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.
Keywords: aneuploidy; cancer biology; cell biology; human; micronuclei; neural net; nuclear envelope.
Healthy cells house most of their genetic information, such as their chromosomes, within a dedicated compartment known as the nucleus. Micronuclei, on the other hand, are small cellular compartments which contain pieces of DNA left behind after improper cell divisions or other abnormal events. Detectable under the microscope, their presence is usually interpreted as a sign of aging, disease or negative perturbations such as exposure to toxic chemicals. However, new research suggests that micronuclei could also be directly harmful. When they rupture – which they almost always do – the sudden presence of unprotected genetic information in the ‘incorrect’ part of the cell could trigger dangerous cascades of events. To better understand these processes, researchers need to be able to quickly identify and isolate micronuclei-carrying cells within a large population, but such techniques are currently lacking. DiPeso et al. aimed to address this gap by developing an AI tool, MNFinder, which can analyse images of live cells and automatically identify micronuclei. This allowed the team to mark and isolate cells in which these compartments were either absent, present, or had ruptured. Further experiments showed that, unexpectedly, patterns of gene expression did not differ between these different groups of cells. This suggests that the cell cannot ‘see’ that it has too many nuclei, or sense when its own DNA is in the wrong place. The disease-associated changes caused by the rupturing of micronuclei may therefore only emerge later, when the cell next divides. At this point, the DNA is heavily damaged and can activate immune checkpoint pathways, which can generate cancer-driving mutations and cellular self-destruct signals. By developing and making MNFinder freely available, DiPeso et al. hope that micronuclei will be easier to study for scientists in a wide range of fields. Interestingly, micronuclei occur frequently during early human embryo development and may affect fertility, highlighting another research area that could benefit from these tools. As with all image analysis programs, however, increased usage, data, and model training will expand the use and ease of applying these tools to critical questions in human health.
© 2024, DiPeso et al.