GRAPHICAL ABSTRACT

Highlights

• A high-fat diet and streptozotocin may serve as a T2DKD model.

• Injured proximal tubules secrete chemokines such as CXCL12, which recruit T cells.

• Elevated CXCL12 level in human diabetic kidneys correlates with disease progression.

• The inflammatory milieu in T2DKD represents a potential therapeutic target.

INTRODUCTION

Diabetic kidney disease (DKD) stands as one of the most common causes of kidney disease, accounting for approximately 50% of kidney failure cases requiring dialysis [1]. The burden of DKD is steadily increasing, projected to impact 550 million patients by 2035, mainly due to the rapid rise in type 2 diabetes mellitus [2]. However, current therapeutic strategies targeting type 2 diabetic kidney disease (T2DKD), such as renin-angiotensin system blockers and sodium-glucose cotransporter 2 inhibitors, fail to address complex pathophysiological aspects of the disease [3]. Consequently, they fall short in completely preventing the deterioration of kidney function. A comprehensive understanding of the pathophysiology, potentially gained through various modeling approaches, is essential for the development of new treatments targeting the underlying mechanisms of the disease [4].

Single-cell RNA sequencing (scRNA-seq) has emerged as an invaluable tool for high-resolution, in-depth gene expression analysis in kidney disease [5]. Researchers studying T2DKD have also leveraged scRNA-seq technique to observe intricate gene expression changes at the single-cell level [6]. However, previous studies primarily focused on genetic models of diabetes and its subsequent progression to DKD, rather than induced models more reflective of T2DKD [7-10]. Considering that T2DKD results from an intricate mix of environmental interactions and polygenic traits, an induced model, effectively mirroring human T2DKD characteristics such as peripheral insulin resistance and diminished β-cell mass, may accurately reflect the actual manifestation of the disease [11]. This subset exhibits distinct patterns of tissue involvement and progression in kidney disease compared to type 1 DKD [12]. Herein, we utilized this induced T2DKD model in mice, coupled with scRNA-seq and other experimental techniques, to elucidate alterations at the single-cell level. This approach enabled us to unravel the intricate interplay between kidney parenchymal tissues and infiltrated immune cells, potentially reflecting the renal response to type 2 diabetes mellitus. We also extended the findings to human T2DKD tissues confirmed by biopsy, enhancing our understanding of the disease.

METHODS

Animals and treatment

C57BL/6N male mice weighing 20 g (Koatech, Pyeongtaek, Korea) were housed under specific pathogen-free conditions at the Seoul National University College of Medicine. Following a one-week adaptation period, a T2DKD model was induced by feeding the mice a high-fat diet consisting of 60% standard laboratory chow and 20% carbohydrates (Research Diets, New Brunswick, NJ, USA) for 24 weeks. Four weeks into the highfat diet, streptozotocin was intraperitoneally injected at a dose of 50 mg/kg per day for 5 consecutive days. The purity of streptozotocin was 98% (Sigma-Aldrich, St. Louis, MO, USA). The control group was fed a standard diet and injected with sodium citrate (i.e., vehicle for streptozotocin) for 5 days. Body weights were measured every 3 days, fasting blood sugar concentrations were assessed every 2 weeks, and urine samples were collected over a 16-hour period. Mice were sacrificed at specified time points after perfusion with phosphate-buffered saline, and their kidneys were harvested for subsequent biochemical and histologic analyses. To assess the therapeutic effects of C-X-C motif chemokine 12 (CXCL12) blockade, mice received intraperitoneal injections of anti-CXCL12 antibody (αCXCL12 Ab) (R&D Systems, Minneapolis, MN, USA) at a dose of 30 µg once weekly for four injections, starting 1 month before the completion of the 6-month T2DKD induction period. The control group received injections of isotype control antibody (R&D Systems).

HOMA-IR score

Plasma insulin levels were measured using a mouse insulin enzyme-linked immunosorbent assay (ELISA) kit (EMINS, ThermoFisher Scientific, Waltham, MA, USA) following the manufacturer’s protocol. Fasting insulin and glucose levels were used to calculate the homeostatic model assessment of insulin resistance (HOMA-IR) score using the following formula: fasting insulin (μU/mL)×fasting glucose (mg/dL)/405.

Flow cytometry

Kidney tissue was minced and digested with 4 mg/mL collagenase D for 30 minutes at 37°C. Cells were filtered through a 40-μm strainer, suspended in 40% Percoll underlain with 80% Percoll (GE HealthCare, Chicago, IL, USA), and centrifuged. The middle layer, enriched with leukocytes, was harvested. Cells were washed, resuspended in staining buffer consisting of 2% horse serum and 0.05% sodium azide, blocked with anti-mouse CD16/CD32 antibodies (eBioscience, San Diego, CA, USA) for 15 minutes, and then incubated with primary antibodies. Alternatively, following surface staining, cells were incubated with fixation-permeabilization buffer, washed with permeabilization buffer (BD Biosciences, Franklin Lakes, NJ, USA), and then incubated with antibodies against intracellular antigens. Samples were processed by a BD Fortessa X-20 (BD Biosciences) and analyzed with FlowJo software (https://www.flowjo.com). The absolute number of live cells per kidney was calculated using trypan blue staining and a C-Chip disposable hemocytometer (INCYTO, Cheonan, Korea). Subsequently, the number of immune cell subsets was calculated using the proportion results from flow cytometric analysis. The proportion of cytokine-producing cells was calculated when the positivity of the relevant isotype control was less than 1%. The antibodies used for flow cytometry are listed in Supplementary Table 1.

Immunohistochemistry

Paraffin-embedded tissue sections were deparaffinized and rehydrated with xylene and ethanol. After retrieval of antigens, sections were blocked in 0.1 M Tris containing 0.1% Triton X-100, 10% normal goat serum, and 1% bovine serum albumin. Blocking reagent was applied for 1 hour at room temperature. Images were captured using a Nikon Eclipse Ci-L microscope (Tokyo, Japan). The antibodies used for immunohistochemistry are listed in Supplementary Table 1.

Immunofluorescence

The steps before blocking were the same as those described in the immunohistochemistry section. After antigen retrieval, the sections were blocked in phosphate-buffered saline containing 10% normal goat/donkey serum and 0.3% Triton X-100. Images were acquired using a Leica STED CW microscope (Leica Microsystems, Wetzlar, Germany). The antibodies used for immunofluorescence are listed in Supplementary Table 1.

Real-time quantitative polymerase chain reaction

RNA was purified from tissues using a Direct-zol RNA MicroPrep kit (Zymo Research, Irvine, CA, USA). Subsequently, RNA was reverse transcribed to cDNA with a PrimeScript RT Reagent kit (TaKaRa Bio Inc., San Jose, CA, USA). Gene expression was evaluated via real-time reverse transcription polymerase chain reaction (PCR) using iQ SYBR Green Supermix (Bio-Rad, Hercules, CA, USA) on a PCR amplification and detection instrument (CFX Connect Real-Time PCR Detection System, Bio-Rad). Gene expression levels were normalized to 18S rRNA, and the mean relative gene expression was calculated using the 2^–ΔΔCt method. The primers used are listed in Supplementary Table 2.

Kidney morphometry

Kidneys were fixed in 4% formalin, paraffin-embedded, and cut into 4 μm sections. These sections were subsequently stained with periodic acid–Schiff. Assessment of mesangial expansion and glomerular volume was conducted by pixel counts on kidney sections under ×400 magnification. Digitized images were scanned, and profile areas were outlined using ImageJ software version 1.5 (National Institute of Health, Bethesda, MD, USA). The mean glomerular volume was determined from the mean glomerular cross-sectional area observed via light microscopy. Mesangial expansion was estimated in a blinded manner using the following grading system: 0, 0%; 1, 1%–24%; 2, 25%–49%; 3, 50%–74%; 4, ≥75%. Glomerular cross-sectional area and mesangial expansion were calculated based on the average area of 50 glomeruli in each group. Sirius red staining assay (Abcam, Cambridge, UK) was conducted following the manufacturer’s instructions, and the areas of fibrosis and positive staining were quantified using ImageJ software.

Single-cell RNA sequencing

A single-cell suspension of mouse kidney cells was prepared following the 10× Genomics single-cell protocol. Mouse kidney cells, with a concentration of approximately 800 to 1,200 cells/μL, were kept on ice for single-cell analysis. The single-cell suspension was mixed with the master mix and loaded, alongside single-cell 5’ gel beads and partitioning oil, into the chip kit. The gel beads were coated with unique molecular identifiers, unique primers containing 10× cell barcodes, and oligo poly(dT) sequences. Single-cell gel bead-in-emulsions were generated, and barcoding occurred after the generation step using a chromium controller (10× Genomics, Oxford, UK). The barcoded full-length cDNA was constructed via reverse transcription of single cells, followed by incubation using PCR.

