Cell-cell communications in multi-cellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell-cell communications at single-cell resolution. Here, we introduce scCrossTalk, a cell-cell communication inference approach for single-cell transcriptomic data based on CellTalkDB by enriching the highly expressed ligand-receptor pairs with the Z-test statistical method. scCrossTalk is an effective method that can help scientists analyze and visualize cell-cell communications for single-cell transcriptomic data.
# install dependent packages `devtools` and install
> install.packages(pkgs = 'devtools')
> devtools::install_github('multitalk/scCrossTalk')
# or download the repository as ZIP
> devtools::install_local("/path/to/scCrossTalk-main.zip")
scCrossTalk method consists of two parts, wherein the first is to enrich the LR pairs mediating cell-cell communications and the second is to visualize the cell-cell communications and the underlying LR interactions. Classification and description of scCrossTalk functions are shown in the document and tutorial
# sc_data: A matrix containing counts of scRNA-seq data
# sc_celltype: A character containing the cell types for scRNA-seq data
> obj <- create_scCrossTalk(sc_data, sc_celltype, species)
>
> obj
An object of class scCrossTalk
0 ligand-receptor interactions found!
# object: scCrossTalk object containg scRNA-seq data
# lrpairs: A data.frame of the system data containing ligand-receptor pairs
> obj <- find_lrpairs(object, lrpairs = lrpairs)
Finding highly expressed LR pairs
[++++++++++++++++++++++++++++++] Finished:100% time:00:00:55
>
> obj
An object of class scCrossTalk
679 ligand-receptor interactions found!
# [a] cell-cell communication analysis
# object: scCrossTalk object after performing find_lrpairs()
> plot_cci_chord(object = obj)
> plot_cci_circle(object = obj)
> plot_cci_heatmap(object = obj)
> plot_cci_sankey(object = obj)
# [b] ligand-receptor interactions analysis
# object: scCrossTalk object after performing find_lrpairs()
# celltype_sender: sender cell type
# celltype_receiver: receiver cell type
> plot_cci_lrpairs_bubble(object = obj)
> plot_cci_lrpairs_heatmap(object = obj)
> plot_lrpairs_chord(object = obj, celltype_sender, celltype_receiver)
> plot_lrpairs_heatmap(object = obj, celltype_sender, celltype_receiver)
scCrossTalk was designed in our manuscript to dissect the different mechanism underlying cell-cell communications within the transplanted livers of EAD and non-EAD patients.
(a, c) Cell-cell communications mediated by LR interactions in non-EAD and EAD patients, where the circle and heat plots displayed the number and score of LR pairs, respectively. (b, d) Comparison of the number and score of LR pairs between non-EAD and EAD patients with one-sided Welch¡¯s test. (e) Cell-cell communications represented by the number of LR pairs with ligands sent from hepatocytes and received by endothelial cells, MAIT cells, GZMB+ GZMK+ NK cells, and S100A12+ neutrophils. (f) Shared LR pairs underlying cell-cell communications among hepatocytes, endothelial cells, MAIT cells, GZMB+ GZMK+ NK cells, and S100A12+ neutrophils in non-EAD and EAD patients. (g) Cell-cell communications represented by the number of LR pairs with ligands sent from endothelial cells, MAIT cells, GZMB+ GZMK+ NK cells, and S100A12+ neutrophils and received by hepatocytes. (h) Top 10 LR pairs mediating cell-cell communications from hepatocytes and endothelial cells to endothelial cells, hepatocytes, MAIT cells, GZMB+ GZMK+ NK cells, and S100A12+ neutrophils in non-EAD and EAD patients. (i) Comparison of the expression of SAA1 in hepatocytes and FPR1 in S100A12+ neutrophils between non-EAD and EAD patients. (j) Expression of SAA1 and FPR1 among endothelial cells, hepatocytes, MAIT cells, GZMB+ GZMK+ NK cells, and S100A12+ neutrophils in non-EAD and EAD patients. (k) Top 100 DEGs of hepatocytes and the enriched pathways and biological processes between non-EAD and EAD patients. (l) Enriched hallmarks of TNF¦Á signaling via NFKB and bile acid metabolism by comparing EAD patients to non-EAD patients with GSEA.
Please cite us as: X. Shao, Z. Wang, K. Wang, X. Lu, P. Zhang, R. Guo, J. Liao, P. Yang, X. Xu, X. Fan, A Single-Cell Landscape of Human Liver Transplantation Reveals a Pathogenic Immune Niche Associated with Early Allograft Dysfunction, Engineering (2024), doi: https://doi.org/10.1016/j.eng.2023.12.004