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Deep Learning–Assisted identification and quantification of cell-associated microplastics using darkfield hyperspectral imaging

Talanta 2026

Summary

Researchers combined darkfield hyperspectral imaging with deep learning to identify and quantify microplastics at the single-cell level, demonstrating that the integrated system can detect 100% of polystyrene-exposed cells and assess dose-response relationships between particle concentration and cell viability.

Polymers

particles/mL), 100% of cells were detected with PS and showed significant reductions in cell viability. Our findings demonstrated that integrating darkfield HSI with deep learning provides a robust quantitative assessment of MPs and cells interaction at single-cell resolution. This approach may be adaptable to other particle types and cell lines, subject to retraining and validation, offering a valuable tool for microplastic toxicology studies and complementing traditional high-throughput assays in evaluating the level of cell association and dose-response relationships.

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