scholarly journals Comprehensive single cell RNAseq analysis of the kidney reveals novel cell types and unexpected cell plasticity

2017 ◽  
Author(s):  
Jihwan Park ◽  
Rojesh Shrestha ◽  
Chengxiang Qiu ◽  
Ayano Kondo ◽  
Shizheng Huang ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Single Cell ◽  
Kidney Function ◽  
Collecting Duct ◽  
Transitional Cell ◽  
Cell Types ◽  
Specific Cell ◽  
Intercalated Cells ◽  
Cell Type ◽  
Cell Plasticity

AbstractA key limitations to understand kidney function and disease development has been that specific cell types responsible for specific homeostatic kidney function or disease phenotypes have not been defined at the molecular level.To fill this gap, we characterized 57,979 cells from healthy mouse kidneys using unbiased single-cell RNA sequencing. We show that genetic mutations that present with similar phenotypes mostly affect genes that are expressed in a single unique differentiated cell type. On the other hand, we found unexpected cell plasticity of epithelial cells in the final segment of the kidney (collecting duct) that is responsible for final composition of the urine. Using computational cell trajectory analysis and in vivo linage tracing, we found that, intercalated cells (that secrete protons) and principal cells (that maintain salt, water and potassium balance) undergo a Notch mediated interconversion via a newly identified transitional cell type. In disease states this transition is shifted towards the principal cell fate. Loss of intercalated cells likely contributes to metabolic acidosis observed in kidney disease.In summary, single cell analysis advanced a mechanistic description of kidney diseases by identifying a defective homeostatic cell lineage.One Sentence SummaryA comprehensive single cell atlas of the kidney reveals a transitional cell type and cell plasticity determined by Notch signaling which is defective in chronic kidney disease.

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

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.

scholarly journals A fate-mapping approach reveals the composite origin of the connecting tubule and alerts on “single-cell”-specific KO model of the distal nephron

2016 ◽  
Vol 311 (5) ◽  
pp. F901-F906 ◽  
Author(s):  
Francesco Trepiccione ◽  
Christelle Soukaseum ◽  
Anna Iervolino ◽  
Federica Petrillo ◽  
Miriam Zacchia ◽  
...  
Keyword(s):  
Single Cell ◽  
Kidney Development ◽  
Fluorescent Protein ◽  
Collecting Duct ◽  
Yellow Fluorescent Protein ◽  
Cell Types ◽  
Intercalated Cells ◽  
Distal Nephron ◽  
Principal Cells ◽  
Different Cell Types

The distal nephron is a heterogeneous part of the nephron composed by six different cell types, forming the epithelium of the distal convoluted (DCT), connecting, and collecting duct. To dissect the function of these cells, knockout models specific for their unique cell marker have been created. However, since this part of the nephron develops at the border between the ureteric bud and the metanephric mesenchyme, the specificity of the single cell markers has been recently questioned. Here, by mapping the fate of the aquaporin 2 (AQP2) and Na+-Cl−cotransporter (NCC)-positive cells using transgenic mouse lines expressing the yellow fluorescent protein fluorescent marker, we showed that the origin of the distal nephron is extremely composite. Indeed, AQP2-expressing precursor results give rise not only to the principal cells, but also to some of the A- and B-type intercalated cells and even to cells of the DCT. On the other hand, some principal cells and B-type intercalated cells can develop from NCC-expressing precursors. In conclusion, these results demonstrate that the origin of different cell types in the distal nephron is not as clearly defined as originally thought. Importantly, they highlight the fact that knocking out a gene encoding for a selective functional marker in the adult does not guarantee cell specificity during the overall kidney development. Tools allowing not only cell-specific but also time-controlled recombination will be useful in this sense.

scholarly journals Histochemical carbonic anhydrase in rat inner medullary collecting duct.

