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Spatiotemporal cell type deconvolution leveraging tissue structure
Macrina Maria Lobo1, Ziqi Zhang1, Xiuwei Zhang1
1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332, Georgia, USA.
Biorxiv : the Preprint Server for Biology
|February 23, 2026
Summary
SpaDecoder improves cell type deconvolution in spatial transcriptomics (ST) by leveraging 3D tissue structure and single-cell (sc) references. This method enhances understanding of cell distributions in tissues.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Spot-based spatial transcriptomics (ST) provides aggregated transcriptomic data from tissue locations.
- Cell type deconvolution is crucial for mapping cell distributions but existing methods struggle with 3D tissue structure and single-cell resolution references.
Purpose of the Study:
- To develop a novel deconvolution method, SpaDecoder, that effectively utilizes 3D tissue architecture and single-cell (sc) RNA-seq references.
- To improve the accuracy of cell type proportion estimation in spatial transcriptomics data.
Main Methods:
- SpaDecoder employs parallelized matrix factorization for per-spot deconvolution across 3D spatial or temporal ST slices.
- It incorporates an adaptively inferred 3D neighborhood Gaussian kernel to leverage tissue structure.
- The method accounts for variability in sc-reference profiles and batch effects.
Main Results:
- SpaDecoder demonstrates improved cell type deconvolution by effectively harnessing 3D tissue structure and sc-reference profiles.
- Ablation tests and comparisons confirm its superior performance across various metrics and datasets.
- The framework enables downstream analyses including gene expression imputation and identification of colocalized cell types.
Conclusions:
- SpaDecoder offers a robust and accurate solution for cell type deconvolution in spatial transcriptomics.
- Its ability to integrate 3D tissue information significantly advances the analysis of spatial cell type distributions.
- The method provides a versatile platform for diverse downstream spatial transcriptomics analyses.
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