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

Jinming Cheng1,2, Pierce Kah Hoe Chow3,4, Nan Liu1,2,5,6,7

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

NAR Genomics and Bioinformatics
|April 13, 2026
PubMed
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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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.