After encapsulation, cDNAs were selectively purified using Dynabeads Cleanup Mix (ThermoFisher Scientific) and then amplified to construct the 5’ Gene Expression Dual Index library. Subsequently, the final single-cell gene expression libraries were sequenced at 50,000 reads per cell with paired-end 150 bp on NovaSeq 6000 (Illumina, San Diego, CA, USA). The 4150 TapeStation instrument (Agilent, Santa Clara, CA, USA) was used to examine the yield and purity of the final libraries. Quality control, alignment to the reference genome (mm10), and the generation of count tables were performed using CellRanger version 7.1.0 (10× Genomics) from the fastq files.

The Seurat package version 5.0.1 (https://satijalab.org/seurat/articles/install_v5.html) was utilized for downstream analysis [13]. Following quality control, which involved retaining cells expressing 200 to 2,500 unique genes and exhibiting ≤50% mitochondrial genes for parenchymal cells and ≤10% mitochondrial genes for immune cells, data from both the control and T2DKD mice were integrated and batch-corrected using the ‘HarmonyIntegration’ method. Subsequently, parenchymal and immune cell clusters were visualized using the ‘RunUMAP’ (Uniform Manifold Approximation and Projection) function and annotated based on previously documented marker gene expression profiles [14-16]. Differentially expressed genes (DEGs) were identified based on average logarithmic fold change >0.5 and adjusted P<0.05. Pathway enrichment analysis was performed using the ‘fgsea’ package (https://bioconductor.org/packages/release/bioc/html/fgsea.html), with gene sets associated with hallmark pathways in MSigDB being selected. To identify crosstalk between cell clusters, the CellChat package (http://www.cellchat.org) was employed. Additionally, cell-cell interactions between tubular cells and immune cells were inferred by plotting canonical chemokine ligand-receptor pairs.

Human samples

Normal human kidney tissues were obtained from patients who underwent radical nephrectomy due to malignancy but did not have hypertension, diabetes, or chronic kidney diseases (n=7). Biopsy-confirmed T2DKD kidney tissues were obtained during routine clinical evaluation of the disease (n=31). Immunohistochemistry was utilized to estimate the areas positive for CXCL12 in the tissue. The tubular expression level of CXCL12 was quantified using ImageJ software, where the tubular area was handcrafted in at least 10 fields per case at a 100× magnification and validated by a nephropathologist. According to the mean level of CXCL12 expression in tubules, patients were categorized into the CXCL12^high and CXCL12^low groups. The risk of kidney progression (e.g., doubling of serum creatinine, decrease in 50% of estimated glomerular filtration rate, and development of kidney failure) was compared between the CXCL12^high and CXCL12^low groups.

Statistical analysis

All analyses and calculations were conducted using GraphPad Prism software version 8.0 (GraphPad Software Inc., San Diego, CA, USA). Differences between groups were assessed using Student’s t-test and analysis of variance with Tukey’s test for comparisons between two and multiple groups, respectively. Survival curves were generated using the Kaplan–Meier method, and a log-rank test was applied to compare survival curves between groups. A P value less than 0.05 was considered statistically significant.

Study approval

All animal experiments were approved by the Seoul National University Institutional Animal Care and Use Committee (no. 22-2074-S1A0). The usage of human samples was approved by the Institutional Review Board of Seoul National University Hospital (no. H-2302-124-1408) and complied with the Declaration of Helsinki; all patients provided written informed consent for the donation and use of their specimens.

Data sharing statement

The dataset for single-cell RNA transcriptomics has been deposited in the Gene Expression Omnibus database (accession number: GSE261356). This study does not report any original code, and the codes used are available in the Methods section. Any additional information required to reanalyze the data is available from the lead contact upon request.

RESULTS

Induced mouse model reflecting T2DKD

We established an induced mouse model mimicking T2DKD using 8-week-old male mice over a period of 6 months. Both hyperlipidemia and hyperglycemia, characteristic findings in T2DKD patients [17], were observed in this model (Fig. 1A). Initially, the T2DKD mice exhibited significant weight gain, followed by weight loss after pancreas disruption via streptozotocin administration, compared to the control mice, due to continuous urinary carbohydrate loss (Fig. 1B). The HOMAIR score, an indicator of insulin resistance, was elevated in the T2DKD mice (Fig. 1C) [18]. In contrast to the overall body weight trend, T2DKD mice exhibited an increase in kidney weight (Fig. 1B), a notable feature observed in T2DKD patients [19]. The blood creatinine level, serving as a marker for tubular injury [20], remained elevated throughout the experiment period in T2DKD mice compared to control mice (Fig. 1D). Additionally, the final creatinine clearance was higher in T2DKD mice than in control mice, indicative of hyperfiltration seen in the early stage of T2DKD (Fig. 1D) [21]. The random urine protein-to-creatinine ratio, used to estimate proteinuria as a marker of glomerular injury, was higher in T2DKD mice than in control mice (Fig. 1E).

