A Novel Platform for Multiplexed N-glycoprotein Biomarker Discovery for Hepatocellular Carcinoma by Antibody Panel Based N-glycan Imaging

The vast majority of biomarkers used in the detection of cancer are glycoproteins, and numerous studies have indicated that the N-glycosylation of serum glycoproteins changes with the development of hepatocellular carcinoma (HCC). However, current biomarkers for HCC are lacking in sensitivity and specificity, and there is a need for higher throughput techniques to discover more powerful biomarkers. The majority of methods that do analyze N-glycans and their protein carriers generally require large amounts of sample preparation and/or look at only one protein at a time, which is a barrier for translating discoveries to the clinic. In response to this need for multiplexed biomarker analysis of protein-specific N-glycan changes, we developed a novel platform for the simultaneous analysis of potentially 100s of N-linked glycoproteins from biofluids with the goal of discovering new clinically-relevant cancer biomarkers. This new mass spectrometry imaging platform for multiplexed N-glycoprotein biomarker was applied to multiple cohorts of cirrhotic and HCC patient serum samples. An antibody panel encompassing antibodies for seven glycoproteins was used in the analysis of two cohorts consisting of 100 patients. These data were used to create biomarker algorithms incorporating protein-specific glycan signatures and clinical information. These models produced AUROCs of 0.9289 and 0.9278 for differentiating HCC from cirrhosis, which were significant improvements on the currently used biomarker AFP. We also expect that this platform can be expanded for biomarker discovery of other types of cancers and diseases from numerous types of biofluids

Utilizing Mass Spectrometry Imaging to Correlate N-Glycosylation of Hepatocellular Carcinoma with Tumor Subtypes for Biomarker Discovery

Hepatocellular carcinoma (HCC) is a leading cause of cancer deaths globally and is a growing clinical problem with poor survival outcomes beyond early-stage disease. Surveillance for HCC has primarily relied on ultrasound and serum α-fetoprotein (AFP), but combined they only have a sensitivity of 63% for early-stage HCC tumors, suggesting a need for improved diagnostic strategies. Alterations to N-glycan expression are relevant to the progression of cancer, and there a multitude of N-glycan-based cancer biomarkers that have been identified with sensitivity for various cancer types including HCC. Spatial HCC tissue profiling of N-linked glycosylation by matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS) serves as a new method to evaluate tumor-correlated N-glycosylation and thereby identify potential HCC biomarkers. Previous work has identified significant changes in the N-linked glycosylation of HCC tumors, but has not accounted for the heterogeneous genetic and molecular nature of HCC, which has led to inadequate sensitivity of N-glycan biomarkers. Therefore, we hypothesized that the incorporation of genetic/molecular information into N-glycan-based biomarker development would result in improved sensitivity for HCC. To determine the correlation between HCC-specific N-glycosylation and genetic/molecular tumor features, we profiled HCC tissue samples with MALDI-IMS and correlated the spatial N-glycosylation with a widely used HCC molecular classification that utilizes histological, genetic, and clinical tumor features (Hoshida subtypes). MALDI-IMS data displayed trends that could approximately distinguish between subtypes, with Subtype 1 demonstrating significantly dysregulated N-glycosylation compared to Subtypes 2 and 3, particularly in regard to fucosylation. In order to further the clinical relevance of subtype-dependent N-glycosylation, we analyzed patient-matching HCC tumor tissue, background liver tissue and serum samples through MALDI-IMS. Results showed a N-glycan based model capable of differentiating tumor tissue from background liver tissue with an AUC of 0.9842. When analyzing the associated serum, 24.7% of detected N-glycans were significantly positively correlated between tumor tissue and serum, suggesting that N-glycosylation trends translate from tissue to serum. Additionally, a serum N-glycan-based model was capable of distinguishing Subtype 1/Subtype 2 tumors from Subtype 3 tumors with an AUC of 0.881. Through the utilization of MALDI-IMS, subtype-dependent N-glycosylation trends were identified in both tissue and serum, which can significantly further the development of HCC biomarkers for clinical application

IMass time: The future, in future!

Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass

Determination of N-Linked Glycosylation Changes in Hepatocellular Carcinoma and the Associated Glycoproteins for Enhanced Biomarker Discovery and Therapeutic Targets

With hepatocellular carcinoma (HCC) remaining as the fifth most common cancer in the world, causing more than 700,000 deaths annually, the need for reliable, early stage diagnoses and preventive treatments is crucial. While serum glycoproteins are hepatic in origin, making them excellent targets for HCC biomarkers, they can originate from both cancerous and non-cancerous regions and direct analysis of cancerous tissue itself is lacking. To counteract this, I hypothesized that direct tissue analysis combined with proteomic analysis could be utilized to identify more potential targets specific to HCC for early detection. This was done with a primary focus on glycosylation—as most clinically approved biomarkers are glycoproteins—and examined direct tissue glycomics in conjunction with glycoproteomic techniques through two specific aims: 1) Determining patterns of N-linked glycan changes in HCC tissue using MALDI imaging mass spectrometry to compare to previously published serum changes and 2) identifying glycopeptides containing changes in observed patterns of N- linked glycans in HCC samples using a targeted glycoproteomic approach. In Aim 1, HCC tissue was examined using MALDI imaging mass spectrometry to v verify changes in glycosylation via direct tissue analysis. Here, it was found that increased branching and fucosylation were directly associated with the cancerous tissue when compared to normal or cirrhotic. To further identify changes in glycosylation, two methods (one novel and one adapted for imaging) were implemented on tissue to further classify N-linked glycan isoforms through linkage analysis, specifically for sialic acids and core fucose. Again, it was shown that core fucose is most directly related to HCC tissue, thus confirming serum findings in the literature. For Aim 2, the novel method of determining core fucosylation was used in conjunction with glycoproteomic techniques to further elucidate the core fucosylated glycoproteins of interest. With the tag left behind following the enzymatic cleavage, targeted glycoproteomics was used to determine glycoproteins of interest while eliminating some biases inherent in the method, such as low ionization efficiencies for more complex N-glycans. This work outlines the first in-depth analysis of HCC tissue specifically regarding N- glycan changes, a novel application to determine N-glycan isoforms, and the application of these methods for glycoproteomic enhancement. With these findings, new trends in glycosylation related to the disease state could be further uncovered, as well as provide new biomarker candidates or therapeutic targets for future studies

Developing and Utilizing Novel Biofluid N-Glycan Profiling Methods for the Classification of Suspicious Mammogram Findings

Biofluids are a great source of biomarkers because they reflect the immune and metabolic status of cells throughout the body and can be collected non-invasively. Changes in the levels and compositions of N-glycans released from serum and plasma glycoproteins have been assessed in many diseases across many large clinical sample cohorts. Assays used for N-glycan profiling in these fluids currently require multiple lengthy processing steps, thus diminishing their potential for use as standard clinical diagnostic assays. In response to this need for rapid, simple, and high-throughput biofluid analysis, a novel slide-based biofluid profiling platform was developed for the detection of N-glycan alterations that can function as biomarkers of disease. This platform was initially validated for the analysis of serum and plasma N-glycan profiles but was also adapted for the evaluation of urine and prostatic fluid N-glycan profiles. Key to these workflows was the immobilization of the fluid glycoproteins to a slide that was washed of all contaminants, followed by the rapid and highly sensitive detection of enzymatically released N-glycans by mass spectrometry. The enzyme used to release the N-glycans can be changed to target and increase the sensitivity for specific N-glycan classes. To demonstrate the utility and feasibility of applying this platform, serum samples from 199 women with breast cancer and 99 women with a benign lesion in their breast were analyzed in order to identify differences in the N-glycan profiles. The overall N-glycan profiles of the two patient groups had no differences, but there were several individual N-glycans with significant differences in intensities between patients with benign lesions and ductal carcinoma in situ (DCIS). For women aged 50 - 74 with a body mass index of 18.5 - 24.9, a model including the intensities of two N-glycans, 1850.666 m/z and 2163.743 m/z, age, and BMI were able to clearly distinguish the breast cancer patients from the patients with benign lesions with an AUROC of 0.899 and an optimal cutoff with 82% sensitivity and 84% specificity. This study indicates that serum N-glycan profiling is a promising approach for providing clarity for breast cancer screening, especially within the subset of healthy weight women in the age group recommended for mammograms. Overall, the workflows described in this dissertation have displayed the sensitivity, adaptability, and throughput to be utilized as a biomarker discovery platform with significant clinical utility

