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Great advances have been made in mass spectrometric data interpretation for intact glycopeptide analysis. However, accurate identification of intact glycopeptides and modified saccharide units at the site-specific level and with fast speed remains challenging. Here, we present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A glycan ion-indexing algorithm developed for glycan-first search makes pGlyco3 5–40 times faster than other glycoproteomic search engines without decreasing accuracy or sensitivity. By combining electron-based dissociation spectra, pGlyco3 integrates a dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization. Our evaluation shows that the site-specific glycan localization probabilities estimated by pGlycoSite are suitable to localize site-specific glycans. With pGlyco3, we confidently identified N-glycopeptides and O-mannose glycopeptides that were extensively modified by ammonia adducts in yeast samples. The freely available pGlyco3 is an accurate and flexible tool that can be used to identify glycopeptides and modified saccharide units.

CD22, a member of Siglec family of sialic acid binding proteins, has restricted expression on B cells. Antibody-based agents targeting CD22 or CD20 on B lymphoma and leukemia cells exhibit clinical efficacy for treating these malignancies, but also attack normal B cells leading to immune deficiency. Here, we report a chemoenzymatic glycocalyx editing strategy to introduce high-affinity and specific CD22 ligands onto NK-92MI and cytokine-induced natural killer cells to achieve tumor-specific CD22 targeting. These CD22-ligand modified cells exhibited significantly enhanced tumor cell binding and killing in vitro without harming healthy B cells. For effective lymphoma cell killing in vivo, we further functionalized CD22 ligand-modified NK-92MI cells with the E-selectin ligand sialyl Lewis X to promote trafficking to bone marrow. The dual-functionalized cells resulted in the efficient suppression of B lymphoma in a xenograft model. Our results suggest that nature killer cells modified with glycan ligands to CD22 and selectins promote both targeted killing of B lymphoma cells and improved trafficking to sites where the cancer cells reside, respectively.

MALDI-MS-based glycan isotope labeling methods have been effectively and widely used for quantitative glycomics. However, interpretation of the data produced by MALDI-MS is inaccurate and tedious because the bioinformatic tools are inadequate. In this work, we present gQuant, an automated tool for MALDI-MS-based glycan isotope labeling data processing. gQuant was designed with a set of dedicated algorithms to improve the efficiency, accuracy and convenience of quantitation data processing. When tested on the reference data set, gQuant showed a fast processing speed, as it was able to search the glycan data of model glycoproteins in a few minutes and reported more results than the manual analysis did. The reported quantitation ratios matched well with the experimental glycan mixture ratios ranging from 1:10 to 10:1. In addition, gQuant is fully open-source and is coded in Python, which is supported by most operating systems, and it has a user-friendly interface. gQuant can be easily adapted by users for specific experimental designs, such as specific glycan databases, different derivatization types and relative quantitation designs and can thus facilitate fast glycomic quantitation for clinical sample analysis using MALDI-MS-based stable isotope labeling.

We present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A novel glycan ion-indexing algorithm developed in this work for glycan-first search makes pGlyco3 5-40 times faster than other glycoproteomic search engines without decreasing the accuracies and sensitivities. By combining electron-based dissociation spectra, pGlyco3 integrates a fast, dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization (SSGL). Our evaluation based on synthetic and natural glycopeptides showed that the SSGL probabilities estimated by pGlycoSite were proved to be appropriate to localize site-specific glycans. With pGlyco3, we found that N-glycopeptides and O-mannose glycopeptides in yeast samples were extensively modified by ammonia adducts on Hex (aH) and verified the aH-glycopeptide identifications based on released N-glycans and15N/13C-labeled data. Thus pGlyco3, which is freely available onhttps://github.com/pFindStudio/pGlyco3/releases, is an accurate and flexible tool to identify glycopeptides and modified saccharide units.

Global in-depth analysis of N-glycosylation, as the most complex post-translational modification of proteins, is requiring methods being as sensitive, selective and reliable as possible. Here, an enhanced strategy for N-glycomics is presented comprising optimized sample preparation yielding enhanced glycoprotein recovery and permethylation efficiency, isotopic labelling for data quality control and relative quantification, integration of new N-glycan libraries (human and mouse), newly developed R-scripts matching experimental MS1 data to theoretical N-glycan compositions and bundled sequencing algorithms for MS2-based structural identification to ultimately enhance the coverage and accuracy of N-glycans. With this strategy the numbers of identified N-glycans are more than doubled compared with previous studies, exemplified by etanercept (more than 3-fold) and chicken ovalbumin (more than 2-fold) at nanogram level. The power of this strategy and applicability to biological samples is further demonstrated by comparative N-glycomics of human acute promyelocytic leukemia cells before and after treatment with all-trans retinoic acid, showing that N-glycan biosynthesis is slowed down and 57 species are significantly altered in response to the treatment. This improved analytical platform enables deep and accurate N-glycomics for glycobiological research and biomarker discovery.

