Research Article

Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease

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Science  18 May 2018:
Vol. 360, Issue 6390, pp. 758-763
DOI: 10.1126/science.aar2131
  • Fig. 1 Cell diversity in mouse kidney cells delineated by single-cell transcriptomic analysis.

    (A) Unsupervised clustering demonstrates 16 distinct cell types shown in a t-distributed stochastic neighbor embedding (tSNE) map (center). Left panels are subclusters of clusters 1, 3, and 7. Percentages of assigned cell types are summarized in the right panel. Endo, containing endothelial, vascular, and descending loop of Henle; Podo, podocyte; PT, proximal tubule; LOH, ascending loop of Henle; DCT, distal convoluted tubule; CD-PC, collecting duct principal cell; CD-IC, collecting duct intercalated cell; CD-Trans, collecting duct transitional cell; Fib, fibroblast; Macro, macrophage; Neutro, neutrophil; lymph, lymphocyte; NK, natural killer cell. (B and C) Violin plots showing the expression levels of representative marker genes across the 16 main clusters. The y axis shows the log-scale normalized read count. (C) Cluster 1 [from (A), left] separates into endothelial cells (Endo), pericytes and vascular smooth muscle cells (Peri), and descending loop of Henle (DLH) cells. Cluster 3 (proximal tubules) separates into S1, S2, and S3 segments or proximal convoluted tubules (PCT) and proximal straight tubules (PST). In cluster 7, intercalated cells (ICs) separate into types A and B.

  • Fig. 2 Discrete human disease phenotypes are due to mutations in single specific cell types.

    Single cell–type specific average expression of human (A) monogenic disease genes and (B) complex-trait genes identified by genome-wide association studies. Mean expression values of the genes were calculated in each cluster. The color scheme is based on z-score distribution; the map only shows genes with maximum z-scores > 2. In the heatmap, each row represents one gene, and each column is a single cell type (defined in Fig. 1). The full list of cell types and genes is shown in figs. S11 and S12.

  • Fig. 3 Identification of a transitional cell type and a conversion process in the kidney collecting duct.

    (A) The expression levels of marker genes across the 16 clusters. The y axis shows the log-scale normalized read count. (B) Gene expression levels in PCs (Aqp2), ICs (Atp6v1g3), and transitional cells (Syt7), demonstrated by a tSNE plot. (C) Representative immunofluorescence images of AQP2 (PC marker), ATP6V1B1 (IC marker), and DAPI (4′,6-diamidino-2-phenylindole) in the kidney collecting duct. The arrow indicates the transitional PC-IC cell type expressing AQP2 and ATP6V1B1. (D) Heatmap showing the expression levels of differentially expressed genes in collecting duct cell types. The color scheme is based on z-score distribution. (E) Venn diagram showing the overlaps of differentially expressed genes between PCs, ICs, and the newly identified cell type. (F) Immunofluorescence staining for PARM1 (transitional cell–specific) and AQP2 (upper panels) or ATP6V1B1 (lower panels) in the kidney collecting duct. “Double-positive” cells are shown by the arrows. (G) Ordering single cells along a cell conversion trajectory using Monocle. Three collecting duct cell clusters were used for ordering and plotted in low-dimensional space with different colors. The tSNE plots next to the trajectory map show differentially expressed genes in the corresponding cell lineages. (H) Aqp2CremT/mG mouse model used for lineage tracing of AQP2-positive cells (left) and immunofluorescence staining for GFP, ATP6V1B1, and AQP2 (right). The far-right panel shows the quantification of GFP-positive cells (mean ± SD; n = 3). AQP2-driven GFP (white) is found in PCs (red and white) and ICs (green and white). (I) Atp6CremT/mG mouse model used for lineage tracing of ATP6ase-positive cells (left) and immunofluorescence staining for GFP, ATP6V1B1, and AQP2 in Atp6CremT/mG mice (right). ATP6V1B1-driven GFP (white) is found in PCs (red and white), ICs (green and white), and transitional cells (red, green, and white).

  • Fig. 4 The IC-to-PC transition is driven by Notch ligand and receptor expression.

    (A) Transcriptional profiles demonstrating the spectrum of expression of Notch genes in the collecting duct. Cells are ordered in pseudotime, and color represents expression levels. (B) Double immunofluorescence staining for AQP2 (red) and JAG1 (green) in the kidney collecting duct. (C) Generation of mice with inducible expression of Notch (ICN1) in kidney tubules (left). Dox, doxycycline. Excess AQP2-positive cells and reciprocally decreased ATP6V1B1-positive cells are found in Pax8rtTA/NICD tubules (mean ± SD; n = 3) (right). *P < 0.01. (D) In silico deconvolution of mouse kidney bulk RNA profiling data. Wild-type and Pax8rtTA/NICD samples were used for analysis. (E) Immunofluorescence quantification of cells labeled with AQP2 and ATP6V1B1 in control mice and a mouse model of CKD induced by folic acid (FA) (mean ± SD; n = 3). *P < 0.01. (F) In silico deconvolution of mouse kidney bulk RNA profiling. Control and kidney samples from FA-injected mice were used for analysis. (G) Immunofluorescence staining for AQP2 and ATP6V1B1 in control, Pax8rtTA/NICD, and FA-induced mouse model collecting ducts. AQP2-positive cells are abundant in the latter two and, conversely, ATP6V1B1-positive cells disappear. (H) In silico deconvolution of bulk RNA profiling data derived from kidney biopsy samples of patients with CKD (n = 91). The histological fibrosis scores and HES1 expression levels for the corresponding samples are also shown. (I) Total serum bicarbonate levels in control mice and in mice with FA-induced kidney fibrosis (mean ± SD; n = 5).

Supplementary Materials

  • Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease

    Jihwan Park, Rojesh Shrestha, Chengxiang Qiu, Ayano Kondo, Shizheng Huang, Max Werth, Mingyao Li, Jonathan Barasch, Katalin Suszták

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Materials and Methods
    • Figs. S1 to S25
    • Captions for tables S1 to S3
    • References
    Table S1
    Cell specific marker gene list for the 16 clusters. P-values and average natural log expression differences were calculated using the Seurat package as described in Materials and Methods. Percent cells 1 and 2 represent the percent of cells expressing the specific gene in the target cell cluster and average of the non-target clusters, respectively.
    Tables S2
    Cell specific marker gene list for subclusters. P-values and average natural log expression differences were calculated using the Seurat package as described in Materials and Methods. Percent cells 1 and 2 represent the percent of cells expressing the specific gene in the target cell cluster and average of the non-target clusters, respectively.
    Tables S3
    Gene expression data matrix. Each column represents one cell group (from this study) and each row represents the expression of a single gene.

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