Abstract

Glycosaminoglycans (GAGs) are a class of polysaccharides with potent biological activities. Due to their complex and heterogeneous composition, varied charge, polydispersity, and presence of isobaric stereoisomers, the analysis of GAG samples poses considerable challenges to current analytical techniques. In the present study, we combined solid-state nanopores - a single molecule sensor with a support vector machine (SVM) - a machine learning algorithm for the analysis of GAGs. Our results indicate that the nanopore/SVM technique could distinguish between monodisperse fragments of heparin and chondroitin sulfate with high accuracy (>90%), allowing as low as 0.8% (w/w) of chondroitin sulfate impurities in a heparin sample to be detected. In addition, the nanopore/SVM technique distinguished between unfractionated heparin (UFH) and enoxaparin (low molecular weight heparin) with an accuracy of ∼94% on average. With a reference sample for calibration, a nanopore could achieve nanomolar sensitivity and a 5-Log dynamic range. We were able to quantify heparin with reasonable accuracy using multiple nanopores. Our studies demonstrate the potential of the nanopore/SVM technique to quantify and identify GAGs.

Original languageEnglish (US)
JournalACS Nano
DOIs
StatePublished - Jan 1 2019

Fingerprint

heparins
Nanopores
Glycosaminoglycans
solid state
Molecules
Support vector machines
Heparin
molecules
Chondroitin Sulfates
sulfates
machine learning
polysaccharides
Enoxaparin
low molecular weights
activity (biology)
Stereoisomerism
Low Molecular Weight Heparin
Polydispersity
dynamic range
Polysaccharides

Keywords

  • glycosaminoglycan
  • heparin
  • machine learning
  • nanopore
  • SVM

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)
  • Physics and Astronomy(all)

Cite this

Single Molecule Identification and Quantification of Glycosaminoglycans Using Solid-State Nanopores. / Im, Jongone; Lindsay, Stuart; Wang, Xu; Zhang, Peiming.

In: ACS Nano, 01.01.2019.

Research output: Contribution to journalArticle

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