SpatialDM: Rapid identification of spatially co-expressed ligand-receptor reveals cell-cell communication patterns

Abstract

Cell-cell communication is a key aspect of dissecting the complex cellular microenvironment. Existing single-cell and spatial transcriptomics-based methods primarily focus on identifying cell-type pairs having a specific interaction, while less attention has been paid to the prioritisation of interaction features. Here, we introduce SpatialDM, a statistical model and toolbox leveraging a bivariant Moran's statistic to detect spatially co-expressed ligand and receptor, their local interacting spots, and communication patterns. By deriving an analytical null distribution, this method is scalable to millions of spots and shows accurate and robust performance in various simulations. On one melanoma and multiple intestinal datasets, SpatialDM reveals promising communication patterns and identifies differential interactions between conditions, hence enabling the discovery of context-specific cell cooperation and signalling.

Screening cell–cell communication in spatial transcriptomics via collective optimal transport

“…Most recently, several methods and packages have been introduced to study CCC with spatial transcriptomics data. SpatialDM 63 evaluates the co-expression of ligand and receptor genes; SpaTalk 64 and stMLnet 65 are focused on signaling target genes; HoloNet 66 studies the joint impact from different combinations of CCC events; and DeepLinc 67 constructs de novo cell-cell interaction landscapes without the need for annotated ligand and receptor genes. Although COMMOT has a different focus, these methods arguably complement each other when studying different aspects of CCC.…”

Organoid-based single-cell spatiotemporal gene expression landscape of human embryonic development and hematopoiesis

“…Fig. 4B) (Li et al, 2022). Among all subtypes, VTN-ITGA2B was the most enriched ligand-receptor pair in HEMO #1.…”

“…It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted September 3, 2022. ; https://doi.org/10.1101/2022.09.02.505700 doi: bioRxiv preprint depleted ligand-receptor pairs were shown in the bar plot. Another tool SpatialDM was also used for predicting local signal interactions at a spatial level (Li et al, 2022). Spot gene count matrix, predicted cell proportion, and spatial coordinate of each fragment were loaded to SpatialDM.…”

“…The resulting enriched or depleted ligand-receptor pairs were shown in the bar plot. Another tool SpatialDM was also used for predicting local signal interactions at a spatial level (Li et al, 2022). Spot gene count matrix, predicted cell proportion, and spatial coordinate of each fragment were loaded to SpatialDM.…”

PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement

“…However, interrogating such models for specific cell-cell interactions is difficult. Previous work toward characterizing surface protein localization includes statistical methods for identifying ligand-receptor pairs in transcriptomics 14,15 , polarity localization measurements in mRNA 16,17 , and co-localization with protein expression 18 .…”