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Related Experiment Video

Updated: Oct 6, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Identify, quantify and characterize cellular communication from single-cell RNA sequencing data with scSeqComm.

Giacomo Baruzzo¹, Giulia Cesaro¹, Barbara Di Camillo^1,2,3

¹Department of Information Engineering, University of Padova, Padova, Italy.

Bioinformatics (Oxford, England)

|January 19, 2022

Summary

This summary is machine-generated.

scSeqComm is a new computational method that analyzes single-cell RNA sequencing (scRNA-seq) data to identify and quantify intercellular and intracellular signaling. This tool enhances the reliability of cellular communication inference in biological studies.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) is increasingly used to investigate cellular communication.
Current bioinformatics methods primarily focus on intercellular signaling, often overlooking intracellular pathways.
There is a need for methods that comprehensively analyze both intercellular and intracellular signaling for more reliable communication inference.

Purpose of the Study:

To introduce scSeqComm, a novel computational method for identifying and quantifying intercellular and intracellular signaling from scRNA-seq data.
To provide functional characterization of inferred cellular communication.
To enhance the reliability and prioritization of cellular communication findings.

Main Methods:

Development of the scSeqComm computational method.
Application of scSeqComm to scRNA-seq data from the tumor microenvironment.
Validation using spatial transcriptomics data and comparison with existing intercellular scoring methods.

Main Results:

scSeqComm successfully identifies and quantifies both intercellular and intracellular signaling pathways.
The method provides functional insights into cellular communication.
Results demonstrate robustness and reliability through validation and comparison with state-of-the-art methods.

Conclusions:

scSeqComm offers a reliable approach to inferring cellular communication by integrating intercellular and intracellular signaling.
Quantification of signaling evidence aids in prioritizing findings.
The method is validated and robust for analyzing scRNA-seq data, particularly in complex biological systems like the tumor microenvironment.