associated with biochemical and physiological traits, as well as different environmental factors.

RESULTS

After regression analysis, we found that AGP N-glycosylation is strongly associated with sex, somewhat less with age, along with multiple biochemical and physiological traits (e.g. BMI, triglycerides, uric acid, glucose, smoking status, fibrinogen).

CONCLUSION

For the first time we have extensively explored the inter-individual variability of AGP N-glycome in a general human population, demonstrating its changes with sex, age, biochemical, and physiological status of individuals, providing the baseline for future population and clinical studies.

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).

Multiplexed Antibody Glycosylation Profiling Using Dual Enzyme Digestion and Liquid Chromatography-Triple Quadrupole Mass Spectrometry Method

Antibody glycosylation plays a crucial role in the humoral immune response by regulating effector functions and influencing the binding affinity to immune cell receptors. Previous studies have focused mainly on the immunoglobulin G (IgG) isotype owing to the analytical challenges associated with other isotypes. Thus, the development of a sensitive and accurate analytical platform is necessary to characterize antibody glycosylation across multiple isotypes. In this study, we have developed an analytical workflow using antibody-light-chain affinity beads to purify IgG, IgA, and IgM from 16 μL of human plasma. Dual enzymes, trypsin and Glu-C, were used during on-bead digestion to obtain enzymatic glycopeptides and protein-specific surrogate peptides. Ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry was used in order to determine the sensitivity and specificity. Our platform targets 95 glycopeptides across the IgG, IgA, and IgM isotypes, as well as eight surrogate peptides representing total IgG, four IgG classes, two IgA classes, and IgM. Four stable isotope-labeled internal standards were added after antibody purification to calibrate the preparation and instrumental bias during analysis. Calibration curves constructed using serially diluted plasma samples showed good curve fitting (R2 > 0.959). The intrabatch and interbatch precision for all the targets had relative standard deviation of less than 29.6%. This method was applied to 19 human plasma samples, and the glycosylation percentages were calculated, which were comparable to those reported in the literature. The developed method is sensitive and accurate for Ig glycosylation profiling. It can be used in clinical investigations, particularly for detailed humoral immune profiling.

RAMZIS: a bioinformatic toolkit for rigorous assessment of the alterations to glycoprotein composition that occur during biological processes

Motivation

Glycosylation elaborates the structures and functions of glycoproteins; glycoproteins are common post-translationally modified proteins and are heterogeneous and non-deterministically synthesized as an evolutionarily driven mechanism that elaborates the functions of glycosylated gene products. Glycoproteins, accounting for approximately half of all proteins, require specialized proteomics data analysis methods due to micro- and macro-heterogeneities as a given glycosite can be divided into several glycosylated forms, each of which must be quantified. Sampling of heterogeneous glycopeptides is limited by mass spectrometer speed and sensitivity, resulting in missing values. In conjunction with the low sample size inherent to glycoproteomics, a specialized toolset is needed to determine if observed changes in glycopeptide abundances are biologically significant or due to data quality limitations.

Results

We developed an R package, Relative Assessment of m/z Identifications by Similarity (RAMZIS), that uses similarity metrics to guide researchers to a more rigorous interpretation of glycoproteomics data. RAMZIS uses a permutation test to generate contextual similarity, which assesses the quality of mass spectral data and outputs a graphical demonstration of the likelihood of finding biologically significant differences in glycosylation abundance datasets. Investigators can assess dataset quality, holistically differentiate glycosites, and identify which glycopeptides are responsible for glycosylation pattern change. RAMZIS is validated by theoretical cases and a proof-of-concept application. RAMZIS enables comparison between datasets too stochastic, small, or sparse for interpolation while acknowledging these issues in its assessment. Using this tool, researchers will be able to rigorously define the role of glycosylation and the changes that occur during biological processes.

Availability and implementation

https://github.com/WillHackett22/RAMZIS.

