Label-free glycan sequencing: new methods to reveal the 'sweet side' of host-pathogen interactions - Project Summary/Abstract Glycoconjugates are formed by the addition of sugars to RNA, proteins, and lipids. It is estimated that up to 1012 different branched glycan structures exist, and these structures can have significant impact on protein, lipid, and RNA function. Glycoconjugates displayed on cell surfaces play crucial roles in regulating a wide range of biological processes, including immune response, cell adhesion, molecular trafficking, and signal transduction. Despite their importance, the vast majority of glycans are unknown and unknowable with existing techniques. Existing methods are either targeted or destructive, and often fall short in providing the resolution and specificity needed to map glycans in detail, especially on their scaffold. This proposal will develop a novel, label-free platform to sequence glycans at high resolution, combining advances in nanophotonics, Raman spectroscopy, enzymatic chemistry, and machine learning. Our initial focus will be on glycoRNA, a newly discovered class of glycosylated RNA molecules found on cell surfaces, which have implications in crucial cellular communication and immune responses. By leveraging a nanostructured silicon chip that significantly enhances Raman scattering signals, we aim to detect and characterize these glycoconjugates at the single-molecule level. Coupled with advanced machine learning algorithms, our platform will enable the construction of a comprehensive library of glycoconjugate Raman spectra and sequences, which will be instrumental in identifying and characterizing the glycome in various biological samples. We will utilize this platform to address important and outstanding questions such as (a) how many RNAs have glycan signatures? (b) how many different glycoRNA signatures do we see in the population (c) are there classes of RNA signatures that have more glycans on them than others? (d) within a given population of RNA signatures which also have glycan signatures, do they all have the same glycan signal or is it heterogeneous? We will use our first-of-its-kind platform to compare mammalian cell glycoRNA with bacterial glycoRNA. We will then measure mammalian cell glycoRNA profiles after exposure to viral or bacterial infection; and also compare those profiles to vaccinated cells against those same viral/bacterial pathogens. We are especially excited to investigate how glycoRNA in model organisms varies as their commensal bacteria are modified to express distinct tumor-associated antigens, and how those glycopatterns might drive immune responses. Subsequently, our platform will be extended to study glycoproteins and glycolipids, broadening its applicability across different classes of glycoconjugates. We ultimately envision using this technology for spatial glycomic investigations, enabling single-cell analysis of glycosylation patterns within complex tissue environments. This research will not only bridge critical gaps in our understanding of the glycome but also provide a versatile, generalizable tool for the scientific community, driving forward discoveries in glycobiology and informing the development of novel therapeutic strategies.
