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Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression

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Abstract

Kidneys are intricate three-dimensional structures in the body, yet the spatial and molecular principles of kidney health and disease remain inadequately understood. We generated high-quality datasets for 81 samples, including single-cell, single-nuclear, spot-level (Visium) and single-cell resolution (CosMx) spatial-RNA expression and single-nuclear open chromatin, capturing cells from healthy, diabetic and hypertensive diseased human kidneys. Combining these data, we identify cell types and map them to their locations within the tissue. Unbiased deconvolution of the spatial data identifies the following four distinct microenvironments: glomerular, immune, tubule and fibrotic. We describe the complex organization of microenvironments in health and disease and find that the fibrotic microenvironment is able to molecularly classify human kidneys and offers an improved prognosis compared to traditional histopathology. We provide a comprehensive spatially resolved molecular roadmap of the human kidney and the fibrotic process, demonstrating the clinical utility of spatial transcriptomics.

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Fig. 1: Comprehensive integrated multimodal human kidney single-cell atlas.
Fig. 2: Spatially resolved human kidneys.
Fig. 3: Human kidney stromal cell atlas.
Fig. 4: Human kidney FME.
Fig. 5: iPT cells in diseased human kidneys.
Fig. 6: FME-GS successfully predicts disease prognosis in a large cohort of human kidney samples.

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Data availability

Raw data, processed data and metadata from the snRNA-seq, scRNA-seq, snATAC–seq and spRNA-seq have been deposited in Gene Expression Omnibus with the accession code GSE211785. The human bulk kidney RNA-seq data are available under the following accession numbers: GSE115098 and GSE173343. The single-cell and nuclear expression and open chromatin and spatial data are also available at www.susztaklab.com (https://susztaklab.com/hk_genemap/).

Code availability

All the codes used for the analysis were deposited on GitHub (https://github.com/amin69upenn/Human_Kidney_Multiomics_and_Spatial_Atlas_ and https://github.com/jlevins2010/FME_atlas).

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Acknowledgements

Work in the Susztak Lab is supported by the National Institutes of Health (NIH; P50DK114786, DK076077, DK087635, DK132630 and DK105821). The study is supported by GSK, Regeneron, Boehringer Ingelheim and Novo Nordisk. The funders have no influence on the reported results. The authors would like to thank the Molecular Pathology and Imaging Core (P30-DK050306) and Diabetes Research Center (P30-DK19525) at the University of Pennsylvania for their services. J.L. is supported by the ASN Ben Lipps Fellowship. B.D. is supported by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation, grant DU 2449/1-1). K.A.K. is supported by the MOLMED Ph.D. program at the Medical University of Graz, a Marietta-Blau Grant and an Austrian Marshall Plan Foundation scholarship. M.S.B. is supported by grants from the German Research Foundation (DFG, BA 6205/2-1), Else Kröner-Fresenius Foundation, Jackstädt Foundation and the Berlin Institute of Health at Charité – Universitätsmedizin Berlin Clinician Scientist Program.

Author information

Author notes

  1. These authors contributed equally: Amin Abedini, Jonathan Levinsohn.

Authors and Affiliations

  1. Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Amin Abedini, Jonathan Levinsohn, Konstantin A. Klötzer, Bernhard Dumoulin, Ziyuan Ma, Julia Frederick, Poonam Dhillon, Michael S. Balzer, Rojesh Shrestha, Hongbo Liu, Steven Vitale, Andi M. Bergeson & Katalin Susztak

  2. Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Amin Abedini, Jonathan Levinsohn, Konstantin A. Klötzer, Bernhard Dumoulin, Ziyuan Ma, Julia Frederick, Poonam Dhillon, Michael S. Balzer, Rojesh Shrestha, Hongbo Liu, Steven Vitale, Andi M. Bergeson, Daniel Traum, Klaus H. Kaestner & Katalin Susztak

  3. Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Amin Abedini, Jonathan Levinsohn, Konstantin A. Klötzer, Bernhard Dumoulin, Ziyuan Ma, Julia Frederick, Poonam Dhillon, Michael S. Balzer, Rojesh Shrestha, Hongbo Liu, Steven Vitale, Andi M. Bergeson & Katalin Susztak

