AM-DMF-ddRPA: an All-in-One Digital Microfluidic Platform for Rapid and Automatic Digital Nucleic Acid Analysis.

Journal: Angewandte Chemie (International ed. in English)
Published Date:

Abstract

Accurate nucleic acid quantification analysis (NQA) is crucial for disease treatment and prevention. However, existing digital NQA methods often lack sufficient automation and speed. In this study, we introduce a novel rapid and automated active-matrix digital microfluidics-based droplet digital recombinase polymerase amplification method (AM-DMF-ddRPA). This method can quantify influenza A and B viruses (IAV and IBV) within a mere 60 min, covering the entire workflow from nucleic acid extraction to digital amplification, fluorescence detection, and statistical analysis. We fabricated an AM-DMF chip equipped with high-throughput electrodes and optimized its control logic. Leveraging an artificial intelligence droplet navigation algorithm, we successfully achieved motion and mixing operations of high-throughput droplets (4608 droplets, each with a volume of 0.9 nL). A dual-target RPA system was designed to detect IAV and IBV, and its detection performance was fine-tuned. Experiments conducted with clinical samples revealed that this method exhibits ultra-high sensitivity for single-molecule detection, with a detection limit of 1.69 copies per reaction, and it achieved 100% concordance with qPCR results. This groundbreaking quantification technology holds tremendous promise for advancing disease diagnosis, treatment, and fundamental research.

Authors

  • Jiajian Ji
    School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, P.R. China.
  • Xinpei Pang
    School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, P.R. China.
  • Chunyu Chang
    CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, P.R. China.
  • Dongping Wang
    CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, P.R. China.
  • Siyi Hu
    CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
  • Zhixin Fang
    Guangdong Second Provincial General Hospital, 466 Middle Xingang Road, Guangzhou, Guangdong, 510317, P.R. China.
  • Chao Yu
    Link Sense Laboratory, Nanjing Research Institute of Electronic Technology, Nanjing, China.
  • Qian Mei
    School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, P.R. China.
  • Hanbin Ma
    CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.

Keywords

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