Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 118, Issue 11, Pages -Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2022806118
Keywords
solid-state nanopore; polysaccharide; glycosaminoglycan; sequencing; single-molecule analysis
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Funding
- NIH [CA231074, DK111958, HL125371]
- NSF [CHE 1808344]
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The application of solid-state nanopore devices for analyzing synthetic heparan sulfate GAG chains is feasible, with clear differences between signals generated by different GAGs detected through machine learning and data analysis. These findings suggest that ultrasensitive GAG analysis could be achieved using SS nanopore detection and well-characterized molecular training sets.
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.
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