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

The efficacy of multiple single nucleotide variants (SNVs) analysis is far from ideal due to the limitations in identification. This study introduced a novel strategy for multiple SNVs analysis at the single particle level, integrating molecular and nanomaterial confinement to significantly accelerate the kinetics of multiplex recognition processes. Leveraging DNA tetrahedra to enhance sample background tolerance, we developed a nano self-assembly approach for the microscopic visualization and single-particle detection of mutations. The incorporation of X-shaped probes on DNA tetrahedra formed high-stability recognition units, which were interconnected via a long-chain confinement mechanism. Upon recognition, the release of the X-probe loop triggered a hybridization chain reaction (HCR) cascade, confined to the surface of gold nanoparticles (AuNPs) to achieve secondary confinement acceleration. Following electrostatic adsorption onto polystyrene (PS) microspheres, the fluorescence signal on AuNPs became microscopically visible. Machine learning algorithms were employed to further enhance the effective discrimination of multiple genomic sites. This work presents a promising and practical approach for multiple SNVs detection with potential applications in genomics and precision medicine.