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Comparative Study
, 30 (1), 23-32

Advantages of Single-Nucleus Over Single-Cell RNA Sequencing of Adult Kidney: Rare Cell Types and Novel Cell States Revealed in Fibrosis

Affiliations
Comparative Study

Advantages of Single-Nucleus Over Single-Cell RNA Sequencing of Adult Kidney: Rare Cell Types and Novel Cell States Revealed in Fibrosis

Haojia Wu et al. J Am Soc Nephrol.

Abstract

Background: A challenge for single-cell genomic studies in kidney and other solid tissues is generating a high-quality single-cell suspension that contains rare or difficult-to-dissociate cell types and is free of both RNA degradation and artifactual transcriptional stress responses.

Methods: We compared single-cell RNA sequencing (scRNA-seq) using the DropSeq platform with single-nucleus RNA sequencing (snRNA-seq) using sNuc-DropSeq, DroNc-seq, and 10X Chromium platforms on adult mouse kidney. We validated snRNA-seq on fibrotic kidney from mice 14 days after unilateral ureteral obstruction (UUO) surgery.

Results: A total of 11,391 transcriptomes were generated in the comparison phase. We identified ten clusters in the scRNA-seq dataset, but glomerular cell types were absent, and one cluster consisted primarily of artifactual dissociation-induced stress response genes. By contrast, snRNA-seq from all three platforms captured a diversity of kidney cell types that were not represented in the scRNA-seq dataset, including glomerular podocytes, mesangial cells, and endothelial cells. No stress response genes were detected. Our snRNA-seq protocol yielded 20-fold more podocytes compared with published scRNA-seq datasets (2.4% versus 0.12%, respectively). Unexpectedly, single-cell and single-nucleus platforms had equivalent gene detection sensitivity. For validation, analysis of frozen day 14 UUO kidney revealed rare juxtaglomerular cells, novel activated proximal tubule and fibroblast cell states, and previously unidentified tubulointerstitial signaling pathways.

Conclusions: snRNA-seq achieves comparable gene detection to scRNA-seq in adult kidney, and it also has substantial advantages, including reduced dissociation bias, compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and successful performance on inflamed fibrotic kidney.

Keywords: RNA-sequencing; fibrosis; single cell.

