Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland 21218, United States.
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.
ACS Nano. 2024 Jun 18;18(24):15729-15743. doi: 10.1021/acsnano.4c02341. Epub 2024 Jun 5.
Lipid nanoparticles (LNP) have emerged as pivotal delivery vehicles for RNA therapeutics. Previous research and development usually assumed that LNPs are homogeneous in population, loading density, and composition. Such perspectives are difficult to examine due to the lack of suitable tools to characterize these physicochemical properties at the single-nanoparticle level. Here, we report an integrated spectroscopy-chromatography approach as a generalizable strategy to dissect the complexities of multicomponent LNP assembly. Our platform couples cylindrical illumination confocal spectroscopy (CICS) with single-nanoparticle free solution hydrodynamic separation (SN-FSHS) to simultaneously profile population identity, hydrodynamic size, RNA loading levels, and distributions of helper lipid and PEGylated lipid of LNPs at the single-particle level and in a high-throughput manner. Using a benchmark siRNA LNP formulation, we demonstrate the capability of this platform by distinguishing seven distinct LNP populations, quantitatively characterizing size distribution and RNA loading level in wide ranges, and more importantly, resolving composition-size correlations. This SN-FSHS-CICS analysis provides critical insights into a substantial degree of heterogeneity in the packing density of RNA in LNPs and size-dependent loading-size correlations, explained by kinetics-driven assembly mechanisms of RNA LNPs.
脂质纳米颗粒(LNP)已成为 RNA 治疗药物的重要递送载体。以往的研究和开发通常假设 LNP 在群体、载药量和组成上是均匀的。由于缺乏合适的工具来在单纳米颗粒水平上表征这些物理化学性质,因此很难检验这种观点。在这里,我们报告了一种集成的光谱-色谱方法,作为一种可推广的策略,用于剖析多组分 LNP 组装的复杂性。我们的平台将圆柱形照明共焦光谱学(CICS)与单纳米颗粒无溶液流体力学分离(SN-FSHS)相结合,以在单颗粒水平上和高通量的方式同时分析 LNP 的群体身份、流体动力学大小、RNA 载药量以及辅助脂质和聚乙二醇化脂质的分布。使用一个基准 siRNA LNP 制剂,我们通过区分七种不同的 LNP 群体、定量表征大小分布和宽范围内的 RNA 载药量、更重要的是,解析组成-大小相关性,证明了该平台的能力。这种 SN-FSHS-CICS 分析提供了关于 LNP 中 RNA 包装密度和尺寸依赖性载药量的显著程度的重要见解,这可以通过 RNA LNP 的动力学驱动组装机制来解释。
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