Characterization of Alternative Splicing in High-Risk Wilms' Tumors.

The significant heterogeneity of Wilms' tumors between different patients is thought to arise from genetic and epigenetic distortions that occur during various stages of fetal kidney development in a way that is poorly understood. To address this, we characterized the heterogeneity of alternative mRNA splicing in Wilms' tumors using a publicly available RNAseq dataset of high-risk Wilms' tumors and normal kidney samples. Through Pareto task inference and cell deconvolution, we found that the tumors and normal kidney samples are organized according to progressive stages of kidney development within a triangle-shaped region in latent space, whose vertices, or "archetypes", resemble the cap mesenchyme, the nephrogenic stroma, and epithelial tubular structures of the fetal kidney. We identified a set of genes that are alternatively spliced between tumors located in different regions of latent space and found that many of these genes are associated with the epithelial-to-mesenchymal transition (EMT) and muscle development. Using motif enrichment analysis, we identified putative splicing regulators, some of which are associated with kidney development. Our findings provide new insights into the etiology of Wilms' tumors and suggest that specific splicing mechanisms in early stages of development may contribute to tumor development in different patients.

Characterization of Continuous Transcriptional Heterogeneity in High-Risk Blastemal-Type Wilms' Tumors Using Unsupervised Machine Learning.

Wilms' tumors are pediatric malignancies that are thought to arise from faulty kidney development. They contain a wide range of poorly differentiated cell states resembling various distorted developmental stages of the fetal kidney, and as a result, differ between patients in a continuous manner that is not well understood. Here, we used three computational approaches to characterize this continuous heterogeneity in high-risk blastemal-type Wilms' tumors. Using Pareto task inference, we show that the tumors form a triangle-shaped continuum in latent space that is bounded by three tumor archetypes with "stromal", "blastemal", and "epithelial" characteristics, which resemble the un-induced mesenchyme, the cap mesenchyme, and early epithelial structures of the fetal kidney. By fitting a generative probabilistic "grade of membership" model, we show that each tumor can be represented as a unique mixture of three hidden "topics" with blastemal, stromal, and epithelial characteristics. Likewise, cellular deconvolution allows us to represent each tumor in the continuum as a unique combination of fetal kidney-like cell states. These results highlight the relationship between Wilms' tumors and kidney development, and we anticipate that they will pave the way for more quantitative strategies for tumor stratification and classification.

Chromatin occupancy and epigenetic analysis reveal new insights into the function of the GATA1 N terminus in erythropoiesis.

Mutations in GATA1, which lead to expression of the GATA1s isoform that lacks the GATA1 N terminus, are seen in patients with Diamond-Blackfan anemia (DBA). In our efforts to better understand the connection between GATA1s and DBA, we comprehensively studied erythropoiesis in Gata1s mice. Defects in yolks sac and fetal liver hematopoiesis included impaired terminal maturation and reduced numbers of erythroid progenitors. RNA-sequencing revealed that both erythroid and megakaryocytic gene expression patterns were altered by the loss of the N terminus, including aberrant upregulation of Gata2 and Runx1. Dysregulation of global H3K27 methylation was found in the erythroid progenitors upon loss of N terminus of GATA1. Chromatin-binding assays revealed that, despite similar occupancy of GATA1 and GATA1s, there was a striking reduction of H3K27me3 at regulatory elements of the Gata2 and Runx1 genes. Consistent with the observation that overexpression of GATA2 has been reported to impair erythropoiesis, we found that haploinsufficiency of Gata2 rescued the erythroid defects of Gata1s fetuses. Together, our integrated genomic analysis of transcriptomic and epigenetic signatures reveals that, Gata1 mice provide novel insights into the role of the N terminus of GATA1 in transcriptional regulation and red blood cell maturation which may potentially be useful for DBA patients.

Single-Cell RNA Sequencing Reveals mRNA Splice Isoform Switching during Kidney Development.

