胞外囊泡
CD63
乳腺癌
数字聚合酶链反应
细胞外小泡
小泡
癌症研究
癌症
生物
医学
微泡
细胞生物学
内科学
聚合酶链反应
生物化学
基因
小RNA
膜
作者
Chunchen Liu,Hao Lin,Jingyun Guo,Chao Yang,Jing Chen,Weilun Pan,Binbin Cui,Junjie Feng,Ye Zhang,Bo Li,Shuhuai Yao,Lei Zheng
标识
DOI:10.1016/j.cej.2023.144364
摘要
Quantification of protein-specific extracellular vesicle (EV) subpopulations at the single-vesicle level is of great significance for cancer diagnosis. Although individual vesicle analysis has been implemented by novel emerging technologies, developing an easy-to-operate and cost-effective single EV analysis approach to promote clinical applications is still challenging. Herein, we constructed a versatile droplet digital immuno-PCR (ddiPCR) assay which integrates the high specificity of immuno-PCR and superior sensitivity of droplet digital PCR to profile the surface proteins of single EVs for multi-subpopulation EVs counting. The clinical application of the ddiPCR assay was validated by simultaneous profiling the EV proteins of CD9/CD63/CD81, HER2, EpCAM in a breast cancer cohort, and CD9/CD63/CD81, GPC-3, EpCAM in a hepatocellular carcinoma cohort (HCC). The results demonstrated that the counting of multi-subpopulation EVs could significantly distinguish patients with breast tumor or HCC from healthy controls. Furthermore, with the assistance of machine learning algorithm and under the best combination of sEV subpopulations, our method exhibited great performance in differentiating breast cancer from healthy individuals. Therefore, this study provides a promising strategy to count multi-subpopulation EVs at the single-vesicle level for cancer diagnosis.
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