Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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).

References

  1. Wynn, T. A. Cellular and molecular mechanisms of fibrosis. J. Pathol. 214, 199–210 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Jager, K. J. et al. A single number for advocacy and communication-worldwide more than 850 million individuals have kidney diseases. Nephrol. Dialysis Transplant. 34, 1803–1805 (2019).

    Article  Google Scholar 

  3. Dhillon, P. et al. The nuclear receptor ESRRA protects from kidney disease by coupling metabolism and differentiation. Cell Metab. 33, 379–394 (2021).

    Article  CAS  PubMed  Google Scholar 

  4. Galli, S. J., Tsai, M. & Piliponsky, A. M. The development of allergic inflammation. Nature 454, 445–454 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Voehringer, D. Protective and pathological roles of mast cells and basophils. Nat. Rev. Immunol. 13, 362–375 (2013).

    Article  CAS  PubMed  Google Scholar 

  6. Inclan-Rico, J. M. et al. Basophils prime group 2 innate lymphoid cells for neuropeptide-mediated inhibition. Nat. Immunol. 21, 1181–1193 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Cohen, M. et al. Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031–1044 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Bosch, X., Lozano, F., Cervera, R., Ramos-Casals, M. & Min, B. Basophils, IgE, and autoantibody-mediated kidney disease. J. Immunol. 186, 6083–6090 (2011).

    Article  CAS  PubMed  Google Scholar 

  9. Schiechl, G. et al. Basophils trigger fibroblast activation in cardiac allograft fibrosis development. Am. J. Transplant. 16, 2574–2588 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Sicklinger, F. et al. Basophils balance healing after myocardial infarction via IL-4/IL-13. J. Clin. Invest. https://doi.org/10.1172/JCI136778 (2021).

  11. Franco, C. B., Chen, C. C., Drukker, M., Weissman, I. L. & Galli, S. J. Distinguishing mast cell and granulocyte differentiation at the single-cell level. Cell Stem Cell 6, 361–368 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Madjene, L. C. et al. Mast cells in renal inflammation and fibrosis: lessons learnt from animal studies. Mol. Immunol. 63, 86–93 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Higashi, A. Y., Aronow, B. J. & Dressler, G. R. Expression Profiling of fibroblasts in chronic and acute disease models reveals novel pathways in kidney fibrosis. J. Am. Soc. Nephrol. 30, 80–94 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Hewitt, K. J. et al. PDGFRβ expression and function in fibroblasts derived from pluripotent cells is linked to DNA demethylation. J. Cell Sci. 125, 2276–2287 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Heng, T. S. & Painter, M. W. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).

    Article  CAS  PubMed  Google Scholar 

  16. Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Conway, B. R. et al. Kidney single-cell atlas reveals myeloid heterogeneity in progression and regression of kidney disease. J. Am. Soc. Nephrol. 31, 2833–2854 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Klinkhammer, B. M., Floege, J. & Boor, P. PDGF in organ fibrosis. Mol. Asp. Med. 62, 44–62 (2018).

    Article  CAS  Google Scholar 

  19. Kusaba, T., Lalli, M., Kramann, R., Kobayashi, A. & Humphreys, B. D. Differentiated kidney epithelial cells repair injured proximal tubule. PNAS 111, 1527–1532 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Nielsen, R., Christensen, E. I. & Birn, H. Megalin and cubilin in proximal tubule protein reabsorption: from experimental models to human disease. Kidney Int. 89, 58–67 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Chen, L., Chou, C. L. & Knepper, M. A. Targeted single-cell RNA-seq identifies minority cell types of kidney distal nephron. J. Am. Soc. Nephrol. 32, 886–896 (2021).

    Article  CAS  PubMed Central  Google Scholar 

  22. Gerhardt, L. M. S., Liu, J., Koppitch, K., Cippà, P. E. & McMahon, A. P. Single-nuclear transcriptomics reveals diversity of proximal tubule cell states in a dynamic response to acute kidney injury. PNAS https://doi.org/10.1073/pnas.2026684118 (2021).

  23. Muto, Y. et al. Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney. Nat. Commun. 12, 2190 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Liu, S. S. et al. Targeting degradation of the transcription factor C/EBPβ reduces lung fibrosis by restoring activity of the ubiquitin-editing enzyme A20 in macrophages. Immunity 51, 522–534 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Petrovic-Djergovic, D. et al. CXCL10 induces the recruitment of monocyte-derived macrophages into kidney, which aggravate puromycin aminonucleoside nephrosis. Clin. Exp. Immunol. 180, 305–315 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ruth, J. H. et al. CXCL16-mediated cell recruitment to rheumatoid arthritis synovial tissue and murine lymph nodes is dependent upon the MAPK pathway. Arthritis Rheum. 54, 765–778 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Baek, J. H. et al. IL-34 mediates acute kidney injury and worsens subsequent chronic kidney disease. J. Clin. Invest. 125, 3198–3214 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Sullivan, B. M. et al. Genetic analysis of basophil function in vivo. Nat. Immunol. 12, 527–535 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. El Hachem, C. et al. Treatment of MCPT8(DTR) mice with high- or low-dose diphtheria toxin leads to differential depletion of basophils and granulocyte-macrophage progenitors. Eur. J. Immunol. 48, 861–873 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Denzel, A. et al. Basophils enhance immunological memory responses. Nat. Immunol. 9, 733–742 (2008).