BACKGROUND: During mammalian kidney development, nephron progenitors undergo a mesenchymal-to-epithelial transition and eventually differentiate into the various tubular segments of the nephron. Recently, Drop-seq single-cell RNA sequencing technology for measuring gene expression from thousands of individual cells identified the different cell types in the developing kidney. However, that analysis did not include the additional layer of heterogeneity that alternative mRNA splicing creates. METHODS: Full transcript length single-cell RNA sequencing characterized the transcriptomes of 544 individual cells from mouse embryonic kidneys. RESULTS: Gene expression levels measured with full transcript length single-cell RNA sequencing identified each cell type. Further analysis comprehensively characterized splice isoform switching during the transition between mesenchymal and epithelial cellular states, which is a key transitional process in kidney development. The study also identified several putative splicing regulators, including the genes Esrp1/2 and Rbfox1/2. CONCLUSIONS: Discovery of the sets of genes that are alternatively spliced as the fetal kidney mesenchyme differentiates into tubular epithelium will improve our understanding of the molecular mechanisms that drive kidney development.

A cluster robustness score for identifying cell subpopulations in single cell gene expression datasets from heterogeneous tissues and tumors.

MOTIVATION: A major aim of single cell biology is to identify important cell types such as stem cells in heterogeneous tissues and tumors. This is typically done by isolating hundreds of individual cells and measuring expression levels of multiple genes simultaneously from each cell. Then, clustering algorithms are used to group together similar single-cell expression profiles into clusters, each representing a distinct cell type. However, many of these clusters result from overfitting, meaning that rather than representing biologically meaningful cell types, they describe the intrinsic 'noise' in gene expression levels due to limitations in experimental precision or the intrinsic randomness of biochemical cellular processes. Consequentially, these non-meaningful clusters are most sensitive to noise: a slight shift in gene expression levels due to a repeated measurement will rearrange the grouping of data points such that these clusters break up. RESULTS: To identify the biologically meaningful clusters we propose a 'cluster robustness score': We add increasing amounts of noise (zero mean and increasing variance) and check which clusters are most robust in the sense that they do not mix with their neighbors up to high levels of noise. We show that biologically meaningful cell clusters that were manually identified in previously published single cell expression datasets have high robustness scores. These scores are higher than what would be expected in corresponding randomized homogeneous datasets having the same expression level statistics. We believe that this scoring system provides a more automated way to identify cell types in heterogeneous tissues and tumors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CDX2 as a Prognostic Biomarker in Stage II and Stage III Colon Cancer.

Background The identification of high-risk stage II colon cancers is key to the selection of patients who require adjuvant treatment after surgery. Microarray-based multigene-expression signatures derived from stem cells and progenitor cells hold promise, but they are difficult to use in clinical practice. Methods We used a new bioinformatics approach to search for biomarkers of colon epithelial differentiation across gene-expression arrays and then ranked candidate genes according to the availability of clinical-grade diagnostic assays. With the use of subgroup analysis involving independent and retrospective cohorts of patients with stage II or stage III colon cancer, the top candidate gene was tested for its association with disease-free survival and a benefit from adjuvant chemotherapy. Results The transcription factor CDX2 ranked first in our screening test. A group of 87 of 2115 tumor samples (4.1%) lacked CDX2 expression. In the discovery data set, which included 466 patients, the rate of 5-year disease-free survival was lower among the 32 patients (6.9%) with CDX2-negative colon cancers than among the 434 (93.1%) with CDX2-positive colon cancers (hazard ratio for disease recurrence, 3.44; 95% confidence interval [CI], 1.60 to 7.38; P=0.002). In the validation data set, which included 314 patients, the rate of 5-year disease-free survival was lower among the 38 patients (12.1%) with CDX2 protein-negative colon cancers than among the 276 (87.9%) with CDX2 protein-positive colon cancers (hazard ratio, 2.42; 95% CI, 1.36 to 4.29; P=0.003). In both these groups, these findings were independent of the patient's age, sex, and tumor stage and grade. Among patients with stage II cancer, the difference in 5-year disease-free survival was significant both in the discovery data set (49% among 15 patients with CDX2-negative tumors vs. 87% among 191 patients with CDX2-positive tumors, P=0.003) and in the validation data set (51% among 15 patients with CDX2-negative tumors vs. 80% among 106 patients with CDX2-positive tumors, P=0.004). In a pooled database of all patient cohorts, the rate of 5-year disease-free survival was higher among 23 patients with stage II CDX2-negative tumors who were treated with adjuvant chemotherapy than among 25 who were not treated with adjuvant chemotherapy (91% vs. 56%, P=0.006). Conclusions Lack of CDX2 expression identified a subgroup of patients with high-risk stage II colon cancer who appeared to benefit from adjuvant chemotherapy. (Funded by the National Comprehensive Cancer Network, the National Institutes of Health, and others.).

