CellGeo: a computational platform for the analysis of shape changes in cells with complex geometries

Area of Science:

• Cell Biology
• Bioinformatics
• Computational Biology

Background:

• Quantitative cell biology relies on image analysis, but current tools struggle with dynamic cell shape features like protrusions.
• Cell protrusions (filopodia, lamellipodia) are crucial for cell migration and morphogenesis.

Purpose of the Study:

• To develop an open-source computational platform, CellGeo, for automated tracking and analysis of dynamic cell shape changes, including protrusions.
• To provide a user-friendly tool for precise, quantitative analysis of cell behavior.

Main Methods:

• Developed CellGeo, a MATLAB application using a tree graph mapping to represent cell shapes, preserving boundary details.
• Implemented automated detection and tracking of geometric cell features, including filopodia and lamellipodia.

Main Results:

• CellGeo successfully maps arbitrary cell shapes onto a tree graph, outperforming traditional skeletonization by preserving complex features.
• Demonstrated CellGeo's utility in analyzing filopodia and lamellipodia dynamics in Drosophila melanogaster cells.
• Applied CellGeo to study the dynamic properties of growth cones in neuroblastoma cells.

Conclusions:

• CellGeo offers a robust and flexible solution for analyzing dynamic cell shape and protrusion behavior.
• The platform enables new quantitative insights into the roles of specific proteins in cell protrusion dynamics and cell growth.
• CellGeo advances computational approaches for cell biology research.