Intercellular Communication Network of CellChat Uncovers Mechanisms of Kidney Fibrosis Based on Single-Cell RNA Sequencing

doi:10.21203/rs.3.rs-3323815/v1

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Intercellular Communication Network of CellChat Uncovers Mechanisms of Kidney Fibrosis Based on Single-Cell RNA Sequencing

https://doi.org/10.21203/rs.3.rs-3323815/v1

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Background

Anti-renal fibrosis and the postponement of renal function are global challenges in treating chronic kidney disease (CKD). However, sodium-glucose cotransporter 2 (SGLT2) inhibitors have opened new avenues for treating diabetic nephropathy. Researchers are currently investigating the potential advantages of SGLT2 inhibitors for non-diabetic CKD.

Methods

We established a CKD model using 5/6 nephrectomy (5/6 Nx) rats and divided them into three groups: placebo-treated sham surgery rats, placebo-treated 5/6 Nx rats, and Empagliflozin (Empa)-treated 5/6 Nx rats. We performed single-cell RNA sequencing on the kidneys of 5/6 Nx rats and used CellChat for quantitative inference and analysis of intercellular communication networks.

Results

Intercellular analysis revealed that the probability and intensity of universal communications were higher in the kidneys of 5/6 Nx rats treated with placebo than in rats treated with sham surgery and placebo. 5/6 Nx up-regulated MIF and GRN expression in tubular epithelial cells, which were activated by paracrine signaling from M2 macrophages. TGF-β also plays an important role in regulating endothelial, mesangial, and fibroblast cells by inducing the up-regulation of fibrogenic genes. This process promotes collagen synthesis and extracellular matrix deposition by activating the COLLAGEN, FN1, LAMNIN, and THBS pathways. In addition, Empa treatment improves renal morphology in 5/6 Nx rats by inhibiting MIF and GRN expression in tubular epithelial cells and down-regulating the COLLAGEN, FN1, THBS, and LAMNIN pathways associated with kidney fibrosis.

Conclusion

We identified critical ligand-receptor pairs and signaling pathways involved in CKD fibrosis by leveraging the intercellular communication network of CellChat. Moreover, our findings indicate that Empa can reduce renal fibrosis in CKD by modulating the intercellular communication network and signaling pathways.

chronic kidney disease

SGLT2 inhibitors

intercellular communication

fibrosis

Chronic kidney disease (CKD) incidence has been rapidly increasing worldwide because of global population aging and an increase in diabetes, obesity, and hypertension ^{1, 2}. End-stage CKD necessitates renal replacement therapy due to its high mortality and morbidity rates ³. Blood pressure regulation and renin-angiotensin system inhibition have been the cornerstones of CKD treatment in recent decades. Therefore, Angiotensin Converting Enzyme Inhibitors (ACEI) and Angiotensin Receptor Blockers (ARB) have become widely used due to their renal protective effects, which include dilation of blood vessels, reduction of intra-glomerular pressure, and proteinuria ⁴. However, renal fibrosis is a chronic and progressive process that is the most common pathological feature of CKD. There are currently few treatment options for renal fibrosis and delaying kidney function loss.

In the proximal renal tubules, the activity of sodium-glucose co-transporter-2 (SGLT2) is inhibited by a novel class of antihyperglycemic agents called SGLT2 inhibitors. These drugs treat diabetes by increasing urinary glucose excretion and decreasing hyperglycemia by inhibiting glucose reabsorption through the kidneys ⁵. Furthermore, it has been shown through studies that SGLT2 inhibitors provide nephro-protection for diabetic kidney disease (DKD). Large, multi-center randomized trials indicated that SGLT2 inhibitors reduce the incidence or worsening of nephropathy, and the risk of cardiovascular disease-related death, hospitalization for heart failure, and all-cause mortality in DKD patients ^{6, 7}. SGLT2 inhibitors protect the kidneys through mechanisms that go beyond simply lowering blood glucose levels, including modulation of the renin-angiotensin-aldosterone system, alteration of energy utilization, and induction of anti-inflammatory, anti-oxidative, and immune-modulatory responses ^8–12. Studies have revealed that SGLT2 inhibitors, such as Empa, can effectively mitigate renal fibrosis and enhance renal function in DKD mice by regulating renal metabolic reprogramming ¹³ and alternatively activate macrophages in rats with 5/6-nephrectomy ¹⁴. The cellular mechanisms contributing to renal fibrosis must be identified to develop targeted anti-fibrotic strategies. Gene regulation analysis can be performed unbiasedly and reliably using single-cell RNA sequencing (scRNA-seq) across numerous cells at a single-cell resolution ¹⁵. scRNA-seq helps to identify new kidney cell types ¹⁶, elucidate the biological functions of the kidney ¹⁷, investigate cellular differentiation pathways, and shed light on intercellular communications ¹⁸ by profiling the transcriptome of a single kidney cell. This technology allows researchers to investigate the underlying mechanisms and pathogenesis of kidney disease, paving the way for more effective treatments ^{19, 20}.

