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Spatially resolved transcriptomics reveals plant host responses to pathogens.


ABSTRACT: Background:Thorough understanding of complex model systems requires the characterisation of processes in different cell types of an organism. This can be achieved with high-throughput spatial transcriptomics at a large scale. However, for plant model systems this is still challenging as suitable transcriptomics methods are sparsely available. Here we present GaST-seq (Grid-assisted, Spatial Transcriptome sequencing), an easy to adopt, micro-scale spatial-transcriptomics workflow that allows to study expression profiles across small areas of plant tissue at a fraction of the cost of existing sequencing-based methods. Results:We compare the GaST-seq method with widely used library preparation methods (Illumina TruSeq). In spatial experiments we show that the GaST-seq method is sensitive enough to identify expression differences across a plant organ. We further assess the spatial transcriptome response of Arabidopsis thaliana leaves exposed to the bacterial molecule flagellin-22, and show that with eukaryotic (Albugo laibachii) infection both host and pathogen spatial transcriptomes are obtained. Conclusion:We show that our method can be used to identify known, rapidly flagellin-22 elicited genes, plant immune response pathways to bacterial attack and spatial expression patterns of genes associated with these pathways.

SUBMITTER: Giolai M 

PROVIDER: S-EPMC6785889 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Spatially resolved transcriptomics reveals plant host responses to pathogens.

Giolai Michael M   Verweij Walter W   Lister Ashleigh A   Heavens Darren D   Macaulay Iain I   Clark Matthew D MD  

Plant methods 20191010


<h4>Background</h4>Thorough understanding of complex model systems requires the characterisation of processes in different cell types of an organism. This can be achieved with high-throughput spatial transcriptomics at a large scale. However, for plant model systems this is still challenging as suitable transcriptomics methods are sparsely available. Here we present GaST-seq (<b>G</b>rid-<b>a</b>ssisted, <b>S</b>patial <b>T</b>ranscriptome <b>seq</b>uencing), an easy to adopt, micro-scale spatia  ...[more]

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