Image-based identification and isolation of micronucleated cells to dissect cellular consequences

Area of Science:

• Cell biology
• Genomics
• Bioinformatics

Background:

• Micronucleus (MN) formation is a key indicator of genome instability.
• Visual analysis of MNs is labor-intensive and limits large-scale studies.
• Automated detection of MNs in microscopy images remains a significant challenge.

Purpose of the Study:

• To develop novel automated methods for micronucleus segmentation and classification.
• To investigate the transcriptional consequences of MN formation and rupture.
• To enable functional genomics studies of MN biology.

Main Methods:

• Development of two new MN segmentation modules: VCS MN (classification) and MNFinder (segmentation).
• Application of VCS MN with photoactivation-based cell isolation and RNA-Seq.
• Transcriptome analysis of human cells with intact or ruptured MNs after chromosome missegregation.

Main Results:

• VCS MN and MNFinder provide rapid and robust MN detection across diverse cell lines.
• Neither MN formation nor rupture elicited a strong, unique transcriptional response.
• Transcriptional changes were correlated with minor increases in aneuploidy.

Conclusions:

• The developed MN segmentation modules overcome challenges in reproducible MN quantification.
• Automated MN detection combined with cell sorting facilitates functional genomics in MN biology.
• Aneuploidy, not MN formation/rupture itself, appears to drive transcriptional alterations.