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. 2010 May;105(5):783–791. doi: 10.1093/aob/mcq048

Analysis of Arabidopsis thaliana root growth kinetics with high temporal and spatial resolution

Nima Yazdanbakhsh 1,*, Joachim Fisahn 1
PMCID: PMC2859919  PMID: 20421235

Abstract

Background

Methods exist to quantify the distribution of growth rate over the root axis. However, non-destructive, high-throughput evaluations of total root elongation in controlled environments and the field are lacking in growth studies. A new imaging approach to analyse total root elongation is described.

Scope

High pixel resolution of the images enables the study of growth in short time intervals and provides high temporal resolution. Using the method described, total root elongation rates are calculated from the displacement of the root tip. Although the absolute root elongation rate changes in response to growth conditions, this set-up enables root growth of Arabidopsis wild-type seedlings to be followed for more than 1 month after germination. The method provides an easy approach to decipher root extension rate and much simpler calculations compared with other methods that use segmental growth to address this question.

Conclusions

The high temporal resolution allows small modifications of total root elongation growth to be revealed. Furthermore, with the options to investigate growth of various mutants in diverse growth conditions the present tool allows modulations in root growth kinetics due to different biotic and abiotic stimuli to be unravelled. Measurements performed on Arabidopsis thaliana wild-type (Col0) plants revealed rhythms superimposed on root elongation. Results obtained from the starchless mutant pgm, however, present a clearly modified pattern. As expected, deviation is strongest during the dark period.

Keywords: Arabidopsis thaliana, image processing, non invasive monitoring, pgm, root phenotyping, total root elongation

INTRODUCTION

Plant root tips growing in soil expand along three-dimensional pathways. Curvatures and elongation rates of these three-dimensional paths depend on root intrinsic and soil environmental parameters. Because roots can actively direct their growth towards regions of higher nutrient availability, it is conceivable that root architecture and curvature might be affected in many ways by external nutrient availability. Furthermore, plant intrinsic parameters such as hormones and carbohydrate metabolism are actively involved in control of the rate of cell production and the size of the root meristem. Thus, detailed analysis of root growth performance will provide valuable information on strategies to improve biomass production.

Root growth studies focus on measurements of root length, curvature, branching and architecture of the root system, as well as elongation rate. In comparison with those for above-ground tissues, these measurements suffer from limited access to the root system. Traditionally, root measurements applied projections of the root system on a transparent surface, such as the glass window of a rhizotron or an acetate paper. Displacement of root tips indicated by consecutive marks enables root growth rates over defined time periods to be calculated. Because it is non-invasive, this approach enables development of an individual to be followed over prolonged periods of time. However, due to limitations in spacing of the labels, relatively large time intervals must elapse before a measurable displacement becomes detectable. Therefore, root growth kinetics obtained with this method are hampered by poor temporal resolution. Image acquisition techniques provide a suitable alternative to analyse length and displacement of the root system. Originally, photographic images were evaluated simply by using a ruler. In parallel with improvements in technology, digital image acquisition replaced classical photography and the ruler was replaced by a video cursor (Leister et al., 1999; van der Weele et al., 2003; Granier et al., 2006). Ultimately, kinematic approaches increased the sampling frequency and thus improved the temporal resolution of root growth kinetics.

Methods developed for investigation of root growth rate have focused on two topics, namely total elongation of the root and the distribution of elongation rate on the growing axis, the latter providing spatial growth profiles. Total elongation of an organ results from detection of the displacement of a terminus (Bengough and Mackenzie, 1994; Frensch, 1997; van der Weele et al., 2003). In particular, displacement of the root tip in consecutive time periods obtained from marks or images enables the relative growth rate (R) to be calculated (Head, 1965; Osulliva and Prendevi, 1973). R relates the velocity of change of a parameter, such as length, to its magnitude at the corresponding time (Evans, 1972; Hunt, 1982).

