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Updated: May 24, 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

STModule: identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes.

Ran Wang^1,2,3, Yan Qian⁴, Xiaojing Guo⁵

¹CUHK-SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China.

Genome Medicine

|March 3, 2025

Summary

STModule is a new Bayesian method for analyzing spatial transcriptomics data. It identifies tissue modules, revealing spatial patterns and characteristics crucial for understanding cancer, immune responses, and disease mechanisms.

Keywords:

Bayesian model Spatial expression components Spatially resolved transcriptomics Tissue module

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Published on: March 14, 2022

Area of Science:

Spatial transcriptomics
Computational biology
Bioinformatics

Background:

Spatially resolved transcriptomics provides insights into tissue architecture and cellular interactions.
Identifying distinct tissue modules is crucial for understanding complex biological systems.
Existing methods may not capture the full spectrum of biological signals in transcriptomic landscapes.

Purpose of the Study:

To develop and present STModule, a novel Bayesian method for identifying tissue modules from spatial transcriptomics data.
To reveal spatial components and essential characteristics of various tissues, including those in cancer.
To facilitate downstream analysis and provide deeper insights into tumor microenvironments and disease mechanisms.

Main Methods:

STModule employs a Bayesian approach to analyze spatially resolved transcriptomic data.
The method identifies tissue modules by uncovering diverse expression signals.
Gene sets characterize the identified tissue modules.

Main Results:

STModule successfully uncovers diverse expression signals in transcriptomic landscapes like cancer and immune infiltrates.
The method detects novel spatial components and captures a broader spectrum of biological signals than other approaches.
Characterized tissue modules demonstrate enhanced robustness and transferability across different biopsies.

Conclusions:

STModule is an effective tool for identifying tissue modules from spatial transcriptomics data.
The identified modules offer valuable insights into tumor microenvironments, disease mechanisms, and histological organization.
STModule enhances downstream analysis and provides a more comprehensive understanding of tissue biology.