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 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; Delta 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 Delta I/I-0 distribution characteristics of the two types for mixtures composed of similar to 75 : 25% and similar to 40 : 60% (in concentration) AAV(empty ): AAV(dsDNA).