Multiple Reaction Monitoring for the Quantitation of Serum Protein Glycosylation Profiles: Application to Ovarian Cancer

Proteomic landscape of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.

Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling

BACKGROUND

There is a need for diagnostic tests for screening, triaging and staging of epithelial ovarian cancer (EOC). Glycoproteomics of blood samples has shown promise for biomarker discovery.

METHODS

We applied glycoproteomics to serum of people with EOC or benign pelvic masses and healthy controls. A total of 653 analytes were quantified and assessed in multivariable models, which were tested in an independent cohort. Additionally, we analyzed glycosylation patterns in serum markers and in tissues.

RESULTS

We identified a biomarker panel that distinguished benign lesions from EOC with sensitivity and specificity of 83.5% and 90.1% in the training set, and of 86.7 and 86.7% in the test set, respectively. ROC analysis demonstrated strong performance across a range of cutoffs. Fucosylated multi-antennary glycopeptide markers were higher in late-stage than in early-stage EOC. A comparable pattern was found in late-stage EOC tissues.

CONCLUSIONS

Blood glycopeptide biomarkers have the potential to distinguish benign from malignant pelvic masses, and early- from late-stage EOC. Glycosylation of circulating and tumor tissue proteins may be related. This study supports the hypothesis that blood glycoproteomic profiling can be used for EOC diagnosis and staging and it warrants further clinical evaluation.

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

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

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.

Water-stable hydrophilic metal organic framework composite for the recognition of N-glycopeptides during diabetes progression by mass spectrometry

A hydrophilic Al-MOFs composite was prepared using cheap and available reagents in water via a suitable large-scale production, an economical and environment-friendly method for capturing N-glycopeptides. The prepared Al-MOFs composite with high hydrolytically stable and hydrophilic 1D channels exhibits an ultralow detection limit (0.5 fmol/μL), and excellent reusability (at least 10 cycles) in the capture of N-glycopeptides from standard bio-samples. Interestingly, the Al-MOFs composite also shows remarkable performance in practical applications, where 300 N-glycopeptides ascribed to 124 glycoproteins were identified in 1 µL human serum and were successfully applied in profiling the differences of N-glycopeptides during diabetes progression. Moreover, 12 specific glycoproteins used as biomarkers to accurately distinguish the progression of diabetes are identified. The present work provides a potential commercial method for large-scale glycoproteomics research in complex clinical samples while offering new guidance for the precise diagnosis of diabetes progression.

Discovery and validation of serum glycoprotein biomarkers for high grade serous ovarian cancer

PURPOSE

This study aimed to identify serum glycoprotein biomarkers for early detection of high-grade serous ovarian cancer (HGSOC), the most common and aggressive histotype of ovarian cancer.

EXPERIMENTAL DESIGN

The glycoproteomics pipeline lectin magnetic bead array (LeMBA)-mass spectrometry (MS) was used in age-matched case-control serum samples. Clinical samples collected at diagnosis were divided into discovery (n = 30) and validation (n = 98) sets. We also analysed a set of preclinical sera (n = 30) collected prior to HGSOC diagnosis in the UK Collaborative Trial of Ovarian Cancer Screening.

RESULTS

A 7-lectin LeMBA-MS/MS discovery screen shortlisted 59 candidate proteins and three lectins. Validation analysis using 3-lectin LeMBA-multiple reaction monitoring (MRM) confirmed elevated A1AT, AACT, CO9, HPT and ITIH3 and reduced A2MG, ALS, IBP3 and PON1 glycoforms in HGSOC. The best performing multimarker signature had 87.7% area under the receiver operating curve, 90.7% specificity and 70.4% sensitivity for distinguishing HGSOC from benign and healthy groups. In the preclinical set, CO9, ITIH3 and A2MG glycoforms were altered in samples collected 11.1 ± 5.1 months prior to HGSOC diagnosis, suggesting potential for early detection.

CONCLUSIONS AND CLINICAL RELEVANCE

Our findings provide evidence of candidate early HGSOC serum glycoprotein biomarkers, laying the foundation for further study in larger cohorts.

