Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat

Abstract

Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.

Synaptic plasticity in human thalamocortical assembloids

“…[55][56][57][58][59][60][61] Next, we investigated the potential cellular origin of the functional connections between hThOs and hCOs. We used NeuronChat 62 to determine the likelihood of neuronal communication between cell clusters in the hThOs and hCOs based on snRNA-seq data. We found that the hThO ExN clusters, which contain glutamatergic thalamic neurons, exhibited the highest probability of TC communication with cells in the Cycling Progenitor and Subplate/DL ExN clusters of the hCOs (Figure 3A).…”

“…with the suggested workflow (https://github.com/Wei-BioMath/NeuronChat/blob/main/vignettes/NeuronChat-Tutorial.html). 62 Cell types were assigned to each cell/cluster based on marker expression and cell cycle analysis. For hThO annotation, markers of interest were identified based on a comparison to previously published scRNA-seq or snRNA-seq studies in developing mouse thalamus or diencephalon.…”

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

“…Specifically, one can adapt LIANA or use existing spatial tools 12 and combine their outputs with Tensor-cell2cell to generate spatially-informed CCC insights across contexts. Similarly, one can also obtain metabolite-mediated intercellular interactions 13,14 , and decompose those into patterns across contexts with Tensor-cell2cell 15 . One can also apply Tensor-cell2cell to extract CCC programs occurring at specific tissues 16 or at a whole-body organism level 16,17 .…”

CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics

“…In addition, in CellChat v2 we have: (a) expanded the CellChatDB to include more than 1000 protein and non-protein interactions (e.g., metabolic and synaptic signaling) with rich annotations, based on the peer reviewed literature and existing databases such as CellPhoneDB 10 and NeuronChatDB 11 ; (b) provided several additional tools to allow easy comparisons between multiple datasets/conditions; and (c) added interactive web browser function to allow intuitive exploration and visualization of CellChat outputs. To facilitate intuitive user guided data interpretation, CellChat provides a variety of visualization outputs, including: circle plot, chord diagram, heatmap, hierarchy plot, spatial plot, bubble plot, and cloud plot.…”