Genomics of Kidney Disease
Open Access

Single Cell Sequencing and Kidney Organoids Generated from Pluripotent Stem Cells

Haojia Wu and Benjamin D. Humphreys
CJASN April 2020, 15 (4) 550-556; DOI: https://doi.org/10.2215/CJN.07470619

Abstract

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.

Introduction

Despite many decades of experience using rodents to model various kidney diseases in the preclinical setting, drugs that effectively treat rodent kidney disease have largely failed when translated to human clinical trials (1). Although there are different reasons for this, an important one is simply that kidney development and genomic regulation between rodents and humans differs (2–7). Variations in the susceptibility of different mouse strains to develop kidney disease both complicates their use preclinically and also raises questions about the generalizability of findings to the clinical setting (8–11). The discovery that human pluripotent stem cells can be differentiated into kidney organoids has generated great enthusiasm among investigators, with hope that this human kidney model system will better predict translation to the clinic.

The first protocols describing the generation of kidney organoids from pluripotent stem cells appeared roughly 5 years ago (12–14). Kidney organoids are differentiated from pluripotent stem cells. These are either human embryonic stem cells or induced pluripotent stem cells. The latter are derived from a patient’s cells—even from a buccal swab or blood draw—and then are reprogrammed to pluripotency, a discovery that won the Nobel Prize in 2012 (15). All differentiation protocols are on the basis of the concept that manipulating pluripotent stem cells to activate the same signaling pathways that lead to the formation of kidney during development can lead to the formation of a self-organizing kidney in vitro. The specifics of these differentiation schemes are beyond the scope of this review, but broadly speaking, stem cells are induced in a stepwise fashion into primitive streak, intermediate mesoderm, and finally into nephron progenitors through modulation of WNT, FGF, and TGFβ signaling pathways (16). Nephron progenitors (metanephric mesenchyme and ureteric bud) attract one another and undergo reciprocal inductive signaling, leading to branching morphogenesis and the generation of all epithelial cell types present in the nephron, from podocytes to collecting duct. The resulting kidney organoids contain between ten and several hundred nephron-like structures with glomeruli, distinct tubule segments, vasculature, and stroma (Figure 1). Many clinically relevant uses have been envisioned for kidney organoids (17–19) (Figure 2).

Figure 1.
Figure 1.

Histology and immunofluorescence of kidney organoids generated by the Takasato protocol (12). (A) Periodic acid–Schiff stained sections of human kidney (left) and kidney organoid (right). Original magnification, ×200 and ×600 for low and high magnification, respectively. (B) Immunofluorescence staining of markers for podocyte (WT1, red), proximal tubule (LTL, white), and distal tubule (ECAD, green) in a kidney organoid. Scale bar, 500μm. Images courtesy of Kohei Uchimura, with permission.

Figure 2.
Figure 2.

Therapeutic potential of kidney organoids. Scheme illustrating the ways that kidney organoids can be used for patient-specific disease modeling and drug screening.

Although the kidney organoid field has developed rapidly, the techniques used to assess organoid cell diversity and maturation state have changed little until recently. Conventional strategies to characterize three-dimensional organoids use a limited number of markers, relying primarily on histology, immunofluorescence, high-resolution microscopy, and bulk gene expression assays (e.g., quantitative PCR and bulk RNA sequencing). These approaches are low throughput and require that the investigator choose candidate markers to measure a priori. Although bulk transcriptomic profiling using RNA sequencing provides a much more comprehensive measure of organoid gene expression and has been applied to kidney organoids (20), it cannot assign gene expression to any particular cell type because it reflects the integrated expression profile of all the cells within the organoid.

By contrast, massively parallel, single-cell RNA sequencing (scRNA-seq) allows for the unbiased and comprehensive gene expression profiling of thousands of single cells in one experiment. It is high throughput, and requires no prior knowledge over what cell types or markers might be present. Organoids are complex structures composed of multiple different cell types and scRNA-seq can assess organoid cell composition and gene regulatory networks. It can also reveal cell lineage relationships, which can be used to improve differentiation protocols. In this review, we highlight the ways in which scRNA-seq is being used to improve the development and use of kidney organoids for human kidney disease modeling, and predict future applications of this transformative technology.

scRNA-seq: The Basics

Microfluidic technology enabled the development of scRNA-seq technologies. At a basic level, scRNA-seq consists of a microfluidic device containing three channels: one for cell lysis buffer containing tiny beads coated with oligonucleotides, one for physiologic saline containing the cell suspension to be profiled, and one containing oil. Precise pumps regulate the flow of all three channels into the microfluidic chip. The lysis buffer with beads flows in into the channel first, and then the second channel containing the cells flows into the first channel. By chance, a cell and a bead will end up adjacent to each other within the combined channel. The third channel containing the oil then flows into the channel as well. When the flow rate is correct, this results in the coencapsulation of the bead, cell, and lysis buffer within a single microdroplet surrounded by oil, i.e., a reverse emulsion.