Growth of plants is not uniform throughout growing organs. Rather, it is confined to distinct zones along which diverse spatial patterns of growth intensity exist (Sachs, 1874; Erickson and Sax, 1956; Peters and Tomos, 1996; Walter et al., 2009). Hence, in the last decade several methods had been developed for detailed quantitative description of spatial growth profiles (Walter et al., 2002; van der Weele, 2003; Walter and Schurr, 2005; Swarup et al., 2005; Nagel et al., 2006; Rahman et al., 2007). Here, segments along an organ are defined by artificial or anatomical markers. Elongation growth of individual segments is measured and compared to identify zones of high growth intensity. Relative growth rate of segments plotted versus segment position on the organ axis was suggested to provide a particularly meaningful measure of spatial growth patterns, especially over short measurement intervals (Green, 1976; Bernstein et al., 1993; Ben-Haj-Salah and Tardieu, 1995). However, it has been argued that spatial growth patterns should be described in terms of relative elemental growth rate (REGR), which relates the change in time of the relevant parameter to the position on the organ (Erickson and Sax, 1956). Indeed, REGR describes the distribution of growth intensities in space, whereas R focuses on size changes of material entities in time (Silk and Erickson, 1979; Gandar, 1980; Feng and Boersma, 1995). Methods have therefore been recently established to investigate REGR. Meanwhile, methods to determine relative growth rate have focused on successive elements along the growth axis rather than the total organ. Application of digital image sequence processing (DISP) approaches (Bigün and Granlund, 1987; Haußecker and Spies, 1999) to time-lapse videos of growing leaves and roots has improved these measurements dramatically. Processing high-resolution time-lapse records captured with shorter time intervals enabled characterization of spatial root growth profiles with high temporal resolution. However, development of methods that address the spatial distribution of growth along the root axis led to studies of total elongation rate of the root being neglected. In particular, profiles of total root elongation rate enable changes in overall growth to be studied over long time periods. Furthermore, investigation of the modulation of root growth due to various biotic and abiotic environmental stimuli relies on these patterns.

A number of software packages exist that aim to automate aspects of the kinematic analysis of growing roots. Many of these software tools attempt to measure growth parameters from high-resolution images. RootFlow (van der Weele et al., 2003) and REGR analysis (Walter et al., 2002) have made use of optical flow-based techniques (Barron et al., 1994; Barron and Liptay, 1994) to recover the motion of texture features through a sequence of root images. Intensity features are identified and matched between frames, and corresponding velocity flow fields are calculated. From these vector fields, estimates of growth can be made across any part of the image, provided that reliable image features are available in the areas of interest. Other tools such as KineRoot (Basu et al., 2007) and Multi-ADAPT (Ishikawa and Evans, 1997) have also been developed to study growth of roots from digital images. However, these tools focus on profiling the distribution of growth along the root axis. Other studies have used cell-scale models of features to determine image-plane motion and hence to estimate growth parameters, cell division pattern and transcription factor activity during root growth (Campilho et al., 2006; Mace et al., 2006; Roberts et al., 2006). RootLM (Qi et al., 2007) can measure the lengths of segments of Arabidopsis roots, allowing per-day growth rates to be determined. However, this tool requires the user to manually mark up the plate on which the roots sit with different coloured marker pens to indicate the growth during different growth phases (e.g. the growth from day to day). Plates are subsequently scanned, and the images are manually manipulated via photo-processing software. Among these methods, Phytomorph (Miller et al., 2007; Spalding, 2009) applies DISP techniques to measure the angle and elongation rate of the root system. Root elongation in this method is determined by sophisticated calculations from the length of the root midline. Midline length is calculated by summing the lengths of the many short midline segments connecting adjacent midline points. However, a midline length obtained by summing these discrete dsi lengths can suffer from an error due to scatter of the points from the true midline. This error is reduced by fitting a spline to the points and smoothing the midline (Miller et al., 2007).

Here we describe a new image processing software tool for detection of total root elongation patterns. The software tool processes large stacks of images and detects the position of the root tip in each. In this method, the total elongation rate of the root is calculated from the displacement of the root tip in consecutive time points. Root elongation profiles of several individuals over different time intervals are calculated from total growth rate values. Visualization of these profiles enables patterns detected from different genotypes in different environmental conditions to be compared, and the effect of biotic and abiotic stimuli on root growth to be addressed. The precise measurement of root tip displacements allows us to investigate the superimposition of rhythms on root elongation kinetics. Arabidopsis thaliana wild-type seedlings (Col0) demonstrate decreasing root growth activity during illumination and increasing root elongation rates during the dark period. Starchless pgm mutants, however, present a completely different root extension pattern.

