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The immune cell landscape in kidneys of patients with lupus nephritis

Nature Immunology volume 20, pages 902–914 (2019)Cite this article

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A Publisher Correction to this article was published on 13 August 2019

This article has been updated

Abstract

Lupus nephritis is a potentially fatal autoimmune disease for which the current treatment is ineffective and often toxic. To develop mechanistic hypotheses of disease, we analyzed kidney samples from patients with lupus nephritis and from healthy control subjects using single-cell RNA sequencing. Our analysis revealed 21 subsets of leukocytes active in disease, including multiple populations of myeloid cells, T cells, natural killer cells and B cells that demonstrated both pro-inflammatory responses and inflammation-resolving responses. We found evidence of local activation of B cells correlated with an age-associated B-cell signature and evidence of progressive stages of monocyte differentiation within the kidney. A clear interferon response was observed in most cells. Two chemokine receptors, CXCR4 and CX3CR1, were broadly expressed, implying a potentially central role in cell trafficking. Gene expression of immune cells in urine and kidney was highly correlated, which would suggest that urine might serve as a surrogate for kidney biopsies.

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Fig. 1: An overview of the approach used for analyzing the cellular contents and molecular states of kidney and urine samples.
Fig. 2: A summary of the stepwise clustering of kidney cells.
Fig. 3: Focused analysis of kidney myeloid cells.
Fig. 4: Focused analysis of kidney T cells and NK cells.
Fig. 5: Focused analysis of kidney B cells.
Fig. 6: The expression of GWAS genes in LN kidneys.
Fig. 7: Chemokine- and cytokine-mediated cellular networks.
Fig. 8: Comparison of immune cells extracted from urine samples and from kidney samples.

Data availability

The data reported in this publication, including the clinical and serological data of the study participants, are deposited in the ImmPort repository (accession code SDY997). The raw single-cell RNA-seq data are also deposited in dbGAP (accession code phs001457.v1.p1). The processed data can be viewed using an interactive browser at https://immunogenomics.io/ampsle, https://immunogenomics.io/cellbrowser/ and https://portals.broadinstitute.org/single_cell/study/amp-phase-1.

Code availability

All R scripts used to analyze the data reported in this publication are available from the corresponding authors on request.

Change history

  • 13 August 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

This work was supported by the Accelerating Medicines Partnership (AMP) in Rheumatoid Arthritis and Lupus Network. AMP is a public–private partnership (AbbVie Inc., Arthritis Foundation, Bristol-Myers Squibb Company, Foundation for the National Institutes of Health, Janssen Pharmaceuticals, Lupus Foundation of America, Lupus Research Alliance, Merck Sharp & Dohme Corp., National Institute of Allergy and Infectious Diseases, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Pfizer Inc., Rheumatology Research Foundation, Sanofi and Takeda Pharmaceuticals International, Inc.) created to develop new ways of identifying and validating promising biological targets for diagnostics and drug development. Funding was provided through grants from the National Institutes of Health (UH2-AR067676, UH2-AR067677, UH2-AR067679, UH2-AR067681, UH2-AR067685, UH2-AR067688, UH2-AR067689, UH2-AR067690, UH2-AR067691, UH2-AR067694 and UM2-AR067678). N.H. was supported by the David P. Ryan, MD Endowed Chair in Cancer Research. We thank participating Lupus Nephritis Trials Network clinical sites and participants.

Author information

Author notes

  1. These authors contributed equally: Arnon Arazi, Deepak A. Rao, Celine C. Berthier.

  2. A list of members and affiliations appears at the end of the paper.

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Arnon Arazi, Paul J. Hoover, Thomas M. Eisenhaure, Shuqiang Li, David J. Lieb, Akiko Noma, Nir Hacohen, Arnon Arazi, Paul J. Hoover, Thomas M. Eisenhaure, Shuqiang Li, David J. Lieb, Akiko Noma & Nir Hacohen

  2. Division of Rheumatology, Immunology, Allergy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Deepak A. Rao, Yanyan Liu, Adam Chicoine, A. Helena Jonsson, Fan Zhang, Kamil Slowikowski, William Apruzzese, Elena Massarotti, Soumya Raychaudhuri, Michael B. Brenner, Deepak A. Rao, Yanyan Liu, Adam Chicoine, A. Helena Jonsson, Fan Zhang, Kamil Slowikowski, William Apruzzese, Elena Massarotti, Soumya Raychaudhuri, Michael B. Brenner & Chamith Fonseka

