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Detection of allele-specific expression in spatial transcriptomics with spASE.

Luli S Zou^1,2,3, Dylan M Cable^2,3,4, Irving A Barrera-Lopez³

¹Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.

|July 8, 2024

Summary

We developed spASE, a new computational tool to analyze spatial allele-specific expression (ASE) in tissues. This method reveals how gene expression varies across locations and cell types within the mouse brain.

Keywords:

Allele-specific expression Spatial transcriptomics

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Published on: December 9, 2016

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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

Area of Science:

Genomics
Computational Biology
Neuroscience

Background:

Spatial transcriptomics enables genome-wide RNA distribution analysis at high resolution.
The study of spatial allele-specific expression (ASE) from these data is currently uncharacterized.

Purpose of the Study:

Introduce spASE, a computational framework for detecting and estimating spatial ASE.
Address challenges of cell type mixtures and low signal-to-noise ratio in spatial transcriptomics data.

Main Methods:

Implement a hierarchical model using additive mixtures of spatial smoothing splines.
Apply the spASE method to allele-resolved Visium and Slide-seq data.
Analyze data from mouse cerebellum and hippocampus.

Main Results:

Successfully detected and estimated spatial ASE from spatial transcriptomics data.
Provided new insights into the landscape of spatial and cell type-specific ASE.
Demonstrated the framework's ability to handle complex biological data.

Conclusions:

spASE is a feasible and effective computational framework for spatial ASE analysis.
The study advances our understanding of gene expression regulation in spatial contexts.
Opens new avenues for investigating genetic variation's role in tissue function.