AI Article Synopsis
- The study explores the application of solid-state nanopore devices for sequencing glycosaminoglycans (GAGs), which is unexpectedly effective despite challenges in single-molecule nucleic acid sequencing.
- Signal magnitude and duration from the nanopore data were analyzed, showing distinct differences in signals from four synthetic GAGs.
- Machine learning techniques were employed to determine the composition of these GAGs, indicating that extremely sensitive analysis might be achievable with low sample amounts using SS nanopore technology.
Article Abstract
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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7980385 | PMC |
| http://dx.doi.org/10.1073/pnas.2022806118 | DOI Listing |
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