Ultrasensitive PD-L1-Expressing Exosome Immunosensors Based on a Chemiluminescent Nickel–Cobalt Hydroxide Nanoflower for Diagnosis and Classification of Lung Adenocarcinoma
Manli Wang, Jiangnan Shu, Yisha Wang, Wencan Zhang, Keying Zheng, Shengnian Zhou, Dongliang Yang, Hua Cui
Abstract
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.