MATERIALS AND METHODS

Plant material and cultivation procedure

Seeds of Arabidopsis thaliana wild-type (accession Col0) were surface-sterilized for 20 min with 10 % sodium hypochlorite solution containing 0·1 % surfactant (Triton X-100). After sterilization, seeds were rinsed several times with sterile water and plated on the surface of solid nutrient agar (7·0 %, w/v) supplemented with half-strength Murashige–Skoog medium (Murashige and Skoog, 1962; M02 555, pH 5·6; Duchefa, Haarlem, the Netherlands). After 4 days of stratification, Petri dishes were placed vertically in the phytotron (21 °C constant day and night temperature, 100 µmol m−2 s−1 photon flux density). Photoperiod was set to 12/12 h light–dark. On day 9, seedlings that had developed roots of at least 1 cm length were selected and transferred to a new 120 × 120-mm Petri dish which was filled with solid medium. The selected seedlings were distributed equally on the surface of the medium along a row 3 cm removed from the upper edge of the Petri dish. After 2 further days in the phytotron, Petri dishes were used for measurement.

Image acquisition

Before recording of root elongation kinetics, the Petri dish was mounted in a vertical position in front of the objective lens of a horizontally placed binocular microscope (Leica MZ6, Leica Microsystems GmbH, Wetzlar, Germany) equipped with an additional camera port. Image stacks were collected by a CCD camera (Panasonic Colour CCTV Camera, WV-CP210/G, Matsushita Communication Industrial Co. Ltd, Yokohama, Japan) mounted on the video port of the microscope. To monitor the seedlings regardless of actinic light requirements, an infrared light source (Infra-Red Illuminator CE-7710, Jenn Huey Enterprise Co. Ltd, Taipeh, Taiwan) continuously provided measuring light. To exclude entry of actinic light into the camera, an infrared filter (Infrarot RG780 E25, Heliopan, Gräfelfing, Germany) was installed between camera and microscope unit. This set-up enabled continuous monitoring of a 4·58 × 3·33-mm view frame area on the surface of the Petri dish through a 768 × 576-pixel channel. As a result, time-lapse records with 5·96 × 5·76-μm pixel resolution were obtained. Alternative image resolutions could be obtained by switching the objective lenses of the microscope.

The microscope camera was connected to the BNC connector (video in) of a Picolo PCI capture board (Picolo series, Euresys Inc., Itasca, IL, USA). Easy Grab for MultiCam (Picolo Demonstrator 3.8.1.1, provided with the capture board) displays the live stream from the camera. Every 10 min a screen shot was grabbed from the live image using SnagIt (http://www.TechSmith.com). Records were stored under a special filename format including important information such as capture time and a capturing index. When the root tip was about to leave the frame of view the Petri dish was moved so that the tip would appear on the top. Consecutive records were stored in a separate folder labelled with another position index. This stack of images was later used to calculate root extension profiles.

RESULTS AND DISCUSSION

The tip of a root growing on the surface of a solid agar plate describes a curve in a plane (Fig. 1A). This curve can be approximated by connecting a finite number of points using line segments to create a polygonal path. It is straightforward to calculate the distance between two successive points by using the theorem of Pythagoras in Euclidean space. If the curve is not already a polygonal path, better approximations to the curve can be obtained by following the shape of the curve more closely by increasing the number of images collected from a root growth experiment. Thus, the displacement of the points can be followed in time (Fig. 1B). In the present method, total growth rate of the root is calculated from these displacements of the root derived from time-lapse records (Fig. 2). Root elongation profiles are calculated from the displacement of the root tip and plotted as growth rate over time. The software applications are written in Visual Basic.Net 2005 (Microsoft Visual Basic 2005 Express Edition Version 8.0.50727.42). The executable files run under the Microsoft Windows operating system. Data visualization was performed using the ZedGraph open source library (http://www.zedgraph.org).

Fig. 1.

Fig. 1.

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Total root elongation is calculated from displacement of the root tip. (A) Tip displacement of a root growing on a surface describes a curve in a plane. This curve can be approximated by connecting a finite number of points using line segments. Dividing the length of consecutive line segments by the respective time interval provides the rate of root elongation in the corresponding period. (B) A stack of images exhibiting elongation of the root. Measuring the displacement of the root tip in this image stack enables the root elongation rates to be calculated.

Fig. 2.

Fig. 2.

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Work flow of automated root growth profiling. The method uses digital images to produce plots of total root elongation rates versus time. Image acquisition provides high-resolution time-lapse records from growing seedlings. The image processing module applies a novel root tip detection algorithm to multiple images and reveals the coordinates of the root tip position. Growth rates are calculated from the displacement of the root tip at consecutive time points. Statistical analysis of these values and averaging them over several time periods provide the root extension profiles, which are plotted over time.

