Spatiotemporal interaction of immune and renal cells controls glomerular crescent formation in autoimmune kidney disease

Abstract

Rapidly progressive glomerulonephritis (RPGN) is the most aggressive group of autoimmune kidney diseases and is characterized by glomerular crescent formation with proliferation of parietal epithelial cells (PECs). However, the underlying mechanisms of glomerular crescent formation are incompletely understood. Here we provide a high-resolution spatial kidney cell atlas of 57 samples from patients with RPGN (ANCA-associated GN, lupus nephritis and anti-glomerular basement membrane-GN) to characterize the cell signaling pathways in glomerular crescent development. Early platelet-derived growth factor (PDGF) signaling from epithelial and mesangial cells caused PEC activation and proliferation in glomerular crescents, whereas later transforming growth factor (TGF)-β signaling from macrophages, T cells and epithelial and mesangial cells triggered expression of extracellular matrix components in PECs associated with glomerulosclerosis and disease progression. These findings were similar across the different GNs and were functionally validated in experimental GN by PDGF and TGFβ blockade. These results highlight a spatiotemporally conserved progression program into glomerular crescents and sclerosis and indicate new treatment options for autoimmune kidney disease.

Fig. 1. Major cell types in the human renal cortex in health and inflammation.

a, Overview of the patient cohort from the Hamburg Glomerulonephritis Registry and the analysis performed. b, Schematic showing the cross-section of a human kidney tissue with the localization of various cell types in different regions. Inset: cross-section of a glomerulus (top) and tubulointerstitial region (bottom) and the cell types therein. c, DAPI-stained images from kidney biopsies of control (left) and a patient with ANCA-GN (right). The images show glomeruli and surrounding regions overlaid with cell boundaries determined using a cell segmentation algorithm. Localization of specific marker genes is indicated, with glomerular boundaries highlighted for clarity. d, Uniform Manifold Approximation and Projection (UMAP) of ~3.2 million cells retained after QC filtering. Cells are colored based on their annotated cell type. e, Stacked bar plot showing the proportions of different cell types from the complete biopsy tissues across the four disease conditions. f, images from control (left four images) and ANCA-GN (right four images) kidney biopsies showing a glomerulus and tubulointerstitial region. For each region, the adjoining plots show the segmented cells color coded according to their cell-type annotation, illustrating cellular composition differences between conditions in different regions of the tissue. aGBM, anti-GBM antibodies; ATL, ascending thin limb of loop of Henle; bx, biopsies; cDC, conventional dendritic cell; CNT, connecting tubule; cycNKC/T, cycling NK cytotoxic T cell; DCT, distal convoluted tubule; DTL, descending thin limb of loop of Henle; FIB, fibroblast; Fib. MC, fibrotic mesangial cell; IC, intercalated cell of collecting duct; KEGG, Kyoto Encyclopedia of Genes and Genomes; MAC, macrophage; MC, mesangial cell; MDC, monocyte-derived cell; Mono, monocyte; N, neutrophil; NEU, neuronal cell; PpE, papillary tip epithelial cells abutting the calyx; pDC, plasmacytoid dendritic cell; PL, plasma cell; POD, podocyte; PT, proximal tubule; TAL, thick ascending limb of loop of Henle; VSM/P, vascular smooth muscle cells or pericytes.

Fig. 2. Spatial transcript analysis of renal compartments in glomerulonephritis.

a, Workflow of the NichePCA algorithm used for automated annotation and spatial mapping of glomeruli. b, Schematic illustration of a glomerulus and its periglomerular niche, defined by expanding the glomerular boundaries to include surrounding regions. c, Representative H&E image showing glomeruli (blue) as defined by NichePCA, their corresponding periglomerular regions (green) and the remaining tubulointerstitial area (orange). d, Representative images highlighting the spatial localization of various kidney and immune cell types. The top panels show the spatial localization of various kidney and immune cells within the glomerular and periglomerular regions, whereas the bottom panels depict the same in tubulointerstitial regions across the four disease conditions. e, Stacked bar plots showing the proportions of different cell types across the four disease conditions within three spatial niches: the glomerular niche (left), periglomerular niche (center) and tubulointerstitial areas (right).

Fig. 3. Trajectory of the immune cell ecosystem in glomerulonephritis.

a, PCA plot showing 782 unique ROIs, each comprising a distinct glomerulus and its associated periglomerular area. ROIs are colored by clustering (left) and by disease condition (right). b, Distribution of PC1 values assigned to the ROIs across the four disease conditions. c, Correlation analysis of the median PC1 and kidney function (eGFR) of each sample (Spearman’s correlation, two-sided). d, Representative H&E images showing glomeruli from each of the four pseudotime quadrants, highlighting morphological variations: cluster 1: control (left); cluster 2: ANCA-GN (middle left); cluster 3: ANCA-GN (middle right); cluster 4: ANCA-GN (right). e, Change in the percentage distribution of different cell types within glomerular regions (left) and periglomerular regions (right) across the crescent formation trajectory (PC1).

Fig. 4. Mapping the immune–epithelial cell interactions in glomerular crescent formation.

a, Circle plot showing cell–cell interactions in cGN compared to control samples. The interaction edges are colored the same as the source cell type whereas edge weights are proportional to the interaction strength. b, Heatmap showing the signaling pathways that have increased activity directed toward PECs in cGN and the source cell types producing the corresponding ligands, colored by the interaction probability. c, Scores for PDGF and TGFβ pathway activation in PECs plotted along pseudotime (lines show quadratic regression fits with 95% confidence intervals). d, Schematic representation of the PDGF signaling pathway, highlighting the genes upregulated in PECs in an snRNA-seq dataset from samples from patients with ANCA-GN (n = 11) over controls (n = 4). e, Multiplexed protein staining on a Xenium slide showing overexpression of Ki-67 in cGN samples (representative images of eight samples: two control (top left), three ANCA-GN (bottom left), two SLE (top right) and one anti-GBM disease (bottom right); representative of two independent experiments). f, Expression of Ki-67 protein in PECs along PC1. The primary axis corresponds to fluorescence intensity values of Ki-67. The secondary axis curve shows the change in percentage of Ki-67-positive cells per glomerulus. g, Schematic representation of the TGFβ signaling pathway, highlighting the genes from this pathway upregulated in PECs in an snRNA-seq dataset from samples from patients with ANCA-GN (n = 11) over controls (n = 4). h, Scatter plot showing correlation of TGFβ pathway score with fibrosis score in PECs (top) and the patient median TGFβ pathway score to the individual kidney function (bottom) (Spearman’s correlation, two-sided). i, Schematic illustrating the role of PDGF-mediated and TGFβ-mediated activation of PECs in crescent formation and glomerulosclerosis in cGN.

Fig. 5. Immunohistochemistry of PECs and intracellular signaling molecules.

a, Exemplified presentation of immunofluorescence staining showing prominent phospho-ERK1/2 and CD44 positivity in claudin-1⁺ or synaptopodin⁻ PECs in human kidney biopsies of patients with ANCA-GN (bottom) compared to healthy control tissue (top). b, Exemplified presentation of immunofluorescence staining showing prominent phospho-SMAD3 staining in PAX8⁺ or synaptopodin⁻ PECs in human kidney biopsies of patients with ANCA-GN (bottom) compared to healthy control tissue (top) in exemplified immunofluorescence staining. Representative staining of four controls and four patients with ANCA-GN is shown.

Fig. 6. PDGF and TGFβ blockade in experimental crescentic glomerulonephritis.

a, Experimental setup of PDGF blockade with nintedanib in experimental cGN. b, Periodic acid–Schiff staining of kidney sections of the respective groups. c, Quantification showing reduction of crescents in mice treated with nintedanib (data pooled from two independent experiments): left, untouched; middle, cGN; right, cGN + nintedanib. d, Exemplified presentation of immunofluorescence staining showing prominent phospho-ERK1/2 and CD44 positivity in claudin-1⁺ or synaptopodin⁻ PECs from cGN mice treated with nintedanib (bottom) in comparison to PBS (top). e, Low expression of the proliferation marker PCNA in claudin-1⁺ or synaptopodin⁻ PECs in exemplified immunofluorescence staining of cGN mice treated with nintedanib (bottom) in comparison to phosphate-buffered saline (PBS; top). f, Experimental setup of TGFβ blockade with fostamatinib in experimental cGN. g, Periodic acid–Schiff staining of kidney sections of the respective groups: left, untouched control; middle, cGN; right, cGN + fostamatinib. h, Quantification showing reduction of glomerulosclerosis in mice treated with fostamatinib (data pooled from two independent experiments). i, Exemplified presentation of immunofluorescence staining showing less prominent phospho-SMAD3 positivity in PAX8⁺ or synaptopodin⁻ PECs. j, Glomerular SMA staining in kidney sections of mice with fostamatinib-treated cGN (bottom) and cGN controls (top). One-sided Student’s t-test was performed (c and h). ^*P < 0.05).

