Inferring spatial and signaling relationships between cells from single cell transcriptomic data

TLDR

Single‑cell RNA sequencing provides detailed cellular profiles but typically lacks spatial context. The study introduces SpaOTsc, a method that uses structured optimal transport and sparse spatial gene measurements to infer spatial properties from scRNA‑seq data. SpaOTsc constructs a spatial metric by mapping cells to spatial gene measurements, infers cell–cell communication through optimal transport of signal senders to receivers, and quantifies intercellular gene–gene information flow via partial information decomposition, with cross‑validation on four datasets to assess spatial gene expression prediction and known communications. SpaOTsc enables integration of non‑spatial single‑cell data with spatial information and reconstructs spatial cellular dynamics, demonstrating broad applicability.

Abstract

Abstract Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell–cell communications are then obtained by “optimally transporting” signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene–gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell–cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues.