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

Abstract

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell–cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.

Spatial and temporal transcriptomics of SHH-medulloblastoma with chromothripsis identifies multiple genetic clones that resist to treatment and lead to relapse

“…3C ). To better understand the signal transduction pathways that drive interactions across cell types, we analysed spatial cell-cell communications using COMMOT [26]. This analysis identified midkine signalling as a major signalling pathway in LFS medulloblastoma ( Fig.…”

“…We used COMMOT [26] with the built-in human CellChat ligand-receptor database. We set the maximum communication distance to 5 times the minimal distance between spot centres.…”

Spatial cancer systems biology resolves heterotypic interactions and identifies disruption of spatial hierarchy as a pathological driver event

“…Such spatial omics studies may lead to a better understanding of the immune drivers of melanoma regression. As a result, they aid in the search for new prognostic and predictive biomarkers in the treatment of migratory metastatic cell populations (Hodis, 2022; Cang, 2023). Going forward, spatial omics will guide clinical decision-making to create durable anti-cancer therapy responses.…”

“…Spatially annotated single-cell datasets, omics-enhanced imaging data coupled with spRNA-Seq and/or sorted scRNA-Seq, provide unprecedented opportunities to dissect cell-cell communication. In skin biology as well as in clinical dermatology, this is particularly useful to advance our understanding of the epithelial tissue program in dermal development and disease (Cang, 2023). Epithelial organization requires several different programs that are tightly regulated.…”

Charting spatial ligand-target activity using Renoir

“…More importantly, cellular communications are often spatially constrained as signalling pathways are activated when ligands diffuse from sender cells to neighbouring receiver cells in close proximity (6). To address this challenge, additional approaches have been developed which either spatially map previously collected scRNA-seq data such as SpaOTsc (10), or make use of highly informative spatial transcriptomic data such as CellPhoneDBv3 (11), Cell2Cell (12), Giotto (13), MISTy (14), stLEARN (15), SVCA (16), NICHES (17) and COMMOT (18). However, some of these methods do not take into account the fine-grained spatial distribution of cell types, or prior information regarding plausible ligand-receptor interactions from scRNA-seq data or examine downstream target gene response caused by ligand-receptor binding.…”