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Issue 46, 2020
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Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness

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Abstract

Solid-state nanopore (SSN)-based analytical methods have found abundant use in genomics and proteomics with fledgling contributions to virology – a clinically critical field with emphasis on both infectious and designer-drug carriers. Here we demonstrate the ability of SSN to successfully discriminate adeno-associated viruses (AAVs) based on their genetic cargo [double-stranded DNA (AAVdsDNA), single-stranded DNA (AAVssDNA) or none (AAVempty)], devoid of digestion steps, through nanopore-induced electro-deformation (characterized by relative current change; ΔI/I0). The deformation order was found to be AAVempty > AAVssDNA > AAVdsDNA. A deep learning algorithm was developed by integrating support vector machine with an existing neural network, which successfully classified AAVs from SSN resistive-pulses (characteristic of genetic cargo) with >95% accuracy – a potential tool for clinical and biomedical applications. Subsequently, the presence of AAVempty in spiked AAVdsDNA was flagged using the ΔI/I0 distribution characteristics of the two types for mixtures composed of ∼75 : 25% and ∼40 : 60% (in concentration) AAVempty : AAVdsDNA.

Graphical abstract: Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness

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Supplementary files

Article information


Submitted
28 Jul 2020
Accepted
12 Nov 2020
First published
24 Nov 2020

Nanoscale, 2020,12, 23721-23731
Article type
Paper

Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness

B. I. Karawdeniya, Y. M. N. D. Y. Bandara, A. I. Khan, W. T. Chen, H. Vu, A. Morshed, J. Suh, P. Dutta and M. J. Kim, Nanoscale, 2020, 12, 23721
DOI: 10.1039/D0NR05605G

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