Exploring Flow Cytometry-Based Micronucleus Scoring for Reliable Nanomaterial Genotoxicity Assessment

The increased use of engineered nanomaterials (ENM) such as SiO 2 and TiO 2 in industrial products, especially in food, raises concerns with regard to their effect on human health. In particular, ENM-induced genotoxicity is crucial to investigate, since DNA damage can cause induction or promotion of carcinogenesis. However, current in vitro and in vivo nanogenotoxicological data are highly contradictory, which impedes interpretation and extrapolation. Hence, robust, reliable, and ideally scalable in vitro methods for nanogenotoxicity assessment are of great interest. This work aimed at evaluating the suitability of flow cytometry-based micronuclei scoring for reliable nanogenotoxicological assessment in human intestinal cells. Therefore, we have evaluated the genotoxicity of differently sized SiO 2 and TiO 2 from different sources (food-relevant, commercially available, and laboratory-synthesized) using the well-established alkaline single cell gel electrophoresis (Comet assay) and the micronucleus (MN) assay employing a flow cytometric readout. Our study demonstrates that physiologically relevant doses of several types of SiO 2 and TiO 2 did not cause genotoxicity, as assessed by the Comet assay, and the MN flow cytometry assay under the particular experimental conditions described. To improve data reliability, we identified ENM-induced interferences with flow cytometric scoring employing a set of interference controls, which is generally applicable for any nanomaterial and any cell line. In conclusion, flow cytometry-based MN scoring appears to be a promising methodology in nanogenotoxicity testing since data acquisition and analysis are significantly faster, highly scalable in terms of throughput, and less operator-dependent compared to the traditional microscopic evaluation. In particular, ENM-induced false-positive or false-negative results, which have not been addressed sufficiently in the literature, can be detected easily, thus enhancing data reliability.