UC Merced

Giant unilamellar vesicles (GUVs) are micrometer-sized compartments that can be assembled from amphiphilic lipid molecules in aqueous environments. The increasing interest in the use of cell-mimicking systems for therapeutics and drug delivery requires techniques that can enable the production of large numbers of GUVs at low costs. Despite the long history of producing GUVs, the mechanism of their assembly has not been understood. Furthermore, the impact of experimental factors such as surface concentration and charge have also not been quantified due to a lack of available methods. Here, I report the quantitative impact of surface concentration, lipid headgroup charge, and salt in the hydration solutions on the yields and size distributions of GUVs using methods previously developed in the Subramaniam lab. I found that the evolution of yield of zwitterionic lipids with surface concentration is peaked while the yields of pure charged lipids, as well as mixtures of zwitterionic and charged lipids, increase with increasing surface concentration. I explain the evolution of yields of zwitterionic lipids with increasing surface concentration and salt concentrations using the framework of the budding and merging (BNM) model. The BNM model describes GUV assembly as a process of nano-scale bud formation from bilayers followed by merging of the buds to form GUVs. I use MATLAB to simulate the process of coating sites on a substrate with lipid bilayers followed by budding and merging of bilayers. The results show that the impact of surface concentration and salt on the yields and size distributions can be predicted by the simulation. I also show that at high surface concentrations, charged lipids form GUVs via an alternative pathway which was named shear-induced fragmentation (SIF). The electrostatic repulsion between charged bilayers causes the bilayers to form an extended Lα mesophase which fragments and closes to form GUVs upon applying shear by pipetting. The quantification of yields has enabled us to systematically determine the effect of various experimental parameters. Furthermore, the ability to model and predict the impact of these parameters opens up avenues to large scale production, where the yields can be optimized depending on the required composition of lipids, hydration solutions, and substrates.