Within the microdroplet, the lysis buffer dissolves the cell membrane, releasing its mRNA. Recall that every mRNA has a polyA tail, i.e., multiple AMP bases in series. These bind to stretches of thymine oligonucleotides bound to every bead. These oligonucleotides also contain a unique DNA barcode that is unique to each bead, which is how a particular mRNA can be traced back to the cell from which it arose. Once the mRNA is bound to the bead, all the beads are collected and the mRNA is reverse transcribed into DNA, then amplified and fragmented. Finally, some adaptor oligonucleotides are ligated onto the ends of each DNA molecule, and the whole group undergoes next-generation sequencing (21).

A typical scRNA-seq experiment might profile 10,000 individual cells (although it would not be unusual to have a 100,000 cell output). Each cell will comprise perhaps 10,000 individual mRNA measurements from about 4000 different genes. As a result, data output from even a simple experiment is massive: 10,000 cells with 10,000 measurement each represents 100 million datapoints. The human brain is not capable of analyzing such large datasets, but a combination of modern processing power and bioinformatic approaches, such as machine learning algorithms and statistical inference, is ideally suited for detecting patterns within these large datasets.

What Is in a Kidney Organoid?

Defining the kidney cell types present in kidney organoids, and their state of differentiation, represents a challenge for the field. Initial studies using different differentiation protocols all confirmed the presence of the major nephron cell types including podocyte, proximal tubule, loop of Henle, and distal tubule. The percentage of collecting duct was insignificant in these protocols and its differentiation was substantially induced in a protocol developed by the Nishinakamura group (14). However, this work was performed in mouse pluripotent cells, and achieving robust ureteric bud differentiation in human kidney organoids remains a challenge for the field. Endothelial cells and fibroblasts were additionally present, although all organoids lack leukocytes (and resident macrophages in particular) even to this day (12–14). Confirming the presence of these cell types required straightforward but relatively laborious immunohistochemical studies as well as measurement of mRNA for cell markers by PCR. The relative abundance of these cell types, and variability between apparently similar cell types within and between organoids, has been largely undefined until recently. These low-throughput assays are also limited in that markers and genes to be measured must be chosen by the investigator. scRNA-seq can comprehensively catalog cell types within a sample without any prior knowledge of what cells might be present, and so is completely unsupervised. The algorithms that analyze the output of the experiment group cells according to their overall transcriptional similarity. This process is unbiased. A good example of why this unbiased analysis is important is our own finding that nonkidney cells, primarily neurons and muscle, are present in kidney organoids derived using certain protocols (22). Because human kidney does not contain these cell types, these “off-target” cells represent differentiation that has gone astray.

Two other findings were revealed by the early scRNA-seq studies of kidney organoids. First, several key kidney cell types are missing or underrepresented in the kidney organoids, such as principal cells, intercalated cells, immune cells, and glomerular endothelium (18,22,23). Second, scRNA-seq revealed different cell states within the same lineage. Such heterogeneity is undetectable in bulk profiling studies and would be difficult to detect without a priori knowledge of markers characterizing those cell states. However, this can be easily detected by scRNA-seq. For example, we have identified that there are actually two proximal tubule states in the kidney organoids by subclustering analysis: one mature and the other expressing an immature signature, such as developmental genes and proliferation markers (22). The same is true for podocytes where three separate podocyte states were detected. The Freedman group similarly identified both early and late proximal tubule and podocyte clusters, using a separate organoid protocol (18).

Kidney Organoids: Immature Cell States

Although the human kidney develops over the course of about 200 days (24), kidney organoid protocols generally last between 15 and 30 days. Thus, it should not be a surprise that organoid cell types do not generally represent fully mature adult kidney cell types. The Little group performed bulk RNA sequencing of their organoids, revealing that kidney organoids most closely resemble the first trimester human fetal kidney (12). Taking a similar approach but at single-cell resolution, we compared human fetal, human adult, and kidney organoid cells by single-cell transcriptomics. This allowed us to quantitatively assess the degree to which organoid cell types are immature. Our results confirmed that kidney organoid cell types are indeed most similar transcriptionally to fetal kidney. As an example, transcription factors determine cell identity, and for podocytes and proximal tubule, we could only detect approximately 20% of the transcription factor repertoire found in these adult cell types compared with the respective organoid cells (22). Whether organoids from other groups or using different protocols or sampled at different time points might yield better results is not known. Certainly continuing to improve differentiation protocols remains a priority for the field. Figure 3 shows that across four kidney cell types, organoid cell types do not express the same degree of terminal differentiation markers as adult cell types, but they do express fetal markers and persist in expressing certain developmental genes.

Figure 3.
Figure 3.

Benchmarking cell-specific markers to kidney organoid cell types. Single-cell RNA sequencing data are from published datasets derived from kidney organoids (22), fetal kidney (44), and adult kidney (22). Datasets were integrated and normalized using Seurat3 (45). Marker genes representing three categories (mature, fetal, and developmental) were selected from the differentially expressed gene list and visualized by the ggplot2 R package.

Brain and Muscle Cells in the Kidney?

