DiPeso Lucian, Pendyala Sriram, Huang Heather Z, Fowler Douglas M, Hatch Emily M
Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, United States.
Molecular & Cellular Biology, University of Washington, Seattle, United States.
Elife. 2025 Jun 2;13:RP101579. doi: 10.7554/eLife.101579.
Recent advances in isolating cells based on visual phenotypes have transformed our ability to identify the mechanisms and consequences of complex traits. Micronucleus (MN) formation is a frequent outcome of genome instability, triggers extensive changes in genome structure and signaling coincident with MN rupture, and is almost exclusively defined by visual analysis. Automated MN detection in microscopy images has proved challenging, limiting discovery of the mechanisms and consequences of MN. In this study we describe two new MN segmentation modules: a rapid model for classifying micronucleated cells and their rupture status (VCS MN), and a robust model for accurate MN segmentation (MNFinder) from a broad range of cell lines. As proof-of-concept, we define the transcriptome of non-transformed human cells with intact or ruptured MN after chromosome missegregation by combining VCS MN with photoactivation-based cell isolation and RNASeq. Surprisingly, we find that neither MN formation nor rupture triggers a strong unique transcriptional response. Instead, transcriptional changes appear correlated with small increases in aneuploidy in these cell classes. Our MN segmentation modules overcome a significant challenge with reproducible MN quantification, and, joined with visual cell sorting, enable the application of powerful functional genomics assays to a wide-range of questions in MN biology.
基于视觉表型分离细胞的最新进展改变了我们识别复杂性状机制及后果的能力。微核（MN）形成是基因组不稳定的常见结果，会引发与MN破裂同时发生的基因组结构和信号传导的广泛变化，并且几乎完全通过视觉分析来定义。在显微镜图像中自动检测MN已被证明具有挑战性，限制了对MN机制及后果的发现。在本研究中，我们描述了两个新的MN分割模块：一个用于对微核化细胞及其破裂状态进行分类的快速模型（VCS MN），以及一个用于从广泛细胞系中准确分割MN的稳健模型（MNFinder）。作为概念验证，我们通过将VCS MN与基于光激活的细胞分离和RNA测序相结合，定义了染色体错分后具有完整或破裂MN的未转化人类细胞的转录组。令人惊讶的是，我们发现MN形成和破裂均未触发强烈的独特转录反应。相反，转录变化似乎与这些细胞类别中非整倍体的小幅增加相关。我们的MN分割模块克服了可重复MN定量的重大挑战，并且与视觉细胞分选相结合，能够将强大的功能基因组学分析应用于MN生物学中的广泛问题。
