Inference of neural cell-cell communication networks using single-cell RNA sequencing data

Authors

• MD Watson
• JC Lee

Abstract

Background: Cell-cell communication (CCC) coordinates brain activity through interactions between ligands and receptors. A recently developed method named CellChat used both single-cell RNA-sequencing (scRNA-seq) and a curated database of ligand-receptor pairs to infer CCC networks. However, applying this method to infer nervous system signaling is currently limited due to a lack of neural pathways in the CellChat database.

Methods: We curated a database of 207 neural ligand-receptor pairs, along with their cofactors and co-receptors, for both humans and mice using KEGG, Ingenuity pathway analysis, and literature review. We tested our database by applying CellChat on scRNA-seq data of adult mouse neocortex (ALM and VISp) obtained by the Allen Brain Institute to infer cell-type specific communication networks. We compared our predicted networks to known molecular interactions between cell types.

Results: CellChat predicted the following interactions in both the ALM and VISp: 1) neuregulin-ERBB signaling from Cajal-Retzius cells onto Pvalb+ chandelier and basket cells; 2) cholecystokinin signaling from Sncg+ interneurons onto layer 6b glutamatergic neurons; and 3) oxytocin signaling from L2/3 intratelencephalic neurons onto Sst+ interneurons. Previous studies support the predicted cholecystokinin network, but not the dominant sender statuses of Cajal-Retzius cells within neuregulin signaling or that of L2/3 neurons within oxytocin signaling.

Conclusion: Our curated database extends RNA-based inference of cell-cell communication to predicting neural signaling across cell classes and can identify known cell-cell interactions in the mouse brain. We plan to extend these studies to examine whether these same cell-cell communication patterns are conserved in humans.

Scientific Focus Area: Neuroscience

This page was last updated on Monday, September 25, 2023