Very Low-Pressure CID Experiments: High Energy Transfer and Fragmentation Pattern at the Single Collision Regime

We have performed CID experiments on a triple quadrupole instrument, lowering the collision gas pressure by 50 times compared to its conventional value. The results show that at very low-collision gas pressure, single collisions dominate the spectra. Indirectly, these results suggest that under conventional conditions, 20-50 collisions may be typical in CID experiments. The results show a marked difference between low- and high-pressure CID spectra, the latter being characterized in terms of 'slow heating' and predominance of consecutive reactions. The results indicate that under single collision conditions, the collisional energy transfer efficiency is very high: nearly 100% of the center of mass kinetic energy is converted to internal energy.

Immunoglobulin A Glycosylation Differs between Crohn's Disease and Ulcerative Colitis

Inflammatory bowel diseases (IBD), such as Crohn's disease (CD) and ulcerative colitis (UC), are chronic and relapsing inflammations of the digestive tract with increasing prevalence, yet they have unknown origins or cure. CD and UC have similar symptoms but respond differently to surgery and medication. Current diagnostic tools often involve invasive procedures, while laboratory markers for patient stratification are lacking. Large glycomic studies of immunoglobulin G and total plasma glycosylation have shown biomarker potential in IBD and could help determine disease mechanisms and therapeutic treatment choice. Hitherto, the glycosylation signatures of plasma immunoglobulin A, an important immunoglobulin secreted into the intestinal mucin, have remained undetermined in the context of IBD. Our study investigated the associations of immunoglobulin A1 and A2 glycosylation with IBD in 442 IBD cases (188 CD and 254 UC) and 120 healthy controls by reversed-phase liquid chromatography electrospray-ionization mass spectrometry of tryptic glycopeptides. Differences of IgA O- and N-glycosylation (including galactosylation, bisection, sialylation, and antennarity) between patient groups were associated with the diseases, and these findings led to the construction of a statistical model to predict the disease group of the patients without the need of invasive procedures. This study expands the current knowledge about CD and UC and could help in the development of noninvasive biomarkers and better patient care.

Collision energies: Optimization strategies for bottom-up proteomics

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

RAMZIS: a bioinformatic toolkit for rigorous assessment of the alterations to glycoprotein structure that occur during biological processes

Motivation

Glycosylation elaborates the structures and functions of glycoproteins; glycoproteins are common post-translationally modified proteins and are heterogeneous and non-deterministically syn-thesized as an evolutionarily driven mechanism that elaborates the functions of glycosylated gene products. While glycoproteins account for approximately half of all proteins, their macro- and micro-heterogeneity requires specialized proteomics data analysis methods as a given glycosite can be divided into several glycosylated forms, each of which must be quantified. Sampling of heterogeneous glycopeptides is limited by mass spectrometer speed and sensitivity, resulting in missing values. In conjunction with the low sample size inherent to glycoproteomics, this necessitated specialized statistical metrics to identify if observed changes in glycopeptide abundances are biologically significant or due to data quality limitations.

Results

We developed an R package, Relative Assessment of m/z Identifications by Similarity (RAMZIS), that uses similarity metrics to guide biomedical researchers to a more rigorous interpretation of glycoproteomics data. RAMZIS uses contextual similarity to assess the quality of mass spectral data and generates graphical output that demonstrates the likelihood of finding biologically significant differences in glycosylation abundance dataset. Investigators can assess dataset quality, holistically differentiate glycosites, and identify which glycopeptides are responsible for glycosylation pattern expression change. Herein RAMZIS approach is validated by theoretical cases and by a proof-of-concept application. RAMZIS enables comparison between datasets too stochastic, small, or sparse for interpolation while acknowledging these issues in its assessment. Using our tool, researchers will be able to rigor-ously define the role of glycosylation and the changes that occur during biological processes.