1992 ◽  
Vol 40 (10) ◽  
pp. 1535-1545 ◽  
Author(s):  
J G Kleinman ◽  
J L Bain ◽  
C Fritsche ◽  
D A Riley
Keyword(s):  
Carbonic Anhydrase ◽  
Collecting Duct ◽  
Light And Electron Microscopy ◽  
Cell Types ◽  
Intercalated Cells ◽  
Cell Type ◽  
Variable Intensity ◽  
Inner Medullary Collecting Duct ◽  
Majority Population ◽  
Medullary Collecting Duct

Rat inner medullary collecting duct (IMCD) secretes substantial amounts of H+. However, carbonic anhydrase (CA), a concomitant of H+ secretion, has been generally reported absent in this segment. To reexamine this problem, we investigated CA and the morphological phenotypes of cells comprising the IMCD by CA histochemistry, using a modified Hansson technique with light and electron microscopy. Throughout the medulla, tubule cells exhibit histochemical CA activity. In the initial third of the inner medulla, a small proportion have features of intercalated cells and demonstrate some degree of CA activity. However, the majority population in the early portions of the IMCD appears to consist of principal cells. These also show CA staining of widely variable intensity, both among and within cells. A third cell type, previously called "IMCD cells", appears in the middle portion of the IMCD and is the only cell type present near the papilla tip. In contrast to previous reports, these "IMCD cells" have histochemical CA staining, also of highly variable intensity. These results demonstrate that stainable carbonic anhydrase to support acidification is present throughout the rat IMCD, both in intercalated cells and in some cells clearly not of this type. Therefore, the presence of CA is not specific for the intercalated cell type and suggests that other cell types may participate in acid secretion in IMCD.

scholarly journals Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

2020 ◽  
Author(s):  
Jiebiao Wang ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Autism Spectrum ◽  
Specific Cell ◽  
Expression Data ◽  
Cell Type ◽  
Disease Etiology ◽  
Cell Type Specific

AbstractWhen assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell-type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer’s disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.

scholarly journals ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

2021 ◽  
Author(s):  
Wenxuan Deng ◽  
Biqing Zhu ◽  
Seyoung Park ◽  
Tomokazu S. Sumida ◽  
Avraham Unterman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Earth Metal ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Proteomics Data ◽  
Data Annotation ◽  
Different Cell Types ◽  
Unique Molecular Identifier

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

scholarly journals Cell-type, single-cell, and spatial signatures of brain-region specific splicing in postnatal development

2020 ◽  
Author(s):  
Anoushka Joglekar ◽  
Andrey Prjibelski ◽  
Ahmed Mahfouz ◽  
Paul Collier ◽  
Susan Lin ◽  
...  
Keyword(s):  
Single Cell ◽  
Brain Region ◽  
Cell Types ◽  
Brain Regions ◽  
Specific Cell ◽  
Cell Type ◽  
Anatomic Structure ◽  
Link Type ◽  
Long Read ◽  
Isoform Expression

AbstractAlternative RNA splicing varies across brain regions, but the single-cell resolution of such regional variation is unknown. Here we present the first single-cell investigation of differential isoform expression (DIE) between brain regions, by performing single cell long-read transcriptome sequencing in the mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 (www.isoformAtlas.com). Using isoform tests for brain-region specific DIE, which outperform exon-based tests, we detect hundreds of brain-region specific DIE events traceable to specific cell-types. Many DIE events correspond to functionally distinct protein isoforms, some with just a 6-nucleotide exon variant. In most instances, one cell type is responsible for brain-region specific DIE. Cell types indigenous to only one anatomic structure display distinctive DIE, where for example, the choroid plexus epithelium manifest unique transcription start sites. However, for some genes, multiple cell-types are responsible for DIE in bulk data, indicating that regional identity can, although less frequently, override cell-type specificity. We validated our findings with spatial transcriptomics and long-read sequencing, yielding the first spatially resolved splicing map in the postnatal mouse brain (www.isoformAtlas.com). Our methods are highly generalizable. They provide a robust means of quantifying isoform expression with cell-type and spatial resolution, and reveal how the brain integrates molecular and cellular complexity to serve function.

