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Related Experiment Video

Updated: Sep 12, 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

LSGI: interpretable spatial gradient analysis for spatial transcriptomics data.

Qingnan Liang¹, Luisa Solis Soto², Cara Haymaker²

¹Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, 7007 Bertner Avenue, Houston, TX, 77030, USA.

|August 8, 2025

Summary

This summary is machine-generated.

We developed Local Spatial Gradient Inference (LSGI) to find spatial gene expression patterns in tumors. This method reveals how spatial transcriptomic gradients contribute to tumor heterogeneity and offers new insights into cancer biology.

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Last Updated: Sep 12, 2025

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Published on: July 6, 2022

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Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

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Published on: March 14, 2022

Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Cellular environments (niches) influence gene expression, creating spatial transcriptomic gradients (STGs).
STGs are a major driver of intra-tumor heterogeneity, impacting cancer progression and treatment response.
Understanding these spatial variations is crucial for deciphering complex tumor biology.

Purpose of the Study:

To introduce Local Spatial Gradient Inference (LSGI), a novel computational framework.
To systematically identify and interpret prominent STGs within spatial transcriptomic (ST) data.
To reveal novel insights into tumor biology and intra-tumor heterogeneity.

Main Methods:

Development of the LSGI computational framework.
Application of LSGI to analyze spatial transcriptomic datasets from tumors.
Identification of pathways exhibiting gradated expression patterns across tumor niches.

Main Results:

LSGI successfully identified significant STGs in tumor ST datasets.
Discovered pan-cancer and tumor-type specific pathways with gradated expression patterns.
Highlighted pathways directly related to spatial transcriptional intratumoral heterogeneity.

Conclusions:

LSGI provides a robust method for interpretable STG analysis.
The framework enhances the understanding of tumor heterogeneity from ST data.
LSGI facilitates the discovery of novel biological insights in cancer research.