Virus-like Plasmonic Nanoprobes for Quick Analysis of Antiviral Efficacy and Mutation-Induced Drug Resistance.

As the pathogenic viruses and the variants of concern greatly threaten human health and global public safety, the development of convenient and robust strategies enabling rapid analysis of antiviral drug efficacy and mutation-induced resistance is quite important to prevent the spread of human epidemics. Herein, we introduce a simple single-particle detection strategy for quick analysis of anti-infective drugs against SARS-CoV-2 and mutation-induced drug resistance, by using the wild-type and mutant spike protein-functionalized AuNPs as virus-like plasmonic nanoprobes. Both the wild-type and mutant virus-like plasmonic nanoprobes can form core-satellite nanoassemblies with the ACE2@AuNPs, providing the opportunity to detect the drug efficacy and mutation-induced resistance by detecting the changes of nanoassemblies upon drug treatment with dark-field microscopy. As a demonstration, we applied the single-particle detection strategy for quantitative determination of antiviral efficacy and mutation-induced resistance of ceftazidime and rhein. The mutations in the receptor-binding domain of Omicron variant could lead to an increase of EC50 values of ceftazidime and rhein, formerly from 49 and 57 µM against wild-type SARS-CoV-2, to 121 and 340 µM, respectively. The mutation-induced remarkable decline in the inhibitory efficacy of drugs was validated with molecule docking analysis and virus-like plasmonic nanoprobe-based cell-incubation assay. Due to the generality and feasibility of the strategy for the preparation of virus-like plasmonic nanoprobes and single-particle detection, we anticipated that this simple and robust method is promising for the discovery and efficacy evaluation of anti-infective drugs against different pathogenic viruses.

Plasmonic Imaging of Dynamic Interactions between Membrane Receptor Clusters beyond the Diffraction Limit in Live Cells.

As a general mechanism, ligand-induced receptor clustering on cell membrane plays determinative roles in pattern recognition and transmembrane signaling. Nevertheless, probing the dynamic characteristics for the complicated interactions between receptor clusters remains difficult because of the lack of strategy for receptor cluster labeling and long-term monitoring in live cells. Herein, we proposed a data-mining-integrated plasmon coupling microscopy to study the dynamic cluster-cluster interactions on cell surface. The receptor clusters were activated and labeled with multivalent plasmonic nanoprobes, which enables the real-time monitoring of individual receptor clusters and the measurement of cluster-cluster interactions from the analysis of plasmonic coupling for the nanoprobe pairs beyond the diffraction limit. Using this method, we found that the protease-activated receptor 1 (PAR1) clusters would experience an initial contact and then form a weakly bound cluster-cluster complex, followed by cluster fusion to generate large-sized signaling complexes. The underlying state transitions for the cluster-cluster fusion process were uncovered using a data-mining technique named the K-means-based hidden Markov model with the scattering intensity of coupled nanoprobe pairs as observations. All of the findings from single-particle analysis and bulk measurements suggested that the allosteric inhibitors could suppress the dynamic transitions from the weakly bound cluster-cluster complexes to fused signaling complexes, leading to the subsequent downregulation of intracellular calcium signaling pathways. We believe that this strategy is promising for imaging and monitoring receptor clustering as well as protein phase separation on the cell surface in various biological and physiological processes.