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Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways

Nature Immunology volume 20, pages 915–927 (2019)Cite this article

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An Author Correction to this article was published on 11 October 2019

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Abstract

The molecular and cellular processes that lead to renal damage and to the heterogeneity of lupus nephritis (LN) are not well understood. We applied single-cell RNA sequencing (scRNA-seq) to renal biopsies from patients with LN and evaluated skin biopsies as a potential source of diagnostic and prognostic markers of renal disease. Type I interferon (IFN)-response signatures in tubular cells and keratinocytes distinguished patients with LN from healthy control subjects. Moreover, a high IFN-response signature and fibrotic signature in tubular cells were each associated with failure to respond to treatment. Analysis of tubular cells from patients with proliferative, membranous and mixed LN indicated pathways relevant to inflammation and fibrosis, which offer insight into their histologic differences. In summary, we applied scRNA-seq to LN to deconstruct its heterogeneity and identify novel targets for personalized approaches to therapy.

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Fig. 1: Cell lineage determination by dimensionality reduction analysis.
Fig. 2: Subclustering of keratinocytes reveals two rare skin-specific cell types.
Fig. 3: Subclustering of tubular cells identifies major tubular cell subtypes of the nephron.
Fig. 4: IFN-response signature differentiates patients with LN from healthy control subjects and response to treatment.
Fig. 5: A fibrotic gene signature as a potential prognostic marker for patients non-responsive to treatment.
Fig. 6: Putative receptor–ligand interactions between kidney and skin cells.
Fig. 7: Differential expression and pathway enrichment analysis of tubular cells and keratinocytes between membranous and proliferative LN.
Fig. 8: Differential expression and pathway enrichment analysis of tubular cells between membranous or proliferative LN and mixed class disease.

Data availability

Raw and processed data will be available from dbGAP with accession number phs001457.v1.p1. Quality-controlled data and data matrices can be obtained from Immport with study number SDY997 and experiment number EXP15077.

Code availability

All software packages and programs are publicly available and open source. Scripts used to analyze the data with these packages are available from https://github.com/evander56/PuttermanLab_scRNA-seq_AMP-PhaseI. There is no restriction on the use of the code or data.

Change history

  • 11 October 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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Acknowledgments

The authors thank A. Hurley and the Research Facilitation Office staff at Rockefeller University for regulatory and administrative assistance who are supported in part by grant no. UL1TR001866 from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program. This work was supported by the Accelerating Medicines Partnership (AMP) in Rheumatoid Arthritis and Lupus Network. AMP is a public–private partnership (AbbVie, Arthritis Foundation, Bristol-Myers Squibb, Foundation for the National Institutes of Health, Lupus Foundation of America, Lupus Research Alliance, Merck Sharp & Dohme, National Institute of Allergy and Infectious Diseases, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Pfizer, Rheumatology Research Foundation, Sanofi and Takeda Pharmaceuticals) 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 (grant nos. UH2-AR067676, UH2-AR067677, UH2-AR067679, UH2-AR067681, UH2-AR067685, UH2-AR067688, UH2-AR067689, UH2-AR067690, UH2-AR067691, UH2-AR067694 and UM2-AR067678). We thank the Rockefeller University Genomics Resource Center for providing access to the Fluidigm C1 system and Illumina sequencing.

Author information

Author notes

  1. These authors contributed equally: Evan Der, Hemant Suryawanshi.

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

Authors and Affiliations

  1. Division of Rheumatology and Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA

    Evan Der, Nicole Jordan, Emma Schulte, Chaim Putterman, Evan Der & Chaim Putterman

  2. Laboratory for RNA Molecular Biology, The Rockefeller University, New York, New York, USA

    Hemant Suryawanshi, Pavel Morozov, Manjunath Kustagi, Thomas Tuschl, Hemant Suryawanshi & Thomas Tuschl

  3. Pediatric Nephrology, The Children’s Hospital at Montefiore, Albert Einstein College of Medicine, Bronx, New York, USA

    Beatrice Goilav

  4. Pediatric Nephrology, Arkansas Children’s Hospital, University of Arkansas Medical Sciences, Little Rock, Arkansas, USA

    Saritha Ranabothu

  5. New York University School of Medicine, New York, New York, USA

    Peter Izmirly, Robert Clancy, H. Michael Belmont, Nicole Bornkamp, Ming Wu, Jill Buyon, Jill Buyon & Robert Clancy

