Cell-Cell Interaction Analyzer, or Cell2Cell, is an innovative software solution designed to revolutionize the quantification and visualization of cell-cell interactions within three-dimensional (3D) multi-channel cancerous tissue data. By meticulously analyzing cellular interactions, Cell2Cell empowers biomedical domain experts to achieve a more precise understanding of the complex relationships between cancer and immune cells.
Traditional methods have primarily relied on inferring interactions based on cell proximity in low-resolution 2D multi-channel imaging data. In contrast, Cell2Cell takes a groundbreaking approach by quantifying cell interactions through the analysis of protein expression intensities derived from high-resolution 3D multi-channel volume data. This shift in perspective enables more accurate and insightful insights into cell interactions, ultimately aiding in the study of cancer biology.
Cell2Cell interprets cell-to-cell interactions as edges in a cell graph. It analyzes the image signal, specifically protein expressions, along these edges. This approach provides both spatial and abstract data visualizations, allowing researchers to gain a comprehensive understanding of the interactions within the cellular network.
Complementing the cell graph analysis, Cell2Cell offers a cell-centered approach. This feature enables scientists to explore polarized distributions of proteins in three dimensions, capturing neighboring cells and their biochemical and cell biological consequences. This holistic perspective is invaluable for studying the intricate dynamics of cancerous tissue.
Cell2Cell has been rigorously evaluated in two case studies, highlighting its capabilities in investigating tumor micro-environments and quantifying T-cell activation in human tissue data. Computational biologists and medical experts have successfully leveraged Cell2Cell to:
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Tumor Micro-Environment Analysis: Gain insights into the complex cellular interactions within tumor micro-environments. Cell2Cell allows for the identification and quantification of these interactions, contributing to a deeper understanding of tumor progression.
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T-cell Activation Quantification: By utilizing Cell2Cell, researchers can explore the activation of T-cells within human tissue data. This analysis is vital for unraveling the immune response against cancerous cells and designing targeted therapies.
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Enhanced Understanding: Cell2Cell facilitates a more accurate and detailed comprehension of cell-cell interactions in 3D multi-channel data, advancing cancer biology research.
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Streamlined Analysis: This tool offers a streamlined and efficient analysis process, saving researchers valuable time and resources.
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Visualizations: Cell2Cell provides intuitive visualizations that aid in conveying complex data insights to both experts and non-experts.
- Install via:
pip install -r requirements.txt
npm install
npx webpack
- After that you can run:
python app.py
- open in any webbrowser
http://localhost:8080