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Related Experiment Video

Updated: Apr 14, 2026

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

SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics.

Jinming Cheng^1,2, Pierce Kah Hoe Chow^3,4, Nan Liu^1,2,5,6,7

¹Centre for Biomedical Data Science, Duke-NUS Medical School, Singapore169857, Singapore.

NAR Genomics and Bioinformatics

|April 13, 2026

Summary

This summary is machine-generated.

SpNeigh is a new R package for spatial transcriptomics analysis. It models local tissue context to reveal gene expression patterns and differences in complex tissues like the brain and tumors.

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Last Updated: Apr 14, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Published on: July 6, 2022

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics technologies like Xenium, MERFISH, and Visium HD offer high-resolution gene expression profiling within tissue architecture.
Existing computational methods often overlook local tissue context, such as boundaries, neighborhoods, and gradients, limiting in-depth spatial analysis.

Purpose of the Study:

To introduce SpNeigh, an R package designed for spatial neighborhood analysis and spatially aware differential expression modeling.
To provide tools for boundary detection, neighborhood extraction, and gradient-based statistical testing in spatial transcriptomics data.

Main Methods:

Development of the SpNeigh R package implementing boundary detection, spatial neighborhood extraction, and distance-based weighting.
Integration of spline-based regression for smooth spatial modeling and a spatial enrichment index for gene identification.
Application of SpNeigh to diverse spatial transcriptomics datasets from mouse brain, human breast cancer, and human liver.

Main Results:

SpNeigh effectively models local tissue context, enabling spatially aware differential expression analysis.
The package identified intermediate cell populations at tissue interfaces, differences in immune microenvironments, and spatially zoned gene expression patterns.
Demonstrated utility across multiple spatial transcriptomics platforms and tissue types.

Conclusions:

SpNeigh provides a flexible and interpretable framework for dissecting spatial gene expression dynamics.
The package enhances the analysis of complex tissues by explicitly modeling spatial relationships and context.
Facilitates deeper understanding of tissue microenvironments and cellular heterogeneity.