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Single-cell analysis identifies the interaction of altered renal tubules with basophils orchestrating kidney fibrosis

Nature Immunology volume 23, pages 947–959 (2022)Cite this article

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Abstract

Inflammation is an important component of fibrosis but immune processes that orchestrate kidney fibrosis are not well understood. Here we apply single-cell sequencing to a mouse model of kidney fibrosis. We identify a subset of kidney tubule cells with a profibrotic-inflammatory phenotype characterized by the expression of cytokines and chemokines associated with immune cell recruitment. Receptor–ligand interaction analysis and experimental validation indicate that CXCL1 secreted by profibrotic tubules recruits CXCR2+ basophils. In mice, these basophils are an important source of interleukin-6 and recruitment of the TH17 subset of helper T cells. Genetic deletion or antibody-based depletion of basophils results in reduced renal fibrosis. Human kidney single-cell, bulk gene expression and immunostaining validate a function for basophils in patients with kidney fibrosis. Collectively, these studies identify basophils as contributors to the development of renal fibrosis and suggest that targeting these cells might be a useful clinical strategy to manage chronic kidney disease.

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Fig. 1: Single-cell atlas of mouse kidney fibrosis.
Fig. 2: Profibrotic proximal tubule cell differentiation in UUO kidneys.
Fig. 3: The interaction between profibrotic PT cells and basophils in kidney fibrosis.
Fig. 4: Mice with genetic or antibody-mediated depletion of basophils are protected from renal fibrosis.
Fig. 5: Basophil respond to IL-18 and IL-33 and release IL-6 in UUO kidneys.
Fig. 6: IL-6 receptor blockade protected from renal fibrosis and TH17 cell expansion.
Fig. 7: Increased number of basophils in kidneys of patients with CKD.
Fig. 8: Graphical abstract.

Data availability

The mouse UUO single-cell data presented in this paper are newly deposited to GSE182256. The publicly available mouse UUO single-cell data were obtained from GSE140023. The human kidney gene expression data can be downloaded from GSE173343, GSE115098. GRCm38 and GRCh38 were used for mice and human reference genome assembly, respectively.

Code availability

The code is available at GitHub (https://github.com/ms-balzer/kidney_basophils).

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Acknowledgements

This work has been supported in the Susztak laboratory by the National Institutes of Health R01 DK087635, DK076077 and DK105821 and in the Lefebvre laboratory by the National Institutes of Health NIAMS R01 grant AR068308. M.S.B. is supported by a German Research Foundation (Deutsche Forschungsgemeinschaft) grant (BA 6205/2-1). The authors thank the Molecular Pathology and Imaging Core (P30-DK050306) and Diabetes Research Center (P30-DK19525) at University of Pennsylvania for their services.

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Authors and Affiliations

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

    Tomohito Doke, Amin Abedini, Ya-Wen Yang, Jihwan Park, Michael S. Balzer, Rojesh Shrestra, Gaia Coppock, Sheng Xin & Katalin Susztak

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

    Daniel L. Aldridge, Gaia Coppock & Christopher A. Hunter

  3. Center for Immunity and Inflammation, Department of Medicine, New Jersey Medical School, Rutgers–The State University of New Jersey, Newark, NJ, USA

    Christina M. Hernandez, Juan M. Inclan Rico & Mark C. Siracusa

  4. Department of Biology, University of Pennsylvania, Philadelphia, PA, USA

    Seung Yub Han & Junhyong Kim

  5. Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA

    Adrian M. Piliponsky

  6. Division of Orthopaedic Surgery, Department of Surgery, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Marco Angelozzi & Veronique Lefebvre

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Contributions

T.D., D.A.A., Y.Y., C.M.H. and R.S. performed experiments and analyzed data. J.P., A.A., M.S.B. and S.X. performed computational analysis. G.C., J.I.R., S.H. and J.K. offered experimental suggestion. A.P., M.A. and V.L. provided resources and advice. K.S. was responsible for overall design and oversight of the experiments. M.C.S., C.A.H. and K.S. supervised the experiment. T.D., J.P. and K.S. wrote the original draft. All authors contributed to and approved the final version of the manuscript.

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Correspondence to Katalin Susztak.

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Nature Immunology thanks Matthias Mack and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Nicholas Bernard, in collaboration with the Nature Immunology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Mcpt8 expression in basophils in kidney fibrosis.

(A) Bubble plots showing the average gene expression and percentage of expressing cells of S100a8, Il5ra, Siglecf, Mcpt8, Fcer1a, Mcpt4 and kit in the basophils. (B) Representative in situ hybridization image of Mcpt8 in kidney fibrosis following UUO or injection of folic acid. Scale bar: 50 µm. Data are representative of two independent experiments.

Extended Data Fig. 2 Integration analysis using published dataset.

