Henan Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases School of Pharmaceutical Sciences Zhengzhou University Zhengzhou 450001 China.
J Extracell Vesicles. 2020 Oct;10(1):e12025. doi: 10.1002/jev2.12025. Epub 2020 Nov 11.
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.
细胞外囊泡 (EV) 因其独特的生物学特性而成为肿瘤标志物,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 间的异质性。在此,开发了一种局部荧光成像方法(称为个体 EV 上的蛋白质数字分析,DPPIE),用于分析个体 EV 上的多种蛋白质。在该测定中,使用抗 CD9 抗体工程化生物芯片从临床血浆样本中捕获 EV。然后,通过多个 DNA 适体(CD63/EpCAM/MUC1)特异性识别捕获的 EV,然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,与批量测量(ELISA)相比,从每个个体 EV 收集的高维数据将提供更精确的信息,并且乳腺癌患者中 CD63/EpCAM/MUC1-三阳性 EV 的百分比明显高于健康供体,并且该方法的总体准确率为 91%。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 EV 上的更多信息以实现多癌种诊断和分类提供了新方法。