Enhanced Discriminability of Viral Vectors in Viscous Nanopores.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Makusu Tsutsui, Yuji Tsunekawa, Mikako Wada, Akihide Arima, Azusa Onodera, Masumi Nishina, Miho Nagoya, Yoshinobu Baba, Tomoji Kawai, Takashi Okada
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引用次数: 0

Abstract

Achieving safe and efficient gene therapy hinges upon the inspection of genomes enclosed within individual nano-carriers to mitigate potential health risks associated with empty or fragment-filled vectors. Here solid-state nanopore sensing is reported for identifications of intermediate adeno-associated virus (AAV) vectors in liquid. The method exploits the phenomenon of translocation slowdown induced by the viscosity of salt water-organic mixtures. This enables real-time ionic current measurements allowing precise tracking of the electroosmotic flow-driven motions of recombinant AAV vectors in a nanopore. The resulting ionic signals facilitate discrimination between replicative intermediates carrying ssDNA fragments and its full vector counterparts based on genome length-derived subtle nanometer differences in the viral diameters. This rapid and non-destructive means of genome analysis within virus capsids provides a promising avenue toward a robust methodology for ensuring the integrity of AAV vectors before administration.

黏性纳米孔中病毒载体的增强辨别能力。
实现安全和有效的基因治疗取决于对单个纳米载体内的基因组进行检查,以减轻与空载体或片段填充载体相关的潜在健康风险。本文报道了固体纳米孔传感技术在液体中鉴定中间腺相关病毒(AAV)载体。该方法利用了盐-有机混合物黏度引起的迁移减速现象。这使得实时离子电流测量能够精确跟踪纳米孔中重组AAV载体的电渗透流驱动运动。由此产生的离子信号有助于区分携带ssDNA片段的复制中间体和基于基因组长度衍生的病毒直径的细微纳米差异的完整载体。这种快速且非破坏性的病毒衣壳内基因组分析方法为在给药前确保AAV载体的完整性提供了一种有前途的方法。
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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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