scholarly journals Urinary Single-Cell Profiling Captures the Cellular Diversity of the Kidney

2021 ◽  
Vol 32 (3) ◽  
pp. 614-627
Author(s):  
Amin Abedini ◽  
Yuan O. Zhu ◽  
Shatakshee Chatterjee ◽  
Gabor Halasz ◽  
Kishor Devalaraja-Narashimha ◽  
...  
Keyword(s):  
Single Cell ◽  
Collecting Duct ◽  
Microscopic Analysis ◽  
Human Kidney ◽  
Cell Types ◽  
Urine Sediment ◽  
Type Composition ◽  
Single Cell Profiling ◽  
Bladder Cells ◽  
Almost All

BackgroundMicroscopic analysis of urine sediment is probably the most commonly used diagnostic procedure in nephrology. The urinary cells, however, have not yet undergone careful unbiased characterization.MethodsSingle-cell transcriptomic analysis was performed on 17 urine samples obtained from five subjects at two different occasions, using both spot and 24-hour urine collection. A pooled urine sample from multiple healthy individuals served as a reference control. In total 23,082 cells were analyzed. Urinary cells were compared with human kidney and human bladder datasets to understand similarities and differences among the observed cell types.ResultsAlmost all kidney cell types can be identified in urine, such as podocyte, proximal tubule, loop of Henle, and collecting duct, in addition to macrophages, lymphocytes, and bladder cells. The urinary cell–type composition was subject specific and reasonably stable using different collection methods and over time. Urinary cells clustered with kidney and bladder cells, such as urinary podocytes with kidney podocytes, and principal cells of the kidney and urine, indicating their similarities in gene expression.ConclusionsA reference dataset for cells in human urine was generated. Single-cell transcriptomics enables detection and quantification of almost all types of cells in the kidney and urinary tract.

scholarly journals Novel Human Kidney Cell Subsets Identified by Mux-Seq

2020 ◽  
Author(s):  
Andrew W. Schroeder ◽  
Swastika Sur ◽  
Priyanka Rashmi ◽  
Izabella Damm ◽  
Arya Zarinsefat ◽  
...  
Keyword(s):  
Single Cell ◽  
Collecting Duct ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Cost Effective ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Proximal Tubular ◽  
Highly Correlated ◽  
First Time

AbstractBackgroundThe kidney is a highly complex organ that performs multiple functions necessary to maintain systemic homeostasis, with complex interplay from different kidney sub-structures and the coordinated response of diverse cell types, few known and likely many others, as yet undiscovered. Traditional global sequencing techniques are limited in their ability to identify unique and functionally diverse cell types in complex tissues.MethodsHerein we characterize over 45,000 cells from 10 normal human kidneys using unbiased single-cell RNA sequencing. We also apply, for the first time, an approach of multiplexing kidney samples (Mux-Seq), pooled from different individuals, to save input sample amount and cost. We applied the computational tool Demuxlet to assess differential expression across multiple individuals by pooling human kidney cells for scRNA sequencing, utilizing individual genetic variability to determine the identity of each cell.ResultsMultiplexed droplet single-cell RNA sequencing results were highly correlated with the singleplexed sample run data. One hundred distinct cell cluster populations in total were identified across the major cell types of the kidney, with varied functional states. Proximal tubular and collecting duct cells were the most heterogeneous, displaying multiple clusters with unique ontologies. Novel proximal tubular cell subsets were identified with regenerative potential. Trajectory analysis demonstrated evolution of cell states between intercalated and principal cells in the collecting duct.ConclusionsHealthy kidney tissue has been successfully analyzed to detect all known renal cell types, inclusive of resident and infiltrating immune cells in the kidney. Mux-Seq is a unique method that allows for rapid and cost-effective single cell, in depth, transcriptional analysis of human kidney tissue.Significance StatementUse of renal biopsies for single cell transcriptomics is limited by small tissue availability and batch effects. In this study, we have successfully employed the use of Mux-Seq for the first time in kidney. Mux-Seq allows the use of single cell technology at a much more cost-effective manner by pooling samples from multiple individuals for a single sequencing run. This is even more relevant in the case of patient biopsies where the input of tissue is significantly limited. We show that the data from overlapping tissue samples are highly correlated between Mux-Seq and traditional Singleplexed RNA seq. Furthermore, the results from Mux-Seq of 4 pooled samples are highly correlated with singleplexed data from 10 singleplex samples despite the inherent variability among individuals.

