A Computational Framework for Extracting Mechanistic Hypotheses from Quantitative Data of Morphological Dynamics

Advances in live-cell imaging and image analysis have made it possible to quantitatively measure the spatiotemporal morphological dynamics of biological phenomena at scale. However, a general framework is still lacking for systematically extracting, from the resulting multivariate data, which relationships between phenotypic characters are mechanistically interpretable and which gene perturbations disrupt those relationships. Here, we propose a computational framework for extracting mechanistic hypotheses and candidate genes from quantitative data of morphological dynamics. First, we detect reproducible correlations between phenotypic characters in wild-type data and interpret them as mechanistic hypotheses in light of existing knowledge. Next, we perform outlier analysis on data obtained under gene perturbation and extract, as candidates, genes that selectively disrupt relationships between phenotypic characters maintained in the wild type. We further integrate the extracted relationships into a spatiotemporal network to provide an overview of how phenotypic characters are linked across the developmental process. As a proof of concept, we applied the framework to quantitative data on nuclear division dynamics during early embryogenesis in Caenorhabditis elegans and recovered relationships between phenotypic characters consistent with known mechanisms while prioritizing candidate genes. This framework provides a useful basis for efficiently generating testable mechanistic hypotheses from quantitative data of morphological dynamics. Author SummaryHow does a single cell give rise to a complex organism? Answering this question requires understanding not only which genes are active, but how the physical behavior of cells--their shapes, positions, and movements--is coordinated across time and space. Live-cell imaging now allows researchers to measure these morphological dynamics in quantitative detail, yet extracting biological meaning from the resulting large, high-dimensional datasets remains a challenge. Here we present a computational framework that addresses this challenge by treating correlations between quantitative morphological measurements as windows into the underlying biological machinery. Applied to the nematode Caenorhabditis elegans, a powerful model organism whose early development is exquisitely reproducible, our approach automatically identifies pairs of cellular measurements that reliably co-vary in normal embryos and interprets these relationships as reflecting shared biological mechanisms. When genes are inactivated one at a time and the resulting embryos deviate from the expected co-variation, those genes are flagged as candidates for the disrupted mechanism. In a systematic test using embryos in which 263 genes had been individually inactivated, the framework correctly prioritized genes with known roles in spindle positioning and cell polarity. By converting large-scale morphodynamic datasets into a network of testable mechanistic hypotheses, this framework offers a broadly applicable strategy for moving from quantitative phenotyping to mechanistic understanding across diverse biological systems.