Abstract

Background: The kidney is a highly complex organ with multiple functions that are essential to health. Kidney disease occurs when the kidneys are damaged and do not function properly. Single-cell analysis is a powerful technique that provides unprecedented insight into normal and abnormal kidney cell types and will change our understanding of the mechanisms of common kidney diseases.

Summary: Our understanding of the pathogenesis of kidney disease is limited by incomplete molecular characterization of the cell types responsible for kidney function. The application of single-cell technology in kidney research has revealed cellular heterogeneity, gene expression profiles, and molecular dynamics in the development and progression of renal disease. Single-cell analysis of renal organoid and allograft tissues has provided new insights into renal organogenesis, disease mechanisms, and therapeutic outcomes. Overall, a better understanding of renal cell heterogeneity and the molecular dynamics of renal disease will improve diagnostic accuracy and help identify new therapeutic strategies in nephrology.

Key message: In this review article, we summarize recent single-cell research on kidney disease and discuss the impact of single-cell technology on basic and clinical kidney research.

Keywords

Single-cell technology; Kidney disease; Immune cell; Kidney organoid; Allograft; Cistanche benefits.

Introduction

The kidneys are two bean-shaped organs responsible for filtering waste, excess water, and other impurities in the blood and producing urine. The kidneys also regulate pH, salt, and potassium levels, and blood pressure; control the production of red blood cells; and activate a form of vitamin D that helps the body absorb calcium. to date, an estimated 850 million people worldwide suffer from kidney disease, including chronic kidney disease (CKD), acute kidney injury, kidney failure, and many other conditions. Kidney disease occurs when the kidneys are damaged and unable to perform their functions. Damage can be caused by diabetes, high blood pressure, and a variety of other chronic (long-term) conditions. Kidney disease can lead to other health problems, including osteoporosis, nerve damage, malnutrition, and heart disease. Current treatment strategies for patients remain kidney transplants or dialysis, which are costly.

A variety of cells in the kidney, including epithelial, thylakoid, endothelial, and neuronal cells, as well as immune cell networks, interact to maintain normal kidney function. A deeper understanding of the heterogeneity of healthy kidneys and the processes underlying renal disease will refine the molecular and histopathological phenotypic definition of the kidney and support the development of new disease classifications. Single-cell technology is potentially advantageous in identifying cellular subtypes, states, and frequency changes during the onset and progression of renal disease. In recent years, with the rapid development of high-throughput single-cell RNA sequencing (scRNA-seq) technology, a comprehensive cellular atlas of the normal kidney has been constructed for renal precision medicine research. The Kidney Precision Medicine Project (KPMP) was developed globally to obtain human kidney biopsies, create kidney tissue atlases, define disease subpopulations, and ultimately identify key cells, pathways, and targets for new therapies. Building on the renal-associated single-cell transcriptional dataset, researchers also analyzed the gene expression profiles of ACE2, TMPRSS2, and SLC6A19 in renal cell subtypes, which is critical for understanding the pathogenesis of severe acute respiratory syndrome coronavirus 2. In this review, we will focus on (1) the development and application of single-cell technology, (2) the use of scRNA-seq to study the development and progression of renal diseases, (3) the molecular mapping of immune cells in renal diseases, (4) the application of scRNA-seq to kidney-like organs, and (5) the use of scRNA-seq to study renal allografts in depth (Figure 1).

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Development and Application of Single-Cell Transcriptomic Technology

A single cell is the basic unit of life. Single-cell analysis techniques have revolutionized our ability to identify cell composition, track molecular dynamics, and reveal pathological mechanisms. Early global single-cell gene expression analysis was performed using microarray and qPCR techniques. Tietjen et al. used single-cell microarrays to monitor the molecular expression profiles of individual neurons and progenitor cells and to define signaling pathways at different developmental stages. In addition, single-cell expression analysis identified several developing cell subtypes that have novel pancreatic genes, providing new insights into pancreatic development. This class developed single-cell qPCR techniques and applied them to the study of key regulatory genes in mouse blastocyst development and hematopoietic lineage differentiation.

