Targeted single-cell RNA-seq identifies minority cell types of kidney distal nephron that regulate blood pressure and calcium balance

ABSTRACTA major objective in modern biology is generation of comprehensive atlases of various organs identifying all cell types and their expressed genes. In kidney, extensive data exists for proximal tubule and collecting duct cells, but not for non-abundant intermediate epithelial cell types. Here, we coupled a FACS-enrichment protocol with single-cell RNA-seq analysis to profile the transcriptomes of 9099 cells from the nephron region adjacent to the macula densa. Clusters containing Slc12a3+/Pvalb+ and Slc12a3+/Pvalb- cells were identified as DCT1 and DCT2 cells. The DCT1 cells appear to be heterogeneously associated with variable expression of Slc8a1, Calb1, and Ckb among other mRNAs. No DCT2-specific transcripts were found. The analysis also identified two distinct cell types in the Slc12a1+ portion of Henle’s loop as well as Nos1+/Avpr1a+ macula densa cells. Thus, we identify unexpected cell diversity in the intermediate region of the nephron and create a web-based data resource for these cells.

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Transcriptomes of major renal collecting-duct cell types in mouse identified by single-cell RNA-Seq

10.1101/183376 ◽

2017 ◽

Author(s):

Lihe Chen ◽

Jae Wook Lee ◽

Chung-Lin Chou ◽

Anilkumar Nair ◽

Maria Agustina Battistone ◽

...

Keyword(s):

Cell Surface ◽

Single Cell ◽

Collecting Duct ◽

Extracellular Fluid ◽

Cell Types ◽

Fluid Composition ◽

Intercalated Cells ◽

Rna Seq ◽

Cell Type ◽

Renal Collecting Duct

ABSTRACTPrior RNA sequencing (RNA-Seq) studies have identified complete transcriptomes for most renal epithelial cell types. The exceptions are the cell types that make up the renal collecting duct, namely intercalated cells (ICs) and principal cells (PCs), which account for only a small fraction of the kidney mass, but play critical physiological roles in the regulation of blood pressure, extracellular fluid volume and extracellular fluid composition. To enrich these cell types, we used fluorescence-activated cell sorting (FACS) that employed well established lectin cell surface markers for PCs and type B ICs, as well as a newly identified cell surface marker for type A ICs, viz. c-Kit. Single-cell RNA-Seq using the 1C- and PC-enriched populations as input enabled identification of complete transcriptomes of A-ICs, B-ICs and PCs. The data were used to create a freely-accessible online gene-expression database for collecting duct cells. This database allowed identification of genes that are selectively expressed in each cell type including cell-surface receptors, transcription factors, transporters and secreted proteins. The analysis also identified a small fraction of hybrid cells expressing both aquapor¡n-2 and either anion exchanger 1 or pendrin transcripts. In many cases, mRNAs for receptors and their ligands were identified in different cells (e.g. Notch2 chiefly in PCs vs Jag1 chiefly in ICs) suggesting signaling crosstalk among the three cell types. The identified patterns of gene expression among the three types of collecting duct cells provide a foundation for understanding physiological regulation and pathophysiology in the renal collecting duct.SIGNIFICANCE STATEMENTA long-term goal in mammalian biology is to identify the genes expressed in every cell type of the body. In kidney, the expressed genes (“transcriptome”) of all epithelial cell types have already been identified with the exception of the cells that make up the renal collecting duct, responsible for regulation of blood pressure and body fluid composition. Here, a technique called "single-cell RNA-Seq" was used in mouse to identify transcriptomes for the major collecting-duct cell types: type A intercalated cells, type B intercalated cells and principal cells. The information was used to create a publicly-accessible online resource. The data allowed identification of genes that are selectively expressed in each cell type, informative for cell-level understanding of physiology and pathophysiology.

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Targeted Single-Cell RNA-seq Identifies Minority Cell Types of Kidney Distal Nephron

Journal of the American Society of Nephrology ◽

10.1681/asn.2020101407 ◽

2021 ◽

pp. ASN.2020101407

Author(s):

Lihe Chen ◽

Chun-Lin Chou ◽

Mark A. Knepper

Keyword(s):

