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- Materials and Methods
Plant growth is controlled by a complex network of factors (Walter & Schurr, 2005). From the level of gene transcription, via hormonal control, to control by environmental parameters, a multitude of factors affect the short-term dynamics and long-term performance of plant growth. Light intensity and nutrient availability are environmental factors that strongly affect plant growth performance from early developmental stages onwards. At the seedling stage the plant is most vulnerable to environmental stress conditions, and cannot buffer deficiencies of factors fostering growth as reserves are sparse. It is this stage in which growth most directly reflects the interaction of the entity of genes driving growth (Meyer et al., 2004). For precise quantification of alterations of growth, noninvasive growth measurements are indispensable. Destructive measurements (such as determining fresh or dry weight) are time-consuming, hence only a small number of replicates can be observed in any given study. Moreover, it is difficult to detect growth effects in small seedlings, as measurement devices have to be precise.
As analysis of plant growth is an essential step in understanding plant performance and productivity, several approaches have been used throughout the past decade to quantify the projected leaf area of dicotyledonous plants as a proxy for overall plant growth performance (Leister et al., 1999; Barbagallo et al., 2003; El-Lithy et al., 2004; Granier et al., 2006). Such procedures work best with seedlings, as the effects of leaf overlap can be neglected because of the rosette stage in which several interesting model species remain for an extended period. Yet standardized automatic procedures for image evaluation of a high number of individuals are still not common. Recently, a very interesting platform named PHENOPSIS has been established that specializes in phenotyping plant responses to soil water deficit (Granier et al., 2006). There, leaf area is determined automatically, plants are weighed, and defined amounts of water are added per individual pot in an automated way. Unfortunately, it is difficult to assess the precision of PHENOPSIS as no details of image acquisition or analysis have been reported.
Relative growth rates originating from area differences of individual plants at consecutive time steps are not reported in any of the above-mentioned studies. Several systems that have been established for high-throughput evaluation of plant growth work with a low degree of automation: Threshold values for distinction between leaf and background have to be defined on a reiterative basis for individual images (Leister et al., 1999); 20–96 plants are displayed within one image to reduce data handling, which leads to an enormous loss of resolution (Leister et al., 1999; Barbagallo et al., 2003; El-Lithy et al., 2004). Industrial solutions for plant phenotyping have been elaborated by several companies, but their applicability has not yet been demonstrated in the literature. Hence there is a clear need for phenotyping solutions that allow automated but flexible image acquisition, robust and transparent data evaluation, and problem-specific precision and interpretation of the data.
Alterations in light climate can often happen very quickly (e.g. in canopy gaps), and it has recently been shown that plants can react within a short time frame to alterations in the light climate by dynamically changing the growth of individual leaves (Lai et al., 2005) or roots (Nagel et al., 2007). Moreover, differences in light intensity and light duration (daily quantum input), resulting in differences of assimilation rate, have recently been shown in two independent meta-analyses to be the major environmental factor causing differences in relative growth rate (RGR) (Kruger & Volin, 2006; Shipley, 2006). Although photosynthesis and the subsequent production of carbohydrates are ultimately major driving factors determining the growth potential of a plant, and although it has become feasible in the past decade to investigate the dynamics of growth and photosynthesis with high resolution, it is still unclear how fast alterations in light climate are transformed into alterations in plant growth rate (Schurr et al., 2006). To elucidate this question, it is necessary to perform noninvasive growth measurements providing a high temporal resolution.
It was the aim of this study to establish a procedure called GROWSCREEN that integrates the strength of modern standard procedures of single-image processing with an automated setup rapidly to acquire and evaluate high-quality images of plants that had been raised in standard laboratory conditions. Using this approach, we investigated whether it is possible to detect expansion differences occurring within days between populations that were caused by differences in daily quantum input and external nutrient availability, and whether these differences in leaf expansion were indicative of differences in biomass growth.
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- Materials and Methods
Phenotyping procedures such as GROWSCREEN are capable of detecting light-induced growth acclimation responses within 24 h. RGR is a more appropriate parameter than total leaf area for quantifying this growth response, as RGR is not affected by the development of the plant before the experimental treatment. This has been pointed out by other authors (Leister et al., 1999); nevertheless, the majority of comparable studies still focus on total leaf area instead of RGR (Barbagallo et al., 2003; El-Lithy et al., 2004; Granier et al., 2006). Our results show that, within a population, smaller and larger plants increase total leaf area by a very similar relative amount per day.
Higher light intensity leads to increased shoot growth within a short time if growth is not nutrient-limited. Plants exposed to low nutrient availability were capable of increasing growth only if the photoperiod was extended from 12 to 24 h. Transfer of plants to continuous light also led to the strongest effects in plants exposed to high nutrient availability. It is conceivable that an extension of the photoperiod can increase the growth performance of N. tabacum more pronouncedly than an amplification of light intensity during a 12-h day, as N. tabacum is adapted to grow in a long photoperiod. Yet extrapolations to later developmental stages from results obtained in this study for seedlings should be treated carefully.
Analysis of fresh weight, dry weight and root growth demonstrated that the entire organism – not merely the area of the leaf canopy – was affected by the treatments. Hence optical phenotyping of seedling plants in the rosette stage is a good proxy of biomass growth processes, as also pointed out by Leister et al. (1999). Increased dry weight of plants from increased light exposure at high nutrient availability is probably caused by increased production of carbohydrates. This hypothesis is supported by findings from a large number of species observed in a time frame of weeks to seasons, for which it was shown that daily quantum input and assimilation rate largely determine plant RGR (Kruger & Volin, 2006; Shipley, 2006).
Because of a lack of measurement methods, the immediate reaction of shoot growth towards increased daily quantum influx has not been assessed up to now. Yet a study analysing single leaf growth of two congener Chamaecyparis species showed recently that leaf growth – not only photosynthesis – reacts within days when light intensity is altered (Lai et al., 2005). Rapid reactions of root growth with increased light exposure of shoots have also been observed recently (Nagel et al., 2007). For root growth, it has been shown conclusively that increased carbohydrate availability generated by amplified shoot photosynthesis causes increased growth at elevated light intensity (Freixes et al., 2002; Nagel et al., 2007).
The optical phenotyping procedure described here is not restricted to analysis of a certain species. As highlighted for the example of A. thaliana, GROWSCREEN can be applied easily to species other than N. tabacum. Magnification of the optical system, number and spatial arrangement of plants can be varied on demand. The optimal parameters hue, saturation and value are easily established for each species and can then be applied throughout the entire experiment. For A. thaliana it was shown that growth of starch-free mutants decreased below wild-type growth soon after measurements started. Carbohydrate reserves of the endosperm might have allowed for comparable growth rates during the first days after germination. Yet the absence of starch in stf1 led to a decrease in growth activity between 3 and 9 d after germination. Transitory starch is a major factor driving nighttime growth activity by providing carbohydrate metabolites for cell-wall assembly and other purposes (Walter & Schurr, 2005). The fact that stf1 mutants remain smaller than wild-type plants has been reported previously (Kofler et al., 2000), but the dynamics of stf1 growth behaviour have not yet been analysed. The finding that growth differences occur at a very early stage in plant development confirms results from the literature obtained with other lines of A. thaliana (Meyer et al., 2004).
The experimental design presented in this study may serve as a protocol for a wide range of future applications. It is conceivable that setups and procedures designed along the lines of GROWSCREEN will be applied to study not only ecophysiology, but also effects of agrochemicals or xenobiotica as well as differences between plant lines caused by their varying genetic backgrounds.