General Post-Regulation Strategy of AIEgens' Photophysical Properties for Intravital Two-Photon Fluorescence Imaging.
Liyun LinJiaxin LiuZhengyuan PanWen PangXinyan JiangMan LeiJucai GaoYujie XiaoBo LiFang HuZhouzhou BaoXunbin WeiWenbo WuBobo GuPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2024)
Fluorogens with aggregation-induced emission (AIEgens) are promising agents for two-photon fluorescence (TPF) imaging. However, AIEgens' photophysical properties are fixed and unoptimizable once synthesized. Therefore, it is urgent and meaningful to explore an efficient post-regulation strategy to optimize AIEgens' photophysical properties. Herein, a general and efficient post-regulation strategy is reported. By simply tuning the ratio of inert AIEgens within binary nanoparticles (BNPs), the fluorescence quantum yield and two-photon absorption cross-section of functional AIEgens are enhanced by 8.7 and 5.4 times respectively, which are not achievable by conventional strategies, and the notorious phototoxicity is almost eliminated. The experimental results, theoretical simulation, and mechanism analysis demonstrated its feasibility and generality. The BNPs enabled deep cerebrovascular network imaging with ≈1.10 mm depth and metastatic cancer cell detection with single-cell resolution. Furthermore, the TPF imaging quality is improved by the self-supervised denoising algorithm. The proposed binary molecular post-regulation strategy opened a new avenue to efficiently boost the AIEgens' photophysical properties and consequently TPF imaging quality.
Keyphrases
- fluorescence imaging
- high resolution
- single cell
- single molecule
- machine learning
- small cell lung cancer
- squamous cell carcinoma
- living cells
- molecular dynamics
- quality improvement
- deep learning
- energy transfer
- mass spectrometry
- convolutional neural network
- sensitive detection
- loop mediated isothermal amplification