...
首页> 外文期刊>Journal of Extracellular Vesicles >Localized fluorescent imaging of multiple proteins on individual extracellular vesicles using rolling circle amplification for cancer diagnosis
【24h】

Localized fluorescent imaging of multiple proteins on individual extracellular vesicles using rolling circle amplification for cancer diagnosis

机译:使用滚动圆扩增对癌症诊断的单个细胞外囊泡上多种蛋白质的局部荧光成像

获取原文

摘要

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.
机译:由于其独特的生物性质,细胞外囊(EV)吸引了随着肿瘤生物标志物的增加。然而,EV分析的常规方法主要基于批量测量,这使得肿瘤诊断和分类中的EV-EV异质性掩盖。这里,开发了一种局部荧光成像方法(蛋白质上称为个体EV,DPPIE),用于分析单个EV上的多种蛋白质。在该测定中,使用抗CD9抗体工程生物芯片从临床血浆样品中捕获EV。然后通过多个DNA适体(CD63 / EPCAM / MUC1)特别识别捕获的EV,然后滚动圆形放大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EV中收集的高维数据比批量测量(ELISA)和乳腺癌中的CD63 / EPCAM / MUC1-三阳性EV的百分比提供更精确的信息患者显着高于健康供体,这种方法可以达到91%的整体准确性。此外,使用DPPIE,我们能够区分肺腺癌和肺鳞状癌患者的EV。这种单独的EV异质性分析策略为挖掘了关于EV的更多信息提供了一种新的方式,以实现多癌症诊断和分类。

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号