Li Pan , Caiqing Yuan , Xiaowei Ma , Dunkai Wu , Yanhao Dong, Jing Ye, Shufan Pan, Donglei Yang, Pengfei Wang
{"title":"DNA编码的细胞外囊泡膜蛋白多轮谱分析用于癌症诊断。","authors":"Li Pan , Caiqing Yuan , Xiaowei Ma , Dunkai Wu , Yanhao Dong, Jing Ye, Shufan Pan, Donglei Yang, Pengfei Wang","doi":"10.1016/j.bios.2025.118055","DOIUrl":null,"url":null,"abstract":"<div><div>The rising global burden of cancer underscores the urgent demand for minimally invasive precision diagnostic methods. Extracellular vesicles (EVs) are emerging cancer liquid biopsy biomarkers carrying promising molecular markers such as membrane proteins. However, conventional approaches to EV membrane protein profiling remain limited by low multiplexing capability, high sample consumption, and complex operational workflows. Herein, we report a <u>D</u>NA <u>e</u>ncoded mul<u>t</u>i-round profiling of <u>e</u>xtracellular vesicle membrane proteins for <u>c</u>ancer diagnos<u>t</u>ics (DETECT) strategy that enables detection of EV membrane proteins with high sensitivity and scalability. This method leverages engineered aptamer probes to facilitate the capture and multi-round <em>in situ</em> detection of 9 EV surface proteins. DETECT integrates aptamer recognition with hybridization chain reaction (HCR) for signal amplification, followed by enzymatic cleavage for complete signal erasure, thereby enabling cyclic detection of multiple protein targets on the same EVs population. Clinical validation with EVs isolated from 48 serum samples of three cancer (gastric, breast, and prostate) demonstrated DETECT's capability to uncover cancer-specific membrane protein fingerprints, which achieved 100 % accuracy in differentiating cancers from noncancers and 83.3 % classification accuracy in differentiating three cancer types. DETECT represents a feasible, robust, and scalable technical platform for profiling EV surface proteins that shall hold great application potential in cancer diagnostics and beyond.</div></div>","PeriodicalId":259,"journal":{"name":"Biosensors and Bioelectronics","volume":"291 ","pages":"Article 118055"},"PeriodicalIF":10.5000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DNA encoded multi-round profiling of extracellular vesicle membrane proteins for cancer diagnostics\",\"authors\":\"Li Pan , Caiqing Yuan , Xiaowei Ma , Dunkai Wu , Yanhao Dong, Jing Ye, Shufan Pan, Donglei Yang, Pengfei Wang\",\"doi\":\"10.1016/j.bios.2025.118055\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The rising global burden of cancer underscores the urgent demand for minimally invasive precision diagnostic methods. Extracellular vesicles (EVs) are emerging cancer liquid biopsy biomarkers carrying promising molecular markers such as membrane proteins. However, conventional approaches to EV membrane protein profiling remain limited by low multiplexing capability, high sample consumption, and complex operational workflows. Herein, we report a <u>D</u>NA <u>e</u>ncoded mul<u>t</u>i-round profiling of <u>e</u>xtracellular vesicle membrane proteins for <u>c</u>ancer diagnos<u>t</u>ics (DETECT) strategy that enables detection of EV membrane proteins with high sensitivity and scalability. This method leverages engineered aptamer probes to facilitate the capture and multi-round <em>in situ</em> detection of 9 EV surface proteins. DETECT integrates aptamer recognition with hybridization chain reaction (HCR) for signal amplification, followed by enzymatic cleavage for complete signal erasure, thereby enabling cyclic detection of multiple protein targets on the same EVs population. Clinical validation with EVs isolated from 48 serum samples of three cancer (gastric, breast, and prostate) demonstrated DETECT's capability to uncover cancer-specific membrane protein fingerprints, which achieved 100 % accuracy in differentiating cancers from noncancers and 83.3 % classification accuracy in differentiating three cancer types. DETECT represents a feasible, robust, and scalable technical platform for profiling EV surface proteins that shall hold great application potential in cancer diagnostics and beyond.</div></div>\",\"PeriodicalId\":259,\"journal\":{\"name\":\"Biosensors and Bioelectronics\",\"volume\":\"291 \",\"pages\":\"Article 118055\"},\"PeriodicalIF\":10.5000,\"publicationDate\":\"2025-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biosensors and Bioelectronics\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0956566325009315\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosensors and Bioelectronics","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0956566325009315","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOPHYSICS","Score":null,"Total":0}
DNA encoded multi-round profiling of extracellular vesicle membrane proteins for cancer diagnostics
The rising global burden of cancer underscores the urgent demand for minimally invasive precision diagnostic methods. Extracellular vesicles (EVs) are emerging cancer liquid biopsy biomarkers carrying promising molecular markers such as membrane proteins. However, conventional approaches to EV membrane protein profiling remain limited by low multiplexing capability, high sample consumption, and complex operational workflows. Herein, we report a DNA encoded multi-round profiling of extracellular vesicle membrane proteins for cancer diagnostics (DETECT) strategy that enables detection of EV membrane proteins with high sensitivity and scalability. This method leverages engineered aptamer probes to facilitate the capture and multi-round in situ detection of 9 EV surface proteins. DETECT integrates aptamer recognition with hybridization chain reaction (HCR) for signal amplification, followed by enzymatic cleavage for complete signal erasure, thereby enabling cyclic detection of multiple protein targets on the same EVs population. Clinical validation with EVs isolated from 48 serum samples of three cancer (gastric, breast, and prostate) demonstrated DETECT's capability to uncover cancer-specific membrane protein fingerprints, which achieved 100 % accuracy in differentiating cancers from noncancers and 83.3 % classification accuracy in differentiating three cancer types. DETECT represents a feasible, robust, and scalable technical platform for profiling EV surface proteins that shall hold great application potential in cancer diagnostics and beyond.
期刊介绍:
Biosensors & Bioelectronics, along with its open access companion journal Biosensors & Bioelectronics: X, is the leading international publication in the field of biosensors and bioelectronics. It covers research, design, development, and application of biosensors, which are analytical devices incorporating biological materials with physicochemical transducers. These devices, including sensors, DNA chips, electronic noses, and lab-on-a-chip, produce digital signals proportional to specific analytes. Examples include immunosensors and enzyme-based biosensors, applied in various fields such as medicine, environmental monitoring, and food industry. The journal also focuses on molecular and supramolecular structures for enhancing device performance.