Quantitative Plant Biology最新文献

Stem cell fates are spatio-temporally regulated during plant development. Time-lapse imaging of fluorescence reporters is the most widely used method for spatio-temporal analysis of biological processes. However, excitation light for imaging fluorescence reporters causes autofluorescence and photobleaching. Unlike fluorescence reporters, luminescence proteins do not require excitation light, and therefore offer an alternative reporter for long-term and quantitative spatio-temporal analysis. We established an imaging system for luciferase, which enabled monitoring cell fate marker dynamics during vascular development in a vascular cell induction system called VISUAL. Single cells expressing the cambium marker, proAtHB8:ELUC, had sharp luminescence peaks at different time points. Furthermore, dual-color luminescence imaging revealed spatio-temporal relationships between cells that differentiated into xylem or phloem, and cells that transitioned from procambium to cambium. This imaging system enables not only the detection of temporal gene expression, but also facilitates monitoring of spatio-temporal dynamics of cell identity transitions at the single cell level.

Signalling and genetic networks underlie most biological processes and are often complex, containing many highly connected components. Modelling these networks can provide insight into mechanisms but is challenging given that rate parameters are often not well defined. Boolean modelling, in which components can only take on a binary value with connections encoded by logic equations, is able to circumvent some of these challenges, and has emerged as a viable tool to probe these complex networks. In this review, we will give an overview of Boolean modelling, with a specific emphasis on its use in plant biology. We review how Boolean modelling can be used to describe biological networks and then discuss examples of its applications in plant genetics and plant signalling.

In recent years, plant biologists interested in quantifying molecules and molecular events in vivo have started to complement reporter systems with genetically encoded fluorescent biosensors (GEFBs) that directly sense an analyte. Such biosensors can allow measurements at the level of individual cells and over time. This information is proving valuable to mathematical modellers interested in representing biological phenomena in silico, because improved measurements can guide improved model construction and model parametrisation. Advances in synthetic biology have accelerated the pace of biosensor development, and the simultaneous expression of spectrally compatible biosensors now allows quantification of multiple nodes in signalling networks. For biosensors that directly respond to stimuli, targeting to specific cellular compartments allows the observation of differential accumulation of analytes in distinct organelles, bringing insights to reactive oxygen species/calcium signalling and photosynthesis research. In conjunction with improved image analysis methods, advances in biosensor imaging can help close the loop between experimentation and mathematical modelling.

Quantitative plant biology is an interdisciplinary field that builds on a long history of biomathematics and biophysics. Today, thanks to high spatiotemporal resolution tools and computational modelling, it sets a new standard in plant science. Acquired data, whether molecular, geometric or mechanical, are quantified, statistically assessed and integrated at multiple scales and across fields. They feed testable predictions that, in turn, guide further experimental tests. Quantitative features such as variability, noise, robustness, delays or feedback loops are included to account for the inner dynamics of plants and their interactions with the environment. Here, we present the main features of this ongoing revolution, through new questions around signalling networks, tissue topology, shape plasticity, biomechanics, bioenergetics, ecology and engineering. In the end, quantitative plant biology allows us to question and better understand our interactions with plants. In turn, this field opens the door to transdisciplinary projects with the society, notably through citizen science.

Quantitative approaches in plant biology have a long history that have led to several ground-breaking discoveries and given rise to new principles, new paradigms and new methodologies. We take a short historical trip into the past to explore some of the many great scientists and influences that have led to the development of quantitative plant biology. We have not been constrained by historical fact, although we have tried not to deviate too much. We end with a forward look, expressing our hopes and ambitions for this exciting interdisciplinary field.

Comparative transcriptomics can be used to translate an understanding of gene regulatory networks from model systems to less studied species. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. We find that different curve registration functions are required for different genes, indicating that there is no single common 'developmental time' between Arabidopsis and B. rapa. A detailed comparison between Arabidopsis and B. rapa and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa and highlights the importance of registration methods for the comparison of developmental gene expression data.

Phenotypic diversity of flowering plants stems from common basic features of the plant body pattern with well-defined body axes, organs and tissue organisation. Cell division and cell specification are the two processes that underlie the formation of a body pattern. As plant cells are encased into their cellulosic walls, directional cell division through precise positioning of division plane is crucial for shaping plant morphology. Since many plant cells are pluripotent, their fate establishment is influenced by their cellular environment through cell-to-cell signaling. Recent studies show that apart from biochemical regulation, these two processes are also influenced by cell and tissue morphology and operate under mechanical control. Finding a proper model system that allows dissecting the relationship between these aspects is the key to our understanding of pattern establishment. In this review, we present the Arabidopsis embryo as a simple, yet comprehensive model of pattern formation compatible with high-throughput quantitative assays.

Stem cells give rise to the entirety of cells within an organ. Maintaining stem cell identity and coordinately regulating stem cell divisions is crucial for proper development. In plants, mobile proteins, such as WUSCHEL-RELATED HOMEOBOX 5 (WOX5) and SHORTROOT (SHR), regulate divisions in the root stem cell niche. However, how these proteins coordinately function to establish systemic behaviour is not well understood. We propose a non-cell autonomous role for WOX5 in the cortex endodermis initial (CEI) and identify a regulator, ANGUSTIFOLIA (AN3)/GRF-INTERACTING FACTOR 1, that coordinates CEI divisions. Here, we show with a multi-scale hybrid model integrating ordinary differential equations (ODEs) and agent-based modeling that quiescent center (QC) and CEI divisions have different dynamics. Specifically, by combining continuous models to describe regulatory networks and agent-based rules, we model systemic behaviour, which led us to predict cell-type-specific expression dynamics of SHR, SCARECROW, WOX5, AN3 and CYCLIND6;1, and experimentally validate CEI cell divisions. Conclusively, our results show an interdependency between CEI and QC divisions.

The light-induced reorientation of the cortical microtubule array in dark-grown Arabidopsis thaliana hypocotyl cells is a striking example of the dynamical plasticity of the microtubule cytoskeleton. A consensus model, based on katanin-mediated severing at microtubule crossovers, has been developed that successfully describes the onset of the observed switch between a transverse and longitudinal array orientation. However, we currently lack an understanding of why the newly populated longitudinal array direction remains stable for longer times and re-equilibration effects would tend to drive the system back to a mixed orientation state. Using both simulations and analytical calculations, we show that the assumption of a small orientation-dependent shift in microtubule dynamics is sufficient to explain the long-term lock-in of the longitudinal array orientation. Furthermore, we show that the natural alternative hypothesis that there is a selective advantage in severing longitudinal microtubules, is neither necessary nor sufficient to achieve cortical array reorientation, but is able to accelerate this process significantly.