Glycomic, Glycoproteomic, and Proteomic Profiling of Philippine Lung Cancer and Peritumoral Tissues: Case Series Study of Patients Stages I-III

Lung cancer is the leading cause of cancer death and non-small cell lung carcinoma (NSCLC) accounting for majority of lung cancers. Thus, it is important to find potential biomarkers, such as glycans and glycoproteins, which can be used as diagnostic tools against NSCLC. Here, the N-glycome, proteome, and N-glycosylation distribution maps of tumor and peritumoral tissues of Filipino lung cancer patients (n = 5) were characterized. We present several case studies with varying stages of cancer development (I-III), mutation status (EGFR, ALK), and biomarker expression based on a three-gene panel (CD133, KRT19, and MUC1). Although the profiles of each patient were unique, specific trends arose that correlated with the role of aberrant glycosylation in cancer progression. Specifically, we observed a general increase in the relative abundance of high-mannose and sialofucosylated N-glycans in tumor samples. Analysis of the glycan distribution per glycosite revealed that these sialofucosylated N-glycans were specifically attached to glycoproteins involved in key cellular processes, including metabolism, cell adhesion, and regulatory pathways. Protein expression profiles showed significant enrichment of dysregulated proteins involved in metabolism, adhesion, cell-ECM interactions, and N-linked glycosylation, supporting the protein glycosylation results. The present case series study provides the first demonstration of a multi-platform mass-spectrometric analysis specifically for Filipino lung cancer patients.

Methods for quantification of glycopeptides by liquid separation and mass spectrometry

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

Higher Temperature Porous Graphitic Carbon Separations Differentially Impact Distinct Glycopeptide Classes

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

N-Glycan and Glycopeptide Serum Biomarkers in Philippine Lung Cancer Patients Identified Using Liquid Chromatography-Tandem Mass Spectrometry

Aberrant glycosylation has been extensively reported in cancer, with fundamental changes in the glycosylation patterns of cell-surface and secreted proteins largely occurring during cancer progression. As such, serum glycan and glycopeptide biomarkers have been discovered using mass spectrometry and proposed for cancer detection. Here, we report for the first time potential serum N-glycan and glycopeptide biomarkers for Philippine lung cancer patients. The N-glycan and glycoprotein profiles of a cohort (n = 26 patients, n = 22 age- and gender-matched) of lung cancer patients were analyzed and compared to identify potential N-glycan and glycopeptide serum biomarkers using nano-QToF-MS/MS and ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry dynamic multiple monitoring methods, respectively. Statistical analyses identified differential N-glycan and glycopeptide abundances. The N-glycans were mostly sialylated and sialofucosylated branched structures. The glycopeptides involved proteins in complement and coagulation cascades (p _adj = 6.418 × 10^-4), innate immunity (p _adj = 6.094 × 10^-3), acute inflammatory response (p _adj = 6.404 × 10^-5), defense response (p _adj = 2.082 × 10^-4), complement activation pathways (p _adj = 1.895 × 10^-2), and immunoglobulin-mediated immune response pathways (p _adj = 4.818 × 10^-2). Biomarker models were constructed using serum N-glycans [area under the curve (AUC) = 0.775; 95% CI: 0.617-0.931] and glycopeptides (AUC = 0.959; 95% CI: 0.85-1.0), with glycopeptides having higher accuracies than N-glycans. The results suggest that in the Philippine lung cancer patient sera, specific N-glycans and site-specific glycans are differentially expressed between cases and controls. This report represents the first serum glycan and glycopeptide biomarkers of Philippine lung cancer patients, further demonstrating the utility of mass spectrometry-based glycomic and glycoproteomic methods.

Quantitative clinical glycomics strategies: A guide for selecting the best analysis approach

Glycans introduce complexity to the proteins to which they are attached. These modifications vary during the progression of many diseases; thus, they serve as potential biomarkers for disease diagnosis and prognosis. The immense structural diversity of glycans makes glycosylation analysis and quantitation difficult. Fortunately, recent advances in analytical techniques provide the opportunity to quantify even low-abundant glycopeptides and glycans derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Understanding the strengths and weaknesses of different quantitative glycomics analysis methods is important for selecting the best strategy to analyze glycosylation changes in any given set of clinical samples. To provide guidance towards selecting the proper approach, we discuss four widely used quantitative glycomics analysis platforms, including fluorescence-based analysis of released N-linked glycans and three different varieties of MS-based analysis: liquid chromatography (LC)-mass spectrometry (MS) analysis of glycopeptides, matrix-assisted laser desorption ionization-time of flight MS, and LC-ESI-MS analysis of released N-linked glycans. These methods' strengths and weaknesses are compared, particularly associated with the figures of merit that are important for clinical biomarker studies, including: the initial sample requirements, the methods' throughput, sample preparation time, the number of species identified, the methods' utility for isomer separation and structural characterization, method-related challenges associated with quantitation, repeatability, the expertise required, and the cost for each analysis. This review, therefore, provides unique guidance to researchers who endeavor to undertake a clinical glycomics analysis by offering insights on the available analysis technologies.

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

Progress of proteomics-driven precision medicine: From a glycosylation view

Currently, cancer is one of the leading causes of death worldwide, partially owing to the lack of early diagnosis methods and effective therapies. With the rapid development of various omics, the precision medicine strategy becomes a promising way to increase the survival rates by considering individual differences. Glycosylation is one of the most essential protein post-translational modifications and plays important roles in a variety of biological processes. Therefore, it is highly possible to acquire understanding of the molecular mechanisms as well as discover novel potential markers for diagnosis and prognosis based on glycoproteomics research. This review summarizes the recent glycoproteomics studies about N-glycosylation of several cancer types, mainly in the past 5 years. We also highlight corresponding mass spectrometry-based analytical methods to give a brief overview on the main techniques applied in glycoproteomics.

Identification of Potential Predictor of Biochemical Recurrence in Prostate Cancer

Background

Prostate cancer is a common malignancy in men. Radical prostatectomy is one of the primary treatment modalities for patients with prostate cancer. However, early identification of biochemical recurrence is a major challenge for post-radical prostatectomy surveillance. There is a lack of reliable predictors of biochemical recurrence. The purpose of this study was to explore potential biochemical recurrence indicators for prostate cancer.

Materials and Methods

We analyzed transcriptomic data of cases with biochemical recurrence in The Cancer Genome Atlas (TCGA). Then, we performed integrative bioinformatics analyses to establish a biochemical recurrence predictor model of prostate cancer.

Results

There were 146 differentially expressed genes (DEGs) between prostate cancer and normal prostate, including 12 upregulated and 134 downregulated genes. Comprehensive pathway enrichment analyses revealed that these DEGs were associated with multiple cellular metabolic pathways. Subsequently, according to the random assignment principle, 208 patients were assigned to the training cohort and 205 patients to the validation cohort. Univariate Cox regression analysis showed that 7 genes were significantly associated with the biochemical recurrence of prostate cancer. A model consisting of 5 genes was constructed using LASSO regression and multivariate Cox regression to predict biochemical recurrence of prostate cancer. Expression of PAH and AOC1 decreased with an increasing incidence of prostate cancer, whereas expression of DDC, LINC01436 and ORM1 increased with increasing incidence of prostate cancer. Kaplan–Meier curves and receiver operator characteristic (ROC) curves indicated that the 5-gene model had reliable utility in identifying the risk of biochemical recurrence of prostate cancer.

Conclusion

This study provides a model for predicting prostate cancer recurrence after surgery, which may be an optional indicator for postoperative follow-up.

