NIR-II Conjugated Electrolytes as Biomimetics of Lipid Bilayers for In Vivo Liposome Tracking.
Yingying MengJi GaoPeirong ZhouXudong QinMiao TianXiaohui WangCheng ZhouKai LiFei HuangYong CaoPublished in: Angewandte Chemie (International ed. in English) (2024)
Liposomes serve as promising and versatile vehicles for drug delivery. Tracking these nanosized vesicles, particularly in vivo, is crucial for understanding their pharmacokinetics. This study introduces the design and synthesis of three new conjugated electrolyte (CE) molecules, which emit in the second near-infrared window (NIR-II), facilitating deeper tissue penetration. Additionally, these CEs, acting as biomimetics of lipid bilayers, demonstrate superior compatibility with lipid membranes compared to commonly used carbocyanine dyes like DiR. To counteract the aggregation-caused quenching effect, CEs employ a twisted backbone, as such their fluorescence intensities can effectively enhance after a fluorophore multimerization strategy. Notably, a "passive" method was employed to integrate CEs into liposomes during the liposome formation, and membrane incorporation efficiency was significantly promoted to nearly 100%. To validate the in vivo tracking capability, the CE-containing liposomes were functionalized with cyclic arginine-glycine-aspartic acid (cRGD) peptides, serving as tumor-targeting ligands. Clear fluorescent images visualizing tumor site in living mice were captured by collecting the NIR-II emission. Uniquely, these CEs exhibit additional emission peak in visible region, enabling in vitro subcellular analysis using routine confocal microscopy. These results underscore the potential of CEs as a new-generation of membrane-targeting probes to facilitate the liposome-based medicine research.
Keyphrases
- drug delivery
- drug release
- cancer therapy
- fluorescent probe
- photodynamic therapy
- living cells
- fluorescence imaging
- energy transfer
- quantum dots
- solid state
- fatty acid
- molecular dynamics simulations
- ionic liquid
- single molecule
- nitric oxide
- small molecule
- deep learning
- mass spectrometry
- convolutional neural network
- machine learning
- type diabetes
- high resolution
- metabolic syndrome
- tandem mass spectrometry