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Development of a Lateral Flow Immunochromatographic Strip for Rapid and Quantitative Detection of Small Molecule Compounds
Published on: November 13, 2021
MicroRNA detection using lateral flow nucleic acid strips with gold nanoparticles
Shao-Yi Hou1, Yi-Ling Hsiao, Ming-Shu Lin
1Institute of Biotechnology, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Road, Taipei 106, Taiwan. ericklin710516@gmail.com
Talanta
|September 13, 2012
Summary
This study presents a novel microRNA detection assay using specific probes and mung-bean nuclease for high sensitivity. The assay accurately quantifies microRNA (miRNA) in about 70 minutes, suitable for point-of-care use.
Area of Science:
- Biotechnology
- Molecular Diagnostics
- Biosensing
Background:
- Traditional microRNA (miRNA) detection methods often face challenges with non-specific signals.
- Developing sensitive and specific assays for miRNA quantification is crucial for diagnostics.
Purpose of the Study:
- To develop a novel, highly sensitive, and specific assay for microRNA detection.
- To enable rapid and convenient microRNA quantification for point-of-care applications.
Main Methods:
- A direct detection method utilizing a specific match between microRNA, detection probe, and capture probe.
- Employing mung-bean nuclease to degrade non-hybridized capture probes, reducing non-specific signals.
- Utilizing gold nanoparticles conjugated to DNA probes and an anti-avidin antibody immobilized on a flow strip for signal detection.
Main Results:
- The assay detected synthetic microRNA down to one femtomole (fmol) and five attomole (amol) with and without silver enhancement, respectively.
- Demonstrated high sensitivity and specificity in microRNA detection.
- Assay completion time is approximately 70 minutes post-RNA preparation.
Conclusions:
- The developed assay offers a simple, convenient, and fast method for microRNA detection and quantification.
- The assay's sensitivity and speed make it suitable for point-of-care diagnostics.
- This approach overcomes limitations of traditional sandwich methods by minimizing non-specific signals.
