High-visual-resolution colorimetric immunoassay with attomolar sensitivity using kinetically controlled growth of Ag in AuAg nanocages and poly-enzyme-boosted tyramide signal amplification.

Colorimetric enzyme-linked immunosorbent assays (CELISAs) have long been used for protein biomarker detection in diagnostics. Unfortunately, as confined by the monochromatic nature of detection signals and the limited catalytic activity of enzymes, CELISAs suffer from poor visual resolution and low sensitivity, hindering their effectiveness for early diagnostics in resource-limited settings. Herein, we report an ultrasensitive, high-visual-resolution CELISA (named PE-TSA-AuAg Cage-CELISA) that combines kinetically controlled growth of Ag in AuAg nanocages with poly-enzyme-boosted tyramide signal amplification (PE-TSA), enabling visual semiquantitative detection of protein biomarkers at attomolar levels with the naked eye. Specifically, the assay begins with the formation of sandwich-type immunocomplexes on a microplate in the presence of targets, and the labeled poly-horseradish peroxidases (poly-HRPs) initiate TSA, resulting in attaching numerous alkaline phosphatases (ALPs) on the microplate. The ALPs further catalyze ascorbic acid 2-phosphate to produce ascorbic acid, triggering the kinetically controlled growth of Ag inside AuAg nanocages. This process induces vivid multicolor variations spanning the visible spectrum range of 691∼477 nm, allowing for visual semiquantitation of protein biomarkers at ultralow levels without requiring specialized equipment. Using interleukin-12 as a model protein biomarker, we demonstrate that the PE-TSA-AuAg Cage-CELISA achieves a visual semiquantitative limit of detection (LOD) of 5 fg mL (67 aM) and an instrumental quantitative LOD of 0.71 fg mL (9.5 aM), representing an 853-fold improvement compared to the conventional HRP-based CELISA. Our findings suggest that the PE-TSA-AuAg Cage-CELISA has the potential to serve as an affordable and effective biosensing platform for early diagnostics in resource-limited settings.