scholarly journals Profiling APOL1 Nephropathy Risk Variants in Genome-Edited Kidney Organoids with Single-Cell Transcriptomics

Kidney360 ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 203-215
Author(s):  
Esther Liu ◽  
Behram Radmanesh ◽  
Byungha H. Chung ◽  
Michael D. Donnan ◽  
Dan Yi ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Single Cell ◽  
Expression Patterns ◽  
Human Kidney ◽  
Model Organisms ◽  
Additional Stress ◽  
Genomic Locus ◽  
Glomerular Epithelial Cell ◽  
Risk Variants ◽  
Kidney Organoids

BackgroundDNA variants in APOL1 associate with kidney disease, but the pathophysiologic mechanisms remain incompletely understood. Model organisms lack the APOL1 gene, limiting the degree to which disease states can be recapitulated. Here we present single-cell RNA sequencing (scRNA-seq) of genome-edited human kidney organoids as a platform for profiling effects of APOL1 risk variants in diverse nephron cell types.MethodsWe performed footprint-free CRISPR-Cas9 genome editing of human induced pluripotent stem cells (iPSCs) to knock in APOL1 high-risk G1 variants at the native genomic locus. iPSCs were differentiated into kidney organoids, treated with vehicle, IFN-γ, or the combination of IFN-γ and tunicamycin, and analyzed with scRNA-seq to profile cell-specific changes in differential gene expression patterns, compared with isogenic G0 controls.ResultsBoth G0 and G1 iPSCs differentiated into kidney organoids containing nephron-like structures with glomerular epithelial cells, proximal tubules, distal tubules, and endothelial cells. Organoids expressed detectable APOL1 only after exposure to IFN-γ. scRNA-seq revealed cell type–specific differences in G1 organoid response to APOL1 induction. Additional stress of tunicamycin exposure led to increased glomerular epithelial cell dedifferentiation in G1 organoids.ConclusionsSingle-cell transcriptomic profiling of human genome-edited kidney organoids expressing APOL1 risk variants provides a novel platform for studying the pathophysiology of APOL1-mediated kidney disease.

scholarly journals Profiling APOL1 Nephropathy Risk Variants in Genome-Edited Kidney Organoids with Single-Cell Transcriptomics

2019 ◽  
Author(s):  
Esther Liu ◽  
Behram Radmanesh ◽  
Byungha H. Chung ◽  
Michael D. Donnan ◽  
Dan Yi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Single Cell ◽  
Human Kidney ◽  
Cell Types ◽  
Genomic Locus ◽  
Glomerular Epithelial Cell ◽  
Risk Variants ◽  
Ifn Γ ◽  
Kidney Organoids

ABSTRACTBackgroundDNA variants in APOL1 associate with kidney disease, but the pathophysiological mechanisms remain incompletely understood. Model organisms lack the APOL1 gene, limiting the degree to which disease states can be recapitulated. Here we present single-cell RNA sequencing (scRNA-seq) of genome-edited human kidney organoids as a platform for profiling effects of APOL1 risk variants in diverse nephron cell types.MethodsWe performed footprint-free CRISPR-Cas9 genome editing of human induced pluripotent stem cells (iPSCs) to knock in APOL1 high-risk G1 variants at the native genomic locus. iPSCs were differentiated into kidney organoids, treated with vehicle, IFN-γ, or the combination of IFN-γ and tunicamycin, and analyzed with scRNA-seq to profile cell-specific changes in differential gene expression patterns, compared to isogenic G0 controls.ResultsBoth G0 and G1 iPSCs differentiated into kidney organoids containing nephron-like structures with glomerular epithelial cells, proximal tubules, distal tubules, and endothelial cells. Organoids expressed detectable APOL1 only after exposure to IFN-γ. scRNA-seq revealed cell type-specific differences in G1 organoid response to APOL1 induction. Additional stress of tunicamycin exposure led to increased glomerular epithelial cell dedifferentiation in G1 organoids.ConclusionsSingle-cell transcriptomic profiling of human genome-edited kidney organoids expressing APOL1 risk variants provides a novel platform for studying the pathophysiology of APOL1-mediated kidney disease.SIGNIFICANCE STATEMENTGaps persist in our mechanistic understanding of APOL1-mediated kidney disease. The authors apply genome-edited human kidney organoids, combined with single-cell transcriptomics, to profile APOL1 risk variants at the native genomic locus in different cell types. This approach captures interferon-mediated induction of APOL1 gene expression and reveals cellular dedifferentiation after a secondary insult of endoplasmic reticulum stress. This system provides a human cellular platform to interrogate complex mechanisms and human-specific regulators underlying APOL1-mediated kidney disease.