In the present study, a non-diabetic CKD model with 5/6 nephrectomy (5/6 Nx) was developed, and scRNA-seq was used to elucidate the intercellular communication network of ligand-receptor pairs and signaling pathways involved in the fibrosis process in CKD. The study also explored the advantages of SGLT2 inhibitors in treating non-diabetic CKD.

2.1 Model for Chronic Kidney Disease

A total of 22 male Wistar rats, aged 6–7 weeks and weighing 200–250 g, were obtained from the Nanjing University Experimental Animal Center. Following a 2-week adaptive feeding period, the rats (n = 22) were divided into three groups: placebo-treated sham surgery rats (sham group, n = 7), placebo-treated 5/6 Nx rats (5/6Nx group, n = 6), and Empa-treated 5/6 Nx rats (Empa group, n = 9). The rats were subjected to the following 5/6 nephrectomy procedure ²¹: we excised the superior and inferior poles of the left kidney in week 2, and the right kidney was removed in week 4. This model replicated CKD conditions by removing approximately 5/6 of the renal mass while preserving a minute portion of functional parenchyma. During the same time, sham laparotomies were performed. While producing 5/6 Nx, all rats were anesthetized with an intraperitoneal injection of 3, 3, 3-tribromoethanol at a concentration of 20 mg/mL (300–400 mg/kg). The rats in the Empa group were given 15 mg/kg of Empa orally through gavage from week 10 to week 16. Similarly, rats in the Sham and 5/6 Nx groups were given 15 mL/kg of sterile water. All rats were housed in a controlled SPF-grade environment with temperatures ranging from 20 to 24 ˚C and humidity levels of 45 to 65%. A 12-h light/dark cycle was maintained throughout the study. There were no restrictions on their diet or water intake.

Urine samples were collected at 24 h, while blood samples were taken at weeks 1 (baseline), 4, 10, and 16. At the end of the 16th week, the rats were anesthetized with an intraperitoneal injection of 2, 2, 2-tribromoethanol, and blood samples were collected from the inferior vena cava to induce hemorrhagic shock and fatality. Subsequently, the blood was centrifuged for 10 min at 3000 rpm, and 4 ˚C. Upper serum was collected and quickly frozen in liquid nitrogen before being stored at -80 ˚C. The kidneys were weighed and cut in half longitudinally; one half was preserved in 4% paraformaldehyde, embedded in paraffin for histology, and the other half for scRNA-seq. We centrifuged the urine for 10 min at 2000 rpm and stored the plasma and supernatant at -80 ˚C for later experiments.

2.2 ScRNA-seq analysis

2.2.1 Sample preparation

The kidney tissues were rinsed thrice with Hanks' Balanced Salt Solution (HBSS) and sectioned into 1–2 mm pieces. These fragments were then digested for 15 min at 37 ˚C using GEXSCOPE tissue dissociation solution. The filtered cells were centrifuged after being separated from cellular debris using a sterile strainer (Falcon) with a pore size of 40 microns. The cellular pellets were re-suspended in phosphate-buffered saline (HyClone). GEXSCOPE erythrocyte lysis buffer (Singleron) was added to remove red blood cells. Finally, we used an automated cell counter (TC20, Bio-Rad) to measure the cell suspension concentration, which was 1 × 10⁵ cells/mL.

2.2.2 ScRNA-seq data processing and analysis

Cellular suspensions (1 × 10⁵ cells/mL) were loaded into a microfluidic apparatus. scRNA-seq libraries were then constructed using the GEXSCOPETM single-cell RNA library kit (Singleron Biotechnologies) and the Singleron GEXSCOPETM protocol. Each library was diluted to 4 nM and combined with 150 bp paired-end reads for Illumina Hiseq X sequencing. FeatureCounts was used to quantify the genes after the Rawraw fastq files were aligned to the Ensembl Rnor_6.0 reference genome. In addition, the Seurat R package (Version 3.1.2) performs the data quality assessment, initial processing, and dimensionality reduction. We screened the cells expressing < 200 or > 3000 genes, with Unique Molecular Identifiers < 30000, and mitochondrial gene expression > 50% to obtain high-quality matrices. After normalizing the data with robust point matching (RPM), 2000 genes with high variability were selected for principal component analysis (PCA). The top 20 dimensions were then visualized and clustered. A total of 14 distinct cell clusters were identified in the cell clustering process using the FindClusters function with a resolution of 0.8. The heterogeneity of T cell clusters and myeloid cells was determined by sub-clustering T cells and myeloid cells with a resolution of 1.2. We identified genes with differential expression between cell clusters using the FindMarkers function and the Wilcoxon rank sum test with adjusted Benjamini-Hochberg P-value < 0.05 as default parameters.

