Automated pipeline improves DNA damage measurement in comet assay images
COMPASS: Comet Object Measurement Pipeline with Automated Selection and Scoring
Computer Vision and Pattern Recognition
Summary
Measuring DNA damage inside individual cells can be tricky because scientists usually look at images by hand or use partly automated tools that can take a lot of time and might miss some details. The authors created COMPASS, a computer program that uses artificial intelligence to automatically find and measure damaged DNA in these images. This makes the results more reliable, saves time, and lets scientists check or change results if they want. COMPASS is freely available for anyone to use.
What this means in practice
- •For biotech lab technicians: Automatically analyze comet assay images to produce consistent DNA damage reports without manual effort.
- •For pharmaceutical screening teams: Use AI-enhanced image analysis to speed up DNA damage evaluation during drug safety testing.
- •For digital pathology software vendors: Integrate automated comet assay scoring into diagnostic products to improve throughput and reproducibility.$Commercial implications: Allows selling enhanced pathology tools with AI-based comet image analysis for clinical and research labs.
Authors
Jack Roberts, Canya Lu, Alexis Michelle Lawson, Kaitlyn Holden, Gerald S. Wilkinson, Anne M. Bronikowski, Ritambhara Singh
Abstract
Summary: The single-cell gel electrophoresis ('comet') assay is a widely used technique for quantifying DNA damage at the individual cell level. However, image analysis often relies on manual inspection or semi-automated software, which can be labor-intensive, difficult to reproduce, and sensitive to image quality and comet morphology. COMPASS automates comet assay image analysis by combining deep learning-based comet segmentation with damage measurement and automated comet selection. The pipeline produces standardized DNA damage measurements while offering robust detection, reducing manual effort and improving reproducibility through transparent selection and optional manual review. Availability and implementation: COMPASS is implemented in Python and is freely available at https://github.com/ rsinghlab/COMPASS. Installation instructions, pretrained weights, and example usage are provided in the repository. Contact: jack_roberts2@brown.edu, ritsingh@illinois.edu Supplementary information: Available onlime upon publication.