Single Cell ◽

Cell Types ◽

The Other ◽

Electrolyte Balance ◽

Thick Ascending Limb ◽

Rna Seq ◽

Distal Nephron ◽

Web Based ◽

Proximal Tubule Cells ◽

Variable Expression

BackgroundProximal tubule cells dominate the kidney parenchyma numerically, although less abundant cell types of the distal nephron have disproportionate roles in water and electrolyte balance.MethodsCoupling of a FACS-based enrichment protocol with single-cell RNA-seq profiled the transcriptomes of 9099 cells from the thick ascending limb (CTAL)/distal convoluted tubule (DCT) region of the mouse nephron.ResultsUnsupervised clustering revealed Slc12a3+/Pvalb+ and Slc12a3+/Pvalb− cells, identified as DCT1 and DCT2 cells, respectively. DCT1 cells appear to be heterogeneous, with orthogonally variable expression of Slc8a1, Calb1, and Ckb. An additional DCT1 subcluster showed marked enrichment of cell cycle–/cell proliferation–associated mRNAs (e.g., Mki67, Stmn1, and Top2a), which fit with the known plasticity of DCT cells. No DCT2-specific transcripts were found. DCT2 cells contrast with DCT1 cells by expression of epithelial sodium channel β- and γ-subunits and much stronger expression of transcripts associated with calcium transport (Trpv5, Calb1, S100g, and Slc8a1). Additionally, scRNA-seq identified three distinct CTAL (Slc12a1+) cell subtypes. One of these expressed Nos1 and Avpr1a, consistent with macula densa cells. The other two CTAL clusters were distinguished by Cldn10 and Ptger3 in one and Cldn16 and Foxq1 in the other. These two CTAL cell types were also distinguished by expression of alternative Iroquois homeobox transcription factors, with Irx1 and Irx2 in the Cldn10+ CTAL cells and Irx3 in the Cldn16+ CTAL cells.ConclusionsSingle-cell transcriptomics revealed unexpected diversity among the cells of the distal nephron in mouse. Web-based data resources are provided for the single-cell data.

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Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.645276 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ernesto Marín-Sedeño ◽

Xabier Martínez de Morentin ◽

Jose M. Pérez-Pomares ◽

David Gómez-Cabrero ◽

Adrián Ruiz-Villalba

Keyword(s):

Single Cell ◽

Single Gene ◽

Cell Lineage ◽

Cell Types ◽

Lineage Tracing ◽

Cardiac Cell ◽

Rna Seq ◽

Cardiovascular Research ◽

Cell Diversity ◽

Unbiased Manner

During the last decade, extensive efforts have been made to comprehend cardiac cell genetic and functional diversity. Such knowledge allows for the definition of the cardiac cellular interactome as a reasonable strategy to increase our understanding of the normal and pathologic heart. Previous experimental approaches including cell lineage tracing, flow cytometry, and bulk RNA-Seq have often tackled the analysis of cardiac cell diversity as based on the assumption that cell types can be identified by the expression of a single gene. More recently, however, the emergence of single-cell RNA-Seq technology has led us to explore the diversity of individual cells, enabling the cardiovascular research community to redefine cardiac cell subpopulations and identify relevant ones, and even novel cell types, through their cell-specific transcriptomic signatures in an unbiased manner. These findings are changing our understanding of cell composition and in consequence the identification of potential therapeutic targets for different cardiac diseases. In this review, we provide an overview of the continuously changing cardiac cellular landscape, traveling from the pre-single-cell RNA-Seq times to the single cell-RNA-Seq revolution, and discuss the utilities and limitations of this technology.

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Transcriptomes of major renal collecting duct cell types in mouse identified by single-cell RNA-seq

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1710964114 ◽

2017 ◽

Vol 114 (46) ◽

pp. E9989-E9998 ◽

Cited By ~ 93

Author(s):

Lihe Chen ◽

Jae Wook Lee ◽

Chung-Lin Chou ◽

Anil V. Nair ◽

Maria A. Battistone ◽

...

Keyword(s):

