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Related Experiment Video

Updated: Aug 17, 2025

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

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

Published on: July 6, 2022

Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial

Zhihua Qiu^1,2, Shaojun Li², Ming Luo²

¹Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.

Frontiers in Neuroscience

|December 16, 2022

Summary

A new method, spatial analysis of spatial transcriptomics (saSpatial), effectively identifies more valuable differentially expressed genes (DEGs) in spatial transcriptomics data by preserving spatial information, unlike traditional methods.

Keywords:

DEGs saSpatial spatial statistics spatial transcriptomics stroke

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics (STs) provides gene expression and location data within tissues.
Current DEG analysis methods in STs often discard crucial spatial information.

Purpose of the Study:

To introduce a novel method, saSpatial, for analyzing spatial transcriptomics data.
To detect differentially expressed genes (DEGs) while retaining spatial context.

Main Methods:

Developed spatial analysis of spatial transcriptomics (saSpatial) based on spatial statistics.
Applied saSpatial to detect DEGs in normal and ischemic stroke brain tissue sections.
Compared saSpatial's DEG detection with the FindMarkers method.

Main Results:

saSpatial successfully identified DEGs in distinct regions of normal brain tissue.
saSpatial revealed DEGs specific to the ischemic core and penumbra in stroke models.
saSpatial characterized genetic heterogeneity in both normal and ischemic brain cortices.
saSpatial identified a greater number of significant DEGs compared to FindMarkers.

Conclusions:

saSpatial is an effective tool for detecting DEGs in spatial transcriptomics data.
The method preserves spatial information, leading to the discovery of more biologically relevant genes.
saSpatial offers a valuable approach for future research in spatial gene expression analysis.