A recent publicationi investigates the characterization of viral and non-viral vectors using multiple Malvern Panalytical technologies. This application note takes a close look at how NanoSight NS300 easily can provide a more detailed understanding of a sample’s size distribution and at the same time inform us of the concentration of the different populations in the sample.
What are mRNA-LNPs?
Lipid nanoparticles (LNPs) have emerged as a highly effective mRNA delivery vector, protecting the mRNA from degradation until it is released in the cytoplasm in the cells, and helping it enter the cell through the endosomal pathway. LNPs typically consist of four components, as shown in Figure 1, which all serve slightly different purposes, including:
- complexing with the mRNA
- helping determine its final size
- enhancing structural stability so the LNPs survive manufacture and storage
- helping the LNP to release its cargo into the cytoplasm
Figure 1 The basic structure and compositions of an mRNA-LNP characterized in this study.
Particle characterization and stability analysis
Average particle diameter and size distribution are commonly measured attributes during the development of mRNA-LNP formulations, often these are measured with Dynamic Light Scattering (DLS), but sometimes the data show indications of changes in the distribution or indicates a higher polydispersity, and then Nanoparticle tracking analysis (NTA) can be used to get a more detailed picture of what is present in the sample.
How is NTA applied in LNP research & manufacture?
In mRNA-LNP formulation and manufacture, NTA is often used as a complementary tool to dynamic light scattering (DLS), when researchers have questions about the detail of the size distribution indicated by DLS. The higher resolution of NTA allows us to understand if there are several close populations. It also helps to answer the question of how much of the sample is present in each population.
ISO 19430 provides guidance on how to evaluate number-based particle size distribution in liquid dispersions using particle tracking analysis.
What is NTA?
Figure 2 Capture, Tracking and Analysis images for nanoparticle tracking analysis
NTA works by capturing the scattered light from illuminated particles – represented by the white dots in the image above. An algorithm tracks the movement of these particles over time - shown as red tracks. The speed of movement is related to size by the Stokes-Einstein equation and allows the software to calculate the size of each particle and create a size versus concentration distribution.
NTA samples often require dilution, and here it is critical to assess sample stability for the measurement’s length. This can be done by tracking particle distribution changes across five repeat measurements. There should be no trending in the particle size distribution: for example, a reduction in sample concentration could indicate sample instability. When using NTA you do not need to know any material properties of the particles. It does not matter what they are made of, unless the buffer they are suspended in has a viscosity that differs from water, as this would affect how fast they can move in the solution. Typical cases for when the viscosity is affected are when you add sugars or glycerol to the samples.
The images from NTA are often erroneously taken for particle images. This is not the case. The video shows the scattering patterns from particles moving under Brownian motion.
NTA & DLS: complementary techniques
The two examples below demonstrate how NTA complements dynamic light scattering (DLS) data. For monodisperse samples, as in the case of the liposome sample shown in Figure 3, we can see that it looks very similar in DLS and NTA. However, it is important to note that NTA gives you a number-based distribution, whereas DLS gives you a light intensity-based distribution and the mean size of DLS will be slightly skewed towards the larger sizes as they scatter more light per particle.
Figure 3 Liposome sample measured with DLS (a) and NTA (b). shows how similar the data are for monodisperse samples
The difference between intensity-based DLS distributions versus number-based size distributions, becomes even more visible for polydisperse samples, as seen in the mRNA-LNP formulation in Figure 4. Here we see a wide size distribution in DLS, with the cumulants analysis giving us a polydispersity index of 0.32 (a monodisperse sample is <0.05) so this is clearly polydisperse. The high polydispersity is partially caused by the second population. However, if the main population polydispersity is considered we can look at Peak %PD, which is 44±5%, much higher than the 20-25% we expect for a monodisperse peak.
When the particle size distribution is measured with NTA, it is clear that this sample actually has multiple populations within what looks like a single population peak in DLS, and the NTA data can be considered to better understand changes in these populations due to different process steps, or different stresses exerted upon the sample. The NTA data do not seem to detect the second larger population captured in DLS data, and this is due to the sensitivity of DLS to the presence of very low number of large particles, so low numbers that in the number-based NTA assay you would have to measure for a very long time to detect even one of them!
Figure 4 mRNA-LNP formulation measured by DLS and NTA shows a broad DLS size distribution, with NTA showing a higher resolution of the sample.
The measure of polydispersity associated with the particle size distribution is used to describe the presence of different populations or aggregates/agglomerates. NTA gives a number-weighted distribution, meaning each particle is given equal weighting irrespective of size. With NanoSight, Standard Deviation (SD) is calculated and reported in units of nanometers and relates to the absolute width of the distribution. It is also common to report a Relative Standard Deviation that is SD*100/mean and expressed as a percentage. Further, inherited from volume-based distributions the parameter of distribution Span can be used as an indication of the sample polydispersity. Span can be calculated by using the parameters D10, D50 and D90, which correspond to the sizes where 10%, 50% and 90% of the population is covered. Span is then calculated via (D90 − D10)/D50) and the closer the value is to 0, the more monodisperse the population.
For the two samples in Figure 3 and 4, the Span from NTA is calculated to 0.38 for the liposome sample and 0.97 for the mrNA-LNP sample, demonstrating the large difference in polydispersity between the samples.
As seen, NTA complements DLS data for mRNA-LNP samples by providing a number-based size distribution, which gives the concentration of each population, as well as its higher size resolution allowing the researcher to monitor impact of different process steps or environmental stress in more detail.
Light scattering-based techniques as these can provide orthogonal measurements of particle concentration for samples across a wide range of concentrations and particle sizes. The applicability of these measurements will depend on the stage of development as it often governs the amount of sample available for measurements. For early development stages of synthetic vectors such as LNPs, typically are >50 nm in diameter and polydisperse, NTA measurement is often employed for quick screen for size and particle concentration.
- Explore how Multi-angle DLS (MADLS) can be used as a complementary technique to NTA: Read more >>
- Read the full peer-reviewed paper on which this application note is based: Read more >>
- Discover more expert tips and advice on LNP characterization in the Vector Analytics Acceleration Center: Learn more >>
- Markova, N.; Cairns, S.; Jankevics-Jones, H.; Kaszuba, M.;Caputo, F.; Parot, J. Biophysical Characterization of Viral and Lipid-Based Vectors for Vaccines and Therapeutics with Light Scattering and Calorimetric Techniques.Vaccines 2022, 10, 49. https://doi.org/10.3390/vaccines10010049