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

作者：Wan, Xiaomeng; Xiao, Jiashun; Tam, Sindy Sing Ting; Cai, Mingxuan; Sugimura, Ryohichi; Wang, Yang; Wan, Xiang; Lin, Zhixiang^*; Wu, Angela Ruohao^*; Yang, Can^*

来源：Nature Communications, 2023, 14(1): 7848.

DOI：10.1038/s41467-023-43629-w

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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. @@@ Spatial transcriptomics (ST) is transforming tissue analysis but has limitations. Here, authors introduce SpatialScope, an integrated approach combining scRNA-seq and ST data using deep generative models, enabling comprehensive spatial characterisation at transcriptome-wide single-cell resolution.