ISIS Neutron and Muon Source Data Journal

Engineering nanoparticles for protein therapeutics - quantifying membrane access and loading

Abstract: The self-assembly properties of lipid systems can be exploited to form lipid membranes with complex morphologies. These include membranes with a lattice-type internal structure and interwoven but not connected water channels. They can be dispersed in the presence of a stabiliser to form uniform nanoparticles termed cubosomes. Loading of these nanoparticles with proteins will enable their application as therapeutic agents. Two significant bottlenecks in this process are 1) to understand how the stabiliser limits access to the internal lipid membrane and 2) characterisation of the structural effects of loading charged molecules into the nanoparticles. The key objectives of this investigation are to characterise the structural effects of loading cubosomes with charged molecules of varying molecular weights as a function of the amount and type of stabiliser used when forming them.

Principal Investigator: Dr Hanna Barriga
Experimenter: Dr Adam Creamer
Experimenter: Miss Valeria Nele
Experimenter: Dr Miina Ojansivu
Experimenter: Dr Christopher Wood
Experimenter: Mr Brian Chen
Experimenter: Dr Omar Rifaie Graham
Experimenter: Dr Adrian Najer
Experimenter: Mr Michael Potter
Experimenter: Miss Nayoung Kim
Experimenter: Professor Molly Stevens
Experimenter: Dr Margaret Holme
Experimenter: Dr Michael Thomas
Local Contact: Dr James Doutch
Experimenter: Dr Jelle Penders
Experimenter: Miss Catherine Saunders

DOI: 10.5286/ISIS.E.RB1910261

ISIS Experiment Number: RB1910261

Publisher: STFC ISIS Neutron and Muon Source

Data format: RAW/Nexus
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Data Citation

The recommended format for citing this dataset in a research publication is as:
[author], [date], [title], [publisher], [doi]

For Example:
Dr Hanna Barriga et al; (2020): Engineering nanoparticles for protein therapeutics - quantifying membrane access and loading, STFC ISIS Neutron and Muon Source, https://doi.org/10.5286/ISIS.E.RB1910261

Data is released under the CC-BY-4.0 license.