Root tip detection

Quantification of huge root image stacks requires automated root tip detection. Tip detection starts by applying thresholds on the red, green and blue values of image pixel data (Fig. 3A–C). Screening the image from right to left and bottom to top, for each pixel whose colour values fall below the applied threshold, two rectangles will be defined, one below and one above (Fig. 3D, yellow and red rectangles). If the number of dark pixels in the lower area falls below a certain value and the number of dark pixels in the upper area exceeds a second threshold, the point is defined as the root tip position. To give the detection algorithm further flexibility, neighbouring pixels can also be included in the latter counting processes. However, it is reasonable to assume that among successive records, the root tip can only progress over a small distance from the previously detected position. In particular, the root tip can be expected not to move upwards or far left or right. Hence, to speed up the image processing procedure, after a successful detection of the root tip, the algorithm enables a user-defined area of interest (AOI) around the previously detected root tip position to be screened.

Fig. 3.

Fig. 3.

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Root tip detection algorithm. Detection of root tip starts by applying thresholds on colour values of the bitmap image pixels. Screening the whole image or the area of interest, below each pixel [P(x, y)] whose colour value does not succeed the threshold, a rectangle (a pixels in width and b pixels in height) is defined. If the number of dark pixels in this region exceeds a user-defined value (C1) screening the image will continue. Only if this condition is not satisfied, a second rectangle (c in width and d in height) is defined above P. If the number of dark pixels in this region exceeds a second user-defined threshold (C2), P(x, y) is reported as the root tip. (A) Flowchart of the root tip detection algorithm; (B) a typical time-lapse record before applying thresholds; (C) bitmap obtained after applying threshold on pixel colour values; (D) P(x, y) is defined as root tip when the number of dark pixels in a rectangle below P (yellow rectangle) fails C1 and the number of dark pixels in the rectangle defined above P (red rectangle) exceeds C2.

The root tip detection algorithm successfully calculates coordinates of root tips when individuals are growing with sufficient spatial separation. However, when roots grow behind each other, or if a dark object (such as an air bubble produced while pouring the medium into the Petri dishes) exists in the background of the image, this algorithm is susceptible to failure. To detect the root tip correctly in these conditions, image subtraction should be activated by the user. Image subtraction will highlight the differences between two images by removing the static parts. After image subtraction the root tip detection can calculate the tip coordinates of the desired root. Furthermore, the image subtraction algorithm allows lateral root growth to be studied from stages of initial development.

Multiple image processing

The sequential image processing module applies the root tip detection algorithm to a stack of images (Fig. 4). In detail, the user defines the root tip detection parameters and prepares a list of the indices of images showing the root tip. For a measurement applied over 4 d with time-lapse records captured every 10 min, the image stack contains approx. 575 records. Upon stack selection and program activation, the root tip detection algorithm scans all records and presents the coordinates of the detected root tip as well as the corresponding image capture time in a list (Fig. 4A). To monitor the precision of selected root tip detection parameters, this application also calculates the root extension rates and plots them online during image processing on the display (Fig. 4B). After analysis of all records of an individual root, the user defines a suitable nomenclature for saving specified data.

Fig. 4.

Fig. 4.

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Multiple image analyser applies the root tip detection algorithm to a stack of time-lapse records. (A) A stack of images is provided by the user, which includes the index numbers of time-lapse records as well as the desired root tip detection parameters. Upon image processing the resulting bitmap is presented and the detected root tip coordinates are listed. (B) To monitor the precision of selected root tip detection parameters, this application also calculates the root extension rates and plots them online during image processing on the display.

The root tip positions obtained are stored in .XML data library structures that possess three hierarchical levels (Fig. 5). On the lowest level, the individual root tip libraries contain the detected root tip coordinates together with the sampling time. These files are labelled with the name assigned to the respective individual. Libraries of the higher hierarchical levels, namely the main library of the software application and the genotype libraries, store the filename and storage address of the genotypes and individual root tip libraries, respectively. Content of the main library is presented in the graphical user interface of this software application. Here the user can select a single entry or groups of individuals and apply colours and symbols to each set. Calculation of root elongation rates of selected individuals and graphical visualization of the profiles obtained enables their growth patterns to be compared.

Fig. 5.

Fig. 5.

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Library structure used in this software application. Detected coordinates as well as times of dawn and dusk for each root tip are stored in the individual root tip .XML library file. For all individuals of the same genotype, filename and storage address of these libraries are stored as a new entry in the genotype library. The same information regarding the genotype libraries are stored as a new entry in the main library of the software application. All entries are accessible through the main form of the software application.