Histological evaluation of the kidneys from T2DKD mice revealed a marked increase in glomerular volume accompanied by mesangial expansion, underscoring the typical glomerular alterations observed in the initial stage of T2DKD (Fig. 1F) [22]. Additionally, immunohistochemical staining for kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin provided clear evidence of tubular damage in T2DKD mice (Fig. 1G) [23]. A pronounced increase in the Sirius red-positive area, serving as a fibrosis marker, was evident in the tubulointerstitial areas of T2DKD kidneys compared to control kidneys (Fig. 1H). Taken together, this model closely mirrors the pathophysiological features of human T2DKD, as demonstrated by significant injuries in both glomerular and tubular structures [24-26].

Interaction between glomerulus and proximal tubular cells

To explore the transcriptomic alterations at a single-cell level, scRNA-seq was conducted in T2DKD and control kidneys. This generated a dataset encompassing a total of 115,412 cells (68,126 cells from control kidneys and 47,286 cells from T2DKD kidneys) after quality control and batch correction. The cells were unbiasedly distributed across the disease status (Fig. 2A). A total of 17 cell types were classified using representative markers [14-16], as follows: Lrp2 for segments of the proximal tubule (PT) (Slc4a4 and Slc5a12 for PT-S1, Slc4a4 and Slco1a1 for PT-S2, and Slc27a2 and Cyp7b1 for PT-S3), Slc4a11 for thin limb of Henle’s loop, Slc12a1 for thick ascending limb, Slc12a3 for distal convoluted tubule, Slc8a1 and Aqp2 for connecting tubule, Atp6v1b1 and Slc26a4 for collecting duct, Flt1 and Pi16 for glomerular endothelial cell (gEC), Flt1 and Plvap for peritubular endothelial cell, Flt1 and Vim for arteriolar endothelial cell, Pdgfrb and Myh11 for mesangial cell, Myh11 and Tagln for pericyte, Nphs1 and Nphs2 for podocyte (PODO), and Ptprc for immune cells, with Cd68 and Csf1r for myeloid cell, Cd3g for T-cell, Thy1 and Nkg7 for natural killer (NK) cell, Igkc and Cd79a for B cell, and Xbp1 and Fkbp11 for plasma cell (Fig. 2B). The expression patterns of canonical markers for each identified cell type are visualized on the dimension reduction plots (Fig. 2C).

When comparison in DEGs was performed, a significant number of genes were found to be upregulated in various types of T2DKD parenchymal cells (Fig. 2D, Supplementary Data 1). Given the fact that the glomeruli are where the initial damage occurs in T2DKD [27], we focused specifically on cell clusters constituting the glomeruli, such as PODO and gECs. Interestingly, while the number of DEGs in PODO remained similar between control and T2DKD kidneys, a notable contrast was observed in gECs, with 330 upregulated genes in T2DKD kidneys compared to only 17 in control kidneys.

Of note, the PT exhibited a considerable variation in DEGs compared to other parenchymal tissue cell types. All segments of PT (e.g., PT-S1, PT-S2, and PT-S3) consistently exhibited high numbers of upregulated DEGs in the T2DKD model [28]. Taking this into account, along with increased tubular injuries observed in histological findings, we directed our attention to the crosstalk between glomeruli and PT. Utilizing ligand-receptor interaction analysis, we uncovered an increased level of interaction in T2DKD kidneys compared to the control kidneys (Fig. 2E). Specifically, our analysis identified 18 ligand-receptor interactions between PODO and PT segments in control kidneys, which increased to 28 interactions in T2DKD kidneys. This suggests that PODO may be indirectly affected by altered crosstalk with PT cells. Such altered signaling could potentially contribute to early glomerular damage, even in the absence of immediate transcriptomic changes within PODO themselves. Similarly, the number of interactions between gEC and PT segments increased from 24 in control kidneys to 51 in T2DKD kidneys. Pathway analysis revealed an upregulation of various inflammatory pathways in the PT segments of T2DKD kidneys (Fig. 2F). Notably, the interferon-γ response was the most significantly upregulated pathway in the PT-S1 and PT-S2, while tumor necrosis factor-α signaling was elevated in the PT-S3. Other inflammatory pathways, such as the inflammatory response and interleukin-6 signaling were upregulated across all PT segments, corroborating previous studies on tubular inflammation in T2DKD kidneys [29,30]. This widespread increase in inflammatory pathways within the PT segments suggests the pivotal role of crosstalk between glomeruli and PT in driving the inflammatory environment within T2DKD kidneys. Additionally, we observed varying degrees of inflammatory responses in other cell types (Supplementary Data 2).

