Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections

Nature Protocols volume 13, pages 2501–2534 (2018)Cite this article

Subjects

Abstract

Spatial resolution of gene expression enables gene expression events to be pinpointed to a specific location in biological tissue. Spatially resolved gene expression in tissue sections is traditionally analyzed using immunohistochemistry (IHC) or in situ hybridization (ISH). These technologies are invaluable tools for pathologists and molecular biologists; however, their throughput is limited to the analysis of only a few genes at a time. Recent advances in RNA sequencing (RNA-seq) have made it possible to obtain unbiased high-throughput gene expression data in bulk. Spatial Transcriptomics combines the benefits of traditional spatially resolved technologies with the massive throughput of RNA-seq. Here, we present a protocol describing how to apply the Spatial Transcriptomics technology to mammalian tissue. This protocol combines histological staining and spatially resolved RNA-seq data from intact tissue sections. Once suitable tissue-specific conditions have been established, library construction and sequencing can be completed in ~5–6 d. Data processing takes a few hours, with the exact timing dependent on the sequencing depth. Our method requires no special instruments and can be performed in any laboratory with access to a cryostat, microscope and next-generation sequencing.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the protocol.
Fig. 2: Bright-field and fluorescence images demonstrating the tissue-optimization stage on mouse olfactory bulb.
Fig. 3: Typical results seen during the library construction steps.
Fig. 4: Flowchart of the experimental part of the Spatial Transcriptomics approach.
Fig. 5: Overview of the computational steps for Spatial Transcriptomics.
Fig. 6: Bright-field images of expected tissue morphology.
Fig. 7: Number and spatial distribution of detected genes and unique transcripts from typical experiments.

References

  1. Coons, A. H., Creech, H. J. & Jones, R. N. Immunological properties of an antibody containing a fluorescent group. Proc. Soc. Exp. Biol. Med. 47, 200–202 (1941).

    Article  CAS  Google Scholar 

  2. Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  PubMed  Google Scholar 

  3. Gall, J. G. & Pardue, M. L. Formation and detection of RNA-DNA hybrid molecules in cytological preparations. Proc. Natl. Acad. Sci. USA 63, 378–383 (1969).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jin, L. & Lloyd, R. V. In situ hybridization: methods and applications. J. Clin. Lab. Anal. 11, 2–9 (1997).

    Article  CAS  PubMed  Google Scholar 

  5. Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–1 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Shah, S., Lubeck, E., Zhou, W. & Cai, L. seqFISH accurately detects transcripts in single cells and reveals robust spatial organization in the hippocampus. Neuron 94, 752–758.e1 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl. Acad. Sci. USA 113, 11046–11051 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  13. Liu, S. & Trapnell, C. Single-cell transcriptome sequencing: recent advances and remaining challenges. F1000Res. 5, F1000 Faculty Rev-182 (2016).

  14. Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643.e4 (2017).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  17. Jemt, A. et al. An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries. Sci. Rep. 6, 37137 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  19. Giacomello, S. et al. Spatially resolved transcriptome profiling in model plant species. Nat. Plants 3, 17061 (2017).

    Article  CAS  PubMed  Google Scholar 

  20. Lein, E., Borm, L. E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64 LP–69 (2017).

    Article  Google Scholar 

  21. Marx, V. Neurobiology: gene expression captured on-site. Nat. Methods 14, 1037–1040 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Giacomello, S. & Lundeberg, J. Preparation of plant tissue to enable Spatial Transcriptomics profiling using barcoded microarrays. Nat. Protoc. https://doi.org/10.1038/s41596-018-0046-1 (2018).

  23. Reuter, J. A., Spacek, D. V., Pai, R. K. & Snyder, M. P. Simul-seq: combined DNA and RNA sequencing for whole-genome and transcriptome profiling. Nat. Methods 13, 953–958 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Nichterwitz, S. et al. Laser capture microscopy coupled with Smart-seq2 (LCM-seq) for robust and efficient transcriptomic profiling of mouse and human cells. Nat. Commun. 7, 12139 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Durruthy-Durruthy, J. et al. Spatiotemporal reconstruction of the human blastocyst by single-cell gene-expression analysis informs induction of naive pluripotency. Dev. Cell 38, 100–115 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Peng, G. et al. Spatial transcriptome for the molecular annotation of lineage fates and cell identity in mid-gastrula mouse embryo. Dev. Cell 36, 681–697 (2016).

    Article  CAS  PubMed  Google Scholar 

  27. Chen, J. et al. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566–580 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Lovatt, D. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat. Methods 11, 190–196 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Medaglia, C. et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 358, 1622–1626 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Junker, J. P. et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662–675 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. Wu, C.-C. et al. Spatially resolved genome-wide transcriptional profiling identifies BMP signaling as essential regulator of zebrafish cardiomyocyte regeneration. Dev. Cell 36, 36–49 (2017).

    Article  Google Scholar 

  32. Lacraz, G. P. A. et al. Tomo-seq identifies SOX9 as a key regulator of cardiac fibrosis during ischemic injury. Circulation 136, 1396–1409 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Femino, A. M., Fay, F. S., Fogarty, K. & Singer, R. H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).