With the advent of next-generation sequencing technologies, scRNA-seq has shown clear advantages in distinguishing different isoforms, allelic expression, and novel transcripts in single cells at a lower cost. in 2009, Tang et al. reported the first single-cell mRNA whole transcriptome sequencing, demonstrating the complexity of transcriptional variants in single mouse oocytes or oocytes. smart-seq was 2012 was developed to detect full-length transcripts in single cells, and by applying it to rare cells, researchers identified candidate biomarkers for melanoma circulating tumor cells. A year later, Smart-seq2 with improved reverse transcription, read coverage, bias, and accuracy was introduced. recent developments in Smart-seq3 have greatly improved its sensitivity in detecting thousands of single-cell transcripts at allelic and isoform resolution. Unlike PCR-based amplification methods, CEL-seq captures efficient single-cell transcriptomes by multiplex linear amplification. A study using CEL-seq to study early Cryptobacterium hidradenoma embryonic development revealed its reproducible and sensitive results at single-cell resolution. scRNA-seq's first automated platform is Fluidigm C1, which uses a microfluidic system to capture single cells in 96 or 384 chambers, followed by cell lysis, reverse transcription, and PCR amplification.

Since 2015, single-cell research has fully entered the era of high-throughput, low-cost, and automation with the continued availability of Drop-seq, inDrop, 10× Genomics, Seq-well, Microwell-seq, and SPLiTseq. Drop-seq and inDrop separate individual cells into nanoliter-sized droplets and mix them with a unique barcode to label each cell. On the other hand, Cyto-seq, Seq-well, and Microwell-seq enable high-throughput single-cell mRNA sequencing by capturing a cell and a barcode bead in a microwell. Our group constructed the first mouse cell atlas and human cell landscape at the single cell level using Microwell-seq. More recently, higher throughput and simpler methods are called SPLiT-seq and sci-RNA-seq for scRNA-seq. These methods use the cell or nucleus itself as the reaction chamber to barcode labeled cells through several rounds of the division of the pool. All cells are then subjected to cDNA PCR amplification and sequencing. Cao et al. used sci-RNA-seq3 to analyze the transcriptomes of approximately 2 million mouse organogenic cells and 4 million human fetal cells, providing a global view of mammalian developmental processes. Other single-cell technologies covering genomic, proteomic, and epigenomic analyses have also flourished; however, they are beyond the scope of this paper.

Occurrence and Progression of Kidney Disease

The kidney can be affected by many common and serious diseases, including acute kidney injury, glomerulonephritis, ascending infections (pyelonephritis), and cancer. Overall, our understanding of the pathogenesis of kidney disease is limited by an incomplete molecular characterization of the cell types responsible for organ-specific functions. To close this knowledge gap, our group and Park et al. constructed a transcriptional atlas of the healthy mouse kidney using scRNA-seq. The major subtypes of renal unit epithelial cells included peduncle cells, proximal tubular epithelial cells, Henle's ring, distal tubules, and collecting duct cells. The study identified a new migratory cell type for collecting ductal cell populations, confirming previous findings of interconversion between intercalated cells and principal cells in scRNA-seq studies. In addition, Ransick et al. analyzed male and female adult kidneys and generated an anatomical atlas of mouse kidney elements at the single-cell level. Our group also performed Microwell-seq analysis of human fetal and adult kidney tissue. In addition to epithelial cells, endothelial cells, stromal cells, and immune cells within the tissue, we identified previously undescribed s-type somatic cell types in the fetal kidney and a new migratory cell type in the adult kidney. Single-cell molecular profiling of the renal vascular system revealed specialized expression profiles of the building nephrons and revealed the pathogenesis of renal disease. Furthermore, all types of glomerular cells were identified from healthy mice and different disease models by single-cell transcriptome clustering analysis, and novel disease-related genes and regulatory pathways were detected in disease models, such as the Hippo pathway activated in podocytes after nephrotoxic immune injury.