Key changes in bovine milk immunoglobulin G during lactation: NeuAc sialylation is a hallmark of colostrum immunoglobulin G N-glycosylation

We monitored longitudinal changes in bovine milk IgG in samples from four cows at 9 time points in between 0.5 and 28 days following calving. We used peptide-centric LC-MS/MS on proteolytic digests of whole bovine milk, resulting in the combined identification of 212 individual bovine milk protein sequences, with IgG making up >50 percent of the protein content of every 0.5 d colostrum sample, which reduced to ≤3 percent in mature milk. In parallel, we analyzed IgG captured from the bovine milk samples to characterize its N-glycosylation, using dedicated methods for bottom-up glycoproteomics employing product ion-triggered hybrid fragmentation; data are available via ProteomeXchange with identifier PXD037755. The bovine milk IgG N-glycosylation profile was revealed to be very heterogeneous, consisting of >40 glycoforms. Furthermore, these N-glycosylation profiles changed substantially over the period of lactation, but consistently across the four individual cows. We identified NeuAc sialylation as the key abundant characteristic of bovine colostrum IgG, significantly decreasing in the first days of lactation, and barely detectable in mature bovine milk IgG. We also report, for the first time to our knowledge, the identification of subtype IgG3 in bovine milk, alongside the better-documented IgG1 and IgG2. The detailed molecular characteristics we describe of the bovine milk IgG, and their dynamic changes during lactation, are important not only for the fundamental understanding of the calf's immune development, but also for understanding bovine milk and its bioactive components in the context of human nutrition.

The 2022 Nobel Prize in Chemistry for the development of click chemistry and bioorthogonal chemistry

The 2022 Nobel Prize in Chemistry recognized the development of biorthogonal chemical ligation reactions known as click chemistry in biomedicine. This concept has catalyzed significant progress in sensing and diagnosis, chemical biology, materials chemistry, and drug discovery and delivery. In proteomics, the ability to incorporate a click tag into proteins has propelled development of powerful new methods for selective enrichment of protein complexes that inform understanding of protein networks. It also has had a strong influence on the ability to enrich for protein post-translational modifications. This feature article summarizes the impacts of biorthogonal click chemistry on proteomics.

Comparative site-specific N-glycoproteome analysis reveals aberrant N-glycosylation and gives insights into mannose-6-phosphate pathway in cancer

N-glycosylation is implicated in cancers and aberrant N-glycosylation is recognized as a hallmark of cancer. Here, we mapped and compared the site-specific N-glycoproteomes of colon cancer HCT116 cells and isogenic non-tumorigenic DNMT1/3b double knockout (DKO1) cells using Fbs1-GYR N-glycopeptide enrichment technology and trapped ion mobility spectrometry. Many significant changes in site-specific N-glycosylation were revealed, providing a molecular basis for further elucidation of the role of N-glycosylation in protein function. HCT116 cells display hypersialylation especially in cell surface membrane proteins. Both HCT116 and DKO1 show an abundance of paucimannose and 80% of paucimannose-rich proteins are annotated to reside in exosomes. The most striking N-glycosylation alteration was the degree of mannose-6-phosphate (M6P) modification. N-glycoproteomic analyses revealed that HCT116 displays hyper-M6P modification, which was orthogonally validated by M6P immunodetection. Significant observed differences in N-glycosylation patterns of the major M6P receptor, CI-MPR in HCT116 and DKO1 may contribute to the hyper-M6P phenotype of HCT116 cells. This comparative site-specific N-glycoproteome analysis provides a pool of potential N-glycosylation-related cancer biomarkers, but also gives insights into the M6P pathway in cancer.

Systems-Wide Site-Specific Analysis of Glycoproteins

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

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.

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

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

Site-specific N-glycosylation analysis of human thyroid thyroglobulin by mass spectrometry-based Glyco-analytical strategies