scholarly journals Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ayshwarya Subramanian ◽  
Eriene-Heidi Sidhom ◽  
Maheswarareddy Emani ◽  
Katherine Vernon ◽  
Nareh Sahakian ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Human Kidney ◽  
Mouse Kidney ◽  
Target Cells ◽  
Rna Seq ◽  
Kidney Capsule ◽  
Time Points ◽  
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 profile four human iPSC lines for a total of 450,118 single cells to show how organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes are largely reproducible across time points, protocols, and replicates, we detect variability in cell proportions between different iPSC lines, largely due to off-target cells. To address this, we analyze organoids transplanted under the mouse kidney capsule and find diminished off-target cells. Our work shows how single cell RNA-seq (scRNA-seq) can 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 Organoid single-cell profiling identifies a transcriptional signature of glomerular disease

2018 ◽  
Author(s):  
Jennifer L. Harder ◽  
Rajasree Menon ◽  
Edgar A. Otto ◽  
Jian Zhou ◽  
Sean Eddy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Single Cells ◽  
Gene Expression Signature ◽  
Human Kidney ◽  
Glomerular Disease ◽  
Podocyte Injury ◽  
Compensation Mechanisms ◽  
Kidney Organoids

ABSTRACTPodocyte injury is central to many forms of kidney disease, but transcriptional signatures reflecting podocyte injury and compensation mechanisms are challenging to analyze in vivo. Human kidney organoids derived from pluripotent stem cells (PSCs), a new model for disease and regeneration, present an opportunity to explore the transcriptional plasticity of podocytes. Here, transcriptional profiling of over 12,000 single cells from human PSC-derived kidney organoid cultures was used to identify robust and reproducible cell-lineage gene expression signatures shared with developing human kidneys based on trajectory analysis. Surprisingly, the gene expression signature characteristic of developing glomerular epithelial cells was also observed in glomerular tissue from a kidney disease cohort. This signature correlated with proteinuria and inverse eGFR, and was confirmed in an independent podocytopathy cohort. Three genes in particular were further identified as critical components of the glomerular disease signature. We conclude that cells in human PSC-derived kidney organoids reliably recapitulate the developmental transcriptional program of podocytes and other cell lineages in the human kidney, and that the early transcriptional profile seen in developing podocytes is reactivated in glomerular disease. Our findings demonstrate an innovative approach to identifying novel molecular programs involved in the pathogenesis of glomerulopathies.

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 High throughput single cell RNA-seq of developing mouse kidney and human kidney organoids reveals a roadmap for recreating the kidney

2017 ◽  
Author(s):  
Alexander N. Combes ◽  
Belinda Phipson ◽  
Luke Zappia ◽  
Kynan T. Lawlor ◽  
Pei Xuan Er ◽  
...  
Keyword(s):  
Single Cell ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Population Based ◽  
Mouse Kidney ◽  
Rna Seq ◽  
Kidney Morphogenesis ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractRecent advances in our capacity to differentiate human pluripotent stem cells to human kidney tissue are moving the field closer to novel approaches for renal replacement. Such protocols have relied upon our current understanding of the molecular basis of mammalian kidney morphogenesis. To date this has depended upon population based-profiling of non-homogenous cellular compartments. In order to improve our resolution of individual cell transcriptional profiles during kidney morphogenesis, we have performed 10x Chromium single cell RNA-seq on over 6000 cells from the E18.5 developing mouse kidney, as well as more than 7000 cells from human iPSC-derived kidney organoids. We identified 16 clusters of cells representing all major cell lineages in the E18.5 mouse kidney. The differentially expressed genes from individual murine clusters were then used to guide the classification of 16 cell clusters within human kidney organoids, revealing the presence of distinguishable stromal, endothelial, nephron, podocyte and nephron progenitor populations. Despite the congruence between developing mouse and human organoid, our analysis suggested limited nephron maturation and the presence of ‘off target’ populations in human kidney organoids, including unidentified stromal populations and evidence of neural clusters. This may reflect unique human kidney populations, mixed cultures or aberrant differentiation in vitro. Analysis of clusters within the mouse data revealed novel insights into progenitor maintenance and cellular maturation in the major renal lineages and will serve as a roadmap to refine directed differentiation approaches in human iPSC-derived kidney organoids.

scholarly journals SAT-118 COMPARATIVE TRANSCRIPTOMIC ANALYSIS OF CULTURED PODOCYTES AND HUMAN KIDNEY ORGANOIDS AT SINGLE-CELL RESOLUTION

2019 ◽  
Vol 4 (7) ◽  
pp. S54-S55
Author(s):  
T. SOARE ◽  
W. Zhang ◽  
A. Westerling-Bui ◽  
A. Tebbe ◽  
L. Walsh ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Kidney ◽  
Transcriptomic Analysis ◽  
Comparative Transcriptomic Analysis ◽  
Kidney Organoids

scholarly journals Comparative analysis of kidney organoid and adult human kidney single cell and single nucleus transcriptomes

