Resolving lipid-nanoparticle bleb structures from small angle scattering measurements
Date
2025
Authors
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Publisher
University of Delaware
Abstract
Lipid nanoparticles (LNPs) are delivery vehicles for nucleic acid payloads, such as siRNA and mRNA. Typical LNPs consist of the ionizable lipid, cholesterol, phospholipid, PEG-lipid, and nucleic acid, self-assembling into a nanostructure for biodistribution, endocytosis and endosomal escape necessary for RNA delivery and transfection. Recent studies report protruding structures on otherwise spherical LNPs, forming a two-compartment lipid-rich and aqueous-rich morphology termed LNP “blebs”. While Cryo-TEM measurements can identify bleb architectures, such measurements are limited in statistical sample size and require delicate sample preparation. Small-Angle Scattering (SAS) measurements of LNPs in formulation provide an alternative route to identify blebs, but existing analytical models are limited in resolving structural features. In this work, we present a computational workflow for efficient analysis of SAS experiments of LNP blebs to resolve their microstructure and composition. This workflow numerically calculates the pair distance distribution function (PDDF) for model LNP bleb structures via a simple Monte Carlo method, weights it by the product of excess scattering lengths based on the composition, and generates simulated SAS profiles by performing a 1D Fourier transform. ☐ A population-based evolutionary optimization algorithm, differential evolution (DE) is used to minimize the discrepancies between the simulated and experimental scattering profiles, refining structural parameters to reconstruct the representative morphology for the experimental sample. Recently published small-angle neutron scattering (SANS) experimental data of LNP-self-amplifying (SAM) RNA formulations (Thelen et al., ACS Nano, 2024) are analyzed. ☐ Simultaneous analysis of multiple SANS profiles measured under different solvent compositions enabled the quantification of average-size, structure and distribution of lipid components and RNA in the LNP bleb representative morphology for the formulations. Our computational workflow provides an efficient, automated, and physics-based approach for SAS analysis of complex morphologies and is shown to be an improvement over the prior, analytical approximations. The LNP model structure development and efficient calculation of PDDF shown in computational workflow is used for generating high-throughput data set of structure and PDDFs for a library of LNP morphologies. The data set developed is utilized in a separate work for developing Physics Informed Machine Learning model for LNP structure prediction. Additionally, a two-compartment core-shell bicelle analytical model is developed and tested as an approximate model for SAS analysis of LNP blebs which can be easily integrated with widely used SAS analysis software. The insights into the structure and composition of LNP blebs aid in the rational design of LNP formulations.
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Keywords
Bleb structures, Computational workflow, Lipid nanoparticles, Small-Angle Scattering, Nucleic acids