Immune cell recruitment triggered by diabetic parenchyma

Human T2DKD kidneys manifest inflammatory characteristics, exacerbating the progression of kidney dysfunctions [29,31]. This induced T2DKD model corroborated this inflammatory feature, showing notable increases in CD45⁺ immune cells and CD3⁺ T cells within the kidneys (Fig. 3A). This finding was further supported by scRNA-seq data, demonstrating a significant change in the immune cell composition along with an increased proportion of T and NK cell clusters (Fig. 3B). The proportion of immune cells increased from 10.2% in control kidney to 15.8% in T2DKD kidney, with the proportion of T and NK cell clusters rising from 19.0% to 28.5% among immune cells. Subsequent flow cytometric analysis revealed significant increases in the numbers of both CD4⁺ and CD8⁺ T cells in the T2DKD mice (Fig. 3C). Conversely, there was a reduction in the number of F4/80^high CD11b^low macrophages in the T2DKD kidneys, which suggests the T2DKD environment adversely affects the residency of kidney macrophages (Fig. 3D) [32]. When T-cell subsets were classified based on surface markers (naïve, CD44⁻ CD62L⁺; effector, CD44⁻ CD62L⁻; effector memory, CD44⁺ CD62L⁻; and central memory, CD44⁺ CD62L⁺), effector memory T cells were predominantly increased (Fig. 3E) [15,33,34]. Furthermore, interleukin-17A levels were elevated in CD4⁺ T cells, while interferon-γ levels were increased in CD8⁺ T cells (Fig. 3F).

After subclustering of the T and NK cell cluster, five distinct subsets were characterized by canonical markers (Fig. 4A and B) [14,16]. Remarkably, there was a consistent increase in the number of cells across all identified subsets (Supplementary Table 3). These findings indicate a significant infiltration of immune cells, especially T cells, following the induction of T2DKD. Building upon the aforementioned crosstalk between the glomeruli and PT segments in promoting inflammation in T2DKD, we further examined whether the altered phenotype of PT induced by T2DKD plays a role in T-cell recruitment. As depicted in Fig. 4C, PT segments exhibited enhanced chemokine secretion following T2DKD induction. Analysis of selected chemokine ligands and receptors revealed elevated expression levels of Cxcl12 gene in PT segments, alongside increased expression of relevant receptor, C-X-C motif chemokine receptor 4 gene (Cxcr4) in T and NK cells (Fig. 4D). Other significant ligand-receptor pair genes, such as Cxcl10-Cxcr3 and Cxcl16-Cxcr3, are also demonstrated in T2DKD kidneys (Supplementary Figs. 1-3). In contrast to the limited interactions observed between PT segments and T and NK cells in control kidneys, T2DKD kidneys highlighted strengthened chemokine ligand-receptor interactions. The protein level of CXCL12 was markedly elevated in the PTs of T2DKD kidneys (Fig. 4E), and CD3⁺ T cells infiltrated the regions surrounding the CXCL12⁺ PTs (Fig. 4F). Cxcl12 gene expression in other kidney disease conditions, such as the mouse model of unilateral ureteral obstruction and human allograft kidneys with rejection, was not elevated in the PTs, suggesting that CXCL12-relevant mechanisms may represent a specific inflammatory feature of T2DKD (Supplementary Fig. 4) [35,36]. Collectively, PTs in T2DKD may actively secrete a broad array of chemokines, thereby facilitating the infiltration of T cells equipped with inflammatory capabilities.