  6. Department of Radiology, Montefiore Medical Center, Bronx, New York, USA

    Mordecai Koenigsberg

  7. Division of Nephrology, Department of Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA

    Michele Mokrzycki

  8. Montefiore Einstein Center for Transplantation, Montefiore Medical Center, Bronx, New York, USA

    Helen Rominieki, Jay A. Graham & Juan P. Rocca

  9. Clinical Pathology, Montefiore Medical Center, Bronx, New York, USA

    James Pullman

  10. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Kamil Slowikowski, Soumya Raychaudhuri, Arnon Arazi, Chamith Fonseka, Nir Hacohen, Paul Hoover, Soumya Raychaudhuri, Kamil Slowikowski & Fan Zhang

  11. Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA

    Joel Guthridge, Judith James, Joel Guthridge & Judith James

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

    Jennifer Anolik

  13. Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY, USA

    Jennifer Anolik

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

    William Apruzzese, Michael Brenner, Chamith Fonseka, Elena Massarotti, Deepak Rao & Fan Zhang

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

    Celine Berthier, Ruba Kado, Mattias Kretzler & William McCune

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

    Sean Connery & Fernanda Payan-Schober

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

    Melissa Cunningham & Diane Kamen

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

    Maria Dall’Era & David Wofsy

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

    Anne Davidson

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

    Andrea Fava, Dan Goldman & Michelle Petri

  21. Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA

    Chamith Fonseka & Fan Zhang

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

    Chamith Fonseka & Fan Zhang

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

    Richard Furie

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

    Rohit Gupta, Mina Pichavant & Paul Utz

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

    David Hildeman

  26. Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    David Hildeman

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

    Raymond Hsu & Meyeon Park

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

    Kenneth Kalunian

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

    Holden Maecker

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

    Maureen McMahon

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

    William Pendergraft

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

    Mina Pichavant & Paul Utz

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

    Dia Waguespack

Authors
  1. Evan Der
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  2. Hemant Suryawanshi
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  3. Pavel Morozov
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  4. Manjunath Kustagi
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  5. Beatrice Goilav
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  6. Saritha Ranabothu
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  7. Peter Izmirly
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  8. Robert Clancy
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  9. H. Michael Belmont
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  10. Mordecai Koenigsberg
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  11. Michele Mokrzycki
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  12. Helen Rominieki
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  13. Jay A. Graham
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  14. Juan P. Rocca
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  15. Nicole Bornkamp
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  16. Nicole Jordan
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  17. Emma Schulte
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  18. Ming Wu
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  19. James Pullman
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  20. Kamil Slowikowski
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  21. Soumya Raychaudhuri
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  22. Joel Guthridge
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  23. Judith James
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Consortia

the Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium

  • Jennifer Anolik
  • , William Apruzzese
  • , Arnon Arazi
  • , Celine Berthier
  • , Michael Brenner
  • , Jill Buyon
  • , Robert Clancy
  • , Sean Connery
  • , Melissa Cunningham
  • , Maria Dall’Era
  • , Anne Davidson
  • , Evan Der
  • , Andrea Fava
  • , Chamith Fonseka
  • , Richard Furie
  • , Dan Goldman
  • , Rohit Gupta
  • , Joel Guthridge
  • , Nir Hacohen
  • , David Hildeman
  • , Paul Hoover
  • , Raymond Hsu
  • , Judith James
  • , Ruba Kado
  • , Kenneth Kalunian
  • , Diane Kamen
  • , Mattias Kretzler
  • , Holden Maecker
  • , Elena Massarotti
  • , William McCune
  • , Maureen McMahon
  • , Meyeon Park
  • , Fernanda Payan-Schober
  • , William Pendergraft
  • , Michelle Petri
  • , Mina Pichavant
  • , Chaim Putterman
  • , Deepak Rao
  • , Soumya Raychaudhuri
  • , Kamil Slowikowski
  • , Hemant Suryawanshi
  • , Thomas Tuschl
  • , Paul Utz
  • , Dia Waguespack
  • , David Wofsy
  •  & Fan Zhang

