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Cell Specific Gene Expression

Cell Specific Gene Expression

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Related Experiment Video

Updated: Aug 30, 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 type-specific inference of differential expression in spatial transcriptomics.

Dylan M Cable^1,2,3, Evan Murray², Vignesh Shanmugam^2,4

¹Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.

|September 1, 2022

Summary

This summary is machine-generated.

We developed C-SIDE, a new method for cell type-specific differential expression analysis in spatial transcriptomics. C-SIDE accurately identifies gene expression changes within specific cell types across complex tissue environments.

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Last Updated: Aug 30, 2025

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Published on: July 6, 2022

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics enables gene expression analysis within intact tissues.
Identifying cell type-specific differential expression (DE) is challenging due to mixed cell populations and varying tissue context.
Existing methods struggle to disentangle gene expression changes within specific cell types amidst complex spatial data.

Purpose of the Study:

To introduce C-SIDE (cell type-specific inference of differential expression), a novel statistical method for robust DE gene detection in spatial transcriptomics.
To provide a framework that accounts for the spatial localization of different cell types when inferring DE.
To enable cell type-specific DE analysis across various biological contexts and experimental replicates.

Main Methods:

Modeled gene expression as an additive mixture of log-linear cell type-specific expression functions.
Developed a statistical framework (C-SIDE) to infer cell type-specific DE, considering the presence of other cell types.
Validated C-SIDE using simulations and diverse spatial transcriptomics datasets (Slide-seq, MERFISH, Visium).

Main Results:

C-SIDE accurately identifies cell type-specific DE genes with reliable uncertainty quantification.
Demonstrated the method's applicability across different spatial transcriptomics technologies and biological scenarios.
Successfully applied C-SIDE to uncover plaque-dependent immune activity in Alzheimer's disease and tumor-immune cell interactions.

Conclusions:

C-SIDE offers a powerful and versatile solution for cell type-specific DE analysis in spatial transcriptomics.
The method enhances understanding of cellular heterogeneity and interactions within complex tissue architectures.
C-SIDE is available as an R package (spacexr) for broader scientific application.