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.
Subscribe to Journal
Get full journal access for 1 year
only $17.42 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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.
Tsokos, G. C. Systemic lupus erythematosus. N. Engl. J. Med. 365, 2110–2121 (2011).
Mohan, C. & Putterman, C. Genetics and pathogenesis of systemic lupus erythematosus and lupus nephritis. Nat. Rev. Nephrol. 11, 329–341 (2015).
Weening, J. J. et al. The classification of glomerulonephritis in systemic lupus erythematosus revisited. J. Am. Soc. Nephrol. 15, 241–250 (2004).
Yu, F. et al. Tubulointerstitial lesions of patients with lupus nephritis classified by the 2003 international society of nephrology and renal pathology society system. Kidney Int. 77, 820–829 (2010).
Hsieh, C. et al. Predicting outcomes of lupus nephritis with tubulointerstitial inflammation and scarring. Arthritis Care Res. 63, 865–874 (2011).
Alsuwaida, A. O. Interstitial inflammation and long-term renal outcomes in lupus nephritis. Lupus 22, 1446–1454 (2013).
Misra, R. & Gupta, R. Biomarkers in lupus nephritis. Int. J. Rheum. Dis. 18, 219–232 (2015).
Reich, A., Marcinow, K. & Bialynicki-Birula, R. The lupus band test in systemic lupus erythematosus patients. Ther. Clin. Risk Manag. 7, 27–32 (2011).
Der, E. et al. Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis. JCI Insight 2(9), e93009 (2017).
Ofengeim, D., Giagtzoglou, N., Huh, D., Zou, C. & Yuan, J. Single-cell RNA sequencing: unraveling the brain one cell at a time. Trends Mol. Med. 23, 563–576 (2017).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Kim, K.-T. et al. Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma. Genome Biol. 17, 80 (2016).
Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).
Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, eaar2131 (2018).
Schittek, B. et al. Dermcidin: a novel human antibiotic peptide secreted by sweat glands. Nat. Immunol. 2, 1133–1137 (2001).
Du, J. et al. MLANA/MART1 and SILV/PMEL17/GP100 are transcriptionally regulated by mitf in melanocytes and melanoma. Am. J. Pathol. 163, 333–343 (2003).
Elkon, K. B. & Stone, V. V. Type I interferon and systemic lupus erythematosus. J. Interferon Cytokine Res. 31, 803–812 (2011).
Lan, H. Y. Tubular epithelial-myofibroblast transdifferentiation mechanisms in proximal tubule cells. Curr. Opin. Nephrol. Hypertens. 12, 25–29 (2003).
Ng, Y. Y. et al. Tubular epithelial-myofibroblast transdifferentiation in progressive tubulointerstitial fibrosis in 5/6 nephrectomized rats. Kidney Int. 54, 864–876 (1998).
Lamouille, S., Xu, J. & Derynck, R. Molecular mechanisms of epithelial–mesenchymal transition. Nat. Rev. Mol. Cell Biol. 15, 178–196 (2014).
Wolf, G. & Ziyadeh, F. N. Renal tubular hypertrophy induced by angiotensin II. Semin. Nephrol. 17, 448–454 (1997).
Zhang, X. et al. TIMP-1 promotes age-related renal fibrosis through upregulating ICAM-1 in human TIMP-1 transgenic mice. J. Gerontol. A Biol. Sci. Med. Sci. 61, 1130–1143 (2006).
Ling, X. B. et al. Integrative urinary peptidomics in renal transplantation identifies biomarkers for acute rejection. J. Am. Soc. Nephrol. 21, 646–653 (2010).
Su, Z. et al. Excessive activation of the alternative complement pathway in autosomal dominant polycystic kidney disease. J. Intern. Med. 276, 470–485 (2014).
Parikh, S. V. et al. Molecular imaging of the kidney in lupus nephritis to characterize response to treatment. Transl. Res. 182, 1–13 (2017).
Strutz, F. et al. Basic fibroblast growth factor expression is increased in human renal fibrogenesis and may mediate autocrine fibroblast proliferation. Kidney Int. 57, 1521–1538 (2000).
Smith, E. R., Tan, S.-J., Holt, S. G. & Hewitson, T. D. FGF23 is synthesised locally by renal tubules and activates injury-primed fibroblasts. Sci. Rep. 7, 3345 (2017).
Meran, S. & Steadman, R. Fibroblasts and myofibroblasts in renal fibrosis. Int. J. Exp. Pathol. 92, 158–167 (2011).
Yang, J. & Liu, Y. Dissection of key events in tubular epithelial to myofibroblast transition and its implications in renal interstitial fibrosis. Am. J. Pathol. 159, 1465–1475 (2001).
Yang, J. & Liu, Y. Blockage of tubular epithelial to myofibroblast transition by hepatocyte growth factor prevents renal interstitial fibrosis. J. Am. Soc. Nephrol. 13, 96–107 (2002).
Castellano, G. et al. Local synthesis of interferon-alpha in lupus nephritis is associated with type I interferons signature and LMP7 induction in renal tubular epithelial cells. Arthritis Res. Ther. 17, 72 (2015).
Stephenson, W. et al. Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation. Nat. Commun. 9, 791 (2018).
MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).
Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal–fetal interface in humans. Nature 563, 347 (2018).
Austin, H. A., Boumpas, D. T., Vaughan, E. M. & Balow, J. E. Predicting renal outcomes in severe lupus nephritis: contributions of clinical and histologic data. Kidney Int. 45, 544–550 (1994).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Pavličev, M. et al. Single-cell transcriptomics of the human placenta: inferring the cell communication network of the maternal–fetal interface. Genome Res. 27, 349–361 (2017).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).
Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014).
Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).
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.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
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.
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 Figures 1–4
Patient demographics, medications, clinical data and sequencing details
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
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
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)
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)
About this article
Cite this article
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
The Journal of Immunology (2021)
Expert Review of Proteomics (2021)
Current Opinion in Organ Transplantation (2021)
Journal of Investigative Dermatology (2021)
Proceedings of the National Academy of Sciences (2021)