Human thyroglobulin (Tg), which has many glycosylation sites, is an essential protein produced by the human thyroid glands. Although human Tg N-glycans play critical roles in the cellular events of the Thyroid gland, the site-specific distribution of glycan structures has not been studied in detail. This study aimed to profile human Tg N-glycosylation sites and their glycan contents by using high-throughput glyco-analytical strategies, including glycopeptide and glycan levels. The sulfated complex and hybrid type N-glycan species were determined by the analysis of the human Tg samples with HPLC-HILIC-FLD-MS/MS. It was found that all fucosylated N-glycans carried fucose residue on their N-glycan core structure. The human Tg was digested with multiple enzymes by applying both in-gel and in-solution protocols to enhance site-specific glycosylation analysis. In total, 17 out of 20 N-glycosylation sites were characterized. It was noticed that 6 N-glycosylation sites contain only high-mannose type glycans, while other regions include complex and hybrid type glycans. In addition, sulfated glycoform structures were detected at the glycopeptide level in glycosylation sites containing complex and hybrid type glycans. It is expected that the results obtained from this study will contribute to functional studies to be conducted on human Tg protein. BIOLOGICAL SIGNIFICANCE: N-glycans of human thyroglobulin modulate thyroid hormone synthesis both in vivo and in vitro. Therefore, a comprehensive analysis of the N-glycosylation sites of human thyroglobulin is essential to improve our understanding of the function of its N-glycans. The present research significantly expanded the knowledge regarding N-glycosylation profiles of human thyroid thyroglobulin protein. For instance, as highlighted here, sulfated N-glycan structures were characterized using comprehensive glyco-analytical strategies. N-glycan patterns for the sites Asn110, Asn1869, and Asn2122 were described for the first time in this current work. In addition, N-glycan structures containing core-fucosylation and bisecting types were confirmed for all determined glycosylation sites.

High-Throughput Human Complement C3 N-Glycoprofiling Identifies Markers of Early Onset Type 1 Diabetes Mellitus in Children

Recently, it was shown that children at the onset of type 1 diabetes (T1D) have a higher proportion of oligomannose glycans in their total plasma protein N-glycome compared to their healthy siblings. The most abundant complement component, glycoprotein C3, contains two N-glycosylation sites occupied exclusively by this type of glycans. Furthermore, complement system, as well as C3, was previously associated with T1D. It is also known that changes in glycosylation can modulate inflammatory responses, so our aim was to characterize the glycosylation profile of C3 in T1D. For this purpose, we developed a novel high-throughput workflow for human C3 concanavalin A lectin affinity enrichment and subsequent LC-MS glycopeptide analysis which enables protein-specific N-glycosylation profiling. From the Danish Childhood Diabetes Register, plasma samples of 61 children/adolescents newly diagnosed with T1D and 84 of their unaffected siblings were C3 N-glycoprofiled. Significant changes of C3 N-glycan profiles were found. T1D was associated with an increase in the proportion of unprocessed glycan structures with more mannose units. A regression model including C3 N-glycans showed notable discriminative power between children with early onset T1D and their healthy siblings with area under curve of 0.879. This study confirmed our previous findings of plasma high-mannose glycan changes in a cohort of recent onset T1D cases, suggesting the involvement of C3 N-glycome in T1D development. Our C3 glycan-based discriminative model could be valuable in assessment of T1D risk in children.

Liquid chromatography and capillary electrophoresis in glycomic and glycoproteomic analysis

Glycosylation is one of the most significant and abundant post-translational modifications in cells. Glycomic and glycoproteomic analyses involve the characterization of oligosaccharides (glycans) conjugated to proteins. Glycomic and glycoproteomic analysis is highly challenging because of the large diversity of structures, low abundance, site-specific heterogeneity, and poor ionization efficiency of glycans and glycopeptides in mass spectrometry (MS). MS is a key tool for characterization of glycans and glycopeptides. However, MS alone does not always provide full structural and quantitative information for many reasons, and thus MS is combined with some separation technique. This review focuses on the role of separation techniques used in glycomic and glycoproteomic analyses, liquid chromatography and capillary electrophoresis. The most important separation conditions and results are presented and discussed.

Graphical abstract

Mass Spectrometry-Based Techniques to Elucidate the Sugar Code

Cells encode information in the sequence of biopolymers, such as nucleic acids, proteins, and glycans. Although glycans are essential to all living organisms, surprisingly little is known about the "sugar code" and the biological roles of these molecules. The reason glycobiology lags behind its counterparts dealing with nucleic acids and proteins lies in the complexity of carbohydrate structures, which renders their analysis extremely challenging. Building blocks that may differ only in the configuration of a single stereocenter, combined with the vast possibilities to connect monosaccharide units, lead to an immense variety of isomers, which poses a formidable challenge to conventional mass spectrometry. In recent years, however, a combination of innovative ion activation methods, commercialization of ion mobility-mass spectrometry, progress in gas-phase ion spectroscopy, and advances in computational chemistry have led to a revolution in mass spectrometry-based glycan analysis. The present review focuses on the above techniques that expanded the traditional glycomics toolkit and provided spectacular insight into the structure of these fascinating biomolecules. To emphasize the specific challenges associated with them, major classes of mammalian glycans are discussed in separate sections. By doing so, we aim to put the spotlight on the most important element of glycobiology: the glycans themselves.

CE-MS for Proteomics and Intact Protein Analysis

This chapter aims to explore various parameters involved in achieving high-end capillary electrophoresis hyphenated to mass spectrometry (CE-MS) analysis of proteins, peptides, and their posttranslational modifications. The structure of the topics discussed in this book chapter is conveniently mapped on the scheme of the CE-MS system itself, starting from sample preconcentration and injection techniques and finishing with mass analyzer considerations. After going through the technical considerations, a variety of relevant applications for this analytical approach are presented, including posttranslational modifications analysis, clinical biomarker discovery, and its growing use in the biotechnological industry.

The Hitchhiker's guide to glycoproteomics

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

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

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

What Can N-glycomics and N-glycoproteomics of Cerebrospinal Fluid Tell Us about Alzheimer Disease?

Proteomics-large-scale studies of proteins-has over the last decade gained an enormous interest for studies aimed at revealing proteins and pathways involved in disease. To fully understand biological and pathological processes it is crucial to also include post-translational modifications in the "omics". To this end, glycomics (identification and quantification of glycans enzymatically or chemically released from proteins) and glycoproteomics (identification and quantification of peptides/proteins with the glycans still attached) is gaining interest. The study of protein glycosylation requires a workflow that involves an array of sample preparation and analysis steps that needs to be carefully considered. Herein, we briefly touch upon important steps such as sample preparation and preconcentration, glycan release, glycan derivatization and quantification and advances in mass spectrometry that today are the work-horse for glycomics and glycoproteomics studies. Several proteins related to Alzheimer disease pathogenesis have altered protein glycosylation, and recent glycomics studies have shown differences in cerebrospinal fluid as well as in brain tissue in Alzheimer disease as compared to controls. In this review, we discuss these techniques and how they have been used to shed light on Alzheimer disease and to find glycan biomarkers in cerebrospinal fluid.

Oxonium Ion Guided Analysis of Quantitative Proteomics Data Reveals Site-Specific O-Glycosylation of Anterior Gradient Protein 2 (AGR2)

Developments in mass spectrometry (MS)-based analyses of glycoproteins have been important to study changes in glycosylation related to disease. Recently, the characteristic pattern of oxonium ions in glycopeptide fragmentation spectra had been used to assign different sets of glycopeptides. In particular, this was helpful to discriminate between O-GalNAc and O-GlcNAc. Here, we thought to investigate how such information can be used to examine quantitative proteomics data. For this purpose, we used tandem mass tag (TMT)-labeled samples from total cell lysates and secreted proteins from three different colorectal cancer cell lines. Following automated glycopeptide assignment (Byonic) and evaluation of the presence and relative intensity of oxonium ions, we observed that, in particular, the ratio of the ions at m/z 144.066 and 138.055, respectively, could be used to discriminate between O-GlcNAcylated and O-GalNAcylated peptides, with concomitant relative quantification between the different cell lines. Among the O-GalNAcylated proteins, we also observed anterior gradient protein 2 (AGR2), a protein which glycosylation site and status was hitherto not well documented. Using a combination of multiple fragmentation methods, we then not only assigned the site of modification, but also showed different glycosylation between intracellular (ER-resident) and secreted AGR2. Overall, our study shows the potential of broad application of the use of the relative intensities of oxonium ions for the confident assignment of glycopeptides, even in complex proteomics datasets.

