Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness

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.