Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics

Mass Spectrometry-Based Glycomics and Proteomics Profiling of On-Slide Digested Tissue from Complex Biological Samples

Mass spectrometry-based investigation of the heterogeneous glycoproteome from complex biological specimens is a robust approach to mapping the structure, function, and dynamics of the glycome and proteome. Sampling whole wet tissues often provides a large amount of starting material; however, there is a reasonable variability in tissue handling prior to downstream processing steps, and it is difficult to capture all the different biomolecules from a specific region. The on-slide tissue digestion approach, outlined in this protocol chapter, is a simple and cost-effective method that allows comprehensive mapping of the glycoproteome from a single spot of tissue of 1 mm or greater diameter. It provides a selection of target areas on tissue slides appropriate for tissue volumes of 10 nL or greater, corresponding to a 1 μL droplet of enzyme solution applied to a 1-mm diameter target on a 10-μm-thick tissue slice. Sequential enzymatic digestions and desalting of the biomolecules without any prior derivatization from the surface of fresh frozen or formalin-fixed paraffin-embedded tissue slides enable the simultaneous identification of glycosaminoglycan disaccharides such as hyaluronan, chondroitin sulfate and heparan sulfate, asparagine or N-linked glycans, and intact (glyco)peptides using liquid chromatography-tandem mass spectrometry. The in-depth information obtained from this method including the disaccharide compositions, glycan structures, peptide abundances, and site-specific glycan occupancies provides a detailed profiling of a single spot of tissue which has the potential to be disseminated to biomedical laboratories.

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.

Impact of glycosylation on viral vaccines

Glycosylation is the most prominent modification important for vaccines and its specific pattern depends on several factors that need to be considered when developing a new biopharmaceutical. Tailor-made glycosylation can be exploited to develop more effective and safer vaccines; for this reason, a deep understanding of both glycoengineering strategies and glycans structures and functions is required. In this review we discuss the recent advances concerning glycoprotein expression systems and the explanation of glycans immunomodulation mechanisms. Furthermore, we highlight how glycans tune the immunological properties among different vaccines platforms (whole virus, recombinant protein, nucleic acid), also comparing commercially available formulations and describing the state-of-the-art analytical technologies for glycosylation analysis. The whole review stresses the aspect of glycoprotein glycans as a potential tool to overcome nowadays medical needs in vaccine field.

Uncovering missing glycans and unexpected fragments with pGlycoNovo for site-specific glycosylation analysis across species

Precision mapping of site-specific glycans using mass spectrometry is vital in glycoproteomics. However, the diversity of glycan compositions across species often exceeds database capacity, hindering the identification of rare glycans. Here, we introduce pGlycoNovo, a software within the pGlyco3 software environment, which employs a glycan first-based full-range Y-ion dynamic searching strategy. pGlycoNovo enables de novo identification of intact glycopeptides with rare glycans by considering all possible monosaccharide combinations, expanding the glycan search space to 16~1000 times compared to non-open search methods, while maintaining accuracy, sensitivity and speed. Reanalysis of SARS Covid-2 spike protein glycosylation data revealed 230 additional site-specific N-glycans and 30 previously unreported O-glycans. pGlycoNovo demonstrated high complementarity to six other tools and superior search speed. It enables characterization of site-specific N-glycosylation across five evolutionarily distant species, contributing to a dataset of 32,549 site-specific glycans on 4602 proteins, including 2409 site-specific rare glycans, and uncovering unexpected glycan fragments.

Novel Pannexin 1 isoform is increased in cancer

Pannexin 1 (PANX1) is upregulated in many cancers, where its activity and signalling promote tumorigenic properties. Here, we report a novel ∼25 kDa isoform of human PANX1 (hPANX1-25K) which lacks the N-terminus and was detected in several human cancer cell lines including melanoma, osteosarcoma, breast cancer and glioblastoma multiforme. This isoform was increased upon hPANX1 CRISPR/Cas9 deletion targeting the first exon near M1, suggesting a potential alternative translation initiation (ATI) site. hPANX1-25K was confirmed to be a hPANX1 isoform via mass spectrometry, can be N-linked glycosylated at N254, and can interact with both β-catenin and full length hPANX1. A double deletion of hPANX1 and hPANX1-25K reduces cell growth and viability in cancer cells. hPANX1-25K is prevalent throughout melanoma progression, and its levels are increased in squamous cell carcinoma cells and patient-derived tumours, compared to keratinocytes and normal skin, indicating that it may be differentially regulated in normal and cancer cells.

