Magnetic nanoparticles (NPs) are a special type of NP with a ferromagnetic, electron-dense core that enables several applications such as cell tracking, hyperthermia, and magnetic separation, as well as multimodality. So far, superparamagnetic iron oxide NPs (SPIONs) are the only clinically approved type of metal oxide NPs, but cobalt ferrite NPs have properties suitable for biomedical applications as well. In this study, we analyzed the cellular responses to magnetic cobalt ferrite NPs coated with polyacrylic acid (PAA) in three cell types: Chinese Hamster Ovary (CHO), mouse melanoma (B16) cell line, and primary human myoblasts (MYO). We compared the internalization pathway, intracellular trafficking, and intracellular fate of our NPs using fluorescence and transmission electron microscopy (TEM) as well as quantified NP uptake and analyzed uptake dynamics. We determined cell viability after 24 or 96 hours' exposure to increasing concentrations of NPs, and quantified the generation of reactive oxygen species (ROS) upon 24 and 48 hours' exposure. Our NPs have been shown to readily enter and accumulate in cells in high quantities using the same two endocytic pathways; mostly by macropinocytosis and partially by clathrin-mediated endocytosis. The cell types differed in their uptake rate, the dynamics of intracellular trafficking, and the uptake capacity, as well as in their response to higher concentrations of internalized NPs. The observed differences in cell responses stress the importance of evaluation of NP-cell interactions on several different cell types for better prediction of possible toxic effects on different cell and tissue types in vivo.
COBISS.SI-ID: 31824089
Cell counting is an important method for evaluation of several phenomena in biology and medicine, however, it is time consuming, prone to user bias and often considered tiresome. To speed up the counting process and two automatic and semi-automatic cell counting programs were written in collaboration with two groups from the Faculty of Computer and Information Sciences (UNI LJ): CellCounter and Learn123. While CellCounter is based on predefined and fine-tuned sequence of filters optimized on sets of chosen experiments, Learn123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. CellCounter also includes an extension for analysis of overlaying images. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Program was validate on severa experiments of nanotoxciciy of nanoparticles, very good agreement with manual counting obatained. The program eneables us to performed nanotoxicity analysis (cell vialbility) much faster.
COBISS.SI-ID: 11059028