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A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data.

Cameron Park1,2, Shouvik Mani2,3, Nicolas Beltran-Velez3

  • 1Department of Biomedical Engineering, Columbia University, New York, New York 10027, USA; cyp2111@columbia.edu ea2690@columbia.edu.

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|September 5, 2024
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Summary
This summary is machine-generated.

Understanding cell-cell communication over time is key for biology and disease. DIISCO, a new Bayesian framework, analyzes single-cell RNA sequencing data to reveal dynamic intercellular interactions and their temporal changes.

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Area of Science:

  • Computational Biology
  • Immunology
  • Genomics

Background:

  • Cell-cell communication is vital for biological processes, development, and disease.
  • Current methods lack the ability to track intercellular interactions dynamically over time.
  • Existing tools often rely on limited contextual databases.

Purpose of the Study:

  • To introduce DIISCO, a novel Bayesian framework for characterizing temporal dynamics of cell-cell interactions.
  • To enable the analysis of time-resolved intercellular communication using multi-timepoint single-cell RNA-sequencing data.
  • To overcome limitations of existing tools in capturing time-dependent interactions.

Main Methods:

  • Utilizes a Bayesian framework with structured Gaussian process regression.
  • Analyzes single-cell RNA-sequencing data from multiple time points.
  • Incorporates prior knowledge of receptor-ligand complexes to model interactions.

Main Results:

  • DIISCO effectively characterizes temporal dynamics of cellular interactions.
  • Demonstrates interpretability in both simulated and experimental T cell-lymphoma coculture data.
  • Successfully uncovers time-resolved cell-cell cross talk.

Conclusions:

  • DIISCO provides a powerful new approach for studying dynamic cell-cell communication.
  • The framework enhances understanding of biological coordination during development and disease.
  • Offers potential for uncovering novel therapeutic targets through dynamic interaction analysis.