MALDI Mass Spectrometry Imaging of N-Linked Glycans in Cancer Tissues

Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements

Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.

The glycosylation landscape of prostate cancer tissues and biofluids

An overview of the role of glycosylation in prostate cancer (PCa) development and progression is presented, focusing on recent advancements in defining the N-glycome through glycomic profiling and glycoproteomic methodologies. Glycosylation is a common post-translational modification typified by oligosaccharides attached N-linked to asparagine or O-linked to serine or threonine on carrier proteins. These attached sugars have crucial roles in protein folding and cellular recognition processes, such that altered glycosylation is a hallmark of cancer pathogenesis and progression. In the past decade, advancements in N-glycan profiling workflows using Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) technology have been applied to define the spatial distribution of glycans in PCa tissues. Multiple studies applying N-glycan MALDI-MSI to pathology-defined PCa tissues have identified significant alterations in N-glycan profiles associated with PCa progression. N-glycan compositions progressively increase in number, and structural complexity due to increased fucosylation and sialylation. Additionally, significant progress has been made in defining the glycan and glycopeptide compositions of prostatic-derived glycoproteins like prostate-specific antigen in tissues and biofluids. The glycosyltransferases involved in these changes are potential drug targets for PCa, and new approaches in this area are summarized. These advancements will be discussed in the context of the further development of clinical diagnostics and therapeutics targeting glycans and glycoproteins associated with PCa progression. Integration of large scale spatial glycomic data for PCa with other spatial-omic methodologies is now feasible at the tissue and single-cell levels.

Advancements in protein glycosylation biomarkers for ovarian cancer through mass spectrometry-based approaches

INTRODUCTION

Ovarian cancer, characterized by metastasis and reduced 5-year survival rates, stands as a substantial factor in the mortality of gynecological malignancies worldwide. The challenge of delayed diagnosis originates from vague early symptoms and the absence of efficient screening and diagnostic biomarkers for early cancer detection. Recent studies have explored the intricate interplay between ovarian cancer and protein glycosylation, unveiling the potential significance of glycosylation-oriented biomarkers.

AREAS COVERED

This review examines the progress in glycosylation biomarker research, with particular emphasis on advances driven by mass spectrometry-based technologies. We document milestones achieved, discuss encountered limitations, and also highlight potential areas for future research and development of protein glycosylation biomarkers for ovarian cancer.

EXPERT OPINION

The association of glycosylation in ovarian cancer is well known, but current research lacks desired sensitivity and specificity for early detection. Notably, investigations into protein-specific and site-specific glycoproteomics have the potential to significantly enhance our understanding of ovarian cancer and facilitate the identification of glycosylation-based biomarkers. Furthermore, the integration of advanced mass spectrometry techniques with AI-driven analysis and glycome databases holds the promise for revolutionizing biomarker discovery for ovarian cancer, ultimately transforming diagnosis and improving patient outcomes.

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Recent Developments and Application of Mass Spectrometry Imaging in N-Glycosylation Studies: An Overview

Among the most typical posttranslational modifications is glycosylation, which often involves the covalent binding of an oligosaccharide (glycan) to either an asparagine (N-linked) or a serine/threonine (O-linked) residue. Studies imply that the N-glycan portion of a glycoprotein could serve as a particular disease biomarker rather than the protein itself because N-linked glycans have been widely recognized to evolve with the advancement of tumors and other diseases. N-glycans found on protein asparagine sites have been especially significant. Since N-glycans play clearly defined functions in the folding of proteins, cellular transport, and transmission of signals, modifications to them have been linked to several illnesses. However, because these N-glycans' production is not template driven, they have a substantial morphological range, rendering it difficult to distinguish the species that are most relevant to biology and medicine using standard techniques. Mass spectrometry (MS) techniques have emerged as effective analytical tools for investigating the role of glycosylation in health and illness. This is due to developments in MS equipment, data collection, and sample handling techniques. By recording the spatial dimension of a glycan's distribution in situ, mass spectrometry imaging (MSI) builds atop existing methods while offering added knowledge concerning the structure and functionality of biomolecules. In this review article, we address the current development of glycan MSI, starting with the most used tissue imaging techniques and ionization sources before proceeding on to a discussion on applications and concluding with implications for clinical research.

Utilizing multimodal mass spectrometry imaging for profiling immune cell composition and N-glycosylation across colorectal carcinoma disease progression

Colorectal cancer (CRC) stands as a leading cause of death worldwide, often arising from specific genetic mutations, progressing from pre-cancerous adenomas to adenocarcinomas. Early detection through regular screening can result in a 90% 5-year survival rate for patients. However, unfortunately, only a fraction of CRC cases are identified at pre-invasive stages, allowing progression to occur silently over 10-15 years. The intricate interplay between the immune system and tumor cells within the tumor microenvironment plays a pivotal role in the progression of CRC. Immune cell clusters can either inhibit or facilitate tumor initiation, growth, and metastasis. To gain a better understanding of this relationship, we conducted N-glycomic profiling using matrix-assisted laser desorption-ionization mass spectrometry imaging (MALDI-MSI). We detected nearly 100 N-glycan species across all samples, revealing a shift in N-glycome profiles from normal to cancerous tissues, marked by a decrease in high mannose N-glycans. Further analysis of precancerous to invasive carcinomas showed an increase in pauci-mannose biantennary, and tetraantennary N-glycans with disease progression. Moreover, a distinct stratification in the N-glycome profile was observed between non-mucinous and mucinous CRC tissues, driven by pauci-mannose, high mannose, and bisecting N-glycans. Notably, we identified immune clusters of CD20+ B cells and CD3/CD44+ T cells distinctive and predictive with signature profiles of bisecting and branched N-glycans. These spatial N-glycan profiles offer potential biomarkers and therapeutic targets throughout the progression of CRC.

Progress of fluorescent probes for protein phosphorylation and glycosylation in atherosclerosis

Exploring the post-translational modification (PTM) of proteins in the course of atherosclerotic disease has important guiding significance for the early warning of atherosclerotic plaque, the development of targeted drugs and the treatment of disease. The advancement advanced detection and imaging methods for phosphorylated and glycosylated proteins is an important tool to further reveal the levels of protein phosphorylation and glycosylation during atherosclerotic plaque formation. We present research strategies for detecting protein phosphorylation and glycosylation from the perspective of fluorescent probes, and discuss the feasibility and future direction of the development of these methods for detecting and imaging phosphorylated and glycosylated proteins in atherosclerotic disease.

