Dual Confinement-Enhanced Multiple Single Nucleotide Variant Detection at the Single-Particle Level.

Area of Science:

• Biotechnology
• Nanotechnology
• Genomics

Background:

• Current methods for analyzing multiple single nucleotide variants (SNVs) face limitations in identification accuracy.
• Single-particle analysis offers potential for improved detection but requires advanced techniques.

Purpose of the Study:

• To develop a novel, highly sensitive strategy for the multiplex analysis of SNVs at the single-particle level.
• To enhance the kinetics and accuracy of mutation detection through molecular and nanomaterial confinement.

Main Methods:

• Utilized DNA tetrahedra with X-shaped probes for stable recognition units and enhanced sample background tolerance.
• Implemented a nano self-assembly approach with a long-chain confinement mechanism and hybridization chain reaction (HCR) cascade on gold nanoparticles (AuNPs).
• Employed machine learning algorithms for enhanced discrimination of multiple genomic sites after signal amplification and microscopic visualization on polystyrene (PS) microspheres.

Main Results:

• Achieved accelerated kinetics of multiplex recognition processes through integrated molecular and nanomaterial confinement.
• Demonstrated microscopic visualization and single-particle detection of mutations with high stability and sensitivity.
• Successfully enhanced the discrimination of multiple genomic sites using machine learning.

Conclusions:

• The developed nano self-assembly approach provides a promising and practical method for multiple SNVs detection.
• This technique has significant potential applications in advancing genomics research and precision medicine.