Using Chicken Embryos to Identify the Key Determinants of Nanoparticles for the Crossing of Air–Blood Barriers

渗透(战争) 化学 生物信息学 纳米颗粒 生物物理学 呼吸系统 纳米技术 生物化学 解剖 生物 材料科学 语言学 哲学 运筹学 工程类 基因
作者
Hui Wang,Weili Wang,Qianqian Xie,Di Wu,Jiayu Cao,Huilin Chen,Meng Gao,Huizhen Zheng,Xi Liu,Jie Jiang,Wenjie Li,Xiaoming Cai,Sergey V. Gudkov,Ruibin Li
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:95 (14): 6009-6019 被引量:2
标识
DOI:10.1021/acs.analchem.3c00034
摘要

Fine particulates (FPs) are a major class of airborne pollutants. In mammals, FPs may reach the alveoli through the respiratory system, cross the air–blood barrier, spread into other organs, and induce hazardous effects. Although birds have much higher respiratory risks to FPs than mammals, the biological fate of inhaled FPs in birds has rarely been explored. Herein, we attempted to disclose the key properties that dictate the lung penetration of nanoparticles (NPs) by visualizing a library of 27 fluorescent nanoparticles (FNPs) in chicken embryos. The FNP library was prepared by combinational chemistry to tune their compositions, morphologies, sizes, and surface charges. These NPs were injected into the lungs of chicken embryos for dynamic imaging of their distributions by IVIS Spectrum. FNPs with diameters <16 nm could cross the air–blood barrier in 20 min, spread into the blood, and accumulate in the yolk sac. In contrast, large FNPs (>30 nm) were mainly retained in the lungs and rarely detected in other tissues/organs. In addition to size, surface charge was the secondary determinant for NPs to cross the air–blood barrier. Compared to cationic and anionic particles, neutrally charged FNPs showed the fastest lung penetration. A predictive model was therefore developed to rank the lung penetration capability of FNPs by in silico analysis. The in silico predictions could be well validated in chicks by oropharyngeal exposure to six FNPs. Overall, our study discovered the key properties of NPs that are responsible for their lung penetration and established a predictive model that will greatly facilitate respiratory risk assessments of nanoproducts.
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