State-of-the-art mass spectrometry imaging applications in biomedical research

Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.

A Guide to MALDI Imaging Mass Spectrometry for Tissues

Imaging mass spectrometry is a well-established technology that can easily and succinctly communicate the spatial localization of molecules within samples. This review communicates the recent advances in the field, with a specific focus on matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) applied on tissues. The general sample preparation strategies for different analyte classes are explored, including special considerations for sample types (fresh frozen or formalin-fixed,) strategies for various analytes (lipids, metabolites, proteins, peptides, and glycans) and how multimodal imaging strategies can leverage the strengths of each approach is mentioned. This work explores appropriate experimental design approaches and standardization of processes needed for successful studies, as well as the various data analysis platforms available to analyze data and their strengths. The review concludes with applications of imaging mass spectrometry in various fields, with a focus on medical research, and some examples from plant biology and microbe metabolism are mentioned, to illustrate the breadth and depth of MALDI IMS.

RaMALDI: Enabling simultaneous Raman and MALDI imaging of the same tissue section

Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain fused images with the advantages of both modalities has the potential of providing spatially resolved, sensitive, specific biomolecular information, but has so far involved two separate sample preparations, or even consecutive tissue sections for RSI and MALDI MSI, resulting in images with inherent disparities. We have developed RaMALDI, a streamlined, integrated, multimodal imaging workflow of RSI and MALDI MSI, performed on a single tissue section with one sample preparation protocol. We show that RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample, which will drive discoveries in cell biology, biomedicine, and pathology, and advance tissue diagnostics.

Enhancing metabolite coverage in MALDI-MSI using laser post-ionisation (MALDI-2)

Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) of metabolites can reveal how metabolism is altered throughout heterogeneous tissues. Here negative ion mode MALDI-MSI has been coupled with laser post-ionisation (MALDI-2) and applied to the MSI of low molecular weight (LMW) metabolites ( Collapse

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MESH Headings

• Animals
• Mice
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
• Molecular Weight
• Drama
• Glutamic Acid
• Lasers
• Thinness

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Affiliation(s)

J C McKinnon Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia.
H H Milioli Garvan Institute of Medical Research, Darlinghurst, NSW, Australia St. Vincent's Clinical School, UNSW Medicine, UNSW Sydney, NSW, Australia The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
C A Purcell Garvan Institute of Medical Research, Darlinghurst, NSW, Australia St. Vincent's Clinical School, UNSW Medicine, UNSW Sydney, NSW, Australia The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
C L Chaffer Garvan Institute of Medical Research, Darlinghurst, NSW, Australia St. Vincent's Clinical School, UNSW Medicine, UNSW Sydney, NSW, Australia The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
B Wadie Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
T Alexandrov Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
T W Mitchell Molecular Horizons, School of Medical, Indigenous and Health Science, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia
S R Ellis Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia.

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Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering

While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.

Spatial Metabolome Lipidome and Glycome from a Single brain Section

Metabolites, lipids, and glycans are fundamental biomolecules involved in complex biological systems. They are metabolically channeled through a myriad of pathways and molecular processes that define the physiology and pathology of an organism. Here, we present a blueprint for the simultaneous analysis of spatial metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging. Complimenting an original experimental protocol, our workflow includes a computational framework called Spatial Augmented Multiomics Interface (Sami) that offers multiomics integration, high dimensionality clustering, spatial anatomical mapping with matched multiomics features, and metabolic pathway enrichment to providing unprecedented insights into the spatial distribution and interaction of these biomolecules in mammalian tissue biology.

Serum N-Glycosylation RPLC-FD-MS Assay to Assess Colorectal Cancer Surgical Interventions

A newly developed analytical strategy was applied to profile the total serum N-glycome of 64 colorectal cancer (CRC) patients before and after surgical intervention. In this cohort, it was previously found that serum N-glycome alterations in CRC were associated with patient survival. Here, fluorescent labeling of serum N-glycans was applied using procainamide and followed by sialic acid derivatization specific for α2,6- and α2,3-linkage types via ethyl esterification and amidation, respectively. This strategy allowed efficient separation of specific positional isomers on reversed-phase liquid chromatography-fluorescence detection-mass spectrometry (RPLC-FD-MS) and complemented the previous glycomics data based on matrix-assisted laser desorption/ionization (MALDI)-MS that did not include such separations. The results from comparing pre-operative CRC to post-operative samples were in agreement with studies that identified a decrease in di-antennary structures with core fucosylation and an increase in sialylated tri- and tetra-antennary N-glycans in CRC patient sera. Pre-operative abundances of N-glycans showed good performance for the classification of adenocarcinoma and led to the revisit of the previous MALDI-MS dataset with regard to histological and clinical data. This strategy has the potential to monitor patient profiles before, during, and after clinical events such as treatment, therapy, or surgery and should also be further explored.

The role of clinical glyco(proteo)mics in precision medicine

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.

The role of protein glycosylation in the occurrence and outcome of acute ischemic stroke

Acute ischemic stroke (AIS) is a serious and life-threatening disease worldwide. Despite thrombolysis or endovascular thrombectomy, a sizeable fraction of patients with AIS have adverse clinical outcomes. In addition, existing secondary prevention strategies with antiplatelet and anticoagulant drugs therapy are not able to adequately decrease the risk of ischemic stroke recurrence. Thus, exploring novel mechanisms for doing so represents an urgent need for the prevention and treatment of AIS. Recent studies have discovered that protein glycosylation plays a critical role in the occurrence and outcome of AIS. As a common co- and post-translational modification, protein glycosylation participates in a wide variety of physiological and pathological processes by regulating the activity and function of proteins or enzymes. Protein glycosylation is involved in two causes of cerebral emboli in ischemic stroke: atherosclerosis and atrial fibrillation. Following ischemic stroke, the level of brain protein glycosylation becomes dynamically regulated, which significantly affects stroke outcome through influencing inflammatory response, excitotoxicity, neuronal apoptosis, and blood-brain barrier disruption. Drugs targeting glycosylation in the occurrence and progression of stroke may represent a novel therapeutic idea. In this review, we focus on possible perspectives about how glycosylation affects the occurrence and outcome of AIS. We then propose the potential of glycosylation as a therapeutic drug target and prognostic marker for AIS patients in the future.