Glycan biomarkers of autoimmunity and bile acid-associated alterations of the human glycome: Primary biliary cirrhosis and primary sclerosing cholangitis-specific glycans

We have recently introduced multiple reaction monitoring (MRM) mass spectrometry as a novel tool for glycan biomarker research and discovery. Herein, we employ this technique to characterize the site-specific glycan alterations associated with primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC). Glycopeptides associated with disease severity were also identified. Multinomial regression modelling was employed to construct and validate multi-analyte diagnostic models capable of accurately distinguishing PBC, PSC, and healthy controls from one another (AUC = 0.93 ± 0.03). Finally, to investigate how disease-relevant environmental factors can influence glycosylation, we characterized the ability of bile acids known to be differentially expressed in PBC to alter glycosylation. We hypothesize that this could be a mechanism by which altered self-antigens are generated and become targets for immune attack. This work demonstrates the utility of the MRM method to identify diagnostic site-specific glycan classifiers capable of distinguishing even related autoimmune diseases from one another.

Recent Advances in Analytical Approaches for Glycan and Glycopeptide Quantitation

Growing implications of glycosylation in physiological occurrences and human disease have prompted intensive focus on revealing glycomic perturbations through absolute and relative quantification. Empowered by seminal methodologies and increasing capacity for detection, identification, and characterization, the past decade has provided a significant increase in the number of suitable strategies for glycan and glycopeptide quantification. Mass-spectrometry-based strategies for glycomic quantitation have grown to include metabolic incorporation of stable isotopes, deposition of mass difference and mass defect isotopic labels, and isobaric chemical labeling, providing researchers with ample tools for accurate and robust quantitation. Beyond this, workflows have been designed to harness instrument capability for label-free quantification, and numerous software packages have been developed to facilitate reliable spectrum scoring. In this review, we present and highlight the most recent advances in chemical labeling and associated techniques for glycan and glycopeptide quantification.

Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer

Inter-tumor heterogeneity is a result of genomic, transcriptional, translational, and post-translational molecular features. To investigate the roles of protein glycosylation in the heterogeneity of high-grade serous ovarian carcinoma (HGSC), we perform mass spectrometry-based glycoproteomic characterization of 119 TCGA HGSC tissues. Cluster analysis of intact glycoproteomic profiles delineates 3 major tumor clusters and 5 groups of intact glycopeptides. It also shows a strong relationship between N-glycan structures and tumor molecular subtypes, one example of which being the association of fucosylation with mesenchymal subtype. Further survival analysis reveals that intact glycopeptide signatures of mesenchymal subtype are associated with a poor clinical outcome of HGSC. In addition, we study the expression of mRNAs, proteins, glycosites, and intact glycopeptides, as well as the expression levels of glycosylation enzymes involved in glycoprotein biosynthesis pathways in each tumor. The results show that glycoprotein levels are mainly controlled by the expression of their individual proteins, and, furthermore, that the glycoprotein-modifying glycans correspond to the protein levels of glycosylation enzymes. The variation in glycan types further shows coordination to the tumor heterogeneity. Deeper understanding of the glycosylation process and glycosylation production in different subtypes of HGSC may provide important clues for precision medicine and tumor-targeted therapy.

Altered protein glycosylation is increasingly recognized as a hallmark of cancer. Here, the authors profile the glycoproteome of 119 high-grade serous ovarian carcinoma tissues, showing that glycosylation patterns correlate with tumor molecular subtypes and clinical outcomes.

Glycoproteomic Analysis of Human Urinary Exosomes

Exosomes represent a class of secreted biological vesicles, which have recently gained attention due to their function as intertissue and interorganism transporters of genetic materials, small molecules, lipids, and proteins. Although the protein constituents of these exosomes are often glycosylated, a large-scale characterization of the glycoproteome has not yet been completed. This study identified 3144 unique glycosylation events belonging to 378 glycoproteins and 604 unique protein sites of glycosylation. With these data, we investigated the level of glycan microheterogeneity within the urinary exosomes, finding on average 5.9 glycans per site. The glycan family abundance on individual proteins showed subtle differences, providing an additional level of molecular characterization compared to the unmodified proteome. Finally, we show protein site-specific changes in regard to the common urinary glycoprotein, uromodulin. While uromodulin is an individual case, these same site-specific analyses provide a way forward for developing diagnostic glycoprotein biomarkers with urine as a noninvasive biological fluid. This study represents an important first step in understanding the functional urinary glycoproteome.

Mass spectrometry in clinical glycomics: The path from biomarker identification to clinical implementation

Over the past decades, the genome and proteome have been widely explored for biomarker discovery and personalized medicine. However, there is still a large need for improved diagnostics and stratification strategies for a wide range of diseases. Post-translational modification of proteins by glycosylation affects protein structure and function, and glycosylation has been implicated in many prevalent human diseases. Numerous proteins for which the plasma levels are nowadays evaluated in clinical practice are glycoproteins. While the glycosylation of these proteins often changes with disease, their glycosylation status is largely ignored in the clinical setting. Hence, the implementation of glycomic markers in the clinic is still in its infancy. This is for a large part caused by the high complexity of protein glycosylation itself and of the analytical techniques required for their robust quantification. Mass spectrometry-based workflows are particularly suitable for the quantification of glycans and glycoproteins, but still require advances for their transformation from a biomedical research setting to a clinical laboratory. In this review, we describe why and how glycomics is expected to find its role in clinical tests and the status of current mass spectrometry-based methods for clinical glycomics.

A site-specific map of the human plasma glycome and its age and gender-associated alterations

Alterations in the human glycome have been associated with cancer and autoimmunity. Thus, constructing a site-specific map of the human glycome for biomarker research and discovery has been a highly sought-after objective. However, due to analytical barriers, comprehensive site-specific glycoprofiling is difficult to perform. To develop a platform to detect easily quantifiable, site-specific, disease-associated glycan alterations for clinical applications, we have adapted the multiple reaction monitoring mass spectrometry method for use in glycan biomarker research. The adaptations allow for highly precise site-specific glycan monitoring with minimum sample prep. Using this technique, we successfully mapped out the relative abundances of the most common 159 glycopeptides in the plasma of 97 healthy volunteers. This plasma glycome map revealed 796 significant (FDR < 0.05) site-specific inter-protein and intra-protein glycan associations, of which the vast majority were previously unknown. Since age and gender are relevant covariants in biomarker research, these variables were also characterized. 13 glycopeptides were found to be associated with gender and 41 to be associated with age. Using just five age-associated glycopeptides, a highly accurate age prediction model was constructed and validated (r2 = 0.62 ± 0.12). The human plasma site-specific glycan map described herein has utility in applications ranging from glycan biomarker research and discovery to the development of novel glycan-altering interventions.

Targeting Oligosaccharides and Glycoconjugates Using Superselective Binding Scaffolds

Recognition of oligosaccharides is associated with very limited specificity due to their strong solvation in water and the high degree of subtle structural variations between them. Here, oligosaccharide recognition sites are created on material surfaces with unmatched, binary on-off binding behavior, sharply discriminating a target oligosaccharide over closely related carbohydrate structures. The basis for the superselective binding behavior relies on the highly efficient generation of a pure, high order complex of the oligosaccharide target with synthetic carbohydrate receptor sites, in which the spatial arrangement of the multiple receptors in the complex is preserved upon material surface incorporation. The synthetic binding scaffolds can easily be tailored to recognize different oligosaccharides and glycoconjugates, opening up a realm of possibilities for their use in a wide field of applications, ranging from life sciences to diagnostics.