Several groups, including our own, have demonstrated the existence of nonkidney cell types, including neurons, muscle cells, and melanocytes, in kidney organoids regardless of the protocols used (22,23,25). Although this has been observed in other areas such as the brain, these off-target cells hamper kidney differentiation and compromise the ability of kidney organoids to faithfully model the human kidney. Here, scRNA-seq can be harnessed to both improve kidney organoid differentiation and to eliminate off-target cell types. Computational models for inferring the lineage trajectory of cells during differentiation— also called pseudotemporal ordering—allows investigators to examine changes in expression of key regulators and signaling pathways that drive cell fate decisions along a particular cell lineage (26). By manipulating gene expression of these regulators and pathways, it is possible to fine-tune the differentiation protocol to improve the outcome; for example, by adding a particular growth factor at a particular time.

In an effort to reduce unwanted off-target cells in kidney organoids, we performed scRNA-seq over the time course of organoid differentiation. We then inferred gene expression changes in each cell lineage during differentiation using pseudotemporal ordering. This analysis revealed that the neuron-specific growth factor Brain-derived neurotrophic factor (BDNF), and its receptor Tropomyosin receptor kinase B (NTRK2) were exclusively expressed in neurons present during kidney organoid differentiation. We hypothesized that autocrine or paracrine BDNF-NTRK2 signaling might be promoting the growth of these off-target neurons because BDNF is known to promote the survival and proliferation of neurons during neurogenesis (27). By simply adding a well characterized, small-molecule inhibitor of NTRK2 during organoid differentiation, we could reduce the number of off-target neurons present in the organoids by 90%. Moreover, we also observed increased differentiation of proximal tubule and podocytes, suggesting that the presence of neurons was inhibiting kidney differentiation (22). These results suggest that a similar strategy can be applied broadly in the organoid field to reduce off-target cell types.

How Has scRNA-seq Improved Our Understanding of Kidney Organoid Biology?

Finally, scRNA-seq is an excellent tool to compare different differentiation protocols or to validate a new protocol. By comparing the cell types in the organoid from two existing protocols, our scRNA-seq results demonstrated that organoids generated from both protocols are relatively similar, despite some variations seen in the cell-type composition and maturation (22). In a similar fashion, Czerniecki et al. (18) used scRNA-seq to demonstrate that the kidney organoid generated by high-throughput screening contained the key nephron cell types. In a massive kidney organoid scRNA-seq effort, Subramanian et al. performed scRNA-seq on >400,000 cells derived from different kidney organoids. They compared organoid batches, protocols, time points, and starting cell lines, and showed that the most variable cell type across conditions were the off-target cell types. They also demonstrated that transplantation of human kidney organoids under the mouse kidney capsule significantly diminished these off-target cells (25).

Kidney organoids have also shown their value in personalized medicine applications. The Freedman group showed that podocalyxin mutant pluripotent stem cell lines generate kidney organoids with defective junctional organization (28) and abnormal assembly of microvilli and lateral spaces (29) in podocyte-like cells. They further showed that PKD1 or PKD2 induces cyst formation from kidney tubules, a phenotype mimicking human polycystic kidney disease (28). Mutations in IFT140 have been linked to nephronophthisis-related ciliopathies in humans. After evaluating kidney organoids derived from a patient with compound-heterozygous variants in IFT140, the Little group found that organoids with the same genotype fully recapitulated the phenotype of nephronophthisis-related ciliopathy such as shortened, club-shaped primary cilia (30). Gene correction of the IFT140 gene using CRISPR/Cas9 rescued all these defects (30). The Nishinakamura team established a pluripotent stem cell line from a patient with an NPHS1 missense mutation. They demonstrated that the defect in slit diaphragm formation in NPHS1 mutant podocytes could be rescued by gene correction (through homologous recombination) (31).

Kidney organoids have been adapted for drug screening studies; for example, the Little group used podocytes derived from kidney organoid for drug toxicity screening. They found that doxorubicin induced fragmentation of glomeruli in a dose-dependent manner—a similar toxicity observed in congenital nephrotic syndrome (19). The Freedman group developed a high-throughput organoid platform to test the efficacy of eight compounds on cyst formation in PKD organoids and identified an unexpected role of myosin pathway in polycystic kidney disease (18).

A very exciting new technology in the kidney organoid field has been the development of protocols for growth of primary kidney tubular epithelial organoids, termed “tubuloids.” In contrast to stem cell–derived kidney organoids, these investigators dissociated adult human or mouse kidney into tubular segments, and developed conditions enabling growth of epithelial structures resembling cysts that are highly polarized and contain fully differentiated, functional epithelial cells. They can be passaged up to 20 times, and can even be generated from cells recovered from human urine (32). They showed that human tubuloids can be used to model BK virus infection, Wilms tumor growth, and hereditary disease. How these epithelial cells differ from those differentiated in kidney organoids remains an open question, but the approach represents a significant step forward for personalized medicine approaches.

What Are Future Applications of scRNA-seq for Organoids?

The future for new single-cell transcriptomic approaches to illuminate kidney organoid biology is bright. One question of importance, both to developmental biologists and pediatric nephrologists, is understanding cell lineage relationships in development. What fetal cells give rise to podocytes, for example? This not just academic. Most genetic mutations that cause congenital abnormalities of the kidney and urogenital tract act in cells that are present only in development, and not in adults. Thus, we need to understand how congenital abnormalities of the kidney and urogenital tract mutations affect progenitor cell types and lead to kidney developmental defects, to formulate strategies to rescue these defects.