Analysis of N-linked Glycan Alterations in Tissue and Serum Reveals Promising Biomarkers for Intrahepatic Cholangiocarcinoma

There is an urgent need for the identification of reliable prognostic biomarkers for patients with intrahepatic cholangiocarcinoma (iCCA) and alterations in N-glycosylation have demonstrated an immense potential to be used as diagnostic strategies for many cancers, including hepatocellular carcinoma (HCC). N-glycosylation is one of the most common post-translational modifications known to be altered based on the status of the cell. N-glycan structures on glycoproteins can be modified based on the addition or removal of specific N-glycan residues, some of which have been linked to liver diseases. However, little is known concerning the N-glycan alterations that are associated with iCCA. We characterized the N-glycan modifications quantitatively and qualitatively in three cohorts, consisting of two tissue cohorts: a discovery cohort (n = 104 cases) and a validation cohort (n = 75), and one independent serum cohort consisting of patients with iCCA, HCC, or benign chronic liver disease (n = 67). N-glycan analysis in situ was correlated to tumor regions annotated on histopathology and revealed that bisected fucosylated N-glycan structures were specific to iCCA tumor regions. These same N-glycan modifications were significantly upregulated in iCCA tissue and serum relative to HCC and bile duct disease, including primary sclerosing cholangitis (PSC) (P < 0.0001). N-glycan modifications identified in iCCA tissue and serum were used to generate an algorithm that could be used as a biomarker of iCCA. We demonstrate that this biomarker algorithm quadrupled the sensitivity (at 90% specificity) of iCCA detection as compared with carbohydrate antigen 19-9, the current "gold standard" biomarker of CCA.

Significance

This work elucidates the N-glycan alterations that occur directly in iCCA tissue and utilizes this information to discover serum biomarkers that can be used for the noninvasive detection of iCCA.

Applications and continued evolution of glycan imaging mass spectrometry

Glycosylation is an important posttranslational modifier of proteins and lipid conjugates critical for the stability and function of these macromolecules. Particularly important are N-linked glycans attached to asparagine residues in proteins. N-glycans have well-defined roles in protein folding, cellular trafficking and signal transduction, and alterations to them are implicated in a variety of diseases. However, the non-template driven biosynthesis of these N-glycans leads to significant structural diversity, making it challenging to identify the most biologically and clinically relevant species using conventional analyses. Advances in mass spectrometry instrumentation and data acquisition, as well as in enzymatic and chemical sample preparation strategies, have positioned mass spectrometry approaches as powerful analytical tools for the characterization of glycosylation in health and disease. Imaging mass spectrometry expands upon these strategies by capturing the spatial component of a glycan's distribution in-situ, lending additional insight into the organization and function of these molecules. Herein we review the ongoing evolution of glycan imaging mass spectrometry beginning with widely adopted tissue imaging approaches and expanding to other matrices and sample types with potential research and clinical implications. Adaptations of these techniques, along with their applications to various states of disease, are discussed. Collectively, glycan imaging mass spectrometry analyses broaden our understanding of the biological and clinical relevance of N-glycosylation to human disease.

In Situ Imaging of O-Linked β-N-Acetylglucosamine Using On-Tissue Hydrolysis and MALDI Mass Spectrometry

Post-translational O-glycosylation of proteins via the addition of N-acetylglucosamine (O-GlcNAc) is a regulator of many aspects of cellular physiology. Processes driven by perturbed dynamics of O-GlcNAcylation modification have been implicated in cancer development. Variability in O-GlcNAcylation is emerging as a metabolic biomarker of many cancers. Here, we evaluate the use of MALDI-mass spectrometry imaging (MSI) to visualize the location of O-GlcNAcylated proteins in tissue sections by mapping GlcNAc that has been released by the enzymatic hydrolysis of glycoproteins using an O-GlcNAc hydrolase. We use this strategy to monitor O-GlcNAc within hepatic VX2 tumor tissue. We show that increased O-GlcNAc is found within both viable tumor and tumor margin regions, implicating GlcNAc in tumor progression.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2017-2018

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization mass spectrometry (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2018. Also included are papers that describe methods appropriate to glycan and glycoprotein analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, new methods, matrices, derivatization, MALDI imaging, fragmentation and the use of arrays. The second part of the review is devoted to applications to various structural types such as oligo- and poly-saccharides, glycoproteins, glycolipids, glycosides, and biopharmaceuticals. Most of the applications are presented in tabular form. The third part of the review covers medical and industrial applications of the technique, studies of enzyme reactions, and applications to chemical synthesis. The reported work shows increasing use of combined new techniques such as ion mobility and highlights the impact that MALDI imaging is having across a range of diciplines. MALDI is still an ideal technique for carbohydrate analysis and advancements in the technique and the range of applications continue steady progress.

Mass Spectrometry-Based Glycoproteomic Workflows for Cancer Biomarker Discovery

Glycosylation has a clear role in cancer initiation and progression, with numerous studies identifying distinct glycan features or specific glycoproteoforms associated with cancer. Common findings include that aggressive cancers tend to have higher expression levels of enzymes that regulate glycosylation as well as glycoproteins with greater levels of complexity, increased branching, and enhanced chain length1. Research in cancer glycoproteomics over the last 50-plus years has mainly focused on technology development used to observe global changes in glycosylation. Efforts have also been made to connect glycans to their protein carriers as well as to delineate the role of these modifications in intracellular signaling and subsequent cell function. This review discusses currently available techniques utilizing mass spectrometry-based technologies used to study glycosylation and highlights areas for future advancement.