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

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.

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 Single-Cell Transcriptomic Map of the Human and Mouse Bladders

2019 ◽  
Vol 30 (11) ◽  
pp. 2159-2176 ◽  
Author(s):  
Zhenyuan Yu ◽  
Jinling Liao ◽  
Yang Chen ◽  
Chunlin Zou ◽  
Haiying Zhang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Epithelial Cells ◽  
Single Cell ◽  
Cell Types ◽  
Specific Cell ◽  
Human Bladder ◽  
Healthy Human ◽  
Bladder Cell ◽  
Bladder Cells ◽  
Human And Mouse

BackgroundHaving a comprehensive map of the cellular anatomy of the normal human bladder is vital to understanding the cellular origins of benign bladder disease and bladder cancer.MethodsWe used single-cell RNA sequencing (scRNA-seq) of 12,423 cells from healthy human bladder tissue samples taken from patients with bladder cancer and 12,884 cells from mouse bladders to classify bladder cell types and their underlying functions.ResultsWe created a single-cell transcriptomic map of human and mouse bladders, including 16 clusters of human bladder cells and 15 clusters of mouse bladder cells. The homology and heterogeneity of human and mouse bladder cell types were compared and both conservative and heterogeneous aspects of human and mouse bladder evolution were identified. We also discovered two novel types of human bladder cells. One type is ADRA2A+ and HRH2+ interstitial cells which may be associated with nerve conduction and allergic reactions. The other type is TNNT1+ epithelial cells that may be involved with bladder emptying. We verify these TNNT1+ epithelial cells also occur in rat and mouse bladders.ConclusionsThis transcriptomic map provides a resource for studying bladder cell types, specific cell markers, signaling receptors, and genes that will help us to learn more about the relationship between bladder cell types and diseases.

scholarly journals Kidney organoid reproducibility across multiple human iPSC lines and diminished off target cells after transplantation revealed by single cell transcriptomics

2019 ◽  
Author(s):  
Ayshwarya Subramanian ◽  
Eriene-Heidi Sidhom ◽  
Maheswarareddy Emani ◽  
Nareh Sahakian ◽  
Katherine Vernon ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Kidney ◽  
Cell Types ◽  
Mouse Kidney ◽  
Target Cells ◽  
Rna Seq ◽  
Kidney Capsule ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractHuman iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we used single cell RNA-Seq (scRNA-Seq) to profile 415,775 cells to show that organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes were largely reproducible across iPSC lines, time points, protocols, and replicates, cell proportions were variable between different iPSC lines. Off-target cell proportions were the most variable. Prolonged in vitro culture did not alter cell types, but organoid transplantation under the mouse kidney capsule diminished off-target cells. Our work shows how scRNA-seq can help score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells.

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 LRcell: detecting the source of differential expression at the sub-cell type level from bulk RNA-seq data

2021 ◽  
Author(s):  
Wenjing Ma ◽  
Sumeet Sharma ◽  
Peng Jin ◽  
Shannon L Gourley ◽  
Zhaohui Qin
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Cell Type ◽  
Reference Dataset ◽  
Cell Type Composition ◽  
Type Composition ◽  
Differential Gene

The rapid proliferation of single-cell RNA-sequencing (scRNA-seq) datasets have revealed cell heterogeneity at unprecedented scales. Several deconvolution methods have been developed to decompose bulk experiments to reveal cell type contributions. However, these methods lack power in identifying the accurate cell type composition when having a considerable amount of sub-cell types in the reference dataset. Here, we present LRcell, a R Bioconductor package (http://bioconductor.org/packages/release/bioc/html/LRcell.html) aiming to identify specific sub-cell type(s) that drives the changes observed in a bulk RNA-seq differential gene expression experiment. In addition, LRcell provides pre-embedded marker genes computed from putative single-cell RNA-seq experiments as options to execute the analyses.