Deep structure-level N-glycan identification using feature-induced structure diagnosis integrated with a deep learning model

Being a widely occurring protein post-translational modification, N-glycosylation features unique multi-dimensional structures including sequence and linkage isomers. There have been successful bioinformatics efforts in N-glycan structure identification using N-glycoproteomics data; however, symmetric "mirror" branch isomers and linkage isomers are largely unresolved. Here, we report deep structure-level N-glycan identification using feature-induced structure diagnosis (FISD) integrated with a deep learning model. A neural network model is integrated to conduct the identification of featured N-glycan motifs and boosts the process of structure diagnosis and distinction for linkage isomers. By adopting publicly available N-glycoproteomics datasets of five mouse tissues (17,136 intact N-glycopeptide spectrum matches) and a consideration of 23 motif features, a deep learning model integrated with a convolutional autoencoder and a multilayer perceptron was trained to be capable of predicting N-glycan featured motifs in the MS/MS spectra with previously identified compositions. In the test of the trained model, a prediction accuracy of 0.8 and AUC value of 0.95 were achieved; 5701 previously unresolved N-glycan structures were assigned by matched structure-diagnostic ions; and by using an explainable learning algorithm, two new fragmentation features of m/z = 674.25 and m/z = 835.28 were found to be significant to three N-glycan structure motifs with fucose, NeuAc, and NeuGc, proving the capability of FISD to discover new features in the MS/MS spectra.

GP-Marker facilitates the analysis of intact glycopeptide quantitative data at different levels

Protein glycosylation is a highly heterogeneous post-translational modification that has been demonstrated to exhibit significant variations in various diseases. Due to the differential patterns observed in disease and healthy populations, the glycosylated proteins hold promise as early indicators for multiple diseases. With the continuous development of liquid chromatography-mass spectrometry (LC-MS) technology and spectrum analysis software, the sensitivity for the decipher of the tandem mass spectra of the glycopeptides carrying intact glycans, i.e., intact glycopeptides, enzymatic hydrolyzed from glycoproteins has been significantly improved. From quantified intact glycopeptides, the difference of protein glycosylation at multiple levels, e.g., glycoprotein, glycan, glycosite, and site-specific glycans, could be obtained for different samples. However, the manual analysis of the intact glycopeptide quantitative data at multiple levels is tedious and time consuming. In this study, we have developed a software tool named "GP-Marker" to facilitate large-scale data mining of spectra dataset of intact N-glycopeptide at multiple levels. This software provides a user-friendly and interactive interface, offering operational tools for machine learning to researchers without programming backgrounds. It includes a range of visualization plots displaying differential glycosylation and provides the ability to extract multi-level data analysis from intact glycopeptide data quantified by Glyco-Decipher.

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.

Immunopeptidomics in the cancer immunotherapy era

Cancer is the primary cause of death worldwide, and conventional treatments are painful, complicated, and have negative effects on healthy cells. However, cancer immunotherapy has emerged as a promising alternative. Principle of cancer immunotherapy is the re-activation of T-cell to combat the tumor that presents the peptide antigen on major histocompatibility complex (MHC). Those peptide antigens are identified with the set of omics technology, proteomics, genomics, and bioinformatics, which referred to immunopeptidomics. Indeed, immunopeptidomics can identify the neoantigens that are very useful for cancer immunotherapies. This review explored the use of immunopeptidomics for various immunotherapies, i.e., peptide-based vaccines, immune checkpoint inhibitors, oncolytic viruses, and chimeric antigen receptor T-cell. We also discussed how the diversity of neoantigens allows for the discovery of novel antigenic peptides while post-translationally modified peptides diversify the overall peptides binding to MHC or so-called MHC ligandome. The development of immunopeptidomics is keeping up-to-date and very active, particularly for clinical application. Immunopeptidomics is expected to be fast, accurate and reliable for the application for cancer immunotherapies.

Recent advances in N-glycan biomarker discovery among human diseases

N-glycans play important roles in a variety of biological processes. In recent years, analytical technologies with high resolution and sensitivity have advanced exponentially, enabling analysts to investigate N-glycomic changes in different states. Specific glycan and glycosylation signatures have been identified in multiple diseases, including cancer, autoimmune diseases, nervous system disorders, and metabolic and cardiovascular diseases. These glycans demonstrate comparable or superior indicating capability in disease diagnosis and prognosis over routine biomarkers. Moreover, synchronous glycan alterations concurrent with disease initiation and progression provide novel insights into pathogenetic mechanisms and potential treatment targets. This review elucidates the biological significance of N-glycans, compares the existing glycomic technologies, and delineates the clinical performance of N-glycans across a range of diseases.

Advancing mass spectrometry-based glycoproteomic software tools for comprehensive site-specific glycoproteome analysis

Glycoproteome analysis at a site-specific level and proteome scale stands out as a highly promising approach for gaining insights into the intricate roles of glycans in biological systems. Recent years have witnessed an upsurge in the development of innovative methodologies tailored for precisely this purpose. Breakthroughs in mass spectrometry-based glycoproteomic techniques, enabling the identification, quantification, and systematic exploration of site-specific glycans, have significantly enhanced our capacity to comprehensively and thoroughly characterize glycoproteins. In this short review, we delve into novel tools in advancing site-specific glycoproteomic analysis and summarize pertinent studies published in the past two years. Lastly, we discuss the ongoing challenges and outline future prospects in the field, considering both the analytical strategies of mass spectrometry and the tools employed for data interpretation.

Prediction of glycopeptide fragment mass spectra by deep learning

Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.