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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

SpaGE: Spatial Gene Enhancement using scRNA-seq.

Tamim Abdelaal^1,2, Soufiane Mourragui^1,3, Ahmed Mahfouz^1,2,4

¹Delft Bioinformatics Lab, Delft University of Technology, Delft 2628XE, The Netherlands.

Nucleic Acids Research

|September 21, 2020

Summary

This summary is machine-generated.

SpaGE integrates spatial and single-cell RNA sequencing (scRNA-seq) data to map whole-transcriptome gene expression within tissues. This method accurately predicts gene patterns in their spatial context.

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell technologies reveal biological system heterogeneity.
Single-cell RNA sequencing (scRNA-seq) provides whole-transcriptome data but lacks spatial information.
Existing spatial transcriptomics methods offer spatial data but limited transcript coverage.

Purpose of the Study:

To develop a computational method, SpaGE, for integrating spatial and scRNA-seq data.
To predict whole-transcriptome gene expression with spatial localization.
To overcome limitations of current single-cell and spatial transcriptomics techniques.

Main Methods:

SpaGE integrates spatial transcriptomics and scRNA-seq datasets.
The method predicts gene expression patterns in their native spatial configuration.
Performance was evaluated using five diverse dataset-pairs.

Main Results:

SpaGE demonstrated superior performance compared to existing methods.
The method is scalable to large-scale biological datasets.
SpaGE identified novel spatial gene expression patterns validated by in situ hybridization.

Conclusions:

SpaGE effectively reconstructs spatial transcriptomes by integrating complementary single-cell and spatial data.
The tool enhances the understanding of tissue architecture and cellular function.
SpaGE offers a scalable solution for spatial gene expression analysis.