CRISPR-associated Plasmonic Colorimeter Method (Ca-PCM): A real-time RGB detection system for gold nanoparticles-based nucleic acid biosensors.

Background: The detection of genetic sequences represents the gold standard procedure for species discrimination, genetic characterisation of tumours, and identification of pathogens. The development of new molecular detection methods, accessible and cost effective, is of great relevance. Biosensors based on plasmonic nanoparticles, such as gold nanoparticles (AuNPs), provide a powerful and versatile platform for highly sensitive, economic, user-friendly and label-free sensing. However, the readout techniques typically employed with such sensors lack temporal and kinetic information, which hampers the ability to perform quantitative detection.

Results: In this study, a novel methodology designated the 'CRISPR-associated Plasmonic Colorimeter Method' (Ca-PCM), has been developed. This method combines RNA target recognition by CRISPR LwaCas13a, AuNPs' aggregation, and real-time colorimetric Red-Green-Blue (RGB) analysis. The system registers the AuNP's plasmonic signatures in real-time using a RGB colour sensor with 3-channel silicon photodiodes having blue, green and red sensitivities. The acquired signals are automatically analysed by an algorithm designed to distinguish between positive and negative samples and to correlate the temporal spectral patterns of aggregation with dose-dependent molecular detection of the RNA target. In addition, the combination of Ca-PCM with a previous isothermal amplification allows the target efficient detection in real clinical applications.

Significance: We have shown that the combination of RGB analysis and continuous temporal measurements is a novel and promising method to characterise the behaviour of gold nanoparticle-based biosensors and to achieve dose-dependent target detection. In addition, the simplicity and cost-effectiveness of this new approach expand the possibilities of other plasmonic-based biosensors and their applicability in low-resources clinical environments.