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

Jiawen Hou^1,2, Wei Zhao^3,4, Qing Nie^5,6,7

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

Nature Communications

|July 2, 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 crucial molecular players in complex regulatory networks.

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Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Cell-cell communication (CCC) involves complex signaling pathways.
Existing methods for analyzing CCC networks using single-cell RNA sequencing (scRNA-seq) data often overlook pathway crosstalk.
Understanding crosstalk is crucial for deciphering cellular responses and disease mechanisms.

Purpose of the Study:

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

Main Methods:

Developed SigXTalk, a machine learning approach utilizing hypergraph learning.
Encoded higher-order relationships among receptors, transcription factors, and target genes.
Quantified signal fidelity and specificity to assess crosstalk effects.

Main Results:

SigXTalk effectively identifies key shared molecules within crosstalk pathways.
The method accurately determines the role of shared molecules in transferring CCC information.
Benchmarking demonstrates SigXTalk's effectiveness, robustness, and accuracy on simulated and real-world data.
SigXTalk successfully identifies disease-specific signals, targets, networks, and CCC patterns.
The method can track the temporal evolution of crosstalk pathways.

Conclusions:

SigXTalk offers a powerful tool for dissecting pathway crosstalk in CCC.
The method enhances the understanding of regulatory networks by incorporating crosstalk.
SigXTalk has significant applications in disease analysis and understanding dynamic biological processes.