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Mapping the human kidney using single-cell genomics

Nature Reviews Nephrology volume 18, pages 347–360 (2022)Cite this article

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Abstract

The field of single-cell genomics and spatial technologies is rapidly evolving and has already provided unprecedented insights into complex tissues. Major advances have been made in dissecting the cellular composition and spatiotemporal interactions that mediate developmental processes in the fetal kidney. Single-cell technologies have also provided detailed insights into the heterogeneity of cell types within the healthy adult and shed light on the complex cellular mechanisms that contribute to kidney disease. The in-depth characterization of specific cell types associated with acute kidney injury and glomerular diseases has potential for the development of prognostic biomarkers and new therapeutics. Analyses of pathway activity in clear-cell renal cell carcinoma can predict the sensitivity of tumour cells to specific inhibitors. The identification of the cell of origin of renal cell carcinoma and of new cell types within the tumour microenvironment also has implications for the development of targeted therapeutics. Similarly, single-cell sequencing has provided new insights into the mechanisms underlying kidney fibrosis, specifically our understanding of myofibroblast origins and the contribution of cell crosstalk within the fibrotic niche to disease progression. These and future studies will enable the creation of a map to aid our understanding of the cellular processes and interactions in the developing, healthy and diseased kidney.

Key points

  • Single-cell RNA sequencing has enabled dissection of the cellular heterogeneity of complex tissues, as well as the characterization of rare cell populations.

  • Differential gene expression analyses and pseudotemporal predictions have yielded insights into the mechanisms of mesenchymal-to-epithelial transition, nephron progenitor self-renewal and podocyte development.

  • Inference of pathway activity for druggable pathways in clear-cell renal cell carcinoma can predict the sensitivity of tumour cells to pathway inhibitors, which could facilitate the identification of optimal combinational therapy.

  • Investigation of fibrosis-related gene expression in single-cell RNA sequencing data has enabled precise understanding of fibroblast and pericyte-to-myofibroblast differentiation trajectories.

  • Single-cell RNA sequencing has facilitated characterization of a dedifferentiated VCAM1+ population of proximal tubule cells and revealed its broad relevance in renal cell carcinoma, kidney injury and kidney fibrosis.

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Fig. 1: Insights into the mechanisms driving kidney development.
Fig. 2: Cellular heterogeneity among different sections of the nephron.
Fig. 3: Clear-cell renal cell carcinoma at a single-cell resolution.
Fig. 4: Pathogenesis of kidney fibrosis.

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References

  1. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  2. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Frommer, M. et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc. Natl Acad. Sci. USA 89, 1827–1831 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).

    Article  CAS  PubMed  Google Scholar 

  11. Takasato, M. & Little, M. H. The origin of the mammalian kidney: implications for recreating the kidney in vitro. Development 142, 1937–1947 (2015).

    Article  CAS  PubMed  Google Scholar 

  12. Rosenblum, N. D. Developmental biology of the human kidney. Semin. Fetal Neonatal Med. 13, 125–132 (2008).

    Article  PubMed  Google Scholar 

  13. Lindström, N. O. et al. Progressive recruitment of mesenchymal progenitors reveals a time-dependent process of cell fate acquisition in mouse and human nephrogenesis. Dev. Cell 45, 651–660.e4 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Matsui, I. et al. Single cell RNA sequencing uncovers cellular developmental sequences and novel potential intercellular communications in embryonic kidney. Sci. Rep. 11, 73 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hochane, M. et al. Single-cell transcriptomics reveals gene expression dynamics of human fetal kidney development. PLoS Biol. 17, e3000152 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Lawlor, K. T. et al. Nephron progenitor commitment is a stochastic process influenced by cell migration. Elife 8, e41156 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Brunskill, E. W. et al. Single cell dissection of early kidney development: multilineage priming. Development 141, 3093–3101 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Magella, B. et al. Cross-platform single cell analysis of kidney development shows stromal cells express Gdnf. Dev. Biol. 434, 36–47 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Lindström, N. O. et al. Spatial transcriptional mapping of the human nephrogenic program. Dev. Cell 56, 2381–2398.e6 (2021).

    Article  PubMed  CAS  Google Scholar 

  20. Wineberg, Y. et al. Single-cell RNA sequencing reveals mRNA splice isoform switching during kidney development. J. Am. Soc. Nephrol. 31, 2278–2291 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Van Itallie, C. M. et al. Two splice variants of claudin-10 in the kidney create paracellular pores with different ion selectivities. Am. J. Physiol. Renal Physiol. 291, F1288–F1299 (2006).

    Article  PubMed  CAS  Google Scholar 

  23. Tran, T. et al. In vivo developmental trajectories of human podocyte inform in vitro differentiation of pluripotent stem cell-derived podocytes. Dev. Cell 50, 102–116.e6 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Menon, R. et al. Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney. Development 145, dev164038 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Yamada, H. et al. MAGI-2 orchestrates the localization of backbone proteins in the slit diaphragm of podocytes. Kidney Int. 99, 382–395 (2021).

