Single Molecule Identification and Quantification of Glycosaminoglycans Using Solid-State Nanopores

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 nano-pores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.

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

“…Sophisticated data analysis methods, including ML, play an important enabling role in nanopore analysis. It is well-established for DNA sequencing (13,35,36), and was recently first applied to the analysis of clinically relevant samples of heparin (31). That study used 16 different signal parameters, including the maximum event amplitude, average event amplitude, duration, blockade (blockage) ratio, and the average magnitude of the cepstrum spectra down-sampled into 51 equal windows of the event.…”

“…SS nanopore analysis of two sulfated GAGs, heparin and a heparin contaminant, oversulfated chondroitin sulfate, using a silicon nitride SS nanopore was able to qualitatively identify these GAGs by either the magnitude or duration of characteristic current blockages (28). SS nanopore data on GAGs, analyzed using a machine-learning (ML) algorithm (i.e., a support vector machine [SVM]), distinguished heparin and chondroitin sulfate oligosaccharides and unfractionated heparin and low molecular weight heparin with >90% accuracy (31).…”

Analysis of the Glycosaminoglycan Chains of Proteoglycans

“…The nanopore can be made of either biological (such as aerolysin and α-hemolysin) 100 or inorganic materials. 101–103 The nanopore filled with electrolyte solution allows ions to pass through unless a charged polymer in the passage and disrupts ion flow, generating a blocked-pore current. Karawdeniya and coworkers 102 reported this technique allowed easy differentiation between a clinical heparin sample and one contaminated with oversulfated chondroitin sulfate (OSCS).…”

“…The nanopore/SVM technique was able to distinguish between monodisperse fragments of HP and CS with high accuracy (>90%), allowing the detection of as little as 0.8% (w/w) CS impurity in a heparin sample. 103…”

Fast Fabrication of Solid-State Nanopores for DNA Molecule Analysis

“…Moreover, because of the integration compatibility of solid-state nanopores with various platforms [ 12 ], resistive-pulse sensing is easily coupled with other detection modalities based on plasmonic sensing [ 13 ], fluorescence spectroscopy [ 14 ], force spectroscopy [ 15 ], field effect transistors [ 16 , 17 , 18 ] and quantum tunnelling [ 19 ]. To date, solid-state nanopores have been used broadly for the detection of RNA [ 20 , 21 ], proteins [ 22 , 23 ], viruses [ 24 ], exosomes [ 25 ], and some other bionanoparticles [ 26 , 27 ], as well as DNA [ 28 ]. Solid-state nanopores also enable measuring the physical characteristics of nanomaterials, including not only the size but also the shape [ 29 ] and stiffness [ 30 ].…”