A self-exciting point process to study multicellular spatial signaling patterns.

Area of Science:

• Computational Biology
• Systems Biology
• Cellular Signaling

Background:

• Multicellular organisms require spatial cell signaling for organization and development.
• Existing models struggle to simultaneously capture single-cell autonomy and neighbor interactions.

Purpose of the Study:

• To propose a novel spatiotemporal model for dynamic cell signaling.
• To simultaneously model autonomous single-cell behavior and intercellular spatial signaling.

Main Methods:

• Developed a cellular point process (CPP) model based on Hawkes processes.
• Modeled signaling within individual cells and spatial couplings between neighboring cells.
• Incorporated heterogeneous cell types with varying signaling behaviors.

Main Results:

• The CPP model captures single-cell pathway activation rates and intercellular signaling dynamics.
• Successfully modeled tissues with heterogeneous cell types and multiple signaling proteins.
• Applied the model to epithelial cells, identifying drug-induced deficits and wound-healing signaling changes.

Conclusions:

• The proposed CPP model offers a unified framework for studying spatial cell signaling.
• This approach advances the understanding of complex multicellular behaviors and responses.
• The model generalizes to multichannel observations and diverse biological contexts.