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

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

This article has been updated


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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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





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 or Thomas Tuschl or Chaim Putterman.

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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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