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Deep Learning–Assisted identification and quantification of cell-associated microplastics using darkfield hyperspectral imaging
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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.
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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Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study
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Researchers developed a label-free method to identify microplastics inside living human cells using enhanced dark-field microscopy combined with deep learning, achieving high classification accuracy for polystyrene microparticles differing only in pigmentation.
Accumulation of nanoplastics in human cells as visualized and quantified by hyperspectral imaging with enhanced dark-field microscopy
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Researchers developed a label-free imaging technique to visualize and count nanoplastic particles that accumulate inside human cells, using enhanced dark-field microscopy combined with hyperspectral imaging. The method successfully tracked polystyrene nanoplastics entering cells over time and measured accumulation rates without needing fluorescent labels. This tool could improve the accuracy of future studies assessing how nanoplastics build up in human tissue and what concentration levels may pose health risks.
Label-free detection of micro- and nanoplastics using dark-field hyperspectral and atomic force microscopies
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Researchers demonstrated that dark-field hyperspectral microscopy could visualize and spectrally identify polystyrene, polymethacrylate, and melamine formaldehyde particles (100 nm to 2 µm) in living Caenorhabditis elegans in vivo, while atomic force microscopy in PeakForce Tapping mode resolved internalized versus surface-bound polystyrene particles (down to 500 nm) in human skin fibroblasts. Deep learning algorithms applied to enhanced dark-field images further enabled automated classification of pigmented microplastics, demonstrating a label-free toolkit for detecting and differentiating particles in biological systems.
Microscopic Hyperspectral Image Analysis via Deep Learning
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This paper reviews deep learning approaches applied to microscopic hyperspectral imaging, a technique that captures detailed spectral data useful for identifying materials including microplastics. Advances in portable cameras and AI analysis are expanding applications for environmental monitoring and pollution detection.
Dark-field/hyperspectral microscopy for detecting nanoscale particles in environmental nanotoxicology research
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Researchers reviewed enhanced dark-field microscopy combined with hyperspectral imaging as a non-destructive optical technique for detecting and chemically characterizing nanoscale particles — including nanoplastics — in live, wet biological samples where electron microscopy is impractical.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.