Extended Data Fig. 1. Quality control metrics and cellular characteristics of Xenium spatial transcriptomics data.

a, Number of samples belonging to control and disease conditions across the 8 Xenium slides. b-c, Number (b) and distribution (c) of cells in each slide and condition after filtering for quality control. d, Boxplot showing distribution of number of unique transcripts (left) and number of unique genes (right) detected in each cell. The number of data points (cells per slide) used to create each boxplot are—Slide 0011216: 501,421, Slide 0011284: 357,492, Slide 0011287: 444,877, Slide 0011546: 407,755, Slide 0011695: 426,766, Slide 0011707: 396,393, Slide 0011762: 303,762, 0018775: 379,774. Boxplots show the median (middle horizontal line), interquartile range (box), Tukey-style whiskers (lines beyond the box), outliers (data points beyond 1.5*interquartile or below −1.5*interquartile). e, Boxplot showing distribution of segmented cell-areas per cell type. The median cell area and approximate diameter overall cells are indicated by a dashed line. The number of datapoints (cells per cell type) used to create boxplots are—POD: 61,319, MC: 26,349, PEC: 68,820, EC: 348,554, FIB: 478,776, Fibrotic MC: 10,288, VSM/P: 109,000, PT: 658,356, DCT: 57,590, CNT: 84,777, TAL: 222,773, ATL: 19,383, DTL: 51,333, PC: 100,663, IC: 92,864, PapE: 18,331, NEU: 5,028, MAST: 12,658, N: 41,570, MAC: 285,800, Mono: 55,537, MDC: 112,695, pDC: 4,164, PL: 61,013, B: 32,780, T: 106,672, cDC: 13,761, cycMNP: 30,547, NKC/T: 42,679, cycNKC/T: 4,130. Boxplots show the median (middle horizontal line), interquartile range (box), Tukey-style whiskers (lines beyond the box), outliers (data points beyond 1.5*interquartile or below −1.5*interquartile).

Extended Data Fig. 2. Evaluation of cell segmentation, classification and profiles.

a, Bar plots showing comparison of segmentation quality metrics across different cell segmentation methods. Metrics include ratio of reads assigned to cells, negative marker purity at level 1 (all immune cells grouped), and negative marker purity at level 2 (with immune cell types as individual clusters). Bar graphs show mean and error bars represent the 95% confidence intervals. Each dot refers to the score of a biopsy (n=32, ANCA-GN biopsies). b, Boxplots showing transcript assignment confidence from Baysor across conditions (Control, SLE, ANCA-associated glomerulonephritis). Assignment confidence remains consistently high (~0.85-0.90) across all conditions, indicating robust transcript-to-cell assignments regardless of disease state. c, Line plots showing negative marker purities of immune and non-immune cells across increasing distances (presented as 9 bins between 0 and 1000 μm) from the center of dense immune infiltrates. Points represent mean values and error bars represent standard deviation. Cell counts per distance bin for immune cells are—0-100 μm: 111, 100-200 μm: 87, 200-300 μm: 114, 300-400 μm: 155, 400-500 μm: 189, 500-600 μm: 207, 600-700 μm: 227, 700-800 μm: 356, 800-900 μm: 444, 900-1000 μm: 279; for non-immune cells: 0-100 μm: 382, 100-200 μm: 747, 200-300 μm: 976, 300-400 μm: 1,292, 400-500 μm: 1,657, 500-600 μm: 1,943, 600-700 μm: 2,277, 700-800 μm: 2,766, 800-900 μm: 2,502, 900-1000 μm: 1,825. d, Line plots showing transcript assignment confidence for immune and non-immune cells across increasing distances similar to panel c. Points represent mean values and error bars represent standard deviation. Cell counts per distance bin are the same as in panel c. e, Matrix showing the classifier confidence (median, mean and standard deviation in prediction of the cell types. f, Heatmap showing the expression of cell type marker genes in annotated cell types.

