About SPASCER

In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location, and has provided novel insights into diverse spatially related biological contexts. We established SPASCER database, which stand for spatial transcriptomics annotation at single-cell resolution, aims to provide spatial transcriptome analysis results in single-cell resolution by integration of single-cell RNA sequencing (scRNA-seq) data. SPASCER database includes 1 082 datasets from 43 studies that across 16 organs. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.

Browse by tissue types.

We use SPARK (S Sun, Nat Methods. 2020) to analyze whether a gene has spatial pattern across the whole tissue sample, and we detected 12216, 16530, 1476, 4915 unique genes for human, mouse, chicken and zebrafish, respectively. For each tissue type, the spatial pattern genes are listed as below (Tissue: number of spatial pattern genes). Abbreviation, H: human, M: mouse, C: Chicken, Z: zebrafish.