Knowledge-graph-based Cell-cell Communication Inference for Spatially Resolved Transcriptomic Data with SpaTalk

Overview

Journal Nat Commun

Specialty Biology

Date 2022 Jul 30

PMID 35908020

Authors

Xin Shao

Chengyu Li

Haihong Yang

Xiaoyan Lu

Jie Liao

Jingyang Qian

Kai Wang

Junyun Cheng

Penghui Yang

Huajun Chen

Xiao Xu

Xiaohui Fan

Affiliations

Soon will be listed here.

Abstract

Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.

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