  3. Internal Medicine, Department of Nephrology, University of Michigan, Ann Arbor, MI, USA

    Celine C. Berthier, Matthias Kretzler, Celine C. Berthier, Matthias Kretzler, William J. McCune & Ruba B. Kado

  4. Center for Autoimmune and Musculoskeletal Diseases, The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, USA

    Anne Davidson, Betty Diamond, Anne Davidson & Betty Diamond

  5. UNC HIV Cure Center and Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Edward P. Browne & Edward P. Browne

  6. Celsius Therapeutics, Cambridge, MA, USA

    Danielle Sutherby & Danielle Sutherby

  7. Cellarity, Inc., Cambridge, MA, USA

    Scott Steelman, Chad Nusbaum, Scott Steelman & Chad Nusbaum

  8. Lupus Nephritis Trials Network, University of California San Francisco, San Francisco, CA, USA

    Dawn E. Smilek, Patti Tosta, Dawn E. Smilek & Patti Tosta

  9. Immune Tolerance Network, University of California San Francisco, San Francisco, CA, USA

    Dawn E. Smilek, Patti Tosta, Dawn E. Smilek & Patti Tosta

  10. Rheumatology Division, University of California San Francisco, San Francisco, CA, USA

    Maria Dall’Era, David Wofsy, Maria Dall’Era & David Wofsy

  11. Division of Nephrology, University of California San Francisco, San Francisco, CA, USA

    Meyeon Park, Meyeon Park & Raymond Hsu

  12. Division of Rheumatology and Immunology, Medical University of South Carolina, Charleston, SC, USA

    Diane L. Kamen, Diane L. Kamen & Melissa A. Cunningham

  13. Division of Rheumatology, Northwell Health, Great Neck, NY, USA

    Richard A. Furie & Richard A. Furie

  14. Department of Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA

    Fernanda Payan-Schober, Fernanda Payan-Schober & Sean M. Connery

  15. The Integrative Medical Clinic of North Carolina, PLLC, Chapel Hill, NC, USA

    William F. Pendergraft III & William F. Pendergraft III

  16. University of North Carolina Kidney Center, Division of Nephrology and Hypertension, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, USA

    Elizabeth A. McInnis & Elizabeth A. McInnis

  17. Department of Medicine, Division of Rheumatology, New York University School of Medicine, New York, NY, USA

    Jill P. Buyon, Jill P. Buyon, Robert Clancy, H. Michael Belmont & Peter M. Izmirly

  18. Division of Rheumatology, Johns Hopkins University, Baltimore, MD, USA

    Michelle A. Petri, Michelle A. Petri, Andrea Fava & Daniel H. Goldman

  19. Division of Rheumatology and Department of Microbiology and Immunology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA

    Chaim Putterman, Chaim Putterman & Evan Der

  20. University of California San Diego School of Medicine, La Jolla, CA, USA

    Kenneth C. Kalunian & Kenneth C. Kalunian

  21. Division of Transplantation, Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    E. Steve Woodle & E. Steve Woodle

  22. Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    James A. Lederer & James A. Lederer

  23. Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA

    David A. Hildeman & David A. Hildeman

  24. Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA

    David A. Hildeman & David A. Hildeman

  25. Department of Medicine, Division of Allergy, Immunology, and Rheumatology, University of Rochester Medical Center, Rochester, NY, USA

    Jennifer H. Anolik & Jennifer H. Anolik

  26. Department of Internal Medicine, University of Texas, Houston, TX, USA

    Dia Waguespack

  27. Department of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Maureen A. McMahon

  28. Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, USA

    Paul J. Utz & Mina Pichavant

  29. Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Holden T. Maecker

  30. Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA

    Rohit Gupta

  31. Arthritis and Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA

    Judith A. James & Joel M. Guthridge

  32. Laboratory of RNA Molecular Biology, Rockefeller University, New York, NY, USA

    Thomas Tuschl, Hemant Suryawanshi, Pavel Morozov & Manjunath Kustagi

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  1. Arnon Arazi
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  2. Deepak A. Rao
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  27. William F. Pendergraft III
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  28. Elizabeth A. McInnis
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  30. Michelle A. Petri
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  32. Kenneth C. Kalunian
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  33. E. Steve Woodle
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  39. Jennifer H. Anolik
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  40. Michael B. Brenner
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  43. Betty Diamond
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Consortia

