Deep Learning-Assisted Fluorescence Single-Particle Detection of Fumonisin B1 Powered by Entropy-Driven Catalysis and Argonaute

Abstract

Timely and accurate detection of trace mycotoxins in agricultural products and food is significant for ensuring food safety and public health. Herein, a deep learning-assisted and entropy-driven catalysis (EDC)-Argonaute powered fluorescence single-particle aptasensing platform was developed for ultrasensitive detection of fumonisin B₁ (FB₁) using single-stranded DNA modified with biotin and red fluorescence-encoded microspheres as a signal probe and streptavidin-conjugated magnetic beads as separation carriers. The binding of aptamer with FB₁ releases the trigger sequence to mediate EDC cycle to produce numerous 5′-phosphorylated output sequences, which can be used as the guide DNA to activate downstream Thermus thermophilus Argonaute (TtAgo) for cleaving the signal probe, resulting in increased number of fluorescence microspheres remaining in the final reaction supernatant after magnetic separation. Subsequently, through fast and accurate counting of red bright particles in the captured confocal fluorescence images from the supernatant via a YOLOv9 deep learning model, the sensitive and specific detection of FB₁ could be realized. This approach has a limit of detection (LOD) of 0.89 pg/mL with a linear range from 1 pg/mL to 100 ng/mL, and satisfactory recovery (87.2–113.5%) in real food samples indicates its practicality. The integration of the aptamer and EDC with TtAgo broadens the target range of Argonaute and enhances sensitivity. Furthermore, incorporating deep learning significantly improves the analytical efficiency of single-particle detection. This work provides a promising analytical strategy in biosensing and promotes the application of fluorescence single-particle detection in food safety monitoring.