2017 ◽  
Author(s):  
Haojia Wu ◽  
Kohei Uchimura ◽  
Erinn Donnelly ◽  
Yuhei Kirita ◽  
Samantha A. Morris ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Kidney ◽  
Cell Types ◽  
Great Promise ◽  
Full Potential ◽  
Diverse Range ◽  
Adult Human ◽  
Cell Diversity ◽  
Single Nucleus ◽  
Kidney Organoids

AbstractKidney organoids differentiated from human pluripotent stem cells hold great promise for understanding organogenesis, modeling disease and ultimately as a source of replacement tissue. Realizing the full potential of this technology will require better differentiation strategies based upon knowledge of the cellular diversity and differentiation state of all cells within these organoids. Here we analyze single cell gene expression in 45,227 cells isolated from 23 organoids differentiated using two different protocols. Both generate kidney organoids that contain a diverse range of kidney cells at differing ratios as well as non-renal cell types. We quantified the differentiation state of major organoid kidney cell types by comparing them against a 4,259 single nucleus RNA-seq dataset generated from adult human kidney, revealing immaturity of all kidney organoid cell types. We reconstructed lineage relationships during organoid differentiation through pseudotemporal ordering, and identified transcription factor networks associated with fate decisions. These results define impressive kidney organoid cell diversity, identify incomplete differentiation as a major roadblock for current directed differentiation protocols and provide a human adult kidney snRNA-seq dataset against which to benchmark future progress.

The Utility of Human Kidney Organoids in Modeling Kidney Disease

2020 ◽  
Vol 40 (2) ◽  
pp. 188-198
Author(s):  
Aneta Przepiorski ◽  
Amanda E. Crunk ◽  
Eugenel B. Espiritu ◽  
Neil A. Hukriede ◽  
Alan J. Davidson
Keyword(s):  
Kidney Disease ◽  
Human Kidney ◽  
Kidney Organoids

scholarly journals A robust semi-supervised NMF model for single cell RNA-seq data

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10091
Author(s):  
Peng Wu ◽  
Mo An ◽  
Hai-Ren Zou ◽  
Cai-Ying Zhong ◽  
Wei Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Prior Knowledge ◽  
Clustering Analysis ◽  
Prior Information ◽  
Expression Patterns ◽  
Biological Significance ◽  
Model Organisms ◽  
Marker Genes ◽  
Specific Gene ◽  
Advanced Analysis

Background Single-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand cell function and constitutes the basis of other advanced analysis. Nonnegative Matrix Factorization (NMF) has been widely used in clustering analysis of transcriptome data and achieved good performance. However, the existing NMF model is unsupervised and ignores known gene functions in the process of clustering. Knowledges of cell markers genes (genes that only express in specific cells) in human and model organisms have been accumulated a lot, such as the Molecular Signatures Database (MSigDB), which can be used as prior information in the clustering analysis of scRNA-seq data. Because the same kind of cells is likely to have similar biological functions and specific gene expression patterns, the marker genes of cells can be utilized as prior knowledge in the clustering analysis. Methods We propose a robust and semi-supervised NMF (rssNMF) model, which introduces a new variable to absorb noises of data and incorporates marker genes as prior information into a graph regularization term. We use rssNMF to solve the clustering problem of scRNA-seq data. Results Twelve scRNA-seq datasets with true labels are used to test the model performance and the results illustrate that our model outperforms original NMF and other common methods such as KMeans and Hierarchical Clustering. Biological significance analysis shows that rssNMF can identify key subclasses and latent biological processes. To our knowledge, this study is the first method that incorporates prior knowledge into the clustering analysis of scRNA-seq data.

scholarly journals DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species

2021 ◽  
Author(s):  
Yanhui Hu ◽  
Sudhir Gopal Tattikota ◽  
Yifang Liu ◽  
Aram Comjean ◽  
Yue Gao ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Model Organisms ◽  
Marker Genes ◽  
Specific Expression ◽  
Web Based ◽  
Manual Curation ◽  
Original Analysis ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AbstractWith the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in studies involving scRNA-seq of several tissues across diverse species including Drosophila. Although a few databases exist for users to query genes of interest within the scRNA-seq studies, search tools that enable users to find orthologous genes and their cell type-specific expression patterns across species are limited. Here, we built a new search database, called DRscDB (https://www.flyrnai.org/tools/single_cell/web/) to address this need. DRscDB serves as a comprehensive repository for published scRNA-seq datasets for Drosophila and the relevant datasets from human and other model organisms. DRscDB is based on manual curation of Drosophila scRNA-seq studies of various tissue types and their corresponding analogous tissues in vertebrates including zebrafish, mouse, and human. Of note, our search database provides most of the literature-derived marker genes, thus preserving the original analysis of the published scRNA-seq datasets. DRscDB serves as a web-based user interface that allows users to mine, utilize and compare gene expression data pertaining to scRNA-seq datasets from the published literature.

Sign in / Sign up

Export Citation Format

Share Document