Logic-Measurer: A Multienzyme-Assisted Ultrasensitive Circuit for Logical Detection of Exosomal MicroRNAs
The logic profiling of exosomal microRNAs (miRNAs) offers broad potential applications in the accurate diagnosis and staging of cancer. However, the logical detection of low-abundance exosomal miRNAs in complex clinical samples remains challenging. This study introduces a logic analysis system termed "Measurer" (a <u>m</u>ulti-<u>e</u>nzyme-<u>as</u>sisted <u>u</u>lt<u>r</u>asensitiv<u>e</u> ci<u>r</u>cuit) that offers ultrasensitive and versatile method for detecting multiple exosomal miRNAs. The Logic-Measurer comprises three modules: a stem-loop hairpin-enhanced CRISPR/Cas13a, a polymerase-driven primer exchange reaction, and an exonuclease III-mediated fluorescence output. The efficient Logic-Measurer was switched by the faster rate of trans-cleavage activity of Cas13a due to its improved affinity for hairpin RNA structures. The mechanistic model of hairpin-enhanced CRISPR/Cas13a was confirmed by molecular dynamics simulations. The Logic-Measurer accurately detected exosomal miRNA-21 or miRNA-375 down to 2.1 and 4.4 fM, with superior specificity, and enabled in situ detection of miRNA-21 and miRNA-375 in as low as 1.4 × 10<sup>2</sup> particles/mL exosomes via membrane fusion. In addition, this method demonstrated 87.3 and 82.1% accuracy in the diagnosis and early detection of breast cancer, respectively, among a cohort of 315 individuals. Subsequent subgroup analysis further confirmed the method's ability to accurately differentiate estrogen receptor-positive patients from healthy individuals. Therefore, the Logic-Measurer offers valuable insights into the development of a CRISPR/Cas-based enhanced diagnostic platform and the next generation of diagnostic technology based on enzyme circuits.
Authors
- Xiaoqi Tang (ORCID: https://orcid.org/0009-0008-9332-7999)
- Ruijia Deng
- Zuowei Xie (ORCID: https://orcid.org/0000-0001-6206-004X)
- Shuang Xie
- Kai Chang (ORCID: https://orcid.org/0009-0001-1665-8697)
- Shuang George Zhao (ORCID: https://orcid.org/0000-0002-9166-6507)
- Feng Liu (ORCID: https://orcid.org/0000-0002-3570-4222)
- Ming Chen (ORCID: https://orcid.org/0000-0003-0613-7932)
- Yan Pi
Institutions
- Army Medical University (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Southwest Hospital (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- ACS Nano
- Published
- 2025-03-20
- DOI
- https://doi.org/10.1021/acsnano.5c00258
- Citations
- 20
- Primary Topic
- Extracellular vesicles in disease
- Type
- article
- Field-Weighted Citation Impact
- 16.12
Funders
- National Natural Science Foundation of China
- Science Fund for Distinguished Young Scholars of Chongqing