STellaris: a web server for accurate spatial mapping of single cells based on spatial transcriptomics data

Abstract

Single-cell RNA sequencing (scRNA-seq) provides insights into gene expression heterogeneities in diverse cell types underlying homeostasis, development and pathological states. However, the loss of spatial information hinders its applications in deciphering spatially related features, such as cell-cell interactions in a spatial context. Here, we present STellaris (https://spatial.rhesusbase.com), a web server aimed to rapidly assign spatial information to scRNA-seq data based on their transcriptomic similarity with public spatial transcriptomics (ST) data. STellaris is founded on 101 manually curated ST datasets comprising 823 sections across different organs, developmental stages and pathological states from humans and mice. STellaris accepts raw count matrix and cell type annotation of scRNA-seq data as the input, and maps single cells to spatial locations in the tissue architecture of properly matched ST section. Spatially resolved information for intercellular communications, such as spatial distance and ligand-receptor interactions (LRIs), are further characterized between annotated cell types. Moreover, we also expanded the application of STellaris in spatial annotation of multiple regulatory levels with single-cell multiomics data, using the transcriptome as a bridge. STellaris was applied to several case studies to showcase its utility of adding value to the ever-growing scRNA-seq data from a spatial perspective.

Graphical Abstract

Figure 1.

Schematic overview of the STellaris workflow. STellaris is built upon a collection of manually curated ST data, consisting of 101 datasets and 823 sections from humans and mice, which can be accessed and visualized in the dataset browser. The major function is spatial mapping, which takes user-uploaded single-cell omics data and performs a screening analysis using the MIA approach. Then, the scRNA-seq data and the selected ST data are integrated into a shared latent space, where a multivariate RF model is fitted to map cells to spatial locations. Based on the spatial cellular map, spatial proximity between cell types are characterized, followed by the identification of LRIs. Additionally, with the gene search tool, users can search for spatial expression patterns of genes of interest.

Figure 2.

Features of STellaris. (A) A summary of ST datasets and sections. (B) Job submission page. (C) Result page of section blast. (D) A summary of preprocessing and filtering at the beginning of the spatial mapping result page. Spatial cellular maps for the scRNA-seq data (E) and the corresponding single-cell omics data (F). (G) Spatial proximity between cell types annotated in the scRNA-seq data and the LRIs atlas between them.

Figure 3.

Highlighted results of case studies 1 and 2. (A) Spatial mapping result of the scRNA-seq data from E14.5 embryonic mouse brain (right), which is compared to the selected ST section (left). (B) Spatial arrangements of representative cell types. (C) Spatial patterning of marker genes for cell types shown in (B) in the selected ST section (left) and mapping result (right). (D) Spatial mapping result of the scRNA-seq data from human cSCC (right), which is compared to the selected ST section (left). The dotted line highlights the leading edges. (E) Euclidean distances between cell types. (F) Euclidean distances of cell type pairs that are divided into three groups using k-means clustering (k= 3). The distances between TSK-fibroblasts and TSK-endothelial cells are highlighted. (G) A summary of detected LRIs between cell types. The TSK cell type is highlighted in red. (H) LRIs between TSK cells (ligands) and fibroblasts (receptors).