ArticleCommunications biology2025
micronuclAI enables automated quantification of micronuclei for assessment of chromosomal instability.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring.Mutagenesis · 2026Review
- Micronucleus quantification from whole-slide haematology images using AI serves as a translatable pharmacodynamic biomarker for DNA damage response inhibitors.Scientific reports · 2026Article
- Image-based identification and isolation of micronucleated cells to dissect cellular consequences.eLife · 2025Article
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13 authors.
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Abstract
Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN may result from chromosome mis-segregation errors and excessive chromatin is frequently packaged in micronuclei (MN), which can be enumerated to quantify CIN. The assessment of CIN remains a predominantly manual and time-consuming task. Here, we present micronuclAI, a pipeline for automated and reliable quantification of MN of varying size and morphology in cells stained only for DNA. micronuclAI can achieve close to human-level performance on various human and murine cancer cell line datasets. The pipeline achieved a Pearson's correlation of 0.9278 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and on several publicly available image datasets where we achieved a Pearson's correlation of 0.9620. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on images that are routinely obtained for research purposes. We release a GUI-implementation for easy access and utilization of the pipeline.
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