cytoNet

cytoNet analyzes and quantifies cell community structure using graph theory to characterize spatial topology and functional relationships in microscopy images.

Key Features:

• Graph-theory-based modeling: Applies graph theory and network-science principles to represent multicellular systems as networks.
• Image-to-graph transformation: Converts cellular microscopy images into graph representations.
• Network feature extraction and quantification: Extracts network features that describe spatial arrangements and functional interactions and quantifies cell–cell interactions and their influence on cell phenotypes.

Scientific Applications:

• Neural differentiation studies: Characterizes the temporal dynamics of neural progenitor cell communities during neural differentiation.
• In vivo neuronal analysis: Identifies communities of pain-sensing neurons in vivo.
• Endothelial cell morphology: Captures the effects of cell community dynamics on endothelial cell morphology.
• Adipose tissue niches: Investigates the impact of laminin α4 on perivascular niches within adipose tissue.

Methodology:

Cellular microscopy images are transformed into graph representations and network features are extracted using graph-theory and network-science methods to quantify spatial topology and functional interactions among cells.