In situ mass spectrometry imaging reveals heterogeneous glycogen stores in human normal and cancerous tissues

Glycogen dysregulation is a hallmark of aging, and aberrant glycogen drives metabolic reprogramming and pathogenesis in multiple diseases. However, glycogen heterogeneity in healthy and diseased tissues remains largely unknown. Herein, we describe a method to define spatial glycogen architecture in mouse and human tissues using matrix-assisted laser desorption/ionization mass spectrometry imaging. This assay provides robust and sensitive spatial glycogen quantification and architecture characterization in the brain, liver, kidney, testis, lung, bladder, and even the bone. Armed with this tool, we interrogated glycogen spatial distribution and architecture in different types of human cancers. We demonstrate that glycogen stores and architecture are heterogeneous among diseases. Additionally, we observe unique hyperphosphorylated glycogen accumulation in Ewing sarcoma, a pediatric bone cancer. Using preclinical models, we correct glycogen hyperphosphorylation in Ewing sarcoma through genetic and pharmacological interventions that ablate in vivo tumor growth, demonstrating the clinical therapeutic potential of targeting glycogen in Ewing sarcoma.

High sensitivity glycomics in biomedicine

Many analytical challenges in biomedicine arise from the generally high heterogeneity and complexity of glycan- and glycoconjugate-containing samples, which are often only available in minute amounts. Therefore, highly sensitive workflows and detection methods are required. In this review mass spectrometric workflows and detection methods are evaluated for glycans and glycoproteins. Furthermore, glycomic methodologies and innovations that are tailored for enzymatic treatments, chemical derivatization, purification, separation, and detection at high sensitivity are highlighted. The discussion is focused on the analysis of mammalian N-linked and GalNAc-type O-linked glycans.

Spatial Multiomics of Lipids, N-Glycans, and Tryptic Peptides on a Single FFPE Tissue Section

Mass spectrometry imaging (MSI) is an emerging technology that is capable of mapping various biomolecules within their native spatial context, and performing spatial multiomics on formalin-fixed paraffin-embedded (FFPE) tissues may further increase the molecular characterization of pathological states. Here we present a novel workflow which enables the sequential MSI of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section and highlight the enhanced molecular characterization that is offered by combining the multiple spatial omics data sets. In murine brain and clear cell renal cell carcinoma (ccRCC) tissue, the three molecular levels provided complementary information and characterized different histological regions. Moreover, when the spatial omics data was integrated, the different histopathological regions of the ccRCC tissue could be better discriminated with respect to the imaging data set of any single omics class. Taken together, these promising findings demonstrate the capability to more comprehensively map the molecular complexity within pathological tissue.

Lectin Drug Conjugates Targeting High Mannose N-Glycans

Cancer-associated alterations to glycosylation have been shown to aid cancer development and progression. An increased abundance of high mannose N-glycans has been observed in several cancers. Here, we describe the preparation of lectin drug conjugates (LDCs) that permit toxin delivery to cancer cells presenting high mannose N-glycans. Additionally, we demonstrate that cancer cells presenting low levels of high mannose N-glycans can be rendered sensitive to the LDCs by co-treatment with a type I mannosidase inhibitor. Our findings establish that an increased abundance of high mannose N-glycans in the glycocalyx of cancer cells can be leveraged to enable toxin delivery.

In situ spatial glycomic imaging of mouse and human Alzheimer's disease brains

N-linked protein glycosylation in the brain is an understudied facet of glucose utilization that impacts a myriad of cellular processes including resting membrane potential, axon firing, and synaptic vesicle trafficking. Currently, a spatial map of N-linked glycans within the normal and Alzheimer's disease (AD) human brain does not exist. A comprehensive analysis of the spatial N-linked glycome would improve our understanding of brain energy metabolism, linking metabolism to signaling events perturbed during AD progression, and could illuminate new therapeutic strategies. Herein we report an optimized in situ workflow for enzyme-assisted, matrix-assisted laser desorption and ionization (MALDI) mass spectrometry imaging (MSI) of brain N-linked glycans. Using this workflow, we spatially interrogated N-linked glycan heterogeneity in both mouse and human AD brains and their respective age-matched controls. We identified robust regional-specific N-linked glycan changes associated with AD in mice and humans. These data suggest that N-linked glycan dysregulation could be an underpinning of AD pathologies.

Mass spectrometry imaging spatially identifies complex-type N-glycans as putative cartilage degradation markers in human knee osteoarthritis tissue

N-Glycan alterations contribute to the pathophysiology and progression of various diseases. However, the involvement of N-glycans in knee osteoarthritis (KOA) progression at the tissue level, especially within articular cartilage, is still poorly understood. Thus, the aim of this study was to spatially map and identify KOA-specific N-glycans from formalin-fixed paraffin-embedded (FFPE) osteochondral tissue of the tibial plateau relative to cadaveric control (CTL) tissues. Human FFPE osteochondral tissues from end-stage KOA patients (n=3) and CTL individuals (n=3), aged >55 years old, were analyzed by matrix‐assisted laser desorption/ionization mass spectrometry imaging (MALDI‐MSI) and liquid chromatography–tandem mass spectrometry (LC-MS/MS). Overall, it was revealed that 22 N-glycans were found in the cartilage region of KOA and CTL tissue. Of those, 15 N-glycans were more prominent in KOA cartilage than CTL cartilage. We then compared sub-regions of KOA and CTL tissues based on the Osteoarthritis Research Society International (OARSI) histopathological grade (1 to 6), where 1 is an intact cartilage surface and 6 is cartilage surface deformation. Interestingly, three specific complex-type N-glycans, (Hex)₄(HexNAc)₃, (Hex)₄(HexNAc)₄, and (Hex)₅(HexNAc)₄, were found to be localized to the superficial fibrillated zone of degraded cartilage (KOA OARSI 2.5-4), compared to adjacent cartilage with less degradation (KOA OARSI 1-2) or relatively healthy cartilage (CTL OARSI 1-2). Our results demonstrate that N-glycans specific to degraded cartilage in KOA patients have been identified at the tissue level for the first time. The presence of these N-glycans could further be evaluated as potential diagnostic and prognostic biomarkers.

Novel Combined Enzymatic Approach to Analyze Nonsialylated N-Linked Glycans through MALDI Imaging Mass Spectrometry

Alterations to N-glycan expression are relevant to the progression of various diseases, particularly cancer. In many cases, specific N-glycan structural features such as sialylation, fucosylation, and branching are of specific interest. A novel MALDI imaging mass spectrometry workflow has been recently developed to analyze these features of N-glycosylation through the utilization of endoglycosidase enzymes to cleave N-glycans from associated glycoproteins. Enzymes that have previously been utilized to cleave N-glycans include peptide-N-glycosidase F (PNGase F) to target N-glycans indiscriminately and endoglycosidase F3 (Endo F3) to target core fucosylated N-glycans. In addition to these endoglycosidases, additional N-glycan cleaving enzymes could be used to target specific structural features. Sialidases, also termed neuraminidases, are a family of enzymes that remove terminal sialic acids from glycoconjugates. This work aims to utilize sialidase, in conjunction with PNGase F/Endo F3, to enzymatically remove sialic acids from N-glycans in an effort to increase sensitivity for nonsialylated N-glycan MALDI-IMS peaks. Improving detection of nonsialylated N-glycans allows for a more thorough analysis of specific structural features such as fucosylation or branching, particularly of low abundant structures. Sialidase utilization in MALDI-IMS dramatically increases sensitivity and increases on-tissue endoglycosidase efficiency, making it a very useful companion technique to specifically detect nonsialylated N-glycans.

Pro-Survival Factor EDEM3 Confers Therapy Resistance in Prostate Cancer

Prostate cancer is the most common cancer in men, and it is primarily driven by androgen steroid hormones. The glycosylation enzyme EDEM3 is controlled by androgen signalling and is important for prostate cancer viability. EDEM3 is a mannosidase that trims mannose from mis-folded glycoproteins, tagging them for degradation through endoplasmic reticulum-associated degradation. Here, we find that EDEM3 is upregulated in prostate cancer, and this is linked to poorer disease-free survival. Depletion of EDEM3 from prostate cancer cells induces an ER stress transcriptomic signature, and EDEM3 overexpression is cyto-protective against ER stressors. EDEM3 expression also positively correlates with genes involved in the unfolded protein response in prostate cancer patients, and its expression can be induced through exposure to radiation. Importantly, the overexpression of EDEM3 promotes radio-resistance in prostate cancer cells and radio-resistance can be reduced through depletion of EDEM3. Our data thus implicate increased levels of EDEM3 with a role in prostate cancer pathology and reveal a new therapeutic opportunity to sensitise prostate tumours to radiotherapy.

Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Many studies have demonstrated that tissue phenotyping (tissue typing) based on mass spectrometric imaging data is possible; however, comprehensive studies assessing variation and classifier transferability are largely lacking. This study evaluated the generalization of tissue classification based on Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometric imaging (MSI) across measurements performed at different sites. Sections of a tissue microarray (TMA) consisting of different formalin-fixed and paraffin-embedded (FFPE) human tissue samples from different tumor entities (leiomyoma, seminoma, mantle cell lymphoma, melanoma, breast cancer, and squamous cell carcinoma of the lung) were prepared and measured by MALDI-MSI at different sites using a standard protocol (SOP). Technical variation was deliberately introduced on two separate measurements via a different sample preparation protocol and a MALDI Time of Flight mass spectrometer that was not tuned to optimal performance. Using standard data preprocessing, a classification accuracy of 91.4% per pixel was achieved for intrasite classifications. When applying a leave-one-site-out cross-validation strategy, accuracy per pixel over sites was 78.6% for the SOP-compliant data sets and as low as 36.1% for the mistuned instrument data set. Data preprocessing designed to remove technical variation while retaining biological information substantially increased classification accuracy for all data sets with SOP-compliant data sets improved to 94.3%. In particular, classification accuracy of the mistuned instrument data set improved to 81.3% and from 67.0% to 87.8% per pixel for the non-SOP-compliant data set. We demonstrate that MALDI-MSI-based tissue classification is possible across sites when applying histological annotation and an optimized data preprocessing pipeline to improve generalization of classifications over technical variation and increasing overall robustness.

Imaging Mass Spectrometry Reveals Alterations in N-Linked Glycosylation That Are Associated With Histopathological Changes in Nonalcoholic Steatohepatitis in Mouse and Human

Nonalcoholic steatohepatitis (NASH) is the progressive form of nonalcoholic fatty liver disease (NAFLD) and is characterized by inflammation, hepatocyte injury, and fibrosis. Further, NASH is a risk factor for cirrhosis and hepatocellular carcinoma. Previous research demonstrated that serum N-glycan profiles can be altered in NASH patients. Here, we hypothesized that these N-glycan modifications may be associated with specific liver damage in NAFLD and NASH. To investigate the N-glycome profile in tissue, imaging mass spectrometry was used for a qualitative and quantitative in situ N-linked glycan analysis of mouse and human NAFLD/NASH tissue. A murine model was used to induce NAFLD and NASH through ad libitum feeding with either a high-fat diet or a Western diet, respectively. Mice fed a high-fat diet or Western diet developed inflammation, steatosis, and fibrosis, consistent with NAFLD/NASH phenotypes. Induction of NAFLD/NASH for 18 months using high caloric diets resulted in increased expression of mannose, complex/fucosylated, and hybrid N-glycan structures compared to control mouse livers. To validate the animal results, liver biopsy specimens from 51 human NAFLD/NASH patients representing the full range of NASH Clinical Research Network fibrosis stages were analyzed. Importantly, the same glycan alterations observed in mouse models were observed in human NASH biopsies and correlated with the degree of fibrosis. In addition, spatial glycan alterations were localized specifically to histopathological changes in tissue like fibrotic and fatty areas. We demonstrate that the use of standard staining's combined with imaging mass spectrometry provide a full profile of the origin of N-glycan modifications within the tissue. These results indicate that the spatial distribution of abundances of released N-glycans correlate with regions of tissue steatosis associated with NAFLD/NASH.

Advances in glycoscience to understand viral infection and colonization

Interactions between carbohydrates and the proteins that bind them, (i.e., lectins), are often some of the first between a host cell and a viral invader. With its highly glycosylated spike protein, SARS-CoV2 is no exception. Interrogating glycosylation is vital for understanding viral infection, yet it has been a challenge. Improvement in methods ranging from mass spectrometry to glycan arrays and modeling simulations are yielding atomic-level information about the glycans that decorate viruses and host cells alike. Through these developments, we are unmasking the sophisticated glycan trickery viruses use. These advances and insights can lead to new types of antiviral agents.

