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贝塞尔光束激发分离技术的声流体病毒分离。

Acoustofluidic Virus Isolation via Bessel Beam Excitation Separation Technology.

机构信息

The Thomas Lord Department of Mechanical Engineering and Materials, Duke University, Durham, North Carolina 27708, United States.

Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, United States.

出版信息

ACS Nano. 2024 Aug 20;18(33):22596-22607. doi: 10.1021/acsnano.4c09692. Epub 2024 Aug 12.

DOI:10.1021/acsnano.4c09692
PMID:39132820
Abstract

The isolation of viruses from complex biological samples is essential for creating sensitive bioassays that assess the efficacy and safety of viral therapeutics and vaccines, which have played a critical role during the COVID-19 pandemic. However, existing methods of viral isolation are time-consuming and labor-intensive due to the multiple processing steps required, resulting in low yields. Here, we introduce the rapid, efficient, and high-resolution acoustofluidic isolation of viruses from complex biological samples via Bessel beam excitation separation technology (BEST). BEST isolates viruses by utilizing the nondiffractive and self-healing properties of 2D, in-plane acoustic Bessel beams to continuously separate cell-free viruses from biofluids, with high throughput and high viral RNA yield. By tuning the acoustic parameters, the cutoff size of isolated viruses can be easily adjusted to perform dynamic, size-selective virus isolation while simultaneously trapping larger particles and separating smaller particles and contaminants from the sample, achieving high-precision isolation of the target virus. BEST was used to isolate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from human saliva samples and Moloney Murine Leukemia Virus from cell culture media, demonstrating its potential use in both practical diagnostic applications and fundamental virology research. With high separation resolution, high yield, and high purity, BEST is a powerful tool for rapidly and efficiently isolating viruses. It has the potential to play an important role in the development of next-generation viral diagnostics, therapeutics, and vaccines.

摘要

从复杂的生物样本中分离病毒对于创建灵敏的生物测定法至关重要,这些方法可评估病毒疗法和疫苗的功效和安全性,在 COVID-19 大流行期间发挥了关键作用。然而,由于需要多个处理步骤,现有的病毒分离方法既耗时又费力,导致产量低。在这里,我们通过贝塞尔光束激发分离技术(BEST)介绍了从复杂生物样本中快速、高效和高分辨率地分离病毒的方法。BEST 通过利用二维平面内无衍射和自修复的 2D 声贝塞尔光束特性,连续从生物流体中分离无细胞病毒,具有高通量和高病毒 RNA 产量。通过调整声学参数,可以轻松调整分离病毒的截止尺寸,从而进行动态、尺寸选择性的病毒分离,同时捕获较大的颗粒,并将较小的颗粒和污染物从样品中分离出来,从而实现目标病毒的高精度分离。BEST 用于从人唾液样本中分离严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)和从细胞培养物中分离莫洛尼鼠白血病病毒,证明了其在实际诊断应用和基础病毒学研究中的潜在用途。BEST 具有高分离分辨率、高产量和高纯度,是一种快速、高效分离病毒的有力工具。它有可能在下一代病毒诊断、治疗和疫苗的开发中发挥重要作用。

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