    Article  CAS  PubMed  Google Scholar 

  31. Yuk, C. M. et al. Basophil-derived IL-6 regulates T(H)17 cell differentiation and CD4 T cell immunity. Sci. Rep. 7, 41744 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Tang, P. M., Nikolic-Paterson, D. J. & Lan, H. Y. Macrophages: versatile players in renal inflammation and fibrosis. Nat. Rev. Nephrol. 15, 144–158 (2019).

    Article  PubMed  Google Scholar 

  33. Turner, J. E., Becker, M., Mittrücker, H. W. & Panzer, U. Tissue-resident lymphocytes in the kidney. J. Am. Soc. Nephrol. 29, 389–399 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Kurts, C., Ginhoux, F. & Panzer, U. Kidney dendritic cells: fundamental biology and functional roles in health and disease. Nat. Rev. Nephrol. 16, 391–407 (2020).

    Article  PubMed  Google Scholar 

  35. Gewin, L. S. Renal fibrosis: primacy of the proximal tubule. Matrix Biol. 68–69, 248–262 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Qiu, C. et al. Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease. Nat. Med. 24, 1721–1731 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kirita, Y., Wu, H., Uchimura, K., Wilson, P. C. & Humphreys, B. D. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. PNAS 117, 15874–15883 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Liang, H. et al. CXCL16 deficiency attenuates renal injury and fibrosis in salt-sensitive hypertension. Sci. Rep. 6, 28715 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Geiser, T., Dewald, B., Ehrengruber, M. U., Clark-Lewis, I. & Baggiolini, M. The interleukin-8-related chemotactic cytokines GRO α, GRO β, and GRO γ activate human neutrophil and basophil leukocytes. J. Biol. Chem. 268, 15419–15424 (1993).

    Article  CAS  PubMed  Google Scholar 

  40. Bani-Hani, A. H. et al. IL-18 neutralization ameliorates obstruction-induced epithelial-mesenchymal transition and renal fibrosis. Kidney Int. 76, 500–511 (2009).

    Article  CAS  PubMed  Google Scholar 

  41. Liang, H. et al. Interleukin-33 signaling contributes to renal fibrosis following ischemia reperfusion. Eur. J. Pharmacol. 812, 18–27 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Hunter, C. A. & Jones, S. A. IL-6 as a keystone cytokine in health and disease. Nat. Immunol. 16, 448–457 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. González, G. E. et al. Deletion of interleukin-6 prevents cardiac inflammation, fibrosis and dysfunction without affecting blood pressure in angiotensin II-high salt-induced hypertension. J. Hypertens. 33, 144–152 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Le, T. T. et al. Blockade of IL-6 trans signaling attenuates pulmonary fibrosis. J. Immunol. 193, 3755–3768 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Fielding, C. A. et al. Interleukin-6 signaling drives fibrosis in unresolved inflammation. Immunity 40, 40–50 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wakahara, K. et al. Human basophils interact with memory T cells to augment Th17 responses. Blood 120, 4761–4771 (2012).

    Article  CAS  PubMed  Google Scholar 

  47. Coppock, G. M. et al. Loss of IL-27Rα results in enhanced tubulointerstitial fibrosis associated with elevated Th17 responses. J. Immunol. 205, 377–386 (2020).

    Article  CAS  PubMed  Google Scholar 

  48. Wilson, M. S. et al. Bleomycin and IL-1β-mediated pulmonary fibrosis is IL-17A dependent. J. Exp. Med. 207, 535–552 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cipolla, E. et al. IL-17A deficiency mitigates bleomycin-induced complement activation during lung fibrosis. FASEB J. 31, 5543–5556 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mitsui, H. et al. Development of human mast cells from umbilical cord blood cells by recombinant human and murine c-kit ligand. PNAS 90, 735–739 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Khanna, D. et al. Tocilizumab in systemic sclerosis: a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Resp. Med. 8, 963–974 (2020).

    Article  CAS  Google Scholar 

  52. Guaraldi, G. et al. Tocilizumab in patients with severe COVID-19: a retrospective cohort study. Lancet Rheumatol. 2, e474–e484 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Doke, T. et al. Transcriptome-wide association analysis identifies DACH1 as a kidney disease risk gene that contributes to fibrosis. J. Clin. Invest. https://doi.org/10.1172/JCI141801 (2021).

  54. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  59. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Angerer, P. et al. Destiny: diffusion maps for large-scale single-cell data in R. Bioinforma. 32, 1241–1243 (2016).

    Article  CAS  Google Scholar 

  62. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Steen, C. B., Liu, C. L., Alizadeh, A. A. & Newman, A. M. Profiling cell type abundance and expression in bulk tissues with CIBERSORTx. Methods Mol. Biol. 2117, 135–157 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

Download references

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.

Author information

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

Authors
  1. Tomohito Doke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amin Abedini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel L. Aldridge
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ya-Wen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jihwan Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christina M. Hernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michael S. Balzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rojesh Shrestra
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Gaia Coppock
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Juan M. Inclan Rico
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Seung Yub Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Junhyong Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sheng Xin
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Adrian M. Piliponsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Marco Angelozzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Veronique Lefebvre
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Mark C. Siracusa
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Christopher A. Hunter
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Katalin Susztak
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to Katalin Susztak.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-022-01200-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing