Gold Nanostars Based Minimalist Sensor Arrays for Protein Isoform and Disease State Discrimination

Signature-based protein detection coupled with machine learning algorithms have revolutionized traditional sensing methods, providing rapid, inexpensive, and selectivity-driven detection without using specialized equipment. This strategy leverages selective interactions with the sensor array to create a global signature pattern using machine learning algorithms. The reference pattern provides an effective tool to stratify the analytes, identify blinded unknowns, and predict altered signatures of the analyte that could be missed by the common specificity-based sensors. Protein corona adsorbed on a nanoparticle surface presents unique signatures of the bound proteins, which can potentially be harnessed to profile protein biomarkers. Herein, we demonstrate that spiky gold nanoparticles (AuNS) generate fingerprint protein corona that dictates the modulation of the AuNS surface plasmons upon etching. The resultant spectrometric signatures are utilized in discriminating between diverse proteins, including isoforms, at significantly low concentrations with a wide dynamic range. Notably, the ability of AuNS to offer binding recognition and signal transduction on a single platform introduces a robust material for signature-based detections, simplifying the sensor fabrication and offering great opportunity for the identification of diverse targets.