Export of defensive glucosinolates is key for their accumulation in seeds.

Plant membrane transporters controlling metabolite distribution contribute key agronomic traits1-6. To eliminate anti-nutritional factors in edible parts of crops, the mutation of importers can block the accumulation of these factors in sink tissues7. However, this often results in a substantially altered distribution pattern within the plant8-12, whereas engineering of exporters may prevent such changes in distribution. In brassicaceous oilseed crops, anti-nutritional glucosinolate defence compounds are translocated to the seeds. However, the molecular targets for export engineering of glucosinolates remain unclear. Here we identify and characterize members of the USUALLY MULTIPLE AMINO ACIDS MOVE IN AND OUT TRANSPORTER (UMAMIT) family-UMAMIT29, UMAMIT30 and UMAMIT31-in Arabidopsis thaliana as glucosinolate exporters with a uniport mechanism. Loss-of-function umamit29 umamit30 umamit31 triple mutants have a very low level of seed glucosinolates, demonstrating a key role for these transporters in translocating glucosinolates into seeds. We propose a model in which the UMAMIT uniporters facilitate glucosinolate efflux from biosynthetic cells along the electrochemical gradient into the apoplast, where the high-affinity H+-coupled glucosinolate importers GLUCOSINOLATE TRANSPORTERS (GTRs) load them into the phloem for translocation to the seeds. Our findings validate the theory that two differently energized transporter types are required for cellular nutrient homeostasis13. The UMAMIT exporters are new molecular targets to improve nutritional value of seeds of brassicaceous oilseed crops without altering the distribution of the defence compounds in the whole plant.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data.

BACKGROUND: Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R. RESULTS: Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples. CONCLUSIONS: The cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis. It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html . The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer .

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data.

Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive data visualization of multiplexed imaging data is necessary for quality control and hypothesis examination. Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. The package operates on SingleCellExperiment, SpatialExperiment and CytoImageList objects and therefore integrates with the Bioconductor framework for single-cell and image analysis. Users of cytoviewer need little coding expertise, and the graphical user interface allows user-friendly navigation. We showcase the functionality of cytoviewer by analysis of an imaging mass cytometry dataset of cancer patients.

Identification of key amino acid residues in AtUMAMIT29 for transport of glucosinolates.

Glucosinolates are key defense compounds of plants in Brassicales order, and their accumulation in seeds is essential for the protection of the next generation. Recently, members of the Usually Multiple Amino acids Move In and Out Transporter (UMAMIT) family were shown to be essential for facilitating transport of seed-bound glucosinolates from site of synthesis within the reproductive organ to seeds. Here, we set out to identify amino acid residues responsible for glucosinolate transport activity of the main seed glucosinolate exporter UMAMIT29 in Arabidopsis thaliana. Based on a predicted model of UMAMIT29, we propose that the substrate transporting cavity consists of 51 residues, of which four are highly conserved residues across all the analyzed homologs of UMAMIT29. A comparison of the putative substrate binding site of homologs within the brassicaceous-specific, glucosinolate-transporting clade with the non-brassicaceous-specific, non-glucosinolate-transporting UMAMIT32 clade identified 11 differentially conserved sites. When each of the 11 residues of UMAMIT29 was individually mutated into the corresponding residue in UMAMIT32, five mutant variants (UMAMIT29#V27F, UMAMIT29#M86V, UMAMIT29#L109V, UMAMIT29#Q263S, and UMAMIT29#T267Y) reduced glucosinolate transport activity over 75% compared to wild-type UMAMIT29. This suggests that these residues are key for UMAMIT29-mediated glucosinolate transport activity and thus potential targets for blocking the transport of glucosinolates to the seeds.

An end-to-end workflow for multiplexed image processing and analysis.

Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .