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用于快速检测外泌体微小RNA以诊断乳腺癌的一体化融合纳米反应器

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

作者信息

Park Chaewon, Chung Soohyun, Kim Hansol, Kim Nayoung, Son Hye Young, Kim Ryunhyung, Lee Sojeong, Park Geunseon, Rho Hyun Wook, Park Mirae, Han Jueun, Song Yejin, Lee Jihee, Jun Sung-Hoon, Huh Yong-Min, Jeong Hyoung Hwa, Lim Eun-Kyung, Kim Eunjung, Haam Seungjoo

机构信息

Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea.

Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, United States.

出版信息

ACS Nano. 2024 Sep 9. doi: 10.1021/acsnano.4c08339.

Abstract

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.

摘要

基于分子谱分析的癌症诊断对于预测疾病预后和选择靶向治疗干预具有重要意义。对癌症来源的细胞外囊泡(EVs)进行分析,为评估癌症的分子格局提供了一种非侵入性的连续方法。在此,我们开发了一种一体化融合纳米反应器(FNR),其封装了由DNA驱动的分子机器(DMMs),用于在一步中快速直接检测EV相关的微小RNA(EV miRNAs)。该平台经过精心设计,可与EVs选择性相互作用,并在特定触发条件下诱导膜融合。融合后,DMMs识别靶miRNA,并在明确的反应体积内启动非酶信号放大,从而在30分钟内产生放大的荧光信号。我们使用FNRs分析了包括细胞培养物、尿液和血浆在内的各种生物流体中三种EV miRNAs的独特表达水平,在三种主要乳腺癌(BC)细胞系的分类中获得了86.7%的准确率,在区分癌症患者和健康供体时的诊断准确率为86.4%。值得注意的是,线性判别分析表明,将miRNAs的数量从一种增加到三种,可将BC患者鉴别的准确率从78.8%提高到95.4%。因此,这种一体化诊断平台在一步中实现了对EV的无损处理和信号放大,为个性化BC治疗提供了一种直接、准确且有效的个体EV miRNA分析策略。

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