the Accelerating Medicines Partnership in SLE network

  • Arnon Arazi
  • , Deepak A. Rao
  • , Celine C. Berthier
  • , Anne Davidson
  • , Yanyan Liu
  • , Paul J. Hoover
  • , Adam Chicoine
  • , Thomas M. Eisenhaure
  • , A. Helena Jonsson
  • , Shuqiang Li
  • , David J. Lieb
  • , Fan Zhang
  • , Kamil Slowikowski
  • , Edward P. Browne
  • , Akiko Noma
  • , Danielle Sutherby
  • , Scott Steelman
  • , Dawn E. Smilek
  • , Patti Tosta
  • , William Apruzzese
  • , Elena Massarotti
  • , Maria Dall’Era
  • , Meyeon Park
  • , Diane L. Kamen
  • , Richard A. Furie
  • , Fernanda Payan-Schober
  • , William F. Pendergraft III
  • , Elizabeth A. McInnis
  • , Jill P. Buyon
  • , Michelle A. Petri
  • , Chaim Putterman
  • , Kenneth C. Kalunian
  • , E. Steve Woodle
  • , James A. Lederer
  • , David A. Hildeman
  • , Chad Nusbaum
  • , Soumya Raychaudhuri
  • , Matthias Kretzler
  • , Jennifer H. Anolik
  • , Michael B. Brenner
  • , David Wofsy
  • , Nir Hacohen
  • , Betty Diamond
  • , Dia Waguespack
  • , Sean M. Connery
  • , Maureen A. McMahon
  • , William J. McCune
  • , Ruba B. Kado
  • , Raymond Hsu
  • , Melissa A. Cunningham
  • , Paul J. Utz
  • , Mina Pichavant
  • , Holden T. Maecker
  • , Rohit Gupta
  • , Judith A. James
  • , Joel M. Guthridge
  • , Chamith Fonseka
  • , Evan Der
  • , Robert Clancy
  • , Thomas Tuschl
  • , Hemant Suryawanshi
  • , Andrea Fava
  • , H. Michael Belmont
  • , Peter M. Izmirly
  • , Pavel Morozov
  • , Manjunath Kustagi
  •  & Daniel H. Goldman

Contributions

A.A., D.A.R, A.D., P.J.H., A.H.J. and D.J.L. analyzed the data. D.A.R., C.C.B., T.M.E., E.P.B., J.A.L. and D.A.H. developed the sample collection and processing protocols. D.A.R., Y.L., P.J.H., A.C., A.N., D.S. and S.S. processed the samples. S.L., D.J.L., A.N., D.S. and S.S. developed the scRNA-seq library preparation protocol. F.Z. and K.S. developed the web-based browser of the data. D.E.S., P.T., E.M., M.D.E., M.P., D.L.K., R.A.F., F.P.S., W.F.P., E.A.M., J.P.B., M.A.P., C.P., K.C.K., E.S.W., D.A.H., D.W. and J.H.A. acquired samples. A.A., D.A.R., C.C.B., A.D., W.A., J.A.L., D.A.H., C.N., D.W., M.K., J.H.A., M.B.B., N.H. and B.D. designed the study. A.D., W.A., D.A.H., C.N., S.R., D.W., M.K., J.H.A., M.B.B., N.H. and B.D. supervised the work. A.A., D.A.R., C.C.B., A.D., P.J.H., N.H. and B.D. wrote the manuscript.

Corresponding authors

Correspondence to Nir Hacohen or Betty Diamond.

Ethics declarations

Competing interests

M.B.B. is a consultant to Roche in the area of stromal cells.

