SpaIM: single-cell spatial transcriptomics imputation via style transfer

Bo Li, Ziyang Tang, Aishwarya Budhkar, Xiang Liu, Tonglin Zhang, Baijian Yang (), Jing Su () and Qianqian Song ()
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Bo Li: Taipa
Ziyang Tang: Purdue University
Aishwarya Budhkar: Indiana University School of Medicine
Xiang Liu: Indiana University School of Medicine
Tonglin Zhang: Purdue University
Baijian Yang: Purdue University
Jing Su: Indiana University School of Medicine
Qianqian Song: University of Florida

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Spatial transcriptomics (ST) technologies have transformed our understanding of cellular organization but are limited by sparse signals and restricted gene coverage. To address these challenges, we introduce SpaIM, a style transfer learning model that leverages single-cell RNA sequencing (scRNA-seq) data to predict unmeasured gene expressions in ST profiles. By disentangling shared content and modality-specific styles, SpaIM effectively integrates scRNA-seq’s rich gene expression with the spatial context of ST. Evaluated across 53 datasets spanning sequencing- and imaging-based spatial technologies in various tissue types, SpaIM consistently outperforms 12 state-of-the-art methods in improving gene coverage and expression accuracy. Furthermore, SpaIM enhances downstream analyses, including ligand-receptor interaction inference, spatial domain characterization, and differential gene expression analysis. Released as open-source software, SpaIM expands accessibility and utility in ST research. Overall, SpaIM represents a robust and generalizable framework for enriching ST data with single-cell information, enabling deeper insights into tissue architecture and cellular function.

Date: 2025
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DOI: 10.1038/s41467-025-63185-9

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