PB-Net: Automatic peak integration by sequential deep learning for multiple reaction monitoring

Mass spectrometry (MS) based proteomics has become an indispensable component of modern molecular and cellular biochemistry analysis. Multiple reaction monitoring (MRM) is one of the most well-established MS techniques for molecule detection and quantification. Despite its wide usage, there lacks an accurate computational framework to analyze MRM data, and expert annotation is often required, especially to perform peak integration. Here we propose a deep learning method PB-Net (Peak Boundary Neural Network), built upon recent advances in sequential neural networks, for fully automatic chromatographic peak integration. To train PB-Net, we generated a large dataset of over 170,000 expert annotated peaks from MS transitions spanning a wide dynamic range, including both peptides and intact glycopeptides. Our model demonstrated outstanding performances on unseen test samples, reaching near-perfect agreement (Pearson's r 0.997) with human annotated ground truth. Systematic evaluations also show that PB-Net is substantially more robust and accurate compared to previous state-of-the-art peak integration software. PB-Net can benefit the wide community of mass spectrometry data analysis, especially in applications involving high-throughput MS experiments. Codes and test data used in this work are available at https://github.com/miaecle/PB-net. SIGNIFICANCE: Human annotations serve an important role in accurate quantification of multiple reaction monitoring (MRM) experiments, though they are costly to collect and limit analysis throughput. In this work we proposed and developed a novel technique for the peak-integration step in MRM, based on recent innovations in sequential deep learning models. We collected in total 170,000 expert-annotated MRM peaks and trained a set of accurate and robust neural networks for the task. Results demonstrated a substantial improvement over the current state-of-the-art software for mass spectrometry analysis and comparable level of accuracy and precision as human annotators.

Distribution of de novo Donor-Specific Antibody Subclasses Quantified by Mass Spectrometry: High IgG3 Proportion Is Associated With Antibody-Mediated Rejection Occurrence and Severity

Donor-specific antibodies (DSAs) are the main risk factor for antibody-mediated rejection (ABMR) and graft loss but could have variable pathogenicity according to their IgG subclass composition. Luminex-based test might lack sensitivity for the detection of IgG subclasses and this test does not allow quantifying the relative abundance of each IgG subclass. We investigated the precise repartition of each DSA subclass and their role in ABMR occurrence and severity, using an innovative mass spectrometry-based method. Between 2014 and 2018, we enrolled 69 patients who developed de novo DSA (n = 29 without ABMR, and n = 40 with ABMR) in two transplant centers. All IgG subclasses were detected in every samples tested: 62.7% were IgG1, 26.6% were IgG2, 6.6% were IgG3, and 4.2% were IgG4. The IgG3 proportion was significantly higher in the ABMR+ compared to the ABMR– group (8.4% vs. 5.6%, p = 0.003). The proportion of IgG1, IgG2, and IgG4 of DSA was similar between the two groups. Higher IgG3 level was associated with higher C4d deposition, higher microvascular inflammation scores, and glomerular filtration rate decline >25%. IgG3 proportion was not correlated with DSA MFI. Multivariate analysis showed that proteinuria and high level of IgG3 DSA were the only two factors independently associated with ABMR. In conclusion, de novo DSA are always composed of the four IgG subclasses, but in different proportions. High IgG3 proportion is associated with ABMR occurrence and severity and with poorer outcome, independently of DSA MFI.

N-Glycosylation profiling of intact target proteins by high-resolution mass spectrometry (MS) and glycan analysis using ion mobility-MS/MS

Glycosylation characterization could lead to the discovery of biomarkers and is crucial in quality control of biopharmaceuticals. Here we present a method to quantify glycoforms on intact proteins, with parallel glycan identification by IMS-MS/MS.

Dramatically changed immune-related molecules as early diagnostic biomarkers of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the main type of lung cancer, with a low 5-year survival rate because of the absence of effective clinical biomarkers for early diagnosis. Based on the immunosurveillance theory, we proposed that changes in the immune system are more pronounced than tumour-associated antigens during the early stage of cancer. Therefore, a new strategy was designed to screen early diagnostic biomarkers from peripheral leukocytes in early-stage NSCLCs with transcriptome sequencing. A total of 358 immune-related differentially expressed genes were identified between early-NSCLC patients and healthy individuals. Orosomucoid-1 (ORM1, a acute phase protein), the total ORM and chitotriosidase-1 (involved in degradation of chitobiose) were selected for further verification in 210 serum samples by western blotting, ELISA and nephelometry immunoassay (based on immuno-scatter turbidmetry). Receiver operating characteristic curve analysis show that ORM1 and total ORM have excellent diagnostic efficacies, with area under the curve of 0.862 and 0.920, respectively, which significantly distinguished very early-NSCLC (IA) from healthy samples. Flow cytometry results showed that CD15⁺ neutrophils made up 73% of ORM1⁺ peripheral leukocytes. In mouse lung cancer model, serum ORM1, but not liver ORM1, changed significantly in the early stage of NSCLC. ORM1 expression in peripheral leukocytes was regulated by TGF-β and mediated by the TGF-β/Smad signalling pathway. Our results indicated that combined ORM and TGF-β could be a promising clinical biomarker in the diagnosis of early NSCLC.