    Article  CAS  PubMed  Google Scholar 

  26. Tsujimoto, H. et al. A modular differentiation system maps multiple human kidney lineages from pluripotent stem cells. Cell Rep. 31, 107476 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Subramanian, A. et al. Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation. Nat. Commun. 10, 5462 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Low, J. H. et al. Generation of human PSC-derived kidney organoids with patterned nephron segments and a de novo vascular network. Cell Stem Cell 25, 373–387.e9 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Combes, A. N., Zappia, L., Er, P. X., Oshlack, A. & Little, M. H. Single-cell analysis reveals congruence between kidney organoids and human fetal kidney. Genome Med. 11, 3 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Phipson, B. et al. Evaluation of variability in human kidney organoids. Nat. Methods 16, 79–87 (2019).

    Article  CAS  PubMed  Google Scholar 

  31. Shankar, A. S. et al. Human kidney organoids produce functional renin. Kidney Int. 99, 134–147 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Wu, H. & Humphreys, B. D. Single cell sequencing and kidney organoids generated from pluripotent stem cells. Clin. J. Am. Soc. Nephrol. 15, 550–556 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lähnemann, D. et al. Eleven grand challenges in single-cell data science. Genome Biol. 21, 31 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Dumas, S. J. et al. Phenotypic diversity and metabolic specialization of renal endothelial cells. Nat. Rev. Nephrol. 17, 441–464 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Stewart, B. J., Ferdinand, J. R. & Clatworthy, M. R. Using single-cell technologies to map the human immune system — implications for nephrology. Nat. Rev. Nephrol. 16, 112–128 (2020).

    Article  PubMed  Google Scholar 

  36. Wu, H., Kirita, Y., Donnelly, E. L. & Humphreys, B. D. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J. Am. Soc. Nephrol. 30, 23–32 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. He, B. et al. Single-cell RNA sequencing reveals the mesangial identity and species diversity of glomerular cell transcriptomes. Nat. Commun. 12, 2141 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lu, Y., Ye, Y., Yang, Q. & Shi, S. Single-cell RNA-sequence analysis of mouse glomerular mesangial cells uncovers mesangial cell essential genes. Kidney Int. 92, 504–513 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Karaiskos, N. et al. A single-cell transcriptome atlas of the mouse glomerulus. J. Am. Soc. Nephrol. 29, 2060–2068 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chung, J.-J. et al. Single-cell transcriptome profiling of the kidney glomerulus identifies key cell types and reactions to injury. J. Am. Soc. Nephrol. 31, 2341–2354 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kuppe, C. et al. Decoding myofibroblast origins in human kidney fibrosis. Nature 589, 281–286 (2021).

    Article  CAS  PubMed  Google Scholar 

  42. Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020).

    Article  PubMed Central  Google Scholar 

  43. Muto, Y. et al. Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney. Nat. Commun. 12, 2190 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ransick, A. et al. Single-cell profiling reveals sex, lineage, and regional diversity in the mouse kidney. Dev. Cell 51, 399–413.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lake, B. B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat. Commun. 10, 2832 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Dhillon, P. et al. The nuclear receptor ESRRA protects from kidney disease by coupling metabolism and differentiation. Cell Metab. 33, 379–394.e8 (2021).

    Article  CAS  PubMed  Google Scholar 

  49. Kirita, Y., Wu, H., Uchimura, K., Wilson, P. C. & Humphreys, B. D. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc. Natl Acad. Sci. USA 117, 15874–15883 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chen, L., Chou, C.-L. & Knepper, M. A. Targeted single-cell RNA-seq identifies minority cell types of kidney distal nephron. J. Am. Soc. Nephrol. https://doi.org/10.1681/ASN.2020101407 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Kompatscher, A. et al. Loss of transcriptional activation of the potassium channel Kir5.1 by HNF1β drives autosomal dominant tubulointerstitial kidney disease. Kidney Int. 92, 1145–1156 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kompatscher, A. et al. Transcription factor HNF1β regulates expression of the calcium-sensing receptor in the thick ascending limb of the kidney. Am. J. Physiol. Renal Physiol. 315, F27–F35 (2018).

    Article  CAS  PubMed  Google Scholar 

  53. Cavodeassi, F., Modolell, J. & Gómez-Skarmeta, J. L. The Iroquois family of genes: from body building to neural patterning. Development 128, 2847–2855 (2001).

