Localized fluorescent imaging of multiple proteins on individual extracellular vesicles using rolling circle amplification for cancer diagnosis.
Localized fluorescent imaging of multiple proteins on individual extracellular vesicles using rolling circle amplification for cancer diagnosis.
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使用滚环放大对单个细胞外囊泡上的多种蛋白质进行局部荧光成像以进行癌症诊断
DOI：
10.1002/jev2.12025
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发表时间：
2020-10
影响因子：
16
通讯作者：
Zhang Z
中科院分区：
文献类型：
--
作者：
Zhang J;Shi J;Zhang H;Zhu Y;Liu W;Zhang K;Zhang Z
细胞外囊泡（extracellular vesicles，EV）由于其独特的生物学特性，作为肿瘤生物标志物受到越来越多的关注。然而，EV分析的传统方法主要基于批量测量，这掩盖了肿瘤诊断和分类中EV到EV的异质性。本文中，开发了局部荧光成像方法（称为个体EV上蛋白质的数字分析，DPPIE）用于分析个体EV上的多种蛋白质。在该测定中，使用抗CD 9抗体工程化生物芯片从临床血浆样品中捕获EV。然后，捕获的EV被多个DNA适体（CD 63/EpCAM/MUC 1）特异性识别，随后进行滚环扩增以产生局部荧光信号。通过分析个体EV的异质性，我们发现从每个个体EV收集的高维数据将提供比批量测量（ELISA）更精确的信息，乳腺癌患者中CD 63/EpCAM/MUC 1-三阳性EV的百分比显著高于健康供体，该方法可以达到91%的总体准确性。此外，使用DPPIE，我们能够区分肺腺癌和肺鳞癌患者的EV。这种个体EV异质性分析策略为挖掘更多EV信息以实现多癌症诊断和分类提供了新的途径。
Extracellular vesicles (EV) have attracted increasing attention as tumour biomarkers due to their unique biological property. However, conventional methods for EV analysis are mainly based on bulk measurements, which masks the EV‐to‐EV heterogeneity in tumour diagnosis and classification. Herein, a localized fluorescent imaging method (termed Digital Profiling of Proteins on Individual EV, DPPIE) was developed for analysis of multiple proteins on individual EV. In this assay, an anti‐CD9 antibody engineered biochip was used to capture EV from clinical plasma sample. Then the captured EV was specifically recognized by multiple DNA aptamers (CD63/EpCAM/MUC1), followed by rolling circle amplification to generate localized fluorescent signals. By‐analyzing the heterogeneity of individual EV, we found that the high‐dimensional data collected from each individual EV would provide more precise information than bulk measurement (ELISA) and the percent of CD63/EpCAM/MUC1‐triple‐positive EV in breast cancer patients was significantly higher than that of healthy donors, and this method can achieve an overall accuracy of 91%. Moreover, using DPPIE, we are able to distinguish the EV between lung adenocarcinoma and lung squamous carcinoma patients. This individual EV heterogeneity analysis strategy provides a new way for digging more information on EV to achieve multi‐cancer diagnosis and classification.
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影响因子：
4.2
作者：
Jing X;Liang H;Hao C;Yang X;Cui X
通讯作者：
Cui X
影响因子：
17.1
作者：
Lee K;Fraser K;Ghaddar B;Yang K;Kim E;Balaj L;Chiocca EA;Breakefield XO;Lee H;Weissleder R
通讯作者：
Weissleder R
影响因子：
4.3
作者：
Kalnina, Zane;Meistere, Irena;Line, Aija
通讯作者：
Line, Aija
影响因子：
15
作者：
Liu, Chao;Zhao, Junxiang;Sun, Jiashu
通讯作者：
Sun, Jiashu
影响因子：
16
作者：
Freitas, Daniela;Balmana, Meritxell;Reis, Celso A.
通讯作者：
Reis, Celso A.