Modeling intercellular communication in tissues using spatial graphs of cells

David S. Fischer; Anna C. Schaar; Fabian J. Theis

doi:10.1038/s41587-022-01467-z

Modeling intercellular communication in tissues using spatial graphs of cells

David S. Fischer
, Anna C. Schaar
, Fabian J. Theis

Chair of Mathematical Modelling of Biological Systems

Helmholtz Zentrum München German Research Center for Environmental Health
Technical University of Munich

Research output: Contribution to journal › Article › peer-review

128 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	332-336
Number of pages	5
Journal	Nature Biotechnology
Volume	41
Issue number	3
DOIs	https://doi.org/10.1038/s41587-022-01467-z
State	Published - Mar 2023

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title = "Modeling intercellular communication in tissues using spatial graphs of cells",

abstract = "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.",

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AU - Schaar, Anna C.

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N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85140896938

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Modeling intercellular communication in tissues using spatial graphs of cells

Abstract

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