    Article  CAS  PubMed  Google Scholar 

  54. Reggiani, L., Raciti, D., Airik, R., Kispert, A. & Brändli, A. W. The prepattern transcription factor Irx3 directs nephron segment identity. Genes Dev. 21, 2358–2370 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Chen, L. et al. Renal-tubule epithelial cell nomenclature for single-cell RNA-sequencing studies. J. Am. Soc. Nephrol. 30, 1358–1364 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Madsen, K. M. & Tisher, C. C. Structural-functional relationship along the distal nephron. Am. J. Physiol. 250, F1–F15 (1986).

    CAS  PubMed  Google Scholar 

  57. Chen, L. et al. Transcriptomes of major renal collecting duct cell types in mouse identified by single-cell RNA-seq. Proc. Natl Acad. Sci. USA 114, E9989–E9998 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hinze, C. et al. Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients. J. Am. Soc. Nephrol. 32, 291–306 (2021).

    Article  CAS  PubMed  Google Scholar 

  59. Saxena, V. et al. Kidney intercalated cells are phagocytic and acidify internalized uropathogenic Escherichia coli. Nat. Commun. 12, 2405 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kim, J., Kim, Y. H., Cha, J. H., Tisher, C. C. & Madsen, K. M. Intercalated cell subtypes in connecting tubule and cortical collecting duct of rat and mouse. J. Am. Soc. Nephrol. 10, 1–12 (1999).

    Article  CAS  PubMed  Google Scholar 

  61. Werth, M. et al. Transcription factor patterns cells in the mouse kidney collecting ducts. Elife 6, e24265 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Trepiccione, F., Capasso, G., Nielsen, S. & Christensen, B. M. Evaluation of cellular plasticity in the collecting duct during recovery from lithium-induced nephrogenic diabetes insipidus. Am. J. Physiol. Renal Physiol. 305, F919–F929 (2013).

    Article  CAS  PubMed  Google Scholar 

  63. Jamous, M. et al. In young primary cultures of rabbit kidney cortical collecting ducts intercalated cells originate from principal or undifferentiated cells. Eur. J. Cell Biol. 66, 192–199 (1995).

    CAS  PubMed  Google Scholar 

  64. Wu, H. et al. Aqp2-expressing cells give rise to renal intercalated cells. J. Am. Soc. Nephrol. 24, 243–252 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Fejes-Tóth, G. & Náray-Fejes-Tóth, A. Differentiation of renal beta-intercalated cells to alpha-intercalated and principal cells in culture. Proc. Natl Acad. Sci. USA 89, 5487–5491 (1992).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Gao, X. et al. Deletion of hensin/DMBT1 blocks conversion of beta- to alpha-intercalated cells and induces distal renal tubular acidosis. Proc. Natl Acad. Sci. USA 107, 21872–21877 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Padala, S. A. et al. Epidemiology of renal cell carcinoma. World J. Oncol. 11, 79–87 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Zhang, Y. et al. Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response. Proc. Natl Acad. Sci. USA 118, e2103240118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Peired, A. J. et al. Acute kidney injury promotes development of papillary renal cell adenoma and carcinoma from renal progenitor cells. Sci. Transl. Med. 12, eaaw6003 (2020).

    Article  CAS  PubMed  Google Scholar 

  70. Lombardi, D., Becherucci, F. & Romagnani, P. How much can the tubule regenerate and who does it? An open question. Nephrol. Dial. Transpl. 31, 1243–1250 (2016).

    Article  CAS  Google Scholar 

  71. Zhang, S. et al. Immune infiltration in renal cell carcinoma. Cancer Sci. 110, 1564–1572 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Wu, T. D. et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 579, 274–278 (2020).

    Article  CAS  PubMed  Google Scholar 

  73. Borcherding, N. et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun. Biol. 4, 122 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Hu, J. et al. Single-cell transcriptome analysis reveals intratumoral heterogeneity in ccRCC, which results in different clinical outcomes. Mol. Ther. 28, 1658–1672 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Iwai, Y. et al. Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1 blockade. Proc. Natl Acad. Sci. USA 99, 12293–12297 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Pruenster, M. & Rot, A. Throwing light on DARC. Biochem. Soc. Trans. 34, 1005–1008 (2006).

    Article  CAS  PubMed  Google Scholar 

  77. Courtney, K. D. & Choueiri, T. K. Updates on novel therapies for metastatic renal cell carcinoma. Ther. Adv. Med. Oncol. 2, 209–219 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Kim, K.-T. et al. Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma. Genome Biol. 17, 80 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Li, P. et al. Histopathologic correlates of kidney function: insights from nephrectomy specimens. Am. J. Kidney Dis. 77, 336–345 (2021).

    Article  PubMed  Google Scholar 

  80. Falke, L. L., Gholizadeh, S., Goldschmeding, R., Kok, R. J. & Nguyen, T. Q. Diverse origins of the myofibroblast — implications for kidney fibrosis. Nat. Rev. Nephrol. 11, 233–244 (2015).

