Enzymatically hydrolyzed oligosaccharide fingerprinting using nanopipettes at the single-molecule level

Abstract

Enzymatic hydrolysis of oligosaccharides plays a pivotal role in important biological processes, but conventional analytical tools struggle to determine these functional components with single-monosaccharide resolution and ultrahigh sensitivity. Here, we report a nanopipette-based strategy for the length discrimination of enzymatically hydrolyzed galacturonic acid oligosaccharides differing by a single monosaccharide unit while achieving femtomolar (fM) level detection limits. Using this approach, we precisely identify six types of enzymatic hydrolysates, even in complex mixtures. Incorporating machine learning further enhances classification performance, achieving 0.98 accuracy. This study establishes an advanced platform for high-precision carbohydrate analysis, offering new possibilities for glycosidase-assisted glycan sequencing, enzymatic degradation studies, and gut microbiota metabolism research.