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基于化学发光共振能量转移和DNA逻辑分析的禽流感病毒生物标志物智能分析

Intelligent Analysis of Avian Influenza Virus Biomarkers Based on Chemiluminescence Resonance Energy Transfer and DNA Logic Analysis.

作者信息

Tian Yafei, Lu Xueyun, Zhang Yujiao, Xiao Dan, Zhou Cuisong

机构信息

College of Chemistry, Sichuan University, Chengdu 610064, P. R. China.

出版信息

ACS Appl Mater Interfaces. 2024 Sep 25;16(38):50360-50368. doi: 10.1021/acsami.4c10356. Epub 2024 Sep 17.

DOI:10.1021/acsami.4c10356
PMID:39285756
Abstract

A one-step logical analysis of multiple targets remains challenging. Herein, we report a one-pot and intelligent DNA logical analysis platform for the diagnosis of avian influenza virus (AIV) biomarkers based on chemiluminescence resonance energy transfer (CRET) and logic operations. On the surface of Lum/PEI/CaCO microparticles, the excited state of luminol underwent CRET with fluorescein isothiocyanate or rhodamine B isothiocyanate, producing three well-separated light emissions at 425, 530, and 590 nm, respectively. Taking advantage of the close distance between fluorophores aligned by the catalytic hairpin assembly reaction, the CRET efficiency was greatly enhanced (53.1%). H1N1, H7N9, and H5N1 were detected with limits of detection values as low as 15, 34, and 58 pM, respectively. Three-input logic circuits were simultaneously conducted on the surface of Lum/PEI/CaCO microparticles, enabling the rapid and accurate discrimination of multiple AIV biomarkers in one solution. In terms of peak positions and the normalized value of the total peak intensity, three biomarkers can be simultaneously discriminated without any other complex operations. In summary, the CRET-based multiple analytical assay was developed as an intelligent biosensor for identifying AIV biomarkers, having promising application prospects in the field of multiple analysis and precise disease diagnosis.

摘要

对多个靶点进行一步式逻辑分析仍然具有挑战性。在此,我们报告了一种基于化学发光共振能量转移(CRET)和逻辑运算的一锅法智能DNA逻辑分析平台,用于诊断禽流感病毒(AIV)生物标志物。在Lum/PEI/CaCO微粒表面,鲁米诺的激发态与异硫氰酸荧光素或异硫氰酸罗丹明B发生CRET,分别在425、530和590nm处产生三个分离良好的光发射。利用催化发夹组装反应排列的荧光团之间的近距离,CRET效率大大提高(53.1%)。对H1N1、H7N9和H5N1的检测限分别低至15、34和58pM。在Lum/PEI/CaCO微粒表面同时进行三输入逻辑电路,能够在一种溶液中快速准确地区分多种AIV生物标志物。就峰位置和总峰强度的归一化值而言,无需任何其他复杂操作即可同时区分三种生物标志物。总之,基于CRET的多重分析方法被开发为一种用于识别AIV生物标志物的智能生物传感器,在多重分析和精确疾病诊断领域具有广阔的应用前景。

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