Calculation and visualization of root elongation profiles, and comparison of extension patterns

A separate module of the software application calculates the extension profile of the user-defined group of individual roots and plots the values over time. An additional graph depicts the position of the root tip over time (Fig. 6A). Extension profiles are calculated from displacements of the root tip in consecutive time points during the measurement (mathematical equations are presented in the Supplementary Data, available online). Visualization of extension profiles show the root elongation during the measuring period (Fig. 6B; supplementary eqns S1 and S2), averaged growth rate during consecutive days as well as light–dark periods (Fig. 6C, D; eqns S3 and S4) and the 24-h growth profile (Fig. 6E; eqn S5). In particular, the 24-h growth profile gives the extension rate of each individual at a certain time of the day averaged over several days of measurement. Additionally, all growth parameters can be averaged among several individuals.

Fig. 6.

Fig. 6.

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Root extension profiles of Arabidopsis thaliana wild-type (Col0) growing under 12/12-h light–dark cycles. Eleven-day-old seedlings were monitored for 4 d (n = 6, 21 °C constant day and night). (A) Positions of the root tip plotted over time. (B) Average root extension rate of all individuals calculated from displacement of the root tip in consecutive time periods. This plot exhibits the robust fluctuations of root growth rate. Light periods are characterized by a general inhibition of growth activity whereas growth speeds up during the night. (C) Averaged root elongation rate of all individuals over consecutive days plotted versus time. (D) Averaged growth rate values of all individuals in consecutive light and dark periods plotted versus time. (E) The 24-h growth profile of all individuals calculated by averaging the growth rate values of all individuals at each time of the day. This plot indicates that over a 24-h period, growth rate mainly increases during the night and decreases during the light period. Maximum growth rate is detected 1–2 h after dawn, after which growth is inhibited. The last 4 h of the light period is characterized by increasing growth rates. Inhibition of growth for 1–2 h upon darkening is followed by increasing growth rates towards the end of the night. (F) Averaged normalized elongation rates of all individuals plotted over the measurement period. (G) Averaged normalized growth rate of all individuals at each time of the day. Error bars indicate standard errors. Values in B–E are absolute elongation rates.

Different light durations, the presence or absence of sugars or other chemicals in the growth medium, as well as genetic mutations all cause variations in absolute root elongation rate. To focus on the pattern of growth, regardless of the absolute growth rate, elongation rates of each seedling were normalized to the median value for each day. As a result, for each day the time point with the median growth rate value is shown as 1. Figure 6F depicts the average of these normalized values for seedlings growing under a 12/12-h light–dark period. Similar to the absolute growth rate values, the normalized values can also be averaged over light–dark periods and the 24-h period of a day (the latter is shown in Fig. 6G).

Root extension pattern of A. thaliana wild-type seedlings in 12-h light periods

The software tool was used to analyse image stacks collected over four consecutive days of A. thaliana wild-type (ecotype Col0) root growth (Fig. 6). Positions of the root tips depicted over time formed nearly linear curves with increasing slope (Fig. 6A). Averaged absolute and normalized growth rates in each successive hourly time interval (Fig. 6B, F) exhibited a rhythmic pattern with the highest extension rate 1–2 h after dawn. Extension rates decreased during most of the light period, with a minimum 8 h after dawn. Subsequently, growth rates stabilized or increased during the last 3 h of the light period. After dark, a transient inhibition of growth was detected. This initial decrease was followed by recovery of elongation during the remainder of the night. Together, two minima could be detected, one before dusk and the second one after dark. Similar growth kinetics were reported for leaf expansion (Wiese et al., 2007). Root growth rate ranged from 120 to 55 µm h−1. Variations of root elongation rates indicated an almost two-fold change over 24 h. Growth rate standard errors ranged between 7·9 and 26·8 µm h−1 (n = 36).

The data were further processed to display additional features of the growth kinetics. Figure 6C shows the averaged daily growth rate (derived from eqn S3). Averaged daily growth rates remained almost constant during the measurement period. The average growth rate observed during this period was 82·15 ± 1·24 µm h−1 (based on 576 time points measured, n = 3456). Figure 6D shows average rates of growth within each light and dark period (derived from eqn S4). Although the growth rate declined during the light period and recovered during the night, the average extension rate detected during illumination was higher. This ratio of day- and night-derived growth rates underlines the importance of high temporal resolution in image sampling.

