Single molecular-based nanoparticles with aggregation-induced emission characteristics for fluorescence imaging and efficient cancer phototherapy
Source abstract
Abstract
Abstract Organic nanomaterials have garnered extensive interest in PDT/PTT due to their excellent photo-stability, efficient photothermal and reactive oxygen species generation ability. However, most organic nanomaterials emit very weak in the aggregated state because of the aggregation-caused quenching phenomenon, which limits their application in bioimaging. In this study, a donor-acceptor-donor (D-A-D) type BODIPY-based compound (BODIPY-TPA) was synthesized by connecting triphenylamine (TPA) with BODIPY structure. To further improve the biocompatibility of the compound, a polyethylene glycol chain (PEG) was introduced into the BODIPY core. BODIPY-TPA could transform into the related nanoparticles (BODIPY-TPA NPs) by the self-assembly process. Remarkably, BODIPY-TPA NPs showed strong near-infrared (NIR) fluorescence because of its aggregation-induced emission (AIE) property, which may be ascribed to the twisted structure of TPA. BODIPY-TPA NPs could simultaneously generate singlet oxygen (1O2) and heat under the irradiation of a single laser (635 nm). The photothermal conversion efficiency (PCE) of BODIPY-TPA NPs was calculated to be 20.7%. The subcellular localization experiment indicated the lysosome-targeting properties of BODIPY-TPA NPs in A549 cells. The cytotoxicity of BODIPY-TPA NPs was studied by CCK-8 assay and AM/PI staining on A549 cells. The half maximal inhibitory concentration (IC50) of BODIY-TPA NPs was calculated to be 28.45 μg/mL. This study revealed BODIPY-TPA NPs could potentially be used in bioimaging and photodynamic/photothermal (PDT/PTT) synergistic cancer treatment.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanoplatforms for cancer theranostics · Luminescence and Fluorescent Materials · Polydiacetylene-based materials and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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