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Deep learning-assisted cellular imaging for evaluating acrylamide toxicity through phenotypic changes.

Zhiyuan Ning¹, Yingming Zhang¹, Shikun Zhang¹

¹State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi, 214122, China.

Food and Chemical Toxicology : an International Journal Published for the British Industrial Biological Research Association

|March 21, 2025

Summary

This study introduces a novel deep learning method to assess acrylamide (AA) toxicology using cell fluorescence imaging. The approach accurately predicts AA levels and analyzes cellular changes, improving upon traditional toxicological methods.

Keywords:

Cellular phenotype Cellular toxicology Deep learning Fluorescence imaging Residual network

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Area of Science:

Toxicology
Biotechnology
Computational Biology

Background:

Acrylamide (AA) is a food processing hazard with significant toxicity.
Conventional toxicological methods for AA are slow and insufficient for cellular analysis.

Purpose of the Study:

To develop a novel, high-throughput method for evaluating acrylamide toxicology.
To link cellular phenotypes to hazard toxicology using advanced computational models.

Main Methods:

Combined deep learning (U-Net, ResNet34) with cell fluorescence imaging.
U-Net for cell segmentation; ResNet34 for classification (80% validation accuracy).
k-means clustering and CellProfiler for phenotypic analysis.

Main Results:

Successfully predicted acrylamide concentration ranges based on cell fluorescence.
Identified and analyzed cellular phenotypic changes induced by acrylamide exposure.
Demonstrated an 80% validation accuracy using the ResNet34 model.

Conclusions:

The novel deep learning approach offers a high-throughput and accurate alternative to traditional AA toxicology methods.
This method refines the understanding of acrylamide's cellular impacts.
Provides a direct link between cell phenotypes and hazard toxicology assessment.