Method Development for the Identification of Alternatively Sialylated Glycoproteins in Non-Small Cell Lung Cancer

Non-small cell lung cancer represents 85% of all lung cancers with an average 5 year life expectancy of 15-20%. A wealth of data suggests that altered glycosylation contributes to the progression of these tumors and efforts to improve the specificity of biomarkers have logically shifted towards glycoproteomic investigations. For this purpose, recent innovations in experimental strategies and analytical techniques could be combined to provide a more detailed characterization of glycans than previously achievable. These new methods have not yet been assessed for feasibility in experimental procedures useful for discovery phase efforts. Therefore, we aimed to investigate the utility of novel glycoproteomic and glycomic approaches for defining alterations in glycosylation which may accompany disease progression. To this end, we implemented an azido-sugar metabolic labeling, alkyne-agarose bead enrichment, and liquid chromatography-tandem mass spectrometry for profiling glycoproteins in a cell model of lung cancer induced to express transforming growth factor beta ligand-1. This approach identified putative changes in the sialylation of glycoproteins related to metabolic, cell adhesion, glycan biosynthesis, and extracellular matrix-related proteins. The application of a secondary digest to glycopeptide-bound beads, using peptide-N-glycosidase-F, was useful for verifying N-glycan sites as well as exposing previously undetected glycoproteins. A stable isotope labeling of amino acids in cell culture approach was used to gauge if altered protein expression was contributing to differential capture, however this method provided limited information due to a low overlap of proteins identified from enriched vs. unenriched fractions. In another set of experiments, we applied a novel derivatization strategy combined with high resolution/high mass accuracy mass spectrometry for discerning glycan structures in human lung cancer proximal fluids. This procedure effectively defined the sialic acid anomeric configuration of several prevalent species and identified preliminary trends in the expression of oligomannose and complex glycans in clinically-relevant materials. Finally, matrix-assisted laser desorption ionization imaging mass spectrometry was used to spatially resolve the distribution of N-glycans in lung tissues matched to the proximal fluids. Histological assessment of these tissues facilitated cross-reference of acquired glycan species to regions of interest and provided a direct means for assessing how these trends correlated in the proximal fluids

Analysis of carbohydrates and glycoconjugates by matrix‐assisted laser desorption/ionization mass spectrometry: An update for 2021–2022

The use of matrix‐assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well‐established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo‐ and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing

Developing an Integrative Glycobiology Workflow for the Identification of Disease Markers for Pancreatic Cancer

A deeper understanding of dysregulated glycosylation in pancreatic cancer can provide insights into disease mechanisms and the identification of novel disease markers. Recent improvements in mass spectrometry techniques have been instrumental in profiling biologically relevant tissue sections in order to identify disease marker candidates, but have either not yet been adopted for studying glycosylation or applied directly to pancreatic cancer. In the dissertation herein, new methods have been developed and adapted to the study of aberrant glycosylation in pancreatic cancer, with the ultimate goal of identifying novel disease marker candidates. For the first time, we describe a mass spectrometry imaging approach to study the localization of N-glycans. This technique demonstrated a histology-derived localization of N-glycans across tissue sections, with identifications displaying remarkable consistency with documented studies. Furthermore, the technique provides superior structural information compared to preexisting methodologies. In the analysis of diseased specimen, changes in glycosylation can be linked to aberrations in glycosyltransferase expression. When applied to pancreatic cancer in a high-throughput and high-dimensional analysis, panels of glycans displayed an improved ability to differentiate tumor from non-tumor tissues compared to current disease markers. Furthermore, the data suggest that glycosylation can identify premalignant lesions, as well as differentiate between malignant and benign conditions. These observations overcome significant limitations that hinder the efficacy of current disease markers. In an effort to link aberrant glycosylation to the modified protein, a subset of glycosylated proteins were enriched and analyzed by mass spectrometry to identify proteins that are integral to disease progression and can be probed for the early detection of pancreatic cancer. Known disease markers were among the glycoproteins identified, validating the utility of the enrichment and detection strategy outlined. This approach also differentiated the role of N- and O-glycosylation in antigen expression. Finally, we outline an integrated workflow that takes advantage of the unique capabilities of high resolution mass spectrometers. This workflow can capitalize on prior glycomic and proteomic experiments to provide a comprehensive analysis of dysregulated protein glycosylation in pancreatic cancer