scholarly journals Enhancer viruses and a transgenic platform for combinatorial cell subclass-specific labeling

2019 ◽  
Author(s):  
Lucas T. Graybuck ◽  
Tanya L. Daigle ◽  
Adriana E. Sedeño-Cortés ◽  
Miranda Walker ◽  
Brian Kalmbach ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Projection Neuron ◽  
Chromatin Accessibility ◽  
Specific Cell ◽  
Cell Type ◽  
Mouse Cortex ◽  
Efficient Access ◽  
Rapid Pace

SummaryThe rapid pace of cell type identification by new single-cell analysis methods has not been met with efficient experimental access to the newly discovered types. To enable flexible and efficient access to specific neural populations in the mouse cortex, we collected chromatin accessibility data from individual cells and clustered the single-cell data to identify enhancers specific for cell classes and subclasses. When cloned into adeno-associated viruses (AAVs) and delivered to the brain by retro-orbital injections, these enhancers drive transgene expression in specific cell subclasses in the cortex. We characterize several enhancer viruses in detail to show that they result in labeling of different projection neuron subclasses in mouse cortex, and that one of them can be used to label the homologous projection neuron subclass in human cortical slices. To enable the combinatorial labeling of more than one cell type by enhancer viruses, we developed a three-color Cre-, Flp- and Nigri-recombinase dependent reporter mouse line, Ai213. The delivery of three enhancer viruses driving these recombinases via a single retroorbital injection into a single Ai213 transgenic mouse results in labeling of three different neuronal classes/subclasses in the same brain tissue. This approach combines unprecedented flexibility with specificity for investigation of cell types in the mouse brain and beyond.

scholarly journals scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jose Alquicira-Hernandez ◽  
Anuja Sathe ◽  
Hanlee P. Ji ◽  
Quan Nguyen ◽  
Joseph E. Powell
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Prediction Method ◽  
Cell Types ◽  
Pancreatic Tissue ◽  
Specific Cell ◽  
Cell Type ◽  
Learning Probability ◽  
Dimension Space

AbstractSingle-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/.

scholarly journals Spatiotemporal mapping of RNA editing in the developing mouse brain using in situ sequencing reveals regional and cell-type-specific regulation

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Elin Lundin ◽  
Chenglin Wu ◽  
Albin Widmark ◽  
Mikaela Behm ◽  
Jens Hjerling-Leffler ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Editing ◽  
Mouse Brain ◽  
Developmental Stages ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Cell Type Specific ◽  
Specific Regulation

Abstract Background Adenosine-to-inosine (A-to-I) RNA editing is a process that contributes to the diversification of proteins that has been shown to be essential for neurotransmission and other neuronal functions. However, the spatiotemporal and diversification properties of RNA editing in the brain are largely unknown. Here, we applied in situ sequencing to distinguish between edited and unedited transcripts in distinct regions of the mouse brain at four developmental stages, and investigate the diversity of the RNA landscape. Results We analyzed RNA editing at codon-altering sites using in situ sequencing at single-cell resolution, in combination with the detection of individual ADAR enzymes and specific cell type marker transcripts. This approach revealed cell-type-specific regulation of RNA editing of a set of transcripts, and developmental and regional variation in editing levels for many of the targeted sites. We found increasing editing diversity throughout development, which arises through regional- and cell type-specific regulation of ADAR enzymes and target transcripts. Conclusions Our single-cell in situ sequencing method has proved useful to study the complex landscape of RNA editing and our results indicate that this complexity arises due to distinct mechanisms of regulating individual RNA editing sites, acting both regionally and in specific cell types.

scholarly journals Optimal marker gene selection for cell type discrimination in single cell analyses

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bianca Dumitrascu ◽  
Soledad Villar ◽  
Dustin G. Mixon ◽  
Barbara E. Engelhardt
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Marker Gene ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Computationally Efficient ◽  
Data Set ◽  
Gene Markers

AbstractSingle-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization.

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