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.
Researchers from Washington University in St. Louis School of Medicine 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. They validated snRNA-seq on fibrotic kidney from mice 14 days after unilateral ureteral obstruction (UUO) surgery.
A total of 11,391 transcriptomes were generated in the comparison phase. The researchers 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. Their 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.
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.2 (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).
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.