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Abstract Understanding crop plants responses to abiotic stress is increasingly important in this changing climate. We asked experts how discoveries in Arabidopsis thaliana have translated into advancements in abiotic crop stress resilience. The theme is that core regulatory networks identified in Arabidopsis are conserved in crops, but the molecular regulation varies among species. For cold tolerance, the regulatory framework is conserved, but MAP Kinase signaling promotes degradation of the INDUCER OF DREB1 EXPRESSION transcription factor in Arabidopsis but inhibits it in rice. For hypoxia, manipulation of the oxygen sensing Arg/N-degron pathway discovered in Arabidopsis has improved waterlogging and flood tolerance in barley, maize, wheat, and soybean. For light signaling, overexpression of PHYTOCHROME B reduces shade avoidance, improving yield under dense planting in potato, soybean, and maize. In rice, understanding of nitrogen responsiveness, uptake, and transport in Arabidopsis has inspired engineering of the NRT1 nitrate transceptor to increase yield. Arabidopsis research has provided leads for genetic manipulations that may improve drought resilience in crop species. Growing plants in space generates a complex array of stresses, and Arabidopsis experiments in the space station prepare for future development of robust crops as integral components of the life support systems. For environmental regulation of flowering time, the role of the GIGANTEA - CONTANS - FLOWERING LOCUS T module elucidated in Arabidopsis is largely conserved in crop plants, although additional regulators modify short-day responsiveness in rice, soybean, chrysanthemum, and potato. 

Abstract BackgroundLive imaging is the gold standard for determining how cells give rise to organs. However, tracking many cells across whole organs over large developmental time windows is extremely challenging. In this work, we provide a comparably simple method for confocal live imaging entireArabidopsis thalianafirst leaves across early development. Our imaging method works for both wild-type leaves and the complex curved leaves of thejaw-1Dmutant. ResultsWe find that dissecting the cotyledons, affixing a coverslip above the samples and mounting samples with perfluorodecalin yields optimal imaging series for robust cellular and organ level analysis. We provide details of our complementary image processing steps in MorphoGraphX software for segmenting, tracking lineages, and measuring a suite of cellular properties. We also provide MorphoGraphX image processing scripts we developed to automate analysis of segmented images and data presentation. ConclusionsOur imaging techniques and processing steps combine into a robust imaging pipeline. With this pipeline we are able to examine important nuances in the cellular growth and differentiation ofjaw-Dversus WT leaves that have not been demonstrated before. Our pipeline is approachable and easy to use for leaf development live imaging. 

One of the fundamental questions in developmental biology is how a cell is specified to differentiate as a specialized cell type. Traditionally, plant cell types were defined based on their function, location, morphology, and lineage. Currently, in the age of single-cell biology, researchers typically attempt to assign plant cells to cell types by clustering them based on their transcriptomes. However, because cells are dynamic entities that progress through the cell cycle and respond to signals, the transcriptome also reflects the state of the cell at a particular moment in time, raising questions about how to define a cell type. We suggest that these complexities and dynamics of cell states are of interest and further consider the roles signaling, stochasticity, cell cycle, and mechanical forces play in plant cell fate specification. Once established, cell identity must also be maintained. With the wealth of single-cell data coming out, the field is poised to elucidate both the complexity and dynamics of cell states. 

Living tissues display fluctuations—random spatial and temporal variations of tissue properties around their reference values—at multiple scales. It is believed that such fluctuations may enable tissues to sense their state or their size. Recent theoretical studies developed specific models of fluctuations in growing tissues and predicted that fluctuations of growth show long-range correlations. Here, we elaborated upon these predictions and we tested them using experimental data. We first introduced a minimal model for the fluctuations of any quantity that has some level of temporal persistence or memory, such as concentration of a molecule, local growth rate, or mechanical property. We found that long-range correlations are generic, applying to any such quantity, and that growth couples temporal and spatial fluctuations, through a mechanism that we call “fluctuation stretching”—growth enlarges the length scale of variation of this quantity. We then analyzed growth data from sepals of the model plant Arabidopsis and we quantified spatial and temporal fluctuations of cell growth using the previously developed cellular Fourier transform. Growth appears to have long-range correlations. We compared different genotypes and growth conditions: mutants with lower or higher response to mechanical stress have lower temporal correlations and longer-range spatial correlations than wild-type plants. Finally, we used theoretical predictions to merge experimental data from all conditions and developmental stages into a unifying curve, validating the notion that temporal and spatial fluctuations are coupled by growth. Altogether, our work reveals kinematic constraints on spatiotemporal fluctuations that have an impact on the robustness of morphogenesis.