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Updated: Jun 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

Precise detection of cell-type-specific domains in spatial transcriptomics.

Zhihan Ruan¹, Weijun Zhou², Hong Liu³

¹Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China.

Cell Reports Methods

|August 10, 2024

Summary

This summary is machine-generated.

De-spot identifies low-proportion cell-type domains in spatial transcriptomics data. This method reveals previously hidden tumor microenvironment domains and cell-type changes in breast cancer.

Keywords:

3D Landscape CP: systems biology cell co-localizations cell-type-specific domains ensemble learning single cell spatial transcriptomics tumor microenvironments

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Last Updated: Jun 17, 2025

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Published on: July 6, 2022

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Spatial transcriptomics
Computational biology
Cancer research

Background:

Cell-type-specific domains are crucial for understanding tissue architecture in spatially resolved transcriptome (SRT) data.
Detecting domains with low-proportion cell types, especially those overlapping or within other domains, remains a computational challenge.

Purpose of the Study:

To develop a novel computational method, De-spot, for detecting low-proportion cell-type-specific domains in SRT data.
To intuitively visualize these identified domains and uncover their biological significance.

Main Methods:

De-spot synthesizes segmentation and deconvolution techniques to create an ensemble approach.
It generates cell-type patterns and detects specific domains, including those with low cell proportions.
The method provides intuitive visualization of the identified domains.

Main Results:

De-spot successfully identified co-localizations between cancer-associated fibroblasts and immune cells, indicating potential tumor microenvironment (TME) domains.
These TME domains were previously obscured by existing computational methods.
Srgn was identified as a potential critical TME marker in SRT slices.
Analysis of T cell-specific domains in breast cancer revealed increased proportions of exhausted T cells in invasive carcinoma compared to ductal carcinoma.

Conclusions:

De-spot enhances the detection and visualization of cell-type-specific domains in SRT data, particularly for low-proportion cell types.
The method facilitates the discovery of novel TME domains and potential biomarkers.
De-spot provides insights into cellular composition changes associated with different cancer subtypes, such as breast cancer progression.