Contributions

J.B., T.T. and C.P. conceived the study with help from S. Ranabothu, J.J., J. Guthridge and S. Raychaudhuri. Input regarding the skin came from R.C. and H.M.B. E.D., H.S. and S. Ranabothu performed all biopsy dissociations and single-cell experiments. B.G., P.I., H.M.B., M. Koenigsberg, M.M., N.J., N.B. and E.S. assisted with patient consent and sample acquisition of LN biopsies. H.R., J.R. and J. Graham assisted with patient consent and sample acquisition of live kidney donor tissue. Renal biopsy histology was evaluated by M.W. and J.P. H.M.B. and P.I. performed all skin biopsies. Analysis was performed by E.D., H.S., P.M., K.S. and M. Kustagi. E.D., J.B., T.T. and C.P. prepared and wrote the manuscript.

Corresponding authors

Correspondence to Jill Buyon, Thomas Tuschl or Chaim Putterman.

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The authors declare no competing interests.

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Integrated supplementary information

Supplementary Figure 1 The tSNE plot with cells colored by patient and tissue of origin.

Points each represent a single cell and are colored to reflect the tissue and patient as labeled. The relative position of each cell (n = 4,019 cells from n =17 skin and n = 21 kidney samples) is based on the analysis which was performed in Fig. 1b.

Supplementary Figure 2 Correlation between cell percentages as determined by histological identification or scRNA-seq.

The cell-type percentages of the major cell types identified (tubular cells, endothelial cells, leukocytes, fibroblasts, mesangial cells, n = 5 cell types) were determined by histological morphology (Y-axis) or scRNA-seq (X-axis) for biopsies (n = 2) and were correlated using Pearson’s correlation. Points represent log2 transformed percentages of cell types with open circles and filled circles representing the two distinct biopsies.

Supplementary Figure 3 Correlation between averaged single-cell and bulk RNA-seq expression.

a, Pearson’s correlation between averaged renal single cells (n = 1 averaged profile) and a bulk sequenced renal biopsy (n = 1 biopsy) for each gene (n = 21,868 gene entries). b, Pearson’s correlation between averaged skin single cells (n = 1 averaged profile) and bulk sequenced skin cells (n = 1 biopsy) for each gene (n = 21,868 gene entries). c, Pearson’s correlation between averaged renal single cells (n = 1 averaged profile) and bulk sequenced skin cells (n = 1 biopsy) for each gene (n = 21,868 gene entries). d, Pearson’s correlation between averaged skin single cells (n = 1 average profile) and a bulk sequenced renal biopsy (n = 1 biopsy) for each gene (n = 21,868 gene entries).

Supplementary Figure 4 Receiver operating characteristic analysis of two logistic regression models predicting response to treatment created by Parikh et al.

a, The 3-gene model shows an AUC of 0.8 when applied to our cohort (n = 18). b,The 5-gene model shows an AUC of 0.81 when applied to our cohort (n = 18).

Supplementary information

Supplementary Information

Supplementary Figures 1–4

Reporting Summary

Supplementary Table 1

Patient demographics, medications, clinical data and sequencing details

Supplementary Table 2

Top 30 most differentially expressed genes between cell type clusters. Differentially expressed genes between each cluster for keratinocytes (n = 1,939), tubular cells (n = 1,221), mesangial cells (n = 63), fibroblasts (n = 95), endothelial cells (n = 130) and leukocytes (n = 120) using Seurat’s differential analysis

Supplementary Table 3

Percentage contribution of each patient to each cell type. Each column is a cell type and each row represents an individual sample’s (n = 44 tissue samples) contribution to that cell type

Supplementary Table 4

Significantly upregulated genes in clinical groups for both tubular cells and keratinocytes. Sheet 1 is upregulated genes in non-responders (n = 13) versus responders (n = 5). Sheet 2 is upregulated genes in responders (n = 5) versus non-responders (n = 13). Sheet 3 is genes upregulated in proliferative (n = 8) versus membranous (n = 6). Sheet 4 is upregulated in membranous (n = 6) versus proliferative (n = 8)

Supplementary Table 5

Genes used for IFN-response cumulative distribution function. Column A contains ubiquitously expressed genes (n = 264) and Column B contatins IFN-responsive genes (n = 212)

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Der, E., Suryawanshi, H., Morozov, P. et al. Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways. Nat Immunol 20, 915–927 (2019). https://doi.org/10.1038/s41590-019-0386-1

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