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. 2017 May 4;2(9):e93009.
doi: 10.1172/jci.insight.93009.

Single Cell RNA Sequencing to Dissect the Molecular Heterogeneity in Lupus Nephritis

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Free PMC article

Single Cell RNA Sequencing to Dissect the Molecular Heterogeneity in Lupus Nephritis

Evan Der et al. JCI Insight. .
Free PMC article

Abstract

Lupus nephritis is a leading cause of mortality among systemic lupus erythematosus (SLE) patients, and its heterogeneous nature poses a significant challenge to the development of effective diagnostics and treatments. Single cell RNA sequencing (scRNA-seq) offers a potential solution to dissect the heterogeneity of the disease and enables the study of similar cell types distant from the site of renal injury to identify novel biomarkers. We applied scRNA-seq to human renal and skin biopsy tissues and demonstrated that scRNA-seq can be performed on samples obtained during routine care. Chronicity index, IgG deposition, and quantity of proteinuria correlated with a transcriptomic-based score composed of IFN-inducible genes in renal tubular cells. Furthermore, analysis of cumulative expression profiles of single cell keratinocytes dissociated from nonlesional, non-sun-exposed skin of patients with lupus nephritis also revealed upregulation of IFN-inducible genes compared with keratinocytes isolated from healthy controls. This indicates the possible use of scRNA-seq analysis of skin biopsies as a biomarker of renal disease. These data support the potential utility of scRNA-seq to provide new insights into the pathogenesis of lupus nephritis and pave the way for exploiting a readily accessible tissue to reflect injury in the kidney.

Keywords: Nephrology.

Conflict of interest statement

Conflict of interest: The authors have declared that no conflict of interest exists.

Figures

Figure 1
Figure 1. Simulated average number of detected genes by the number of mRNA transcripts sampled from simulated single cell transcriptomes.
Three sizes of a single cell transcriptome were simulated: 50,000 (blue); 250,000 (green); and 500,000 (red) mRNA transcripts. The simulation was based on gene frequencies from bulk HEK293 polyA RNA-seq data with 18,101 distinctive genes with 100 iterations for each point. (A) Complete graph for sampled simulated single cell transcriptome sizes from 1–500,000. Because simulated single cell transcriptomes almost never have all 18,101 genes detected in bulk RNA-seq, the average number of detected genes is asymptotically approaching the maximal number of genes with an increase of the size of simulated single cell transcriptome. (B) Enlarged fragment of near-linear part of A corresponding to the approximate number of genes detected in individual cells (700 genes). The gray dotted diagonal (y = x) represents a hypothetical linear relationship between number of transcripts sampled and number of genes detected.
Figure 2
Figure 2. Pearson’s correlation between the average kidney single cell expression and a bulk sequenced renal biopsy.
Kidney single cells (n = 361) were averaged into a single “reconstructed” biopsy and correlated with a conventionally sequenced biopsy. Each dot represents a gene. Cell lineage markers for endothelial cells (light green: SELE, PECAM1, FLT1, LYVE1, VWF, MCAM, FLK1, CDH5, ARHGDIB, A2M, PTPRB ), fibroblasts (dark green: MFAP4, MFAP5, COL3A1, COL1A2, PRG4, COL1A1, PLA2G2A, APOD), tubular cells (yellow: UMOD, SLC12A1, SPP1, CA12, ALDOB, CALB1, PDZK1IP1, NAT8, SLC22A6, SLC22A8, AQP1, SLC34A1, SLC12A3, SCNN1B, CLCN5, CLDN16, GPX3, DEFB1, KCNJ1, KNG, SCNN1A, SLC22A2, PAX8, SLC23A3, KCNJ15, MT1G, SLC12A6, BHMT, ALDOB, SLC13A1), and leukocytes (pink: CD14, LYZ, FCER1G, CD4, MS4A6A, PTPRC, ITGAX, MRC1, CD247, FCGR2A, LTB, MARCO, EMR1, IL10RA ) are highlighted.
Figure 3
Figure 3. Lineage determination of single cells from skin, kidney, and peripheral blood mononuclear cells (PBMCs).
(A) Clustering of cells (n = 899) by t-distributed stochastic neighbor embedding (t-SNE). Cells are colored based on tissue of origin from skin (blue), kidney cells (red), and PBMCs (yellow). (B) Six distinct clusters generated by t-SNE plotting. (C) Differentially expressed genes across 6 cell clusters. In this heat map, rows correspond to individual genes found to be selectively upregulated in individual clusters (P < 0.01). (D) Violin plots demonstrating expression of lineage markers that indicate the identity of the clusters generated by t-SNE plotting.
Figure 4
Figure 4. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) plot of tubular cell clusters from renal biopsies.
Expression of markers of the different tubular compartments are overlaid, indicating the lineage of the cluster. ALDOB (purple), UMOD (cyan), CDH16 (red), and KLF5 (blue) for proximal tubules, loop of Henle, distal tubules, and collecting duct cells, respectively.
Figure 5
Figure 5. Keratinocytes in lupus nephritis (LN) patients demonstrate an increased IFN response signature as compared with healthy controls.
(A) MA-plot comparing differential expression of genes between keratinocytes from LN patient skin biopsies (n = 240) and healthy controls (n = 89). Genes above the red line indicate increased expression in LN patients. Significantly differentially expressed genes as determined by the Wald test are colored red (P < 0.0001), and the 4 most highly significant genes are outlined and labeled in blue. (B) Violin plots of the 4 most significantly differentially expressed genes. (C) Cumulative distribution function (CDF) of the ratio of averaged patient to healthy control keratinocytes for IFN-inducible genes (n = 212) and ubiquitous genes (n = 262) compared using the Mann-Whitney U test.
Figure 6
Figure 6. IFN scores of lupus nephritis (LN) tubular cells correlate with clinical scores and with the response to treatment.
(A) Spearman’s correlation between patient (n = 8–9) clinical scores of chronicity, proteinuria, and glomerular IgG deposition and tubular IFN scores. (B) Patients with complete response to treatment at 12 months after biopsy (urinary protein creatinine ratio of < 0.5; normal serum creatinine, or if abnormal, ≤ 125% of baseline) (n = 4) had significantly lower IFN scores (2-tailed Student’s t test P < 0.05) at the time of biopsy than patients who did not completely respond to treatment (the latter group included 4 partial responders [i.e., with decreased proteinuria but not to < 0.5] and 1 nonresponder). Only patients with at least 10 tubular cells were included.

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