To further evaluate the therapeutic potential of CXCL12 signaling, T2DKD mice were administrated with αCXCL12 Ab. The overall numbers of both CD4⁺ and CD8⁺ T cells did not differ between the αCXCL12 Ab-treated and control Ab-treated groups (Fig. 4G). However, within the CD4⁺ and CD8⁺ T subsets, the proportions of effector (CD44⁻ CD62L⁻) and effector memory (CD44⁺ CD62L⁻) T cells were reduced in the αCXCL12 Ab-treated group (Fig. 4H), indicating that CXCL12 plays a prominent role in recruiting activated T cells. These findings suggest that targeting CXCL12 signaling may serve a therapeutic strategy for mitigating inflammation in T2DKD.

CXCL12 expression in human T2DKD kidneys

The role of the CXCL12-CXCR4 ligand-receptor pair was validated in human DKD kidneys to broaden our understanding of its impact on kidney dysfunction. Analysis of kidney biopsies from patients with T2DKD revealed increased expression of CXCL12 compared to patients with normal kidney function (Fig. 5A). CD3⁺ T cells infiltrated regions surrounding CXCL12⁺ PTs of human T2DKD kidneys (Fig. 5B). Notably, high CXCL12 expression in kidney tubules was associated with kidney dysfunction among T2DKD patients (Fig. 5C). Additionally, we discovered a positive correlation between tubular CXCL12 expression and the random urine protein-to-creatinine ratio, as a marker of glomerular injury (Fig. 5C). Importantly, patients with elevated tubular CXCL12 expression showed a trend of accelerated deterioration of kidney function (referred to as kidney progression) (Fig. 5D). Accordingly, tubular CXCL12 expression may serve as a valuable marker for assessing disease severity and predicting disease progression, and potentially as a promising therapeutic target.

DISCUSSION

Various types of mouse model can be utilized to closely simulate human DKD. Notably, genetically modified mice, such as those with genetic mutation in the leptin receptor (db/db mice), have been employed for scRNA-seq to explore the pathophysiology of human DKD [8-10]. Previous study with 12-week-old db/db mice uncovered hyperglycemia-independent responses in glomeruli [9]. A study with 14-week-old db/db mice investigated altered gene signature in glomeruli and tubules, such as promoted endothelium-mesenchymal transition in gEC and impaired cell repair activity in tubular cells [10]. Another study using OVE26 mice, which is a model for progressive type 1 diabetes mellitus, identified changes in myeloid subsets during disease progression [8]. However, these genetic models develop overt and often severe diabetes at a very young age, which are not closely pertinent to real human cases with T2DKD. The induced model we report here involves mice at 6 months of age without genetic alteration, which develop the early stage of DKD, more appropriate for studying the precise pathophysiology of human DKD based on type 2 diabetes mellitus, as evidenced by insulin resistance, proteinuria, hyperfiltration, and relevant histological changes [37].

Emerging evidence supports the significance of immune cells and inflammation in T2DKD pathogenesis, involving both innate and adaptive immune responses [38]. Hyperglycemia triggers T-cell recruitment, activation, differentiation, and cytokine profile alterations [39,40]. Increases in interferon γ and receptor for interleukin-2 were observed in the blood from patients with T2DKD [41]. Furthermore, treating DKD mice with mycophenolate mofetil suppressed interleukin-17-producing cells within the kidney, consequently reducing albuminuria and interstitial fibrosis [42]. Our findings also showed an elevated T cells in T2DKD kidneys, highlighting the critical role of T cells in the pathophysiology. However, this contrasts with the findings from scRNA-seq analyses of genetically modified mice, which showed a decreased proportion of T cells [10]. This discrepancy may be due to the different inducing methods and pathophysiology between genetically modified and induced DKD models. Furthermore, through scRNA-seq, we identified a pivotal role for crosstalk between glomeruli and PT segments in exacerbating inflammation. Previous studies indicate that gEC interact with kidney tubular epithelial cells, including PT, by secreting insulin-like growth factor binding proteins and hepatocyte growth factor, which can either injure or protect tubules [43,44]. Nevertheless, how the interplay induces the transformation of PT fate towards a chemokine-secreting phenotype remain unclear. Further investigation is necessary to delineate the impact of glomerulotubular crosstalk on the development of inflammatory phenotypes in T2DKD.

The chemokine ligand, CXCL12, is a chemotactic cytokine that facilitates the migration and localization of immune cells or sometimes neoplastic cells [45,46]. Its function in mobilizing monocytes and spleen lymphocytes has been well-documented [47]. Additionally, mesenchymal stem cells have exhibited chemotaxis towards CXCL12 in an arthritis model, particularly in regions of inflammatory bone destruction [48]. Our scRNA-seq data indicated that the CXCL12-CXCR4 signaling pathway is involved in T-cell recruitment, with immunohistochemistry demonstrating elevated protein expression levels of CXCL12 in T2DKD kidneys.

