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

• Published in: Journal of extracellular vesicles 2020-10, Vol.10 (1), p.e12025-n/a
• Main Authors: Zhang, Junli, Shi, Jinjin, Zhang, Hongling, Zhu, Yifan, Liu, Wei, Zhang, Kaixiang, Zhang, Zhenzhong
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Antibodies; Aptamers; Biomarkers; Breast cancer; cancer diagnosis; cancer subtype differentiation; Carcinoma, Squamous Cell - blood; Carcinoma, Squamous Cell - diagnosis; Carcinoma, Squamous Cell - pathology; CD63 antigen; CD9 antigen; Diagnosis; DNA polymerase; DNA, Neoplasm - blood; Enzyme-linked immunosorbent assay; Extracellular vesicles; Extracellular Vesicles - metabolism; Extracellular Vesicles - pathology; Female; Humans; individual extracellular vesicles heterogeneity; localized fluorescent imaging; Lung cancer; Lung carcinoma; Lung Neoplasms - blood; Lung Neoplasms - diagnosis; Lung Neoplasms - pathology; Male; MCF-7 Cells; Medical diagnosis; Neoplasm Proteins - blood; Nucleic Acid Amplification Techniques; Optical Imaging; Penicillin; Proteins; rolling circle amplification; Tumors
• ISSN: 2001-3078; 2001-3078
• DOI: 10.1002/jev2.12025

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.