Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope

Xiaomeng Wan, Jiashun Xiao, Sindy Sing Ting Tam, Mingxuan Cai, Ryohichi Sugimura, Yang Wang, Xiang Wan, Zhixiang Lin (), Angela Ruohao Wu () and Can Yang ()
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Xiaomeng Wan: The Hong Kong University of Science and Technology
Jiashun Xiao: Shenzhen Research Institute of Big Data
Sindy Sing Ting Tam: The Hong Kong University of Science and Technology
Mingxuan Cai: City University of Hong Kong
Ryohichi Sugimura: University of Hong Kong
Yang Wang: The Hong Kong University of Science and Technology
Xiang Wan: Shenzhen Research Institute of Big Data
Zhixiang Lin: The Chinese University of Hong Kong
Angela Ruohao Wu: The Hong Kong University of Science and Technology
Can Yang: The Hong Kong University of Science and Technology

Nature Communications, 2023, vol. 14, issue 1, 1-22

Abstract: Abstract The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope’s utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.

Date: 2023
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DOI: 10.1038/s41467-023-43629-w

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