T2DKD human biopsy samples showed a correlation between increased tubular CXCL12 expression and poor disease prognosis, although this did not reach statistical significance. This may be due to differences in medication or therapeutic interventions among patients. For example, some patients might be receiving extensive antiproteinuric treatments despite high disease activity and corresponding CXCL12 levels, which could account for the outlier data points observed in our clinical data. Nevertheless, the observed association aligns with the known chemotactic role of CXCL12 and the potential detrimental effects of infiltrated T-cell subsets in T2DKD.

In addition, we observed an increase in the expression of Cxcl10-Cxcl3 and C-X3-C motif chemokine ligand 1 (Cx3cl1)-C-X3-C motif chemokine receptor 1 (Cx3cr1) pairs. Previous research has highlighted the significantly increased number of CX3CR1⁺ cells in the early stages of T2DKD, which might contribute to the progression of this disease, although the exact role of these cells was not discussed [49,50]. Collectively, these findings demonstrate that the crosstalk between glomeruli and PT alters the fate of PT segments, leading to the exacerbation of T2DKD with a highly inflammatory milieu.

In conclusion, this study offers a comprehensive overview of transcriptomic changes, particularly related to the inflammatory response, in an induced T2DKD model. The insights into T-cell recruitment via specific chemokine signals from parenchymal crosstalk lay the groundwork for further investigation into their roles and contributions to T2DKD pathophysiology, with the aim of developing targeted therapies.

SUPPLEMENTARY MATERIALS

NOTES

CONFLICTS OF INTEREST

Soo Heon Kwak has been an associate editor of the Diabetes & Metabolism Journal since 2022. He was not involved in the review process of this article. Otherwise, there was no conflict of interest.

AUTHOR CONTRIBUTIONS

Conception or design: H.K., S.S.H.

Acquisition, analysis, or interpretation of data: P.G.P., J.H., Y.K., M.H., D.Y., H.Y., C.K., Y.C.K., D.S.L., Y.S.K., H.G.K., H.K., S.S.H.

Drafting the work or revising: P.G.P., J.H., Y.K., S.H.K., Y.S.K., H.K., S.S.H.

Final approval of the manuscript: all authors.

FUNDING

This work was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI23C1531 to Seung Seok Han) and the SNUH Research Fund (0320230090 to Seung Seok Han).

ACKNOWLEDGMENTS

The human biospecimens were provided by the Biobank of Seoul National University Hospital, a member of Korea Biobank Network (KBN4_A03), which is supported by the Korea Disease Control and Prevention Agency (#4845-303).

Fig. 1.

Biochemical and histological parameters in type 2 diabetic kidney disease (T2DKD) and control kidneys. (A) Serum cholesterol and glucose levels over 6 months. (B) Total body and kidney weights. (C) Homeostatic model assessment of insulin resistance (HOMA-IR) scores. (D) Trend in serum creatinine over 6 months and creatinine clearance at 6 months of the model. (E) Urinary protein-to-creatinine ratio (uPCR) over 6 months. (F) Representative images of periodic acid–Schiff-stained glomeruli, and measurements of glomerular volume and mesangial expansion. Scale bar=50 μm. (G) Representative images of kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL)-stained kidneys, and their expression levels. Scale bar=100 μm. (H) Representative Sirius red-stained images, and their expression levels. Scale bar=100 μm. ^aP<0.05, ^bP<0.001.

Fig. 2.

Exploring crosstalk between glomeruli and tubules using single-cell RNA sequencing. (A) Uniform Manifold Approximation and Projection (UMAP) visualization of 115,412 cells, categorized by cell type, including proximal tubule segments 1, 2, and 3 (PT-S1, PT-S2, PT-S3), thin limb of Henle’s loop (tL), thick ascending limb (TAL), combined TAL and distal convoluted tubule (DCT), connecting tubule (CNT), collecting duct (CD), glomerular endothelial cell (gEC), peritubular endothelial cell (ptEC), arteriolar endothelial cell (Arteriole), mesangial cell and pericyte (MES and PERI), podocyte (PODO), myeloid cell, T-cell and natural killer (NK) cell, B cell, and plasma cell (PC). (B) Dot plot highlighting canonical marker genes for each identified cell type, with the color intensity reflecting the mean scaled expression of each gene and the dot size representing the proportion of cells expressing the gene. (C) Expression profiles of canonical marker genes for each cell types, with color intensity denoting mean scaled gene expression. (D) Number of upregulated differentially expressed genes in type 2 diabetic kidney disease (T2DKD) and control kidneys. (E) Circle plots showing predicted ligand-receptor interactions between PODO or gECs and PT segments in T2DKD and control kidneys. (F) Visualization of hallmark pathways significantly activated in PT segments of T2DKD kidneys. TNFα, tumor necrosis factor-α; NFκB, nuclear factor-κB; IL, interleukin; STAT, signal transducer and activator of transcription; TGFβ, transforming growth factor-β; UV, ultraviolet.

