by Nature Biotechnology (2022). DOI: 10.1038/s41587-022-01467-z” width=”800″ height=”418″/> Modeling ligand-receptor signaling with NCEM. (a) UMAP of cells in MERFISH—elements of fetal liver. (b) Imputation of MERFISH data with scRNA-seq increases the number of genes that can be modeled with NCEMs, including receptor genes, ligand genes, and ligand-receptor pairs. (c) Distribution of selected marker genes observed in both scRNA-seq and MERFISH across cell types. Credit: Nature Biotechnology (2022). DOI: 10.1038/s41587-022-01467-z
Cells interact in a variety of ways and at multiple length scales. The interaction of a cell with its tissue location can be described through cell communication events. To understand these events, researchers around the world are creating models based on different strategies. The knowledge is critical to understanding and identifying emerging phenomena in the tissue microenvironment, such as genetic changes in a tumor.
The issue is that many of the models are based on dissociated cells, meaning that the cells are separated into single cells when analyzed and are no longer integrated into their natural environment. Other models are limited to receptor-ligand signaling, a certain type of communication between cells.
These models therefore ignore the spatial proximity of a group of cells (a site) to their natural tissue environment. Researchers led by Fabian Theis from the Health Computing Center at Helmholtz Munich and the Technical University of Munich (TUM) have now developed a new method, which defines the complexity and improves the understanding of cellular communication: the Node Expression Models (NCEM).
A flexible framework
NCEM is a computational method based on graph neural networks, which combines transcriptional variation attribution and cellular communication modeling into a single model of tissue niches.
The model is therefore able to predict the gene expression profile of a cell based on the presence of surrounding cell types. Furthermore, it estimates the effect of a tissue site composition on gene expression in an unbiased manner from spatial molecular patterning data.
In their model, the researchers developed a flexible framework to explain gene expression variations observed in spatial transcriptomics, a technology that provides spatially analyzed gene expression information. Gene expression variations can then be associated with known molecular processes associated with cell communication events.
They showed that NCEMs robustly localize cell-cell dependencies at different spatial transcriptional technologies and at length scales characteristic of known communication mechanisms. With this method, first authors David Fischer and Anna Schaar were able to recover signatures of molecular processes known to underlie cellular communication.
A new way to identify cell communication
The framework limits communication events to cells that are close in space. The identified dependencies are not limited to ligand-receptor based communication, but can also explain, for example, physical interactions or metabolite exchange.
NCEM is a flexible computational method that can be extended to more complex data sets, such as 3D spatial transcription data and higher throughput data. As such, it provides a versatile set of tools for the analysis of cell-cell communication in space. The new methodology complements recent efforts to characterize gene expression in single cells in single-cell “atlas” projects, taking tissue location into account.
The research is published in Nature Biotechnology.
Neighboring cell types influence single-cell gene expression variability
Fabian Theis, Modeling intercellular communication in tissues using spatial cell graphs, Nature Biotechnology (2022). DOI: 10.1038/s41587-022-01467-z. www.nature.com/articles/s41587-022-01467-z
Provided by the Helmholtz Association of German Research Centers
Reference: Node-centered expression models (NCEMs): Graph neural networks reveal cell-to-cell communication (2022, October 27) Retrieved October 27, 2022 from https://phys.org/news/2022-10-node- centric-ncems-graph -neural-networks-reveal.html
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