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Related Experiment Video

Updated: Sep 2, 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

Sprod for de-noising spatially resolved transcriptomics data based on position and image information.

Yunguan Wang¹, Bing Song¹, Shidan Wang¹

¹Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.

|August 4, 2022

Summary

This summary is machine-generated.

Spatially resolved transcriptomics (SRT) data can be noisy. Our new method, Sprod, uses location and imaging data to accurately impute gene expression, improving downstream analyses for biomedical discoveries.

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Last Updated: Sep 2, 2025

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) offers high-resolution gene expression data with spatial context.
SRT data is prone to high noise levels due to shallow sequencing coverage and experimental procedures.
Noise in SRT hinders accurate downstream analyses, limiting its application in biomedical research.

Purpose of the Study:

To develop a novel computational method for denoising SRT data.
To leverage spatial location and pathology imaging data for accurate gene expression imputation.
To enhance the reliability of SRT data for biological discovery.

Main Methods:

Developed Sprod, a latent graph learning framework.
Integrated matched spatial location and pathology imaging data.
Validated Sprod against existing methods for gene expression imputation and dropout removal.

Main Results:

Sprod effectively imputes accurate gene expression in SRT data.
Demonstrated superior performance over previous methods in removing dropouts.
Imputed data led to more accurate differential expression analyses, pathway enrichment, and cell-to-cell interaction inferences.

Conclusions:

Sprod significantly improves the quality of SRT data by reducing noise.
This denoising approach is crucial for unlocking the full potential of SRT technologies.
Sprod is poised to become an essential first step for biomedical discoveries using SRT.