Genomic analysis at the single-cell level.

Studying complex biological systems such as a developing embryo, a tumor, or a microbial ecosystem often involves understanding the behavior and heterogeneity of the individual cells that constitute the system and their interactions. In this review, we discuss a variety of approaches to single-cell genomic analysis.

Quantitative assessment of single-cell RNA-sequencing methods.

Interest in single-cell whole-transcriptome analysis is growing rapidly, especially for profiling rare or heterogeneous populations of cells. We compared commercially available single-cell RNA amplification methods with both microliter and nanoliter volumes, using sequence from bulk total RNA and multiplexed quantitative PCR as benchmarks to systematically evaluate the sensitivity and accuracy of various single-cell RNA-seq approaches. We show that single-cell RNA-seq can be used to perform accurate quantitative transcriptome measurement in individual cells with a relatively small number of sequencing reads and that sequencing large numbers of single cells can recapitulate bulk transcriptome complexity.

KIT Signaling Promotes Growth of Colon Xenograft Tumors in Mice and Is Up-Regulated in a Subset of Human Colon Cancers.

BACKGROUND & AIMS: Receptor tyrosine kinase (RTK) inhibitors have advanced colon cancer treatment. We investigated the role of the RTK KIT in development of human colon cancer. METHODS: An array of 137 patient-derived colon tumors and their associated xenografts were analyzed by immunohistochemistry to measure levels of KIT and its ligand KITLG. KIT and/or KITLG was stably knocked down by expression of small hairpin RNAs from lentiviral vectors in DLD1, HT29, LS174T, and COLO320 DM colon cancer cell lines, and in UM-COLON#8 and POP77 xenografts; cells transduced with only vector were used as controls. Cells were analyzed by real-time quantitative reverse transcription polymerase chain reaction, single-cell gene expression analysis, flow cytometry, and immunohistochemical, immunoblot, and functional assays. Xenograft tumors were grown from control and KIT-knockdown DLD1 and UM-COLON#8 cells in immunocompromised mice and compared. Some mice were given the RTK inhibitor imatinib after injection of cancer cells; tumor growth was measured based on bioluminescence. We assessed tumorigenicity using limiting dilution analysis. RESULTS: KIT and KITLG were expressed heterogeneously by a subset of human colon tumors. Knockdown of KIT decreased proliferation of colon cancer cell lines and growth of xenograft tumors in mice compared with control cells. KIT knockdown cells had increased expression of enterocyte markers, decreased expression of cycling genes, and, unexpectedly, increased expression of LGR5 associated genes. No activating mutations in KIT were detected in DLD1, POP77, or UM-COLON#8 cells. However, KITLG-knockdown DLD1 cells formed smaller xenograft tumors than control cells. Gene expression analysis of single CD44(+) cells indicated that KIT can promote growth via KITLG autocrine and/or paracrine signaling. Imatinib inhibited growth of KIT(+) colon cancer organoids in culture and growth of xenograft tumors in mice. Cancer cells with endogenous KIT expression were more tumorigenic in mice. CONCLUSIONS: KIT and KITLG are expressed by a subset of human colon tumors. KIT signaling promotes growth of colon cancer cells and organoids in culture and xenograft tumors in mice via its ligand, KITLG, in an autocrine or paracrine manner. Patients with KIT-expressing colon tumors can benefit from KIT RTK inhibitors.

Geometry of the Gene Expression Space of Individual Cells.

There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.