Renal fibrosis is a hallmark of CKD and affects more than 10% of the world's population. In a heterogeneous coexistence model of renal fibrosis, Kramann et al. used scRNA-seq to confirm that monocytes contributed a small proportion of myofibroblasts, but that most myofibroblasts were derived from mesenchymal cells. Subsequently, Kuppe et al. [37] analyzed the transcriptome of approximately 135,000 cells from proximal and nonproximal tubules and further validated that different isoforms of MSCs are the major contributors to human renal fibrosis. Furthermore, in a comparative analysis of healthy and fibrotic renal monocytes, a myofibroblast-specific gene, naked keratinocyte homolog 2 (NKD2), was identified as a potential therapeutic target for human renal fibrosis. Based on 402 kidney biopsies, urinary fibrinogen has been predicted as a non-invasive biomarker in patients with CKD.

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Diabetic nephropathy (DN) is characterized by simultaneous damage to the glomeruli and tubular interstitium. However, relatively little is known about how cellular status and frequency change with disease onset and progression of gene expression. To elucidate changes in glomerular cell gene expression in mouse DN, Fu et al. performed scRNA-seq analysis and identified several novel potential markers of glomerular cells, including Magi2, Robo2, Ramp3, and Fabp4. In diabetic nephropathy (DKDs), the regulation of angiogenic and migratory pathways is altered in endothelial cells, while translation and protein-stable regulatory pathways are highly enriched in thylakoid cells. Overall, changes in the molecular dynamics of endothelial and thylakoid cells will help identify important pathophysiological factors contributing to DN progression. Chung et al. depicted the single-cell transcriptome of glomeruli in a mouse model of diabetes mellitus. Gene expression was altered in thylakoid and podocytes of ob/ob mice compared to controls. Proliferative pathways were induced in thylakoid cells and cell death-related pathways were induced in podocytes, consistent with changes in cell number ratios. Comprehensive analysis of published DKD scRNA-seq datasets identified 17 pivotal genes, enriching our understanding of the molecular mechanisms underlying the pathogenesis of DKDs. In addition, unbiased single nucleus RNA sequencing (snRNA-seq) of cryopreserved human diabetic kidney samples yielded 23,980 transcriptomes from three control and three early DN samples. The results showed cell type-specific changes in gene expression suggesting increased potassium secretion in human DN. A previous study comparing scRNA-seq and snRNA-seq in adult mouse kidneys showed that the latter had a more efficient capture capacity. For example, glomerular podocytes, thylakoid cells, and endothelial cells were captured by snRNA-seq rather than by scRNA-seq. The application of snRNA-seq minimizes the pseudo-effects of enzymatic digestion and can be performed on frozen samples, which is expected to lead to a wider application to accelerate the study of pathological mechanisms in various renal diseases.

A precise cellular transcriptome can reveal the origin of renal tumor cells and the transcriptional trajectories supporting malignant transformation. young et al. defined normal and cancerous human renal cell types from a catalog of 72,501 single-cell transcriptomes. By identifying specific normal cellular correlates of renal cancer cells (RCCs), a study provided evidence for the hypothesis that Wilms' tumor cells are aberrant fetal cells and that RCCs may originate from a little-known subtype of proximal tubular cells. Thus, scRNA-seq provides a scalable experimental strategy for characterizing human renal cancer cells with precise quantitative cellular molecular resolution.

Molecular Atlas of Immune Cells in Kidney Disease

Immune cells are a fundamental component of human tissues and play an important role in physiological and pathological metabolism. Indeed, the ability of the immune system to recognize pathogenic or danger signals or malignant cells is crucial. The application of single-cell technology to the study of renal immune cells has the potential to facilitate a better understanding of the role of the immune system in the pathogenesis of healthy kidneys and disease, as well as to identify new therapeutic strategies. These efforts are beginning to map the complex immune landscape within the kidney and reveal the relationship between tissue-resident immune cells and their mutually activated neighbors.

