Skip to main content

Spatial Transcriptomics in Kidney Tissue

  • Protocol
  • First Online:
Kidney Research

Abstract

Unlike bulk and single-cell/single-nuclei RNA sequencing methods, spatial transcriptome sequencing (ST-seq) resolves transcriptome expression within the spatial context of intact tissue. This is achieved by integrating histology with RNA sequencing. These methodologies are completed sequentially on the same tissue section placed on a glass slide with printed oligo-dT spots, termed ST-spots. Transcriptomes within the tissue section are captured by the underlying ST-spots and receive a spatial barcode in the process. The sequenced ST-spot transcriptomes are subsequently aligned with the hematoxylin and eosin (H&E) image, giving morphological context to the gene expression signatures within intact tissue. We have successfully employed ST-seq to characterize mouse and human kidney tissue. Here, we describe in detail the application of Visium Spatial Tissue Optimization (TO) and Visium Spatial Gene Expression (GEx) protocols for ST-seq in fresh frozen kidney tissue.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ståhl PL, Salmén F, Vickovic S et al (2016) Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353:78–82

    Article  PubMed  Google Scholar 

  2. Vickovic S, Ståhl PL, Salmén F et al (2016) Massive and parallel expression profiling using microarrayed single-cell sequencing. Nat Commun 7:13182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rusk N (2016) Spatial transcriptomics. Nat Methods 13:710–710

    Article  CAS  Google Scholar 

  4. Salmén F, Ståhl PL, Mollbrink A et al (2018) Barcoded solid-phase RNA capture for spatial transcriptomics profiling in mammalian tissue sections. Nat Protoc 13:2501–2534

    Article  PubMed  Google Scholar 

  5. Wong K, Navarro JF, Bergenstråhle L et al (2018) ST spot detector: a web-based application for automatic spot and tissue detection for spatial transcriptomics image datasets. Bioinformatics 34:1966–1968

    Article  CAS  PubMed  Google Scholar 

  6. Asp M, Salmén F, Ståhl PL et al (2017) Spatial detection of fetal marker genes expressed at low level in adult human heart tissue. Sci Rep 7:12941

    Article  PubMed  PubMed Central  Google Scholar 

  7. Thrane K, Eriksson H, Maaskola J et al (2018) Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma. Cancer Res 78:5970–5979

    Article  CAS  PubMed  Google Scholar 

  8. Lundmark A, Gerasimcik N, Båge T et al (2018) Gene expression profiling of periodontitis-affected gingival tissue by spatial transcriptomics. Sci Rep 8:9370

    Article  PubMed  PubMed Central  Google Scholar 

  9. Berglund E, Maaskola J, Schultz N et al (2018) Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat Commun 9:2419

    Article  PubMed  PubMed Central  Google Scholar 

  10. Maniatis S, Äijö T, Vickovic S et al (2019) Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 364:89–93

    Article  CAS  PubMed  Google Scholar 

  11. Carlberg K, Korotkova M, Larsson L et al (2019) Exploring inflammatory signatures in arthritic joint biopsies with spatial transcriptomics. Sci Rep 9:18975

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Asp M, Giacomello S, Larsson L et al (2019) A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179:1647–1660.e19

    Article  CAS  PubMed  Google Scholar 

  13. Ortiz C, Navarro JF, Jurek A et al (2020) Molecular atlas of the adult mouse brain. Sci Adv 6:eabb3446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Moncada R, Barkley D, Wagner F et al (2020) Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat Biotechnol 38:333–342

    Article  CAS  PubMed  Google Scholar 

  15. Chen W-T, Lu A, Craessaerts K et al (2020) Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell 182:976–991.e19

    Article  CAS  PubMed  Google Scholar 

  16. Ji AL, Rubin AJ, Thrane K et al (2020) Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182:1661–1662

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Melo Ferreira R, Sabo AR, Winfree S et al (2021) Integration of spatial and single-cell transcriptomics localizes epithelial cell-immune cross-talk in kidney injury. JCI Insight 6. https://doi.org/10.1172/jci.insight.147703

