Systematic Position Mapping of Split CRISPR-Cas12a Activators Enables Highly Sensitive miRNA Detection and Cancer...

Area of Science:

• Molecular Biology
• Biotechnology
• CRISPR Technology

Background:

• CRISPR-Cas12a diagnostics offer amplification-free, programmable target recognition using split CRISPR RNA (crRNA).
• Predictable control over Cas12a trans-cleavage activity and diagnostic sensitivity is limited by incomplete understanding of DNA activator architecture.

Purpose of the Study:

• To systematically map DNA activator configurations and understand their impact on Cas12a trans-cleavage activity.
• To engineer enhanced split activators for improved sensitivity in CRISPR-Cas12a diagnostics.
• To generalize the engineered Cas12a system for detecting non-nucleic acid targets.

Main Methods:

• Systematic mapping of over 200 split DNA activator configurations by introducing nicks at all positions across both DNA strands.
• Analysis of nick position effects on Cas12a trans-cleavage activity, identifying suppressive (target strand) and enhancing (non-target strand) effects.
• Engineering of an optimized split activator pair based on identified rules and application to multiplexed live-cell profiling and non-nucleic acid detection.

Main Results:

• Target strand nicks suppress Cas12a activity in a position-dependent manner, while non-target strand nicks enhance activity.
• Engineered split activators achieved attomolar microRNA detection (LOD: 112 aM), representing a ~480-fold sensitivity increase over intact activators.
• Demonstrated multiplexed live-cell profiling of five miRNAs for cancer cell stratification and generalized detection of APE1 enzyme and HClO.

Conclusions:

• Provides a systematic understanding of DNA activator architecture's role in CRISPR-Cas12a trans-cleavage activity and sensitivity.
• Developed a generalizable methodology for engineering CRISPR-Cas12a performance, significantly enhancing diagnostic sensitivity and expanding biosensing capabilities.
• The engineered Cas12a system shows promise for advanced diagnostics, including machine learning-based cancer cell stratification and detection of diverse analytes.