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Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Updated: Sep 10, 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

Deciphering context-specific gene programs from single-cell and spatial transcriptomics data with DeCEP.

Lin Li¹, Xianbin Su¹, Ze-Guang Han²

¹Key Laboratory of Systems Biomedicine (Ministry of Education) and State Key Laboratory of Medical Genomics, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.

Genome Research

|August 21, 2025

Summary

This summary is machine-generated.

DeCEP is a new computational framework that identifies context-specific gene programs using single-cell RNA sequencing and spatial transcriptomics data. This tool enhances understanding of gene regulation in health and disease by analyzing transcriptional heterogeneity.

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Area of Science:

Genomics
Computational Biology
Systems Biology

Background:

Functional gene programs are crucial for cell identity and are implicated in various health and disease states.
Deciphering complex gene programs at single-cell and spatial resolutions is essential but challenging.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) offer powerful tools for comprehensive gene program characterization.

Purpose of the Study:

To introduce DeCEP, a computational framework for characterizing context-specific gene programs.
To leverage scRNA-seq and ST data for detailed analysis of gene networks and program activity.
To identify context-dependent hub genes and assign gene program activity to individual cells or spatial locations.

Main Methods:

DeCEP utilizes functional gene lists and directed graphs to build functional networks for specific cellular or spatial contexts.
Network topology is employed to identify context-dependent hub genes associated with gene programs.
Gene program activity is assigned to individual cells or spatial locations within the analyzed data.

Main Results:

DeCEP enables a more fine-grained characterization of gene programs within specific biological contexts.
The framework excels in analyzing contexts with significant transcriptional heterogeneity.
Evaluations on simulated and real biological datasets confirm DeCEP's complementary strengths over existing methods.

Conclusions:

DeCEP provides a robust computational approach for dissecting context-specific gene programs.
The framework facilitates deeper biological insights into complex diseases like Alzheimer's and cancer.
DeCEP advances the understanding of gene regulation in normal tissues and disease states.