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

By Zixuan Cang, Qing Nie. Nature Communications, 2020

378 citations recorded by the source corpus.

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. Dissociation of tissues allows high-throughput expression profiling of single cells, but spatial information is lost. Here the authors apply an unbalanced and structured optimal transport method to infer spatial and signalling relationships between cells from scRNA-seq data by integrating it with spatial imaging data.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/de63dfe83e384f97a2e46020e5cac0ca27477854

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

An introduction to spatial transcriptomics for biomedical research (2022, background): “More recent mapping approaches include SpaOTsc, which relies on an optimal transport model to map single-cell transcriptomes to spatial data and also includes functions for inferring ligand-receptor interaction [100].”
Spatial components of molecular tissue biology (2022, positive): “Recently, new methods emerged that reconstruct the spatial arrangement of cells mainly based on scRNA-seq data with little or no spatially resolved experimental data 29,150 .”
Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, methodology): “An in situ spatial transcriptomic map was generated by integrating an scRNA-seq dataset with spatial single-cell resolution data 60 using SpaOTsc 31 .”
Learning single-cell perturbation responses using neural optimal transport (2021, background): “Optimal transport has previously been applied in the domain of single-cell biology to uncover trajectories of single-cell reprogramming and to link rich, non-spatially-resolved with sparse, spatially resolved measurements (Schiebinger et al., 2019; Cang and Nie, 2020; Demetci et al., 2020; Huizing e”
CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics (2024, positive): “Recently, several methods have been developed for spatially resolved transcriptomics 4 , such as SpaOTsc 36 , SpaTalk 37 , COMMOT 38 , CellPhoneDB v3 13 and HoloNet 39 , which are better at detecting spatially proximal cell–cell communication.”
Deciphering cell–cell interactions and communication from gene expression (2020, methodology): “Most recently, a method called ‘SpaOTsc’ 108 formulates intercellular communication as an optimal transport problem 109 using RNA-seq and spatial information.”
Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “…non-negative matrix factorization 19 and shared factor neighborhood graphs to predict gene expression levels in space; novoSpaRc 20 and SpaOTsc 21 each use optimal transport methods 22 to construct spatial metrics of cells on the basis of scRNA-seq data; stPlus 23 combines the auto-encoder…”