Structural and Functional Characterization of SARS-CoV-2 RBD Domains Produced in Mammalian Cells

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is still ongoing and dramatically influences our life, the need for recombinant viral proteins for diagnostics, vaccine development, and research is very high. The spike (S) protein, and particularly its receptor-binding domain (RBD), mediates the interaction with the angiotensin-converting enzyme 2 (ACE2) receptor on host cells and may be modulated by its structural features. Therefore, well-characterized recombinant RBDs are essential. We have performed an in-depth structural and functional characterization of RBDs expressed in Chinese hamster ovary (CHO) and human embryonic kidney 293 (HEK293) cells. To structurally characterize the native RBDs (comprising N- and O-glycans and additional post translational modifications), a multilevel mass spectrometric approach was employed. Released glycan and glycopeptide analysis were integrated with intact mass analysis, glycan-enzymatic dissection, and top-down sequencing for comprehensive annotation of RBD proteoforms. The data showed distinct glycosylation for CHO- and HEK293-RBD with the latter exhibiting antenna fucosylation, a higher level of sialylation, and a combination of core 1 and core 2 type O-glycans. Additionally, using an alternative approach based on N-terminal cleavage of the O-glycosylation, the previously unknown O-glycosylation site was localized at T323. For both RBDs, the binding to SARS-CoV-2 antibodies of positive patients and affinity to the ACE2 receptor was addressed showing comparable results. This work not only offers insights into RBD structural and functional features but also provides an analytical workflow for characterization of new RBDs and batch-to-batch comparison.

Importance of evaluating protein glycosylation in pluripotent stem cell-derived cardiomyocytes for research and clinical applications

Proper protein glycosylation is critical to normal cardiomyocyte physiology. Aberrant glycosylation can alter protein localization, structure, drug interactions, and cellular function. The in vitro differentiation of human pluripotent stem cells into cardiomyocytes (hPSC-CM) has become increasingly important to the study of protein function and to the fields of cardiac disease modeling, drug testing, drug discovery, and regenerative medicine. Here, we offer our perspective on the importance of protein glycosylation in hPSC-CM. Protein glycosylation is dynamic in hPSC-CM, but the timing and extent of glycosylation are still poorly defined. We provide new data highlighting how observed changes in hPSC-CM glycosylation may be caused by underlying differences in the protein or transcript abundance of enzymes involved in building and trimming the glycan structures or glycoprotein gene products. We also provide evidence that alternative splicing results in altered sites of glycosylation within the protein sequence. Our findings suggest the need to precisely define protein glycosylation events that may have a critical impact on the function and maturation state of hPSC-CM. Finally, we provide an overview of analytical strategies available for studying protein glycosylation and identify opportunities for the development of new bioinformatic approaches to integrate diverse protein glycosylation data types. We predict that these tools will promote the accurate assessment of protein glycosylation in future studies of hPSC-CM that will ultimately be of significant experimental and clinical benefit.

Screening Trimethoprim Primary Metabolites for Covalent Binding to Albumin

Modification of endogenous proteins by drugs and drug metabolites are thought to be a cause of idiosyncratic adverse drug reactions (IADRs). Trimethoprim (TMP) is a commonly prescribed antibiotic that has been implicated in IADRs; however, there is no known mechanism by which this drug or its metabolites modify proteins. This study describes the results of screening trimethoprim and its primary metabolites for the ability to covalently modify human serum albumin (HSA). The first step of the screen was in vitro reactions of the compounds with HSA followed by western blotting with antisera specific to drug-modified proteins. Compounds with positive signal in the western blot were then screened using an untargeted peptide profiling method to discover modified peptides. This strategy identified two sites in HSA that are modified by incubation with a TMP metabolite, α-hydroxy trimethoprim (Cα-OH-TMP).

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.