Figures

Figure 1.
Figure 1.
Single nucleus RNA sequencing performs equivalent to or better than single cell RNA sequencing as long as intronic reads are mapped. (A) Reads mapped to exonic, intronic, and intergenic regions according to the platform. (B) Average number of reads per cell (nRead), average number of unique genes per cell (nGene), and average percentage of mitochondrial reads per cell across platforms and according to using exonic reads only or exonic and intronic reads. (C) Percentage of nonzero reads per cell across all techniques on the basis of exonic reads alone or exonic and intronic reads. (D) Mapped reads to a gene plot using different platforms. Single-cell DropSeq (scDropSeq) and DroNc-seq show an advantage in the low- (10,000 mapped reads per cell) to middle-range (20,000 mapped reads per cell) sequencing depths. (E) The t-distributed stochastic neighbor embedding (tSNE) plot of 1469 epithelial cells from the DroNc-seq dataset on the basis of mapped exonic reads alone. (F) tSNE of 1469 matched epithelial cells from the scDropSeq dataset (also on the basis of exonic reads alone). (G) Improved clustering from 1469 epithelial cells from the DroNc-seq dataset using exonic plus intronic reads. (H) Few changes in clustering of 1469 matched epithelial cells from the scDropSeq dataset using exonic plus intronic reads. Cluster cohesion (average within-cluster coclustering) and separation (difference between within-cluster coclustering and maximum between-cluster coclustering) plotted for (I) nuclei and (J) cells. Gene expression quantification, including introns, increases cohesion and separation of nuclei but not cell clusters. CD-PC, collecting duct-principal cell; DCT, distal convoluted tubule; LH, loop of Henle; PT, proximal tubule.
Figure 1.
Figure 1.
Single nucleus RNA sequencing performs equivalent to or better than single cell RNA sequencing as long as intronic reads are mapped. (A) Reads mapped to exonic, intronic, and intergenic regions according to the platform. (B) Average number of reads per cell (nRead), average number of unique genes per cell (nGene), and average percentage of mitochondrial reads per cell across platforms and according to using exonic reads only or exonic and intronic reads. (C) Percentage of nonzero reads per cell across all techniques on the basis of exonic reads alone or exonic and intronic reads. (D) Mapped reads to a gene plot using different platforms. Single-cell DropSeq (scDropSeq) and DroNc-seq show an advantage in the low- (10,000 mapped reads per cell) to middle-range (20,000 mapped reads per cell) sequencing depths. (E) The t-distributed stochastic neighbor embedding (tSNE) plot of 1469 epithelial cells from the DroNc-seq dataset on the basis of mapped exonic reads alone. (F) tSNE of 1469 matched epithelial cells from the scDropSeq dataset (also on the basis of exonic reads alone). (G) Improved clustering from 1469 epithelial cells from the DroNc-seq dataset using exonic plus intronic reads. (H) Few changes in clustering of 1469 matched epithelial cells from the scDropSeq dataset using exonic plus intronic reads. Cluster cohesion (average within-cluster coclustering) and separation (difference between within-cluster coclustering and maximum between-cluster coclustering) plotted for (I) nuclei and (J) cells. Gene expression quantification, including introns, increases cohesion and separation of nuclei but not cell clusters. CD-PC, collecting duct-principal cell; DCT, distal convoluted tubule; LH, loop of Henle; PT, proximal tubule.
Figure 2.
Figure 2.
Reduced dissociation bias from single-nucleus techniques. (A) The t-distributed stochastic neighbor embedding (tSNE) projection of the combined datasets reveals 13 separate clusters. CD-PC, collecting duct-principal cell; CNT, connecting tubule; DCT, distal convoluted tubule; EC, endothelial cell; IC-A, intercalated cell type A; IC-B, intercalated cell type B; LH(AL), loop of Henle ascending loop; LH(DL), loop of Henle descending loop; MΦ, macrophage; MC, mesangial cell; Pod, podocyte; PT, proximal tubule. (B) Marker gene expression across clusters for the combined dataset. (C) tSNE showing the contribution of data from each platform to all clusters. (D) Percentage of cells contributed by each platform reveals a very low contribution to podocytes, endothelial cells, and intercalated cells type A and type B from single-cell DropSeq (scDropSeq) compared with single-nucleus platforms. (E) We combined podocyte frequencies obtained from our scDropSeq (n=1) as well as those from Park et al. (n=7) and compared them with the frequencies observed in our single-nucleus RNA sequencing (snRNA-seq) datasets (n=3). This revealed 20-fold more podocytes from snRNA-seq (2.4%) compared with single-cell RNA sequencing (scRNA-seq; 0.12%; P=0.02).
Figure 3.
Figure 3.
