Non-invasive Assay for Chlorophyll Biosynthesis Kinetics Determination during Early Stages of Arabidopsis De-etiolation.

Chlorophyll biosynthesis is a hallmark of de-etiolation, one of the most dramatic stages in the plant life cycle. The tightly controlled and highly dynamic process of chlorophyll biosynthesis is triggered during the shift from the dark to the light in flowering plants. At the moment when etiolated seedlings are exposed to the first traces of sunlight, rapid (in order of seconds) conversion of protochlorophyllide into chlorophyllide is mediated by unique light-accepting protein complexes, leading via subsequent metabolic steps to the production of fully functional chlorophyll. Standard techniques for chlorophyll content analysis include pigment extraction from detached plant tissues, which does not apply to studying such fast processes. To investigate chlorophyll kinetics in vivo with high accuracy and spatiotemporal resolution in the first hours after light-induced de-etiolation, an instrument and protocol were developed. Here, we present a detailed procedure designed for statistically robust quantification of chlorophyll in the early stages of Arabidopsis de-etiolation.

iReenCAM: automated imaging system for kinetic analysis of photosynthetic pigment biosynthesis at high spatiotemporal resolution during early deetiolation.

Seedling de-etiolation is one of the key stages of the plant life cycle, characterized by a strong rearrangement of the plant development and metabolism. The conversion of dark accumulated protochlorophyllide to chlorophyll in etioplasts of de-etiolating plants is taking place in order of ns to µs after seedlings illumination, leading to detectable increase of chlorophyll levels in order of minutes after de-etiolation initiation. The highly complex chlorophyll biosynthesis integrates number of regulatory events including light and hormonal signaling, thus making de-etiolation an ideal model to study the underlying molecular mechanisms. Here we introduce the iReenCAM, a novel tool designed for non-invasive fluorescence-based quantitation of early stages of chlorophyll biosynthesis during de-etiolation with high spatial and temporal resolution. iReenCAM comprises customized HW configuration and optimized SW packages, allowing synchronized automated measurement and analysis of the acquired fluorescence image data. Using the system and carefully optimized protocol, we show tight correlation between the iReenCAM monitored fluorescence and HPLC measured chlorophyll accumulation during first 4h of seedling de-etiolation in wild type Arabidopsis and mutants with disturbed chlorophyll biosynthesis. Using the approach, we demonstrate negative effect of exogenously applied cytokinins and ethylene on chlorophyll biosynthesis during early de-etiolation. Accordingly, we identify type-B response regulators, the cytokinin-responsive transcriptional activators ARR1 and ARR12 as negative regulators of early chlorophyll biosynthesis, while contrasting response was observed in case of EIN2 and EIN3, the components of canonical ethylene signaling cascade. Knowing that, we propose the use of iReenCAM as a new phenotyping tool, suitable for quantitative and robust characterization of the highly dynamic response of seedling de-etiolation.

Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences.

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.