Myocardial B cells have specific gene expression and predicted interactions in Dilated Cardiomyopathy and Arrhythmogenic Right Ventricular Cardiomyopathy

Figures

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  Figure 1.. Graphic depicting the in silico workflow UMAP and plots generated. a): two human heart single cell datasets were downloaded, loaded into R and analyzed using Seurat. Standard quality control and clustering methods were first performed. B cells were then identified using ScType, and ScTransform was used to normalize, scale, and integrate the datasets. Finally, differentially expressed genes were identified and pathway analysis as well as cell-cell communication analysis was performed. Panel b shows a UMAP with the subset of cells identified as B cells that were subsetted from the integrated data and sub-classified into B cell subtypes. Cells with an identity that went unresolved were classified as “unknown” and excluded from the analysis. Panels c-e show the UMAPs of the integrated data sets from sc/sn-RNA sequencing cells split by condition, including, control (panel c), ARVC (panel d), and DCM (panel e).

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  Figure 2.. B cell interaction network is notably altered by disease state. Intercellular communications plot of the interactions from B cell ligands to other receptors in a) controls, c) ARVC, e) DCM, and from other cell ligands to B cell receptors in b) controls, d) ARVC and f) DCM. Thickness of the line is relative to the maximum communication probability to or from B cells. Line color represents the source (ligand provider) of the interaction. Size of circle is proportional to the number of cells of that type. Open circles represent cell types that are not detected in the disease state. DC = dendritic cell. Statistical significance determined by CellChat’s permutation test.

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  Figure 3.. B cell ligand-receptor interactions are significantly altered by disease states. Dot plot of all probable interactions that reached statistical significance. Color represents condition (Controls = turquoise, DCM = purple, ARVC = red). Bar plot represents counts of ligand-receptor interactions for each cell type per disease state. Statistical significance was determined by CellChat’s permutation test. L-R = ligand-receptor, Probability = communication probability as determined by CellChat’s algorithm.

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  Figure 4.. Dysregulated Pathways Identified using IPA in both Naive B cells and Plasma cells clusters from DCM samples as compared to controls. The bar plots depict the dysregulated pathways identified by Ingenuity Pathway Analysis (IPA). a) Dysregulated Ingenuity Canonical Pathways in the Plasma cells cluster. b) Dysregulated Ingenuity Canonical Pathways in the Naive B cells cluster. The plots show pathways involving immune system response and metabolism. The pathways shown here meet the statistical criteria of a p-value below 0.05 and an absolute z-score exceeding 1.5. ARVC data did not show any relevant pathway on IPA based on the defined threshold criteria.