A FRET-based toolkit for quantifying lipid incorporation into nanoparticles

Abstract

Understanding how lipids integrate and self-assemble into nanoparticles is needed to design functional lipid-based drug delivery systems. While lipid-based nanoparticles offer a modular and chemically diverse design space, tools to quantitatively assess the assembly and distribution of diverse lipids within nanoparticles remain limited. Here, we present a Förster Resonance Energy Transfer (FRET)-based approach to quantify lipid incorporation within liposomes and lipid nanoparticles (LNPs). This approach is modular and adaptable, compatible with a broad range of lipids with chemically modifiable moieties. To guide experimental design and optimal labeling, we developed a theoretical model describing the distribution of labeled lipids within a nanoparticle and experimentally validated it using established liposomal formulations. We applied our FRET-based approach to monitor lipid post-insertion into liposomes and quantify incorporation of a range of phospholipids. This includes an understudied but therapeutically relevant class of lipids known as plasmalogens, which have self-assembly properties that are difficult to predict. We further extended our toolkit to study lipid incorporation in LNPs and found that lipid incorporated uniformly without disrupting Fluorescence nanoparticle tracking analysis (F-NTA) confirmed our findings. Overall, by enabling quantitative analysis of individual lipid components within complex formulations, this toolkit fills a critical gap in nanoparticle characterization and can accelerate the rational design of next-generation lipid-based therapeutics.

Supplementary files

Article information

Article type
Communication
Submitted
03 Sep 2025
Accepted
07 Dec 2025
First published
09 Dec 2025

Nanoscale, 2025, Accepted Manuscript

A FRET-based toolkit for quantifying lipid incorporation into nanoparticles

P. D. Rosales, A. Viravalli, A. Schneider and N. Boehnke, Nanoscale, 2025, Accepted Manuscript , DOI: 10.1039/D5NR03734D

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