Issue 2, 2024

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.

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

Supplementary files

Article information

Article type
Paper
Submitted
09 Sept. 2023
Accepted
02 Janv. 2024
First published
09 Janv. 2024
This article is Open Access
Creative Commons BY-NC license

Environ. Sci.: Nano, 2024,11, 507-517

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

E. Ostovich, A. Henke, C. Green, R. Hamers and R. Klaper, Environ. Sci.: Nano, 2024, 11, 507 DOI: 10.1039/D3EN00629H

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements