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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jul 8, 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

Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity.

Qingnan Liang¹, Yuefan Huang¹, Shan He¹

¹Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA.

Nature Communications

|December 18, 2023

Summary

This summary is machine-generated.

GSDensity, a novel graph-modeling method, enhances single-cell and spatial transcriptomics analysis by focusing on pathways instead of clusters. This approach reveals new cell-pathway links and spatial patterns in development and cancer.

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Last Updated: Jul 8, 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

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Published on: July 6, 2022

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Area of Science:

Single-cell and spatial transcriptomics
Computational biology
Bioinformatics

Background:

Single-cell technologies offer high-resolution analysis of biological samples.
Current cluster-centric methods struggle with highly heterogeneous and dynamic single-cell data.
A need exists for methods that provide pathway-centric interpretation.

Purpose of the Study:

To introduce GSDensity, a graph-modeling approach for pathway-centric analysis of single-cell and spatial transcriptomics data.
To demonstrate GSDensity's ability to dissect complex biological systems without prior clustering.
To reveal novel cell-pathway associations and spatial pathway patterns.

Main Methods:

Development of GSDensity, a graph-modeling framework.
Application of GSDensity to single-cell and spatial transcriptomics datasets.
Integration of GSDensity with trajectory analysis for developmental studies.
Creation of a pan-cancer spatial transcriptomics map.

Main Results:

GSDensity accurately identifies biologically distinct cells and novel cell-pathway associations.
The method reveals pathways active during mouse brain development.
GSDensity identifies spatially relevant pathways in mouse brains and human tumors, including complex organizational patterns.
A pan-cancer map highlights recurrently active pathways across diverse tumor types.

Conclusions:

GSDensity provides a powerful alternative to clustering for interpreting single-cell and spatial transcriptomics data.
The approach facilitates deeper understanding of biological heterogeneity, development, and disease.
GSDensity enables the discovery of spatially organized biological processes and potential therapeutic targets.