In 2018, the application of scRNA-seq in mouse kidney tissue provided a comprehensive immune cell landscape including resident macrophages, neutrophils, B and T lymphocytes, and NK cells. One year later, an unprecedented single-cell study of the spatiotemporal organization of human kidney immune cells was established. Tissue-resident myeloid and lymphoid immune cell networks were identified in fetal and adult kidneys, and studies identified postnatally acquired transcriptional programs that promote infection defense. In addition, cross-talk between mature kidney epithelial cells and immune cells was predicted to recruit antibacterial macrophages and neutrophils to the most susceptible regions of the kidney. Overall, the comprehensive immune landscape of the kidney contributes to the study of pathogenic mechanisms and the identification of therapeutic targets in immune and infectious kidney diseases.

Lupus nephritis (LN) is a potentially fatal autoimmune disease for which current therapies are ineffective and often toxic. The molecular and cellular processes leading to renal damage and the heterogeneity of LN remain unclear. scRNA-seq was used by Arazi et al. to identify 21 disease-specific subpopulations of myeloid, T, natural killer, and B cells in LN patients. Similarly, Der et al. applied scRNA-seq to kidney and skin biopsy tissues from LN patients. Type I interferon response scores of tubular epithelial cells were reported to be higher in LN patients than in healthy controls. In addition, inflammatory and fibrotic features of tubular cells were associated with treatment ineffectiveness. In DKD, scRNA-seq analysis showed significantly higher numbers of immune cells in diabetic glomeruli than in controls. These studies suggest that the gene expression profiles of immune cells in urine, skin, and kidney are highly correlated, suggesting that urine and skin biopsies may be a potential source of diagnostic and prognostic markers for kidney disease.

Recent studies have demonstrated the role of immune cells in models of renal disease with single-cell resolution. Unilateral ureteral obstruction models are widely used for renal interstitial fibrosis, where macrophages and inflammatory cells infiltrate the renal interstitium, leading to marked changes in renal hemodynamics and metabolism. scRNA-seq has been used in reversible unilateral ureteral obstruction models to dissect the myeloid cell landscape during fibrosis progression and regression. Conway et al. revealed heterogeneity in myeloid cells, with dynamic changes in the relative proportions of subpopulations, monocytes are recruited early in the injury, Ccr2+ macrophages accumulate late in the injury, and a new cluster of Mmp12+ macrophages plays a role in the repair process. In renal disease and transplantation, ischemia-reperfusion injury (IRI) is associated with inflammation and leukocyte recruitment. experimental models of IRI were used to assess the functional role of 2 groups of innate lymphoid-like cells in the kidney, and the data suggest that these cells have redundant functions in renal injury. By applying scRNA-seq to a renal transplant IRI model, Kremann et al. found that subsequent CXCR5+ leukocyte infiltration resulted in elevated systemic CXCL13 levels. knRNA-seq was also applied to an IRI mouse model by Kirita et al. to characterize detailed cellular responses after injury and to identify a distinct pro-inflammatory and pro-fibrotic proximal tubule cell state. Uric acid, the final oxidation product of purine metabolism, is closely associated with renal inflammation in several disease models. For example, NLRP3 (NOD-, LRR-, and pyrin-containing structural domain 3) senses signals of uric acid excess, and inflammatory vesicles are activated in hyperuricemic nephropathy. However, the specific immune mechanisms underlying hyperuricemia-induced kidney injury are not known. The construction of an inflammatory body landscape at the single-cell level in a model of hyperuricemia-induced kidney injury is expected to be realized in the near future.

Immune infiltration exists within renal tumors and is influenced by the tumor microenvironment. A previous study showed that tumor-infiltrating macrophage populations express vascular endothelial growth factor A and are involved in the complex VEGF signaling pathway in RCC tissue. The present study demonstrates that scRNA-seq can address tumorigenesis and identify putative pathophysiological mechanisms and cellular signaling networks that may serve as targets for drug therapy. Overall, the study highlights advances in scRNAseq that provide insights into the immune system and the cellular networks operating in healthy and diseased kidneys.

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Application of scRNA-seq to Kidney Organoids

In addition to the study of diseased renal tissues, renal organoids have become a key tool for the study of organogenesis and disease mechanisms, and have the potential to accelerate the development of therapies as a source of alternative tissues. In parallel, advances in scRNA-seq have led to more detailed analysis of various cellular subpopulations and gene expression changes in kidney-like organs.

Overall, a better understanding of kidney embryonic development at the single-cell level is necessary to guide the maturation of kidney-like organs. Single-cell transcriptomics studies of the human fetal kidney have identified 22 cell types and corresponding marker genes.

The comparison of different developmental stages shows the continuity of molecular dynamics of foot cells. Another single-cell analysis was performed on human fetal kidney and embryonic stem cell-derived kidney-like organs. Comparative analysis of single-cell developmental trajectories in the kidney revealed similar gene expression profiles in vivo and in vitro, except for late-stage podocytes, suggesting an incomplete maturation process of podocyte-like organs. Transplantation experiments further suggest that the thylakoid and vascular lumen can be optimized using organoid models. A recent study using posterior renal mesenchymal and ureteral bud-like cells to generate renal organoids showed that scRNA-seq improved proximal tubule maturation and reduced off-target cell populations. scRNA-seq analysis of human pluripotent stem cell (PSC)-derived renal organoids was performed by Subramanian et al. and Wu et al. Compared to human fetal and adult kidney single-cell datasets, the different kidney-like organs showed largely reproducible cell types, but with different cell proportions due to the presence of off-target cells. In addition, transcription factor network analysis revealed renal organoid differentiation pathways, highlighting the power of single-cell technology in characterizing and guiding organoid differentiation.

In conclusion, human kidney-like organs are a useful resource for mining disease models, potential regulatory mechanisms, high-throughput drug screening, and ultimately regenerative therapies. These studies highlight the potential use of scRNA-seq in renal model systems to elucidate in vivo physiological and pathological processes and provide guidance for future research in the diagnosis and treatment of renal diseases.

Insight into Kidney Allografts by scRNA-seq

Allogeneic kidney transplantation is one of the most effective clinical treatments for end-stage renal disease. Single-cell technology offers the opportunity to accurately and precisely characterize renal cell types and status using human biopsy specimens after allogeneic transplantation. The first published report of renal transplantation biopsy specimens analyzed 8746 single-cell transcriptomes and defined distinct inflammatory responses. Monocytes formed the non-classical CD16+ group and the classical CD16- group; endothelial cells presented a resting state and 2 antibody-mediated rejection states. These findings contribute to our better understanding of immune rejection in kidney transplantation. In a comparative study of renal monocytes from recipient and donor sources, renal biopsy specimens showed significantly different transcriptional gene expression. Inflammation-activated macrophages and cytotoxically expressed T cells were observed in the recipients. Similarly, Liu et al. analyzed cells from chronic renal transplant rejection and matched healthy adult kidneys at the single-cell level. Unsupervised clustering analysis revealed that increased numbers of immune cells and myofibroblasts in the chronic renal transplant rejection group may contribute to renal rejection and fibrosis. Notably, a recent study depicted the first single-cell atlas of adult urine and identified SOX9+ renal stem/progenitor cell populations in urine. The progenitor cells successfully proliferated and differentiated in vivo, acquiring some of the properties of tubular cells and providing a potentially useful resource for future renal transplantation therapy. Overall, scRNA-seq technology provides novel and insightful insights into human renal transplant rejection, ultimately improving diagnostic accuracy and accelerating the adoption of molecular biopsy interpretation.

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Conclusion

In the last decade, scRNA-seq has become an indispensable tool for transcriptome-wide differential gene expression analysis to study physiological biology and identify molecular regulatory abnormalities in disease. In contrast to traditional discrete phenotypes, molecular characterization at the single-cell level can provide a systematic standard for characterizing renal disease phenotypes. The application of single-cell technology in healthy kidney tissue or clinical kidney biopsy samples provides a comprehensive molecular atlas of the kidney and broadens our understanding of nephrology. The Renal Single Cell Atlas will be an integral part of the international work of the Human Cell Atlas, which aims to produce a comprehensive and systematic reference map of the human body. In future studies, single-cell ultra-high-throughput and spatial transcriptome analyses are expected to expand our understanding of the kidney. The integration of multi-omics data will further improve the personalized diagnosis and treatment of kidney diseases.