  4. Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Amin Abedini, Jonathan Levinsohn, Konstantin A. Klötzer, Bernhard Dumoulin, Andi M. Bergeson, Daniel Traum, Klaus H. Kaestner & Katalin Susztak

  5. Nephrology, Charité – Universitätsmedizin, Berlin, Germany

    Michael S. Balzer

  6. Cardiovascular and Renal Research, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA

    Kishor Devalaraja-Narashimha & Lori Morton

  7. Genomic Sciences, GSK-Cellzome, Heidelberg, Germany

    Paola Grandi

  8. Research and Development, GSK, Crescent Drive, Philadelphia, PA, USA

    Tanmoy Bhattacharyya, Erding Hu & Lea Sarov-Blat

  9. Department of Cardiometabolic Diseases Research, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, USA

    Steven S. Pullen, Carine M. Boustany-Kari & Paolo Guarnieri

  10. Novo Nordisk, Lexington, MA, USA

    Anil Karihaloo

  11. Department of Epidemiology, Biostatistics and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Hanying Yan, Kyle Coleman & Mingyao Li

  12. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Matthew Palmer

  13. Department of Pathobiology, University of Pennsylvania School of Veterinary Medicine, Philadelphia, PA, USA

    Christopher A. Hunter

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  1. Amin Abedini
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  2. Jonathan Levinsohn
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Contributions

A.A., J.L., Z.M., J.F., R.S., P.D., D.T., A.M.B. and T.B. performed experiments. A.A., J.L., K.A.K., M.S.B., H.L., S.V., M.S.B., H.Y. and K.C. performed computational analysis. K.D., B.D., L.M., E.H., S.P., C.B.K., L.S.B., C.A.H., P.G., A.K., P.G., C.M.B., K.D.N, K.H.K. and M.L. offered experimental and analytical suggestions. K.S. was responsible for the overall design and oversight of the experiments. M.P. performed pathological scorings. K.S. supervised the experiment. A.A. and K.S. wrote the original draft. All authors contributed to and approved the final version of the manuscript.

Corresponding author

Correspondence to Katalin Susztak.

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Competing interests

K.D. and L.M. are employees of Regeneron Pharmaceuticals. G.P., T.B., E.H. and L.S.B. are employees of GSK. S.P., C.M.B. and P.G. are employees of Boehringer Ingelheim. A.K. is an employee of Novo Nordisk. The remaining authors declare no competing interests.

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Nature Genetics thanks Rafel Kramann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Multimodal single-cell atlas.

(a) UMAP of snRNA-seq, scRNA-seq and snATAC-seq datasets before integration. (b) UMAP of integrated snRNA-seq, scRNA-seq and snATAC-seq datasets of 338,565 cells and nuclei using the SCVI tool. (c) Annotations of cell types on integrated UMAP. (d) The dot plots of marker genes used to annotate 44 main cell types in the integrated dataset. The size of the dot indicates the percent of positive cells, and the darkness of the color indicates average expression.

Extended Data Fig. 2 Integrations of snRNA-seq, scRNA-seq and snATAC-seq datasets from multiple sources.

(a) UMAP of integrated snRNA-seq, scRNA-seq and snATAC-seq datasets (n = 588,425 cells and nuclei) from Susztak Lab and KPMP using the SCVI tool. Left, the new annotation after integration of the dataset. Right, the original annotation used by KPMP. (b) Bar charts showing cell abundance in each cell cluster (Method, Lab, present study annotation and KPMP annotation).

Extended Data Fig. 3 CosMx cell populations.

(a) Original UMAP of all cells that passed QC along with their annotations. (b) Cell populations with annotatable markers of the CosMx data on UMAP. (c) UMAP of all CosMx cells by sample. (d) CosMx cell annotations across the UMAP with a single population being shown for a given UMAP demonstrating that these clusters are indeed relatively localized within the UMAP. (e) Dot plot showing markers for each annotated CosMx population. (f) Annotation frequency for each cell type.

Extended Data Fig. 4 CosMx SCVI integration with snRNA-seq data.

After integrating with the snRNA-seq data, we compared annotations from our CosMx analysis and the original snRNA-seq annotation. (a) UMAP of integrated data demonstrating the technology type of each cell within the UMAP. (b) Comparison of CosMx annotations and snRNA-seq annotations, demonstrating concordance of location within the integrated UMAP.

