Direct Glycan Analysis of Biological Samples and Intact Glycoproteins by Integrating Machine Learning-Driven Surface-Enhanced Raman Scattering and Boronic Acid Arrays.

ABSTRACT: Frequent monitoring of glycan patterns is a critical step in studying glycan-mediated cellular processes. However, the current glycan analysis tools are resource-intensive and less suitable for routine use in standard laboratories. We developed a novel glycan detection platform by integrating surface-enhanced Raman spectroscopy (SERS), boronic acid (BA) receptors, and machine learning tools. This sensor monitors the molecular fingerprint spectra of BA binding to cis-diol-containing glycans. Different types of BA receptors could yield different stereoselective reactions toward different glycans and exhibit unique vibrational spectra. By integration of the Raman spectra collected from different BA receptors, the structural information can be enriched, eventually improving the accuracy

SUBMITTER: Hu Q 

PROVIDER: S-EPMC11191725 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature