micronuclAI enables automated quantification of micronuclei for assessment of chromosomal instability.

IF 5.1 1区 生物学 Q1 BIOLOGY Communications Biology Pub Date : 2025-03-04 DOI:10.1038/s42003-025-07796-4
Miguel A Ibarra-Arellano, Lindsay A Caprio, Aroj Hada, Niklas Stotzem, Luke L Cai, Shivem B Shah, Zachary H Walsh, Johannes C Melms, Florian Wünneman, Kresimir Bestak, Ibrahim Mansaray, Benjamin Izar, Denis Schapiro
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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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microuclai能够自动量化微核以评估染色体的不稳定性。
染色体不稳定性(CIN)是癌症转移、免疫逃避和治疗抵抗的标志。CIN可能是由染色体误分离错误引起的,过量的染色质经常被包装在微核(MN)中,可以通过枚举来量化CIN。CIN的评估仍然是一个主要的人工和耗时的任务。在这里,我们提出了微核lai,这是一种自动化和可靠的管道,用于在仅用于DNA染色的细胞中对不同大小和形态的MN进行定量。微核可以在各种人类和小鼠癌细胞系数据集上达到接近人类水平的性能。管道在10倍放大率下获得的图像上实现了0.9278的Pearson相关性。我们在其他等基因细胞系中测试了该方法,在这些细胞系中,我们通过遗传方式提高或降低CIN率,并在几个公开可用的图像数据集中测试了该方法,我们获得了0.9620的Pearson相关性。鉴于人们对开发CIN驱动型癌症的治疗方法越来越感兴趣,该方法提供了一种重要的、可扩展的、快速的方法来量化用于研究目的的常规图像上的CIN。我们发布了一个gui实现,以便于访问和利用管道。
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来源期刊
Communications Biology
Communications Biology Medicine-Medicine (miscellaneous)
CiteScore
8.60
自引率
1.70%
发文量
1233
审稿时长
13 weeks
期刊介绍: Communications Biology is an open access journal from Nature Research publishing high-quality research, reviews and commentary in all areas of the biological sciences. Research papers published by the journal represent significant advances bringing new biological insight to a specialized area of research.
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