An Imaging Mass Spectrometry Investigation Into the N-linked Glycosylation Landscape of Pancreatic Ductal Adenocarcinoma and the Development of Associated Tools for Enhanced Glycan Separation and Characterization

The severity of pancreatic ductal adenocarcinoma (PDAC) is largely attributed to a failure to detect the disease before metastatic spread has occurred. CA19-9, a carbohydrate biomarker, is used clinically to surveille disease progression, but due to specificity challenges is not suitable for early discovery. As CA19-9 and other prospective markers are glycan epitopes, there is great clinical interest in understanding the glycobiology of pancreatic cancer. Unfortunately, few studies have been able to link glycosylation changes directly to pancreatic tumors and instead have focused on peripheral glycan alterations in the serum of PDAC patients. To address this gap in our understanding, we applied an imaging mass spectrometry (IMS) approach with complementary enzymatic and chemical isomer separation techniques to spatially assess the PDAC N-glycome in a cohort of pancreatic cancer patients. Orthogonally, we characterized the expression of CA19-9 and a new biomarker, sTRA, by multi-round immunofluorescence (IF) in the same cohort. These analyses revealed increased sialylation, fucosylation and branching amongst other structural themes in areas of PDAC tumor tissue. CA19-9 expressing tumors were defined by multiply branched, fucosylated bisecting N-glycans while sTRA expressing tumors favored tetraantennary N-glycans with polylactosamine extensions. IMS and IF-derived glycan and biomarker features were used to build classification models that detected PDAC tissue with an AUC of 0.939, outperforming models using either dataset individually. While studying sialylation isomers in our PDAC cohort, we saw an opportunity to enhance the chemical derivatization protocol we were using to address its shortcomings and expand its functionality. Subsequently, we developed a set of novel amidation-amidation strategies to stabilize and differentially label 2,3 and 2,6-linked sialic acids. In our alkyne-based approach, the differential mass shifts induced by the reactions allow for isomeric discrimination in imaging mass spectrometry experiments. This scheme, termed AAXL, was further characterized in clinical tissue specimens, biofluids and cultured cells. Our azide-based approach, termed AAN3, was more suitable for bioorthogonal applications, where the azide tag installed on 2,3 and 2,8-sialic acids could be reacted by click chemistry with a biotin-alkyne for subsequent streptavidin-peroxidase staining. Furthering the use of AAN3, we developed two additional techniques to fluorescently label (SAFER) and preferentially enrich (SABER) 2,3 and 2,8-linked sialic acids for more advanced glycomic applications. Initial experiments with these novel approaches have shown successful fluorescent staining and the identification of over 100 sialylated glycoproteins by LC-MS/MS. These four bioorthogonal strategies provide a new glycomic tool set for the characterization of sialic acid isomers in pancreatic and other cancers. Overall, this work furthers our collective understanding of the glycobiology underpinning pancreatic cancer and potentiates the discovery of novel carbohydrate biomarkers for the early detection of PDAC

Post-translational modifications and mass spectrometry detection

In this review, we provide a comprehensive bibliographic overview of the role of mass spectrometry and the recent technical developments in the detection of post-translational modifications (PTMs). We briefly describe the principles of mass spectrometry for detecting PTMs and the protein and peptide enrichment strategies for PTM analysis, including phosphorylation, acetylation and oxidation. This review presents a bibliographic overview of the scientific achievements and the recent technical development in the detection of PTMs is provided. In order to ascertain the state of the art in mass spectrometry and proteomics methodologies for the study of PTMs, we analyzed all the PTM data introduced in the Universal Protein Resource (UniProt) and the literature published in the last three years. The evolution of curated data in UniProt for proteins annotated as being post-translationally modified is also analyzed. Additionally, we have undertaken a careful analysis of the research articles published in the years 2010 to 2012 reporting the detection of PTMs in biological samples by mass spectrometry. © 2013 Elsevier Inc