Extended Data Fig. 3. Cell type composition and analysis of glomerular regions.

a, Stacked bar plot showing the cell type proportions for individual glomerular regions across control and disease conditions. b, Boxplots showing comparisons of the cell type proportions in glomerular regions between control, SLE, ANCA-GN, and anti-GBM conditions. The number of datapoints (cells per disease) used to create boxplots are—Cntrl: 29,035, SLE: 47,266, ANCA: 61,708, GBM: 31,744. Boxplots show the median (middle horizontal line), interquartile range (box), Tukey-style whiskers (lines beyond the box), outliers (data points beyond 1.5*interquartile or below −1.5*interquartile). c, Heatmap showing Pearson correlation coefficients between different ROI cell types (glomerular and abundant immune cell types) based on their transcriptional profiles. The heatmap displays strong positive correlations between MCs and fibrotic MCs (r = 0.55). d, Dot plot showing expression patterns of immune, glomerular, and fibrosis-related genes across cell types. Dot sizes represent the fraction of cells within the ROIs expressing each gene, and dot colors indicate scaled mean expression levels. e, Heatmap displaying the mean expression of selected genes in MC and fibrotic MC populations, highlighting transcriptional differences between these cell states. f, Line plot showing the change in proportions of MCs and fibrotic MCs ordered by PC1. The inverse relationship demonstrates potential transition from MC to fibrotic MC phenotype along this axis. Pearson’s correlation coefficient (r) and associated two-sided p-value (1.97e-174).

Extended Data Fig. 4. Cell type composition analysis of periglomerular and tubulointerstitial regions.

a, c Stacked bar plot showing the cell type proportions for individual periglomerular regions (a) and tubulointerstitial regions (c) across control and disease conditions. b, d Boxplots comparing cell type proportions in periglomerular regions (b) and tubulointerstitial regions (d) between control, SLE, ANCA-GN, and anti-GBM conditions. The number of datapoints (cells per disease) used to create boxplots in panel b are—Cntrl: 59,045, SLE: 118,474, ANCA: 192,361, GBM: 66,024. The number of datapoints (cells per disease) used to create boxplots in panel d are Cntrl: 296,135; SLE: 632,488; ANCA: 1,318,963; GBM: 364,967. Boxplots show the median (middle horizontal line), interquartile range (box), Tukey-style whiskers (lines beyond the box), outliers (data points beyond 1.5*interquartile or below −1.5*interquartile).

Extended Data Fig. 5. Principal component analysis, clustering of ROIs and pseudotime analysis.

a, Cumulative variance ratio explained by the first 10 principal components. PC1 captures variance in the dataset. b, Heatmap showing Pearson correlation coefficients between cell type compositions and the first four principal components (PC1-4) across ROIs. Cell type composition was computed and correlated with its PC values. The cell types in the X-axis were ordered according to their correlation with the first principal component. c, Matrix showing the percentage distribution of conditions (Control, SLE, ANCA, and aGBM) across the four clusters. d, Heatmap showing the mean expression of top 10 markers across clusters C1-C4, with immune and crescent scores shown below. e, Bar plot showing the number of markers identified in each cluster. f, Dot plot showing enriched GO terms specifically related to fibrosis and extracellular matrix (ECM) remodeling across clusters. The dot size indicates the fraction of ROIs in each group, and colors represent GSVA scores for the enriched GO terms. g, Scatter plots showing linear associations between the first four principal components (PC1-4) and pseudotime for ROIs (Pearson correlation coefficients (r) and two-sided p-values). h, Matrix showing the percentage distribution of conditions (Control, SLE, ANCA, and aGBM) across the pseudotime quadrant. i, Matrix showing the percentage distribution of ROI clusters across the pseudotime quadrants. j, Bar plot showing the number of markers identified in each pseudotime quadrant. k, Heatmap displaying the mean expression of top 10 markers for each pseudotime quadrant, with immune and crescent scores shown below. l, Dotplot showing enriched GO terms specifically related to fibrosis and extracellular matrix (ECM) remodeling. The dot size indicates the fraction of ROIs in each group, and colors represent GSVA scores for the enriched GO terms.