(a) UMAP dimension reduction showing 26 distinct cell types identified by unsupervised clustering. Sample number, Sham; n = 7, UUO; n = 5. GEC: glomerular endothelial cells, Endo: endothelial, Podo: podocyte, PT: proximal tubule, ALOH: ascending loop of Henle, DCT: distal convoluted tubule, CNT: connecting tubule, CD PC: collecting duct principal cell, A-IC: alpha intercalated cell, B-IC: beta intercalated cell, Trans-IC: transitional intercalated cell, Neutro: Neutrophils, Mono: monocyte, DC: dendritic cell, Macro: macrophage, pDC: plasmacytoid DC, Baso: Basophile B Lymph: B lymphocyte, NK: natural killer cell. (b) The percentage of basophils detected in single-cell RNA-seq in UUO kidneys. n = 3 from the published database (Conway et al), n = 2 from this paper. Data are presented as mean values ± SEM. (c) Bubble plots showing the expression of cell cluster marker genes in the combined dataset.

Extended Data Fig. 3 Marker gene expressions of renal tubule cells in UUO kidneys.

(a) Violin plots showing the average expression and percentage of cells expressing Lrp2, Hnf4a, Slc34a1, Slc13a3, Slc22a6, Cp, Cryab, and Aqp1 in the PT subcluster in UUO kidneys. (b) Venn diagram of differentially expression genes in profibrotic proximal renal tubules in UUO kidneys and ischemia reperfusion injury (IRI) kidneys. (c) Bubble plots showing the average expression and percentage of cells expressing Epcam in UUO kidneys.

Extended Data Fig. 4 SCENIC regulon activity of PT clusters in UUO kidneys.

(Left panel) Heat map of predicted transcription factor activity in PT subclusters (Precursor, S2, S3, Transient mix, Proliferating, Immune, S1, and Profibrotic PT). Red indicates higher regulon activity, blue indicates lower regulon activity. (Right panel) Feature plots of the regulon activity (AUC) for representative transcription factors of Bcl3, Cebpb, Stat3, Klf6, Stat5a, and Ddit3.

Extended Data Fig. 5 The expression levels of Il18, Il33 in UUO kidneys.

(a) Whole kidney gene and protein expression levels in sham and UUO kidneys of wild-type (WT) mice (n = 3 respectively). (b) Bubble plots showing the average gene expression and percentage of expressing cells of Il18 and Il33 in sham and UUO kidneys. Gene expression levels are shown as FPKM values (quantified by RNA-seq) and protein levels (quantified by ELISA) are corrected with total protein levels. *p < 0.05, ***p < 0.001. Data are presented as mean values ± SEM. Data were analyzed using DEseq2. (c) The representative image of in situ hybridization of Il18 and Il33 with Hnf4a in sham and UUO kidneys. Scale bar; 20 µm. Data are representative of two independent experiments.

Extended Data Fig. 6 Immune cell survey of MCPT8Cre-DTR mice kidneys.

Relative transcript level of immune cell markers Tcf7, Cd8a, Foxp3, Ccr6, Ncr1, Cd79a, Adgre, Cd206, Clec10a, and Siglech in kidneys of experimental groups (n = 6 in each group). Gene expression levels in whole kidney samples were normalized to Gapdh. *p < 0.05. N.S. not significant. Data are presented as mean values ± SEM. All data were analyzed using a one-way ANOVA followed by Tukey post hoc test for multigroup comparison.

Extended Data Fig. 7 Changes in immune cell population in kidneys of Mcpt8cre-DTR mice.

(a) The number of Th17 cells in kidneys of experimental groups identified by FACS (n = 3 in WT sham, MCPT8Cre-DTR sham, and WT UUO. n = 4 in MCPT8Cre-DTR UUO). *p < 0.05, **p < 0.01. (b) The number of CD4, CD8, Treg, and mast cells in kidneys of experimental groups identified by flow sorting (n = 3 in WT sham, MCPT8Cre-DTR sham, and WT UUO. n = 4 in MCPT8Cre-DTR UUO). **p < 0.01. N.S. not significant. (a-b) Data are presented as mean values ± SEM. All data were analyzed using a one-way ANOVA followed by Tukey post hoc test for multigroup comparison.

Extended Data Fig. 8 Gene expression correlation in microdissected human kidney tubule samples.

(a) Correlation between IL6, CXCL1, IL18, IL33, IL17d normalized expression levels in human kidney samples (x axis) and eGFR (ml/min) (y axis). (b) Correlation between CXCL1, IL18, IL33, IL17d normalized expression levels (x axis) and IL6 normalized expression levels (y axis) in human kidney samples. Pearson’s correlation coefficient values are shown.

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Doke, T., Abedini, A., Aldridge, D.L. et al. Single-cell analysis identifies the interaction of altered renal tubules with basophils orchestrating kidney fibrosis. Nat Immunol 23, 947–959 (2022). https://doi.org/10.1038/s41590-022-01200-7

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