Machine Learning-Assisted Nanoenzyme/Bioenzyme Dual-Coupled Array for Rapid Detection of Amyloids

Array-based sensing methods offer significant advantages in the simultaneous detection of multiple amyloid biomarkers and thus have great potential for diagnosing early-stage Alzheimer's disease. Yet, detecting low concentrations of amyloids remains exceptionally challenging. Here, we have developed a fluorescent sensor array based on the dual coupling of a nanoenzyme (AuNPs) and bioenzyme (horseradish peroxidase) to detect amyloids. Various ss-DNAs were bound to the nanoenzyme for regulating enzymatic activity and recognizing amyloids. A simplified sensor array was generated from a screening model via machine learning algorithms and achieved signal amplification through a two-step enzymatic reaction. As a result, our sensing system could discriminate the aggregation species and aggregation kinetics at 200 nM with 100% accuracy. Moreover, AD model mice and healthy mice were distinguished with 100% accuracy through the sensor array, providing a powerful sensing platform for diagnosing AD.

Automated summary

Array-based sensing methods with a fluorescent sensor array that combines nanoenzymes and bioenzymes have been developed for the detection of amyloids, allowing for the accurate diagnosis of early-stage Alzheimer's disease. Through the use of machine learning algorithms, a simplified sensor array is created, which can amplify the signals and achieve 100% accuracy in discriminating the aggregation species and kinetics at a concentration of 200 nM. Additionally, the sensor array can also differentiate between AD model mice and healthy mice with 100% accuracy, providing a powerful tool for diagnosing AD.