Measurement of Arabidopsis thaliana Plant Traits Using the PHENOPSIS Phenotyping Platform

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Plant Physiology
Jul 2017


High-throughput phenotyping of plant traits is a powerful tool to further our understanding of plant growth and its underlying physiological, molecular, and genetic determinisms. This protocol describes the methodology of a standard phenotyping experiment in PHENOPSIS automated platform, which was engineered in INRA-LEPSE (https://www6.montpellier.inra.fr/lepse) and custom-made by Optimalog company. The seminal method was published by Granier et al. (2006). The platform is used to explore and test various ecophysiological hypotheses (Tisné et al., 2010; Baerenfaller et al., 2012; Vile et al., 2012; Bac-Molenaar et al., 2015; Rymaszewski et al., 2017). Here, the focus concerns the preparation and management of experiments, as well as measurements of growth-related traits (e.g., projected rosette area, total leaf area and growth rate), water status-related traits (e.g., leaf dry matter content and relative water content), and plant architecture-related traits (e.g., stomatal density and index and lamina/petiole ratio). Briefly, a completely randomized (block) design is set up in the growth chamber. Next, the substrate is prepared, its initial water content is measured and pots are filled. Seeds are sown onto the soil surface and germinated prior to the experiment. After germination, soil watering and image (visible, infra-red, fluorescence) acquisition are planned by the user and performed by the automaton. Destructive measurements may be performed during the experiment. Data extraction from images and estimation of growth-related trait values involves semi-automated procedures and statistical processing.

Keywords: Phenotyping (表型), PHENOPSIS (PHENOPSIS), Water deficit (缺水), Arabidopsis thaliana (拟南芥), Growth (生长)


Phenotyping of plant traits is an important aspect of plant sciences. It can be defined as a set of methodologies used to measure plant traits with certain accuracy and precision at different scales of organization (Fiorani and Schurr, 2013). A renaissance of plant phenotyping was brought by the development of automated phenotyping platforms and the creation of new tools for image analysis (Granier and Vile, 2014). Automated plant phenotyping is useful in many aspects of plant biology, such as ecophysiology (Vile et al., 2012), genetics (Bac-Molenaar et al., 2016), and molecular biology (Baerenfaller et al., 2012). It is then important for the broader readership to understand how these platforms work and what can be achieved. This protocol focuses on one particular installation, namely PHENOPSIS (Granier et al., 2006). PHENOPSIS is a custom-made phenotyping platform (growth chamber, automaton, and computer software), manufactured by Optimalog company and it is especially well-suited for analyses of small plants, such as Arabidopsis thaliana. The platform enables growing up to 504 A. thaliana plants simultaneously. Each plant is grown in a separate pot. Each pot can be automatically weighed and watered to a target value, thus it is feasible to monitor soil water content (SWC) individually and automatically adjust it. This feature of the platform makes it perfect for the application of water deficit treatments. PHENOPSIS automatically takes images of plant rosettes. Images can be of four types: RGB zenithal, RGB lateral, infrared, and fluorescence. In addition, many other non-destructive or destructive measurements are also available with some human involvement, e.g., transpiration, stomatal conductance, phenological stage, individual leaf area, epidermal cell density, extent of endoreduplication, and root weight. Because each of these measurements would require a separate lengthy manual, this protocol focuses on the basic platform operation, as well as measurements of leaf morphology and water status. The video of the platform in action is available online (http://bioweb.supagro.inra.fr/phenopsis/InfoBDD.php).

