Modeling intercellular communication in tissues using spatial graphs of cells

被引：79
|
作者：
Fischer, David S. [1 ,2 ]
Schaar, Anna C. [1 ,3 ]
Theis, Fabian J. [1 ,2 ,3 ]
机构：
[1] Helmholtz Zentrum Munchen, Inst Computat Biol, Neuherberg, Germany
[2] Tech Univ Munich, TUM Sch Life Sci Weihenstephan, Freising Weihenstephan, Germany
[3] Tech Univ Munich, Dept Math, Garching, Germany
来源：
NATURE BIOTECHNOLOGY | 2023年 / 41卷 / 03期
基金：
英国惠康基金;
关键词：
Cell-be - Cell/B.E - Cell/BE - Expression modeling - Genes expression - Graph neural networks - Intercellular communications - Receptor ligands - Spatial graphs - Spatial proximity;
D O I：
10.1038/s41587-022-01467-z
中图分类号：
Q81 [生物工程学（生物技术）]; Q93 [微生物学];
学科分类号：
071005 ; 0836 ; 090102 ; 100705 ;
摘要：
How cells in a tissue communicate is modeled with a graph neural network. Models of intercellular communication in tissues are based on molecular profiles of dissociated cells, are limited to receptor-ligand signaling and ignore spatial proximity in situ. We present node-centric expression modeling, a method based on graph neural networks that estimates the effects of niche composition on gene expression in an unbiased manner from spatial molecular profiling data. We recover signatures of molecular processes known to underlie cell communication.
引用
收藏
页码：332 / +
页数：23
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