    Article  CAS  PubMed  Google Scholar 

  34. Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lee, J. H. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc. 10, 442–458 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Grundberg, I. et al. In situ mutation detection and visualization of intratumor heterogeneity for cancer research and diagnostics. Oncotarget 4, 2407–2418 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kiflemariam, S. et al. In situ sequencing identifies TMPRSS2-ERG fusion transcripts, somatic point mutations and gene expression levels in prostate cancers. J. Pathol. 234, 253–261 (2014).

    CAS  PubMed  Google Scholar 

  40. Hansen, K. D., Brenner, S. E. & Dudoit, S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res 38, e131 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2017).

    Article  Google Scholar 

  44. Van Gelder, R. N. et al. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc. Natl. Acad. Sci. USA 87, 1663–1667 (1990).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    Article  CAS  PubMed  Google Scholar 

  46. Rong, M., Durbin, R. K. & McAllister, W. T. Template strand switching by T7 RNA polymerase. J. Biol. Chem. 273, 10253–10260 (1998).

    Article  CAS  PubMed  Google Scholar 

  47. Nacheva, G. A. & Berzal-Herranz, A. Preventing nondesired RNA-primed RNA extension catalyzed by T7 RNA polymerase. Eur. J. Biochem. 270, 1458–1465 (2003).

    Article  CAS  PubMed  Google Scholar 

  48. Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Navarro, J. F., Sjöstrand, J., Salmén, F., Lundeberg, J. & Ståhl, P. L. ST Pipeline: an automated pipeline for spatial mapping of unique transcripts. Bioinformatics 33, 2591–2593 (2017).

    Article  PubMed  Google Scholar 

  50. Wong, K., Navarro, J. F., Bergenstråhle, L., Ståhl, P. L. & Lundeberg, J. ST spot detector: a web-based application for automatic spot and tissue detection for spatial transcriptomics image datasets. Bioinformatics 34, 1966–1968 (2018).

    Article  PubMed  Google Scholar 

  51. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner . Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. R Development Core Team. R: A Language and Environment for Statistical Computing. (The R Foundation for Statistical Computing: Vienna, Austria, 2011. Available online at http://www.R-project.org/ ISBN: 3-900051-07-0.

  53. van Rossum, G. & Drake, F. L. Python tutorial. History 42, 1–122 (2010).

    Google Scholar 

Download references

Acknowledgements

We thank M. Asp for valuable contributions. This work was supported by the Knut and Alice Wallenberg Foundation, the Swedish Research Council, the Swedish Foundation for Strategic Research, the Swedish Cancer Society, the Karolinska Institutet, Ragnar Söderbergs Stiftelse, Torsten Söderbergs Stiftelse, Tobias Stiftelsen, the Åke Wiberg Foundation, StratRegen and the Jeansson Foundations. P.L.S. was supported by a grant from the Swedish Research Council. We also thank the Swedish National Genomics Infrastructure at SciLifeLab for providing sequencing assistance and infrastructure, and the Swedish National Infrastructure for Computing–Uppsala Multidisciplinary Center for Advanced Computational Science and Bioinformatics Long-Term Support for providing computational assistance.

Author information

Author notes

  1. These authors contributed equally: Fredrik Salmén, Patrik L. Ståhl.

Authors and Affiliations

  1. Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden

    Fredrik Salmén, Patrik L. Ståhl, Annelie Mollbrink, José Fernández Navarro, Sanja Vickovic & Joakim Lundeberg

  2. Hubrecht Institute–KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, The Netherlands

    Fredrik Salmén

  3. Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA

    Sanja Vickovic

  4. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden

    Jonas Frisén

Authors
  1. Fredrik Salmén
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrik L. Ståhl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Annelie Mollbrink
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Fernández Navarro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sanja Vickovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jonas Frisén
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joakim Lundeberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.S. and P.L.S. developed the method and wrote the manuscript; A.M. and S.V. contributed to the protocol development; J.F.N. developed computational methods for the method; F.S. and A.M. wrote the protocol; and J.L., P.L.S. and J.F. planned the study.

Corresponding authors

Correspondence to Patrik L. Ståhl or Joakim Lundeberg.

Ethics declarations

Competing interests

J.F., J.L., P.L.S. and F.S. are authors on patents owned by Spatial Transcriptomics AB covering the technology. The remaining authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Ståhl, P.L. et al. Science 353, 78–82 (2016): https://doi.org/10.1126/science.aaf2403

Jemt, A. et al. Sci. Rep. 7, 41109 (2017): https://doi.org/10.1038/srep37137

Asp, M. et al. Sci. Rep. 7, 12941 (2017): https://doi.org/10.1038/s41598-017-13462-5

Berglund, E. et al. Nat. Commun. 9, 2419 (2018): https://doi.org/10.1038/s41467-018-04724-5

An extension to this protocol adapted for use on plant material

Giacomello, S. & Lundeberg, J. Nat. Protoc. (2018): https://doi.org/10.1038/s41596-018-0046-1

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salmén, F., Ståhl, P.L., Mollbrink, A. et al. Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections. Nat Protoc 13, 2501–2534 (2018). https://doi.org/10.1038/s41596-018-0045-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-018-0045-2

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing