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Updated: Jun 28, 2026

A Mouse Model for Pathogen-induced Chronic Inflammation at Local and Systemic Sites

A Mouse Model for Pathogen-induced Chronic Inflammation at Local and Systemic Sites

Published on: August 8, 2014

Redefining Mucosal Inflammation with Spatial Genomics.

A J Caetano¹, P T Sharpe²

¹Centre for Oral Immunobiology and Regenerative Medicine, Barts Centre for Squamous Cancer, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK.

Journal of Dental Research

|January 3, 2024

Summary

This summary is machine-generated.

Spatial transcriptomics reveals the complex cellular network of the human oral mucosa. These advanced techniques preserve tissue structure, offering new insights into cell states and disease neighborhoods for improved mucosal homeostasis understanding.

Keywords:

bioinformatics mucosal immunology multiomics, machine learning oral mucosa periodontal diseases

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Area of Science:

Oral biology
Molecular biology
Genomics

Background:

The human oral mucosa is a complex cellular system vital for physiology and pathogen defense.
Single-cell RNA sequencing (scRNA-seq) has advanced understanding but loses spatial context.
Dissociation of tissue samples in scRNA-seq disrupts cellular interrelationships.

Purpose of the Study:

To discuss the contribution of spatial technologies in understanding the oral mucosa.
To explore how spatial transcriptomics impacts the identification of disease cellular neighborhoods.
To examine how spatial information defines cell states within the oral mucosa.

Main Methods:

Review of spatial transcriptomic technologies and their application to oral mucosa research.
Integration of machine learning advances with spatial sequencing data.
Analysis of gene expression within the context of tissue structural organization.

Main Results:

Spatial transcriptomics retains crucial spatial information lost in tissue dissociation.
These methods enable assessment of gene expression across thousands of cells in situ.
Spatial context is key to defining cell states and identifying disease-associated cellular neighborhoods.

Conclusions:

Spatial technologies are revolutionizing the study of the oral mucosa's complex cellular network.
Understanding spatial relationships is critical for comprehending mucosal homeostasis and disease.
Future directions include leveraging machine learning to address open questions in mucosal biology.