MaGNet: A Network-Based Method for Quantitative Analysis of the Mammary Ductal Tree in Developing Female Mice.

Abstract

The mammary gland is a uniquely dynamic organ with a branching architecture that develops entirely after birth in response to hormonal cues. A common approach in mammary gland biology is the evaluation of branching morphogenesis to characterize the role of developmental, physiological and molecular perturbations on branching tissue invasion, growth, and maintenance. Yet, the field still lacks a fully open-sourced, quantitative framework to analyze whole-mount mammary tissue images, as a commonly utilized methodology. Here, we present MaGNet (Mammary Gland Network analysis tool), a method that leverages network theory to characterize key features of ductal branching during mammary gland development. Applying this pipeline to mammary gland images captured at three pubertal timepoints, we achieved reproducible quantification of ductal tree expansion across development. In addition, this network analysis pipeline captures ductal expansion induced by pregnancy hormones. By providing open-source tools to the research community, this method may increase reproducibility and broad applicability across diverse organ systems, model organisms, and developmental stages.