Global and site-specific analysis of protein glycosylation in complex biological systems with Mass Spectrometry

Protein glycosylation is ubiquitous in biological systems and plays essential roles in many cellular events. Global and site-specific analysis of glycoproteins in complex biological samples can advance our understanding of glycoprotein functions and cellular activities. However, it is extraordinarily challenging because of the low abundance of many glycoproteins and the heterogeneity of glycan structures. The emergence of mass spectrometry (MS)-based proteomics has provided us an excellent opportunity to comprehensively study proteins and their modifications, including glycosylation. In this review, we first summarize major methods for glycopeptide/glycoprotein enrichment, followed by the chemical and enzymatic methods to generate a mass tag for glycosylation site identification. We next discuss the systematic and quantitative analysis of glycoprotein dynamics. Reversible protein glycosylation is dynamic, and systematic study of glycoprotein dynamics helps us gain insight into glycoprotein functions. The last part of this review focuses on the applications of MS-based proteomics to study glycoproteins in different biological systems, including yeasts, plants, mice, human cells, and clinical samples. Intact glycopeptide analysis is also included in this section. Because of the importance of glycoproteins in complex biological systems, the field of glycoproteomics will continue to grow in the next decade. Innovative and effective MS-based methods will exponentially advance glycoscience, and enable us to identify glycoproteins as effective biomarkers for disease detection and drug targets for disease treatment. © 2019 Wiley Periodicals, Inc. Mass Spec Rev 9999: XX-XX, 2019.

Analyzing protein glycosylation using UHPLC: a review

Ultrahigh performance liquid chromatography (UHPLC) uses small stationary-phase particle size (<2 μm) and high pressure in order to achieve rapid and efficient separations. The speed and high resolution of this method has made it a valuable tool for analyzing the complex glycosylation patterns found in post-translationally modified proteins. This article highlights the differences between UHPLC and HPLC and reviews recent UHPLC applications and developments for detecting glycosylated proteins (e.g., glycomics studies) and characterizing glycosylated pharmaceuticals (e.g., monoclonal antibodies).

Glycopeptides as a Tool for Early Detection of Cancer

Protein glycosylation, as one of the most common and complex posttranslational modifications, plays an important role in many biological processes. Along with the intensive progress in MS techniques and development of glycan search tools and databases, glycoproteomics has become a popular subject of studies. The possibility of simultaneous identification of amino acid sequence, glycosylation sites, and glycan composition enabled the monitoring of changes in glycosylation patterns in various pathological states. In this issue, Saraswat et al. describe MS-based investigations of glycopeptide changes in oral cavity squamous cell carcinoma (OSCC). Their findings indicate glycopeptides with changed expression levels and the presence of altered glycan forms observed in four proteins derived from OSCC patients' sera. Proteins carrying this distinctive pattern are in the group of the most abundant components of serum, IgG1, IgG4, HPT, and TRFE, which makes their identification more accessible. Described changes, characteristic for cancer serum samples, may be considered as potential diagnostic indicators of OSCC; however, there is still a need to establish a universal glycopeptide-based biomarkers database, where all glycoproteomic data can be collected from all types of cancer studies and evaluated using meta-analyses. Only then, early diagnosis of patients using MS-based approach will make sense, as those investigations are very convoluted, and all efforts made during sample preparation and analysis will pay off when comprehensive anticancer prevention will be achieved during single measurement.