    Article  CAS  PubMed  Google Scholar 

  81. Kramann, R. et al. Perivascular Gli1+ progenitors are key contributors to injury-induced organ fibrosis. Cell Stem Cell 16, 51–66 (2015).

    Article  CAS  PubMed  Google Scholar 

  82. Kramann, R. et al. Parabiosis and single-cell RNA sequencing reveal a limited contribution of monocytes to myofibroblasts in kidney fibrosis. JCI Insight 3, e99561 (2018).

    Article  PubMed Central  Google Scholar 

  83. Chevalier, R. L. The proximal tubule is the primary target of injury and progression of kidney disease: role of the glomerulotubular junction. Am. J. Physiol. Renal Physiol. 311, F145–F161 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Conway, B. R. et al. Kidney single-cell atlas reveals myeloid heterogeneity in progression and regression of kidney disease. J. Am. Soc. Nephrol. 31, 2833–2854 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Rudman-Melnick, V. et al. Single-cell profiling of AKI in a murine model reveals novel transcriptional signatures, profibrotic phenotype, and epithelial-to-stromal crosstalk. J. Am. Soc. Nephrol. 31, 2793–2814 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Wilson, P. C. et al. The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc. Natl Acad. Sci. USA 116, 19619–19625 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Lake, B. B. et al. An atlas of healthy and injured cell states and niches in the human kidney. bioRxiv https://doi.org/10.1101/2021.07.28.454201 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Abedini, A. et al. Urinary single-cell profiling captures the cellular diversity of the kidney. J. Am. Soc. Nephrol. 32, 614–627 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Lusco, M. A., Najafian, B., Alpers, C. E. & Fogo, A. B. AJKD atlas of renal pathology: Pierson syndrome. Am. J. Kidney Dis. 71, e3–e4 (2018).

    Article  PubMed  Google Scholar 

  90. Zhang, L. et al. Genetic and preimplantation diagnosis of cystic kidney disease with ventriculomegaly. J. Hum. Genet. 65, 455–459 (2020).

    Article  CAS  PubMed  Google Scholar 

  91. Arazi, A. et al. Publisher correction: the immune cell landscape in kidneys of patients with lupus nephritis. Nat. Immunol. 20, 1404 (2019).

    Article  CAS  PubMed  Google Scholar 

  92. Zhang, T. et al. Association of urine sCD163 with proliferative lupus nephritis, fibrinoid necrosis, cellular crescents and intrarenal M2 macrophages. Front. Immunol. 11, 671 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Fava, A. et al. Integrated urine proteomics and renal single-cell genomics identify an IFN-γ response gradient in lupus nephritis. JCI Insight 5, e138345 (2020).

    Article  PubMed Central  Google Scholar 

  94. Der, E. et al. Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways. Nat. Immunol. 20, 915–927 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Der, E. et al. Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis. JCI Insight 2, e93009 (2017).

    Article  PubMed Central  Google Scholar 

  96. Zheng, Y. et al. Single-cell transcriptomics reveal immune mechanisms of the onset and progression of IgA nephropathy. Cell Rep. 33, 108525 (2020).

    Article  CAS  PubMed  Google Scholar 

  97. Tang, R. et al. A partial picture of the single-cell transcriptomics of human IgA nephropathy. Front. Immunol. 12, 645988 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Fu, J. et al. Single-cell RNA profiling of glomerular cells shows dynamic changes in experimental diabetic kidney disease. J. Am. Soc. Nephrol. 30, 533–545 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Sheng, X. et al. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Nat. Genet. https://doi.org/10.1038/s41588-021-00909-9 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Doke, T. et al. Transcriptome-wide association analysis identifies DACH1 as a kidney disease risk gene that contributes to fibrosis. J. Clin. Invest 131, e141801 (2021).

    Article  CAS  PubMed Central  Google Scholar 

  101. Miao, Z. et al. Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets. Nat. Commun. 12, 2277 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Marshall, J. L. et al. High resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways. bioRxiv https://doi.org/10.1101/2021.10.10.463829 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany

    Felix Schreibing & Rafael Kramann

  2. Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany

    Felix Schreibing & Rafael Kramann

  3. Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands

    Rafael Kramann

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Glossary

RNA-velocity

A computational method that predicts future states of cells by comparing the quantitative abundance of spliced and unspliced mRNA of genes.

Pseudotime analysis

A computational method that orders single cells along a developmental trajectory by identifying similarities and continuous changes in the transcriptome of these cells.

Alternative splicing

A process, in which different mRNA molecules are generated from the same gene by removing different parts of the gene during transcription. This process enables different proteins to be encoded by the same gene.

Isoform switching

The process by which a cell switches from producing one isoform of a transcript to another isoform.

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Schreibing, F., Kramann, R. Mapping the human kidney using single-cell genomics. Nat Rev Nephrol 18, 347–360 (2022). https://doi.org/10.1038/s41581-022-00553-4

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