If understanding cellular lineage relationships during nephrogenesis has clinical implications, then how can we better define them in human kidney? Very sophisticated methods for tracking cell fate in mice have been developed over the past 15 years (33). These involve genetic manipulations that cause marked cells to heritably express a fluorescent protein, and cannot be applied to humans. We understand lineage relationships in mouse kidney development quite well, but in humans we know very little, mostly because the genetic tools for tracking cell lineage have been unavailable for human studies. However, this type of genetic lineage analysis can be applied to human pluripotent stem cells, and can be used to track the fate of human progenitor cells during kidney organoid differentiation. The Little laboratory has recently done just this (34). They asked whether the self-renewing SIX2+ progenitor cells can give rise to nephron cell types during human kidney organoid differentiation, a competency that was demonstrated in mouse kidney nephrogenesis (35). They demonstrated that the SIX2-expressing cells were able to differentiate into proximal nephron segments and that they did not contribute to the collecting duct lineage (34). This result replicates what is known about Six2+ cell potential in mouse and provides a proof of principle for tracking cell fate in human kidney organoids.

Recently, a new and versatile lineage tracing technique using scRNA-seq has been developed by the Morris laboratory (36). This technique, CellTagging, has been successfully implemented to track lineage relationships during direct conversion of fibroblasts to induced endoderm progenitors. The technique involves lentiviral expression of an 8 bp barcode unique to each cell, which can be read out by scRNA-seq. With this technique, clonal information can be captured by scRNA-seq and uncovered by downstream scRNA-seq data analysis (36). Because scRNA-seq also records the gene expression information of each single cell, this cell-tagging technique enables simultaneous single-cell profiling of transcriptome and clonal history (36). If such an approach could be applied to kidney organoid differentiation, it would provide a detailed developmental map for all organoid cell types, not just a single lineage, that to date has been lacking.

The lack of leukocytes in kidney organoids means that we cannot study how the immune system regulates kidney development or disease, and this is a current limitation. However, researchers have successfully introduced leukocytes in other organoid models and provide examples for how it might be done in kidney. For example, Dijkstra et al. (37) showed that coculture of autologous tumor organoids and PBMCs can enrich tumor-reactive T cells from patients with colorectal cancer and lung cancer. In a separate study, Neal et al. (38) reported that an air–liquid interface method can generate patient-derived organoids with tumor epithelia retaining native immune cells.

An international consortium called the Human Cell Atlas seeks to create a comprehensive reference “atlas” of every human cell type as the basis to understand human health, and how changes in this atlas underlie disease (39). Although this is a critically important effort, it is fundamentally a descriptive one. By contrast, human kidney organoids offer the opportunity to perturb cellular processes to uncover regulatory mechanisms. Because it offers such high information content, scRNA-seq is well suited to be combined with perturbations to uncover the mechanisms of cell differentiation, communication, and tissue organization. Such efforts are already under way in other fields. For example, Perturb-seq is a promising technique that uses gene-editing technologies to inhibit expression of one specific gene while simultaneously labeling that cell with an expressed barcode (40,41). When scRNA-seq is subsequently performed, it captures the expressed barcode and the associated change of gene expression that is caused by inhibiting that single gene. This approach could be applied to screen thousands of different transcription factors, to identify those critical ones that are required for kidney cell differentiation.

Conclusion

These are exciting times in both basic and clinical kidney investigation. We now have both recent, positive, phase 3 clinical trial results for diabetic nephropathy for the first time in memory (42,43) and remarkably powerful technologic advances like kidney organoids, tubuloids, and scRNA-seq that promise to help us develop a more predictive pipeline for therapeutic target identification, toxicity prediction, disease modeling, leading to drug development and randomized, clinical trial testing. Stay tuned.

Disclosures

Dr. Humphreys reports receiving grants from Chinook Therapeutics and Janssen; receiving consulting fees from Celgene, Chinook Therapeutics, Indalo Therapeutics, Janssen, Medimmune, and Merck; receiving honoraria from Genentech; and equity ownership in Chinook Therapeutics, all outside of the submitted work. Dr. Wu has nothing to disclose.

Footnotes

  • Published online ahead of print. Publication date available at www.cjasn.org.

References

  1. ↵
    1. de Caestecker M,
    2. Humphreys BD,
    3. Liu KD,
    4. Fissell WH,
    5. Cerda J,
    6. Nolin TD,
    7. Askenazi D,
    8. Mour G,
    9. Harrell FE Jr..,
    10. Pullen N,
    11. Okusa MD,
    12. Faubel S
    ; ASN AKI Advisory Group: Bridging translation by improving preclinical study design in AKI. J Am Soc Nephrol 26: 2905–2916, 2015
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Schmidt D,
    2. Wilson MD,
    3. Ballester B,
    4. Schwalie PC,
    5. Brown GD,
    6. Marshall A,
    7. Kutter C,
    8. Watt S,
    9. Martinez-Jimenez CP,
    10. Mackay S,
    11. Talianidis I,
    12. Flicek P,
    13. Odom DT
    : Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science 328: 1036–1040, 2010
    OpenUrlAbstract/FREE Full Text
    1. Odom DT,
    2. Dowell RD,
    3. Jacobsen ES,
    4. Gordon W,
    5. Danford TW,
    6. MacIsaac KD,
    7. Rolfe PA,
    8. Conboy CM,
    9. Gifford DK,
    10. Fraenkel E
    : Tissue-specific transcriptional regulation has diverged significantly between human and mouse. Nat Genet 39: 730–732, 2007
    OpenUrlCrossRefPubMed
    1. Lin S,
    2. Lin Y,
    3. Nery JR,
    4. Urich MA,
    5. Breschi A,
    6. Davis CA,
    7. Dobin A,
    8. Zaleski C,
    9. Beer MA,
    10. Chapman WC,
    11. Gingeras TR,
    12. Ecker JR,
    13. Snyder MP
    : Comparison of the transcriptional landscapes between human and mouse tissues. Proc Natl Acad Sci U S A 111: 17224–17229, 2014
    OpenUrlAbstract/FREE Full Text
    1. Lindström NO,
    2. Guo J,
    3. Kim AD,
    4. Tran T,
    5. Guo Q,
    6. De Sena Brandine G,
    7. Ransick A,
    8. Parvez RK,
    9. Thornton ME,
    10. Baskin L,
    11. Grubbs B,
    12. McMahon JA,
    13. Smith AD,
    14. McMahon AP
    : Conserved and divergent features of mesenchymal progenitor cell types within the cortical nephrogenic niche of the human and mouse kidney. J Am Soc Nephrol 29: 806–824, 2018
    OpenUrlAbstract/FREE Full Text
    1. Lindström NO,
    2. Tran T,
    3. Guo J,
    4. Rutledge E,
    5. Parvez RK,
    6. Thornton ME,
    7. Grubbs B,
    8. McMahon JA,
    9. McMahon AP
    : Conserved and divergent molecular and anatomic features of human and mouse nephron patterning. J Am Soc Nephrol 29: 825–840, 2018
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Lindström NO,
    2. McMahon JA,
    3. Guo J,
    4. Tran T,
    5. Guo Q,
    6. Rutledge E,
    7. Parvez RK,
    8. Saribekyan G,
    9. Schuler RE,
    10. Liao C,
    11. Kim AD,
    12. Abdelhalim A,
    13. Ruffins SW,
    14. Thornton ME,
    15. Baskin L,
    16. Grubbs B,
    17. Kesselman C,
    18. McMahon AP
    : Conserved and divergent features of human and mouse kidney organogenesis. J Am Soc Nephrol 29: 785–805, 2018
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Lu X,
    2. Li N,
    3. Shushakova N,
    4. Schmitt R,
    5. Menne J,
    6. Susnik N,
    7. Meier M,
    8. Leitges M,
    9. Haller H,
    10. Gueler F,
    11. Rong S
    : C57BL/6 and 129/Sv mice: Genetic difference to renal ischemia-reperfusion. J Nephrol 25: 738–743, 2012
    OpenUrlCrossRefPubMed
    1. Burne MJ,
    2. Haq M,
    3. Matsuse H,
    4. Mohapatra S,
    5. Rabb H
    : Genetic susceptibility to renal ischemia reperfusion injury revealed in a murine model. Transplantation 69: 1023–1025, 2000
    OpenUrlPubMed
    1. Arif E,
    2. Solanki AK,
    3. Nihalani D
    : Adriamycin susceptibility among C57BL/6 substrains. Kidney Int 89: 721–723, 2016
    OpenUrl
  11. ↵
    1. Qi Z,
    2. Fujita H,
    3. Jin J,
    4. Davis LS,
    5. Wang Y,
    6. Fogo AB,
    7. Breyer MD
    : Characterization of susceptibility of inbred mouse strains to diabetic nephropathy. Diabetes 54: 2628–2637, 2005
    OpenUrlAbstract/FREE Full Text
  12. ↵
    1. Takasato M,
    2. Er PX,
    3. Chiu HS,
    4. Maier B,
    5. Baillie GJ,
    6. Ferguson C,
    7. Parton RG,
    8. Wolvetang EJ,
    9. Roost MS,
    10. Chuva de Sousa Lopes SM,
    11. Little MH
    : Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis. Nature 526: 564–568, 2015
    OpenUrlCrossRefPubMed
