Despite the increasing prevalence and economic cost of Alzheimer?s disease (AD), there are still no effective diagnostic methods or treatments to prevent, halt, or cure the disease. The long-recognized relationship between aberrant sugar metabolism and AD, especially in early stages of the disease, has led many to hypothesize that altered glycoprotein biosynthesis, processing, and/or metabolism might play a major role in AD pathogenesis. Multiple glycans and glycoproteins are also beginning to be explored as novel biomarkers or therapeutic targets. Nevertheless, very little is known about how specific glycan structures, their associated glycoproteins, and related biosynthesis and processing enzymes are altered in AD. Moreover, we have no information regarding the relative contributions of these disease-associated glycans to different cell types in the brain (e.g., glial vs neuronal cells, activated vs resting neurons), severely hindering our understanding of how aberrant glycosylation contributes to AD pathogenesis. The overall objective of this program is to develop a new method, called SUGAR-seq (Single-cell Unified Glycan And RNA sequencing), for the detection and quantification of specific learning and memory-relevant glycan structures in AD at the level of single cells. This method takes advantage of chemoenzymatic labeling techniques developed by our laboratory to tag fucose ?(1-2) galactose (Fuc?(1- 2)Gal), core fucose, or terminal N-acetylglucosamine (GlcNAc) glycans with DNA oligo ?barcodes.? These barcodes will then be read out during single-cell RNA sequencing to yield quantitative information about the levels of each glycan in a given cell, while simultaneously profiling the transcriptome. Importantly, this couples the detection and quantification of glycans with specific information about cell type, cell state, and gene expression in AD. We will also apply our state-of-the-art chemoproteomics methods to understand how individual glycoproteins, glycosylation sites, and glycan structures change in AD brains. Finally, we will develop novel bioinformatics tools to integrate quantitative glycoproteomic, glycan expression, and transcriptomic data, revealing novel inter-relationships between these biomolecules.
In Aim 1, we will demonstrate the ability of SUGAR-seq to quantify both glycan and gene expression levels across diverse cell populations.
In Aim 2, we will apply SUGAR-seq to reveal, for the first time, how GlcNAc, Fuc?(1-2)Gal, and core fucose glycan expression changes in response to neuronal activation and across different mouse brain regions in vivo. Finally, in Aim 3, we will apply SUGAR-seq, along with our recently developed, quantitative glycoproteomic methods, to AD vs matched control brain tissue. Together, these studies will provide new, fundamental insights into the role of protein glycosylation in the early pathogenesis and progression of AD. Moreover, the data generated herein will define the first cell type-, cell state-, and glycosylation-specific phenotypes of AD, significantly broadening our understanding of the disease and potentially suggesting new targets for the development of innovative diagnostic approaches and disease-modifying interventions.

Public Health Relevance

Although aberrant protein glycosylation has been strongly associated with Alzheimer?s disease (AD), the precise changes in glycan structure that occur on individual cell types and their roles in AD pathogenesis are poorly understood. The proposed research will develop a new technology, called SUGAR-seq (Single-cell Unified Glycan And RNA sequencing), to reveal both glycosylation and transcriptomic changes associated with AD at the level of single cells. This technology is expected to (1) significantly advance a fundamental understanding of how glycan and glycoprotein expression levels change and possibly contribute to AD pathogenesis, and (2) provide novel biomarker candidates and targets for therapeutic intervention.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Multi-Year Funded Research Project Grant (RF1)
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Special Emphasis Panel (ZRG1)
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Yang, Austin Jyan-Yu
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California Institute of Technology
Schools of Arts and Sciences
United States
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