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Preparation of single-cell suspensions of mouse glomeruli for high-throughput analysis

Nature Protocols volume 16, pages 4068–4083 (2021)Cite this article

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Abstract

The kidney glomerulus is essential for proper kidney function. Until recently, technical challenges associated with glomerular isolation and subsequent dissolution into single cells have limited the detailed characterization of cells in the glomerulus. Previous techniques of kidney dissociation result in low glomerular cell yield, which limits high-throughput analysis. The ability to efficiently purify glomeruli and digest the tissue into single cells is especially important for single-cell characterization methods. Here, we present a detailed and comprehensive technique for the extraction and preparation of mouse glomerular cells, with high yield and viability. The method includes direct renal perfusion of Dynabeads via the renal artery followed by kidney dissociation and isolation of glomeruli by magnet; these steps provide a high number and purity of isolated glomeruli, which are further dissociated into single cells. The balanced representation of podocytes, mesangial and endothelial cells in single-cell suspensions of mouse glomeruli, and the high cell viability observed, confirm the efficiency of our method. With some practice, the procedure can be done in <3 h (excluding equipment setup and data analysis). This protocol provides a valuable technique for advancing future single-cell-based studies of the glomerulus in health, injury and disease.

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Fig. 1: Schematic description of protocol design and steps.
Fig. 2: Glomerulus structure and cell composition.

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Data availability

The authors confirm that the data supporting the findings of this study are available within the article, its Supplementary Information and the primary supporting research paper12.

References

  1. Xie, L. et al. Micro-CT imaging and structural analysis of glomeruli in a model of Adriamycin-induced nephropathy. Am. J. Physiol. Ren. Physiol. 316, F76–F89 (2019).

    Article  CAS  Google Scholar 

  2. Baldelomar, E. J. et al. Phenotyping by magnetic resonance imaging nondestructively measures glomerular number and volume distribution in mice with and without nephron reduction. Kidney Int 89, 498–505 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Baldelomar, E. J., Charlton, J. R., deRonde, K. A. & Bennett, K. M. In vivo measurements of kidney glomerular number and size in healthy and Os/+ mice using MRI. Am. J. Physiol. Ren. Physiol. 317, F865–F873 (2019).

    Article  CAS  Google Scholar 

  4. Holdsworth, S. R., Thomson, N. M., Glasgow, E. F., Dowling, J. P. & Atkins, R. C. Tissue culture of isolated glomeruli in experimental crescentic glomerulonephritis. J. Exp. Med. 147, 98–109 (1978).

    Article  CAS  PubMed  Google Scholar 

  5. Spiro, R. G. Studies on the renal glomerular basement membrane preparation and chemical composition. J. Biol. Chem. 242, 1915–1922 (1967).

    Article  CAS  PubMed  Google Scholar 

  6. Kreisberg, J. I., Hoover, R. L. & Karnovsky, M. J. Isolation and characterization of rat glomerular epithelial cells in vitro. Kidney Int. 14, 21–30 (1978).

    Article  CAS  PubMed  Google Scholar 

  7. Samuel, T., Hoy, W. E., Douglas-Denton, R., Hughson, M. D. & Bertram, J. F. Applicability of the glomerular size distribution coefficient in assessing human glomerular volume: the Weibel and Gomez method revisited. J. Anat. 210, 578–582 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Takemoto, M. et al. A new method for large scale isolation of kidney glomeruli from mice. Am. J. Pathol. 161, 799–805 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Cook, W. F. & Pickering, G. W. A rapid method for separating glomeruli from rabbit kidney. Nature 182, 1103–1104 (1958).

    Article  CAS  PubMed  Google Scholar 

  10. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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 

  13. Boerries, M. et al. Molecular fingerprinting of the podocyte reveals novel gene and protein regulatory networks. Kidney Int. 83, 1052–1064 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. 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 

  15. LIU, X. et al. Isolating glomeruli from mice: a practical approach for beginners. Exp. Ther. Med. 5, 1322–1326 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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 

  18. Chen, G., Ning, B. & Shi, T. Single-cell RNA-Seq technologies and related computational data analysis. Front. Genet. 10, (2019).

  19. McGinnis, C. S. et al. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat. Methods 16, 619–626 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Stoeckius, M. et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gaublomme, J. T. et al. Nuclei multiplexing with barcoded antibodies for single-nucleus genomics. Nat. Commin 10, 2907 (2019).