Integrated supplementary information

Supplementary Figure 1 Analysis of cells obtained from cryopreserved viable kidney tissue.

a, Leukocyte yields from kidney tissue samples processed fresh or after freeze/thaw. b, Leukocyte yields from kidney tissue samples cryopreserved either post-dissociation (frozen cells) or pre-dissociation (frozen tissue). n = 9 segments pooled from 4 different donors. * p < 0.01 by two-tailed Mann-Whitney U-test. c, Cell yields and leukocyte frequencies from kidney samples either cryopreserved or shipped overnight on wet ice. Wilcoxon matched-pairs signed rank test. d, Example of flow cytometry analysis of cells from a cryopreserved lupus nephritis kidney biopsy. e, RNA-seq quality metrics of bulk leukocytes from kidney cells analyzed before (fresh cells) and after freeze/thaw (frozen cells), or obtained from kidney tissue shipped overnight or shipped after tissue cryopreservation (frozen tissue). f-h, The distribution of the number of genes per cell on each processed 384-well plate, in kidney leukocytes (f), kidney epithelial cells (g) and urine leukocytes (h).

Supplementary Figure 2 An overview of the isolated cells.

a-d, Leukocyte yields and composition from LN kidney biopsies, as determined by flow cytometry. a, Number of leukocytes sorted as single cells from LN and control biopsy samples. b, Number of leukocytes sorted from LN biopsies stratified by histologic glomerulonephritis class. c, Flow cytometric assessment of the proportion of leukocytes that are B cells, T cells, monocyte/macrophages, or other in biopsy samples. d, Percentage of B cells among leukocytes in biopsy samples (p = 0.0031; two-tailed Mann-Whitney U-test). e, Projection of the gene expression data of kidney cells following principal component analysis (PCA), in the PC1-PC2 (left) and PC2-PC3 (right) planes. Cells are colored based on their identity as either leukocytes (cyan) or epithelial cells (pink), as determined by flow cytometry. The labeling of clusters in the PC2-PC3 plane is based on the identity of the leading genes in each principal component. f, Same as (e), except cells are colored based on the 384-well plate they were processed on. g, The expression of selected genes in the 10 low-resolution clusters; these were among the genes used to determine the putative lineage of each cell. h, The number of clusters encountered as a function of the number of patients included in the analysis. The presented curve was generated by randomly down-sampling the number of patients analyzed, while counting in each case the number of clusters spanned by the cells of these patients. Patients that had a small number of high quality cells (less than 70) were not included in this analysis, as such patients would lead to a false saturation. Furthermore, to account for the fact that for a cluster to be identified it should contain a sufficient number of cells, we only counted here clusters that had more cells than the smallest cluster identified when analyzing the full cohort (17 cells—cluster CB2b). i, A tSNE plot of the analyzed cells, highlighting the LD controls as opposed to the LN patients. j, A tSNE plot of the analyzed cells, colored by the donor identity.

Supplementary Figure 3 Focused analysis of kidney myeloid cells.

a, The expression of selected genes over the 5 myeloid cell clusters. b-c, The expression of the canonical markers CD16 (FCGR3A) and CD14 on the 5 myeloid clusters. d-f, The changes in the expression of 3 selected genes, along the trajectory from CM0 to CM4; ‘pseudotime’ represents the ordering of the cells along this trajectory. The violin plots (shades) show the distribution of expression levels in equally-spaced intervals along the pseudotime axis (and do not directly correspond to cell clusters). g, The scRNA-seq data of myeloid blood cells sorted from 2 LN patients, projected in the tSNE plane. Low-resolution clustering identified a cluster of CD16+ monocytes; other myeloid cells are grouped here into an ‘other’ set. h, The results of comparing the blood CD16+ monocytes to the 5 myeloid cells clusters found in kidney (CM0-CM4). For each blood cell, the most similar kidney cluster was determined; the bar plot denotes the percentage of myeloid cells most similar to each of the kidney myeloid clusters (only values larger than 0 are shown). i, The increase in the phagocytosis score, computed as the average expression of several genes associated with phagocytosis, in cluster CM1 compared to cluster CM0, in blood myeloid cells. j, Same as (i), but with regard to kidney myeloid cells. ***—p-value < 0.001 (two-tailed Mann-Whitney U-test).

