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Updated: Jun 5, 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

Cell-specific priors rescue differential gene expression in spatial spot-based technologies.

Ornit Nahman¹, Timothy J Few-Cooper¹, Shai S Shen-Orr¹

¹Department of Immunology, Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, 1 Efron St., Haifa, 3525433, Israel.

Briefings in Bioinformatics

|December 16, 2024

Summary

This summary is machine-generated.

Spatial transcriptomics (ST) analysis using standard gene expression algorithms struggles to identify true differentially expressed genes (DEGs). Cellular heterogeneity within spots causes poor performance, but a cell-type specific gene selection method improves accuracy.

Keywords:

deconvolution differentially expressed genes gene specificity spatial transcriptomics

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

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Radioactive in situ Hybridization for Detecting Diverse Gene Expression Patterns in Tissue

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Published on: April 27, 2012

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Spatial transcriptomics (ST) enables gene expression profiling within tissue context.
Spot-based ST platforms like Visium are widely adopted.
Current ST data analysis often relies on algorithms developed for single-cell (SC) and bulk RNA-seq.

Purpose of the Study:

To evaluate the performance of traditional differentially expressed gene (DEG) algorithms on ST data.
To identify the causes of poor DEG detection in ST analysis.
To develop an improved method for DEG identification in ST data.

Main Methods:

Construction of an in silico spatial transcriptomics data simulator with known DEG ground truth.
Performance evaluation of classic DEG algorithms on simulated ST data.
Development and testing of a gene-selection scheme based on cell-type specificity.

Main Results:

Classic DEG algorithms show limited accuracy in identifying known DEGs in ST data.
Cellular heterogeneity within ST spots is a major factor limiting DEG detection performance.
The proposed cell-type specific gene-selection scheme significantly improved DEG recovery and reliability.

Conclusions:

Existing DEG algorithms are not optimal for spatial transcriptomics data analysis.
Cellular heterogeneity necessitates specialized approaches for accurate DEG identification in ST.
A novel gene-selection strategy enhances the reliability and accuracy of spatial transcriptomics studies.