Association of reactive oxygen species levels and radioresistance in cancer stem cells.

The metabolism of oxygen, although central to life, produces reactive oxygen species (ROS) that have been implicated in processes as diverse as cancer, cardiovascular disease and ageing. It has recently been shown that central nervous system stem cells and haematopoietic stem cells and early progenitors contain lower levels of ROS than their more mature progeny, and that these differences are critical for maintaining stem cell function. We proposed that epithelial tissue stem cells and their cancer stem cell (CSC) counterparts may also share this property. Here we show that normal mammary epithelial stem cells contain lower concentrations of ROS than their more mature progeny cells. Notably, subsets of CSCs in some human and murine breast tumours contain lower ROS levels than corresponding non-tumorigenic cells (NTCs). Consistent with ROS being critical mediators of ionizing-radiation-induced cell killing, CSCs in these tumours develop less DNA damage and are preferentially spared after irradiation compared to NTCs. Lower ROS levels in CSCs are associated with increased expression of free radical scavenging systems. Pharmacological depletion of ROS scavengers in CSCs markedly decreases their clonogenicity and results in radiosensitization. These results indicate that, similar to normal tissue stem cells, subsets of CSCs in some tumours contain lower ROS levels and enhanced ROS defences compared to their non-tumorigenic progeny, which may contribute to tumour radioresistance.

Innate immune response to homologous rotavirus infection in the small intestinal villous epithelium at single-cell resolution.

"Bulk" measurements of antiviral innate immune responses from pooled cells yield averaged signals and do not reveal underlying signaling heterogeneity in infected and bystander single cells. We examined such heterogeneity in the small intestine during rotavirus (RV) infection. Murine RV EW robustly activated type I IFNs and several antiviral genes (IFN-stimulated genes) in the intestine by bulk analysis, the source of induced IFNs primarily being hematopoietic cells. Flow cytometry and microfluidics-based single-cell multiplex RT-PCR allowed dissection of IFN responses in single RV-infected and bystander intestinal epithelial cells (IECs). EW replicates in IEC subsets differing in their basal type I IFN transcription and induces IRF3-dependent and IRF3-augmented transcription, but not NF-κB-dependent or type I IFN transcripts. Bystander cells did not display enhanced type I IFN transcription but had elevated levels of certain IFN-stimulated genes, presumably in response to exogenous IFNs secreted from immune cells. Comparison of IRF3 and NF-κB induction in STAT1(-/-) mice revealed that murine but not simian RRV mediated accumulation of IkB-α protein and decreased transcription of NF-κB-dependent genes. RRV replication was significantly rescued in IFN types I and II, as well as STAT1 (IFN types I, II, and III) deficient mice in contrast to EW, which was only modestly sensitive to IFNs I and II. Resolution of "averaged" innate immune responses in single IECs thus revealed unexpected heterogeneity in both the induction and subversion of early host antiviral immunity, which modulated host range.

Single-cell genomics.

Methods for genomic analysis at single-cell resolution enable new understanding of complex biological phenomena. Single-cell techniques, ranging from flow cytometry and microfluidics to PCR and sequencing, are used to understand the cellular composition of complex tissues, find new microbial species and perform genome-wide haplotyping.

Identification of a cKit(+) colonic crypt base secretory cell that supports Lgr5(+) stem cells in mice.

BACKGROUND & AIMS: Paneth cells contribute to the small intestinal niche of Lgr5(+) stem cells. Although the colon also contains Lgr5(+) stem cells, it does not contain Paneth cells. We investigated the existence of colonic Paneth-like cells that have a distinct transcriptional signature and support Lgr5(+) stem cells. METHODS: We used multicolor fluorescence-activated cell sorting to isolate different subregions of colon crypts, based on known markers, from dissociated colonic epithelium of mice. We performed multiplexed single-cell gene expression analysis with quantitative reverse transcriptase polymerase chain reaction followed by hierarchical clustering analysis to characterize distinct cell types. We used immunostaining and fluorescence-activated cell sorting analyses with in vivo administration of a Notch inhibitor and in vitro organoid cultures to characterize different cell types. RESULTS: Multicolor fluorescence-activated cell sorting could isolate distinct regions of colonic crypts. Four major epithelial subtypes or transcriptional states were revealed by gene expression analysis of selected populations of single cells. One of these, the goblet cells, contained a distinct cKit/CD117(+) crypt base subpopulation that expressed Dll1, Dll4, and epidermal growth factor, similar to Paneth cells, which were also marked by cKit. In the colon, cKit(+) goblet cells were interdigitated with Lgr5(+) stem cells. In vivo, this colonic cKit(+) population was regulated by Notch signaling; administration of a γ-secretase inhibitor to mice increased the number of cKit(+) cells. When isolated from mouse colon, cKit(+) cells promoted formation of organoids from Lgr5(+) stem cells, which expressed Kitl/stem cell factor, the ligand for cKit. When organoids were depleted of cKit(+) cells using a toxin-conjugated antibody, organoid formation decreased. CONCLUSIONS: cKit marks small intestinal Paneth cells and a subset of colonic goblet cells that are regulated by Notch signaling and support Lgr5(+) stem cells.

OCT4 induces long-lived dedifferentiated kidney progenitors poised to redifferentiate in 3D kidney spheroids.

Upscaling of kidney epithelial cells is crucial for renal regenerative medicine. Nonetheless, the adult kidney lacks a distinct stem cell hierarchy, limiting the ability to long-term propagate clonal populations of primary cells that retain renal identity. Toward this goal, we tested the paradigm of shifting the balance between differentiation and stemness in the kidney by introducing a single pluripotency factor, OCT4. Here we show that ectopic expression of OCT4 in human adult kidney epithelial cells (hKEpC) induces the cells to dedifferentiate, stably proliferate, and clonally emerge over many generations. Control hKEpC dedifferentiate, assume fibroblastic morphology, and completely lose clonogenic capacity. Analysis of gene expression and histone methylation patterns revealed that OCT4 represses the HNF1B gene module, which is critical for kidney epithelial differentiation, and concomitantly activates stemness-related pathways. OCT4-hKEpC can be long-term expanded in the dedifferentiated state that is primed for renal differentiation. Thus, when expanded OCT4-hKEpC are grown as kidney spheroids (OCT4-kSPH), they reactivate the HNF1B gene signature, redifferentiate, and efficiently generate renal structures in vivo. Hence, changes occurring in the cellular state of hKEpC following OCT4 induction, long-term propagation, and 3D aggregation afford rapid scale-up technology of primary renal tissue-forming cells.

Characterization of alternative mRNA splicing in cultured cell populations representing progressive stages of human fetal kidney development.

Nephrons are the functional units of the kidney. During kidney development, cells from the cap mesenchyme-a transient kidney-specific progenitor state-undergo a mesenchymal to epithelial transition (MET) and subsequently differentiate into the various epithelial cell types that create the tubular structures of the nephron. Faults in this transition can lead to a pediatric malignancy of the kidney called Wilms' tumor that mimics normal kidney development. While human kidney development has been characterized at the gene expression level, a comprehensive characterization of alternative splicing is lacking. Therefore, in this study, we performed RNA sequencing on cell populations representing early, intermediate, and late developmental stages of the human fetal kidney, as well as three blastemal-predominant Wilms' tumor patient-derived xenografts. Using this newly generated RNAseq data, we identified a set of transcripts that are alternatively spliced between the different developmental stages. Moreover, we found that cells from the earliest developmental stage have a mesenchymal splice-isoform profile that is similar to that of blastemal-predominant Wilms' tumor xenografts. RNA binding motif enrichment analysis suggests that the mRNA binding proteins ESRP1, ESRP2, RBFOX2, and QKI regulate alternative mRNA splicing during human kidney development. These findings illuminate new molecular mechanisms involved in human kidney development and pediatric kidney cancer.

Applications of Community Detection Algorithms to Large Biological Datasets.