Gene Expression ◽

Cell Surface ◽

Single Cell ◽

Collecting Duct ◽

Extracellular Fluid ◽

Cell Types ◽

Rna Seq ◽

Duct Cells ◽

Collecting Duct Cells ◽

Renal Collecting Duct

Prior RNA sequencing (RNA-seq) studies have identified complete transcriptomes for most renal epithelial cell types. The exceptions are the cell types that make up the renal collecting duct, namely intercalated cells (ICs) and principal cells (PCs), which account for only a small fraction of the kidney mass, but play critical physiological roles in the regulation of blood pressure, extracellular fluid volume, and extracellular fluid composition. To enrich these cell types, we used FACS that employed well-established lectin cell surface markers for PCs and type B ICs, as well as a newly identified cell surface marker for type A ICs, c-Kit. Single-cell RNA-seq using the IC- and PC-enriched populations as input enabled identification of complete transcriptomes of A-ICs, B-ICs, and PCs. The data were used to create a freely accessible online gene-expression database for collecting duct cells. This database allowed identification of genes that are selectively expressed in each cell type, including cell-surface receptors, transcription factors, transporters, and secreted proteins. The analysis also identified a small fraction of hybrid cells expressing aquaporin-2 and anion exchanger 1 or pendrin transcripts. In many cases, mRNAs for receptors and their ligands were identified in different cells (e.g., Notch2 chiefly in PCs vs. Jag1 chiefly in ICs), suggesting signaling cross-talk among the three cell types. The identified patterns of gene expression among the three types of collecting duct cells provide a foundation for understanding physiological regulation and pathophysiology in the renal collecting duct.

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treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses

Genome Biology ◽

10.1186/s13059-021-02368-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ruizhu Huang ◽

Charlotte Soneson ◽

Pierre-Luc Germain ◽

Thomas S.B. Schmidt ◽

Christian Von Mering ◽

...

Keyword(s):

Single Cell ◽

Synthetic Data ◽

Cell Types ◽

Data Driven ◽

Rna Seq ◽

Hierarchical Trees

AbstracttreeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

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Annotating cell types in human single-cell RNA-seq data with CellO

STAR Protocols ◽

10.1016/j.xpro.2021.100705 ◽

2021 ◽

Vol 2 (3) ◽

pp. 100705

Author(s):

Matthew N. Bernstein ◽

Colin N. Dewey

Keyword(s):

Single Cell ◽

Cell Types ◽

Rna Seq

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Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽

10.1186/s12864-020-07358-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tracy M. Yamawaki ◽

Daniel R. Lu ◽

Daniel C. Ellwanger ◽

Dev Bhatt ◽

Paolo Manzanillo ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Immune Cell ◽

Cell Types ◽

Data Interpretation ◽

Detection Sensitivity ◽

Rna Seq ◽

Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

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Single-cell RNA sequencing reveals differential cell cycle activity in key cell populations during nephrogenesis

Scientific Reports ◽

10.1038/s41598-021-01790-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Abha S. Bais ◽

Débora M. Cerqueira ◽

Andrew Clugston ◽

Andrew J. Bodnar ◽

Jacqueline Ho ◽

...

Keyword(s):

Cell Cycle ◽

Single Cell ◽

Molecular Mechanisms ◽

Kidney Development ◽

Collecting Duct ◽

Cell Types ◽

Nephron Progenitors ◽

Developing Kidney ◽

Distal Tubules ◽

Cycle Regulation

AbstractThe kidney is a complex organ composed of more than 30 terminally differentiated cell types that all are required to perform its numerous homeostatic functions. Defects in kidney development are a significant cause of chronic kidney disease in children, which can lead to kidney failure that can only be treated by transplant or dialysis. A better understanding of molecular mechanisms that drive kidney development is important for designing strategies to enhance renal repair and regeneration. In this study, we profiled gene expression in the developing mouse kidney at embryonic day 14.5 at single-cell resolution. Consistent with previous studies, clusters with distinct transcriptional signatures clearly identify major compartments and cell types of the developing kidney. Cell cycle activity distinguishes between the “primed” and “self-renewing” sub-populations of nephron progenitors, with increased expression of the cell cycle-related genes Birc5, Cdca3, Smc2 and Smc4 in “primed” nephron progenitors. In addition, augmented expression of cell cycle related genes Birc5, Cks2, Ccnb1, Ccnd1 and Tuba1a/b was detected in immature distal tubules, suggesting cell cycle regulation may be required for early events of nephron patterning and tubular fusion between the distal nephron and collecting duct epithelia.

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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

10.1101/2020.10.06.327601 ◽

2020 ◽

Author(s):

Mohit Goyal ◽

Guillermo Serrano ◽

Ilan Shomorony ◽

Mikel Hernaez ◽

Idoia Ochoa

Keyword(s):

Single Cell ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Rna Seq ◽

Batch Effects ◽

Cell Type ◽

Latent Space ◽

Cell Type Specific ◽

Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

10.1101/786285 ◽

2019 ◽

Cited By ~ 4

Author(s):

Marcus Alvarez ◽

Elior Rahmani ◽

Brandon Jew ◽

Kristina M. Garske ◽

Zong Miao ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Supervised Machine Learning ◽

Data Sets ◽

Rna Seq ◽

Novel Approach ◽

Single Nucleus ◽

Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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