Quantitative Analysis of Chromatin Condensate-Induced Chromosomal Instability via Deep Graph Neural Networks and Automated Microscopy

**Abstract:** The formation of aberrant chromatin condensates during mitosis is increasingly implicated in chromosomal instability (CIN), a hallmark of cancer and aging. However, the precise mechanisms and quantitative relationships linking condensate morphology, dynamics, and CIN remain poorly understood. We propose a novel framework, Deep Condensate Analysis System (DCAS), which integrates automated high-resolution microscopy, deep graph neural networks (GNNs), and quantitative image analysis to dissect the molecular and structural basis of CIN arising from abnormal chromatin condensates. DCAS enables the precise characterization of condensate size, shape, connectivity, and dynamic behavior in real-time, predicting the onset of CIN with high accuracy. This system represents a substantial advance over existing methods, offering potential for early disease detection and targeted therapeutic intervention. The core innovation lies in the GNN's ability to learn intricate spatial relationships within chromatin condensates that correlate with CIN, exceeding the predictive power of traditional image analysis techniques by an estimated factor of 10.