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Related Experiment Video

Updated: Jun 18, 2026

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

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

Cameron Park^1,2,3, Shouvik Mani^2,4,3, Nicolas Beltran-Velez⁴

¹Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.

Biorxiv : the Preprint Server for Biology

|November 28, 2023

Summary

This summary is machine-generated.

DIISCO is a new Bayesian framework that tracks cell-cell communication dynamics over time using single-cell RNA sequencing. It reveals how cellular interactions evolve, offering insights into biological processes and disease progression.

Keywords:

Cell-cell communication Probabilistic modeling Single-cell omics Time-series data Variational inference

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Last Updated: Jun 18, 2026

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Published on: December 7, 2021

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Understanding dynamic cell-cell communication is crucial for biological processes, disease, and therapy response.
Current methods struggle to capture time-dependent intercellular interactions and rely on limited databases.

Conclusions:

DIISCO provides a powerful approach for dissecting the temporal dynamics of cell-cell communication.
The framework enhances understanding of biological systems, disease mechanisms, and therapeutic interventions.
DIISCO facilitates the discovery of novel intercellular signaling pathways from multi-time-point single-cell data.