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


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.

SUBMITTER: Wan X 

PROVIDER: S-EPMC10687049 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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

Wan Xiaomeng X   Xiao Jiashun J   Tam Sindy Sing Ting SST   Cai Mingxuan M   Sugimura Ryohichi R   Wang Yang Y   Wan Xiang X   Lin Zhixiang Z   Wu Angela Ruohao AR   Yang Can C  

Nature communications 20231129 1


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 integratin  ...[more]

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