Raman Spectroscopy and Hyperspectral Analysis of Living Cells Exposed to Nanoparticles

Abstract: Nanoparticles, i.e. particles with at least one dimension smaller than 100 nm, are present in large quantities in ambient air and can also be found in an increasing amount of consumer products. It is known that many nanomaterials have physicochemical properties that differ from physicochemical properties of the same material in bulk size. It is therefore important to characterize nanoparticles and to evaluate their toxicity. To understand mechanisms behind nanotoxicity, it is important to study the uptake of nanoparticles, and how they are accumulated. For these purposes model studies of cellular uptake are useful. In this thesis metal oxide and carbon-based nanoparticles have been studied in living cells using Raman spectroscopy. Raman spectroscopy is a method that facilitates a non-destructive analysis without using any fluorescent labels, or any other specific sample preparation. It is possible to collect Raman images, i.e. images where each pixel corresponds to a Raman spectrum, and to use the spectral information to detect nanoparticles, and to identify organelles in cells. In this thesis the question whether or not nanoparticles can enter the cell nucleus of lung epithelial cells has been addressed using hyperspectral analysis. It is shown that titanium dioxide nanoparticles and iron oxide nanoparticles are taken up by cells, and also in the cell nucleus. In contrast, graphene oxide nanoparticles are mainly found attached on the outside of the cell membrane and very few nanoparticles are found in the cell, and none have been detected in the nucleus. It is concluded that graphene oxide nanoparticles are not cytotoxic. However, a comparison of Raman spectra of biomolecules in cells exposed to graphene oxide, unexposed cells and apoptotic cells, shows that the graphene oxide nanoparticles do affect lipid and protein structures. In this thesis, several multivariate data analysis methods have been used to analyze Raman spectra and Raman images. In addition, super-resolution algorithms, which originally have been developed to improve the resolution in photographic images, were optimized and applied to Raman images of cells exposed to submicron polystyrene particles in living cells.