    Article  CAS  Google Scholar 

  22. Srivatsan, S. R. et al. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367, 45–51 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Allison, S. J. A single-cell, 2D atlas of the normal human kidney using imaging mass cytometry. Nat. Rev. Nephrol. 15, 528–528 (2019).

    PubMed  Google Scholar 

  24. Singh, N. et al. Development of a 2-dimensional atlas of the human kidney with imaging mass cytometry. JCI Insight 4, (2019).

  25. Ptacek, J. et al. Multiplexed ion beam imaging (MIBI) for characterization of the tumor microenvironment across tumor types. Lab. Investig. 100, 1111–1123 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  PubMed  CAS  Google Scholar 

  28. Torban, E. et al. From podocyte biology to novel cures for glomerular disease. Kidney Int 96, 850–861 (2019).

    Article  PubMed  Google Scholar 

  29. Agrawal, S., He, J. C. & Tharaux, P.-L. Nuclear receptors in podocyte biology and glomerular disease. Nat. Rev. Nephrol. https://doi.org/10.1038/s41581-020-00339-6 (2020)

  30. Hastie, N. D. Wilms’ tumour 1 (WT1) in development, homeostasis and disease. Development 144, 2862–2872 (2017).

    Article  CAS  PubMed  Google Scholar 

  31. Morito, N. et al. Overexpression of Mafb in podocytes protects against diabetic nephropathy. J. Am. Soc. Nephrol. 25, 2546–2557 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ilicic, T. et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17, 29 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Bacher, R. & Kendziorski, C. Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol. 17, 63 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Hwang, B., Lee, J. H. & Bang, D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 50, 1–14 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wu, Y. & Zhang, K. Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat. Rev. Nephrol. 16, 408–421 (2020).

    Article  PubMed  Google Scholar 

  36. Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Efremova, M. & Teichmann, S. A. Computational methods for single-cell omics across modalities. Nat. Methods 17, 14–17 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. Chen, H., Ye, F. & Guo, G. Revolutionizing immunology with single-cell RNA sequencing. Cell. Mol. Immunol. 16, 242–249 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Potter, S. S. Single-cell RNA sequencing for the study of development, physiology and disease. Nat. Rev. Nephrol. 14, 479–492 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Rao, D. A., Arazi, A., Wofsy, D. & Diamond, B. Design and application of single-cell RNA sequencing to study kidney immune cells in lupus nephritis. Nat. Rev. Nephrol. 16, 238–250 (2020).

    Article  CAS  PubMed  Google Scholar 

  42. 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 

  43. 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 

  44. Baran-Gale, J., Chandra, T. & Kirschner, K. Experimental design for single-cell RNA sequencing. Brief. Funct. Genomics 17, 233–239 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. AlJanahi, A. A., Danielsen, M. & Dunbar, C. E. An introduction to the analysis of single-cell RNA-sequencing data. Mol. Ther. Methods Clin. Dev. 10, 189–196 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Nguyen, Q. H., Pervolarakis, N., Nee, K. & Kessenbrock, K. Experimental considerations for single-cell RNA sequencing approaches. Front. Cell Dev. Biol. 6, (2018).

  47. LeBleu, V. S. et al. Origin and function of myofibroblasts in kidney fibrosis. Nat. Med. 19, 1047–1053 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 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 

  49. 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 

  50. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    Article  PubMed  CAS  Google Scholar 

  51. Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Naylor, R. W., Morais, M. R. P. T. & Lennon, R. Complexities of the glomerular basement membrane. Nat. Rev. Nephrol. https://doi.org/10.1038/s41581-020-0329-y (2020)

  56. Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38, 737–746 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 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. PNAS 117, 15874–15883 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Krebs, C. F., Schmidt, T., Riedel, J.-H. & Panzer, U. T helper type 17 cells in immune-mediated glomerular disease. Nat. Rev. Nephrol. 13, 647–659 (2017).

    Article  CAS  PubMed  Google Scholar 

  59. Kitching, A. R. & Hutton, H. L. The players: cells involved in glomerular disease. Clin. J. Am. Soc. Nephrol. 11, 1664–1674 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ruiz-Ortega, M., Rayego-Mateos, S., Lamas, S., Ortiz, A. & Rodrigues-Diez, R. R. Targeting the progression of chronic kidney disease. Nat. Rev. Nephrol. 16, 269–288 (2020).

