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

• Published in: Scientific reports Vol. 15; no. 1; p. 15
• Main Authors: DuPlissis, Andrew, Medewar, Abhishri, Hegarty, Evan, Laing, Adam, Shen, Amber, Gomez, Sebastian, Mondal, Sudip, Ben-Yakar, Adela
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/136/334/1582/712; 631/61/32; 639/166/985; 639/705/794; 639/925/930/2735; Animals; C. elegans; Caenorhabditis elegans - drug effects; Caenorhabditis elegans - growth & development; Data collection; Developmental toxicity; Few-shot learning; High-throughput screening; Humanities and Social Sciences; Image processing; Image Processing, Computer-Assisted - methods; Lab-On-A-Chip Devices; Learning algorithms; Machine Learning; Microfluidics; multidisciplinary; Phenotyping; Science; Science (multidisciplinary); Toxicity; Toxicity testing; Toxicity Tests - instrumentation; Toxicity Tests - methods; U-Net; High-throughput screening; Few-shot learning; Developmental toxicity; U-Net; Microfluidics; C. elegans
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-84842-x

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.