Gold nanorods and machine learning for paper-based genetic assays

Thanks to their photophysical properties and the ease of synthesis and functionalization, gold nanoparticles (AuNPs) represent an ideal tool to develop colorimetric paper-based biosensors. Colloidal suspensions of AuNPs exhibit different colors depending on their size, shape and state of aggregation and their surface is suitable for functionalization with a wide variety of biomolecules. Here, we used anisotropic gold nanorods (AuNRs) for their multiplexability and their intrinsic brightness (10-fold higher than standard gold nanospheres), to label oligonucleotides for identifying a specific target DNA, both as PCR amplified fragment and as transgene into a cloning vector, by a dot-blot assay. The recognition of pathogenetic targets indeed represents a perspective of extreme interest in the clinical and environmental fields, e.g., to identify the microorganisms involved in infections and to trace the diffusion of antibiotic resistance or genetically modified organisms. To improve the analytical sensitivity and to obtain an automated and reproducible quantification of samples, we have also assessed the perspective to analyze dot-blot membranes with a supervised machine learning approach after a dedicated methodology for the acquisition of standardized photographs. Our work demonstrated the feasibility of a synergic use of plasmonic particles and artificial intelligence paradigms to accurately realize a rapid colorimetric paper-based detection.