All-in-One Fusogenic Nanoreactor for the Rapid Detection of Exosomal MicroRNAs for Breast Cancer Diagnosis

纳米反应器 乳腺癌 小RNA 癌症检测 癌症 纳米技术 计算生物学 材料科学 生物 纳米颗粒 遗传学 基因
作者
Chaewon Park,Soo‐Hyun Chung,H.Y. Kim,Nayoung Kim,Hye Young Son,Ryunhyung Kim,Sojeong Lee,Geunseon Park,Hyun Wook Rho,Mirae Park,Jueun Han,Yejin Song,Ji Hee Lee,Sung‐Hoon Jun,Yong‐Min Huh,Hyoung Hwa Jeong,Eun‐Kyung Lim,Eunjung Kim,Seungjoo Haam
出处
期刊:ACS Nano [American Chemical Society]
标识
DOI:10.1021/acsnano.4c08339
摘要

Molecular-profiling-based cancer diagnosis has significant implications for predicting disease prognosis and selecting targeted therapeutic interventions. The analysis of cancer-derived extracellular vesicles (EVs) provides a noninvasive and sequential method to assess the molecular landscape of cancer. Here, we developed an all-in-one fusogenic nanoreactor (FNR) encapsulating DNA-fueled molecular machines (DMMs) for the rapid and direct detection of EV-associated microRNAs (EV miRNAs) in a single step. This platform was strategically designed to interact selectively with EVs and induce membrane fusion under a specific trigger. After fusion, the DMMs recognized the target miRNA and initiated nonenzymatic signal amplification within a well-defined reaction volume, thus producing an amplified fluorescent signal within 30 min. We used the FNRs to analyze the unique expression levels of three EV miRNAs in various biofluids, including cell culture, urine, and plasma, and obtained an accuracy of 86.7% in the classification of three major breast cancer (BC) cell lines and a diagnostic accuracy of 86.4% in the distinction between patients with cancer and healthy donors. Notably, a linear discriminant analysis revealed that increasing the number of miRNAs from one to three improved the accuracy of BC patient discrimination from 78.8 to 95.4%. Therefore, this all-in-one diagnostic platform performs nondestructive EV processing and signal amplification in one step, providing a straightforward, accurate, and effective individual EV miRNA analysis strategy for personalized BC treatment.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Odette完成签到,获得积分20
1秒前
杳鸢应助LUTT采纳,获得10
1秒前
赘婿应助八九采纳,获得10
2秒前
nushell完成签到,获得积分10
2秒前
3秒前
ossantu完成签到,获得积分10
5秒前
顾矜应助科研通管家采纳,获得10
5秒前
科目三应助科研通管家采纳,获得10
5秒前
Ava应助科研通管家采纳,获得10
5秒前
华仔应助科研通管家采纳,获得10
5秒前
笨笨石头应助科研通管家采纳,获得10
5秒前
852应助科研通管家采纳,获得10
5秒前
郭郭要努力ya完成签到 ,获得积分10
6秒前
叶公子发布了新的文献求助10
7秒前
乐乐应助飘逸的蚂蚁采纳,获得10
8秒前
张秋雨完成签到,获得积分20
11秒前
bxj发布了新的文献求助10
12秒前
BIT_lulu完成签到,获得积分10
13秒前
luckyru完成签到,获得积分10
13秒前
充电宝应助叶公子采纳,获得10
13秒前
14秒前
14秒前
科研通AI2S应助张秋雨采纳,获得10
15秒前
搜集达人应助鹿梨白之樱采纳,获得10
15秒前
爆米花应助直率的拉米采纳,获得30
15秒前
莫愁完成签到,获得积分10
15秒前
烟火还是永恒完成签到,获得积分10
16秒前
调研昵称发布了新的文献求助10
17秒前
17秒前
19秒前
19秒前
恶恶么v发布了新的文献求助10
19秒前
20秒前
健壮小懒猪完成签到,获得积分10
22秒前
popo完成签到,获得积分10
22秒前
yatou5651发布了新的文献求助10
22秒前
23秒前
大胆胡萝卜完成签到,获得积分10
23秒前
24秒前
心随风飞应助呼呼采纳,获得20
25秒前
高分求助中
Evolution 10000
Sustainability in Tides Chemistry 2800
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
A new species of Velataspis (Hemiptera Coccoidea Diaspididae) from tea in Assam 500
Diagnostic immunohistochemistry : theranostic and genomic applications 6th Edition 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3155908
求助须知:如何正确求助?哪些是违规求助? 2807136
关于积分的说明 7871997
捐赠科研通 2465497
什么是DOI,文献DOI怎么找? 1312260
科研通“疑难数据库(出版商)”最低求助积分说明 629958
版权声明 601905