Decoding G0 somatic mutants through deep phenotyping and mosaic pattern analysis in the zebrafish skeleton

Abstract

Genetic mosaicism manifests as spatially variable phenotypes, whose detection and interpretation remains challenging. This study identifies biological factors influencing spatial phenotypic patterns in the skeletons of somatic mutant zebrafish, and tests methods for their analysis using deep phenotyping. We explore characteristics of loss-of-function clusters in the skeleton of CRISPR-edited G0 (\"crispant\") zebrafish, and identify a distinctive size distribution shown to arise from clonal fragmentation and merger events. Using microCT-based phenomics, we describe diverse phenotypic manifestations in somatic mutants for genes implicated in monogenic (plod2 and bmp1a) and polygenic (wnt16) bone diseases, each showing convergence with germline mutant phenomes. Finally, we describe statistical frameworks for phenomic analysis which confers heightened sensitivity in discriminating somatic mutant populations, and quantifies spatial phenotypic variation. Our studies provide strategies for decoding spatially variable phenotypes which, paired with CRISPR-based screens, can identify genes contributing to skeletal disease.