To substantiate further the detected time constants of the root elongation kinetics, the averaged 24-h growth pattern of seedlings at different times of the day were calculated (Fig. 6E, eqn S5). In agreement with the curve of average elongation rate with time, this plot demonstrates that growth rate rises during the night, peaks 1–2 h in the light (0900–1000 h), declines over the next hours to a minimum at 1600–1800 h, and after slight recovery towards the end of the light period shows a second minimum at 2100 h (1 h after dark).

All growth rate values in Fig. 6B–E are averaged absolute elongation rates. As the standard error indicates, they are affected by the different mean elongation rates of individuals. Normalizing growth rates to the median of each day (starting from midnight) removes this source of noise and provides a clearer insight into the pattern of growth. The normalized elongation rate over time and normalized 24-h growth profile are depicted in Fig. 6F and 6G, respectively. In parallel to the previously described patterns, these graphs display similar kinetics. Together, the results demonstrate the rhythmic root elongation of Arabidopsis seedlings growing under constant temperature conditions. Furthermore, this method enabled us to investigate these rhythms in various light durations including free running (results of these experiments will be published separately). In addition, the modulation of growth in response to mutations in the starch metabolism pathway and circadian clock were studied as well as the presence of different concentrations of extracellular sucrose in the growth medium (see below; detailed results will be published separately).

Mutation in PGM modifies root growth kinetics

Sugars facilitate growth by the supply of energy and are major building blocks of cell-wall compounds (Cosgrove, 2005). During the light period, carbohydrate metabolism is mainly driven by photosynthesis. At night, growth processes are maintained by degradation products of transitory starch (Geiger and Servaites, 1994; Zeeman et al., 2007). Starchless mutants of A. thaliana lacking the plastid enzyme phosphoglucomutase (PGM; Caspar et al., 1985) accumulate high levels of sucrose and reducing sugars during the light period. However, sugars fall to very low levels by the middle of the night (Gibon et al., 2004). To unravel the impact of carbohydrate availability on root elongation, root elongation profiles of pgm mutants were studied. Roots of pgm seedlings elongated with a completely modified elongation pattern during the diurnal cycle. In the first 4 h of the dark period, growth rate declined to 50 % of its initial value (Fig. 7). During the rest of the night, inhibition of growth continued at a slower rate. This brought the elongation rate at the end of the dark period down to about 20 % of the maximum daily value. After onset of light, however, growth increased gradually during the first 5 h of the light period, reaching 85 % of its maximum daily value. A slight trough emerged 6 h after dawn, which was followed by an increase of the elongation rate throughout the rest of the day. At the end of the day, roots of pgm mutants had their highest elongation rate. By this time the absolute growth rate was comparable with that of wild-type Col0. Previous studies have reported that starchless mutants grow like wild-type plants in continuous light, but growth is progressively impaired as the duration of the night is increased, and is arrested under short days (Caspar et al., 1985; Lin et al., 1988; Huber and Hanson, 1992). Although these previous studies were made under longer time periods, the high temporal resolution of the present method allowed the kinetics of root elongation in pgm mutants to be unravelled.

Fig. 7.

Fig. 7.

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Root extension pattern detected from pgm mutants deviates dramatically from that of wild-type plants. Root elongation of Col0 seedlings and pgm mutants was monitored for 72 h (n = 3). Averaged root extension rates of the pgm seedlings over time show an increase of growth rate during the light period and inhibition of elongation in the dark. This pattern reflects the absence of starch as a substrate for growth in the dark period and thus clearly deviates from that of the wild-type.

Conclusions

The present paper describes a software tool to decipher the kinetics of total root elongation by video imaging. Growing the seedlings in 12 × 12-cm rectangular Petri dishes enabled the elongation of the roots to be monitored until they reach a length of about 9 cm. An algorithm was developed that detects the root tip and reports its coordinates. Total root elongation rate in time is calculated from the displacement of the root tip in consecutive time-lapse records. Further calculations result in several growth profiles, which are visualized in terms of growth rate versus time plots. Application of this method enabled the modulation of root elongation in response to abiotic and biotic stimuli to be deciphered. Furthermore, with the possibility of investigating several mutants in various growth media, this method allows the physiological processes controlling root growth and its observed rhythms to be unravelled.

SUPPLEMENTARY DATA

Supplementary data are available online at www.aob.oxfordjournals.org and consist of the mathematical description of extension profiling (eqns S1–S5).

Supplementary Material

[Supplementary Data]
mcq048_index.html (736B, html)

AKNOWLEDGEMENTS

This work was supported by the Max Planck Society and by a contract to N.Y. We thank Professor M. Stitt (MPIMP, Potsdam, Germany) for valuable discussion during the development of this method.

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