Materials and Reagents

  1. Double-sided tape (Scotch 12 x 6.3 mm dispensed, permanent, clear)
  2. Single-sided tape (Crystal clear tape Scotch 19 x 33 mm)
  3. Template sheets for leaf scanning
  4. Outer pots (APTE Society, http://www.apte.fr/)
  5. Inner perforated pots (outer pots hand perforated with a Dremel 4000)
  6. Pot labels (Point label, soft plastic 1.3 x 6 cm, BIER: http://www.fournitures-horticoles.com/magasin/catproduits.php?idgdf=9)
  7. Wooden toothpicks
  8. Microscope slide (Knittel Glass 76 x 26 mm, Starfrost)
  9. Pencil
  10. Fine permanent marker (Staedtler permanent, Lumacolor)
  11. Cylindrical containers for soil drying (50 x 50 mm, Servilab, catalog number: 8668770 )
  12. Paper bags for plant tissue drying (7 x 12 cm: http://www.beaumont-group.fr/produit/kraft-blanchi-frictionne-neutre-2/)
  13. Scalpel (Swann-Morton, Carbon Steel Surgical blades)
  14. Pincers (S MurrayTM Stainless Steel Watchmaker Forceps 11 cm, Fisher Scientific)
  15. A. thaliana seeds
  16. Agricultural soil (from Mauguio city, near Montpellier, France: N 43 37′ 01″, E 4 00′ 33″)
  17. Compost (Neuhaus N2)
  18. Clear nail polish (Maybelline New York, express Finish 40)
  19. Ammonium dihydrogen phosphate (H2PO4NH4) (VWR, catalog number: 21305.290 )
  20. Potassium nitrate (KNO3) (VWR, catalog number: 26869.291 )
  21. Fe (E.D.D.H.A) (SEQUESTRENE 138 FE 100 SG)
  22. Nitric acid (HNO3) 52% (VWR, catalog number: 20412.362 )
  23. Boric acid (H3BO3) (Merck, catalog number: 1.00165.0100 )
  24. Manganese(II) sulfate monohydrate (MnSO4·H2O) (VWR, catalog number: 25303.233 )
  25. Copper(II) sulfate pentahydrate (CuSO4·5H2O) (Sigma-Aldrich, catalog number: C2857-250G )
    Note: This product has been discontinued.
  26. Zinc sulfate heptahydrate (ZnSO4·7H2O) (Avantor Performance Materials, J.T. Baker, catalog number: 4382-01 )
  27. Ammonium molybdate tetrahydrate, (NH4)6Mo7O24∙4H2O (Sigma-Aldrich, catalog number: A7302-100G )
  28. Nutrient solution (see Recipes)
  29. Microelement solution (see Recipes)


  1. PHENOPSIS phenotyping platform (Figure 1)
    1. Growth chamber
    2. Robotic arm (custom made by Optimalog: https://www.optimalog.com/phenopsis)
    3. Precision balance (Precisa, model: XB620C )
    4. Watering tubes
    5. RBG camera (Prosilica GC1600 (Allied Vision Technologies, model: PROSILICA GC 1600 ) with Computar Varifocal Megapixel M3Z1228C-MP (CBC, Computar, model: M3Z1228C-MP ), 12-36 mm, monture C zoom lens)
    6. Controlling computer (Dell)
  2. Laptop computer for phenological stage notations (HP ProBook 650 G1 (HP Development, model: HP ProBook 650 G1 ), Intel Core i3-4000M Dual Core, 8 GB 1600 DDR3L 2DM)
  3. Desktop scanner (Epson, model: Epson Perfection 4990 Photo )
  4. Desktop computer for image analyses (HP Compaq Pro 6300 MT PC+ (HP Development, model: HP Compaq Pro 6300 Microtower ) Intel Core i5-3470 3.2 G 6 M HD 2500 CPU, GB DDR3-1600 DIMM (2 x 4 GB) RAM + 250 GB 7200 RPM 3.5 HDD, AMD Radeon HD 7450 DP)
  5. Tablet monitor with a pen (Wacom, model: Cintiq 22HD )
  6. Stereo microscope with camera attachment (Leitz DMRB, Manta G-201B camera)
  7. Green LED lamp (Bulb LED GU10 Spot 1 W = 10 W green)

    Figure 1. PHENOPSIS phenotyping platform. A. Overview of the growth chamber; B-C. Robotic arm with RBG camera and watering tube visible; D. Precision balance; E. A screenshot from Optimalog software controlling the platform.


  1. Microsoft Excel
  2. ImageJ v1.51 (https://imagej.nih.gov/ij/) (Schneider et al., 2012)
  3. PHENOPSIS ImageJ macros (http://bioweb.supagro.inra.fr/phenopsis/MacroImageJ.php)
  4. Application for automaton control:
    OPTIMA PLC software (https://optimalog.com/index.php?q=optimaplc_presentation)
  5. Camera control: ProsilicaGigE (RGB Camera, Allied Vision); optionally: ThermaCAM Researcher (IR Camera, Flir), ImagingWin (Fluorescence camera, Walz)
    Note: All the software is adapted by Optimalog to be used with Phenopsis.
  6. R v3.4.1 (https://www.r-project.org/)
  7. Software for monitoring of environmental conditions:
    Campbell Logger Net (https://www.campbellsci.com/loggernet)