    1. Morizane R,
    2. Lam AQ,
    3. Freedman BS,
    4. Kishi S,
    5. Valerius MT,
    6. Bonventre JV
    : Nephron organoids derived from human pluripotent stem cells model kidney development and injury. Nat Biotechnol 33: 1193–1200, 2015
    OpenUrlCrossRefPubMed
  14. ↵
    Taguchi A, Nishinakamura R: Higher-order kidney organogenesis from pluripotent stem cells. Cell Stem Cell 21: 730–746 e6, 2017
  15. ↵
    1. Rossant J,
    2. Mummery C
    : NOBEL 2012 Physiology or medicine: Mature cells can be rejuvenated. Nature 492: 56, 2012
    OpenUrl
  16. ↵
    1. Islam M,
    2. Nishinakamura R
    : How to rebuild the kidney: Recent advances in kidney organoids. J Biochem 166: 7–12, 2019
    OpenUrl
  17. ↵
    1. Cruz NM,
    2. Song X,
    3. Czerniecki SM,
    4. Gulieva RE,
    5. Churchill AJ,
    6. Kim YK,
    7. Winston K,
    8. Tran LM,
    9. Diaz MA,
    10. Fu H,
    11. Finn LS,
    12. Pei Y,
    13. Himmelfarb J,
    14. Freedman BS
    : Organoid cystogenesis reveals a critical role of microenvironment in human polycystic kidney disease. Nat Mater 16: 1112–1119, 2017
    OpenUrlCrossRefPubMed
  18. ↵
    Czerniecki SM, Cruz NM, Harder JL, Menon R, Annis J, Otto EA, Gulieva RE, Islas LV, Kim YK, Tran LM, Martins TJ, Pippin JW, Fu H, Kretzler M, Shankland SJ, Himmelfarb J, Moon RT, Paragas N, Freedman BS: High-throughput screening enhances kidney organoid differentiation from human pluripotent stem cells and enables automated multidimensional phenotyping. Cell Stem Cell 22: 929–940 e4, 2018
  19. ↵
    1. Hale LJ,
    2. Howden SE,
    3. Phipson B,
    4. Lonsdale A,
    5. Er PX,
    6. Ghobrial I,
    7. Hosawi S,
    8. Wilson S,
    9. Lawlor KT,
    10. Khan S,
    11. Oshlack A,
    12. Quinlan C,
    13. Lennon R,
    14. Little MH
    : 3D organoid-derived human glomeruli for personalised podocyte disease modelling and drug screening. Nat Commun 9: 5167, 2018
    OpenUrlCrossRefPubMed
  20. ↵
    1. Phipson B,
    2. Er PX,
    3. Combes AN,
    4. Forbes TA,
    5. Howden SE,
    6. Zappia L,
    7. Yen HJ,
    8. Lawlor KT,
    9. Hale LJ,
    10. Sun J,
    11. Wolvetang E,
    12. Takasato M,
    13. Oshlack A,
    14. Little MH
    : Evaluation of variability in human kidney organoids. Nat Methods 16: 79–87, 2019
    OpenUrlCrossRefPubMed
  21. ↵
    1. Wu H,
    2. Humphreys BD
    : The promise of single-cell RNA sequencing for kidney disease investigation. Kidney Int 92: 1334–1342, 2017
    OpenUrlPubMed
  22. ↵
    Wu H, Uchimura K, Donnelly EL, Kirita Y, Morris SA, Humphreys BD: Comparative analysis and refinement of human PSC-derived kidney organoid differentiation with single-cell transcriptomics. Cell Stem Cell 23: 869–881 e8, 2018
  23. ↵
    1. Combes AN,
    2. Zappia L,
    3. Er PX,
    4. Oshlack A,
    5. Little MH
    : Single-cell analysis reveals congruence between kidney organoids and human fetal kidney. Genome Med 11: 3, 2019
    OpenUrlCrossRefPubMed
  24. ↵
    1. McMahon AP
    : Development of the mammalian kidney. Curr Top Dev Biol 117: 31–64, 2016
    OpenUrlCrossRefPubMed
  25. ↵
    1. Subramanian A,
    2. Sidhom E-H,
    3. Emani M,
    4. Sahakian N,
    5. Vernon K,
    6. Zhou Y,
    7. Kost-Alimova M,
    8. Weins A,
    9. Slyper M,
    10. Waldman J,
    11. Dionne D,
    12. Nguyen LT,
    13. Marshall J,
    14. Rosenblatt-Rosen O,
    15. Regev A,
    16. Greka A
    : Kidney organoid reproducibility across multiple human iPSC lines and diminished off target cells after transplantation revealed by single cell transcriptomics. bioRxiv, 2019
  26. ↵
    1. Saelens W,
    2. Cannoodt R,
    3. Todorov H,
    4. Saeys Y
    : A comparison of single-cell trajectory inference methods. Nat Biotechnol 37: 547–554, 2019
    OpenUrlCrossRefPubMed
  27. ↵
    1. Huang EJ,
    2. Reichardt LF
    : Neurotrophins: Roles in neuronal development and function. Annu Rev Neurosci 24: 677–736, 2001
    OpenUrlCrossRefPubMed
  28. ↵
    1. Freedman BS,
    2. Brooks CR,
    3. Lam AQ,
    4. Fu H,
    5. Morizane R,
    6. Agrawal V,
    7. Saad AF,
    8. Li MK,
    9. Hughes MR,
    10. Werff RV,
    11. Peters DT,
    12. Lu J,
    13. Baccei A,
    14. Siedlecki AM,
    15. Valerius MT,
    16. Musunuru K,
    17. McNagny KM,
    18. Steinman TI,
    19. Zhou J,
    20. Lerou PH,
    21. Bonventre JV
    : Modelling kidney disease with CRISPR-mutant kidney organoids derived from human pluripotent epiblast spheroids. Nat Commun 6: 8715, 2015
    OpenUrlCrossRefPubMed
  29. ↵
    1. Kim YK,
    2. Refaeli I,
    3. Brooks CR,
    4. Jing P,
    5. Gulieva RE,
    6. Hughes MR,
    7. Cruz NM,
    8. Liu Y,
    9. Churchill AJ,
    10. Wang Y,
    11. Fu H,
    12. Pippin JW,
    13. Lin LY,
    14. Shankland SJ,
    15. Vogl AW,
    16. McNagny KM,
    17. Freedman BS
    : Gene-edited human kidney organoids reveal mechanisms of disease in podocyte development. Stem Cells 35: 2366–2378, 2017
    OpenUrlCrossRef
  30. ↵
    1. Forbes TA,
    2. Howden SE,
    3. Lawlor K,
    4. Phipson B,
    5. Maksimovic J,
    6. Hale L,
    7. Wilson S,
    8. Quinlan C,
    9. Ho G,
    10. Holman K,
    11. Bennetts B,
    12. Crawford J,
    13. Trnka P,
    14. Oshlack A,
    15. Patel C,
    16. Mallett A,
    17. Simons C,
    18. Little MH
    : Patient-iPSC-derived kidney organoids show functional validation of a ciliopathic renal phenotype and reveal underlying pathogenetic mechanisms. Am J Hum Genet 102: 816–831, 2018
    OpenUrlCrossRefPubMed
  31. ↵
    1. Tanigawa S,
    2. Islam M,
    3. Sharmin S,
    4. Naganuma H,
    5. Yoshimura Y,
    6. Haque F,
    7. Era T,
    8. Nakazato H,
    9. Nakanishi K,
    10. Sakuma T,
    11. Yamamoto T,
    12. Kurihara H,
    13. Taguchi A,
    14. Nishinakamura R
    : Organoids from nephrotic disease-derived iPSCs identify impaired NEPHRIN localization and slit diaphragm formation in kidney podocytes. Stem Cell Reports 11: 727–740, 2018
    OpenUrl
  32. ↵
    1. Schutgens F,
    2. Rookmaaker MB,
    3. Margaritis T,
    4. Rios A,
    5. Ammerlaan C,
    6. Jansen J,
    7. Gijzen L,
    8. Vormann M,
    9. Vonk A,
    10. Viveen M,
    11. Yengej FY,
    12. Derakhshan S,
    13. de Winter-de Groot KM,
    14. Artegiani B,
    15. van Boxtel R,
    16. Cuppen E,
    17. Hendrickx APA,
    18. van den Heuvel-Eibrink MM,
    19. Heitzer E,
    20. Lanz H,
    21. Beekman J,
    22. Murk JL,
    23. Masereeuw R,
    24. Holstege F,
    25. Drost J,
    26. Verhaar MC,
    27. Clevers H
    : Tubuloids derived from human adult kidney and urine for personalized disease modeling. Nat Biotechnol 37: 303–313, 2019
    OpenUrlCrossRefPubMed
  33. ↵
    1. Humphreys BD,
    2. DiRocco DP
    : Lineage-tracing methods and the kidney. Kidney Int 86: 481–488, 2014
    OpenUrlCrossRefPubMed
  34. ↵
    1. Howden SE,
    2. Vanslambrouck JM,
    3. Wilson SB,
    4. Tan KS,
    5. Little MH
    : Reporter-based fate mapping in human kidney organoids confirms nephron lineage relationships and reveals synchronous nephron formation. EMBO Rep 20: e47483, 2019
    OpenUrlAbstract/FREE Full Text
  35. ↵
    1. Little MH,
    2. McMahon AP
    : Mammalian kidney development: Principles, progress, and projections. Cold Spring Harb Perspect Biol 4: a008300, 2012
    OpenUrlAbstract/FREE Full Text
  36. ↵
    1. Biddy BA,
    2. Kong W,
    3. Kamimoto K,
    4. Guo C,
    5. Waye SE,
    6. Sun T,
    7. Morris SA
    : Single-cell mapping of lineage and identity in direct reprogramming. Nature 564: 219–224, 2018
    OpenUrlCrossRefPubMed
  37. ↵
    Dijkstra KK, Cattaneo CM, Weeber F, Chalabi M, van de Haar J, Fanchi LF, Slagter M, van der Velden DL, Kaing S, Kelderman S, van Rooij N, van Leerdam ME, Depla A, Smit EF, Hartemink KJ, de Groot R, Wolkers MC, Sachs N, Snaebjornsson P, Monkhorst K, Haanen J, Clevers H, Schumacher TN, Voest EE: Generation of tumor-reactive T cells by co-culture of peripheral blood lymphocytes and tumor organoids. Cell 174: 1586–1598 e12, 2018
  38. ↵
    Neal JT, Li X, Zhu J, Giangarra V, Grzeskowiak CL, Ju J, Liu IH, Chiou SH, Salahudeen AA, Smith AR, Deutsch BC, Liao L, Zemek AJ, Zhao F, Karlsson K, Schultz LM, Metzner TJ, Nadauld LD, Tseng YY, Alkhairy S, Oh C, Keskula P, Mendoza-Villanueva D, De La Vega FM, Kunz PL, Liao JC, Leppert JT, Sunwoo JB, Sabatti C, Boehm JS, Hahn WC, Zheng GXY, Davis MM, Kuo CJ: Organoid modeling of the tumor immune microenvironment. Cell 175: 1972–1988 e16, 2018
