Single-Cell Diagnosis of Cancer Drug Resistance through the Differential Endocytosis of Nanoparticles between Drug-Resistant and Drug-Sensitive Cancer Cells.
Lingshan LiuQiurui ZhangChenglong WangHeze GuoVincent MukwayaRong ChenYichun XuXiaohui WeiXiaoyan ChenSujiang ZhangMin ZhouHongjing DouPublished in: ACS nano (2023)
Single-cell diagnosis of cancer drug resistance is highly relevant for cancer treatment, as it can be used to identify the subpopulations of drug-resistant cancer cells, reveal the sensitivity of cancer cells to treatment, and monitor the progress of cancer drug resistance. However, simple and effective methods for cancer drug resistance detection at the single-cell level are still lacking in laboratory and clinical studies. Inspired by the fact that nanoparticles with diverse physicochemical properties would generate distinct and specific interactions with drug-resistant and drug-sensitive cancer cells, which have distinctive molecular signatures, here, we have synthesized a library of fluorescent nanoparticles with various sizes, surface charges, and compositions (SiO 2 nanoparticles (SNPs), organic PS- co -PAA nanoparticles (ONPs), and ZIF-8 nanoparticles (ZNPs)), thus demonstrating that the composition has a critical influence on the interaction of nanoparticles with drug-resistant cancer cells. Furthermore, the clathrin/caveolae-independent endocytosis of ZNPs together with the P-glycoprotein-related decreased cell membrane fluidity resulted in a lower cellular accumulation of ZNPs in drug-resistant cancer cells, consequently causing the distinct cellular accumulation of ZNPs between the drug-resistant and drug-sensitive cancer cells. This difference was further quantified by detecting the fluorescence signals generated by the accumulation of nanoparticles at the single-cell level via flow cytometry. Our findings provide another insight into the nanoparticle-cell interactions and offer a promising platform for the diagnosis of cancer drug resistance of various cancer cells and clinical cancer samples at the single-cell level.