Multimodal Label-Free Nanosensor for Single Virus Characterization and Content Analysis

项目总结 最近，病毒颗粒因其作为变革性治疗载体的潜力而受到极大的关注。 例如，FDA批准的生物Luxturna是一种腺相关病毒(AAV)，用于携带基因 治疗遗传性失明的材料。在药物输送中，质量控制是非常重要的，而 这种样品中的空衣壳和部分填充衣壳需要准确测定，以避免可能的灾难性后果 过量服药的影响。虽然现有的技术如ELISA法和QPCR法可用于此目的， 盲法研究显示，样本内标准偏差高得惊人。我们提出了一个更简单但更多的 根据货物含量区分AAVs的有效技术--等离子体纳米孔传感器 自动重新捕获功能，用于电子传感，与光学陷阱配合运行 系统。病毒是柔软的纳米颗粒，AAV衣壳的变形性取决于它们的货物含量。我们 将通过我们的三个具体目标实施我们的双峰病毒表征平台：(1A)表征 单个AAVs在通过固态纳米孔移位过程中的变形动力学；(1b)重新捕获每个 移位后的病毒并自动执行重新捕获协议；(2)通过自我诱导的反作用捕获单个AAVs (SIBA)驱动纳米孔电泳光捕获与电捕获串联；(3) 将机器学习应用于光电领域，实现基于载重的AAVs稳健分类 在实验期间依赖于病毒变形性的信号。首先，我们将跟踪电压感应 三种不同货物含量的AAV样品的变形性：填充单链DNA(AAVssDNA)、双链DNA(AAVdsDNA)、 以及通过一定范围的电压的空壳(AAVEmpty)。每种类型的AAV预计都会诱导出一种独特的 相对易位电流随外加电压的可重复性变化。狭窄的电压范围显示 对不同比例的AAVssDNA+AAVEmpty和AAVEmpty进行最佳判别 AAVdsDNA+AAVEmpty。将对条件进行优化，以获得装满货物的AAVs和 空的/半满的衣壳类种群。一旦这一步成功，我们将合并单个/多个 重新捕获平台的能力，以获得每个AAV粒子的统计声音数据。接下来，一个金色的双纳米孔 将在纳米孔的顶部制作结构，通过SIBA力光学捕获AAVs，这将进一步变形 并减缓它们的移位。重要的是，光电串联传感将允许表征 单个AAV尺寸变形与装药效应解耦。对于病毒的自动分类，我们将 利用光电数据段建立概率(贝叶斯)机器学习模型 单个AAVs的信号可实现非常高的预测精度。所提出的机器学习环辅助电动- 光学传感将从复杂的AAV混合物中检测病毒颗粒的部分(例如，AAVdsDNA部分 同时仅使用微微到纳米摩尔浓度，与当前的分析性超速离心法形成对比 在质量保证步骤中使用大量AAV制剂的技术。

PROJECT SUMMARY Recently virus particles have gained immense attention for their potential as transformative therapeutic carriers. For example, the FDA-approved biologic Luxturna is an adeno-associated virus (AAV) used to carry genetic materials to treat hereditary blindness. In drug delivery, quality control is very important and the percentage of empty and part-filled capsids in such a sample needs to be determined accurately to avoid possible catastrophic effects of overdosing. While there are existing techniques like ELISA and qPCR that are used for this purpose, blind studies have shown alarmingly high intra-sample standard deviations. We propose a simpler yet more effective technique to discriminate AAVs depending on their cargo content – a plasmonic nanopore sensor with automated recapture capability for electrical sensing, aligned to operate in tandem with an optical trapping system. Viruses are soft nanoparticles and the deformability of AAV capsids depends on their cargo content. We will implement our bimodal virus characterization platform through our three Specific Aims: (1a) Characterize deformation dynamics of single AAVs during translocations through solid-state nanopores; (1b) Recapture each virus after translocation and automate a recapture protocol; (2) Capture single AAVs by self-induced back-action (SIBA) actuated nanopore electrophoresis (SANE) optical trapping in tandem with electrical recapturing; (3) Achieve robust classification of AAVs based on their cargo load by applying machine learning to optical-electrical signals that depend on virus deforamability during experiments. First, we will track the voltage-induced deformability of three AAV samples of different cargo contents: filled with ssDNA (AAVssDNA), dsDNA (AAVdsDNA), and empty capsids (AAVempty) through a range of voltages. Each type of AAV is expected to induce a unique and reproducible change in relative translocation current versus applied voltage. A narrow voltage range that shows the best discrimination will be applied on different percentage mixtures of AAVssDNA+AAVempty and AAVdsDNA+AAVempty. Conditions will be optimized to get the best discrimination between cargo-filled AAVs and the empty/part-filled capsid populations. Once this step is successful, we will incorporate single/multiple recapture capability to the platform to gain statistically sound data per AAV particle. Next, a gold double nanohole structure will be fabricated on top of the nanopore to optically trap AAVs by SIBA force, which will further deform AAVs and slow down their translocation. Importantly, optical-electrical tandem sensing will allow characterization of single AAV size deformations decoupled from charge effects. For automatic classification of viruses, we will develop a probabilistic (Bayesian) machine learning model with multiple data segments from optical-electrical signals of single AAVs for very high accuracy predictions. The proposed machine leanring assisted electrical- optical sensing will detect the fraction of viral particles (e.g., the AAVdsDNA fraction) from a complex AAV mixture while using only pico- to nano-molar concentrations, in contrast to current analytical ultracenrifugation technologies that use up significant amounts of an AAV preparation in the quality assurance step.