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Machine Learning-Assisted “Shrink-Restricted” SERS Strategy for Classification of Environmental Nanoplastic-Induced Cell Death
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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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Machine Learning-Aided 3D Dynamic SERS Strategy for Physiological Mapping: Biotoxicity of Environmentally Dimensional Aged Nanoplastics and Corresponding Protein Corona Complexes
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Researchers used a new combination of 3D surface-enhanced Raman spectroscopy and machine learning to study the toxicity of nanoplastics on cells. They found that aged nanoplastics and those coated with proteins from the environment caused different types of cell damage depending on the plastic type. This approach could help scientists more rapidly assess the biological hazards of nanoplastics found in the environment.
Cell Responseto Nanoplastics and Their Carrier EffectsTracked Real-Timely with Machine Learning-Driven Smart Surface-EnhancedRaman Spectroscopy Slides
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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.
Cell Response to Nanoplastics and Their Carrier Effects Tracked Real-Timely with Machine Learning-Driven Smart Surface-Enhanced Raman Spectroscopy Slides
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Researchers developed a novel smart sensor slide that can track in real time how living cells respond to nanoplastic exposure at the molecular level. Using specially designed core-shell plastic nanoparticles with embedded tracking signals, they could monitor each stage from initial cell contact through absorption and eventual cell damage. The technology offers a powerful new tool for studying how nanoplastics interact with human cells and carry other pollutants into the body.
Integrating Metal Phenolic Networks-Mediated Separation and Machine Learning-Aided SERS for High-Precision Quantification and Classification of Nanoplastics
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Scientists combined metal-phenolic network chemistry — which rapidly concentrates and captures nanoplastics — with machine-learning-enhanced surface-enhanced Raman spectroscopy (SERS) to accurately identify and quantify nanoplastics at very low environmental concentrations. This integrated approach addresses one of the biggest technical obstacles in nanoplastic research: detecting particles that are too small and too sparse for conventional methods to reliably find.
Trapping tiny pollutants: SERS-driven strategies for microplastics and nanoplastics detection
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This review explores how surface-enhanced Raman spectroscopy (SERS) is being developed as a highly sensitive tool for detecting and identifying micro- and nanoplastics in environmental and biological samples. Researchers highlight recent advances in sensor design, the integration of machine learning for improved accuracy, and the technique's potential for real-world monitoring. The study also identifies key challenges, including signal variability and the lack of standardized methods, that need to be resolved for broader adoption.
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