Machine Learning-Assisted "Shrink-Restricted" SERS Strategy for Classification of Environmental Nanoplastic-Induced...

Area of Science:

• Environmental Science
• Biotechnology
• Toxicology

Background:

• Nanoplastics (NPs) pose emerging environmental risks.
• The biotoxicity and carrier effect of NPs are under-researched.
• Current cell death assays lack temporal resolution and holistic molecular insight.

Purpose of the Study:

• To develop a novel method for monitoring cellular secretome changes upon NP exposure.
• To investigate the toxicity of environmental NPs and the NP carrier effect.
• To compare the metabolic vulnerability of different cell types to NP exposure.

Main Methods:

• A machine learning-assisted "shrink-restricted" surface-enhanced Raman spectroscopy (SERS) strategy (SRSS) was developed.
• Three-dimensional (3D) Ag@hydrogel substrates were used for active molecular targeting.
• Machine learning analyzed spectral profiles, biochemical signatures, and time-dependent cellular changes.

Main Results:

• Environmentally sourced NPs demonstrated higher toxicity to BEAS-2B and L02 cells.
• The "NPs carrier effect" (pollutant adsorption) significantly increased NP harm.
• NP exposure shifted BEAS-2B cell death from apoptosis/ferroptosis to primarily ferroptosis.
• L02 cells exhibited greater metabolic vulnerability to NPs, especially with the carrier effect.

Conclusions:

• The ML-assisted SRSS approach enables comprehensive monitoring of NP-induced cellular and molecular changes.
• Environmental NPs and their carrier effect pose significant risks, altering cellular death pathways and metabolism.
• This study advances understanding of plastic pollution's impact on cellular health.