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Related Experiment Video

Updated: Aug 29, 2025

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

CINS: Cell Interaction Network inference from Single cell expression data.

Ye Yuan^1,2, Carlos Cosme³, Taylor Sterling Adams³

¹Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.

Plos Computational Biology

|September 12, 2022

Summary

This summary is machine-generated.

This study introduces Cell Interaction Network Inference (CINS), a new computational tool. CINS identifies changing cell-cell interactions and underlying proteins in single-cell RNA sequencing data.

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Area of Science:

Computational biology
Genomics
Systems biology

Background:

Single-cell RNA sequencing (scRNA-Seq) studies often analyze cell type proportions or gene expression.
Inferring changes in cell-cell interactions between conditions is challenging without spatial data.
Cellular communication is crucial for biological processes and disease pathogenesis.

Purpose of the Study:

To develop a computational pipeline, Cell Interaction Network Inference (CINS), for identifying differential cell-type interactions from scRNA-Seq data.
To pinpoint specific proteins mediating these altered interactions.
To provide a method for analyzing condition-specific cell communication patterns.

Main Methods:

Developed the Cell Interaction Network Inference (CINS) pipeline.
Integrated Bayesian network analysis with regression-based modeling.
Applied CINS to disease case-control and mouse aging scRNA-Seq datasets.

Main Results:

CINS successfully identified differential cell-type interactions in both tested datasets.
The pipeline accurately pinpointed the specific ligands involved in these interactions.
Results demonstrated improved performance over existing methods for predicting cell interactions.

Conclusions:

CINS is an effective tool for inferring condition-specific cell-cell interactions from scRNA-Seq data.
The method advances the understanding of how cellular communication changes in disease and aging.
Further experiments validated the interactions predicted by CINS.