GlycoFibroTyper: A Novel Method for the Glycan Analysis of IgG and the Development of a Biomarker Signature of Liver Fibrosis

Our group has recently developed the GlycoTyper assay which is a streamlined antibody capture slide array approach to directly profile N-glycans of captured serum glycoproteins including immunoglobulin G (IgG). This method needs only a few microliters of serum and utilizes a simplified processing protocol that requires no purification or sugar modifications prior to analysis. In this method, antibody captured glycoproteins are treated with peptide N-glycosidase F (PNGase F) to release N-glycans for detection by MALDI imaging mass spectrometry (IMS). As alterations in N-linked glycans have been reported for IgG from large patient cohorts with fibrosis and cirrhosis, we utilized this novel method to examine the glycosylation of total IgG, as well as IgG1, IgG2, IgG3 and IgG4, which have never been examined before, in a cohort of 106 patients with biopsy confirmed liver fibrosis. Patients were classified as either having no evidence of fibrosis (41 patients with no liver disease or stage 0 fibrosis), early stage fibrosis (10 METAVIR stage 1 and 18 METAVIR stage 2) or late stage fibrosis (6 patients with METAVIR stage 3 fibrosis and 37 patients with METAVIR stage 4 fibrosis (cirrhosis)). Several major alterations in glycosylation were observed that classify patients as having no fibrosis (sensitivity of 92% and a specificity of 90%), early fibrosis (sensitivity of 84% with 90% specificity) or significant fibrosis (sensitivity of 94% with 90% specificity).

Establishment of an antibody specific for AMIGO2 improves immunohistochemical evaluation of liver metastases and clinical outcomes in patients with colorectal cancer

Instruction

The human amphoterin-induced gene and open reading frame (AMIGO) was identified as a novel cell adhesion molecule of type I transmembrane protein. AMIGO2 is one of three members of the AMIGO family (AMIGO1, 2, and 3), and the similarity between them is approximately 40% at the amino acid level. We have previously shown that AMIGO2 functions as a driver of liver metastasis. Immunohistochemical analysis of AMIGO2 expression in colorectal cancer (CRC) using a commercially available anti-AMIGO2 mouse monoclonal antibody clone sc-373699 (sc mAb) correlated with liver metastasis and poor prognosis. However, the sc mAb was found to be cross-reactive with all three molecules in the AMIGO family.

Methods

We generated a rat monoclonal antibody clone rTNK1A0012 (rTNK mAb) for human AMIGO2. The rTNK mAb was used to re-evaluate the association between AMIGO2 expression and liver metastases/clinical outcomes using the same CRC tissue samples previously reported with sc mAb.

Results

Western blot analysis revealed that a rTNK mAb was identified as being specific for AMIGO2 protein and did not cross-react with AMIGO1 and AMIGO3. The rTNK mAb and sc mAb showed higher AMIGO2 expression, which correlates with a high frequency of liver metastases (65.3% and 47.5%, respectively), while multivariate analysis showed that AMIGO2 expression was an independent prognostic factor for liver metastases (p = 7.930E-10 and p = 1.707E-5). The Kaplan-Meier analyses showed that the rTNK mAb (p = 0.004), but not sc mAb (p = 0.107), predicted worse overall survival in patients with high AMIGO2 expression. The relationship between AMIGO2 expression and poor disease-specific survival showed a higher level of significance for rTNK mAb (p = 0.00004) compared to sc mAb (p = 0.001).

Conclusions

These results indicate that the developed rTNK1A0012 mAb is an antibody that specifically recognizes AMIGO2 by immunohistochemistry and can be a more reliable and applicable method for the diagnostic detection of liver metastases and worse prognosis in patients with high AMIGO2-expressing CRC.

Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging of Glycogen In Situ

Histopathological detection and quantitation of glycogen in situ are important for the assessment of glycogen storage diseases and different types of cancer. The current standard method for defining the regionality of glycogen rely almost exclusively on Periodic Acid-Schiff (PAS) staining, a workflow that lacks specificity and sensitivity. Herein, we describe a new and much improved workflow to detect microenvironmental glycogen in situ using enzyme-assisted matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI). This method provides superior sensitivity and can elucidate the molecular features of glycogen structure, with 50 μm spatial resolution for a next-generation histopathological assessment of glycogen.

On-tissue spatially resolved glycoproteomics guided by N-glycan imaging reveal global dysregulation of canine glioma glycoproteomic landscape

Here, we present an approach to identify N-linked glycoproteins and deduce their spatial localization using a combination of matrix-assisted laser desorption ionization (MALDI) N-glycan mass spectrometry imaging (MSI) and spatially resolved glycoproteomics. We subjected glioma biopsies to on-tissue PNGaseF digestion and MALDI-MSI and found that the glycan HexNAc4-Hex5-NeuAc2 was predominantly expressed in necrotic regions of high-grade canine gliomas. To determine the underlying sialo-glycoprotein, various regions in adjacent tissue sections were subjected to microdigestion and manual glycoproteomic analysis. Results identified haptoglobin as the protein associated with HexNAc4-Hex5-NeuAc2, thus directly linking glycan imaging with intact glycopeptide identification. In total, our spatially resolved glycoproteomics technique identified over 400 N-, O-, and S- glycopeptides from over 30 proteins, demonstrating the diverse array of glycosylation present on the tissue slices and the sensitivity of our technique. Ultimately, this proof-of-principle work demonstrates that spatially resolved glycoproteomics greatly complement MALDI-MSI in understanding dysregulated glycosylation.

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation

MOTIVATION

Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation.

RESULTS

We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine.

AVAILABILITY AND IMPLEMENTATION

https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review

OBJECTIVE

A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeons ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines.

METHODS

We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies.

RESULTS

Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively.

CONCLUSION

Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible.

SIGNIFICANCE

By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.