  1. Preparation of the experiment
    1. Design the experiment: experimental conditions (aerial conditions, photoperiod and water deficit treatments if considered), the duration of the experiment, tissue harvests, total number of plants in the platform and numbers of biological replicates. An example experiment could be: 21 A. thaliana genotypes grown in three watering regimes (well-watered, moderate and severe water deficit), 8 biological replicates per genotype per watering conditions. Using four experimental blocks, there will be 2 replicates per block (Figure 2). 
    2. Create a randomized experimental design, for example using a customized R script (Figure 2). Pot coordinate randomization is advisable to diminish the effect of the position in the growth chamber which might significantly affect the results, e.g., growth rate and flowering time. Block numbers can be assigned to different areas of the chamber to test for the pot position effect. Assign numbers to pots and enter the data to a spreadsheet.

      Figure 2. Schematic randomized experimental design in PHENOPSIS for a specific experiment with 21 A. thaliana genotypes, 3 watering regimes, and 8 biological replicates per genotype per watering conditions. Each point corresponds to one pot. Shapes correspond to watering regimes and colors to genotypes. Black lines divide the trays in PHENOPSIS growth chamber, and grey/white shading signify 4 blocks. Randomization reduces the effect of pot position in the growth chamber, and blocks can be used to investigate this effect.

    3. Prepare two series of pots (inner and outer) labeled with an adhesive tape. Inner pots are perforated with elongated holes, to enhance the soil drying process if necessary (Figure 3).

      Figure 3. Pots used in PHENOPSIS experiments. Inner pots (left) have extended holes for enhanced soil drying (visible as two lines on the surface of the pot). Holes are nearly identical in all inner pots, thus differences in soil drying dynamics are minimal. Outer pots (right) are not perforated. Source: http://bioweb.supagro.inra.fr/phenopsis/.

    4. Weigh the outer (Pout) and inner (Pin) pots. Enter the weights of the two empty pots into the spreadsheet.
    5. Prepare the substrate: Mix 50% agricultural soil with 50% compost.
      Note: This substrate is dedicated for water deficit experiments, as it provides optimal water retention for such purpose. Other substrates may be used for other types of experiments.
    6. Place the inner pot in the outer one and fill the inner pot with the mixed substrate, weigh the pots with the substrate (Total) and enter this weight into the spreadsheet. Collect and weigh one small soil sample (approx. 10 g) per every 10 pots (Swet).
    7. Dry the soil samples at 80 °C for 3 days.
    8. Rinse the soil surface with 10 ml of nutrient solution (see Recipes) for better seed imbibition.
    9. Sow approximately 5 A. thaliana seeds onto the soil surface using a wet toothpick and put the pots in the growth chamber (20.5 °C and 70% relative air humidity) in the dark for two days.
    10. Transfer the pots into the PHENOPSIS growth-chamber and adjust the environmental conditions (vapor pressure deficit, photoperiod, temperature, PAR [max: 200 µmol]).
    11. Spray the soil surface in all pots with deionized water 3 times per day until germination.
    12. Measure the weights of soil samples (Sdry) and include them in the spreadsheet. Each soil sample will represent 10 pots. Calculate the initial SWC of the substrate as follows:

    13. Calculate the wet (PSwet) and dry (PSdry) weights of soil as follows:

    14. Calculate the target weights of pots in two combinations for desired SWCs–with single (Targetsingle) and double (Targetdouble) pot. SWC will be defined here as g H2O g-1 dry soil.

      If you plan to apply water deficit treatments, calculate target weights for both well-watered conditions and water deficit conditions.

  2. Management of the experiment
    1. Declare the experiment in the database (PhenopsisDB, http://bioweb.supagro.inra.fr/phenopsis/). Include information about seed stocks, watering regimes and other details of the experiment.
    2. Note the germination date for each pot.
    3. After germination, adjust the watering conditions in the Optimalog software. This software controls functions of the robotic arm, including measurements of pot weight, watering and imaging, and cycles during the day. The watering can be programmed using a spreadsheet with calculated pot weights for desired conditions. Set 1-2 weighing/watering cycles per day. Set the target SWC to 0.35 g H2O g-1 dry soil.