Recent advances in data acquiring technologies in biology have led to major challenges in mining relevant information from large datasets. For example, single-cell RNA sequencing technologies are producing expression and sequence information from tens of thousands of cells in every single experiment. A common task in analyzing biological data is to cluster samples or features (e.g., genes) into groups sharing common characteristics. This is an NP-hard problem for which numerous heuristic algorithms have been developed. However, in many cases, the clusters created by these algorithms do not reflect biological reality. To overcome this, a Networks Based Clustering (NBC) approach was recently proposed, by which the samples or genes in the dataset are first mapped to a network and then community detection (CD) algorithms are used to identify clusters of nodes.Here, we created an open and flexible python-based toolkit for NBC that enables easy and accessible network construction and community detection. We then tested the applicability of NBC for identifying clusters of cells or genes from previously published large-scale single-cell and bulk RNA-seq datasets.We show that NBC can be used to accurately and efficiently analyze large-scale datasets of RNA sequencing experiments.

An analytically solvable model for rapid evolution of modular structure.

Biological systems often display modularity, in the sense that they can be decomposed into nearly independent subsystems. Recent studies have suggested that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals. Such modularly varying goals can also dramatically speed up evolution, relative to evolution under a constant goal. These studies were based on simulations of model systems, such as logic circuits and RNA structure, which are generally not easy to treat analytically. We present, here, a simple model for evolution under modularly varying goals that can be solved analytically. This model helps to understand some of the fundamental mechanisms that lead to rapid emergence of modular structure under modularly varying goals. In particular, the model suggests a mechanism for the dramatic speedup in evolution observed under such temporally varying goals.

Human kidney clonal proliferation disclose lineage-restricted precursor characteristics.

In-vivo single cell clonal analysis in the adult mouse kidney has previously shown lineage-restricted clonal proliferation within varying nephron segments as a mechanism responsible for cell replacement and local regeneration. To analyze ex-vivo clonal growth, we now preformed limiting dilution to generate genuine clonal cultures from one single human renal epithelial cell, which can give rise to up to 3.4 * 106 cells, and analyzed their characteristics using transcriptomics. A comparison between clonal cultures revealed restriction to either proximal or distal kidney sub-lineages with distinct cellular and molecular characteristics; rapidly amplifying de-differentiated clones and a stably proliferating cuboidal epithelial-appearing clones, respectively. Furthermore, each showed distinct molecular features including cell-cycle, epithelial-mesenchymal transition, oxidative phosphorylation, BMP signaling pathway and cell surface markers. In addition, analysis of clonal versus bulk cultures show early clones to be more quiescent, with elevated expression of renal developmental genes and overall reduction in renal identity markers, but with an overlapping expression of nephron segment identifiers and multiple identity. Thus, ex-vivo clonal growth mimics the in-vivo situation displaying lineage-restricted precursor characteristics of mature renal cells. These data suggest that for reconstruction of varying renal lineages with human adult kidney based organoid technology and kidney regeneration ex-vivo, use of multiple heterogeneous precursors is warranted.

Ex Vivo Expanded 3D Human Kidney Spheres Engraft Long Term and Repair Chronic Renal Injury in Mice.

End-stage renal disease is a worldwide epidemic requiring renal replacement therapy. Harvesting tissue from failing kidneys and autotransplantation of tissue progenitors could theoretically delay the need for dialysis. Here we use healthy and end-stage human adult kidneys to robustly expand proliferative kidney epithelial cells and establish 3D kidney epithelial cultures termed "nephrospheres." Formation of nephrospheres reestablishes renal identity and function in primary cultures. Transplantation into NOD/SCID mice shows that nephrospheres restore self-organogenetic properties lost in monolayer cultures, allowing long-term engraftment as tubular structures, potentially adding nephron segments and demonstrating self-organization as critical to survival. Furthermore, long-term tubular engraftment of nephrospheres is functionally beneficial in murine models of chronic kidney disease. Remarkably, nephrospheres inhibit pro-fibrotic collagen production in cultured fibroblasts via paracrine modulation, while transplanted nephrospheres induce transcriptional signatures of proliferation and release from quiescence, suggesting re-activation of endogenous repair. These data support the use of human nephrospheres for renal cell therapy.