micronuclAI enables automated quantification of micronuclei for assessment of chromosomal instability

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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Publication Details

Journal
Communications Biology
Published
2025-03-04
DOI
https://doi.org/10.1038/s42003-025-07796-4
Citations
7
Primary Topic
DNA Repair Mechanisms
Type
article
Field-Weighted Citation Impact
5.64
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article

micronuclAI enables automated quantification of micronuclei for assessment of chromosomal instability

Aroj Hada, Shivem B. Shah, Zachary Walsh, Luke L. Cai et al.
7 citations
Communications Biology
DNA Repair Mechanisms
5.64
article

micronuclAI enables automated quantification of micronuclei for assessment of chromosomal instability

Aroj Hada, Shivem B. Shah, Zachary Walsh, Luke L. Cai, Johannes C. Melms, Benjamin Izar, Lindsay Caprio, Denis Schapiro, Miguel A. Ibarra-Arellano, Kresimir Bestak, Niklas Stotzem, Florian Wünneman, Ibrahim Mansaray
article en
7 citations

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.

Communications BiologyVol. 8(1)
Heidelberg University (DE), Columbia University Irving Medical Center (US), University Hospital Heidelberg (DE), Helmholtz Zentrum München (DE), German Center for Lung Research (DE), Excellence Cluster Universe (DE), Technical University of Munich (DE), Columbia University (US)
Openalex Percentile: Top 4%
DNA Repair Mechanisms
5.64
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