Extended Data Fig. 5 Location of CosMx annotated cell types within the slide.

(a) Location of annotated cell types within the two tissue sections. (b) Location of glomerular cell subtypes. (c) Location of iPT, fibroblasts and immune cells.

Extended Data Fig. 6 Microanatomy of the CosMx slide.

(a) Location of glomerular cell types within a subsection of tissue and in a single field of view (right). (b) Location of injured thick ascending limb, healthy injured thick ascending limb, principal cells and immune cell types within a subsection of tissue and in a single field of view (right). (c) Location of distal nephron cell subtypes within a subsection of tissue and in a single field of view (right). (d) Single field of view showing many cell types.

Extended Data Fig. 7 Neighborhood characteristics of CosMx slide.

(a) Relative type cell frequency between each sample. Orange indicates HK3039 (healthy), and blue indicates HK2844 (diseased). Right, frequency of neighbor annotations for each cell type for a 20-micron neighborhood. (b) Neighborhood enrichment by permuting annotations for the 20-micron neighborhood size. Lighter color indicates higher enrichment and colocalization of a given population. (c) Dot plots for iPT, PEC, podocytes and PT cells expression of iPT and PEC markers across genomics modalities and protein staining of VCAM1 in PECs from the Human Protein Atlas: https://www.proteinatlas.org/.

Extended Data Fig. 8 Cell–cell interaction analysis in the spRNA-seq dataset in fibrotic microenvironment.

(a) Weighted total interaction strength of the CXCL, SPP1, TGFβ and PDGF pathways in control and diseased samples in the fibrotic microenvironment (left). The spatial location of the identified cell–cell communications pathways (CXCL, SPP1, TGFβ and PDGF) in control and diseased sample in the fibrotic microenvironment (right). The arrows indicate the source and targets of the identified pathways. (b) Expression of CD34 and CDH5 as the markers of high endothelial venules (HEVs) in the fibrotic microenvironments. Scale bar is 1 mm in length. (c) Volcano plot of differentially expressed genes from CosMx data. Cells with an immune neighbor within 20 microns were compared against cells without an immune neighbor for both the PT and iPT population. log(fold change) >0 indicates increased expression in cells with an immune neighbor, while log(fold change) <0 indicates increased expression in cells without an immune neighbor. log10(p value) is indicated by the y axis. Genes with an adjusted p-value < 0.01 are marked in orange. (d) The dot plot of expression of ligands and receptors in regions of FME in integrated snRNA-seq/scRNA-seq and snATAC-seq data. The size of the dot indicates the percent positive cells and the darkness of the color indicates average expression (right). The gray indicates control, and the red indicates diseased group.

Extended Data Fig. 9 Spatial characteristics of injured PT subclusters on CosMx.

(a) The CosMx iPT population for each sample was subclustered using a Leiden algorithm with a resolution of 0.3. (b) The top 10 differentially expressed genes for each subcluster. (c) iPT subcluster localization within the entire slide. Views of specific regions indicated by inset boxes are shown in Supplementary Fig. 9d. (d) iPT subclusters visualized on H&E. Subset images showing populations on H&E. Blue cells correspond with cluster 0 (iPT_APOE), orange with cluster 1 (iPT_SPP1) and green with cluster 2 (iPT_KRT7). (e) Frequency of immune and fibroblast neighbors for each iPT subtype within each sample within 20 microns is shown below. We performed testing using a Wilcoxon rank-sum test between each population within a sample. These subtypes had significantly different immune neighbors and fibroblast neighbors with each sample. HK3039 fibroblasts: iPT_APOE vs iPT_SPP1, p-value = 7E-39. HK3039 immune cells: iPT_APOE vs iPT_SPP1, p-value = 4E-67. HK2844 fibroblasts iPT_APOE vs iPT_SPP1, p-value = 9E-9, iPT_KRT7 vs iPT_SPP1, p-value = 9E-46. HK2844 immune cells iPT_APOE vs iPT_SPP1, p-value = 3E-16, iPT_KRT7 vs iPT_SPP1, p-value = 9E-11. (f) iPT subcluster neighborhood enrichment within a 20-micron neighborhood size. Lighter color indicates higher enrichment and colocalization of a given population.