Mass Spectrometry Approaches to Glycomic and Glycoproteomic Analyses

Glycomic and glycoproteomic analyses involve the characterization of oligosaccharides (glycans) conjugated to proteins. Glycans are produced through a complicated nontemplate driven process involving the competition of enzymes that extend the nascent chain. The large diversity of structures, the variations in polarity of the individual saccharide residues, and the poor ionization efficiencies of glycans all conspire to make the analysis arguably much more difficult than any other biopolymer. Furthermore, the large number of glycoforms associated with a specific protein site makes it more difficult to characterize than any post-translational modification. Nonetheless, there have been significant progress, and advanced separation and mass spectrometry methods have been at its center and the main reason for the progress. While glycomic and glycoproteomic analyses are still typically available only through highly specialized laboratories, new software and workflow is making it more accessible. This review focuses on the role of mass spectrometry and separation methods in advancing glycomic and glycoproteomic analyses. It describes the current state of the field and progress toward making it more available to the larger scientific community.

Understanding Ovarian Cancer: iTRAQ-Based Proteomics for Biomarker Discovery

Despite many years of studies, ovarian cancer remains one of the top ten cancers worldwide. Its high mortality rate is mainly due to lack of sufficient diagnostic methods. For this reason, our research focused on the identification of blood markers whose appearance would precede the clinical manifestation of the disease. ITRAQ-tagging (isobaric Tags for Relative and Absolute Quantification) coupled with mass spectrometry technology was applied. Three groups of samples derived from patients with: ovarian cancer, benign ovarian tumor, and healthy controls, were examined. Mass spectrometry analysis allowed for highlighting the dysregulation of several proteins associated with ovarian cancer. Further validation of the obtained results indicated that five proteins (Serotransferrin, Amyloid A1, Hemopexin, C-reactive protein, Albumin) were differentially expressed in ovarian cancer group. Interestingly, the addition of Albumin, Serotransferrin, and Amyloid A1 to CA125 (cancer antigen 125) and HE4 (human epididymis protein4) improved the diagnostic performance of the model discriminating between benign and malignant tumors. Identified proteins shed light on the molecular signaling pathways that are associated with ovarian cancer development and should be further investigated in future studies. Our findings indicate five proteins with a strong potential to use in a multimarker test for screening and detection of ovarian cancer.

Quantitative Analysis of Sex-Hormone-Binding Globulin Glycosylation in Liver Diseases by Liquid Chromatography-Mass Spectrometry Parallel Reaction Monitoring

Sex-hormone-binding globulin (SHBG) is a liver-secreted glycoprotein and a major regulator of steroid distribution. It has been reported that the serum concentration of SHBG changes in liver disease. To explore the involvement of SHBG in liver disease of different etiologies in greater detail, we developed a sensitive and selective liquid chromatography-mass spectrometry parallel reaction monitoring workflow to achieve quantitative analysis of SHBG glycosylation microheterogeneity. The method uses energy-optimized "soft" fragmentation to extract informative Y ions for maximal coverage of glycoforms and their quantitative comparisons. A total of 15 N-glycoforms of two N-glycosites and 3 O-glycoforms of 1 O-glycosite of this low-abundance serum protein were simultaneously analyzed in the complex samples. At the same time, we were able to partially resolve linkage isoforms of the fucosylated glycoforms and to identify and quantify SHBG N-glycoforms that were not previously reported. The results show that both core and outer-arm fucosylation of the N-glycoforms increases with liver cirrhosis but that a further increase of fucosylation is not observed with hepatocellular carcinoma (HCC). In contrast, the α-2-6 sialylated glycoform of the O-glycopeptide of SHBG increases in liver cirrhosis, and a significant 2-fold further increase is observed in HCC. In general, we do not find a significant contribution of different liver disease etiologies to the observed changes in glycosylation; however, elevation of the newly reported HexNAc(4)Hex(6) N-glycoform is associated with alcoholic liver disease.