Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness. Issue 46 (24th November 2020) - Karawdeniya, Buddini Iroshika; Bandara, Y. M. Nuwan D. Y.; Khan, Aminul Islam; Chen, Wei Tong; Vu, Hoang-Anh; Morshed, Adnan; Suh, Junghae; Dutta, Prashanta; Kim, Min Jun
Abstract : Solid-state nanopore based electro-deformation coupled with deep learning to distinguish AAV particles based on their cargo content. 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; Δ I / I 0 ). 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 Δ I / I 0 distribution characteristics of the two types for mixtures composed of ∼75 : 25% and ∼40 : 60% (in concentration) AAVempty : AAVdsDNA .