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Spatially resolved transcriptome profiling in model plant species

Nature Plants volume 3, Article number: 17061 (2017) Cite this article

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Abstract

Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study high-resolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes high-throughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.

The study of specialized tissue domains and their interactions is essential for a comprehensive understanding of the function of biological systems, including the wide variety of plant species. Functional aspects such as growth, development, biochemistry and physiology ultimately derive from underlying gene expression programmes that are actively regulated in different organs and tissues. Specific molecular conditions are present at every morphological level (whole organism, organ, tissue, cell), rendering spatially resolved, high-resolution and high-throughput analyses of tissues crucial. This need has motivated the development of several methods capable of generating such spatial information1–3. However, these methods have so far only been established for mammalian systems, and have been lacking for plants4. Nonetheless, RNA-sequencing5,6 (RNA-seq) or microarray profiling7 studies at the level of individual tissues or cell types have been achieved in plants. Techniques to isolate the input material for such type of studies include fluorescence-activated cell sorting (FACS) of fluorescently labelled cells or nuclei8–10, isolation of nuclei tagged in specific cell types (INTACT)11 and laser capture microdissection (LCM)12–16 of tissue sections. Although these techniques can provide spatially resolved or cell-type-specific gene expression data, they each have inherent limitations. The FACS and INTACT methods require plant transgenic lines respectively expressing fluorescent proteins or the biotinylated nuclear envelope protein in the cell type of interest. Moreover, FACS in plants requires the formation of protoplasts, which in certain species can be difficult to produce. A major drawback of LCM is the limited sample throughput because of laborious experimental procedures. Furthermore, LCM and FACS can be limited by the yield and purity of the targeted cell types.

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Figure 1: Spatially resolved transcriptome profiling in plants.
Figure 2: Method reproducibility in angiosperm and gymnosperm species.
Figure 3: Validation of the method on three A. thaliana tissue sections.
Figure 4: Types of gene expression studies allowed by spatial transcriptomics data in A. thaliana.

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Acknowledgements

We thank the Swedish National Genomics Infrastructure hosted at SciLifeLab, the National Bioinformatics Infrastructure Sweden (NBIS) for providing computational assistance, and the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) for providing computational infrastructure. This work was supported by Knut and Alice Wallenberg Foundation, and Swedish Research Council. N.R.S. and B.K.T. are supported by the Trees and Crop for the Future (TC4F) project. This work was supported by a grant to N.R.S. from the Carl Tryggers Stiftelse för Vetenskaplig Forskning.

Author information

Author notes

  1. Fredrik Salmén and Barbara K. Terebieniec: These authors contributed equally to this work.

Authors and Affiliations

  1. Division of Gene Technology, School of Biotechnology, KTH Royal Institute of Technology, Science for Life Laboratory, 17165 Solna, Sweden

    Stefania Giacomello, Fredrik Salmén, Sanja Vickovic & Joakim Lundeberg

  2. Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, 17165 Solna, Sweden

    Stefania Giacomello

  3. Department of Plant Physiology, Umeå Plant Science Centre, Umeå University, 90736 Umeå, Sweden

    Barbara K. Terebieniec, Chanaka Mannapperuma & Nathaniel R. Street

  4. Department of Cell and Molecular Biology, Karolinska Institute, 17165 Solna, Sweden

    José Fernandez Navarro & Patrik L. Ståhl

  5. Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, 17165 Solna, Sweden

    Andrey Alexeyenko

  6. National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, 17121 Solna, Sweden

    Andrey Alexeyenko

  7. Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, 75237 Uppsala, Sweden

    Johan Reimegård

  8. Division of Glycoscience, School of Biotechnology, KTH Royal Institute of Technology, AlbaNova University Centre, 11421 Stockholm, Sweden

    Lauren S. McKee & Vincent Bulone

  9. ARC Centre of Excellence in Plant and Cell Walls and School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, Urrbrae, Adelaide, 5064, South Australia, Australia

    Vincent Bulone

  10. Department of Plant Biology, Uppsala BioCenter, Linnean Center for Plant Biology, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden

    Jens F. Sundström

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  1. Stefania Giacomello
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Contributions

S.G. and J.L. designed the project. S.G. developed the plant-specific protocol, performed and guided experiments and data analyses, prepared figures and wrote the manuscript. F.S. and P.L.S. developed the original protocol for mammalian tissue. F.S. contributed to some code used for the analyses. B.K.T. performed experiments. S.V. developed barcoded arrays. J.F.N. developed the alignment and demultiplexing pipeline. A.A. developed the linear model and performed its computations. J.R. performed a part of data analysis and a part of figure preparation. L.S.M. and V.B. provided consultation on plant cell-wall degradation enzymes. C.M. developed the visualization and data sharing tool. J.F.S. provided A. thaliana and P. abies samples, contributed to data interpretation. N.S. provided P. tremula samples, contributed to data interpretation, and guided the development of the visualization tool. A.A., L.S.M., J.F.S., N.R.S. and J.L. edited the manuscript.

Corresponding authors

Correspondence to Stefania Giacomello or Joakim Lundeberg.

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Competing interests

P.L.S. and J.L are founders of a company that holds IP rights to the presented technology.

Supplementary information

Supplementary Information

Supplementary Figures 1-13. (PDF 19503 kb)

Supplementary Table 1

Differential expressed genes between developing and dormant Populus tremula leaf buds. (XLSX 327 kb)

Supplementary Table 2

Number of TP, TN, FP and FN per each tissue domain in A. thaliana replicates. (XLSX 44 kb)

Supplementary Table 3

Linear model P-values per genes and pathways at the macro- and micro-category level. (XLSX 1617 kb)

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Giacomello, S., Salmén, F., Terebieniec, B. et al. Spatially resolved transcriptome profiling in model plant species. Nature Plants 3, 17061 (2017). https://doi.org/10.1038/nplants.2017.61

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