Deep origin and gradual evolution of transporting tissues: Perspectives from across the land plants.

The evolution of transporting tissues was an important innovation in terrestrial plants that allowed them to adapt to almost all nonaquatic environments. These tissues consist of water-conducting cells and food-conducting cells and bridge plant-soil and plant-air interfaces over long distances. The largest group of land plants, representing about 95% of all known plant species, is associated with morphologically complex transporting tissue in plants with a range of additional traits. Therefore, this entire clade was named tracheophytes, or vascular plants. However, some nonvascular plants possess conductive tissues that closely resemble vascular tissue in their organization, structure, and function. Recent molecular studies also point to a highly conserved toolbox of molecular regulators for transporting tissues. Here, we reflect on the distinguishing features of conductive and vascular tissues and their evolutionary history. Rather than sudden emergence of complex, vascular tissues, plant transporting tissues likely evolved gradually, building on pre-existing developmental mechanisms and genetic components. Improved knowledge of the intimate structure and developmental regulation of transporting tissues across the entire taxonomic breadth of extant plant lineages, combined with more comprehensive documentation of the fossil record of transporting tissues, is required for a full understanding of the evolutionary trajectory of transporting tissues.

Advances and Opportunities in Single-Cell Transcriptomics for Plant Research.

Single-cell approaches are quickly changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell. The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications. In this review, we give an overview of the main advances in plant single-cell transcriptomics over the past few years and provide the reader with an accessible guideline covering all steps, from sample preparation to data analysis. We end by offering a glimpse of how these technologies will shape and accelerate plant-specific research in the near future.

SeedGerm: a cost-effective phenotyping platform for automated seed imaging and machine-learning based phenotypic analysis of crop seed germination.

Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination- and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists' scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications.