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 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 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 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.

scholarly journals Single cell transcriptomics of human nephritis reveal the cellular origin of kidney disease associated genes

2020 ◽  
Author(s):  
zhejun chen ◽  
ting zhang ◽  
qin wang ◽  
zhenyuan li ◽  
yuanyuan xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Single Cell ◽  
Antigen Processing ◽  
Expression Profiles ◽  
Human Kidney ◽  
Signalling Pathway ◽  
Specific Gene ◽  
Cellular Origin ◽  
Disease Associated Genes

Abstract Background Single cell RNA sequencing (scRNA-seq) have become a powerful tool in discovering a novel cell type and pinpointing cellular specific gene within tissues. However, in diseased kidneys, especially caused by glomerulonephritis, there have few study focusing on revealing gene expression changes at the cellular level. Methods To reveal cellular gene expression profiles of glomerulonephritis, we performed scRNA-seq of 2 human kidney transplantation donor samples, 4 human glomerulonephritis samples, 1 human malignant hypertension sample and 1 human chronic interstitial nephritis sample, all tissues were taken from biopsy. Results Upon disease occur, immune cells infiltrate which can be proved by our dataset. In the cluster of podocyte, two glomerulonephritis related genes named FXYD5 and CD74 were found, and interferon alpha/beta signalling pathway and antigen processing and presentation was enriched in podocyte of LN and IgAN comparing with other samples, thus inhibiting these signalling pathway may alleviate the symptom of these disease. Conclusions Cellular origin of kidney disease associated genes and the activated signaling pathways the genes involved in can be found by scRNA-seq.

scholarly journals Single Cell Sequencing and Kidney Organoids Generated from Pluripotent Stem Cells

2020 ◽  
Vol 15 (4) ◽  
pp. 550-556 ◽  
Author(s):  
Haojia Wu ◽  
Benjamin D. Humphreys
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Pluripotent Stem Cells ◽  
High Throughput Screening ◽  
Kidney Development ◽  
Toxicity Testing ◽  
Human Kidney ◽  
Cell Types ◽  
Time Frame ◽  
Kidney Organoids

Methods to differentiate human pluripotent stem cells into kidney organoids were first introduced about 5 years ago, and since that time, the field has grown substantially. Protocols are producing increasingly complex three-dimensional structures, have been used to model human kidney disease, and have been adapted for high-throughput screening. Over this same time frame, technologies for massively parallel, single-cell RNA sequencing (scRNA-seq) have matured. Now, both of these powerful approaches are being combined to better understand how kidney organoids can be applied to the understanding of kidney development and disease. There are several reasons why this is a synergistic combination. Kidney organoids are complicated and contain many different cell types of variable maturity. scRNA-seq is an unbiased technology that can comprehensively categorize cell types, making it ideally suited to catalog all cell types present in organoids. These same characteristics also make scRNA-seq a powerful approach for quantitative comparisons between protocols, batches, and pluripotent cell lines as it becomes clear that reproducibility and quality can vary across all three variables. Lineage trajectories can be reconstructed using scRNA-seq data, enabling the rational adjustment of differentiation strategies to promote maturation of desired kidney cell types or inhibit differentiation of undesired off-target cell types. Here, we review the ways that scRNA-seq has been successfully applied in the organoid field and predict future applications for this powerful technique. We also review other developing single-cell technologies and discuss how they may be combined, using “multiomic” approaches, to improve our understanding of kidney organoid differentiation and usefulness in modeling development, disease, and toxicity testing.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Single-cell RNA sequencing reveals the mesangial identity and species diversity of glomerular cell transcriptomes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bing He ◽  
Ping Chen ◽  
Sonia Zambrano ◽  
Dina Dabaghie ◽  
Yizhou Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Human Kidney ◽  
Cell Types ◽  
Model Organisms ◽  
Mural Cell ◽  
Glomerular Cell ◽  
Individual Gene ◽  
The Individual

AbstractMolecular characterization of the individual cell types in human kidney as well as model organisms are critical in defining organ function and understanding translational aspects of biomedical research. Previous studies have uncovered gene expression profiles of several kidney glomerular cell types, however, important cells, including mesangial (MCs) and glomerular parietal epithelial cells (PECs), are missing or incompletely described, and a systematic comparison between mouse and human kidney is lacking. To this end, we use Smart-seq2 to profile 4332 individual glomerulus-associated cells isolated from human living donor renal biopsies and mouse kidney. The analysis reveals genetic programs for all four glomerular cell types (podocytes, glomerular endothelial cells, MCs and PECs) as well as rare glomerulus-associated macula densa cells. Importantly, we detect heterogeneity in glomerulus-associated Pdgfrb-expressing cells, including bona fide intraglomerular MCs with the functionally active phagocytic molecular machinery, as well as a unique mural cell type located in the central stalk region of the glomerulus tuft. Furthermore, we observe remarkable species differences in the individual gene expression profiles of defined glomerular cell types that highlight translational challenges in the field and provide a guide to design translational studies.

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