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

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All-in-One Fusogenic Nanoreactor for the Rapid Detection of Exosomal MicroRNAs for Breast Cancer Diagnosis
ACS Nano ( IF 15.8 ) Pub Date : 2024-09-09 , DOI: 10.1021/acsnano.4c08339
Chaewon Park ₁ , Soohyun Chung ₁ , Hansol Kim ₁ , Nayoung Kim _{2,

3,

4} , Hye Young Son _{5,

6} , Ryunhyung Kim ₁ , Sojeong Lee ₁ , Geunseon Park ₁ , Hyun Wook Rho ₅ , Mirae Park ₅ , Jueun Han ₇ , Yejin Song ₇ , Jihee Lee ₈ , Sung-Hoon Jun ₉ , Yong-Min Huh _{5,

6} , Hyoung Hwa Jeong ₁₀ , Eun-Kyung Lim _{11,

12,

13} , Eunjung Kim _{7,

8} , Seungjoo Haam ₁

Affiliation

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.
Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, United States.
Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts 02139, United States.
Department of Radiology, College of Medicine, Yonsei University, Seoul 03722, Republic of Korea.
YUHS-KRIBB Medical Convergence Research Institute, Seoul 03722, Republic of Korea.
Department of Bioengineering and Nano-Bioengineering, Research Center for Bio Materials and Process Development, Incheon National University, Incheon 22012, Republic of Korea.
Division of Bioengineering, Incheon National University, Incheon 22012, Republic of Korea.
Electron Microscopy & Spectroscopy Team, Korea Basic Science Institute, Chungbuk 28119, Republic of Korea.
Huvet Bio Inc., 127 Beobwon-ro, Seoul 05836, Republic of Korea.
BioNanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology(KRIBB), Daejeon 34141, Republic of Korea.
Department of Nanobiotechnology, KRIBB School of Biotechnology, University of Science and Technology (UST), Daejeon 34113, Republic of Korea.
School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea.

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.

中文翻译：

用于快速检测外泌体 MicroRNA 进行乳腺癌诊断的多合一融合纳米反应器

基于分子谱的癌症诊断对于预测疾病预后和选择有针对性的治疗干预措施具有重要意义。对癌症来源的细胞外囊泡 (EV) 的分析提供了一种无创且连续的方法来评估癌症的分子景观。在这里，我们开发了一种封装 DNA 驱动的分子机器 (DMM) 的一体化融合纳米反应器 (FNR)，用于一步快速直接检测 EV 相关 microRNA (EV miRNA)。该平台经过战略设计，可选择性地与电动汽车相互作用，并在特定触发条件下诱导膜融合。融合后，DMM 识别目标 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 FNR 分析了三种 EV miRNA 在各种生物液体（包括细胞培养物、尿液和血浆）中的独特表达水平，并在三种主要乳腺癌 (BC) 细胞系的分类和诊断中获得了 86.7% 的准确度。区分癌症患者和健康捐赠者的准确度为 86.4%。值得注意的是，线性判别分析表明，将 miRNA 的数量从 1 个增加到 3 个，可将 BC 患者辨别的准确性从 78.8% 提高到 95.4%。因此，这一一体化诊断平台可一步执行无损 EV 处理和信号放大，为个性化 BC 治疗提供简单、准确且有效的个体 EV miRNA 分析策略。

更新日期：2024-09-09

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