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank C. Ising, S. Braehler, L. Xie and our Genentech colleagues in the Research Biology, Laboratory Animal Resources, Microscopy and Pathology Departments for their support of this study. This work was supported by Genentech.

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Author notes

  1. These authors contributed equally: Ben Korin, Jun-Jae Chung.

Authors and Affiliations

  1. Department of Research Biology, Genentech, South San Francisco, CA, USA

    Ben Korin, Jun-Jae Chung, Shimrit Avraham & Andrey S. Shaw

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  1. Ben Korin
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Contributions

B.K. wrote the manuscript and contributed to the development of the protocol; J-J.C. designed and developed the protocol and revised the manuscript; S.A. contributed to the development of the protocol and writing of the manuscript; and A.S. supervised the experimental design and revised the manuscript.

Corresponding author

Correspondence to Andrey S. Shaw.

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Competing interests

B.K., S.A. and A.S. are employees of Genentech Research and Early Development. J-J.C. reports employment at Pin Pharmaceuticals.

Additional information

Peer review information Nature Protocols thanks Benjamin Humphreys, Katalin Susztak and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Key reference using this protocol

Chung, J.-J. et al. J. Am. Soc. Nephrol. 31, 2341–2354 (2020): https://doi.org/10.1681/ASN.2020020220

Extended data

Extended Data Fig. 1 Preparation of a semi-dulled needle for renal perfusion.

Representative image of a semi-dulled 27½ gauge needle used for direct perfusion via the renal arteries. Images were taken using 1080P Full HD Digital Microscope.

Extended Data Fig. 2 Kidney extraction with renal arteries.

a, A C57BL/6 mouse following the removal of most of the intestine. White arrows indicate right and left kidneys, and the top and bottom of the aorta/inferior vena cava. b, Extraction of the kidney-blood-vessel tissue complex by gently pulling the top part of the aorta/inferior vena cava (left white arrow) while separating attached tissues (lower right white arrow). Dashed white arrow indicates direction of pulling. Appropriate institutional regulatory board permission was obtained for all animal experiments.

Extended Data Fig. 3 Mouse kidney extraction, perfusion and preliminary dissociation.

a, En bloc kidney extraction and transfer to a Petri dish for perfusion. Kidney before (right) and kidney after (left) direct perfusion via the renal artery. b, Kidneys after direct perfusion via the renal arteries. c, Representative image of properly minced kidneys. Images were taken using a 1080P Full HD Digital Microscope. Appropriate institutional regulatory board permission was obtained for all animal experiments performed.

Extended Data Fig. 4 Validation of glomeruli purity following magnetic separation and washes.

a, Representative image of glomeruli and magnetic Dynabeads sample following first enzymatic digestion; before washes, tubular contamination is visible. b–d, The sample was washed with 10 ml HBSS−/−, and 10 µl aliquots were taken after the third (b), fourth (c), and fifth (d) washes to check for glomeruli purity under the microscope. e, Representative image of the resuspended glomeruli sample. Images were taken using the Leica Thunder Imaging System. Appropriate institutional regulatory board permission was obtained for all animal experiments.

Extended Data Fig. 5 Confirmation of single-cell suspension purity following magnetic separation and washes.

a, Representative image of a single-cell sample following second enzymatic digestion, before removal of Dynabeads. b, Representative image following removal of magnetic Dynabeads using a magnetic separator. Images were taken using the Leica Thunder Imaging System. Appropriate institutional regulatory board permission was obtained for all animal experiments.

Supplementary information

Supplementary Information

Supplementary Table 1.

Supplementary Video 1

Dissection of aorta and direct renal perfusion. Kidneys are placed in a Petri dish with HBSS−/− and further handled using a dissecting microscope. Dumont forceps are used to remove muscle and fatty tissue over the aorta and inferior vena cava. Then, Vannas spring scissors are used to dissect the abdominal aorta to expose the openings to the renal arteries. Next, the kidney is perfused by inserting the tip of a semi-dulled needle into the opening of the renal artery and slowly injecting the solution. The video was recorded using a Dino-Lite Edge Digital Microscope. Appropriate institutional regulatory board permission was obtained for all animal experiments.

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Korin, B., Chung, JJ., Avraham, S. et al. Preparation of single-cell suspensions of mouse glomeruli for high-throughput analysis. Nat Protoc 16, 4068–4083 (2021). https://doi.org/10.1038/s41596-021-00578-2

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