Microfluidic Mixing: A General Method for Encapsulating Macromolecules in Lipid Nanoparticle Systems

双层 微流控 高分子 纳米颗粒 脂质双层 化学 胶体金 乙二醇 纳米技术 生物物理学 材料科学 阳离子聚合 纳米结构 化学工程 生物化学 有机化学 工程类 生物
作者
Alex K. K. Leung,Yuen Yi C. Tam,Sam Chen,Ismail M. Hafez,Pieter R. Cullis
出处
期刊:Journal of Physical Chemistry B [American Chemical Society]
卷期号:119 (28): 8698-8706 被引量:331
标识
DOI:10.1021/acs.jpcb.5b02891
摘要

Previous work has shown that lipid nanoparticles (LNP) composed of an ionizable cationic lipid, a poly(ethylene glycol) (PEG) lipid, distearoylphosphatidylcholine (DSPC), cholesterol, and small interfering RNA (siRNA) can be efficiently manufactured employing microfluidic mixing techniques. Cryo-transmission electron microscopy (cryo-TEM) and molecular simulation studies indicate that these LNP systems exhibit a nanostructured core with periodic aqueous compartments containing siRNA. Here we examine first how the lipid composition influences the structural properties of LNP-siRNA systems produced by microfluidic mixing and, second, whether the microfluidic mixing technique can be extended to macromolecules larger than siRNA. It is shown that LNP-siRNA systems can exhibit progressively more bilayer structure as the proportion of bilayer DSPC lipid is increased, suggesting that the core of LNP-siRNA systems can exhibit a continuum of nanostructures depending on the proportions and structural preferences of component lipids. Second, it is shown that the microfluidic mixing technique can also be extended to encapsulation of much larger negatively charged polymers such mRNA (1.7 kb) or plasmid DNA (6 kb). Finally, as a demonstration of the generality of the microfluidic mixing encapsulation process, it is also demonstrated that negatively charged gold nanoparticles (5 nm diameter) can also be efficiently encapsulated in LNP containing cationic lipids. Interestingly, the nanostructure of these gold-containing LNP reveals a "currant bun" morphology as visualized by cryo-TEM. This structure is fully consistent with LNP-siRNA structure predicted by molecular modeling.
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