2.2.3 analysis of general intercellular communication

We used the CellChat 1.0.0 algorithm to analyze and quantify cell-cell communication at the single-cell level ²². CellChat calculated the probability of ligand-receptor interaction to assess the communication incidence of the signaling pathway. P-value < 0.05 indicate a significant interaction. The number of cell-cell interactions and communication strength were then depicted using chord and circle diagrams.

Signaling pathways were classified based on structural and functional similarities to investigate their characteristics further. The Euclidean distance was used to measure the distance between signaling pathways to develop an impression of signaling pathway adaptation among groups. The information flow value in each signaling pathway was compared across groups, and the synergistic mode of cell populations and signaling pathways was illustrated. Hierarchical diagrams were used to examine the network structure formed by the cellular signal senders and receptors in target signaling pathways. The specific roles of cell populations in signaling pathways, such as senders, receivers, mediators, and regulators, were illustrated by heat maps. Moreover, we used the online database STRING (string-db.org) to predict the function of the target gene.

3.1 Comparison analysis reveals alterations of intercellular contact in CKD

Three kidney samples were randomly selected from each group for scRNA-seq atlas generation. After filtering, normalizing, and reducing the dimensionality of the data, 14 cell clusters were grouped from 89,420 cells. Stacked bar graphs depict the differences in the proportion of various cell subsets between groups (Fig. 1A, Supplementary Table S1). We classified the cells into clusters based on their marker genes: endothelial cells (ECs; marker: CDH5), mesangial cells (MCs; marker: ITGA8), pericytes (Per; marker: RGS5), proximal tubule cells (PTCs; marker: LRP2), loop of Henle cells (LOHs; marker: SLC12A1), distal tubule cells (DTCs; marker: SLC12A3), collecting duct principal cells (PCs; marker: AQP2), collecting duct intercalated cells (ICs; marker: FOXIL), Urothelial cells (UT_epithelial; marker: UPK3A), smooth muscle cells (SMCs; marker: TAGLN), fibrocytes (Fib; marker: DCN), proliferating cells (marker: TOP2A), myeloid cells (marker: LYZ2), and T cells (marker: CD3G). We also subclustered the T-cells into four subpopulations, including regulatory T cells (Treg; marker: TNFRSF4), IL-17-producing helper T cells (Th17; marker: CCR6), follicular helper T cells (Tfh; marker: CXCR6), and CD8⁺ effector T cells (marker: NKG7). Myeloid cells were further divided into six subpopulations, including monocytes (marker: SELL), M1 macrophage (M1 macro; marker: IL1B), M2 macrophage (M2 macro; marker: C1QB), and three dendritic cell subsets. Further details are provided in our previous study ¹⁴.

CellChat 1.0.0 was then used to infer intercellular communication networks systematically. Intercellular analysis revealed that the incoming and outgoing interaction strength was significantly higher in the 5/6 Nx rats than in the sham group (Fig. 1B). Our findings also revealed a significant increase in ligand-receptor pairs involved in cell-cell communication in 5/6 Nx rats. Moreover, we observed that Empa inhibited cell communication (Fig. 1C). Based on the above findings, we further investigated the incoming communication patterns of target cells and the outgoing communication patterns of secreting cells in 5/6 Nx rats. There are five distinct patterns of outgoing signaling and five distinct patterns of incoming signaling. Notably, endothelial and mesangial cell communication patterns are significantly similar. Particularly, pattern 4 is shared by both types of cells for incoming and outgoing signaling (Fig. 1D).

3.2 Predictive modeling of pathway networks

Because of their structural and functional similarities, all signaling pathways were represented on two-dimensional images, identifying four functional and four structural groups. Our findings revealed that different states of each signaling pathway were altered. For instance, MIF, LAMININ, and COLLAGEN had structural similarities in the sham group. They were assigned to separate groups in the 5/6 Nx group, indicating that their architecture of cell-cell communication changed in CKD (Fig. 2A). In addition, we compared the information flow for each signaling pathway that reflects the cumulative communication probabilities between cell populations in the inferred network. The 5/6Nx group had significant enrichment of multiple signaling pathways compared to the control group, such as fibrotic signaling pathways COLLAGEN, FN1, THBS, and LAMININ (Fig. 2B). This indicates that CKD effectively triggers fibrotic responses. The diversity was determined by measuring the Euclidean distance between each pair of shared signaling pathways. The 5/6Nx group demonstrated a more pronounced MIF pathway remodeling than the control group.

Moreover, Empa treatment resulted in GRN pathway reconstruction (Fig. 2C, Supplementary Table 2, Additional File 1). For the functional network association analysis, we focused on the ligand-receptor genes that were important in the pathways above (Fig. 2D, Supplementary Figs. 1–4, Additional File 2). We used the Search Tool for the Retrieval of Interacting Genes/Proteins database (STRING, http://string-db.org) for this analysis (Fig. 2E). In addition, we performed functional annotations of the genes using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (www.genome.jp/kegg). These genes were enriched in various functional categories, including ECM-receptor interaction, cell-matrix proliferation, TGF-β pathway, cell adhesion, and focal adhesion (Fig. 2F, Supplementary Table 3, Additional File 1).

3.3 Overview of intercellular communication in TGF-β signaling pathway

We first focused on the TGF-β pathway because of its important role in renal fibrosis. The chord diagram revealed higher interaction strength between macrophages and other cell types in the 5/6 Nx group compared to the sham group. M2 macrophages had the highest sender, receiver, and mediator scores in the 5/6 Nx group, indicating their central role in the communication flow within the TGF-β signaling network in CKD. Figure 3A demonstrates that the Empa therapy significantly reduced these interactions. Furthermore, we used a hierarchical diagram to illustrate the intercellular communication mediated by each cell type and their roles in the TGF-β pathway. Cross-referencing source and target signaling patterns revealed information about the autocrine and paracrine pathways of specific cell types. ECs were the primary sources of the TGF-β pathway in the sham group. In contrast, M2 macrophages were identified as the primary source and target of the TGF-β pathway in the 5/6 Nx group. Furthermore, M2 macrophages were autocrine and paracrine to ECs, MCs, and Fib. Figure 3B demonstrates that the Empa group has inhibited autocrine and paracrine interactions between M2 macrophages and other cells. To investigate the potential correlation between cell communication intensity and gene expression, alterations in the ligand and receptor genes in the TGF-β pathway were analyzed across three groups. The maximum was contributed by TGFB1-(TGFBR1 + TGFBR2) ligand-receptor pair, followed by the TGFB3-(TGFBR1 + TGFBR2) ligand-receptor pair (Fig. 3C). The expression of TGFB1 in Fib, TGFB2 in Fib and MCs, and TGFBR2 in ECs were up-regulated in the 5/6Nx group.

In contrast, the expression of these genes was down-regulated in the Empa group. Moreover, ligand and receptor gene expression levels in other cell types, such as M2 macrophages, were noticeably low in all three groups (Fig. 3D, Supplementary Tables 5–7, Additional File 1). The Empa group had higher TGFB3 gene expression than the 5/6Nx group, but this difference was not statistically significant (Supplementary Table 7, Additional File 1). Although there is a strong correlation between cells in the TGF-β pathway in the 5/6 Nx group, it is important to note that the communication strength of each cell is not always positively correlated with gene expression levels. Given the preceding, we hypothesize that Empa may reduce intercellular communication by inhibiting gene expression. However, it is essential to recognize that other factors may influence the strength of intercellular communication. The findings shed light on the intricate intercellular communication mechanisms of the TGF-β pathway.

3.4 Cross-talk among M2 macrophage and the other cells in the 5/6 nephrectomy CKD

During our investigation of additional pathways involving M2 macrophages in the 5/6 Nx group, we observed that M2 macrophages communicate with tubular cells via the MIF and GRN pathways (Figs. 4A and 4B). The MIF pathway in the sham group primarily depends on PTCs and the LOHs as signal transmitters, with collecting duct PCs serving as primary receptors. However, M2 macrophages emerged as the primary signal recipients in the 5/6Nx group (Fig. 4A), receiving signals from tubular cells via paracrine signaling (Supplementary Fig. 4A, Additional File 2). A significant feature of CKD progression was the observed transition in receptor contribution from ACR3 to CD74 and CXCR4 (Fig. 4A). MIF expression was observed ubiquitously across multiple cell types, indicating its presence in the kidney.