Extended Data Fig. 6. Profiling of renal biopsies of patients reveals PC1-associated transcriptional and clinical heterogeneity.

a, Distribution of PC1 values across regions of interest (ROIs) from an individual patient’s biopsy. ROIs are color-coded by cluster identity and ordered along the Y-axis by increasing PC1 values and clinical condition. b, Correlation between the median PC1 value of ROIs from each patient and clinical variables: estimated glomerular filtration rate (eGFR), albuminuria and age (Pearson´s correlation, two-sided). c, Correlation between the ANCA Renal Risk (ARR) score of patients diagnosed with ANCA-GN and the median PC1 values of ROIs of their biopsy sample (Spearman’s correlation, two-sided). d, Differential gene expression analysis between ROIs classified as cluster 1 compared to those classified under clusters 2, 3 or 4. e, Expression of genes higher in cluster 1 ROIs shown in representative patients. f, Expression of genes higher in cluster 2-4 ROIs shown in representative patients.

Extended Data Fig. 7. Crescent cell type composition and communication in ANCA-GN.

a, Exemplified crescentic glomerulus annotated in white overlaid on DAPI staining (top panel) and corresponding cell type annotations (bottom panel). b. Box plots comparing cell type proportions between crescentic and non-crescentic glomerular regions (n=44). It shows the ten most abundant cell types in crescentic areas, ranked by average abundance (p-values for PEC: 6.2990e-12, EC: 8.3491e-10, MAC: 0.0066, MC: 0.02911; Mono: 0.0002, VSM/P : 0.0010). *p-value<=0.05, *p<=0.01, ***p<=0.001 (two-sided paired t-test comparing crescent and non-crescent regions of the same glomeruli. P-value correction was not performed. Boxplots show the median (middle horizontal line), interquartile range (box), Tukey-style whiskers (lines beyond the box), outliers (data points beyond 1.5*interquartile or below −1.5*interquartile). c, Circular network diagrams depicting intercellular communication patterns within the ROIs (glomerular+periglomerular regions) in SLE, ANCA-GN, and anti-GBM. Nodes represent different cell types and edges indicate communication strength between cell pairs, with edge thickness proportional to interaction probability. d, Heatmap showing disease-specific cell-type sources and their signaling pathways targeting PECs across SLE, ANCA-GN, and anti-GBM conditions. e, Intercellular communication patterns in the tubulointerstitial regions in SLE, ANCA-GN, and anti-GBM.

Extended Data Fig. 8. Single nuclear mRNA sequencing (snRNA-seq) QC and cell type annotation.

a, Violin plots show quality control metrics, distribution of number of genes, total counts, percentage of mitochondrial and ribosomal genes across samples. Barplots show the total number of nuclei per sample. b, Matrix showing the classifier confidence (median, mean and standard deviation in prediction of the cell types. c, Heatmap showing the expression of cell type marker genes in annotated cell types (Wilcoxon test, adjusted p-value <0.05).

Extended Data Fig. 9. Comparison of PDGF and TGF-β pathway gene activation in PECs in crescentic glomerulonephritis (cGN) versus non-immune-mediated kidney diseases.

a, UMAP showing major kidney cell types identified in the scRNA-seq dataset from Lake et al. (Nature, 2023), based on biopsies from patients with kidney diseases such as diabetic nephropathy or hypertension. b, Fold change in expression of PDGF and TGF-β pathway genes in PECs from ANCA patients versus controls (as also shown in Fig. 4d,g), compared to the log fold change observed in PECs from AKI or CKD patients relative to controls. c–d, Barplots showing average fold change of all genes used in the PDGF (C, n=89 genes)) and TGF-β (D, n=54 genes) pathway score calculations (barplots show mean ± standard error. The y-axes are split to show all data points).

Extended Data Fig. 10. Comparison of cell type proportions and differential gene expression in ANCA-associated biopsies using single-nucleus RNA sequencing and Xenium spatial transcriptomics.

a, Comparison of cell type proportions in biopsies from ANCA patients between NucSeq and Xenium datasets. b–d, Log fold change in expression of specific gene groups between ANCA and control samples, as measured by NucSeq and Xenium: MMP genes (b), ADAM genes (c), and fibrosis-related genes (d). The fibrosis related genes for human kidney were taken from Fibrotic Disease-associated RNAome database (http://www.medsysbio.org/FDRdb). * show p-value < 0.05, two-sided Wilcoxon test with Benjamini-Hochberg correction.