Single nucleus RNA-seq detects similar genes to single cell RNA-seq without artifactual transcriptional stress responses. (A) Binned scatterplot showing the proportion of genes detected with greater reliability in cells versus nuclei. The gray lines show the variation in detection expected by chance (95% confidence interval). (B) Binned scatterplot showing that 5.0% of genes are significantly more highly expressed (fold change >1.5; adjusted P value <0.05) in cells and that 6.4% of genes are significantly more highly expressed in nuclei. (C) Cell-enriched genes include mitochondrial and ribosomal genes as well as heat shock response genes. (D) Nuclei-enriched genes predominantly encode drivers of cell identity, such as solute carriers, transcription factors, and long noncoding RNA. (E) The 650 glomerular cells from DroNc-seq and single-nucleus DropSeq (snDropSeq) plus the 650 matched cells from a glomerular cell atlas coprojected by the t-distributed stochastic neighbor embedding (tSNE) reveal podocyte (Pod), mesangial cell (MC), and endothelial cell (EC) clusters. (F) Equal representation of cell and nucleus RNA sequencing data in all clusters. (G) Strong replicability of glomerular cell types between cell and nucleus datasets as defined by the area under the receiver operator characteristic curve (AUROC) score. (H) tSNE of epithelia from single-cell DropSeq (scDropSeq) highlighting an artifactual cluster defined by stress response gene expression induced during proteolytic dissociation. CD-PC, collecting duct-principal cell; DCT, distal convoluted tubule; LH, loop of Henle; PT, proximal tubule. (I) Immediate early gene expression in the artifactual cluster. (J) Reanalysis of the glomerular cell atlas reveals strong stress response gene expression among podocytes, mesangial cells, and endothelial cells. The same cells isolated by nuclear dissociation lack a stress response signature. (K) Heat map comparison of the same glomerular cell types showing strong mitochondria, heat shock, and apoptosis gene expression signature among the single-cell but not the single-nucleus dataset. FC, fold change; TF, transcription factor; UMI, unique molecular identifier.
Figure 3.
Figure 3.
Single nucleus RNA-seq detects similar genes to single cell RNA-seq without artifactual transcriptional stress responses. (A) Binned scatterplot showing the proportion of genes detected with greater reliability in cells versus nuclei. The gray lines show the variation in detection expected by chance (95% confidence interval). (B) Binned scatterplot showing that 5.0% of genes are significantly more highly expressed (fold change >1.5; adjusted P value <0.05) in cells and that 6.4% of genes are significantly more highly expressed in nuclei. (C) Cell-enriched genes include mitochondrial and ribosomal genes as well as heat shock response genes. (D) Nuclei-enriched genes predominantly encode drivers of cell identity, such as solute carriers, transcription factors, and long noncoding RNA. (E) The 650 glomerular cells from DroNc-seq and single-nucleus DropSeq (snDropSeq) plus the 650 matched cells from a glomerular cell atlas coprojected by the t-distributed stochastic neighbor embedding (tSNE) reveal podocyte (Pod), mesangial cell (MC), and endothelial cell (EC) clusters. (F) Equal representation of cell and nucleus RNA sequencing data in all clusters. (G) Strong replicability of glomerular cell types between cell and nucleus datasets as defined by the area under the receiver operator characteristic curve (AUROC) score. (H) tSNE of epithelia from single-cell DropSeq (scDropSeq) highlighting an artifactual cluster defined by stress response gene expression induced during proteolytic dissociation. CD-PC, collecting duct-principal cell; DCT, distal convoluted tubule; LH, loop of Henle; PT, proximal tubule. (I) Immediate early gene expression in the artifactual cluster. (J) Reanalysis of the glomerular cell atlas reveals strong stress response gene expression among podocytes, mesangial cells, and endothelial cells. The same cells isolated by nuclear dissociation lack a stress response signature. (K) Heat map comparison of the same glomerular cell types showing strong mitochondria, heat shock, and apoptosis gene expression signature among the single-cell but not the single-nucleus dataset. FC, fold change; TF, transcription factor; UMI, unique molecular identifier.
Figure 4.
Figure 4.
snRNA-seq of day 14 unilateral ureteral obstruction (UUO) kidney identifies rare cell types and and intercellular communication networks. (A, inset) Periodic acid–Schiff stain of UUO kidney showing dilated and cast-filled tubules and expanded and fibrotic interstitium. (A) The t-distributed stochastic neighbor embedding (tSNE) shows 17 separate cell clusters. (B) Projection of cell cycle state onto the tSNE, revealing limited proliferation primarily in the proliferating proximal tubule cluster. (C) Violin plot showing cluster-specific gene expression. (D) Dock10 expression through the proximal tubule but enriched within the dedifferentiating proximal tubule cluster. (E) Three stromal clusters could be identified, including juxtaglomerular apparatus cells expressing Endra and the stem cell marker Hopx. Immunohistochemistry images are from the Human Protein Atlas (https://www.proteinatlas.org/). (F) Cell-specific ligand-receptor analysis reveals intercellular signaling pathways. (G) Known and new intercellular signaling within the tubulintersitial compartment as revealed by this snRNA-seq analysis. Act., activating; CD-PC, collecting duct-principal cell; CNT, connecting tubule; DCT, distal convoluted tubule; Dediff., dedifferentiated; DL + tAL, descending limb + thin ascending limb; EC, endothelial cell; Fib., fibroblast; IC, intercalated cell; JGA, juxtaglomerular apparatus; MΦ, macrophage; PC, principal cell; Pod, podocyte; Prolif, proliferating; PT, proximal tubule; TAL, thick ascending limb; UMI, unique molecular identifier.

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