Analytical chemistry · 2026
Multidimensional Pathogen Fingerprinting via DNA Scaffold-Confined Enzymatic Synthesis of Silicon Quantum Dots.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Wang Yiting, Wu Na, Jiang Yi, Yan Changjian, Zhang Wei, Hua Ruinian, Peng Huaping
Аннотация
Accurate and highly sensitive detection of pathogenic bacteria is essential to public health. Conventional biosensors often rely on bulk signal amplification, which suffers from diffusion-limited kinetics and high background signals. Herein, we present a programmable, surface-confined biosensing strategy that constructs a target-responsive nucleic acid-enzyme microenvironment directly on the bacterial surface, enabling localized catalytic reactions for ultrasensitive detection. Upon target recognition, the aptamer-primer (AP) strand triggers surface-confined rolling circle amplification (RCA) to form high-density DNA network scaffolds. These scaffolds recruit alkaline phosphatase (ALP), establishing a confined enzymatic reaction microenvironment. The localized ALP catalyzes the hydrolysis of p-aminophenol phosphate (APP) to generate p-aminophenol (PAP), creating a reducing microenvironment that drives in situ nucleation of silicon quantum dots (SiQDs) from the silane precursor N-[3-(trimethoxysilyl)propyl] ethylenediamine (DAMO). Using Staphylococcus aureus as a model pathogen, this approach provides dual-mode readouts: an ultrasensitive fluorescence response with a detection limit of 23 CFU mL-1 and a rapid colorimetric response with a detection limit of 134 CFU mL-1. Importantly, the recognition module was highly programmable. By simply replacing the target-specific aptamer domain within the AP strand, this strategy can be universally adapted for various pathogens. Moreover, by generating complementary multidimensional signals, including fluorescence, UV-vis absorbance, and hydrodynamic diameter, the strategy enables the construction of a multidimensional optical sensor array. Integration with machine-learning algorithms allows the platform to interpret distinct chemo-optical fingerprints, thereby enabling the accurate classification of multiple pathogenic bacteria. Overall, this strategy provides a versatile framework for intelligent multiplexed pathogen diagnostics by integrating programmable biomolecular recognition, spatially confined enzymatic nanosynthesis, and data-driven analyses.