Protein and lipid-based nanoparticles have been widely used in clinical applications, such as drug and gene delivery and vaccine development. Their unique physical and chemical properties at the nanoscale level have paved the way for various breakthroughs in medicine and healthcare. Characterizing the homogeneity of nanoparticles is crucial for ensuring their safety, efficacy, and performance in clinical settings.
Cryo-TEM imaging is a powerful technique used to directly visualize nanoparticles, such as liposomes and lipid nanoparticles (LNPs), viruses and virus like particles (VLPs), in their near-native state. This technique can evaluate the size, morphology, internal structure, and surface features of the nanoparticles. Therefore, cryo-TEM imaging is immensely valuable in categorizing nanoparticles based on specific properties of interest.
Visualize formulation characteristics on a per-particle basis with cryo-TEM direct imaging
NIS provides the following types of fraction counting analysis services using cryo-TEM direct imaging and automated analysis of nanoparticles:
- Payload Location and Density
- Lamellarity Analysis
- Antigen Distribution
- Virus Maturation State
Automated Payload Location and Density Analysis
When nanoparticles are designed to carry specific payloads, understanding how the payload is distributed within the nanoparticle structure is crucial for optimizing their properties and applications. Determination of the loaded/unloaded ratios for nanocarriers is a critical measure of how effectively the delivery system can hold and protect the drug until it reaches its target site. Besides being unable to provide therapeutic benefit, empty nanocarriers also have the potential to increase unwanted immune responses.
Combined with fraction counting analysis, cryo-TEM imaging is powerful for studying payload encapsulation within nanoparticles such as virus and virus-like particle (VLP) capsids, particularly adeno-associated virus (AAV), adenovirus (AV), lipid nanoparticles (LNPs), and liposomes.
In the case of AAV, it is possible to determine the empty/full ratio of a formulation using automated image analysis. Please contact us to see an example report of our automated capsid ratio analysis.
Lamellarity Analysis
Lamellarity of lipid nanoparticles (LNPs) refers to the various arrangements of lipid bilayer within the particle, which is an important factor in determining their effectiveness as drug delivery vehicles. Similar to size, morphology, and encapsulation state, lamellarity is a critical attribute that can affect their bioavailability, stability, and therapeutic outcome.
Researchers work on optimizing the lamellarity of lipid nanoparticles to improve their performance as drug carriers. LNP and liposome lamellarity evaluation with cryo-TEM imaging is a valuable tool to differentiate between for example unilamellar, multilamellar, multicompartmental (or “blebbing” when looking at LNPs), and multivesicular particles.
Antigen Distribution
Self-assembling nanoparticles have gained significant attention in vaccine development due to their ability to provide a stable platform for presenting antigens to the immune system. These nanoparticles can display numerous foreign antigens on their surface, and in some systems multiple types of antigens can be expressed on the same surface. This presentation enhances the recognition of antigens by immune cells compared to using discrete antigens as part of a vaccine formulation, leading to a stronger and more durable immune response.
Characterizing the antigen spatial distribution on the nanoparticle surface using cryo-TEM images into classes such as densely decorated, sparsely decorated, or no antigen decorated nanoparticles, can help optimize nanoparticle-based vaccine design. In addition, cryo-TEM imaging can also capture conformational variations of the surface-expressed antigens.
Nanoparticles We Can Perform Particle Classification Analysis On
Frequently Asked Questions
Can I see an example report for your particle classification analysis?
How many particles do you count, or images do you analyze per sample or grid?