In contrast, ACR3 was primarily expressed in PCs, whereas CD74 was predominantly expressed in M2 macrophages. Lower expression of CXCR4 and CD44 supported ACR3 and CD74 as key receptor genes in the MIF pathway (Fig. 4C). Despite the lack of significant changes in gene expression in the Empa treatment group, we identified a significant difference in intercellular communication strength. The chord and heatmap diagrams revealed decreased signal reception from M2 macrophages, with Empa PCs resuming their role as signal receivers. Notably, the most significant ligand-receptor pair returned to MIF-ACR3 in the Empa group, indicating a return to normalcy (Fig. 4A). The GRN pathway is primarily involved in each of the three groups by the ligand-receptor pair GRN-SORT1. The chord diagram demonstrated that all cell types engaged in extensive intercellular communication.

Moreover, heatmap analysis revealed alterations in GRN signaling pathway communication in CKD. Under sham conditions, tubular cells were identified as the primary signal transmitters. However, there is a significant shift in CKD, where M2 macrophages assumed the role of primary signal transmitters (Fig. 4B). The hierarchy diagram reveals that M2 macrophages significantly increased the paracrine effects of collecting duct intercalated cells (ICs) and PCs. However, Empa treatment significantly reduced this effect (Supplementary Fig. 5B, Additional File 2). The violin plot depicted GRN expression in all cells, revealing significantly higher levels in M2 macrophages of the 5/6Nx group (Fig. 4D). Empa effectively down-regulated GRN gene expression (P < 0.05, Supplementary Table 4, Additional File 1). These findings indicate that Empa has the potential to inhibit the interaction between M2 macrophages and other cells by weakening signal transmission in the MIF pathway and inhibiting gene expression in the GRN pathway.

3.5 Key signaling pathways regulating renal fibrosis in the 5/6 Nx rat

The pro-fibrotic signaling pathways of COLLAGEN, FN1, LAMININ, and THBS were significantly altered in CKD, compared to the sham group. Fib and MCs signal output in these pathways was significantly higher in the 5/6 Nx group, with Fib having the highest output. Figure 5A depicts EC-tubular cell communication in the COLLAGEN and LAMININ pathways. The heat map analysis revealed that tubular cells were the primary targets of signals in all four pro-fibrotic pathways, demonstrating the significant impact of the fibrotic process on tubular cells (Fig. 5B).

Our findings revealed a significant up-regulation of ligand genes, such as COL4A1 and COL4A2 in the COLLAGEN pathway, FN1 in the FN1 pathway, LAMB1 in the LAMININ pathway, and THBS1 and THBS2 in the THBS pathway. These up-regulations were particularly evident in the Fib and MCs of the 5/6 Nx group (Fig. 5C). In ECs, the circle plot illustrated that the COL4A1 and COL4A2 expression levels in the COLLAGEN pathway, and LAMB2 in the LAMININ pathway, were associated with their communication strength (Fig. 5B). In addition, the primary receptor genes involved in the four pathways (ITGA1, ITGB1, SDC4, and CD47) had significant expression levels in tubular cells, supporting the findings of the heat map that tubular cells are the primary signal recipients. Although the receptor gene expression was higher in Fib and MCs (Fig. 5B), the heatmap did not identify them as the primary signal recipients. These findings suggest that ligand genes facilitate cell communication between Fib, MCs, and tubular cells in CKD. The decreased ligand gene expression in Fib within the relevant pathways due to Empa treatment explained the observed attenuation in cellular communication strength in the Empa group.

3.6 Potential mechanisms of renal fibrosis in the 5/6 Nx rat

In the 5/6 Nx CKD model, TGF-β pathway activation has promoted the proliferation and activation of M2 macrophages (Fig. 1A). In addition, CD74 receptor gene expression has been up-regulated in M2 macrophages (Fig. 4C). The interaction between tubular cell-expressed MIF and CD74 promotes the recruitment of inflammatory cells. Furthermore, up-regulation of the ligand gene GRN on M2 macrophages (Fig. 4C, Supplementary Table 4, Additional File 1) facilitates M2 macrophage polarization, aggravating renal damage. Moreover, M2 macrophages communicate with ECs, Fib, and MCs via the TGF-β pathway, promoting fibrosis progression. Increased expression of fibrotic genes (COL4A1, FN1, LAMB1, and THB1) in Fib and MCs, combined with up-regulation of receptor genes (ITGA1, ITGB1, SDC4, and CD47) in tubular cells, promotes extracellular matrix (ECM) deposition, improves cell adhesion, and accelerates the progression of renal fibrosis. The number of M2 macrophages was significantly reduced after Empa treatment (Fig. 1A). Empa inhibits the expression of the TGF-β1 gene in Fib and the GRN gene in M2 macrophages, reducing intercellular communication in the TGF-β pathway between M2 macrophages, ECs, Fib, and MCs. This attenuation is critical for modulating the cellular interactions in renal fibrosis progression. Moreover, there was a significant inhibition of communication between Fib, MCs, and tubular cells in the fibrotic pathway during Empa treatment. Therefore, we hypothesize that Empa slows renal fibrosis progression by reducing the number of M2 macrophages and inhibiting signal transduction in both pro-inflammatory and fibrotic pathways.

