Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness.
Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness.
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DOI：
10.1039/d0nr05605g
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发表时间：
2020-12-08
期刊：
影响因子：
6.7
通讯作者：
Kim MJ
中科院分区：
文献类型：
--
作者：
Karawdeniya BI ;Bandara YMNDY ;Khan AI ;Chen WT ;Vu HA ;Morshed A ;Suh J ;Dutta P ;Kim MJ
基于固态纳米孔（SSN）的分析方法已在基因组学和蛋白质组学中得到了广泛的应用，对病毒学的贡献刚刚起步-这是一个临床关键领域，重点是感染性和设计药物载体。在这里，我们证明了SSN基于其遗传货物[双链DNA（AAVds-DNA）、单链DNA（AAVss-DNA）或无（AAVEmpty）]成功区分腺相关病毒（AAV）的能力，没有消化步骤，通过纳米孔诱导的电变形（以相对电流变化为特征; ΔI/I 0）。发现变形顺序为AAVEmpty > AAVssDNA > AAVdsDNA。通过将支持向量机与现有神经网络集成开发了一种深度学习算法，该算法成功地将AAV从SSN连续脉冲（遗传货物的特征）中分类，准确率>95%-这是临床和生物医学应用的潜在工具。随后，对于由约75：25和约40：60（浓度）AAVEmpty：AAVds-DNA组成的混合物，使用两种类型的ΔI/I 0分布特征标记掺入的AAVds-DNA中AAVEmpty的存在。基于固态纳米孔的电变形与深度学习相结合，以基于其货物含量区分AAV颗粒
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 (AAVds-DNA), single-stranded DNA (AAVss-DNA) or none (AAVEmpty)], devoid of digestion steps, through nanopore-induced electro-deformation (characterized by relative current change; ΔI/I0). 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 AAVds-DNA was flagged using the ΔI/I0 distribution characteristics of the two types for mixtures composed of ~75:25 and ~40:60 (in concentration) AAVEmpty: AAVds-DNA. Solid-state nanopore based electro-deformation coupled with deep learning to distinguish AAV particles based on their cargo content
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影响因子：
7.4
作者：
Freedman, Kevin J.;Jurgens, Maike;Kim, Min Jun
通讯作者：
Kim, Min Jun
影响因子：
11.8
作者：
Hoenen T;Groseth A;Rosenke K;Fischer RJ;Hoenen A;Judson SD;Martellaro C;Falzarano D;Marzi A;Squires RB;Wollenberg KR;de Wit E;Prescott J;Safronetz D;van Doremalen N;Bushmaker T;Feldmann F;McNally K;Bolay FK;Fields B;Sealy T;Rayfield M;Nichol ST;Zoon KC;Massaquoi M;Munster VJ;Feldmann H
通讯作者：
Feldmann H
影响因子：
16.6
作者：
Karawdeniya BI;Bandara YMNDY;Nichols JW;Chevalier RB;Dwyer JR
通讯作者：
Dwyer JR
影响因子：
4.6
作者：
Arima A;Tsutsui M;Harlisa IH;Yoshida T;Tanaka M;Yokota K;Tonomura W;Taniguchi M;Okochi M;Washio T;Kawai T
通讯作者：
Kawai T
影响因子：
4.1
作者：
Hossana, Mohammad Robiul;Dillonc, Robert;Duttaa, Prashanta
通讯作者：
Duttaa, Prashanta