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Machine Learning-Assisted “Shrink-Restricted” SERS Strategy for Classification of Environmental Nanoplastic-Induced Cell Death

Environmental Science & Technology 2024 6 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 55 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Ruili Li, Ruili Li, Ruili Li, Ruili Li, Ruili Li, Shu-Ting Huang, Xiaotong Sun, Qi Liu, Xiaoqing Chen Yuyang Hu, Yuyang Hu, Xiaotong Sun, Shenghong Liu, Yuyang Hu, Xiaotong Sun, Qi Liu, Zhipeng Zhang, Shenghong Liu, Kecen Chen, Xiaoqing Chen Shu-Ting Huang, Zhipeng Zhang, Kecen Chen, Zhipeng Zhang, Shu-Ting Huang, Kecen Chen, Kecen Chen, Shenghong Liu, Qi Liu, Xiaoqing Chen Xiaotong Sun, Qi Liu, Qi Liu, Xiaoqing Chen Xiaoqing Chen

Summary

Researchers developed a machine learning-assisted technique using surface-enhanced Raman spectroscopy to track how nanoplastics from environmental sources affect human cells. They found that environmentally derived nanoplastics were more toxic than pristine laboratory versions, largely because pollutants adsorbed onto their surfaces amplified the harmful effects. The study reveals that the real-world "carrier effect" of nanoplastics, where they transport other pollutants into cells, may pose a greater health risk than the plastic particles alone.

The biotoxicity of nanoplastics (NPs), especially from environmental sources, and "NPs carrier effect" are in the early stages of research. This study presents a machine learning-assisted "shrink-restricted" SERS strategy (SRSS) to monitor molecular changes in the cellular secretome exposure to six types of NPs. Utilizing three-dimensional (3D) Ag@hydrogel-based SRSS, active targeting of molecules within adjustable nanogaps was achieved to track information. Machine learning was employed to analyze the overall spectral profiles, biochemical signatures, and time-dependent changes. Results indicate that environmentally derived NPs exhibited higher toxicity to BEAS-2B and L02 cells. Notably, the "NPs carrier effect," resulting from pollutant adsorption, proved to be more harmful. This effect altered the death pathway of BEAS-2B cells from a combination of apoptosis and ferroptosis to primarily ferroptosis. Furthermore, L02 cells demonstrated greater metabolic vulnerability to NPs exposure than that of BEAS-2B cells, especially concerning the "NPs carrier effect." Traditional detection methods for cell death often rely on end point assays, which limit temporal resolution and focus on single or multiple markers. In contrast, our study pioneers a machine learning-assisted SERS approach for monitoring overall metabolic levels post-NPs exposure at both cellular and molecular levels. This endeavor has significantly advanced our understanding of the risks associated with plastic pollution.

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