纳米花
微泡
镍
化学发光
钴
腺癌
化学
肺腺癌
材料科学
纳米技术
癌症研究
医学
生物化学
色谱法
无机化学
冶金
内科学
癌症
小RNA
纳米结构
基因
作者
Manli Wang,Jiangnan Shu,Yisha Wang,Wencan Zhang,Keying Zheng,Shengnian Zhou,Dongliang Yang,Hua Cui
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2024-06-07
卷期号:9 (6): 3444-3454
被引量:16
标识
DOI:10.1021/acssensors.4c00954
摘要
Programmed death ligand-1 (PD-L1)-expressing exosomes are considered a potential marker for diagnosis and classification of lung adenocarcinoma (LUAD). There is an urgent need to develop highly sensitive and accurate chemiluminescence (CL) immunosensors for the detection of PD-L1-expressing exosomes. Herein, N-(4-aminobutyl)-N-ethylisopropanol-functionalized nickel–cobalt hydroxide (NiCo-DH-AA) with a hollow nanoflower structure as a highly efficient CL nanoprobe was synthesized using gold nanoparticles as a “bridge”. The resulting NiCo-DH-AA exhibited a strong and stable CL emission, which was ascribed to the exceptional catalytic capability and large specific surface area of NiCo-DH, along with the capacity of AuNPs to facilitate free radical generation. On this basis, an ultrasensitive sandwich CL immunosensor for the detection of PD-L1-expressing exosomes was constructed by using PD-L1 antibody-modified NiCo-DH-AA as an effective signal probe and rabbit anti-CD63 protein polyclonal antibody-modified carboxylated magnetic bead as a capture platform. The immunosensor demonstrated outstanding analytical performance with a wide detection range of 4.75 × 10 3 –4.75 × 10 8 particles/mL and a low detection limit of 7.76 × 10 2 particles/mL, which was over 2 orders of magnitude lower than the reported CL method for detecting PD-L1-expressing exosomes. Importantly, it was able to differentiate well not only between healthy persons and LUAD patients (100% specificity and 87.5% sensitivity) but also between patients with minimally invasive adenocarcinoma and invasive adenocarcinoma (92.3% specificity and 52.6% sensitivity). Therefore, this study not only presents an ultrasensitive and accurate diagnostic method for LUAD but also offers a novel, simple, and noninvasive approach for the classification of LUAD.
科研通智能强力驱动
Strongly Powered by AbleSci AI