Artificial intelligence-driven high-content imaging decodes for NLRP7-mutant recurrent hydatidiform moles

Recurrent hydatidiform moles (RHMs) are a gestational disorder primarily caused by maternal-effect loss-of-function mutations in NLRP7. This study established an in vitro model of NLRP7 mutation using patient-derived induced pluripotent stem cells (iPSCs) and integrated high-content imaging (HCI) with artificial intelligence (AI) to dissect mutation-induced multi-organellar dysfunction. Multiparametric HCI enabled synchronous capture of cellular and subcellular phenotypes, including mitochondrial function and lysosomal distribution. We developed a bio-inspired AI framework (BioVision-Segmentation) that combines a hybrid Transformer-Voronoi architecture for segmentation with mutual information analysis to quantify organelle interactions. Our findings reveal that NLRP7 mutations disrupt the lysosome-mitochondria crosstalk hub, leading to energy metabolism dysregulation, Reactive Oxygen Species (ROS) accumulation, and organelle spatial distribution defects. Furthermore, transcriptome Sequencing corroborated these findings. This study elucidates the organellar pathogenesis of RHMs and provides a technological platform for exploring therapeutic targets, facilitating a shift toward early embryo protection strategies.

Biological sciences; Machine learning; Methodology in biological sciences.