Spatially resolved transcriptome profiling in model plant species


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.

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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.


  1. 1

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

    PubMed  PubMed Central  Google Scholar 

  2. 2

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

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

    CAS  PubMed  Google Scholar 

  4. 4

    Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015).

    CAS  PubMed  Google Scholar 

  5. 5

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

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Ortiz-Ramírez, C. et al. A transcriptome atlas of Physcomitrella patens provides insights into the evolution and development of land plants. Mol. Plant 9, 205–220 (2015).

    PubMed  Google Scholar 

  7. 7

    Rensink, W. A. & Buell, C. R. Microarray expression profiling resources for plant genomics. Trends Plant Sci. 10, 603–609 (2005).

    CAS  PubMed  Google Scholar 

  8. 8

    Birnbaum, K. et al. A gene expression map of the Arabidopsis root. Science 302, 1956–1960 (2003).

    CAS  PubMed  Google Scholar 

  9. 9

    Brady, S. M. et al. A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318, 801–806 (2007).

    CAS  PubMed  Google Scholar 

  10. 10

    Yadav, R. K., Tavakkoli, M., Xie, M., Girke, T. & Reddy, G. V. A high-resolution gene expression map of the Arabidopsis shoot meristem stem cell niche. Development 17, 2735–2744 (2014).

    Google Scholar 

  11. 11

    Deal, R. B. & Henikoff, S. The INTACT method for cell type – specific gene expression and chromatin profiling in Arabidopsis thaliana. Nat. Protoc. 19, 56–68 (2011).

    Google Scholar 

  12. 12

    Nelson, T., Tausta, S. L., Gandotra, N. & Liu, T. Laser microdissection of plant tissue: what you see is what you get. Annu. Rev. Plant Biol. 57, 181–201 (2006).

    CAS  PubMed  Google Scholar 

  13. 13

    Anjam, M. S. et al. An improved procedure for isolation of high-quality RNA from nematode-infected Arabidopsis roots through laser capture microdissection. Plant Methods 12, 25 (2016).

    PubMed  PubMed Central  Google Scholar 

  14. 14

    Gautam, V., Singh, A., Singh, S. & Sarkar, A. K. An efficient LCM-based method for tissue specific expression analysis of genes and miRNAs. Sci. Rep. 6, 21577 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Takacs, E. M. et al. Ontogeny of the maize shoot apical meristem. Plant Cell 24, 3219–3234 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Jiao, Y. et al. A transcriptome atlas of rice cell types uncovers cellular, functional and developmental hierarchies. Nat. Genet. 41, 258–263 (2009).

    CAS  PubMed  Google Scholar 

  17. 17

    Cosgrove, D. J. Growth of the plant cell wall. Nat. Rev. Mol. Cell Biol. 6, 850–861 (2005).

    CAS  PubMed  Google Scholar 

  18. 18

    Bourgaud, F., Gravot, A., Milesi, S. & Gontier, E. Production of plant secondary metabolites: a historical perspective. Plant Sci. 161, 839–851 (2001).

    CAS  Google Scholar 

  19. 19

    Li, Y., Pearl, S. A. & Jackson, S. A. Gene networks in plant biology: approaches in reconstruction and analysis. Trends Plant Sci. 20, 664–675 (2015).

    CAS  PubMed  Google Scholar 

  20. 20

    Nystedt, B. et al. The Norway spruce genome sequence and conifer genome evolution. Nature 497, 579–584 (2013).

    CAS  PubMed  Google Scholar 

  21. 21

    Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    CAS  PubMed  Google Scholar 

  22. 22

    Fu, G. K., Hu, J., Wang, P.-H. & Fodor, S. P. A. Counting individual DNA molecules by the stochastic attachment of diverse labels. Proc. Natl Acad. Sci. USA 108, 9026–9031 (2011).

    CAS  PubMed  Google Scholar 

  23. 23

    Koonjul, P. K., Brandt, W. F., Farrant, J. M. & Lindsey, G. G. Inclusion of polyvinylpyrrolidone in the polymerase chain reaction reverses the inhibitory effects of polyphenolic contamination of RNA. Nucleic Acids Res. 27, 915–916 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Petterle, A., Karlberg, A. & Bhalerao, R. P. Daylength mediated control of seasonal growth patterns in perennial trees. Curr. Opin. Plant Biol. 16, 301–306 (2013).

    PubMed  Google Scholar 

  25. 25

    Street, N. R. et al. A cross-species transcriptomics approach to identify genes involved in leaf development. BMC Genomics 9, 589 (2008).

    PubMed  PubMed Central  Google Scholar 

  26. 26

    Street, N. R., Jansson, S. & Hvidsten, T. R. A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation. BMC Plant Biol. 11, 13 (2011).

    PubMed  PubMed Central  Google Scholar 

  27. 27

    Schmid, M. et al. A gene expression map of Arabidopsis thaliana development. Nat. Genet. 37, 501–506 (2005).

    CAS  PubMed  Google Scholar 

  28. 28

    Wellmer, F., Alves-Ferreira, M., Dubois, A., Riechmann, J. L. & Meyerowitz, E. M. Genome-wide analysis of gene expression during early Arabidopsis flower development. PLoS Genet. 2, 1012–1024 (2006).

    CAS  Google Scholar 

  29. 29

    Rubinelli, P., Hu, Y. & Ma, H. Identification, sequence analysis and expression studies of novel anther-specific genes of Arabidopsis thaliana. Plant Mol. Biol. 37, 607–619 (1998).