      Figure 4. Example zenithal rosette image. A. Unprocessed image of A. thaliana rosette; B. Image processed using custom ImageJ macro that applies a color threshold to the picture. The resulting binary mask is created on the basis of green pixels. Source: http://bioweb.supagro.inra.fr/phenopsis/.

    4. Adjust the properties of zenithal imaging in prosilicaGigE software. Program the robot to take one picture per day after the light was turned on in the growth chamber (Figure 4).
    5. Perform phenological stage notations three times per week. Notations take ca. 2 h for 2 people and 504 pots, i.e., one in the chamber looking at the plant phenological stages and the other noting on the laptop. Critical developmental stages should be noted (Boyes et al., 2001), as well as leaf number for each plant. Notations are best stored in spreadsheets.
    6. At the 4-leaf stage determined individually for each pot, remove excessive plants, leaving one healthy plant per pot.
    7. If the experiment involves different SWC treatments, the target weight should be appropriately adjusted (Targetsingle) upon the appearance of first true leaves, and outer pots should be removed for the soil drying period. After reaching the desired SWC, reintroduce the outer pot and adjust the target weight to Targetdouble. This needs to be done individually for each pot.
    8. Harvest plant tissue at previously specified growth stages. If metabolomic and transcriptomic analyses are considered, it is advisable to harvest the tissue at the specific time of the day to avoid diurnal fluctuations. Night time harvests can be done under green light in order to avoid activation of photosynthetic processes and induction of light-dependent gene expression.

  3. Biomass partitioning and leaf scans
    1. Remove the flowering stem and cut the rosette near the soil surface. Measure the rosette fresh weight (FWR).
    2. Enclose the rosette between sheets of wet paper towel in the Petri dish, and place the Petri dish in the cold room for 24 h.
    3. Carefully remove water droplets from the rosette with a paper towel, and measure the water-saturated (turgid) weight of the rosette (TWR).
    4. Excise all laminas from the rosette and stick them with double-sided tape to an A4 sheet of paper, according to their rank. The template for this sheet includes a calibration square for further analysis, and boxes for sample description (Figure 5). Measure the water-saturated weight of petioles after stripping the rosettes of laminas (TWP), and stick the petioles to the same sheet of paper.

      Figure 5. Example leaf scan with indicated weights and final leaf numbers

    5. Count the final number of rosette (LN) and cauline leaves (LCN), and write down these numbers.
    6. Scan the paper sheets with leaves and collect the laminas and petioles of each plant to separate bags.
    7. Place the bags at 80 °C for 3 days, and afterwards measure the dry weights of laminas (DWL) and petioles (DWP).

  4. Stomatal density measurement
    1. Remove one leaf blade from the rosette (the largest leaf or a specific leaf rank). Cut it in half along the central nerve and stick both halves to a sheet of paper with double-sided tape, opposite sides up (Figure 6A).
    2. Cover the leaf blade with a thin layer of transparent nail polish. The nail polish needs to be not too viscous, and formation of air bubbles should be avoided.
    3. After 5 min of drying, stick one-sided tape to the nail polish layer, remove it carefully from the leaf blade, and stick it to the labeled microscope slide.

      Figure 6. Epidermal imprints. A. Leaf blades after peeling-off the nail polish; B. Example microphotograph of an epidermal imprint.

Data analysis

  1. Leaf scans
    Analyze the leaf scans using a custom ImageJ macro (http://bioweb.supagro.inra.fr/phenopsis/MacroImageJ.php). The macro will prompt you to click on all the leaves and then calculate areas for each leaf. The sum of these areas for each plant is total leaf area (TLA).

  2. Stomatal traits
    1. Place the slide under a stereo microscope. Take at least 3 good quality representative images from each epidermal imprint with 50-200 stomata visible at the area of the image (Iarea) (e.g., Figure 6B).
    2. At each image count both the number of stomata (Snum) and the number of the pavement cells (Pavnum). Stomatal counting can be performed using a custom ImageJ macro available online (http://bioweb.supagro.inra.fr/phenopsis/MacroImageJ.php). The provided link also contains adequate documentation with clear step-by-step description of how to perform cell counting.

  3. Rosette projections
    1. Collect zenithal images that are to be used in the analysis. For successful curve fitting, one image per 4 days is required.
    2. Extract projected rosette areas (PRA) using custom ImageJ macros (http://bioweb.supagro.inra.fr/phenopsis/MacroImageJ.php).