The present study aimed to establish 5/6 Nx non-diabetic CKD rats and use scRNA-seq to uncover the communication network of ligand-receptor pairs and signaling pathways associated with the fibrosis process in CKD, and to investigate how Empa influences the cell-cell communication in CKD. Empa treatment significantly weakened the intercellular interactions, which were more assertive in CKD patients. In addition, we identified and analyzed key cell types and signaling pathways in the TGF-β signaling pathway and fibrosis pathway, emphasizing the importance of M2 macrophages and Fib in CKD and demonstrating that Empa can inhibit renal fibrosis progression by inhibiting intercellular signaling among these cell types.

M2 macrophages are recognized for their role in tissue repair and immune regulation ^{23, 24}. However, overactivation of M2 macrophages can result in the deposition of excessive ECM and the recruitment of fibrocytes, leading to tissue fibrosis ²⁵. Considering renal fibrosis, numerous studies ^26–28 demonstrated that M2 macrophages play an important role in fibrosis development and progression. Our team recently investigated the role of macrophage subpopulations in CKD progression in 5/6 Nx rats ¹⁴. The main pro-fibrotic process involved M2 macrophage polarization from CD206⁻CD68⁻ to CD206⁺CD68⁺ state, increasing pro-fibrotic genes and transforming into fibrocytes ²⁷. Empa inhibited the expression of the pro-fibrotic genes IFG1 and TREM2, which promote the transformation of M2 macrophages into fibroblasts. It also inhibited the genes GPNMB, LGALS3, PRDX5, and CTSB that promoted polarization by regulating the mTOR pathway and mitophagy and attenuated the inflammatory signaling of CD8⁺effector T cells. Therefore, the 5/6 Nx group in this study demonstrated increased interaction strength between macrophages and other cell types compared to the sham group. In CKD, M2 macrophages have been identified as key players in the communication flow of the TGF-β signaling network. The Empa treatment effectively reduced these interactions. The hierarchical diagram revealed the specific roles of different cell types and their autocrine and paracrine pathways in the TGF-β pathway. The ligand-receptor pairs TGFB1-(TGFBR1 + TGFBR2) and TGFB3-(TGFBR1 + TGFBR2) contributed the most. Gene expression analyses revealed altered TGFB1, TGFB2, and TGFBR2 expression levels in several cell types, which were suppressed in the Empa group. These findings provide valuable insights into the complex mechanisms of intercellular communication in the TGF-β pathway.

Numerous studies have demonstrated that glomerular and tubular cells may contribute to renal fibrosis through mechanisms such as endothelial dysfunction ^29–32, mesangial cell apoptosis ^{33, 34}, and tubular cell necrosis ^35–37. Similarly, we used scRNA-seq to analyze intercellular communications between M2 macrophages and tubular epithelial cells to investigate further the potential contribution of M2 macrophages in the development of renal fibrosis via cellular cross-talk. M2 macrophages communicate with tubular cells through the MIF and GRN pathways. The MIF pathway in the sham group primarily used PTCs and LOHs as signal transmitters, with collecting duct PCs as the primary receivers. However, M2 macrophages emerged as the primary signal receivers in the 5/6 Nx group, receiving signals from tubular cells via paracrine signaling. The observed transition in receptor contribution from ACR3 to CD74 + CXCR4 was a critical aspect of CKD progression. Empa treatment can potentially reduce interaction between M2 macrophages and other cells by inhibiting signal transmission in the MIF pathway and gene expression in the GRN pathway. MIF is a potent cytokine that recruits leukocytes and stimulates pro-inflammatory responses by binding to CXCR4 or CD74 ^{38, 39}.

The increased MIF expression in endothelial and renal tubular epithelial cells is associated with macrophage accumulation and renal dysfunction ⁴⁰. Anti-MIF therapy has been shown to ameliorate IgA nephropathy and decrease TGF-β expression in glomerulonephritis ⁴¹. The TGF-β pathway is a classic pathway in renal fibrosis, and numerous studies ^{35, 42, 43} demonstrated that it plays an important role in matrix protein synthesis by inhibiting matrix degradation. GRN is a multifaceted protein involved in cell growth, tissue remodeling, and inflammation. In addition, it has been associated with systemic lupus erythematosus ^{44, 45}.