scholarly journals Single cell derived mRNA signals across human kidney tumors

2020 ◽  
Author(s):  
Matthew D Young ◽  
Thomas J Mitchell ◽  
Lars Custers ◽  
Thanasis Margaritis ◽  
Francisco Morales ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Cancer ◽  
Human Kidney ◽  
Cell Types ◽  
Renal Tumors ◽  
Tumor Type ◽  
Kidney Tumors ◽  
Cell Renal Cell Carcinoma ◽  
Cellular Origin ◽  
Normal Tissues

AbstractThe cellular transcriptome may provide clues into the differentiation state and origin of human cancer, as tumor cells may retain patterns of gene expression similar to the cell they derive from. Here, we studied the differentiation state and cellular origin of human kidney tumors, by assessing mRNA signals in 1,300 childhood and adult renal tumors, spanning seven different tumor types. Using single cell mRNA reference maps of normal tissues generated by the Human Cell Atlas project, we measured the abundance of reference “cellular signals” in each tumor. Quantifying global differentiation states, we found that, irrespective of tumor type, childhood tumors exhibited fetal cellular signals, thus replacing the long-held presumption of “fetalness” with a precise, quantitative readout of immaturity. By contrast, in adult cancers our assessment refuted the suggestion of dedifferentiation towards a fetal state in the overwhelming majority of cases, with the exception of lethal variants of clear cell renal cell carcinoma. Examining the specific cellular phenotype of each tumor type revealed an intimate connection between the different mesenchymal populations of the developing kidney and childhood renal tumors, whereas adult tumors mostly represented specific mature tubular cell types. RNA signals of each tumor type were remarkably uniform and specific, indicating a possible therapeutic and diagnostic utility. We demonstrated this utility with a case study of a cryptic renal tumor. Whilst not classifiable by clinical pathological work-up, mRNA signals revealed the diagnosis. Our findings provide a cellular definition of human renal tumors through an approach that is broadly applicable to human cancer.

scholarly journals Comprehensive benchmarking of computational deconvolution of transcriptomics data

2020 ◽  
Author(s):  
Francisco Avila Cobos ◽  
José Alquicira-Hernandez ◽  
Joseph Powell ◽  
Pieter Mestdagh ◽  
Katleen De Preter
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Type ◽  
Factors Affecting ◽  
Marker Selection ◽  
Cell Type Composition ◽  
Type Composition ◽  
Comparable Performance ◽  
Transcriptomics Data ◽  
Combined Impact

AbstractMany computational methods to infer cell type proportions from bulk transcriptomics data have been developed. Attempts comparing these methods revealed that the choice of reference marker signatures is far more important than the method itself. However, a thorough evaluation of the combined impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the results is still lacking.Using different single-cell RNA-sequencing (scRNA-seq) datasets, we generated hundreds of pseudo-bulk mixtures to evaluate the combined impact of these factors on the deconvolution results. Along with methods to perform deconvolution of bulk RNA-seq data we also included five methods specifically designed to infer the cell type composition of bulk data using scRNA-seq data as reference.Both bulk and single-cell deconvolution methods perform best when applied to data in linear scale and the choice of normalization can have a dramatic impact on the performance of some, but not all methods. Overall, single-cell methods have comparable performance to the best performing bulk methods and bulk methods based on semi-supervised approaches showed higher error and lower correlation values between the computed and the expected proportions. Moreover, failure to include cell types in the reference that are present in a mixture always led to substantially worse results, regardless of any of the previous choices. Taken together, we provide a thorough evaluation of the combined impact of the different factors affecting the computational deconvolution task across different datasets and propose general guidelines to maximize its performance.

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