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

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.

Predicting cell-to-cell communication networks using NATMI

“…We acknowledge that NATMI is not the first tool to attempt cell-to-cell communication analyses at the single-cell level. Supplementary Data 9 systematically compares features and approaches used in NATMI and 13 other methods [18][19][20][21][55][56][57][58][59][60][61][62][63] . The major discriminating features incorporated in NATMI are that (1) NATMI uses connectomeDB2020, the most comprehensive set of ligand-receptor pairs with primary literature support to date (note, a substantial number of ligand-receptor pairs in other resources lack primary literature support, Supplementary Data 1), (2) NATMI can identify and visualise the cell-types that are communicating the most or the most specifically (both the directed heatmap visualisation and summed-specificity weighting of cell-connectivity summary edges is unique to NATMI) ( Fig.…”

Deciphering cell–cell interactions and communication from gene expression

“…The former computes a probability, while the latter uses a personalized PageRank algorithm , in both cases to evaluate the effect of ligand–receptor co-expression on downstream signalling genes in the receiver cell and, thus, obtain a continuous score for ranking ligands and receptors based on this effect. Most recently, a method called ‘SpaOTsc’ 108 formulates intercellular communication as an optimal transport problem 109 using RNA-seq and spatial information. All these tools use not only the expression levels of ligands and receptors to compute interaction scores but also expression levels of downstream signalling targets, which is intended to be a strength of these techniques.…”

“…As more approaches emerge for spatial-based transcriptomics 99 – 104 and proteomics 133 , 134 , future studies and algorithms should include this information. Accounting for the physical distance between cells will lead to the generation of new scoring functions that may better capture the potential of cells to communicate and interact 53 , 98 , 108 , 111 , 135 . As an example, ligand-specific diffusion constants can be considered to reflect how effectively gene products can mediate long-distance communication 136 , 137 .…”

Mapping multicellular programs from single-cell profiles

“…analyze single cell data the vast majority were geared to map and explore single cell states (13,(19)(20)(21)(22)(23)(24)(25). Those developed to study cell-cell interactions, are focused on reconstructing the tissue's spatial organization (e.g., by combining single cell and spatial data or based on assumptions on spatial patterning) (26)(27)(28)(29), on inferring putative physical cell-cell interactions based on known receptor-ligand pairs and known signaling pathways (30)(31)(32), or on highlighting recurring cell type compositions of cellular neighborhoods using multiplex molecular spatial data (33,34). Thus, while these methods revealed important properties of cell biology and tissue structure, we still lack a computational basis to study tissue biology and functional multicellular processes at scale.…”