Copper nanocluster-labeled hybridization chain reaction for potentiometric immunoassay of matrix metalloproteinase-7 in acute kidney injury and renal cancer. Issue 19 (7th May 2019) - Zhuang, Wei; Li, Yining; Chen, Jiabi; Liu, Weihui; Huang, Hongyuan
Abstract : A simple and portable potentiometric immunoassay was designed for the quantitative detection of matrix metalloproteinase-7 (MMP7) in acute kidney injury and renal cancer on a copper ion-selective electrode (Cu-ISE). Abstract : A simple and portable potentiometric immunoassay was designed for the quantitative detection of matrix metalloproteinase-7 (MMP7) in acute kidney injury and renal cancer on a copper ion-selective electrode (Cu-ISE). To construct such an assay system, a sandwich-type immunoreaction was initially carried out on monoclonal anti-MMP7 capture antibody-coated microplates by using an initiator DNA strand-labeled anti-MMP7 secondary antibody. Thereafter, a hybridization chain reaction (HCR) was used for the signal amplification between copper nanocluster-labeled two hairpin DNA probes. Accompanying the formation of the sandwiched immunocomplex, the labeled DNA initiator strand on the secondary antibody triggered the HCR between two alternating DNA hairpins to form a nicked double-helix with copper nanoclusters. The subsequent potentiometric measurement of copper ions released from the concatenated copper nanoclusters under acidic conditions was conducted on a portable handheld potentiometer. The immuno-HCR assay combined an easy-operation potentiometric assay with signal amplification by using an HCR-induced copper nanocluster concatemer. Under optimum conditions, the developed immuno-HCR assay exhibited a dynamic linear range of 0.01–100 ng mL −1 with a detection limit of 5.3 pg mL −1 and good reproducibility. Also, the immuno-HCR assay gave good anti-interfering capacity toward other biomarkers. The analysis for clinical human serum specimens revealed good accordance with the results obtained by an enzyme-linked immunosorbent assay. Importantly, our strategy is promising for enzyme-free and cost-effective analysis of low-abundance cancer biomarkers.