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

Cihang Yang1, Xiaohui Lin2, Jun-Li Xu1

  • 1School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin, 4, Ireland.

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Summary
This summary is machine-generated.

This study introduces an automated method using hyperspectral imaging and deep learning to quantify microplastic interactions with human cells. The advanced technique accurately measures microplastic uptake and its dose-dependent effects on cell viability.

Keywords:
Caco-2Darkfield hyperspectral imagingMask R-CNNMicroplastics

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Area of Science:

  • Environmental Science
  • Toxicology
  • Biomedical Imaging

Background:

  • Microplastic (MP) interaction with human cells presents health risks.
  • Quantifying MP-cell interactions and their extent is challenging.
  • Existing methods lack single-cell resolution and high throughput.

Purpose of the Study:

  • To develop an automated strategy for detecting and quantifying cell-associated microplastics at the single-cell level.
  • To assess the dose-dependent effects of polystyrene microplastics on Caco-2 cells.
  • To establish a robust method for microplastic toxicology studies.

Main Methods:

  • Combined darkfield hyperspectral imaging (HSI) with a deep learning pipeline.
  • Utilized Mask R-CNN for cell segmentation, LS-SVM for particle classification, and CHT for particle counting.
  • Validated the pipeline's performance with high precision and accuracy metrics.

Main Results:

  • The automated pipeline achieved high performance: 95% cell detection precision, 99.7% particle classification accuracy, and 99.6% particle detection precision.
  • Demonstrated a dose-dependent relationship between PS MP concentration and uptake in Caco-2 cells.
  • Observed no impact on cell viability at low concentrations, but significant viability reduction at high concentrations.

Conclusions:

  • The integrated HSI and deep learning approach provides robust, single-cell resolution for quantifying MP-cell interactions.
  • This method offers a valuable tool for microplastic toxicology, adaptable to various particle types and cell lines.
  • The findings highlight the potential health risks associated with microplastic exposure at high concentrations.