Single molecule identification and quantification of glycosaminoglycans using solid-state nanopores

Jongone Im, Stuart Lindsay, Xu Wang, Peiming Zhang

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


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)
Pages (from-to)6308-6318
Number of pages11
JournalACS nano
Issue number6
StatePublished - Jun 25 2019


  • Glycosaminoglycan
  • Heparin
  • Machine learning
  • Nanopore
  • SVM

ASJC Scopus subject areas

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


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