Title

Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis

Document Type

Article

Date of Original Version

3-16-2021

Abstract

The application of solid-state (SS) nanopore devices to singlemolecule nucleic acid sequencing has been challenging. Thus, the early successes in applying SS nanopore devices to the more difficult class of biopolymer, glycosaminoglycans (GAGs), have been surprising, motivating us to examine the potential use of an SS nanopore to analyze synthetic heparan sulfate GAG chains of controlled composition and sequence prepared through a promising, recently developed chemoenzymatic route. A minimal representation of the nanopore data, using only signal magnitude and duration, revealed, by eye and image recognition algorithms, clear differences between the signals generated by four synthetic GAGs. By subsequent machine learning, it was possible to determine disaccharide and even monosaccharide composition of these four synthetic GAGs using as few as 500 events, corresponding to a zeptomole of sample. These data suggest that ultrasensitive GAG analysis may be possible using SS nanopore detection and well-characterized molecular training sets.

Publication Title

Proceedings of the National Academy of Sciences of the United States of America

Volume

118

Issue

11

Share

COinS