Streamlining Asymmetry Quantification in Fetal Mouse Imaging: A Semi-Automated Pipeline Supported by Expert Guidance

Asymmetry is a key feature of numerous developmental disorders and in phenotypic screens is often used as a readout for environmental or genetic perturbations to normal development. A better understanding of the genetic basis of asymmetry and its relationship to disease susceptibility will help unravel the complex genetic and environmental factors and their interactions that increase risk in a range of developmental disorders. Large-scale imaging datasets offer opportunities to work with sample sizes needed to detect and quantify differences in morphology beyond severe deformities while also posing challenges to manual phenotyping protocols. In this work, we introduce a semi-automated open-source workflow to quantify abnormal asymmetry of craniofacial structures that integrates expert anatomical knowledge. We apply this workflow to explore the role of genes contributing to abnormal asymmetry by deep phenotyping 3D fetal microCT images from knockout strains acquired as part of the Knockout Mouse Phenotyping Program (KOMP2). Four knockout strains: Ccdc186, Acvr2a, Nhlh1, and Fam20c were identified with highly significant asymmetry in craniofacial regions, making them good candidates for further analysis into their potential roles in asymmetry and developmental disorders.