SUMMARYPlant roots integrate environmental signals and developmental programs using exquisite spatiotemporal control. This is apparent in the deposition of suberin, an apoplastic diffusion barrier, which regulates the entry and exit of water, solutes and gases, and is environmentally plastic. Suberin is considered a hallmark of endodermal differentiation, but we find that it is absent in the tomato endodermis during normal development. Instead, suberin is present in the exodermis, a cell type that is absent in the model organismArabidopsis thaliana. Here, we uncover genes driving exodermal suberization and describe its effects on drought responses in tomato, unravelling the similarities and differences with the paradigmatic Arabidopsis endodermis. Cellular resolution imaging, gene expression, and mutant analyses reveal loss of this program from the endodermis, and its co-option in the exodermis. Functional genetic analyses of the tomato MYB92 transcription factor and ASFT enzyme demonstrate the importance of exodermal suberin for a plant water-deficit response. Controlling the degree of exodermal suberization could be a new strategy for breeding climate-resilient plants.
Summary An essential step in the analysis of single‐cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single‐cell predictive marker (SPmarker) to identify novel cell‐type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single‐cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non‐Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell‐type marker genes from scRNA‐seq data and pave the way for cross‐species mapping of scRNA‐seq data in plants.
Climate change may lead to the emergence of novel plant diseases caused by yet unknown pathogens. Surveillance for emerging plant diseases is crucial to reduce their threat to food security.
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