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

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

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

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

High-Throughput and Site-Specific N-Glycosylation Analysis of Human Alpha-1-Acid Glycoprotein Offers a Great Potential for New Biomarker Discovery

Alpha-1-acid glycoprotein (AGP) is an acute phase glycoprotein in blood, which is primarily synthetized in the liver and whose biological role is not completely understood. It consists of 45% carbohydrates that are present in the form of five N-linked complex glycans. AGP N-glycosylation was shown to be changed in many different diseases, and some changes appear to be disease-specific; thus, it has a great diagnostic and prognostic potential. However, AGP glycosylation was mainly analyzed in small cohorts and without detailed site-specific glycan information. Here, we developed a cost-effective method for a high-throughput and site-specific N-glycosylation LC-MS analysis of AGP which can be applied on large cohorts, aid in search for novel disease biomarkers, and enable better understanding of AGP's role and function in health and disease. The method does not require isolation of AGP with antibodies and affinity chromatography, but AGP is enriched by acid precipitation from 5 μl of bloodplasma in a 96-well format. After trypsinization, AGP glycopeptides are purified using a hydrophilic interaction chromatography-based solid-phase extraction and analyzed by reversed-phase-liquid chromatography-electrospray ionization-MS. We used our method to show for the first time that AGP N-glycan profile is stable in healthy individuals (14 individuals in three time points), which is a requirement for evaluation of its diagnostic potential. Furthermore, we tested our method on a population including individuals with registered hyperglycemia in critical illness (59 cases and 49 controls), which represents a significantly increased risk of developing type 2 diabetes. Individuals at higher risk of diabetes presented increased N-glycan branching on AGP's second glycosylation site and lower sialylation of N-glycans on AGP's third and AGP1's fourth glycosylation site. Although this should be confirmed on a larger prospective cohort, it indicates that site-specific AGP N-glycan profile could help distinguish individuals who are at risk of type 2 diabetes.

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.

Mapping O-glycosylation Sites Using OpeRATOR and LC-MS

O-glycosylation is a difficult posttranslational modification to analyze. O-glycans are labile and often cluster making their analysis by LC-MS very challenging. OpeRATOR is an O-glycan specific protease that cleaves the protein backbone N-terminally of glycosylated serine and threonine residues. This enables the generation of glycopeptides of suitable size for mapping O-glycosylation sites in detail by bottom-up LC-MS analysis. In this chapter we demonstrate a simple workflow for in-depth analysis of O-glycosylation sites on heavily glycosylated proteins using OpeRATOR digestion and HILIC-MS/MS analysis.

Mass Spectrometry-Based Methods for Immunoglobulin G N-Glycosylation Analysis

Mass spectrometry and its hyphenated techniques enabled by the improvements in liquid chromatography, capillary electrophoresis, novel ionization, and fragmentation modes are truly a cornerstone of robust and reliable protein glycosylation analysis. Boost in immunoglobulin G (IgG) glycan and glycopeptide profiling demands for both applied biomedical and research applications has brought many new advances in the field in terms of technical innovations, sample preparation, improved throughput, and confidence in glycan structural characterization. This chapter summarizes mass spectrometry basics, focusing on IgG and monoclonal antibody N-glycosylation analysis on several complexity levels. Different approaches, including antibody enrichment, glycan release, labeling, and glycopeptide preparation and purification, are covered and illustrated with recent breakthroughs and examples from the literature omitting excessive theoretical frameworks. Finally, selected highly popular methodologies in IgG glycoanalytics such as liquid chromatography-mass spectrometry and matrix-assisted laser desorption ionization are discussed more thoroughly yet in simple terms making this text a practical starting point either for the beginner in the field or an experienced clinician trying to make sense out of the IgG glycomic or glycoproteomic dataset.

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

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

Glycomics and glycoproteomics: Approaches to address isomeric separation of glycans and glycopeptides

Changes in the glycome of human proteins and cells are associated with the progression of multiple diseases such as Alzheimer's, diabetes mellitus, many types of cancer, and those caused by viruses. Consequently, several studies have shown essential modifications to the isomeric glycan moieties for diseases in different stages. However, the elucidation of extensive isomeric glycan profiles remains challenging because of the lack of analytical techniques with sufficient resolution power to separate all glycan and glycopeptide iso-forms. Therefore, the development of sensitive and accurate approaches for the characterization of all the isomeric forms of glycans and glycopeptides is essential to tracking the progression of pathology in glycoprotein-related diseases. This review describes the isomeric separation achievements reported in glycomics and glycoproteomics in the last decade. It focuses on the mass spectrometry-based analytical strategies, stationary phases, and derivatization techniques that have been developed to enhance the separation mechanisms in liquid chromatography systems and the detection capabilities of mass spectrometry systems.

