Cell2Cell: Explorative Cell Interaction Analysis in Multi-Volumetric Tissue Data.

Area of Science:

• Computational Biology
• Biomedical Imaging
• Bioinformatics

Background:

• Analyzing cell-cell interactions is crucial for understanding cancer biology and immune responses.
• Existing methods often rely on low-resolution 2D imaging and cell proximity, limiting detailed analysis.
• High-resolution 3D multi-channel tissue data offers richer information but presents significant analytical challenges.

Purpose of the Study:

• To introduce Cell2Cell, a novel visual analytics approach for quantifying and visualizing cell-cell interactions in 3D cancerous tissue data.
• To enable biomedical experts to gain a more accurate understanding of cancer and immune cell relationships.
• To provide semi-automated methods for interactive analysis of large and complex 3D imaging datasets.

Main Methods:

• Quantifying cell interactions by analyzing protein expressions in high-resolution 3D multi-channel volume data.
• Utilizing two complementary semi-automated approaches: a cell graph-based method analyzing protein expressions along interaction edges and a cell-centered approach for polarized protein distribution analysis.
• Integrating spatial and abstract visualizations for interactive exploration.

Main Results:

• Cell2Cell successfully quantifies and visualizes cell-cell interaction networks in 3D cancerous tissue.
• The approach enables detailed analysis of protein expressions and polarized distributions around cells.
• Case studies demonstrated the tool's effectiveness in identifying and quantifying T-cell activation in human tissue data.

Conclusions:

• Cell2Cell provides a powerful and streamlined method for analyzing complex cell-cell interactions in 3D tumor microenvironments.
• The visual analytics approach integrates domain expertise for robust biological insights.
• The tool effectively addresses the need for detailed investigation of cellular relationships in cancer research.