    CAS  PubMed  Google Scholar 

  30. 30

    Irish, V. F. & Sussex, I. M. Function of the apetala-1 gene during Arabidopsis floral development. Plant Cell 2, 741–753 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Jack, T., Brockman, L. L. & Meyerowitz, E. M. The homeotic gene apetala 3 of Arabidopsis thaliana encodes a MADS box and is expressed in petals and stamens. Cell 68, 683–697 (1992).

    CAS  PubMed  Google Scholar 

  32. 32

    Goto, K. & Meyerowitz, E. M. Function and regulation of the Arabidopsis floral homeotic gene PISTILLATA. Genes Dev. 8, 1548–1560 (1994).

    CAS  PubMed  Google Scholar 

  33. 33

    Yanofsky, M. et al. The protein encoded by the Arabidopsis homeotic gene AGAMOUS resembles transcription factors. Nature 346, 35–39 (1990).

    CAS  PubMed  Google Scholar 

  34. 34

    Van Der Maaten, L. J. P. & Hinton, G. E. Visualizing high-dimensional data using t-sne. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  35. 35

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. Edger: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

    PubMed  PubMed Central  Google Scholar 

  36. 36

    Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  37. 37

    Truernit, E., Stadler, R., Baier, K. & Sauer, N. A male gametophyte-specific monosaccharide transporter in Arabidopsis. Plant J. 17, 191–201 (1999).

    CAS  PubMed  Google Scholar 

  38. 38

    Alexeyenko, A. et al. Network enrichment analysis: extension of gene-set enrichment analysis to gene networks. BMC Bioinformatics 13, 226 (2012).

    PubMed  PubMed Central  Google Scholar 

  39. 39

    Sundell, D. et al. The plant genome integrative explorer resource: New Phytol. 208, 1149–1156 (2015).

    CAS  PubMed  Google Scholar 

  40. 40

    Karlebach, G. & Shamir, R. Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9, 770–780 (2008).

    CAS  PubMed  Google Scholar 

  41. 41

    Thompson, D., Regev, A. & Roy, S. Comparative analysis of gene regulatory networks: from network reconstruction to evolution. Annu. Rev. Cell Dev. Biol. 31, 399–428 (2015).

    CAS  PubMed  Google Scholar 

  42. 42

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

    CAS  PubMed  Google Scholar 

  43. 43

    Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Smyth, J. L. Bowman & E. M. & Meyerowitz, D. R. Early flower development in Arabidopsis. Plant Cell 2, 755–767 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Vickovic, S. et al. Massive and parallel expression profiling using microarrayed single-cell sequencing. Nat. Commun. 7, 1–9 (2016).

    Google Scholar 

  46. 46

    Lundin, S., Stranneheim, H., Pettersson, E., Klevebring, D. & Lundeberg, J. Increased throughput by parallelization of library preparation for massive sequencing. PLoS ONE 5, e10029 (2010).

    PubMed  PubMed Central  Google Scholar 

  47. 47

    Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).

    CAS  PubMed  Google Scholar 

  48. 48

    The Arabidopsis Genome Initiative. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796–815 (2000).

  49. 49

    Tuskan, G. A . et al. The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313, 1596–1604 (2006).

    CAS  PubMed  Google Scholar 

  50. 50

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

    CAS  Google Scholar 

  51. 51

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Costea, P. I., Lundeberg, J. & Akan, P. TagGD: fast and accurate software for DNA tag generation and demultiplexing. PLoS ONE 8, e57521 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Trygg, J. & Wold, S. Orthogonal projections to latent structures (O-PLS). J. Chemometrics 16, 119–128 (2002).

    CAS  Google Scholar 

  54. 54

    Kjellqvist, S. et al. A combined proteomic and transcriptomic approach shows diverging molecular mechanisms in thoracic aortic aneurysm development in patients with tricuspid- and bicuspid aortic valve. Mol. Cell. Proteomics 12, 407–425 (2013).

    CAS  PubMed  Google Scholar 

  55. 55

    Lindholm, M. E. et al. The impact of endurance training on human skeletal muscle memory, global isoform expression and novel transcripts. PLoS Genet. 12, e1006294 (2016).

    PubMed  PubMed Central  Google Scholar 

  56. 56

    Martens, H., Høy, M., Westad, F., Folkenberg, D. & Martens, M. Analysis of designed experiments by stabilised PLS regression and jack-knifing. Chemom. Intell. Lab. Syst. 58, 151–170 (2001).

    CAS  Google Scholar 

  57. 57

    Du, Z., Zhou, X., Ling, Y., Zhang, Z. & Su, Z. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 38, 64–70 (2010).

    Google Scholar 

  58. 58

    Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Google Scholar 

  59. 59

    Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. 102, 15545–15550 (2005).

    CAS  PubMed  Google Scholar 

  60. 60

    Schmitt, T., Ogris, C. & Sonnhammer, E. L. L. Funcoup 3.0: Database of genome-wide functional coupling networks. Nucleic Acids Res. 42, 380–388 (2014).

    Google Scholar 

  61. 61

    Alexeyenko, A. & Sonnhammer, E. L. L. Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Res. 19, 1107–1116 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Jeggari, A. & Alexeyenko, A. NEArender: an R package for functional interpretation of ‘omics’ data via network enrichment analysis. BMC Bioinform. 18, 118 (2017).

    Google Scholar 

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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




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).

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