  4. Calculated traits
    1. Calculate the trait values: rosette dry weight (DWR), lamina/petiole ratio (DWL/P), water content (WC), relative water content (RWC), leaf dry matter content (LDMC), and leaf mass per area (LMA):

    2. Calculate stomatal density (Sdens) and stomatal index (SI):

    3. Estimate the dynamics of leaf formation (Vile et al., 2012) using LOESS or curve fitting (Figure 7A):

      Lnum is leaf number at the time of measurement,
      a is the number of days after the formation of first true leaves,
      a0 is the time when LN/2 leaves developed,
      LN is the maximum leaf number,
      b is the maximum rate of leaf production.
    4. Estimate growth rate parameters (Bac-Molenaar et al., 2015) using curve fitting to projected rosette area data (Figure 7B). Depending on the complexity of the dataset, different functions can be used, such as simple exponential function:

      A0 is initial rosette size,
      r is the growth rate; or more complex sigmoidal function (Gompertz function):

      Amax is the final rosette size, and b is the position along the time axis.

      Figure 7. Dynamic growth measurement of A. thaliana Col-0 accession under well-watered conditions (blue), and moderate (red) and severe (black) water deficit. A. Changes in rosette leaf number. Data are derived from phenological stage notations. Lines and shading correspond to LOESS and standard error respectively. B. Changes in projected rosette area. Lines correspond to Gompertz curves.

    All traits that can be measured using this protocol are listed in Table 1.

    Table 1. Traits measured in a basic PHENOPSIS experiment

    1. The effects of the genotype (G), environment (E) and G x E interaction can be calculated using two-way analysis of variance (ANOVA). Multivariate effects may be analyzed by multivariate ANOVA (MANOVA). For these statistical methods, additional data analysis software is needed.
      In A. thaliana accessions, the genotype effect tends to be strong, what makes direct observation of plant responses difficult. It is then advisable to, in addition to the analysis of absolute trait values, calculate and analyze the response ratios to different conditions as it was done by Rymaszewski et al. (2017). A response ratio is a logarithmized ratio of mean trait value under treatment conditions and mean trait value under control conditions.
    2. Working on multiple genotypes and multiple traits, it is often beneficial to reduce the dimensionality using such methods as principal component analysis (PCA). These methods can also be utilized to visualize and better understand the data structure prior to the in-depth analysis.


Note: Add deionized water to the required volume.

  1. Nutrient solution (150 L)
    60 ml of 28.76 g/L stock
    120 ml of 88.45 g/L stock
    Microelement solution
    60 ml
     Fe (E.D.D.H.A)
    7.2 g
    Adjust pH to 5.2-5.8 with HNO3
  2. Microelement solution (5 L)
    1.85 g
    1.90 g
    0.03 g
    1.44 g
    0.06 g


Access to the PHENOPSIS platform was permitted thanks to funds from the European Plant Phenotyping Network (EPPN, grant agreement No. 284443) funded by the FP7 Research Infrastructures Programme of the European Union. This research was also supported by a PRELUDIUM (2012/05/N/NZ9/01396) grant from the National Science Centre, Poland, awarded to W.R. PHENOPSIS developments are supported by FEDER-FSE-IEJ 2014-2020 Languedoc-Roussillon APSEVIR and PHENOPSIS 2.0 projects. Authors declare no conflict of interest.


