0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Environmental Sources Human Health Effects Nanoplastics Policy & Risk Remediation Sign in to save

Cell Responseto Nanoplastics and Their Carrier EffectsTracked Real-Timely with Machine Learning-Driven Smart Surface-EnhancedRaman Spectroscopy Slides

Figshare 2025
Ruili Li (470065), Shuting Huang (2938800), Yuyang Hu (10219874), Xiaotong Sun (6535064), Zhipeng Zhang (756024), Zaixuan Yang (20998808), Qi Liu (33068), Xiaoqing Chen (89898)

Summary

Researchers developed smart SERS slides to monitor in real-time how cells respond to polystyrene nanoplastics and their ability to carry contaminants (the 'carrier effect'). The platform captured intracellular metabolic changes at the molecular level, showing nanoplastics extended cell cycle S and G2 phases in lung cells and that carried pollutants caused additional distinct cellular effects.

Polymers

Research on nanoplastic (NP) toxicity and their “carrier effects” on human health remains nascent, especially for real-time, in situ monitoring of metabolic reactions in live cells. Herein, we developed smart surface-enhanced Raman spectroscopy (SERS) slides using a cyclic centrifugation-enhanced electrostatic loading (CCEL) method to facilitatively track live-cell metabolic signals. The designed core–shell polystyrene NPs (mPS) with embedded Raman probes successfully identified intracellular accumulation via a distinct Raman-silent peak. The smart SERS slide effectively monitored the metabolic changes induced by mPS at the molecular level, distinguishing different stages of membrane interaction, the endocytosis process, endosomal aggregation, and cell apoptosis. Besides, this platform was employed to perform a real-time, in situ comparison of cell cycle alterations induced by bare NPs and their “carrier effects”, revealing that NPs extended both the S and G2 phases in BEAS-2B cells, while the “carrier effects” further prolonged G2 and disrupted S-phase progression. Additionally, we integrated machine learning algorithms to accurately predict the cell cycle impacts associated with mPS and their “carrier effects”. This study provides a label-free, in situ, real-time method for monitoring NP-induced metabolic changes in live cells, laying the groundwork for further investigation into cytotoxic behaviors and strategies to mitigate NP toxicity.

Share this paper