REFERENCES

1. Kriz W, Bankir L. A standard nomenclature for structures of the kidney. The renal commission of the international union of physiological sciences (IUPS). Kidney Int. 1988 Jan; 33(1):1–7.

2. Kurts C, Panzer U, Anders HJ, Rees AJ. The immune system and kidney disease: basic concepts and clinical implications. Nat Rev Immunol. 2013 Oct;13(10):738–53.

3. Luyckx VA, Al-Aly Z, Bello AK, Bellorin Font E, Carlini RG, Fabian J, et al. Sustainable development goals are relevant to kidney health: an update on progress. Nat Rev Nephrol. 2021 Jan;17(1):15–32.

4. Clark AR, Greka A. The power of one: advances in single-cell genomics in the kidney. Nat Rev Nephrol. 2020 Feb;16(2):73–4.

5. Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, et al. Mapping the mouse cell atlas by microwell-seq. Cell. 2018 Feb;172(5):1091–107.e17.

6. Park J, Shrestha R, Qiu C, Kondo A, Huang S, Werth M, et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science. 2018 May; 360(6390):758–63.

7. Hochane M, van den Berg PR, Fan X, Bérenger-Currias N, Adegeest E, Bialecka M, et al. Single-cell transcriptomics reveals gene expression dynamics of human fetal kidney development. PLoS Biol. 2019 Feb;17(2): e3000152.

8. Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, et al. Construction of a human cell landscape at the single-cell level. Nature. 2020 May; 581(7808):303–9.

9. Liao J, Yu Z, Chen Y, Bao M, Zou C, Zhang H, et al. Single-cell RNA sequencing of the human kidney. Sci Data. 2020 Jan;7(1):4.

10. Precision medicine in nephrology. Nat Rev Nephrol. 2020 Nov;16(11):615.

11. Chen QL, Li JQ, Xiang ZD, Lang Y, Guo GJ, Liu ZH. Localization of cell receptor-related genes of SARS-CoV-2 in the kidney through single-cell transcriptome analysis. Kidney Dis. 2020 Jul;6(4):258–70.

12. Tietjen I, Rihel JM, Cao Y, Koentges G, Zakhary L, Dulac C. Single-cell transcriptional analysis of neuronal progenitors. Neuron. 2003 Apr;38(2):161–75.

13. Chiang MK, Melton DA. Single-cell transcript analysis of pancreas development. Dev Cell. 2003 Mar;4(3):383–93.

14. Guo G, Huss M, Tong GQ, Wang C, Li Sun L, Clarke ND, et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev Cell. 2010 Apr;18(4):675–85.

15. Guo G, Luc S, Marco E, Lin TW, Peng C, Kerenyi MA, et al. Mapping cellular hierarchy by single-cell analysis of the cell surface repertoire. Cell Stem Cell. 2013 Oct;13(4):492– 505.

16. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009 Jan;10(1):57–63.

17. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009 May;6(5):377–82.

18. Ramsköld D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012 Aug;30(8):777–82.

19. Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013 Nov;10(11): 1096–8.

20. Hagemann-Jensen M, Ziegenhain C, Chen P, Ramsköld D, Hendriks GJ, Larsson AJM, et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat Biotechnol. 2020 Jun;38(6):708–14.

21. Hashimshony T, Wagner F, Sher N, Yanai I. CEL-seq:single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2012 Sep;2(3): 666–73.

22. Yanai I, Hashimshony T. CEL-seq2-single-cell RNA sequencing by multiplexed linear amplification. Methods Mol Biol. 2019;1979: 45–56.

23. Streets AM, Zhang X, Cao C, Pang Y, Wu X, Xiong L, et al. Microfluidic single-cell whole transcriptome sequencing. Proc Natl Acad Sci U S A. 2014 May;111(19):7048–53.

24. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015 May;161(5): 1187–201.

25. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015 May;161(5):1202–14.

26. Fan HC, Fu GK, Fodor SP. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science. 2015 Feb; 347(6222):1258367.

27. Gierahn TM, Wadsworth MH, Hughes TK, Bryson BD, Butler A, Satija R, et al. Seq-well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods. 2017 Apr;14(4):395–8.

28. Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C, Daza R, et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science. 2017 Aug;357(6352): 661–7.

29. Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P, Yao Z, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018 Apr;360(6385):176–82.

30. Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496–502.

31. Cao J, O’Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, et al. A human cell atlas of fetal gene expression. Science. 2020; 370(6518):370.

32. Chen L, Lee JW, Chou CL, Nair AV, Battistone MA, Păunescu TG, et al. Transcriptomes of major renal collecting duct cell types in mice identified by single-cell RNA-seq. Proc Natl Acad Sci U S A. 2017 Nov;114(46): E9989–98.

33. Ransick A, Lindström NO, Liu J, Zhu Q, Guo JJ, Alvarado GF, et al. Single-cell profiling reveals sex, lineage, and regional diversity in the mouse kidney. Dev Cell. 2019 Nov;51(3):399– 413.e7.

34. Barry DM, McMillan EA, Kunar B, Lis R, Zhang T, Lu T, et al. Molecular determinants of nephron vascular specialization in the kidney. Nat Commun. 2019 Dec;10(1):5705.

35. Chung JJ, Goldstein L, Chen YJ, Lee J, Webster JD, Roose-Girma M, et al. Single-cell transcriptome profiling of the kidney glomerulus identifies key cell types and reactions to injury. J Am Soc Nephrol. 2020 Oct;31(10): 2341–54.

36. Kramann R, Machado F, Wu H, Kusaba T, Hoeft K, Schneider RK, et al. Parabiosis and single-cell RNA sequencing reveal a limited contribution of monocytes to myofibroblasts in kidney fibrosis. JCI Insight. 2018 May;3(9): e99561.

37. Kuppe C, Ibrahim MM, Kranz J, Zhang X, Ziegler S, Perales-Patón J, et al. Decoding myofibroblast origins in human kidney fibrosis. Nature. 2021 Jan;589(7841):281–6.

38. Wang H, Zheng C, Lu Y, Jiang Q, Yin R, Zhu P, et al. Urinary fibrinogen as a predictor of progression of CKD. Clin J Am Soc Nephrol. 2017 Dec;12(12):1922–9.

39. Fu J, Akat KM, Sun Z, Zhang W, Schlondorff D, Liu Z, et al. Single-cell RNA profiling of glomerular cells shows dynamic changes in experimental diabetic kidney disease. J Am Soc Nephrol. 2019 Apr;30(4):533–45.

40. Zhang Y, Li W, Zhou Y. Identification of hub genes in diabetic kidney disease via multiplemicroarray analysis. Ann Transl Med. 2020 Aug;8(16):997.

41. Wilson PC, Wu H, Kirita Y, Uchimura K, Ledru N, Rennke HG, et al. The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc Natl Acad Sci U S A. 2019 Sep;116(39):19619–25.

42. Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of single-nucleus over single-cell RNA sequencing of the adult kidney: rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol. 2019 Jan;30(1):23–32.

43. Young MD, Mitchell TJ, Vieira Braga FA, Tran MGB, Stewart BJ, Ferdinand JR, et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science. 2018 Aug;361(6402):594–9.

44. Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep;365(6460):1461–6.

45. Arazi A, Rao DA, Berthier CC, Davidson A, Liu Y, Hoover PJ, et al. The immune cell landscape in kidneys of patients with lupus nephritis. Nat Immunol. 2019 Jul;20(7):902–14.

46. Der E, Ranabothu S, Suryawanshi H, Akat KM, Clancy R, Morozov P, et al. Single-cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis. JCI Insight. 2017 May;2(9):e93009.

47. Der E, Suryawanshi H, Morozov P, Kustagi M, Goilav B, Ranabothu S, et al. Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis-relevant pathways. Nat Immunol. 2019 Jul;20(7):915–27.

48. Conway BR, O’Sullivan ED, Cairns C, O’Sullivan J, Simpson DJ, Salzano A, et al. Kidney single-cell atlas reveals myeloid heterogeneity in the progression and regression of kidney disease. J Am Soc Nephrol. 2020 Dec; 31(12):2833–54.