  39. ↵
    1. Regev A,
    2. Teichmann SA,
    3. Lander ES,
    4. Amit I,
    5. Benoist C,
    6. Birney E,
    7. Bodenmiller B,
    8. Campbell P,
    9. Carninci P,
    10. Clatworthy M,
    11. Clevers H,
    12. Deplancke B,
    13. Dunham I,
    14. Eberwine J,
    15. Eils R,
    16. Enard W,
    17. Farmer A,
    18. Fugger L,
    19. Göttgens B,
    20. Hacohen N,
    21. Haniffa M,
    22. Hemberg M,
    23. Kim S,
    24. Klenerman P,
    25. Kriegstein A,
    26. Lein E,
    27. Linnarsson S,
    28. Lundberg E,
    29. Lundeberg J,
    30. Majumder P,
    31. Marioni JC,
    32. Merad M,
    33. Mhlanga M,
    34. Nawijn M,
    35. Netea M,
    36. Nolan G,
    37. Pe’er D,
    38. Phillipakis A,
    39. Ponting CP,
    40. Quake S,
    41. Reik W,
    42. Rozenblatt-Rosen O,
    43. Sanes J,
    44. Satija R,
    45. Schumacher TN,
    46. Shalek A,
    47. Shapiro E,
    48. Sharma P,
    49. Shin JW,
    50. Stegle O,
    51. Stratton M,
    52. Stubbington MJT,
    53. Theis FJ,
    54. Uhlen M,
    55. van Oudenaarden A,
    56. Wagner A,
    57. Watt F,
    58. Weissman J,
    59. Wold B,
    60. Xavier R,
    61. Yosef N
    ; Human Cell Atlas Meeting Participants: The human cell atlas. eLife 6: e27041, 2017
    OpenUrlCrossRefPubMed
  40. ↵
    Dixit A, Parnas O, Li B, Chen J, Fulco CP, Jerby-Arnon L, Marjanovic ND, Dionne D, Burks T, Raychowdhury R, Adamson B, Norman TM, Lander ES, Weissman JS, Friedman N, Regev A: Perturb-Seq: Dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167: 1853–1866 e17, 2016
  41. ↵
    Adamson B, Norman TM, Jost M, Cho MY, Nunez JK, Chen Y, Villalta JE, Gilbert LA, Horlbeck MA, Hein MY, Pak RA, Gray AN, Gross CA, Dixit A, Parnas O, Regev A, Weissman JS: A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167: 1867–1882 e21, 2016
  42. ↵
    1. Perkovic V,
    2. Jardine MJ,
    3. Neal B,
    4. Bompoint S,
    5. Heerspink HJL,
    6. Charytan DM,
    7. Edwards R,
    8. Agarwal R,
    9. Bakris G,
    10. Bull S,
    11. Cannon CP,
    12. Capuano G,
    13. Chu PL,
    14. de Zeeuw D,
    15. Greene T,
    16. Levin A,
    17. Pollock C,
    18. Wheeler DC,
    19. Yavin Y,
    20. Zhang H,
    21. Zinman B,
    22. Meininger G,
    23. Brenner BM,
    24. Mahaffey KW
    ; CREDENCE Trial Investigators: Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med 380: 2295–2306, 2019
    OpenUrlCrossRefPubMed
  43. ↵
    1. Heerspink HJL,
    2. Parving HH,
    3. Andress DL,
    4. Bakris G,
    5. Correa-Rotter R,
    6. Hou FF,
    7. Kitzman DW,
    8. Kohan D,
    9. Makino H,
    10. McMurray JJV,
    11. Melnick JZ,
    12. Miller MG,
    13. Pergola PE,
    14. Perkovic V,
    15. Tobe S,
    16. Yi T,
    17. Wigderson M,
    18. de Zeeuw D
    ; SONAR Committees and Investigators: Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): A double-blind, randomised, placebo-controlled trial. Lancet 393: 1937–1947, 2019
    OpenUrlCrossRefPubMed
  44. ↵
    1. Hochane M,
    2. van den Berg PR,
    3. Fan X,
    4. Bérenger-Currias N,
    5. Adegeest E,
    6. Bialecka M,
    7. Nieveen M,
    8. Menschaart M,
    9. Chuva de Sousa Lopes SM,
    10. Semrau S
    : Single-cell transcriptomics reveals gene expression dynamics of human fetal kidney development. PLoS Biol 17: e3000152, 2019
    OpenUrlCrossRefPubMed
  45. ↵
    Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck 3rd WM, Hao Y, Stoeckius M, Smibert P, Satija R: Comprehensive integration of single-cell data. Cell 177: 1888–1902 e21, 2019
Back to top

In this issue

View Selected Citations (0)
Print
Download PDF
Email Article
Citation Tools
Request Permissions
Single Cell Sequencing and Kidney Organoids Generated from Pluripotent Stem Cells
Haojia Wu, Benjamin D. Humphreys
CJASN Apr 2020, 15 (4) 550-556; DOI: 10.2215/CJN.07470619
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo

Jump to section

Keywords