  1. Bac-Molenaar, J. A., Granier, C., Keurentjes, J. J. and Vreugdenhil, D. (2016). Genome-wide association mapping of time-dependent growth responses to moderate drought stress in Arabidopsis. Plant Cell Environ 39(1): 88-102.
  2. Bac-Molenaar, J. A., Vreugdenhil, D., Granier, C. and Keurentjes, J. J. (2015). Genome-wide association mapping of growth dynamics detects time-specific and general quantitative trait loci. J Exp Bot 66(18): 5567-5580.
  3. Baerenfaller, K., Massonnet, C., Walsh, S., Baginsky, S., Buhlmann, P., Hennig, L., Hirsch-Hoffmann, M., Howell, K. A., Kahlau, S., Radziejwoski, A., Russenberger, D., Rutishauser, D., Small, I., Stekhoven, D., Sulpice, R., Svozil, J., Wuyts, N., Stitt, M., Hilson, P., Granier, C. and Gruissem, W. (2012). Systems-based analysis of Arabidopsis leaf growth reveals adaptation to water deficit. Mol Syst Biol 8: 606.
  4. Boyes, D. C., Zayed, A. M., Ascenzi, R., McCaskill, A. J., Hoffman, N. E., Davis, K. R. and Gorlach, J. (2001). Growth stage-based phenotypic analysis of Arabidopsis: a model for high throughput functional genomics in plants. Plant Cell 13(7): 1499-1510.
  5. Fiorani, F. and Schurr, U. (2013). Future scenarios for plant phenotyping. Annu Rev Plant Biol 64: 267-291.
  6. Granier, C., Aguirrezabal, L., Chenu, K., Cookson, S. J., Dauzat, M., Hamard, P., Thioux, J. J., Rolland, G., Bouchier-Combaud, S., Lebaudy, A., Muller, B., Simonneau, T. and Tardieu, F. (2006). PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytol 169(3): 623-635.
  7. Granier, C. and Vile, D. (2014). Phenotyping and beyond: modelling the relationships between traits. Curr Opin Plant Biol 18: 96-102.
  8. Rymaszewski, W., Vile, D., Bediee, A., Dauzat, M., Luchaire, N., Kamrowska, D., Granier, C. and Hennig, J. (2017). Stress-related gene expression reflects morphophysiological responses to water deficit. Plant Physiol 174(3): 1913-1930.
  9. Schneider, C. A., Rasband, W. S. and Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9(7): 671-675.
  10. Tisné, S., Schmalenbach, I., Reymond, M., Dauzat, M., Pervent, M., Vile, D. and Granier, C. (2010). Keep on growing under drought: genetic and developmental bases of the response of rosette area using a recombinant inbred line population. Plant Cell Environ 33(11): 1875-1887.
  11. Vile, D., Pervent, M., Belluau, M., Vasseur, F., Bresson, J., Muller, B., Granier, C. and Simonneau, T. (2012). Arabidopsis growth under prolonged high temperature and water deficit: independent or interactive effects? Plant Cell Environ 35(4): 702-718.


植物性状的高通量表型分析是进一步了解植物生长及其潜在生理,分子和遗传决定论的有力工具。该协议描述了在INRA-LEPSE( https: //www6.montpellier.inra.fr/lepse )并由Optimalog公司定制。开创性的方法由Granier等人发表(2006年)。该平台用于探索和测试各种生理生理假设(Tisnéet al。,2010; Baerenfaller et al。,2012; Vile et al。 > 2012; Bac-Molenaar 等,2015; Rymaszewski 等,2017)。在这里,重点关注实验的准备和管理,以及与生长相关的性状(例如,预测莲座面积,总叶面积和生长速率),水状况相关性状(<例如叶干物质含量和相对含水量)以及与植物结构相关的性状(例如气孔密度和指数以及叶片/叶柄比率)。简而言之,在生长室中设置完全随机的(块)设计。接下来,准备基底,测量其初始含水量并填充盆。种子播种到土壤表面并在实验之前发芽。发芽后,用户计划土壤浇水和图像(可见光,红外线,荧光)采集,并由自动机执行。在实验过程中可能会进行破坏性测量。从图像中提取数据并估计与生长相关的性状值涉及半自动程序和统计处理。

【背景】植物性状的表型分析是植物科学的一个重要方面。它可以被定义为一套方法论,用于在不同规模的组织中以一定的精度和精度测量植物性状(Fiorani and Schurr,2013)。自动化表型分析平台的发展和图像分析的新工具的创建带来了植物表型的复兴(Granier and Vile,2014)。自动植物表型分型在植物生物学的许多方面是有用的,例如生态生理学(Vile等人,2012),遗传学(Bac-Molenaar等人,2016),和分子生物学(Baerenfaller等人,2012)。对于更广泛的读者来说,了解这些平台如何工作以及可以实现什么非常重要。该协议专注于一个特定的安装,即PHENOPSIS(Granier et。,2006)。 PHENOPSIS是由Optimalog公司生产的定制的表型分析平台(生长室,自动机和计算机软件),特别适用于分析小型植物,如拟南芥。该平台可以长到504平方米。 thaliana 植物。每个植物都生长在一个独立的盆中。每罐可以自动称量并浇灌到目标值,因此单独监测土壤含水量(SWC)并自动调整土壤含水量是可行的。该平台的这一特点使其成为缺水处理应用的完美选择。 PHENOPSIS自动拍摄植物玫瑰花图像。图像可以有四种类型:RGB天顶,RGB横向,红外和荧光。此外,许多其他非破坏性或破坏性测量还可用于一些人类参与,例如蒸发,气孔导度,物候阶段,单个叶面积,表皮细胞密度,内部复制程度和根重量。由于每个测量需要单独的冗长的手册,因此该协议侧重于基本的平台操作,以及叶片形态和水状态的测量。该平台的视频可以在线获取( http://bioweb.supagro.inra。 FR / phenopsis / InfoBDD.php )。

