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

The application of solid-state (SS) nanopore devices to single-molecule 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 pot...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2021-03, Vol.118 (11), p.1-7
Main Authors: Xia, Ke, Hagan, James T., Fu, Li, Sheetz, Brian S., Bhattacharya, Somdatta, Zhang, Fuming, Dwyer, Jason R., Linhardt, Robert J.
Format: Article
Language:English
Subjects:
Algorithms
Biological Sciences
Biopolymers
Composition
Disaccharides
Glycosaminoglycans
Heparan sulfate
Learning algorithms
Machine learning
Monosaccharides
Nucleic acids
Object recognition
Physical Sciences
Porosity
Solid state
Sulfates
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Summary:The application of solid-state (SS) nanopore devices to single-molecule 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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.2022806118