Proteomic Distributions in CD34+ Microvascular Niche Patterns of Glioblastoma

The poor clinical prognosis and microvascular patterns of glioblastoma (GBM) are of serious concern to many clinicians and researchers. However, very few studies have examined the correlation between microvascular niche patterns (MVNPs) and proteomic distribution. In this study, CD34 immunofluorescence staining and matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-IMS) technology were used to investigate the protein distributions in MVNPs. CD34+ microvascular phenotype could be divided into four types: microvascular sprouting (MS), vascular cluster (VC), vascular garland (VG), and glomeruloid vascular proliferation (GVP). Based on such characteristics, MVNPs were divided into two types by cluster analysis, namely, type I, comprising primarily MS and VC, and type II, comprising many VGs and GVPs. Survival analysis indicated the type of MVNPs to be an independent prognostic factor for progression-free and overall survival in GBM. MALDI-IMS results showed the peaks at m/z 1037 and 8960 to exhibit stronger ion signals in type II, while those at m/z 3240 and 3265 exhibited stronger ion signals in type I. The findings may assist future research on therapy and help predict prognosis in GBM. However, due to the limited number of studies, more well-designed studies are warranted to further verify our results.

Emerging roles of N-linked glycosylation in brain physiology and disorders

N-linked glycosylation is a complex, co- and post-translational series of events that connects metabolism to signaling in almost all cells. Metabolic assembly of N-linked glycans spans multiple cellular compartments, and early N-linked glycan biosynthesis is a central mediator of protein folding and the unfolded protein response (UPR). In the brain, N-linked glycosylated proteins participate in a myriad of processes, from electrical gradients to neurotransmission. However, it is less clear how perturbations in N-linked glycosylation impact and even potentially drive aspects of neurological disorders. In this review, we discuss our current understanding of the metabolic origins of N-linked glycans in the brain, their role in modulating neuronal function, and how aberrant N-linked glycosylation can drive neurological disorders.

N-Glycosylation Patterns Correlate with Hepatocellular Carcinoma Genetic Subtypes

Hepatocellular carcinoma (HCC) is the second leading cause of cancer deaths globally, and the incidence rate in the United States is increasing. Studies have identified inter- and intratumor heterogeneity as histologic and/or molecular subtypes/variants associated with response to certain molecular targeted therapies. Spatial HCC tissue profiling of N-linked glycosylation by matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS) may serve as a new method to evaluate the tumor heterogeneity. 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. To determine the correlation between HCC-specific N-glycosylation changes 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 (Hoshida subtypes). MALDI-IMS data displayed trends that could approximately distinguish between subtypes, with subtype 1 demonstrating significantly dysregulated N-glycosylation versus adjacent nontumor tissue. Although there were no individual N-glycan structures that could identify specific subtypes, trends emerged regarding the correlation of branched glycan expression to HCC as a whole and fucosylated glycan expression to subtype 1 tumors specifically. IMPLICATIONS: Correlating N-glycosylation to specific subtypes offers the specific detection of subtypes of HCC, which could both enhance early HCC sensitivity and guide targeted clinical therapies.

Apo-H (beta-2-glycoprotein) intact N-glycan analysis by MALDI-TOF-MS using sialic acid derivatization

Apo-H is a plasma glycoprotein. Nearly 19% of the molecular weight of this protein is composed of glycans. Up- and down-regulation and structural changes in protein glycans provide diagnostic value for disease detection. Here, an efficient, sensitive, and optimized method is developed for Apo-H N-glycans analysis by MALDI-TOF-MS in positive mode. This bioanalytical method includes sample preparation, sample purification, and detection. An Apo-H enrichment method is developed using standard proteins by anti-Apo-H beads followed by enrichment from plasma samples. SDS-PAGE confirms the Apo-H protein enrichment, which is further verified by LC-MS/MS analysis. The lower ionization efficiency of sialylated glycan hampers their analysis by MALDI-MS. For this, stabilization of sialic acids is done by selective derivatization of carboxyl groups to differentiate between α(2,3)- and α(2,6)-linked sialic acids. Glycans are further purified by HILIC-SPE and analyzed by MALDI-MS. Several branched bi- and tri-antennary glycans with fucosylation and sialylation are identified. The reproducibility of the developed method is tested by analyzing multiple replicates of human plasma, where the same glycans are consistently identified. This method could be applied for the Apo-H glycan profiling of large clinical cohorts for diagnostic purposes.

Direct N-Glycosylation Profiling of Urine and Prostatic Fluid Glycoproteins and Extracellular Vesicles

Expressed prostatic secretions (EPS), also called post digital rectal exam urines, are proximal fluids of the prostate that are widely used for diagnostic and prognostic assays for prostate cancer. These fluids contain an abundant number of glycoproteins and extracellular vesicles secreted by the prostate gland, and the ability to detect changes in their N-glycans composition as a reflection of disease state represents potential new biomarker candidates. Methods to characterize these N-glycan constituents directly from clinical samples in a timely manner and with minimal sample processing requirements are not currently available. In this report, an approach is described to directly profile the N-glycan constituents of EPS urine samples, prostatic fluids and urine using imaging mass spectrometry for detection. An amine reactive slide is used to immobilize glycoproteins from a few microliters of spotted samples, followed by peptide N-glycosidase digestion. Over 100 N-glycan compositions can be detected with this method, and it works with urine, urine EPS, prostatic fluids, and urine EPS-derived extracellular vesicles. A comparison of the N-glycans detected from the fluids with tissue N-glycans from prostate cancer tissues was done, indicating a subset of N-glycans present in fluids derived from the gland lumens. The developed N-glycan profiling is amenable to analysis of larger clinical cohorts and adaptable to other biofluids.

