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

Area of Science:

• Developmental Biology
• Biophysics
• Computational Biology

Background:

• The mammary gland exhibits complex branching morphogenesis crucial for its function.
• Existing methods for analyzing mammary gland development lack open-source quantitative frameworks for whole-mount images.
• Understanding ductal branching is key to studying mammary gland development and disease.

Purpose of the Study:

• To introduce MaGNet, an open-source computational framework for analyzing mammary gland ductal branching.
• To provide a reproducible and quantitative method for assessing mammary gland development using network theory.
• To characterize ductal expansion during pubertal development and in response to hormonal cues.

Main Methods:

• Leveraging network theory to analyze whole-mount mammary tissue images.
• Developing the MaGNet (Mammary Gland Network analysis tool) pipeline.
• Applying the MaGNet pipeline to images from three distinct pubertal timepoints.

Main Results:

• MaGNet enabled reproducible quantification of ductal tree expansion during mammary gland development.
• The network analysis pipeline successfully captured ductal expansion induced by pregnancy hormones.
• Demonstrated the tool's ability to characterize key features of ductal branching.

Conclusions:

• MaGNet offers a robust, open-source solution for quantitative analysis of mammary gland development.
• This network-based approach enhances reproducibility and broad applicability across biological systems.
• Facilitates further research into mammary gland biology, disease, and therapeutic interventions.