Uptake and effects of polystyrene nanoplastics in comparison to non-plastic silica nanoparticles on small intestine cells (IPEC-J2).

Nanoplastics smaller than 1 µm accumulate as anthropogenic material in the food chain, but only little is known about their uptake and possible effects on potentially strongly exposed cells of the small intestine. The aim of the study was to observe the uptake of 100 nm polystyrene nanoplastics into a non-tumorigenic small intestine cell culture model (IPEC-J2 cells) and to monitor the effects on cell growth and gene regulation, compared to a 100 nm non-plastic silica nanoparticle reference. The intracellular uptake of both types of nanoparticles was proven via (confocal) fluorescence microscopy and complemented with transmission electron microscopy. Fluorescence microscopy showed a growth phase-dependent uptake of nanoparticles into the cells, hence further experiments included different time points related to epithelial closure, determined via electric cell substrate impedance sensing. No retardations in epithelial closure of cells after treatment with polystyrene nanoparticles could be found. In contrast, epithelial cell closure was partly negatively influenced by silica nanoparticles. An increased production of organic nanoparticles, like extracellular vesicles, was not measurable via nanoparticle tracking analysis. An assessment of messenger RNA by next generation sequencing and subsequent pathway analysis revealed that the TP53 pathway was influenced significantly by the polystyrene nanoparticle treatment. In both treatments, dysregulated mRNAs were highly enriched in the NOTCH signaling pathway compared to the non-particle control.

Modelling and Differential Quantification of Electric Cell-Substrate Impedance Sensing Growth Curves.

Measurement of cell surface coverage has become a common technique for the assessment of growth behavior of cells. As an indirect measurement method, this can be accomplished by monitoring changes in electrode impedance, which constitutes the basis of electric cell-substrate impedance sensing (ECIS). ECIS typically yields growth curves where impedance is plotted against time, and changes in single cell growth behavior or cell proliferation can be displayed without significantly impacting cell physiology. To provide better comparability of ECIS curves in different experimental settings, we developed a large toolset of R scripts for their transformation and quantification. They allow importing growth curves generated by ECIS systems, edit, transform, graph and analyze them while delivering quantitative data extracted from reference points on the curve. Quantification is implemented through three different curve fit algorithms (smoothing spline, logistic model, segmented regression). From the obtained models, curve reference points such as the first derivative maximum, segmentation knots and area under the curve are then extracted. The scripts were tested for general applicability in real-life cell culture experiments on partly anonymized cell lines, a calibration setup with a cell dilution series of impedance versus seeded cell number and finally IPEC-J2 cells treated with 1% and 5% ethanol.