An Efficient Luminol–H2O2 Electrochemiluminescence System with Porous Bimetallic Organic Gels as Signal Booster and Elaborate Heterosequence Aptamer as Recognition Component for Ultrasensitive Biosensing

化学 鲁米诺 电化学发光 适体 双金属片 生物传感器 化学发光 信号(编程语言) 多孔性 纳米技术 电极 色谱法 物理化学 有机化学 催化作用 生物化学 生物 遗传学 计算机科学 材料科学 程序设计语言
作者
Zhe Tang,Kai Wen,Yu-Zhuo Guo,Pan Xie,Kun Li,Yifei Chen,Jia‐Li Liu,Ruo Yuan,Kanfu Peng
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:97 (20): 10772-10781 被引量:4
标识
DOI:10.1021/acs.analchem.5c00980
摘要

Herein, an efficient luminol-H2O2 electrochemiluminescence (ECL) system with bimetallic organic gels as an ECL signal booster and an innovative heterosequence aptamer recognition as a target conversion strategy is used to construct a sensitive and specific ECL aptasensor for the detection of β2-microglobulin (B2M), a key biomarker for end-stage renal disease (ESRD). Impressively, the porous Fe@Cu bimetallic organic gels (Fe@Cu MOGs) act as coreaction accelerators and confinement-enhanced reactors of the luminol-H2O2 ECL system, amplifying the ECL signal by 21-fold compared to the traditional luminol-H2O2 ECL system, which greatly enhanced the sensitivity of the biosensor. Compared to the homosequence aptamer approach with competitive binding of aptamers to a single site, the heterosequence aptamer approach with synergistic binding to multiple sites could greatly improve the specificity of aptasensor, which is validated by experiments and molecular docking simulations. Therefore, the developed aptasensor exhibits a remarkable dynamic range of 10 fg/mL-1 μg/mL with an ultralow detection limit of 0.9 fg/mL, which is superior to previously reported works. Additionally, the aptasensor demonstrated consistent performance with conventional clinical immunoturbidimetric assays for high B2M concentration detection in 14 clinical samples, as well as exhibiting superior sensitivity for trace B2M levels that are undetectable by immunoturbidimetry. This strategy offers a sensitive and accurate platform for biomarker recognition, with promising applications in trace clinical biomarker detection, disease diagnosis, and therapeutic monitoring, as well as in advancing scientific research on early pathological changes and biomarker discovery.
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