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Updated: May 28, 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

STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues.

Peisen Sun¹, Stephen J Bush¹, Songbo Wang¹

¹School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China; MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.

|February 13, 2025

Summary

This summary is machine-generated.

STMiner analyzes spatial transcriptomics data by focusing on gene expression patterns, not cell locations. This approach overcomes biases in tumor samples, revealing hidden biological insights and spatial structures.

Keywords:

Gaussian mixture model bioinformatics gene-centric machine learning optimal transport theory scRNA-seq spatial transcriptomics spatial variable genes tumor

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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

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Published on: July 22, 2020

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics analysis of tumors faces challenges like unclear boundaries, uneven cell density, and high heterogeneity.
These factors bias background identification, leading to misidentified spatial structures and hindering pathological insights.

Purpose of the Study:

To develop a novel computational method, STMiner, for accurate analysis of spatial transcriptomics data in complex tissues.
To overcome limitations of existing spot-based methods in identifying spatially variable genes and structures.

Main Methods:

STMiner utilizes 2D Gaussian mixture models and optimal transport theory to directly characterize spatial gene distribution.
The method analyzes overall gene expression patterns, mitigating background bias and data sparsity.

Main Results:

STMiner effectively identifies key gene sets and spatial structures missed by traditional spot-based tools.
The approach enhances the discovery of novel biological insights from spatial transcriptomics data.

Conclusions:

STMiner provides a robust framework for analyzing spatial transcriptomics, improving the identification of biological patterns in challenging tumor microenvironments.
Its core methodology offers potential for broader applications in evolving spatial omics technologies.