A powerful and fast glycopeptide/glycan enrichment method is critical for the efficiency and throughput of mass spectrometry (MS)-based glycoproteomic and glycomic analyses, especially for large-scale sample analysis. Here, we report an ultrafast and effective method for both intact N-glycopeptide and N-glycan enrichment and apply it to human serum samples. In this method, a natural hydrophilic material, bacterial cellulose (BC), was adopted and fully optimized for enrichment. This method offers the following advantages: (i) The enrichment material has natural hydrophilicity and is low-cost, biocompatible, biodegradable and easily accessible; (ii) the whole enrichment procedure is remarkably simple and fast. It takes only 10 min for intact glycopeptides/glycans to be easily purified from mixtures; (iii) the specificity of this method is over 94% for both glycan and glycopeptide enrichment; and (iv) the outstanding specificity of this technique enables high isolation efficiency for the enrichment of both intact glycopeptides and glycans. A total of 36 N-glycans and 31 N-glycopeptides were identified from human immunoglobulin G (IgG). The glycan and glycopeptide absorption capacity of BC was as high as 333 μg/mg and 250 μg/mg (IgG/BC) respectively. The selectivity for glycan and glycopeptide enrichment reached 1:100 (IgG/bovine serum albumin (BSA), molar ratio) and 1:200 (maltoheptaose (DP7)/BSA, molar ratio), respectively. Furthermore, a total of 159 N-glycans and 523 N-glycopeptides were identified in human serum by using this method. Overall, the BC-based enrichment method we present here provides an ultrafast and highly efficient method for the enrichment of both N-glycopeptides and N-glycans in complex samples and shows great potential in large-scale glycoproteomic and glycomic analyses.

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

PNGase F-catalyzed glycosylation site (18)O-labeling is a widely used method for glycoprotein quantitation owing to its efficiency and simplicity. However, PNGase F-catalyzed glycan (18)O-labeling, which offers advantages for glycomics, has not yet been developed. In this study, PNGase F-mediated incorporation of (18)O into glycans during the N-glycan release from glycoproteins by PNGase F was finally realized, named as PCGOL (PNGase F-catalyzed glycan (18)O-labeling), which offers a potential strategy for relative glycan quantitation. This new method showed good linearity and high reproducibility within at least 2 orders of magnitude in the dynamic range. Furthermore, PCGOL combined with our previously developed TOSIL method (tandem (18)O stable isotope labeling for N-glycoproteome quantitation) can be used for comprehensive N-glycosylation quantification, achieving simultaneous quantification of glycans, glycopeptides and glycoproteins in a single workflow, which was also used to analyze glycosylation changes in immunoglobulin G (IgG) associated with hepatocellular carcinoma in the present work.

GREDIL for quantitative N-glycomics largely improves the stability of N-glycan 18O-labeling and greatly decreases the interference of isotopic cluster overlap.

Confident characterization of the microheterogeneity of protein glycosylation through identification of intact glycopeptides remains one of the toughest analytical challenges for glycoproteomics. Recently proposed mass spectrometry (MS)-based methods still have some defects such as lack of the false discovery rate (FDR) analysis for the glycan identification and lack of sufficient fragmentation information for the peptide identification. Here we proposed pGlyco, a novel pipeline for the identification of intact glycopeptides by using complementary MS techniques: 1) HCD-MS/MS followed by product-dependent CID-MS/MS was used to provide complementary fragments to identify the glycans and a novel target-decoy method was developed to estimate the false discovery rate of the glycan identification; 2) data-dependent acquisition of MS3 for some most intense peaks of HCD-MS/MS was used to provide fragments to identify the peptide backbones. By integrating HCD-MS/MS, CID-MS/MS and MS3, intact glycopeptides could be confidently identified. With pGlyco, a standard glycoprotein mixture was analyzed in the Orbitrap Fusion and 309 non-redundant intact glycopeptides were identified with detailed spectral information of both glycans and peptides.

The Human Disease Glycomics/Proteome Initiative (HGPI) is an activity in the Human Proteome Organization (HUPO) supported by leading researchers from international institutes and aims at development of disease-related glycomics/glycoproteomics analysis techniques. Since 2004, the initiative has conducted three pilot studies. The first two were N- and O-glycan analyses of purified transferrin and immunoglobulin-G and assessed the most appropriate analytical approach employed at the time. This paper describes the third study, which was conducted to compare different approaches for quantitation of N- and O-linked glycans attached to proteins in crude biological samples. The preliminary analysis on cell pellets resulted in wildly varied glycan profiles, which was probably the consequence of variations in the pre-processing sample preparation methodologies. However, the reproducibility of the data was not improved dramatically in the subsequent analysis on cell lysate fractions prepared in a specified method by one lab. The study demonstrated the difficulty of carrying out a complete analysis of the glycome in crude samples by any single technology and the importance of rigorous optimization of the course of analysis from preprocessing to data interpretation. It suggests that another collaborative study employing the latest technologies in this rapidly evolving field will help to realize the requirements of carrying out the large-scale analysis of glycoproteins in complex cell samples.

Playing an important role in a broad range of biological and pathological processes, sialylation has been drawing wide interest. The efficient sialoglycopeptides enrichment methods are therefore attracting considerable attention. In this paper, we first compared two conventional enrichment methods, lectin and TiO(2), and analyzed their characteristics. Furthermore, considering the highly negatively charged nature of sialic acids, we developed a new strategy, peptide immobilized pH gradient isoelectric focusing (IPG-IEF) assisted TiO(2) chromatography (PIAT), for the highly efficient enrichment of sialoglycopeptides. In this method, peptides were first separated into 24 fractions using peptide IPG-IEF. Sialoglycopeptides were relatively concentrated in low-pH fractions of the immobilized pH strips and were captured using TiO(2) chromatography. As a result, 614 N-glycosylation sites were identified in 582 sialoglycopeptides within 322 sialoglycoproteins from rat liver using PIAT. To our knowledge, this work represents one of the most comprehensive sialoglycoproteomic analyses in general and exhibits the largest database of sialoglycoproteome in rat liver currently. So the new strategy introduced here exhibits high efficiency and universality in the sialoglycopeptide enrichment, and is a powerful tool for sialoglycoproteome exploration.