Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness

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 discrimina...

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Bibliographic Details
Published in:Nanoscale 2020-12, Vol.12 (46), p.23721-23731
Main Authors: Karawdeniya, Buddini Iroshika, Bandara, Y. M. Nuwan D. Y, Khan, Aminul Islam, Chen, Wei Tong, Vu, Hoang-Anh, Morshed, Adnan, Suh, Junghae, Dutta, Prashanta, Kim, Min Jun
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Language:English
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Summary: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 (AAV dsDNA ), single-stranded DNA (AAV ssDNA ) or none (AAV empty )], devoid of digestion steps, through nanopore-induced electro-deformation (characterized by relative current change; Δ I / I 0 ). The deformation order was found to be AAV empty > AAV ssDNA > AAV dsDNA . 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 AAV empty in spiked AAV dsDNA was flagged using the Δ I / I 0 distribution characteristics of the two types for mixtures composed of ∼75 : 25% and ∼40 : 60% (in concentration) AAV empty  : AAV dsDNA . Solid-state nanopore based electro-deformation coupled with deep learning to distinguish AAV particles based on their cargo content.
ISSN:2040-3364
2040-3372
DOI:10.1039/d0nr05605g