Trends in oligomannosylation and α1,2-mannosidase expression in human cancers

Aberrant protein glycosylation is a prominent cancer feature. While many tumour-associated glycoepitopes have been reported, advances in glycoanalytics continue to uncover new associations between glycosylation and cancer. Guided by a comprehensive literature survey suggesting that oligomannosylation (Man_5–9 GlcNAc₂) is a widespread and often regulated glycosignature in human cancers, we here revisit a valuable compilation of nearly 500 porous graphitized carbon LC-MS/MS N-glycomics datasets acquired across 11 human cancer types to systematically test for oligomannose-cancer associations. Firstly, the quantitative glycomics data obtained across 34 cancerous cell lines demonstrated that oligomannosylation is a pan-cancer feature spanning in a wide abundance range. In keeping with literature, our quantitative glycomics data of tumour and matching control tissues and new MALDI-MS imaging data of tissue microarrays showed a strong cancer-associated elevation of oligomannosylation in both basal cell (p = 1.78 × 10^–12) and squamous cell (p = 1.23 × 10^–11) skin cancer and colorectal cancer (p = 8.0 × 10^–4). The glycomics data also indicated that some cancer types including gastric and liver cancer exhibit unchanged or reduced oligomannose levels, observations also supported by literature and MALDI-MS imaging data. Finally, expression data from public cancer repositories indicated that several α1,2-mannosidases are regulated in tumour tissues suggesting that these glycan-processing enzymes may contribute to the cancer-associated modulation of oligomannosylation. This omics-centric study has compiled robust glycomics and enzyme expression data revealing interesting molecular trends that open avenues to better understand the role of oligomannosylation in human cancers.

Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE

Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high-throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. Here, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI data sets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus, facilitating applications of MSI in glycobiology.

Alterations in protein expression and site-specific N-glycosylation of prostate cancer tissues

Identifying molecular alterations occurring during cancer progression is essential for a deeper understanding of the underlying biological processes. Here we have analyzed cancerous and healthy prostate biopsies using nanoLC-MS(MS) to detect proteins with altered expression and N-glycosylation. We have identified 75 proteins with significantly changing expression during disease progression. The biological processes involved were assigned based on protein-protein interaction networks. These include cellular component organization, metabolic and localization processes. Multiple glycoproteins were identified with aberrant glycosylation in prostate cancer, where differences in glycosite-specific sialylation, fucosylation, and galactosylation were the most substantial. Many of the glycoproteins with altered N-glycosylation were extracellular matrix constituents, and are heavily involved in the establishment of the tumor microenvironment.

In Situ Analysis of N-Linked Glycans as Potential Biomarkers of Clinical Course in Human Prostate Cancer

Prostate cancer is the most common cancer in men worldwide. Despite its prevalence, there is a critical knowledge gap in understanding factors driving disparities in survival among different cohorts of patients with prostate cancer. Identifying molecular features separating disparate populations is an important first step in prostate cancer research that could lead to fundamental hypotheses in prostate biology, predictive biomarker discovery, and personalized therapy. N-linked glycosylation is a cotranslational event during protein folding that modulates a myriad of cellular processes. Recently, aberrant N-linked glycosylation has been reported in prostate cancers. However, the full clinical implications of dysregulated glycosylation in prostate cancer has yet to be explored. Herein, we performed direct on-tissue analysis of N-linked glycans using matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) from tissue microarrays of over 100 patient tumors with over 10 years of follow-up metadata. We successfully identified a panel of N-glycans that are unique between benign and prostate tumor tissue. Specifically, high-mannose as well as tri-and tetra-antennary N-glycans were more abundant in tumor tissue and increase proportionally with tumor grade. Further, we expanded our analyses to examine the N-glycan profiles of Black and Appalachian patients and have identified unique glycan signatures that correlate with recurrence in each population. Our study highlights the potential applications of MALDI-MSI for digital pathology and biomarker discovery for prostate cancer. IMPLICATIONS: MALDI-MSI identifies N-glycan perturbations in prostate tumors compared with benign tissue. This method can be utilized to predict prostate cancer recurrence and study prostate cancer disparities.

Glycan Imaging Mass Spectrometry: Progress in Developing Clinical Diagnostic Assays for Tissues, Biofluids, and Cells

N-glycan imaging mass spectrometry (IMS) can rapidly and reproducibly identify changes in disease-associated N-linked glycosylation that are linked with histopathology features in standard formalin-fixed paraffin-embedded tissue samples. It can detect multiple N-glycans simultaneously and has been used to identify specific N-glycans and carbohydrate structural motifs as possible cancer biomarkers. Recent advancements in instrumentation and sample preparation are also discussed. The tissue N-glycan IMS workflow has been adapted to new glass slide-based assays for effective and rapid analysis of clinical biofluids, cultured cells, and immunoarray-captured glycoproteins for detection of changes in glycosylation associated with disease.

Proteomic and Glyco(proteo)mic tools in the profiling of cardiac progenitors and pluripotent stem cell derived cardiomyocytes: Accelerating translation into therapy

Research in stem cells paved the way to an enormous amount of knowledge, increasing expectations on cardio regenerative therapeutic approaches in clinic. While the first generation of clinical trials using cell-based therapies in the heart were performed with bone marrow and adipose tissue derived mesenchymal stem cells, second generation cell therapies moved towards the use of cardiac-committed cell populations, including cardiac progenitor cells and pluripotent stem cell derived cardiomyocytes. Despite all these progresses, translating the aptitudes of R&D and pre-clinical data into effective clinical treatments is still highly challenging, partially due to the demanding regulatory and safety concerns but also because of the lack of knowledge on the regenerative mechanisms of action of these therapeutic products. Thus, the need of analytical methodologies that enable a complete characterization of such complex products and a deep understanding of their therapeutic effects, at the cell and molecular level, is imperative to overcome the hurdles of these advanced therapies. Omics technologies, such as proteomics and glyco(proteo)mics workflows based on state of the art mass-spectrometry, have prompted some major breakthroughs, providing novel data on cell biology and a detailed assessment of cell based-products applied in cardiac regeneration strategies. These advanced 'omics approaches, focused on the profiling of protein and glycan signatures are excelling the identification and characterization of cell populations under study, namely unveiling pluripotency and differentiation markers, as well as paracrine mechanisms and signaling cascades involved in cardiac repair. The leading knowledge generated is supporting a more rational therapy design and the rethinking of challenges in Advanced Therapy Medicinal Products development. Herein, we review the most recent methodologies used in the fields of proteomics, glycoproteomics and glycomics and discuss their impact on the study of cardiac progenitor cells and pluripotent stem cell derived cardiomyocytes biology. How these discoveries will impact the speed up of novel therapies for cardiovascular diseases is also addressed.