Glycomics & Glycoproteomics: From Analytics to Function

Morten Thaysen-Andersen, Daniel Kolarich and Nicolle H. Packer introduce the Molecular Omics themed issue on Glycomics & Glycoproteomics: From Analytics to Function.

Tailoring to Search Engines: Bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence

Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide–spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptide-level results, we designed methods with one or two liquid chromatography–tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10–40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).

Glycoproteomics Technologies in Glycobiotechnology

Glycosylation is a key factor determining the pharmacological properties of biotherapeutics, including their stability, solubility, bioavailability, pharmacokinetics, and immunogenicity. As such, comprehensive information about glycosylation of biotherapeutics is critical to demonstrate similarity. Regulatory agencies also require extensive documentation of the comprehensive analyses of glycosylation-related critical quality attributes (CQAs) during the development, manufacturing, and release of biosimilars. Mass spectrometry has catalysed tremendous advancements in the characterisation of glycosylation CQAs of biotherapeutics. Here we provide a perspective overview on the MS-based technologies relevant for biotherapeutic product characterisation with an emphasis on the recent developments that allow determination of glycosylation features such as site of glycosylation, sialic acid linkage, glycan structure, and content.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

Optimal Dissociation Methods Differ for N- and O-Glycopeptides

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

Advancing Target Identification of Nitrated Phospholipids in Biological Systems by HCD Specific Fragmentation Fingerprinting in Orbitrap Platforms

Nitrated phospholipids have recently been detected in vitro and in vivo and associated with beneficial health effects. They were identified and quantified in biological samples by lipidomics methodologies using liquid chromatography-collision-induced dissociation (CID) tandem mass spectrometry (MS/MS) acquired with the linear ion trap mass spectrometer. Only a few studies have used higher-energy collision dissociation (HCD)-MS/MS in high-resolution Orbitraps to characterize nitrated phosphatidylserines and nitrated cardiolipins, highlighting the marked differences in the fragmentation patterns when using CID or HCD fragmentation methods. In this study, we aimed to evaluate the fragmentation of nitrated phosphatidylcholine and nitrated phosphatidylethanolamine species under HCD-MS/MS. We studied the effect of normalized collision energy (NCE) in the fragmentation pattern to identify the best acquisition conditions and reporter ions to detect nitrated phospholipids. The results showed that the intensity of the typical neutral loss of nitrous acid (HNO₂) diminishes with increasing NCE, becoming non-detectable for a higher NCE. Thus, the loss of HNO₂ could not be the most suitable ion/fragment for the characterization of nitrated phospholipids under HCD. In HCD-MS/MS new fragment ions were identified, corresponding to the nitrated fatty acyl chains, NO₂-RCOO⁻, (NO₂-RCOOH-H₂O + H)⁺, and (NO₂-RCOOH + H)⁺, suggested as potential reporter ions to detect nitrated phospholipids when using the HCD-MS/MS lipidomics analysis.

Relative Retention Time Estimation Improves N-Glycopeptide Identifications by LC-MS/MS

Glycopeptides identified by tandem mass spectrometry rely on the identification of the peptide backbone sequence and the attached glycan(s) by the incomplete fragmentation of both moieties. This may lead to ambiguous identifications where multiple structures could explain the same spectrum equally well due to missing information in the mass spectrum or incorrect precursor mass determination. To date, approaches to solving these problems have been limited, and few inroads have been made to address these issues. We present a technique to address some of these challenges and demonstrate it on previously published data sets. We use a linear modeling approach to learn the influence of the glycan composition on the retention time of a glycopeptide and use these models to validate glycopeptides within the same experiment, detecting over 400 incorrect cases during the MS/MS search and correcting 75 cases that could not be identified based on mass alone. We make this technique available as a command line executable program, written in Python and C, freely available at https://github.com/mobiusklein/glycresoft in source form, with precompiled binaries for Windows.