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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Published in: | Nanoscale 2020-12, Vol.12 (46), p.23721-23731 |
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Main Authors: | , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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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. |
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ISSN: | 2040-3364 2040-3372 |
DOI: | 10.1039/d0nr05605g |