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Deep Learning-Enhanced DNAzyme-Driven Rolling-Circle Amplification Encoding for Multibacterial Detection
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DOI：10.1002/anie.4446117.png)
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Abstract
En 中文
Food-borne outbreaks are frequently caused by multiple live pathogens that conventional methods cannot process simultaneously. We report a DNAzyme-driven rolling-circle amplification/molecular-beacon encoding system (DRM-ES) coupled with a smartphone-based convolutional neural network (CNN) that simultaneously identifies and quantifies three live bacteria from 32 real-world samples. Bacteria-secreted proteins cleave bead-immobilized DNAzymes, releasing primers that initiate RCA and generate long concatemers; each opens a spectrally distinct molecular beacon, producing blue, green, or red fluorescence captured in one smartphone image and decoded by a CNN trained on 2800 images. DRM-ES achieves 101–102 CFU/mL sensitivity for S. aureus, B. cocovenenans, and E. coli in food, clinical, and environmental samples; shows 100% positive and ≥95.2% negative agreement with culture; and correctly identifies 29/32 samples naturally contaminated with these three bacteria in a 32-tube array. The platform offers culture-comparable sensitivity and live-cell specificity, providing a generalizable blueprint for large-scale multiplex pathogen screening.
Keywords:
bacterial pathogens
deep learning
DNAzyme
multiplex detection
rolling-circle amplification
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