Mechanisms of Adeno-Associated Virus Serotype 9 Vector Characterization and Quality Control through Solid-State Nanopores

  • Navod Thyashan
  • , Janeesha Manawasinghe
  • , Chaoming Gu
  • , Santosh Khatri
  • , Christopher Nelson
  • , Merve Emecen Sanli
  • , Steven J. Gray
  • , Sangyoup Lee
  • , Prashanta Dutta
  • , George Alexandrakis
  • , Min Jun Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Adeno-associated virus (AAV) vectors are excellent gene-delivery carriers in gene therapy; however, improperly packaged capsids produced during manufacturing can reduce potency and raise safety concerns. We introduce a machine-learning-assisted, low-cost, label-free nanopore sensing platform with single-particle resolution to enhance AAV quality control. Using solid-state nanopore (SSN) devices on SixNy membranes, we optimized in vitro conditions for AAV9 detection and classification. We observed pH-dependent capsid denaturation under strong alkaline conditions. Buffer-specific, selective translocation of emptyAAV9 capsids from cargo-loaded samples enabled clear discrimination and revealed potential avenues for in situ filtration. We also observed distinct translocation behaviors between vectors encapsulating single-stranded DNA and those encapsulating self-complementary DNA. In addition, unsupervised clustering algorithms demonstrated high accuracy in distinguishing capsids with truncated genomes from those with full genomes, further facilitating AAV production quality. These findings support practical avenues for AAV filtration and analysis, providing a basis for label-free, high-throughput, precise, and scalable quality control in AAV vector manufacturing.

Original languageEnglish
Pages (from-to)2148-2163
Number of pages16
JournalACS Nano
Volume20
Issue number2
DOIs
StatePublished - Jan 20 2026

Funding

This work was supported by the National Institute of Health (1R01GM149949-01), the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2023-00259957), and the Nano & Material Technology Development Program through the NRF, funded by the Ministry of Science and ICT (RS-2024-00449882). The authors also acknowledge Dr. Vy Tran (UTA) for conducting zeta potential experiments, Mr. Kamruzzaman Joty, Dr. Matthew O’Donohue, Dr. Nuwan Bandara at the Ohio State University, USA, and Dr. Jugal Saharia at the University of Houston, Clear Lake, for their insightful discussion and technical support. The UTSW Translational Gene Therapy Core is acknowledged for the production of the AAV vectors, and Dr. Chad Brautigam and the UTSW Macromolecular Biophysics Core for AUC analysis of the AAV vectors.

Keywords

  • AAV vector quality control
  • buffer-specific filtration
  • chemically tuned pores
  • gene delivery vectors
  • single-molecule sensing
  • solid-state nanopores
  • unsupervised learning

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