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High Resolution Image Download MS PowerPoint Slide Signature-based protein detection coupled with machine learning algorithms has revolutionized traditional sensing methods, providing rapid, inexpensive, and selectivity-driven detection without the use of specialized equipment. This strategy leverages selective interactions with a sensor array to create a global signature pattern using machine learning. 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 fingerprint signatures of the bound proteins, which can potentially be harnessed to profile protein biomarkers. Herein, we demonstrate that gold nanostars (AuNS) generate a 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 and with a wide dynamic range. The approach effectively discerns the protein corona signatures of healthy controls from two preclinical disease models of autoimmune dextran sodium sulfate-induced colitis and Salmonella Typhimurium infection-induced sepsis, demonstrating its clinical diagnostic potential. 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 a great opportunity for the identification of diverse targets.
Bhatt et al. (Wed,) studied this question.