Stereo-seq data of coronal sections derived from an embryonic mouse brain at E14.5

Single-cell RNA sequencing (scRNA-seq) provides insights into gene expression heterogeneities in diverse cell types underlying homeostasis, development and pathological states. However, spatial information is lost, hindering the further interpretation of crosstalk between cell types in the spatial context. Emerging spatial transcriptomics (ST) methods can measure gene expression while preserving spatial information but may not achieve single-cell resolution or transcriptome-wide profiling. Here, we present a web server that can be used to rapidly assign spatial information to scRNA-seq data based on the transcriptomic similarity with ST data.