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Related Experiment Video

Updated: Jun 29, 2026

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics.

Axel A Almet^1,2, Yuan-Chen Tsai^3,4,5, Momoko Watanabe^3,4,5

¹Department of Mathematics, University of California, Irvine, Irvine, CA, USA.

|August 26, 2024

Summary

This summary is machine-generated.

FlowSig infers intercellular communication flows from gene expression data. This method reveals how cell signaling drives gene module activity and uncovers complex biological signaling patterns.

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Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) provide high-dimensional gene expression data.
Gene expression patterns are often described by intercellular communication networks or gene modules, typically assumed to operate independently.
Existing methods lack the ability to describe directed intercellular information flows mediated by intracellular gene modules.

Purpose of the Study:

To present FlowSig, a novel method for inferring communication-driven intercellular flows from scRNA-seq or ST data.
To address the gap in methodologies for describing intercellular signaling dynamics.
To analyze how intercellular communication influences intracellular gene module activity.

Main Methods:

FlowSig utilizes graphical causal modeling and conditional independence to infer intercellular flows.
The method is benchmarked using experimental cortical organoid data and synthetic data from mathematical modeling.
Application to diverse biological systems to demonstrate its capabilities.

Main Results:

FlowSig successfully infers communication-driven intercellular flows from transcriptomic data.
Benchmarking confirms the method's accuracy on both experimental and synthetic datasets.
The method captures stimulation-induced paracrine signaling changes in pancreatic islets.
FlowSig identifies shifts in intercellular flows associated with COVID-19 severity.
Reconstruction of morphogen-driven activator-inhibitor patterns in mouse embryogenesis is achieved.

Conclusions:

FlowSig provides a robust framework for dissecting intercellular communication dynamics from gene expression data.
The method reveals the interplay between intercellular signaling and intracellular gene modules.
FlowSig has broad applicability in understanding various biological processes, from development to disease.