Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis
Date of Original Version
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, e.g., Journal
Proceedings of the National Academy of Sciences of the United States of America
Xia, Ke, James T. Hagan, Li Fu, Brian S. Sheetz, Somdatta Bhattacharya, Fuming Zhang, Jason R. Dwyer, and Robert J. Linhardt. "Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis." Proceedings of the National Academy of Sciences of the United States of America 118, 11 (2021). doi: 10.1073/pnas.2022806118.