Extended Data Fig. 10 FME gene expression predicts kidney outcomes.

(a) Hierarchical clustering of 245 human kidney tubule samples based on the expression of 1,100 randomly picked genes. (b) Kaplan–Meier analysis with log-rank test was used to compare the survival of the 3 different clusters. Renal survival was defined as cases reaching end-stage renal disease or greater than 40% eGFR decline. (c) Single-cell expression enrichment of genes associated with eGFR decline. The heatmap shows the cell-type enrichment of genes associated with eGFR decline (red indicates more genes with cell-type expression). Endo, endothelial cells; Stroma, stromal cells; PEC, parietal epithelial cells; Podo, podocyte; PT, proximal tubule cells; LOH, loop of Henle; DCT, distal convoluted tubule; CNT, connecting tubule; PC, principal cells of collecting duct; IC_A, type alpha intercalated cells. Spatial expression and microenvironment enrichment of genes associated with eGFR decline. GME, glomerular; TME, tubule; FME, fibrosis; IME, immune microenvironment. (d) Using the LASSO regression of all FME genes against eGFR, Kaplan–Meier analysis was re-performed using clustering of subsets of gene—those with a LASSO coefficient that is nonzero and the genes with the most negative coefficients (Supplementary Table 13).

Supplementary information

Supplementary Information

Supplementary Note and Supplementary Figs. 1–27.

Reporting Summary

Supplementary Tables 1–26

Supplementary Table 1: Demographic and clinical characteristics of the participants in the study. Supplementary Table 2: QC metrics of the snRNA-seq, scRNA-seq, snATAC–seq and spRNA-seq data. Supplementary Table 3: Cell-type marker genes in the snRNA-seq data, 144 clusters. Supplementary Table 4: Cell-type marker genes in the snRNA-seq data, 44 clusters. Supplementary Table 5: Cell-type marker genes in the scRNA-seq data, 144 clusters. Supplementary Table 6: Cell-type marker genes in the scRNA-seq data, 44 clusters. Supplementary Table 7: Cell-type marker ATAC–seq, chromatin accessibility. Supplementary Table 8: Cell-type marker ATAC–seq, gene activity. Supplementary Table 9: Cell-type markers ATAC–seq, 144 clusters. Supplementary Table 10: Cell-type markers ATAC–seq, 44 clusters. Supplementary Table 11: Cell-type specific markers genes in the integrated snRNA-seq, snATAC–seq and scRNA-seq, 144 clusters. Supplementary Table 12: Cell-type-specific markers genes in the integrated snRNA-seq, snATAC–seq and scRNA-seq, 44 clusters. Supplementary Table 13: Cell-type-specific TF motif enrichment. Supplementary Table 14: Module-specific genes and scores in fibroblast (WGCNA). Supplementary Table 15: Module-specific genes and scores in myofibroblast (WGCNA). Supplementary Table 16: Differentially expressed genes between diseased and control samples in each cell type in the integrated snRNA-seq, snATAC–seq and scRNA-seq—DKD versus control. Supplementary Table 17: Differentially expressed genes between diseased and control samples in each cell type in the integrated snRNA-seq, snATAC–seq and scRNA-seq: all disease versus control. Supplementary Table 18: Differentially expressed genes between diseased and control samples in each cell type in the integrated snRNA-seq, snATAC–seq and scRNA-seq—CKD versus control. Supplementary Table 19: CellChat interaction scores, integrated. Supplementary Table 20: CellChat interaction scores, HK2671_ST. Supplementary Table 21: CellChat interaction scores, HK2844_ST. Supplementary Table 22: Differentially expressed genes along injured PT trajectory in the integrated snRNA-seq, snATAC–seq and scRNA-seq. Supplementary Table 23: Fibrotic microenvironment specific genes. Supplementary Table 24: Clinical characteristics of the 245 tubule bulk RNA-seq cohorts, summary. Supplementary Table 25: Clinical characteristics of the 245 tubule bulk RNA-seq cohorts, cluster analysis. Supplementary Table 26: LASSO score for each FME gene against eGFR.

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Abedini, A., Levinsohn, J., Klötzer, K.A. et al. Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression. Nat Genet 56, 1712–1724 (2024). https://doi.org/10.1038/s41588-024-01802-x

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