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Updated: Feb 1, 2026

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE
Published on: May 16, 2017
Assessment of in vitro particle dosimetry models at the single cell and particle level by scanning electron
Thomas Kowoll1, Susanne Fritsch-Decker2, Silvia Diabaté2
1Laboratory for Electron Microscopy, Karlsruhe Institute of Technology (KIT), Campus South, Engesserstr. 7, 76131, Karlsruhe, Germany. thomas.kowoll@kit.edu.
Background:
Particokinetic models are important to predict the effective cellular dose, which is key to understanding the interactions of particles with biological systems. For the reliable establishment of dose-response curves in, e.g., the field of pharmacology and toxicology, mostly the In vitro Sedimentation, Diffusion and Dosimetry (ISDD) and Distorted Grid (DG) models have been employed. Here, we used high resolution scanning electron microscopy to quantify deposited numbers of particles on cellular and intercellular surfaces and compare experimental findings with results predicted by the ISDD and DG models.
Results:
Exposure of human lung epithelial A549 cells to various concentrations of differently sized silica particles (100, 200 and 500 nm) revealed a remarkably higher dose deposited on intercellular regions compared to cellular surfaces. The ISDD and DG models correctly predicted the areal densities of particles in the intercellular space when a high adsorption ("stickiness") to the surface was emulated. In contrast, the lower dose on cells was accurately inferred by the DG model in the case of "non-sticky" boundary conditions. Finally, the presence of cells seemed to enhance particle deposition, as aerial densities on cell-free substrates were clearly reduced.
Conclusions:
Our results further validate the use of particokinetic models but also demonstrate their limitations, specifically, with respect to the spatial distribution of particles on heterogeneous surfaces. Consideration of surface properties with respect to adhesion and desorption should advance modelling approaches to ultimately predict the cellular dose with higher precision.
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