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Published on: December 23, 2022
CRISPR/Cas12a dual-mode biosensor for Staphylococcus aureus detection via enzyme-free isothermal amplification
Hongmin Gao1, Hehua Zhang2, Xue Qi2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China; Collaborative Research Center, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, People's Republic of China.
This study introduces a new dual-mode biosensor for detecting Staphylococcus aureus (S. aureus). It uses CRISPR/Cas12a and amplification methods for highly sensitive and accurate detection in various samples.
Area of Science:
- Biotechnology
- Biosensor Technology
- Molecular Diagnostics
Background:
- Accurate detection of Staphylococcus aureus (S. aureus) is critical for healthcare and food safety.
- Existing detection methods may lack the required sensitivity or speed for certain applications.
Purpose of the Study:
- To develop a novel dual-mode biosensor for sensitive and reliable detection of S. aureus.
- To integrate CRISPR/Cas12a with enzyme-free isothermal amplification for enhanced detection.
Main Methods:
- Utilized CRISPR/Cas12a system with hybridization chain reaction (HCR) and catalytic hairpin assembly (CHA) for signal amplification.
- Employed dual-mode detection leveraging CRISPR/Cas12a's cis-cleavage for fluorescence and trans-cleavage for electrochemical signals.
- Developed an aptamer-triggered response for S. aureus detection.
Main Results:
- Achieved a low limit of detection (LOD) of 5.7 CFU mL⁻¹ (electrochemical) and 133.7 CFU mL⁻¹ (fluorescence).
- Demonstrated high accuracy, stability, and sensitivity in detecting S. aureus.
- Successfully validated the biosensor's performance in analyzing actual samples.
Conclusions:
- The developed dual-mode biosensor provides a powerful and sensitive tool for rapid S. aureus identification.
- This approach shows significant potential for next-generation biosensors in clinical and food safety applications.
- Future work will focus on simplifying the detection process for resource-limited settings.
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