Moreover, GRN is involved in M2 macrophage polarization in lupus nephritis ⁴⁵, which can exacerbate renal fibrosis ⁴⁶. Importantly, Empa treatment effectively down-regulated the GRN gene expression in our study. Therefore, our findings indicate that Empa treatment has the potential to attenuate the interaction between M2 macrophages and other cells by weakening the signal transmission in the MIF pathway and inhibiting gene expression in the GRN pathway.

Excessive ECM deposition within the tubulointerstitium of the kidney is the defining feature of interstitial fibrosis. The ECM primarily comprises four types of proteins: collagen, laminin, fibronectin, and thrombospondin ⁴⁷. Under pathological conditions, these proteins can be excessively synthesized and deposited, causing renal fibrosis progression. The present study observed significant alterations in pro-fibrotic signaling pathways in CKD, specifically collagen synthesis (COL4A1), fibronectin synthesis (FN1), laminin synthesis (LAMB1), and thrombospondin synthesis (THBS1). The 5/6 Nx group revealed increased signal output from Fib and MCs in all four pathways, with Fib having the highest signal output. Communication between ECs and tubular cells was also observed in the collagen and laminin pathways. Collagens and fibronectin, essential ECM proteins, regulate various biological processes such as tissue repair, wound healing, and embryonic development ^{48, 49}. After tissue injury, collagens and fibronectin are the first proteins secreted by fibroblasts and other migratory cells at the injury site ⁵⁰. Collagens are also the most abundant proteins in the ECM, which provide mechanical support and structure to tissues ⁴⁸.

In contrast, fibronectin acts as a scaffold for cell migration and tissue regeneration, as well as facilitating cell adhesion ⁵¹. Collagens and fibronectin function as a fundamental framework, allowing ECM molecules such as proteoglycans and glycosaminoglycans to accumulate and increase the complexity and diversity of the ECM. Therefore, collagens and fibronectin deposition are critical in the wound-healing process. However, dysregulation in their expression or function can lead to pathological conditions such as fibrosis. Type IV collagen, with laminins, proteoglycans, and entactin/nidogen, is the primary structural component of the glomerular basement membrane (GBM), forming a mesh-like structure. Previous studies have demonstrated that type IV alpha 1 collagen (COL4A1) plays an important role in the fibrosis progression ⁵². TGF-β is thought to be responsible for increased COL4A1 and FN1 expression, which leads to renal fibrosis ⁵³. Ligand genes COL4A1, COL4A2, FN1, LAMB1, THBS1, and THBS2 were significantly up-regulated in this study, particularly in the Fib and MCs of the 5/6 Nx group. The significant expression of receptor genes ITGA1, ITGB1, SDC4, and CD47 in tubular cells is consistent with their role as primary signal receivers. Our findings emphasize the importance of ligand genes in facilitating cell communication between Fib, MCs, and tubular cells in CKD. The observed decrease in ligand gene expression in Fib as a result of Empa therapy explains the weakened cellular communication in the Empa group.

LAMB1, an ECM protein 54, mediates cell adhesion and migration. Researchers have focused increasingly on its role in tissue fibrosis, a common aging-related organ dysfunction ⁵⁵. Recent research suggests that renal tubular epithelial cells can act as key mediators of renal fibrosis, a common age-related organ dysfunction ^{56, 57}. Multiple ECM molecules, including LAMB1, are produced by renal tubular epithelial cells, which can interact with various immune cells to promote fibrosis progression ^{58, 59}. Activated platelets secrete THBS proteins that activate the CD47 receptor on target cells, activating fibrosis-related downstream signaling pathways.

Furthermore, the THBS/CD47 signaling pathway has been identified as a key regulator of renal fibrosis ⁶⁰. Importantly, this pathway inhibits the fibrosis progression in experimental models of various diseases, including diabetes, liver cirrhosis, and pulmonary fibrosis ⁶¹. Thrombospondin-1 (THBS1) is a glycoprotein found in numerous tissues, including the kidney. THBS1 is up-regulated in glomerular mesangial cells and tubular epithelial cells in DKD, contributing to the development of renal fibrosis ⁶². TGF-β signaling activation is one way by which THBS1 promotes fibrosis. THBS1 can bind to and activate latent TGF-β, causing phosphorylation and nuclear translocation of Smad proteins, which regulate the ECM gene expression. In addition, THBS1 can induce the TGF-β receptor expression and improve TGF-β signaling sensitivity in cells. These effects of THBS1 on TGF-β signaling contribute to renal fibrosis in diabetic nephropathy ⁶².

In conclusion, THBS1 is an important endogenous TGF-β activator in diabetic nephropathy and promotes the development of renal fibrosis. THBS1 and its downstream signaling pathways are potential therapeutic targets to treat diabetic nephropathy. These findings highlight the importance of LAMB1 and the THBS/CD47 signaling pathway in the development of fibrosis in organ dysfunction associated with aging and suggest that targeting these pathways may have therapeutic potential for reducing tissue fibrosis.

The 5/6 Nx CKD model revealed that the TGF-β pathway stimulates M2 macrophages. The up-regulation of the CD74 receptor gene in M2 macrophages and the interaction between MIF expressed in tubular cells, and CD74 facilitates the recruitment of inflammatory cells. Furthermore, the GRN gene up-regulation on M2 macrophages promotes polarization, thereby exacerbating renal injury. In addition, increased fibrotic gene expression in Fib and MCs, in conjunction with the receptor gene up-regulation in tubular cells, promotes increased ECM deposition, cell adhesion, and renal fibrosis progression. Empa treatment significantly reduced the number of M2 macrophages and down-regulated the expression of TGF-β1 in Fib and the GRN gene in M2 macrophages. This effectively suppressed the intercellular communication within the TGF-β pathway between M2 macrophages, ECs, Fib, and MCs, modulating the cellular interactions involved in renal fibrosis progression. Therefore, we believe Empa slows the progression of renal fibrosis by reducing the number of M2 macrophages and inhibiting signal transduction in both pro-inflammatory and fibrotic pathways.

Ethics approval and consent to participate

Animal experiments in the present study strictly followed the National Law for Laboratory Animal Experimentation guidelines. They were approved by the Experimental Animal Ethics Committee of Jinan University (Approval number: No. 00245787). Animal welfare and well-being were prioritized throughout the study. The animals received appropriate care, housing, and veterinary support, and efforts were made to minimize any potential distress or discomfort. The Experimental Animal Ethics Committee of Jinan University (Approval number: No. 00245787) verifies that this study was conducted in compliance with ethical standards and guidelines.

Consent for publication

All the authors have read and approved the paper and declare no potential conflicts of interest in the paper. All the authors agree to publish this paper.

Competing interests

No potential conflict of interest relevant to this article was reported.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 8210032527 and 82170690), GuangDong Basic and Applied Basic Research Foundation (Grant No. 2019A1515110488, 2020A1515111209 and 2022A1515110267), The Shenzhen Science and Technology Innovation Committee of Guangdong Province of China (Grant No. JCYJ20210324123200003).

Authors' contributions

Prof Zheng, Prof Hocher, and Dr Zhu designed and supervised the study. Dr Lu, Dr Lei , Dr Luo, Hong-Wei Wu, Chun Tang, Tian-Jiao Cui, Lian Lin, Xue-Mei Zhang, Wen-Jing Sun, Denis Delic, Thomas Klein and Xiao-Hua Wang performed the experiments and collected the samples. Dr Luo performed single cell RNA sequencing. Dr Lu , Dr Lei and Dr Luo analyzed the scRNA-seq data. Dr Zhu interpreted the data. Dr Lu and Dr Lei wrote the manuscript. Prof Zheng, Prof Hocher edited the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We are thankful to Singleron Biotechnologies for their technical support in single-cell sequencing and data analysis. Their assistance were instrumental in the success of this research project.

Availability of data and materials

The data supporting the findings of this study are available in Additional files and from the corresponding author.

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Intercellular Communication Network of CellChat Uncovers Mechanisms of Kidney Fibrosis Based on Single-Cell RNA Sequencing

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Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Methods

2.1 Model for Chronic Kidney Disease

2.2 ScRNA-seq analysis

2.2.1 Sample preparation

2.2.2 ScRNA-seq data processing and analysis

2.2.3 analysis of general intercellular communication

3. Results

3.1 Comparison analysis reveals alterations of intercellular contact in CKD

3.2 Predictive modeling of pathway networks

3.3 Overview of intercellular communication in TGF-β signaling pathway

3.4 Cross-talk among M2 macrophage and the other cells in the 5/6 nephrectomy CKD

3.5 Key signaling pathways regulating renal fibrosis in the 5/6 Nx rat

3.6 Potential mechanisms of renal fibrosis in the 5/6 Nx rat

4. Discussion

Conclusion

Declarations

References

Supplementary Files

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