Spatially resolved transcriptome profiling in model plant species

Identification: Giacomello, Stefania


Spatially resolved transcriptome profiling in model plant species

Stefania Giacomello1*, Barbara K. Terebieniec2, Andrey Alexeyenko3, Johan Reimegård4, Fredrik Salmén1, Sanja Vickovic1, José Fernandez Navarro5,Patrik L. Ståhl5, Jens F. Sundström6, Nathaniel R. Street2, Joakim Lundeberg1

1Science for Life Laboratory, Stockholm, Sweden; 2Umeå Plant Science Centre, Sweden; 3National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Sweden; 4Science for Life Laboratory, Uppsala, Sweden; 5Department of Cell and Molecular Biology, Karolinska Institute, Sweden; 6Linnean Center for Plant Biology, Sweden

* Corresponding author

Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analyzing high-throughput spatial expression profiles were developed for a limited range of organisms - primarily mammals. We present the first available method to generate 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 feasibility by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections (Arabidopsis thaliana, Populus tremula and Picea abies). We achieved high reproducibility of our method in the three species, and identify a high specificity (92.9%), and a low false positive rate (6.5%).

In A. thaliana we used the spatial data to identify 141 differential expressed genes and 189 altered pathways among eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling represents a powerful new strategy that can be applied to a broad range of plant species, providing an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology. To facilitate this goal, the data have been made available to the community for visual exploration, representing the first high-resolution spatially resolved gene expression resource in plants.


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