  18. Lake BB, Menon R, Winfree S et al (2021) An atlas of healthy and injured cell states and niches in the human kidney. bioRxiv. https://doi.org/10.1101/2021.07.28.454201

  19. Raghubar AM, Pham DT, Tan X et al (2020) Spatially resolved transcriptome profiles of mammalian kidneys illustrate the molecular complexity of functional nephron segments, cell-to-cell interactions and genetic variants. bioRxiv. https://doi.org/10.1101/2020.09.29.317917

  20. (2003) RNeasy® Micro Handbook, Document Number 1023761. Qiagen

    Google Scholar 

  21. (2015) Qubit® RNA HS Assay Kits, Manual Number MAN0002327 | MP32852. Life Technologies

    Google Scholar 

  22. (2016) Agilent RNA 6000 Pico Kit Guide, Document Number G2938-90046. Agilent Technologies

    Google Scholar 

  23. (2020) Visium Spatial Slide Reset Demonstrated Protocol, Document Number CG000332. 10x Genomics

    Google Scholar 

  24. (2018) NextSeq System Denature and Dilute Libraries Guide, Document Number 15048776. Illumina

    Google Scholar 

  25. (2017) Technical Note SPRIselect: DNA Ratios Affect the Size Range of Library Fragments – v2 Reagents, Document Number CG000061. 10x Genomics

    Google Scholar 

  26. (2020) NEBNext® Multiplex Small RNA Library Prep Set for Illumina® Set 1, Set 2, Index Primers 1–48 and Multiplex Compatible NEB #E7300S/L, E7580S/L, E7560S, E7330S/L. New England BioLabs

    Google Scholar 

  27. (2012) XCell SureLock Mini-Cell User Guide, Publication Part number IM-9003. Life Technologies

    Google Scholar 

Download references

Acknowledgments

This work was supported by Pathology Queensland-Study, Education and Research Committee, Royal Brisbane and Women’s Hospital Foundation Project Grant 2019, Robert and Janelle Bird Postdoctoral Research Fellowship 2020, and the University of Queensland (UQ)-Genome Innovation Hub. AR is supported by an Australian Government Research Training Program (RTP) Scholarship. AJM was supported by a Metro North Hospital and Health Service Clinical Research Fellowship and is supported by a Queensland Health Advancing Clinical Research Fellowship. The authors would like to thank the tissue donors; Queensland Health clinicians, pathologists, and scientists; Conjoint Internal Medicine Laboratory; the University of Queensland School of Biomedical Sciences Imaging Facility; and Institute for Molecular Bioscience Sequencing Facility. In particular, we thank Quan Nguyen, Leo Francis, Anne Stewart, Jane Ilsley, and Quinn Jones for their support and assistance with this work. Visium, 10×, and 10× Genomics are trademarks of 10× Genomics, Inc. All Rights Reserved. NextSeq and Illumina are trademarks of Illumina, Inc. All Rights Reserved. All trademarks used herein are the property of their respective owners and are used under license from the respective owners.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrew J. Mallett .

Editor information

Editors and Affiliations

1 Electronic Supplementary Material

Freezing of kidney tissue. An overview of kidney tissue dissection, freezing, and storage, including required equipment, is provided in this video. This technical video links with Subheading 3.3 Visium TO and Subheading 3.6 Visium GEx (MP4 323050 kb)

Cryosectioning of kidney tissue. An overview of the cryosectioning process, including required equipment, cryostat setup, sectioning, placement of tissue sections within the capture array, and storage of slides, is provided in this video. This technical video links with Subheading 3.3 VisiumTO and Subheading 3.6 Visium GEx (MP4 225485 kb)

Placing a slide in the slide cassette. Slide cassette assembly, slide placement, and slide removal are demonstrated in this video. This technical video links with Subheading 3.3 Visium TO and Subheading 3.6 Visium GEx (MP4 181803 kb)

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Raghubar, A.M. et al. (2023). Spatial Transcriptomics in Kidney Tissue. In: Hewitson, T.D., Toussaint, N.D., Smith, E.R. (eds) Kidney Research. Methods in Molecular Biology, vol 2664. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3179-9_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3179-9_17

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3178-2

  • Online ISBN: 978-1-0716-3179-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics