Dataset Information

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

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.

SUBMITTER: Wu H

PROVIDER: S-EPMC6317600 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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

Wu Haojia H Kirita Yuhei Y Donnelly Erinn L EL Humphreys Benjamin D BD

Journal of the American Society of Nephrology : JASN 20181203 1

<h4>Background</h4>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.<h4>Methods</h4>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 m ...[more]

PMID: 30510133

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Project description:Understanding the development and function of an organ requires the characterization of all of its cell types. Traditional methods for visualizing and isolating sub-populations of cells are based on mRNA or protein expression of only few known marker genes. The unequivocal identification of a specific marker gene, however, poses a major challenge, particularly if this cell type is rare. Identifying rare cell types, such as stem cells, short-lived progenitors, cancer stem cells, or circulating tumor cells is crucial to acquire a better understanding of normal or diseased tissue biology. To address this challenge we sequenced the transcriptome of hundreds of randomly selected cells from mouse intestinal organoids, cultured self-organizing epithelial structures that contain all cell lineages of the mammalian intestine. Organoid buds, like intestinal crypts, harbor stem cells that continuously differentiate into a variety of cell types, occurring at widely different abundances. Since available computational methods can only resolve more abundant cell types, we developed RaceID, an algorithm for rare cell type identification in complex populations of single cells. We demonstrate that this algorithm can resolve cell types represented by only a single cell in a population of randomly sampled organoid cells. We use this algorithm to identify Reg4 as a novel marker for enteroendocrine cells, a rare population of hormone producing intestinal cells. Next, we use Reg4 expression to enrich for these rare cells and investigate the heterogeneity within this population. Reassuringly, RaceID confirmed the existence of known enteroendocrine lineages, and moreover, discovered novel subtypes, which we subsequently validated in vivo. Having validated RaceID by this proof-of-principle experiment we then apply the algorithm to ex vivo isolated LGR5 positive cells and their direct progeny and demonstrate homogeneity of the stem cell pool. We envision broad applicability of our method for discovering rare cell types and the corresponding marker genes in healthy and diseased organs.

Dataset Information

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

Publications

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

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