Supplementary Figure 4 Focused analysis of kidney T cells and NK cells.

a, The expression of selected genes across the 7 high-level T/NK cell clusters. b, The expression of selected genes across the two subclusters of cluster CT5, and in comparison to cluster CT1. c, The expression of genes associated with exhaustion, across the different T/NK cell clusters. d, A heatmap showing the separation of LN patients (orange in line above heatmap) and healthy controls (blue), based on expression data of CD8+ T cells sorted from blood samples, and considering a published list of exhaustion markers. Both rows and columns are clustered based on Euclidian distance. Note that out of the 10 LN patients for which blood samples were available, only 8 had sufficient numbers of blood CD8+ T cells to allow sequencing. e, The exhaustion score as calculated in blood CD8+ T cells, comparing the 8 LN patients to the 10 healthy controls. Each point represents a single patient; lines represent the median per group. **—p-value < 0.01 (two-tailed Mann-Whitney U-test). f, The exhaustion score in kidney CD8+ T cells, comparing LN patients to LD controls (same 8 LN patients as in (e)). Each point refers to a cell. g, The expression of selected genes across the two subclusters of cluster CT3. h, The expression of selected genes across the two subclusters of cluster CT0a.

Supplementary Figure 5 Focused analysis of kidney B cells.

a, The expression of selected genes, across the 4 high-level B cell clusters. b, A tSNE projection of all B cells. Note that the cells of cluster CB2 (in cyan) occupy two main separate regions in the tSNE1-tSNE2 plane. c, The expression of selected genes, across the two subclusters of cluster CB2. d-f, The results of classifying the CB2 cells by correlating their gene expression with that of 3 sets of reference samples—FANTOM5 (d), 13 immune cell populations sorted from healthy individuals (Browne et al.; e), and the clusters identified in Villani et al. (f). For each of the 2 subclusters in CB2, the bars denote the percentage of cells most similar to each of the reference samples. For readability, only relevant reference samples are specified, and the rest are collapsed into an ‘other’ category. For all cells in all comparisons, the correlation used for classification was above the corresponding assignability threshold. Note that the reference samples in Villani et al. do not include B cells, and therefore are not relevant for the classification of the cells in cluster CB2a.

Supplementary Figure 6 Focused analysis of cluster C9.

a, The identification of 3 subclusters, the first of which pertains to cells undergoing mitosis. b, The percentage of reads mapped to mitochondrial genes in each subcluster. c, Classification results for each subcluster, comparing the cells of cluster C9 to reference samples found in FANTOM5.

Supplementary Figure 7 Analysis of chemokines, cytokines and their receptors.

a, The distribution of the maximum fraction of expressing cells (taken over all clusters for each receptor), for all receptors. Based on this distribution, a threshold of 0.3 was chosen to define frequently expressed receptors. b–f, The percentage of cells expressing selected chemokine receptors, specified for each cluster. The dashed line in all panels pertains to the above threshold. g–j, The distribution of expression levels of selected chemokines, for each cluster.

Supplementary Figure 8 Analysis of urine cells.

A comparison of gene expression in kidney (x axis) and urine (y axis), for 3 of the clusters represented in both compartments.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8.

Reporting Summary

Supplementary Table 1: Clinical characteristics of kidney biopsy donors.

Two worksheets are provided—one containing a summary of the clinical characteristics of the studied cohort, the other containing detailed information per sample.

Supplementary Table 2: Sensitivity analysis of clustering parameters.

The results of a detailed sensitivity analysis with regard to the low-resolution clustering step, varying several parameters. Consistency of clustering across various parameter combinations, as defined using the Rand index, is reported.

Supplementary Table 3: Number of cells per cluster and patient.

Rows correspond to patients; columns correspond to clusters.

Supplementary Table 4: Number of cells per cluster and processed 384-well plate.

Rows correspond to plates; columns correspond to clusters.

Supplementary Table 5: Differential expression analysis, comparing LN and LD samples.

Results are reported (in separate worksheets) per cluster, looking only at clusters that had a sufficient number of cells in the LD samples

Supplementary Table 6: Gene lists.

The various gene lists that were used throughout the analysis, each in a separate worksheet.

Supplementary Table 7: Myeloid cells classification by correlation.

The Pearson correlation scores, comparing each kidney myeloid cell (rows) to each reference sample (columns) in Villani et al.

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Arazi, A., Rao, D.A., Berthier, C.C. et al. The immune cell landscape in kidneys of patients with lupus nephritis. Nat Immunol 20, 902–914 (2019). https://doi.org/10.1038/s41590-019-0398-x

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