Rapid, portable, and sensitive detection of CaMV35S by RPA-CRISPR/Cas12a-G4 colorimetric assays with high accuracy deep learning object recognition and classification.

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Abstract

Fast, sensitive, and portable detection of genetic modification contributes to agricultural security and food safety. Here, we developed RPA-CRISPR/Cas12a-G-quadruplex colorimetric assays that can combine with intelligent recognition by deep learning algorithms to achieve sensitive, rapid, and portable detection of the CaMV35S promoter. When the crRNA-Cas12a complex recognizes the RPA amplification product, Cas12 cleaves the G-quadruplex, causing the G4-Hemin complex to lose its peroxide mimetic enzyme function and be unable to catalyze the conversion of ABTS2- to ABTS, allowing CaMV35S concentration to be determined based on ABTS absorbance. By utilizing the RPA-CRISPR/Cas12a-G4 assay, we achieved a CaMV35S limit of detection down to 10 aM and a 0.01 % genetic modification sample in 45 min. Deep learning algorithms are designed for highly accurate classification of color results. Yolov5 objective finding and Resnet classification algorithms have been trained to identify trace (0.01 %) CaMV35S more accurately than naked eye colorimetry. We also coupled deep learning algorithms with a smartphone app to achieve portable and rapid photo identification. Overall, our findings enable low cost ($0.43), high accuracy, and intelligent detection of the CaMV35S promoter.Copyright © 2024 Elsevier B.V. All rights reserved.