Journal of Histochemistry & Cytochemistry

The six mammalian glycosaminoglycans (GAGs), chondroitin sulfate, dermatan sulfate, heparin, heparan sulfate, hyaluronan, and keratan sulfate, are linear polysaccharides. Except for hyaluronan, they are sulfated to various extent, and covalently attached to proteins to form proteoglycans. GAGs interact with growth factors, morphogens, chemokines, extracellular matrix proteins and their bioactive fragments, receptors, lipoproteins, and pathogens. These interactions mediate their functions, from embryonic development to extracellular matrix assembly and regulation of cell signaling in various physiological and pathological contexts such as angiogenesis, cancer, neurodegenerative diseases, and infections. We give an overview of GAG–protein interactions (i.e., specificity and chemical features of GAG- and protein-binding sequences), and review the available GAG–protein interaction networks. We also provide the first comprehensive draft of the GAG interactome composed of 832 biomolecules (827 proteins and five GAGs) and 932 protein–GAG interactions. This network is a scaffold, which in the future should integrate structures of GAG–protein complexes, quantitative data of the abundance of GAGs in tissues to build tissue-specific interactomes, and GAG interactions with metal ions such as calcium, which plays a major role in the assembly of the extracellular matrix and its interactions with cells. This contextualized interactome will be useful to identify druggable GAG–protein interactions for therapeutic purpose:

Heparan sulfate proteoglycans consist of a small family of proteins decorated with one or more covalently attached heparan sulfate glycosaminoglycan chains. These chains have intricate structural patterns based on the position of sulfate groups and uronic acid epimers, which dictate their ability to engage a large repertoire of heparan sulfate–binding proteins, including extracellular matrix proteins, growth factors and morphogens, cytokines and chemokines, apolipoproteins and lipases, adhesion and growth factor receptors, and components of the complement and coagulation system. This review highlights recent progress in the characterization of the so-called “heparan sulfate interactome,” with a major focus on systems-wide strategies as a tool for discovery and characterization of this subproteome. In addition, we compiled all heparan sulfate–binding proteins reported in the literature to date and grouped them into a few major functional classes by applying a networking approach.

Glycosaminoglycans (GAGs) are heterogeneous, negatively charged, macromolecules that are found in animal tissues. Based on the form of component sugar, GAGs have been categorized into four different families: heparin/heparan sulfate, chondroitin/dermatan sulfate, keratan sulfate, and hyaluronan. GAGs engage in biological pathway regulation through their interaction with protein ligands. Detailed structural information on GAG chains is required to further understanding of GAG–ligand interactions. However, polysaccharide sequencing has lagged behind protein and DNA sequencing due to the non-template-driven biosynthesis of glycans. In this review, we summarize recent progress in the analysis of GAG chains, specifically focusing on techniques related to mass spectroscopy (MS), including separation techniques coupled to MS, tandem MS, and bioinformatics software for MS spectrum interpretation. Progress in the use of other structural analysis tools, such as nuclear magnetic resonance (NMR) and hyphenated techniques, is included to provide a comprehensive perspective.

Advances in reagents, methodologies, analytic platforms, and tools have resulted in a dramatic transformation of the research pathology laboratory. These advances have increased our ability to efficiently generate substantial volumes of data on the expression and accumulation of mRNA, proteins, carbohydrates, signaling pathways, cells, and structures in healthy and diseased tissues that are objective, quantitative, reproducible, and suitable for statistical analysis. The goal of this review is to identify and present how to acquire the critical information required to measure changes in tissues. Included is a brief overview of two morphometric techniques, image analysis and stereology, and the use of artificial intelligence to classify cells and identify hidden patterns and relationships in digital images. In addition, we explore the importance of preanalytical factors in generating high-quality data. This review focuses on techniques we have used to measure proteoglycans, glycosaminoglycans, and immune cells in tissues using immunohistochemistry and in situ hybridization to demonstrate the various morphometric techniques. When performed correctly, quantitative digital pathology is a powerful tool that provides unbiased quantitative data that are difficult to obtain with other methods.