Single-cell RNA sequencing of human kidney - PubMed (original) (raw)

doi: 10.1038/s41597-019-0351-8.

Jinling Liao # 1 2 3, Zhenyuan Yu # 1 2 3 4 5, Yang Chen # 1 2 3 4 5, Mengying Bao 1 2 3, Haiying Zhang 1 2 3, Deyun Liu 4 5, Tianyu Li 4 5, Qingyun Zhang 1 2 3 4 8, Jiaping Li 9 10 11, Jiwen Cheng 12 13 14 15 16, Zengnan Mo 17 18 19 20 21

Affiliations

Single-cell RNA sequencing of human kidney

Jinling Liao et al. Sci Data. 2020.

Abstract

A comprehensive cellular anatomy of normal human kidney is crucial to address the cellular origins of renal disease and renal cancer. Some kidney diseases may be cell type-specific, especially renal tubular cells. To investigate the classification and transcriptomic information of the human kidney, we rapidly obtained a single-cell suspension of the kidney and conducted single-cell RNA sequencing (scRNA-seq). Here, we present the scRNA-seq data of 23,366 high-quality cells from the kidneys of three human donors. In this dataset, we show 10 clusters of normal human renal cells. Due to the high quality of single-cell transcriptomic information, proximal tubule (PT) cells were classified into three subtypes and collecting ducts cells into two subtypes. Collectively, our data provide a reliable reference for studies on renal cell biology and kidney disease.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1

Fig. 1

scRNA-seq reveals the cell populations of the human kidney. (a) Overview of the scRNA-seq process using human kidney tissue samples. (b) Uniform manifold approximation and projection (UMAP) plot showing the unbiased classification of renal cells. (c) Pie chart showed the proportion of each kidney cell type. (d) Heat map showing the marker genes of each cluster, highlighting the selected marker genes for each cluster.

Fig. 2

Fig. 2

Quality control (QC) of human kidney single cell data. (a) Scatterplot illustrating the number of genes, unique molecular identifiers (UMIs) and the percentage of mitochondrial genes in each cell of three kidney samples. (b) The relationship between the percentage of mitochondrial genes and the mRNA reads, together with the relationship between the amount of mRNA and the reads of mRNA. (c) We detected the batch effect between three different kidney samples. (d) UMAP plot showing the cell cycle status of each cell. (e) Violin plot illustrating the number of genes, unique molecular identifiers (UMIs) and the percentage of mitochondrial genes in previous kidney single-cell data from GSE107585.

Fig. 3

Fig. 3

Subpopulations of PT cells and reconstructing the developmental trajectory of PT cells. (a) Violin plots representing the expression of marker genes in PT cells. Clusters 1, 2 and 3 refer to the proximal convoluted tubule, proximal tubule and proximal straight tubule, respectively. (b) Monocle2-generated pseudotemporal trajectory of three PT cell types (n = 20,308); imported Seurat data are coloured according to the cell name designation. (c) Pseudotime was coloured in a gradient from dark to light blue, and the start of pseudotime is dark. (d) The pseudotime trajectory was divided into three different states by Monocle2. (e) The trajectory showing the distribution of cells from three samples. (f) The top six genes influencing fate decisions are shown as line plots displayed as the expression level over pseudotime by Monocle2. (g) Heat map for clustering the top 50 genes that affected cell fate decisions. These 50 genes were divided into three clusters (cluster 1, cluster 2 and cluster 3), showing genes at the beginning stage, the transitory stage and the end stage of the developmental trajectory, respectively.

Fig. 4

Fig. 4

Detailed classification of collecting duct cells and NK-T cells by scRNA-seq. (a) Violin plot showing the expression of the collecting duct principal cell marker AQP2 and the collecting duct intercalated cell markers ATP6V1B1, ATP6V0D2 and ATP6V1G3. Clusters 8 and 10 are collecting duct principal cells and collecting duct intercalated cells, respectively. (b) UMAP plot showing the spatial location of NKT cells and T cells after dimensionality reduction. The red dots represent NKT cells and the green dots represent T cells. (c–g) Violin plots showing the expression of the NK cells markers GNLY and NKG7. Violin plots showing the expression of the T cell markers CD3D, CD3E and IL7R.

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