Reaction Pathway Differentiation Enabled Fingerprinting Signal for Single Nucleotide Variant Detection

Area of Science:

• Biotechnology
• Molecular Diagnostics
• Bioinformatics

Background:

• Accurate single-nucleotide variant (SNV) identification is crucial for disease diagnosis.
• Conventional DNA hybridization probes have limited specificity, hindering clinical applications.
• Existing methods for SNV detection often lack the required sensitivity and accuracy.

Purpose of the Study:

• To develop a novel detection method, the differential reaction pathway probe (DRPP), for highly accurate SNV identification.
• To leverage dynamic DNA reaction networks and machine learning for enhanced signal analysis and classification.
• To improve sensitivity for variant allele frequency (VAF) detection and demonstrate clinical applicability.

Main Methods:

• A differential reaction pathway probe (DRPP) based on a dynamic DNA reaction network was designed.
• DRPP utilizes differences in reaction intermediate concentrations to create distinct signal pathways for SNVs and wild-type (WT) DNA.
• Machine learning algorithms were applied to analyze fluorescence kinetic data for automated classification.

Main Results:

• DRPP achieved 99.6% accuracy in classifying SNV and WT signals, surpassing conventional methods (80.7%).
• Sensitivity for VAF detection was enhanced to 0.1%, a ten-fold improvement.
• Accurate identification of SARS-CoV-2 SNVs (D614G, N501Y) and cancer mutations (KRAS, NRAS, BRAF) in clinical samples was demonstrated.

Conclusions:

• DRPP offers a highly accurate and sensitive method for SNV detection, outperforming traditional approaches.
• The technology shows significant potential for clinical diagnostics, including early disease detection and personalized medicine.
• Integration with rapid amplification techniques could further enhance DRPP's clinical utility.