Fig. 6

Characterization of signal transmissions forming the fibrogenic niche in the kidney. a The mouse scRNA-seq dataset of the fibrotic kidney comprises vascular smooth muscle cells (VSMCs), injured VSMCs, mesangial cells, pericytes, parietal epithelial cells (PECs), and myofibroblasts. UUO, unilateral ureteral obstruction. b EV-derived MiTI-mediated CCC inferred by miRTalk, displaying the number of MiTIs between pairwise cell types in normal and fibrotic kidneys. c Heatmap showing the miRNA scores of inferred miRNAs derived from different senders. d Gene expression of inferred miRNAs in the sender cell types of uninjured and injured kidneys with the scRNA-seq data. The number of data points in uninjured and injured groups involving the VSMC are 273 and 250, respectively. The number of data points in uninjured and injured groups involving the pericyte are 75 and 121, respectively. e Gene expression of inferred miRNAs in the uninjured and injured kidneys with the bulk miRNA-seq data. f Inferred MiTIs from injured VSMCs to myofibroblasts. g UMAP plot showing the gene expression of Mir132 and its target gene Btg2 among kidney cells. h Functional annotations of miR- 132 - 3p and its target gene Btg2. i Specificity for all miRNAs in senders regarding target genes in PECs. j UMAP plot showing the gene expression of Mir27a and its target gene Epha4 among kidney cells. k Functional annotations of miR- 132 - 3p and its target gene Btg2. l Correlation between the scaled module scores of the negative regulation of EMT signatures and the scaled expression of the target gene Epha4 of miR- 125b- 5p. Spearman coefficient (rho) was used. m Single-cell trajectory analysis with the monocle3 for the construction of the pseudotime trajectory. n GSVA score for the EMT hallmark collected from the MSigDB. The bar plot showed the mean ± SEM. Significant differences between the two groups were calculated with the one-sided (less) Wilcoxon test for scRNA-seq data and the T-test for bulk miRNA-seq data