Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness

Buddini Iroshika Karawdeniya, Y. M.Nuwan D.Y. Bandara, Aminul Islam Khan, Wei Tong Chen, Hoang Anh Vu, Adnan Morshed, Junghae Suh, Prashanta Dutta, Min Jun Kim*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)23721-23731
Number of pages11
JournalNanoscale
Volume12
Issue number46
DOIs
Publication statusPublished - 14 Dec 2020
Externally publishedYes

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