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. 2024 Apr 13;25(8):4320.
doi: 10.3390/ijms25084320.

Renal Endothelial Single-Cell Transcriptomics Reveals Spatiotemporal Regulation and Divergent Roles of Differential Gene Transcription and Alternative Splicing in Murine Diabetic Nephropathy

Alex-Xianghua Zhou  1 , Marie Jeansson  2   3 , Liqun He  2   3 , Leif Wigge  4 , Pernilla Tonelius  1 , Ramesh Tati  1 , Linda Cederblad  1 , Lars Muhl  2 , Martin Uhrbom  1   2 , Jianping Liu  2 , Anna Björnson Granqvist  1 , Lilach O Lerman  5 , Christer Betsholtz  2   3 , Pernille B L Hansen  1
Affiliations

Renal Endothelial Single-Cell Transcriptomics Reveals Spatiotemporal Regulation and Divergent Roles of Differential Gene Transcription and Alternative Splicing in Murine Diabetic Nephropathy

Alex-Xianghua Zhou et al. Int J Mol Sci. .

Abstract

Endothelial cell (EC) injury is a crucial contributor to the progression of diabetic kidney disease (DKD), but the specific EC populations and mechanisms involved remain elusive. Kidney ECs (n = 5464) were collected at three timepoints from diabetic BTBRob/ob mice and non-diabetic littermates. Their heterogeneity, transcriptional changes, and alternative splicing during DKD progression were mapped using SmartSeq2 single-cell RNA sequencing (scRNAseq) and elucidated through pathway, network, and gene ontology enrichment analyses. We identified 13 distinct transcriptional EC phenotypes corresponding to different kidney vessel subtypes, confirmed through in situ hybridization and immunofluorescence. EC subtypes along nephrons displayed extensive zonation related to their functions. Differential gene expression analyses in peritubular and glomerular ECs in DKD underlined the regulation of DKD-relevant pathways including EIF2 signaling, oxidative phosphorylation, and IGF1 signaling. Importantly, this revealed the differential alteration of these pathways between the two EC subtypes and changes during disease progression. Furthermore, glomerular and peritubular ECs also displayed aberrant and dynamic alterations in alternative splicing (AS), which is strongly associated with DNA repair. Strikingly, genes displaying differential transcription or alternative splicing participate in divergent biological processes. Our study reveals the spatiotemporal regulation of gene transcription and AS linked to DKD progression, providing insight into pathomechanisms and clues to novel therapeutic targets for DKD treatment.

Keywords: diabetic nephropathy; endothelium; transcriptomics.

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Conflict of interest statement

A.-X.Z., P.T., L.W., A.B.G., M.U., R.T., L.C. and P.B.L.H. are employed by AstraZeneca Gothenburg, Sweden, and L.O.L is an advisor. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Figures

Figure 1
Figure 1
Experimental design and EC populations. (A) Overview of the experimental design. (B) Kidneys were harvested and dissociated into single-cell suspensions before labeling with Pecam1 antibody to sort endothelial cells into 384-well plates. (C) Blood glucose levels and (D) urinary albumin–creatinine ratios (UACRs) in 11-week-old non-diabetic (BTBRLean) and diabetic (BTBRob/ob) mice. (E) Single-cell sequencing utilized SmartSeq2 technology, and data were subjected to bioinformatic analysis. UMAPs of endothelial cells from non-diabetic (BTBRLean) mice were used to identify 13 separate clusters of EC annotated as afferent arteriole/arterioles (AA), ascending vasa recta (AVR), proliferating (cycling), descending vasa recta (DVR), efferent arteriole (EA), glomerulus (GEC), lymphatics (LVEC), peritubular capillaries (PCEC), tip cells (tip), and veins (VEC). GEC and PCEC each had two clusters with one cluster displaying a high immediate early gene expression (GECIEG-high and PCECIEG-high). (F) Schematic of renal EC populations. (G) Violin plots showing pan EC markers including Pecam1, Cdh5, Tek, Kdr, and Notch4 in BTBRLean mice (H). Dot blot of markers for each EC population in BTBRLean mice. (I) Calca expression in efferent arteriole of C57BL6/J mice. Scale bars = 50 µm. Data expressed as mean ± SD. Other EC population validations are shown in Supplementary Figure S1.
Figure 2
Figure 2
Heterogeneity of different EC compartments in BTBRLean mice. (A) Selection of significant canonical pathways identified through pathway analysis based on DEGs between EC populations in non-diabetic (Lean) mice (complete IPA results shown in Supplementary File S2). Dot blot and schematic representation of EC populations for genes involved in (B) immune zonation, (C) coagulation zonation, (D) vascular tone and calcium homeostasis, and (E) IGF signaling.
Figure 3
Figure 3
Renal EC response to DKD progression in BTBRob/ob mice. (A) UMAP of endothelial cells in non-diabetic (Lean) and diabetic(ob/ob) mice. (B) Schematic of DEGs of different EC populations in non-diabetic and diabetic mice at 6, 11, and 20 weeks. (C,D) Venn diagrams of DEGs for PCEC and GEC, respectively, comparing non-diabetic (Lean) with diabetic (ob/ob) mice at different timepoints. Expression heatmaps (blue, low; red, high) of the relative expression are shown for each comparison.
Figure 4
Figure 4
Pathway analysis (IPA) of PCEC based on DEGs comparing non-diabetic (Lean) and diabetic (ob/ob) mice. (A) The selection of significant canonical pathways, downstream effects, and upstream regulators identified through IPA for PCEC (the complete IPA results are shown in Supplementary File S2). (B–D) Graphical summaries of pathway analysis showing the pathway activation (red) or pathway inhibition (blue) in PCEC in diabetic 6-, 11-, and 20-week-old mice, respectively.
Figure 5
Figure 5
Pathway analysis (IPA) of GEC based on DEGs comparing non-diabetic (Lean) and diabetic (ob/ob) mice. (A) The selection of significant canonical pathways, downstream effects, and upstream regulators identified through IPA for GEC. The complete IPA results can be found in Supplementary File S2. (B,C) Graphical summaries of the pathway analysis showing activation (red) or inhibition (blue) in diabetic GEC in 6- and 20-week-old mice, respectively. No significant pathway regulation was found in 11-week-old mice. * multiple identifiers in the dataset file map to a single gene.
Figure 6
Figure 6
Identification of DSEs in DKD progression in BTBRob/ob mice. (A) Schematics of five different modes of AS distinguished through rMATS. (B) Number of significant DSEs identified in comparison between BTBRLean and BTBRob/ob GEC and PCEC at different timepoints. (C) Venn diagrams of DSE genes for PCEC and GEC, respectively, displaying number of overlapping or distinct DSE genes at different timepoints. Exemplary Sashimi plots visualizing read counts of splice junctions and inclusion levels of exons at selected genomic locations for Lias and Chek2 in GEC (D) and Tbc1d31 in PCEC (E) in BTBRLean and BTBRob/ob mice.
Figure 7
Figure 7
Differentially related biological processes with DSE genes and DEGs. (A) Venn diagrams of DSE genes and DEGs for PCEC and GEC, displaying number of distinct genes and overlapping genes that are both differentially expressed and alternatively spliced. GO-term enrichment analysis for DSE genes (B) and DEGs (C) displaying top 10 significant GOBPs at three timepoints in GEC and PCEC, respectively. Scale bar represents -log10 (adjusted p-value). (D) Proposed working model displaying the link between DEGs and DSEs and their divergently regulated biological processes.
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