Simultaneous subset tracing and miRNA profiling of tumor-derived exosomes via dual-surface-protein orthogonal barcoding.
Simultaneous subset tracing and miRNA profiling of tumor-derived exosomes via dual-surface-protein orthogonal barcoding.
复制标题
DOI:
10.1126/sciadv.adi1556
复制
发表时间:
2023-10-06
期刊:
影响因子:
13.6
通讯作者:
Yang, Chaoyong
中科院分区:
文献类型:
--
作者:
Lei, Yanmei;Fei, Xiaochen;Ding, Yue;Zhang, Jianhui;Zhang, Guihua;Dong, Liang;Song, Jia;Zhuo, Ying;Xue, Wei;Zhang, Peng;Yang, Chaoyong
The clinical potential of miRNA-based liquid biopsy has been largely limited by the heterogeneous sources in plasma and tedious assay processes. Here, we develop a precise and robust one-pot assay called dual-surface-protein-guided orthogonal recognition of tumor-derived exosomes and in situ profiling of microRNAs (SORTER) to detect tumor-derived exosomal miRNAs and enhance the diagnostic accuracy of prostate cancer (PCa). The SORTER uses two allosteric aptamers against exosomal marker CD63 and tumor marker EpCAM to create an orthogonal labeling barcode and achieve selective sorting of tumor-specific exosome subtypes. Furthermore, the labeled barcode on tumor-derived exosomes initiated targeted membrane fusion with liposome probes to import miRNA detection reagents, enabling in situ sensitive profiling of tumor-derived exosomal miRNAs. With a signature of six miRNAs, SORTER differentiated PCa and benign prostatic hyperplasia with an accuracy of 100%. Notably, the diagnostic accuracy reached 90.6% in the classification of metastatic and nonmetastatic PCa. We envision that the SORTER will promote the clinical adaptability of miRNA-based liquid biopsy. The one-pot SORTER assay profiled tumor-derived exosomal miRNAs in 0.2 μl plasma, allowing for early diagnosis of prostate cancer.
登录
查看更多内容
DOI:
10.1126/science.aau6977
发表时间:
2020-02-07
期刊:
Science (New York, N.Y.)
影响因子:
--
作者:
Kalluri R;LeBleu VS
通讯作者:
LeBleu VS
影响因子:
13.3
作者:
Chen, Xiaohui;Jia, Mei;Luo, Yang
通讯作者:
Luo, Yang
影响因子:
48
作者:
Chen, Yuchao;Zhu, Qingfu;Liu, Fei
通讯作者:
Liu, Fei
影响因子:
16.6
作者:
Gao, Xihui;Li, Sha;Zhang, Chuan
通讯作者:
Zhang, Chuan
影响因子:
16.6
作者:
Deng, Jinqi;Zhao, Shuai;Sun, Jiashu
通讯作者:
Sun, Jiashu