Griottes

Griottes constructs network-based representations from segmented 2D and 3D tissue microscopy images to encode cellular properties and neighborhood interactions for quantitative analysis.

Key Features:

• Network representation: Transforms segmented microscopy images into network models with cells encoded as nodes and intercellular contacts encoded as edges.
• Cellular and neighborhood metrics: Computes an extensive array of biologically relevant metrics capturing individual cell attributes and cell-cell interaction features at multiple scales.
• 2D and 3D support: Supports construction of network "twins" from both 2D and 3D segmented tissue images.
• Scalability and efficiency: Implements a computationally efficient conversion process to enable analysis of large imaging datasets with reduced resource demands.
• Versatility across image and data types: Adaptable to multiple image and data types derived from advanced microscopy and segmentation outputs.

Scientific Applications:

• Cellular interaction analysis: Enables quantitative analysis of neighborhood interactions and cellular dynamics within tissues.
• Tissue organization and microenvironments: Facilitates study of tissue organization and spatial relationships between cell types.
• Developmental biology: Supports investigations into developmental processes by revealing spatial patterns of cell behavior.
• High-throughput imaging studies: Applied to large-scale imaging datasets to extract multiscale biological insights from segmented images.

Methodology:

Converts segmented images into network models by encoding cells as nodes with intrinsic properties and links as neighborhood interactions, implementing an optimized, computationally efficient conversion process.