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Related Experiment Video

Updated: Jun 13, 2025

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.

Jiawen Hou^1,2, Wei Zhao^1,2, Qing Nie^1,2,3

¹Department of Mathematics, University of California Irvine, 92697 Irvine, CA, USA.

Biorxiv : the Preprint Server for Biology

|June 12, 2025

Summary

This summary is machine-generated.

This study introduces SigXTalk, a machine learning method to analyze pathway crosstalk in cell-cell communication (CCC) using single-cell RNA sequencing (scRNA-seq) data. SigXTalk quantifies signal fidelity and specificity to reveal complex regulatory networks.

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Last Updated: Jun 13, 2025

Transcriptome Analysis of Single Cells

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Published on: April 25, 2011

Single-cell Microinjection for Cell Communication Analysis

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Published on: February 26, 2017

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Cell-cell communication (CCC) involves complex signaling pathways.
Existing methods for CCC analysis using single-cell RNA sequencing (scRNA-seq) data overlook pathway crosstalk.
Pathway crosstalk significantly impacts downstream cellular responses.

Purpose of the Study:

To introduce SigXTalk, a novel machine learning-based method for analyzing pathway crosstalk in CCC.
To quantify signal fidelity and specificity to measure the effects of crosstalk.
To provide a systematic analysis of CCC-induced regulatory networks considering crosstalk.

Main Methods:

Developed SigXTalk, a machine learning method utilizing hypergraph learning.
Encoded higher-order relationships among receptors, transcription factors, and target genes.
Benchmarked SigXTalk using simulation data and analyzed disease and time-series scRNA-seq data.

Main Results:

SigXTalk effectively identifies key shared molecules and their roles in crosstalk pathways.
The method accurately quantifies signal fidelity and specificity.
Analysis revealed crucial signals, targets, and regulatory networks distinguishing disease states and tracked pathway evolution over time.

Conclusions:

SigXTalk offers a robust approach to analyze pathway crosstalk in CCC using scRNA-seq data.
The method enhances understanding of regulatory networks by considering crosstalk.
SigXTalk has applications in disease mechanism elucidation and dynamic pathway analysis.