Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk.

Autor: Shao, Xin, Li, Chengyu, Yang, Haihong, Lu, Xiaoyan, Liao, Jie, Qian, Jingyang, Wang, Kai, Cheng, Junyun, Yang, Penghui, Chen, Huajun, Xu, Xiao, Fan, Xiaohui
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Zdroj: Nature Communications; 7/30/2022, Vol. 13 Issue 1, p1-15, 15p
Abstrakt: 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. Cell-cell communication is a vital feature involving numerous biological processes. Here, the authors develop SpaTalk, a cell-cell communication inference method using knowledge graph for spatially resolved transcriptomic data, providing valuable insights into spatial intercellular tissue dynamics. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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