Fig. 3.

Immune cell-infiltrated milieu in type 2 diabetic kidney disease (T2DKD) kidneys. (A) Representative images of CD45- and CD3-stained kidneys, and their respective expression levels. Scale bar=100 μm. (B) Proportions of whole immune cells, and T and natural killer (NK) cell cluster, calculated from single-cell RNA sequencing data. (C) Flow cytometric analysis of cell number of lymphocytes. (D) Flow cytometric analysis of cell number of myeloid cells. (E) Flow cytometric analysis of proportions of CD4⁺ (left) and CD8⁺ (right) T-cell subsets. (F) Flow cytometric analysis of cytokine production in CD4⁺ and CD8⁺ T cells. Mϕ, macrophage; Nϕ, neutrophil; MHCII, major histocompatibility complex class II; DC, dendritic cell; IFNγ, interferon γ; IL, interleukin; TNFα, tumor necrosis factor-α. ^aP<0.05, ^bP<0.01, ^cP<0.001.

Fig. 4.

Recruitment of T and natural killer (NK) cells in type 2 diabetic kidney disease (T2DKD) kidneys. (A) Uniform Manifold Approximation and Projection (UMAP) plot illustrating cell distributions of T and NK subsets. (B) Dot plot highlighting canonical marker genes for each cell type, with the color intensity reflecting the mean scaled expression of each gene and the dot size representing the proportion of cells expressing the gene. (C) Selected chemokine ligands and receptors on segments of proximal tubule (PT) and T and NK cell subsets in T2DKD kidneys compared to control kidneys. (D) Mapping of C-X-C motif chemokine 12 (Cxcl12) and C-X-C motif chemokine receptor 4 gene (Cxcr4) genes in PT segments and T and NK cell subsets, respectively. (E) Representative images of CXCL12-stained kidneys, and their expression levels. Scale bar=100 μm. (F) Representative image of kidney sections immunostained for CXCL12 and CD3 from patients with T2DKD. Scale bars=100 μm (left) and 50 μm (right). (G) Flow cytometric analysis of T-cell numbers following administration with anti-CXCL12 antibody (αCXCL12 Ab) or control Ab. (H) Flow cytometric analysis of CD4⁺ (left) and CD8⁺ (right) T-cell subset proportions following administration with αCXCL12 Ab or control Ab. PT-S, proximal tubule segment; EM, effector memory; DAPI, 4´,6-diamidino-2-phenylindole. ^aP<0.05, ^bP<0.01, ^cP<0.001.

Fig. 5.

Human translation of C-X-C motif chemokine 12 (CXCL12) expression. (A) Representative image of CXCL12-stained kidneys, and their expression levels in tubules. Scale bar=100 μm. (B) Representative image of kidney sections immunostained for CXCL12 and CD3. Scale bar=100 μm. (C) Correlation between tubular CXCL12 expression and estimated glomerular filtration rate (eGFR) or urinary protein-to-creatinine ratio (uPCR). Line and gray area indicate the trend line and 95% confidence interval, respectively. (D) Relationship between tubular CXCL12 expression and cumulative rate curves of kidney progression. T2DKD, type 2 diabetic kidney disease; DAPI, 4´,6-diamidino-2-phenylindole. ^aP<0.001.

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Park PG, Hwang J, Kim Y, Hong M, Yun D, Yoon H, Kang C, Bae S, Kwak SH, Kim YC, Moon KC, Lee DS, Kim YS, Kang HG, Kim HJ, Han SS. Inflammatory Milieu by Crosstalk between Glomerulus and Proximal Tubular Cells in Type 2 Diabetes Mellitus Kidney Disease. Diabetes Metab J. 2025;49(5):1024-1039.

Received: Sep 04, 2024; Accepted: Dec 12, 2024

DOI: https://doi.org/10.4093/dmj.2024.0535.

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