Aging-related changes in intercellular communication from the paper Single-cell transcriptomic profiling of the aging mouse brain by Ximerakis et al. Nat Neurosci, 2019.
Predictions and interactive viewer powered by CCInx. Back to Aging Mouse Brain data portal
Nodes represent ligands or receptors expressed in the denoted cell type, and edges represent protein-protein interactions between them. Node color represents magnitude of differential gene expression (logFC as estimated by the MAST model), such that the most significantly age-upregulated genes are in magenta, and age downregulated are in blue. Node borders indicate statistical significance of differential expression, specifically the false-discovery rate expected from the MAST analysis. Edge color represents the sum of scaled differential expression magnitudes from each contributing node, while width and transparency are determined by the magnitude of scaled differential expression (see full Methods below).
Methods
Cell-cell interactions were predicted by a method similar to that described by Kirouac et al., 2010. First, a cell communication interactome was created, collecting known protein protein interactions between receptor, ligand, and extracellular matrix (ECM) proteins. Receptor genes were defined based on a set of GO terms (GO: 0043235 - receptor complex; GO: 0008305 - integrin complex; GO: 0072657 - protein localized to membrane; GO: 0043113 - receptor clustering; GO: 0004872 - receptor activity; GO: 0009897 - external side of plasma membrane) and UniProt (search term: “Receptor [KW-0675]” GO: 0005886 organism: human). Ligand genes were defined based on a GO term (GO: 0005102 - receptor binding) and the set of proteins labeled as secreted in the Secretome dataset. ECM genes were defined based on a set of GO terms (GO: 0031012 - extracellular matrix; GO: 0005578 - proteinacious extracellular matrix; GO: 0005201 - extracellular matrix structural constituent; GO: 1990430 - extracellular matrix protein binding; and GO: 0035426 - extracellular matrix cell signalling). Gene lists were manually curated to correct or remove genes that were misclassified. Using the curated list of receptors, ligands, and ECM genes, known protein-protein interactions were collected from iRefindex (version 14) 12 153 , Pathway Commons (version 8) 13, and BioGRID (version 3.4.147), keeping only those occurring between genes from the different classes (ligand, receptor, ECM). This dataset is available for download.
To predict cell-cell interactions, the ligand-receptor interaction dataset was filtered for genes detected to be expressed at the mRNA transcript level in our cell types. To investigate aging-related perturbations in these putative cell-cell interaction networks, differential gene expression metrics from the MAST analysis outlined above were used to build subnetworks for each set of interactions between cell types. In these networks, nodes represent ligands or receptors expressed in the denoted cell type, and edges represent protein-protein interactions between them. Nodes were colored to represent the magnitude of differential gene expression (logFC as estimated by the MAST model). These values were scaled per cell type and summed to determine edge weight. The R package CCInx was built to generate and visualize these predicted cell-cell interaction networks.