Construction of InstantPC derivatized glycan GU database: A foundation work for high-throughput and high-sensitivity glycomic analysis

Updates of the current strategies of labeling for N-glycan analysis

This mini review summarizes the current methods used for screening N-glycosylation of glycoproteins, with a specific focus on therapeutic proteins and on techniques involving the release of N-glycans. With the continuous development of biopharmaceuticals, particularly monoclonal antibodies (mAbs), which are N-glycosylated proteins, monitoring has gained importance in recent decades. Glycosylation of therapeutic glycoproteins is considered a critical quality attribute because it can impact the efficacy and safety of these therapeutic drugs. The protocols and instrumentation have evolved with the advancement of technologies. Nowadays, methods are becoming increasingly robust, rapid, and sensitive. For the release of N-glycans, the most commonly used method is enzymatic release using PNGase F. The latter is discussed in light of the advent of rapid release that is now possible. The strategy for separating N-glycans using either liquid chromatography (LC) with hydrophilic interaction liquid chromatography (HILIC) chemistry or capillary electrophoresis will be discussed. The selection of the labeling agent is a crucial step in sample preparation for the analysis of released N-glycans. This review also discusses labeling agents that are compatible with and dependent on the separation and detection techniques employed. The emergence of multiplex labeling agents is also summarized. The latter enables the analysis of multiple samples in a single run, but it requires MS analysis.

N-glycomic profiling of capsid proteins from Adeno-Associated Virus serotypes

Adeno-associated virus (AAV) vector has become the leading platform for gene delivery. Each serotype exhibits a different tissue tropism, immunogenicity, and in vivo transduction performance. Therefore, selecting the most suitable AAV serotype is critical for efficient gene delivery to target cells or tissues. Genome divergence among different serotypes is due mainly to the hypervariable regions of the AAV capsid proteins. However, the heterogeneity of capsid glycosylation is largely unexplored. In the present study, the N-glycosylation profiles of capsid proteins of AAV serotypes 1 to 9 have been systemically characterized and compared using a previously developed high-throughput and high-sensitivity N-glycan profiling platform. The results showed that all 9 investigated AAV serotypes were glycosylated, with comparable profiles. The most conspicuous feature was the high abundance mannosylated N-glycans, including FM3, M5, M6, M7, M8, and M9, that dominated the chromatograms within a range of 74 to 83%. Another feature was the relatively lower abundance of fucosylated and sialylated N-glycan structures, in the range of 23%-40% and 10%-17%, respectively. However, the exact N-glycan composition differed. These differences may be utilized to identify potential structural relationships between the 9 AAV serotypes. The current research lays the foundation for gaining better understanding of the importance of N-glycans on the AAV capsid surface that may play a significant role in tissue tropism, interaction with cell surface receptors, cellular uptake, and intracellular processing.

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.

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.

MS-based glycomics: An analytical tool to assess nervous system diseases

Neurological diseases affect millions of peopleochemistryorldwide and are continuously increasing due to the globe's aging population. Such diseases affect the nervous system and are characterized by a progressive decline in brain function and progressive cognitive impairment, decreasing the quality of life for those with the disease as well as for their families and loved ones. The increased burden of nervous system diseases demands a deeper insight into the biomolecular mechanisms at work during disease development in order to improve clinical diagnosis and drug design. Recently, evidence has related glycosylation to nervous system diseases. Glycosylation is a vital post-translational modification that mediates many biological functions, and aberrant glycosylation has been associated with a variety of diseases. Thus, the investigation of glycosylation in neurological diseases could provide novel biomarkers and information for disease pathology. During the last decades, many techniques have been developed for facilitation of reliable and efficient glycomic analysis. Among these, mass spectrometry (MS) is considered the most powerful tool for glycan analysis due to its high resolution, high sensitivity, and the ability to acquire adequate structural information for glycan identification. Along with MS, a variety of approaches and strategies are employed to enhance the MS-based identification and quantitation of glycans in neurological samples. Here, we review the advanced glycomic tools used in nervous system disease studies, including separation techniques prior to MS, fragmentation techniques in MS, and corresponding strategies. The glycan markers in common clinical nervous system diseases discovered by utilizing such MS-based glycomic tools are also summarized and discussed.

Serum N-glycomic profiling may provide potential signatures for surveillance of COVID-19

Disease development and progression are often associated with aberrant glycosylation, indicating that changes in biological fluid glycome may potentially serve as disease signatures. The corona virus disease-2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents a significant threat to global human health. However, the effect of SARS-CoV-2 infection on the overall serum N-glycomic profile has been largely unexplored. Here, we extended our 96-well-plate-based high-throughput, high-sensitivity N-glycan profiling platform further with the aim of elucidating potential COVID-19-associated serum N-glycomic alterations. Use of this platform revealed both similarities and differences between the serum N-glycomic fingerprints of COVID-19 positive and control cohorts. Although there were no specific glycan peaks exclusively present or absent in COVID-19 positive cohort, this cohort showed significantly higher levels of glycans and variability. On the contrary, the overall N-glycomic profiles for healthy controls were well-contained within a narrow range. From the serum glycomic analysis, we were able to deduce changes in different glycan subclasses sharing certain structural features. Of significance was the hyperbranched and hypersialylated glycans and their derived glycan subclass traits. T-distributed stochastic neighbour embedding (tSNE) and hierarchical heatmap clustering analysis were performed to identify 13 serum glycomic variables that potentially distinguished the COVID-19 positive from healthy controls. Such serum N-glycomic changes described herein may indicate or correlate to the changes in serum glycoproteins upon COVID-19 infection. Furthermore, mapping the serum N-glycome following SARS-CoV-2 infection may help us better understand the disease and enable "Long-COVID" surveillance to capture the full spectrum of persistent symptoms.