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<p>Developmental toxicity (DevTox) tests evaluate the adverse effects of chemical exposures on an organism’s development. Although current testing primarily relies on large mammalian models, the emergence of new approach methodologies (NAMs) is encouraging industries and regulatory agencies to evaluate novel assays. <i>C. elegans</i> have emerged as NAMs for rapid toxicity testing because of its biological relevance and suitability to high throughput studies. However, current low-resolution and labor-intensive methodologies prohibit its application for sub-lethal DevTox studies at high throughputs. With the recent advent of the large-scale microfluidic device, vivoChip, we can now rapidly collect 3D high-resolution images of ~ 1000 <i>C. elegans</i> from 24 different populations. While data collection is rapid, analyzing thousands of images remains time-consuming. To address this challenge, we developed a machine-learning (ML)-based image analysis platform using a 2.5D U-Net architecture (vivoBodySeg) that accurately segments <i>C. elegans</i> in images obtained from vivoChip devices, achieving a Dice score of 97.80%. vivoBodySeg processes 36&#xa0;GB data per device, phenotyping multiple body parameters within 35&#xa0;min on a desktop PC. This analysis is ~ 140 × faster than the manual analysis. This ML approach delivers highly reproducible DevTox parameters (4–8% CV) to assess the toxicity of chemicals with high statistical power.</p>

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Machine learning-based analysis of microfluidic device immobilized C. elegans for automated developmental toxicity testing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月2日

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  • Andrew DuPlissis,
  • Abhishri Medewar,
  • Evan Hegarty,
  • Adam Laing,
  • Amber Shen,
  • Sebastian Gomez,
  • Sudip Mondal,
  • Adela Ben-Yakar

摘要

Developmental toxicity (DevTox) tests evaluate the adverse effects of chemical exposures on an organism’s development. Although current testing primarily relies on large mammalian models, the emergence of new approach methodologies (NAMs) is encouraging industries and regulatory agencies to evaluate novel assays. C. elegans have emerged as NAMs for rapid toxicity testing because of its biological relevance and suitability to high throughput studies. However, current low-resolution and labor-intensive methodologies prohibit its application for sub-lethal DevTox studies at high throughputs. With the recent advent of the large-scale microfluidic device, vivoChip, we can now rapidly collect 3D high-resolution images of ~ 1000 C. elegans from 24 different populations. While data collection is rapid, analyzing thousands of images remains time-consuming. To address this challenge, we developed a machine-learning (ML)-based image analysis platform using a 2.5D U-Net architecture (vivoBodySeg) that accurately segments C. elegans in images obtained from vivoChip devices, achieving a Dice score of 97.80%. vivoBodySeg processes 36 GB data per device, phenotyping multiple body parameters within 35 min on a desktop PC. This analysis is ~ 140 × faster than the manual analysis. This ML approach delivers highly reproducible DevTox parameters (4–8% CV) to assess the toxicity of chemicals with high statistical power.