3D electron microscopy for analyzing nanoparticles in the tumor endothelium

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Cell Biology

Background:

• Delivering medical agents to diseased tissues is challenging.
• Existing imaging techniques lack single nanoparticle resolution in 3D tissue context.
• Understanding in vivo nanoparticle transport is crucial for improving drug delivery.

Purpose of the Study:

• To develop a novel 3D electron microscopy (3D EM) machine learning strategy for imaging and mapping single nanoparticle distributions (NPD) in tissues.
• To provide unbiased visualization and quantification of individual nanoparticles within organs.
• To establish a metric for quantifying nanoparticle transport at the single-nanoparticle level.

Main Methods:

• Developed 3DEM-NPD, a 3D EM machine learning strategy.
• Applied the technique to quantify nanoparticle transport through tumor blood vessel endothelial cells.
• Utilized machine learning to locate over 550,000 nanoparticles with >82% accuracy in under 3 hours.

Main Results:

• 3DEM-NPD enables accurate measurement of cell morphology (diameter, surface area, volume) in 3D, surpassing 2D EM.
• Quantified nanoparticle transport and vesicle morphology (~2,800 vesicles).
• Found an average of 2.4 nanoparticles per vesicle, with a theoretical maximum of 158, indicating low encapsulation efficiency.

Conclusions:

• The developed 3DEM-NPD method offers precise visualization and quantification of nanoparticle distribution in tissues.
• Results highlight the need to enhance vesicle encapsulation efficiency for improved nanoparticle transport and delivery.
• This technique provides a benchmark for future studies on nanoparticle-cell interactions and drug delivery systems.