Predicting the phytotoxic mechanism of action of LiCoO2 nanomaterials using a novel multiplexed algal cytological imaging (MACI) assay and machine learning

Abstract

Currently, there is a lack of knowledge of how complex metal oxide nanomaterials, like LiCoO2 (LCO) nanosheets, interact with eukaryotic green algae. Previously, LCO was reported to cause a number of physiological impacts to Raphidocelis subcapitata including endpoints related to growth, reproduction, pigment & lipid biosynthesis, and carbon biomass assimilation. Furthermore, LCO was proven to physically enter the cells, thus indicating the possibility for it to directly interact with key subcellular components. However, the mechanisms through which LCO interacts with these key subcellular components is still unknown. This study assesses the interactions of LCO at the biointerface of R. subcapitata using a novel multiplexed algal cytological imaging (MACI) assay and machine learning in order to predict its phytotoxic mechanism of action (MoA). Algal cells were exposed to varying concentrations of LCO, and their phenotypic profiles were compared to that of cells treated with reference chemicals which had already established MoAs. Hierarchical clustering and machine learning analyses indicated photosynthetic electron transport to be the most probable phytotoxic MoA of LCO. Additionally, single-cell chlorophyll fluorescence results demonstrated an increase in irreversibly oxidized photosystem II proteins. Lastly, LCO-treated cells were observed to have less nuclei/cell and less DNA content/nucleus when compared to non-treated cell controls.