High-fidelity detection and sorting of nanoscale vesicles in viral disease and cancer
High-fidelity detection and sorting of nanoscale vesicles in viral disease and cancer
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DOI：
10.1080/20013078.2019.1597603
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发表时间：
2019-12-01
影响因子：
16
通讯作者：
Jones, Jennifer C.
中科院分区：
文献类型：
--
作者：
Morales-Kastresana, Aizea;Musich, Thomas A.;Jones, Jennifer C.
生物纳米粒子，包括病毒和细胞外囊泡（EV），作为疾病的生物标志物和介质或治疗方法引起了许多医学领域的兴趣。然而，由于颗粒相关的散射光信号与漫散射光背景仪器噪声的检测重叠，外泌体和小病毒低于传统流式细胞仪的检测限。为了识别、分类和研究 EV 和其他纳米颗粒的不同子集（作为单个颗粒），我们开发了纳米级荧光分析和流式细胞分选 (nanoFACS) 方法，以最大限度地利用高速、高分辨率流式细胞仪获得的信息和材料。这种 nanoFACS 方法需要分析仪器背景噪声（本文定义为“参考噪声”）。通过这些方法，我们展示了使用荧光和散射光参数检测具有特定肿瘤抗原的肿瘤细胞衍生的 EV。我们通过将两种不同的 HIV 毒株分选至 >95% 的纯度，进一步验证了 nanoFACS 的性能，并确认了用 nanoFACS 分选的生物纳米材料的活力（感染性）和分子特异性（特定细胞向性）。这种 nanoFACS 方法提供了一种独特的方法来分析和分类功能性 EV 和病毒子集，同时保留囊泡结构、表面蛋白特异性和 RNA 货物活性。
Biological nanoparticles, including viruses and extracellular vesicles (EVs), are of interest to many fields of medicine as biomarkers and mediators of or treatments for disease. However, exosomes and small viruses fall below the detection limits of conventional flow cytometers due to the overlap of particle-associated scattered light signals with the detection of background instrument noise from diffusely scattered light. To identify, sort, and study distinct subsets of EVs and other nanoparticles, as individual particles, we developed nanoscale Fluorescence Analysis and Cytometric Sorting (nanoFACS) methods to maximise information and material that can be obtained with high speed, high resolution flow cytometers. This nanoFACS method requires analysis of the instrument background noise (herein defined as the "reference noise"). With these methods, we demonstrate detection of tumour cell-derived EVs with specific tumour antigens using both fluorescence and scattered light parameters. We further validated the performance of nanoFACS by sorting two distinct HIV strains to >95% purity and confirmed the viability (infectivity) and molecular specificity (specific cell tropism) of biological nanomaterials sorted with nanoFACS. This nanoFACS method provides a unique way to analyse and sort functional EV- and viral-subsets with preservation of vesicular structure, surface protein specificity and RNA cargo activity.