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Published on: October 31, 2013
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Gold Nanoparticle-Labeled CRISPR-Cas13a Assay for the Sensitive Solid-State Nanopore Molecular Counting
Li Liu1,2, Zhiheng Xu3, Kamel Awayda4
1Department of Microsystems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Summary
A new gold nanoparticle (AuNP)-labeled CRISPR-Cas13a assay offers sensitive RNA detection using nanopore technology. This method enables rapid, portable nucleic acid biomarker detection, even in low-resource settings.
Area of Science:
- Biotechnology
- Nanotechnology
- Molecular Diagnostics
Background:
- CRISPR-Cas13a systems offer precise RNA targeting capabilities.
- Nanopore sensing provides a platform for label-free electrical detection of biomolecules.
- Existing nucleic acid detection methods can be complex and require specialized equipment.
Purpose of the Study:
- To develop a sensitive and quantitative nucleic acid assay using gold nanoparticles (AuNPs) and CRISPR-Cas13a.
- To enable rapid, portable detection of RNA biomarkers via solid-state nanopore sensing.
- To achieve high sensitivity and a wide dynamic range for RNA quantification.
Main Methods:
- A CRISPR-Cas13a assay was developed, labeling RNA targets with AuNPs via non-covalent conjugation.
- Upon CRISPR activation, AuNPs were liberated, isolated, and detected by a solid-state nanopore sensor.
- Detection was based on monitoring changes in ionic current caused by AuNP translocation, with each event enumerated.
Main Results:
- Achieved a sensitive detection limit of 50 fM for SARS-CoV-2 RNA segments without amplification.
- Demonstrated a broad dynamic range of six orders of magnitude for quantitative RNA sensing.
- The assay operates at physiological temperature and utilizes a smartphone-sized nanopore reader.
Conclusions:
- The developed AuNP-labeled CRISPR-Cas13a assay provides a sensitive, quantitative, and simplified method for RNA detection.
- The system's portability and ease of use make it suitable for rapid biomarker detection in diverse settings.
- This technology has potential applications in both clinical diagnostics and resource-limited environments.

