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

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.

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Institutions

Publication Details

Journal
ACS Nano
Published
2024-09-09
DOI
https://doi.org/10.1021/acsnano.4c08339
Citations
10
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
2.34

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article

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

Seungjoo Haam, Mirae Park, Eun‐Kyung Lim, Hye Young Son et al.
10 citations
ACS Nano
Extracellular vesicles in disease
2.34
article

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

Seungjoo Haam, Mirae Park, Eun‐Kyung Lim, Hye Young Son, Jueun Han, Hyoung Hwa Jeong, Yejin Song, Sung‐Hoon Jun, Geunseon Park, Ji Hee Lee, Yong‐Min Huh, Hansol Kim, Nayoung Kim, Eunjung Kim, Sojeong Lee, Chaewon Park, Soo‐Hyun Chung, Ryunhyung Kim, Hyun Wook Rho
article en
10 citations

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.

ACS Nano
Harvard University (US), Incheon National University (KR), Yonsei University (KR), Korea Basic Science Institute (KR), Yonsei University Health System (KR), Massachusetts Institute of Technology (US), Korea Research Institute of Bioscience and Biotechnology (KR), Sungkyunkwan University (KR), Korea University of Science and Technology (KR)
Ministry of Trade, Industry and Energy, Korea Research Institute of Bioscience and Biotechnology, National Research Foundation of Korea
Openalex Percentile: Top 11%
Extracellular vesicles in disease
2.34
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