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在经济高效的数字微流控平台上快速自动化细胞外囊泡分离和 miRNA 制备。

Rapid automated extracellular vesicle isolation and miRNA preparation on a cost-effective digital microfluidic platform.

机构信息

State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.

Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.

出版信息

Anal Chim Acta. 2024 Apr 1;1296:342337. doi: 10.1016/j.aca.2024.342337. Epub 2024 Feb 6.


DOI:10.1016/j.aca.2024.342337
PMID:38401929
Abstract

As a prerequisite for extracellular vesicle (EV) -based studies and diagnosis, effective isolation, enrichment and retrieval of EV biomarkers are crucial to subsequent analyses, such as miRNA-based liquid biopsy for non-small-cell lung cancer (NSCLC). However, most conventional approaches for EV isolation suffer from lengthy procedure, high cost, and intense labor. Herein, we introduce the digital microfluidic (DMF) technology to EV pretreatment protocols and demonstrate a rapid and fully automated sample preparation platform for clinical tumor liquid biopsy. Combining a reusable DMF chip technique with a low-cost EV isolation and miRNA preparation protocol, the platform completes automated sample processing in 20-30 min, supporting immediate RT-qPCR analyses on EV-derived miRNAs (EV-miRNAs). The utility and reliability of the platform was validated via clinical sample processing for EV-miRNA detection. With 23 tumor and 20 non-tumor clinical plasma samples, we concluded that EV-miR-486-5p and miR-21-5p are effective biomarkers for NSCLC with a small sample volumn (20-40 μL). The result was consistent to that of a commercial exosome miRNA extraction kit. These results demonstrate the effectiveness of DMF in EV pretreatment for miRNA detection, providing a facile solution to EV isolation for liquid biopsy.

摘要

作为基于细胞外囊泡 (EV) 的研究和诊断的前提,有效地分离、富集和回收 EV 生物标志物对于随后的分析至关重要,例如用于非小细胞肺癌 (NSCLC) 的基于 miRNA 的液体活检。然而,大多数用于 EV 分离的常规方法都存在过程冗长、成本高和劳动强度大的问题。在此,我们将介绍数字微流控 (DMF) 技术到 EV 预处理方案中,并展示了一种用于临床肿瘤液体活检的快速、全自动样本制备平台。该平台将可重复使用的 DMF 芯片技术与低成本的 EV 分离和 miRNA 制备方案相结合,可在 20-30 分钟内完成自动化样本处理,支持对 EV 衍生的 miRNA (EV-miRNA) 进行即时 RT-qPCR 分析。该平台通过对 EV-miRNA 检测的临床样本处理进行验证,证明了其有效性和可靠性。使用 23 个肿瘤和 20 个非肿瘤临床血浆样本,我们得出结论,EV-miR-486-5p 和 miR-21-5p 是用于 NSCLC 的有效生物标志物,样本量小(20-40μL)。结果与商业外泌体 miRNA 提取试剂盒的结果一致。这些结果表明 DMF 在 EV 预处理中用于 miRNA 检测的有效性,为液体活检中的 EV 分离提供了一种简便的解决方案。

相似文献

[1]
Rapid automated extracellular vesicle isolation and miRNA preparation on a cost-effective digital microfluidic platform.

Anal Chim Acta. 2024-4-1

[2]
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[3]
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[4]
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[5]
The potential diagnostic value of extracellular vesicle miRNA for human non-small cell lung cancer: a systematic review and meta-analysis.

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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Nucleic acid amplification tests in digital microfluidics: the promise of next-generation point-of-care diagnostics.

Microsyst Nanoeng. 2025-8-18

[2]
Extracellular Vesicles for Clinical Diagnostics: From Bulk Measurements to Single-Vesicle Analysis.

ACS Nano. 2025-8-12

[3]
Diagnostic Potential of Exosomal and Non-Exosomal Biomarkers in Lung Cancer: A Comparative Analysis Using a Rat Model of Lung Carcinogenesis.

Noncoding RNA. 2025-6-16

[4]
Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.

Adv Sci (Weinh). 2025-1

[5]
Extracellular vesicles for cancer therapy: potential, progress, and clinical challenges.

Front Bioeng Biotechnol. 2024-9-27

[6]
Mesenchymal Stem Cell-Derived Exosomes as Drug Delivery Vehicles in Disease Therapy.

Int J Mol Sci. 2024-7-14

[7]
Recent progress of exosomal lncRNA/circRNA-miRNA-mRNA axis in lung cancer: implication for clinical application.

Front Mol Biosci. 2024-7-3

[8]
Exosomes in the Diagnosis of Neuropsychiatric Diseases: A Review.

Biology (Basel). 2024-5-28

[9]
Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review.

Adv Sci (Weinh). 2024-8

[10]
Exosomal Proteomics: Unveiling Novel Insights into Lung Cancer.

Aging Dis. 2024-4-9

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