Machine Learning-Aided 3D Dynamic SERS Strategy for Physiological Mapping: Biotoxicity of Environmentally Dimensional Aged Nanoplastics and Corresponding Protein Corona Complexes.

Abstract

Nanoplastics (NPs) are emerging pollutants that undergo inevitable aging in the environment, raising concerns about human exposure and health hazards. Research on the cytotoxicity of various polymer types of NPs, aged nanoplastics (aNPs), and their interactions with proteins (aNPs-protein corona) is still nascent. Traditional cytotoxicity detection methods often rely on end point assays with restricted temporal resolution and analysis of single or multiple biomarkers. Here, we propose a novel approach integrating the 3D dynamic SERS strategy (DSS) with machine learning to rapidly analyze the cell fate and death modes induced by NPs, aNPs, and aNPs-protein corona complexes at the molecular level. PS, PVC, PMMA, and PC products from the water environment were used to prepare the corresponding NPs, and the impact of UV irradiation on their physicochemical properties was examined. DSS systematically maps the molecular changes in the cellular secretome caused by these NPs. Machine learning effectively extracts information from complex spectra, differentiating between biological samples. Results show prolonged UV exposure increases cell sensitivity to ferroptosis and cytotoxicity in various aNPs, while the protein corona on aNPs significantly mitigates toxicity associated with surface oxygen-containing functional groups, resulting in a reduced similarity to ferroptosis signatures. 3D DSS with machine learning technique analyzes the overall metabolite profile at the molecular level rather than individual biomarkers. This study is the first attempt to compare the biotoxicity of diverse polymer NPs, aNPs, and aNPs-protein coronas at cellular and molecular levels in human hepatocytes, enhancing our understanding of the complex biological impacts of NPs.