49. Cameron GJM, Cautivo KM, Loering S, Jiang SH, Deshpande AV, Foster PS, et al. Group 2 innate lymphoid cells are redundant in experimental renal ischemia-reperfusion injury. Front Immunol. 2019;10:826.

50. Kreimann K, Jang MS, Rong S, Greite R, von Vietinghoff S, Schmitt R, et al. Ischemia-reperfusion injury triggers CXCL13 release and Bcell recruitment after allogenic kidney transplantation. Front Immunol. 2020;11:1204.

51. Kirita Y, Wu H, Uchimura K, Wilson PC, Humphreys BD. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc Natl Acad Sci U S A. 2020 Jul;117(27):15874–83.

52. Wen L, Yang H, Ma L, Fu P. The roles of NLRP3 inflammasome-mediated signaling pathways in hyperuricemic nephropathy. Mol Cell Biochem. 2021 Mar;476(3):1377–86.

53. Nishinakamura R. Human kidney organoids: progress and remaining challenges. Nat Rev Nephrol. 2019 Oct;15(10):613–24.

54. Tran T, Lindström NO, Ransick A, De Sena Brandine G, Guo Q, Kim AD, et al. In vivo developmental trajectories of human podocytes inform in vitro differentiation of pluripotent stem cell-derived podocytes. Dev Cell. 2019 Jul;50(1):102–e6.

55. Uchimura K, Wu H, Yoshimura Y, Humphreys BD. Human pluripotent stem cell-derived kidney organoids with improved collecting duct maturation and injury modeling. Cell Rep. 2020 Dec;33(11):108514.

56. Subramanian A, Sidhom EH, Emani M, Vernon K, Sahakian N, Zhou Y, et al. Single-cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation. Nat Commun. 2019 Nov;10(1):5462.

57. Wu H, Uchimura K, Donnelly EL, Kirita Y, Morris SA, Humphreys BD. Comparative analysis and refinement of human PSC-de-rived kidney organoid differentiation with single-cell transcriptomics. Cell Stem Cell. 2018 Dec;23(6):869–e8.

58. Czerniecki SM, Cruz NM, Harder JL, Menon R, Annis J, Otto EA, et al. High-throughput screening enhances kidney organoid differentiation from human pluripotent stem cells and enables automated multidimensional phenotyping. Cell Stem Cell. 2018 Jun;22(6): 929–e4.

59. Dvela-Levitt M, Kost-Alimova M, Emani M, Kohnert E, Thompson R, Sidhom EH, et al. The small molecule targets TMED9 and promotes lysosomal degradation to reverse proteinopathy. Cell. 2019 Jul;178(3):521–e23.

60. Wu H, Malone AF, Donnelly EL, Kirita Y, Uchimura K, Ramakrishnan SM, et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol. 2018 Aug;29(8):2069–80.

61. Malone AF, Wu H, Fronick C, Fulton R, Gaut JP, Humphreys BD. Harnessing expressed single nucleotide variation and single-cell RNA sequencing to define immune cell chimerism in the rejecting kidney transplant. J Am Soc Nephrol. 2020 Sep;31(9):1977–86.

62. Liu Y, Hu J, Liu D, Zhou S, Liao J, Liao G, et al. Single-cell analysis reveals the immune landscape in the kidneys of patients with chronic transplant rejection. Theranostics. 2020; 10(19):8851–62.

63. Wang Y, Zhao Y, Zhao Z, Li D, Nie H, Sun Y, et al. Single-cell RNA-Seq analysis identified kidney progenitor cells from human urine. Protein Cell. 2021 Apr;12(4):305–12.

64. Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage years. Nat Rev Genet. 2019 Nov;20(11):631–56. 65 Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, et al. The human cell atlas. Elife. 2017;12:6.

Mengmeng Jiang ^a,b; Haide Chen^{b, c} ; Guoji Guo^{a, b, c, d, e}

a: Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China;

b: Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China;

c: Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin, Center for Stem Cell and Regenerative Medicine, Hangzhou, China;

d: Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China;

e: Institute of Hematology, Zhejiang University, Hangzhou, China