Bio-Conjugated Magnetic-Fluorescence Nanoarchitectures for the Capture and Identification of Lung-Tumor-Derived Programmed Cell Death Lighand 1-Positive Exosomes

Autor: Avijit Pramanik, Shamily Patibandla, Ye Gao, Lauren R. Corby, Md Mhahabubur Rhaman, Sudarson Sekhar Sinha, Paresh Chandra Ray
Rok vydání: 2022
Předmět:
Zdroj: ACS Omega. 7:16035-16042
ISSN: 2470-1343
DOI: 10.1021/acsomega.2c01210
Popis: As per the American Cancer Society, lung cancer is the leading cause of cancer-related death worldwide. Since the accumulation of exosomal programmed cell death ligand 1 (PD-L1) is associated with therapeutic resistance in programmed cell death 1 (PD-1) and PD-L1 immunotherapy, tracking PD-L1-positive (PD-L1 (+)) exosomes is very important for predicting anti-PD-1 and anti-PD-L1 therapy for lung cancer. Herein, we report the design of an anti-PD-L1 monoclonal antibody-conjugated magnetic-nanoparticle-attached yellow fluorescent carbon dot (YFCD) based magnetic-fluorescence nanoarchitecture for the selective separation and accurate identification of PD-L1-expressing exosomes. In this work, photostable YFCDs with a good photoluminescence quantum yield (23%) were synthesized by hydrothermal treatment. In addition, nanoarchitectures with superparamagnetic (28.6 emu/g), biocompatible, and selective bioimaging capabilities were developed by chemically conjugating the anti-PD-L1 antibody and YFCDs with iron oxide nanoparticles. Importantly, using human non-small-cell lung cancer H460 cells lines, which express a high amount of PD-L1 (+) exosomes, A549 lung cancer cells lines, which express a low amount of PD-L1 (+) exosomes, and the normal skin HaCaT cell line, which does not express any PD-L1 (+) exosomes, we demonstrate that nanoarchitectures are capable of effectively separating and tracking PD-L1-positive exosomes simultaneously. Furthermore, as a proof-of-concept of clinical setting applications, a whole blood sample infected with PD-L1 (+) exosomes was analyzed, and our finding shows that this nanoarchitecture holds great promise for clinical applications.
Databáze: OpenAIRE