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

doi:10.1039/D0NR05605G

Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness†

Buddini Iroshika Karawdeniya,

‡^a Y. M. Nuwan D. Y. Bandara,

‡^a Aminul Islam Khan,‡^b Wei Tong Chen,^c Hoang-Anh Vu,^d Adnan Morshed,

^b Junghae Suh,^cdef Prashanta Dutta^b and Min Jun Kim

*^a

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* Corresponding authors

^a Department of Mechanical Engineering, Southern Methodist University, Dallas, TX, USA
E-mail: mjkim@lyle.smu.edu

^b School of Mechanical and Materials Engineering, Washington State University, Pullman, WA, USA

^c Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, USA

^d Department of Bioengineering, Rice University, Houston, TX, USA

^e Department of Biosciences, Rice University, Houston, TX, USA

^f Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, USA

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 (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; ΔI/I₀). 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 ΔI/I₀ distribution characteristics of the two types for mixtures composed of ∼75 : 25% and ∼40 : 60% (in concentration) AAV_empty: AAV_dsDNA.

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DOI: https://doi.org/10.1039/D0NR05605G
Article type: Paper
Submitted: 28 Jul 2020
Accepted: 12 Nov 2020
First published: 24 Nov 2020

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Nanoscale, 2020,12, 23721-23731

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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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Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness†

Abstract

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Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness

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