关键字:表型, PHENOPSIS, 缺水, 拟南芥, 生长


  1. 双面胶带(Scotch 12 x 6.3 mm分液,永久性,清洁)
  2. 单面胶带(透明胶带Scotch 19 x 33 mm)
  3. 叶子扫描的模板表
  4. 外盆(APTE Society, http://www.apte.fr/
  5. 内部穿孔锅(用Dremel 4000穿孔的外锅手)
  6. 锅标签(点标签,软塑料1.3 x 6厘米,BIER: http:
  7. 木牙签
  8. 显微镜幻灯片(Knittel玻璃76 x 26毫米,Starfrost)
  9. 铅笔
  10. 细永久性标记(Staedtler永久,Lumacolor)

  11. 用于土壤干燥的圆柱形容器(50 x 50 mm,Servilab,目录号:8668770)
  12. 用于植物组织干燥的纸袋(7 x 12厘米: http: //www.beaumont-group.fr/produit/kraft-blanchi-frictionne-neutre-2/
  13. 手术刀(Swann-Morton,碳钢外科手术刀)
  14. (S Murray 不锈钢制表匠钳11厘米,Fisher Scientific)
  15. 甲。 thaliana 种子
  16. 农业土壤(来自法国蒙彼利埃附近的Mauguio市:N 43 37'01“,E 4 00'33”)
  17. 堆肥(Neuhaus N2)
  18. 清除指甲油(美宝莲纽约,快递完成40)
  19. 磷酸二氢铵(H 2 PO 4 4 NH 4)(VWR,目录号:21305.290)
  20. 硝酸钾(KNO 3)(VWR,目录号:26869.291)
  21. Fe(E.D.D.H.A)(SEQUESTRENE 138 FE 100 SG)
  22. 硝酸(HNO 3)52%(VWR,目录号:20412.362)
  23. 硼酸(H 3 BO 3)(Merck,目录号:1.00165.0100)
  24. 硫酸锰(II)一水合物(MnSO 4•H 2 O)(VWR,目录号:25303.233)
  25. 硫酸铜(II)五水合物(CuSO 4•5H 2 O)(Sigma-Aldrich,目录号:C2857-250G)
  26. 硫酸锌七水合物(ZnSO 4•7H 2 O)(Avantor Performance Materials,J.T.Baker,目录号:4382-01)
  27. 钼酸铵四水合物,(NH4)6Mo7O24•4H2 / 2 > O(Sigma-Aldrich,目录号:A7302-100G)
  28. 营养液(见食谱)
  29. 微量元素解决方案(请参阅食谱)


  1. PHENOPSIS表型分析平台(图1)
    1. 成长室
    2. 机器人手臂(由Optimalog定制: https://www.optimalog.com/phenopsis ) >
    3. 精密天平(Precisa,型号:XB620C)
    4. 浇水管
    5. RBG相机(带有Computar Varifocal Megapixel M3Z1228C-MP(CBC,Computar,型号:M3Z1228C-MP),12-36mm,C型变焦镜头的Prosilica GC1600(Allied Vision Technologies,型号:PROSILICA GC 1600))
    6. 控制电脑(戴尔)
  2. 物理阶段符号的笔记本电脑(HP ProBook 650 G1(HP开发,型号:HP ProBook 650 G1),Intel Core i3-4000M双核,8 GB 1600 DDR3L 2DM)
  3. 桌面扫描仪(爱普生,型号:爱普生完美4990照片)
  4. 用于图像分析的台式机(HP Compaq Pro 6300 MT PC +(HP开发,型号:HP Compaq Pro 6300微型立式机)Intel Core i5-3470 3.2 G 6 M HD 2500 CPU,GB DDR3-1600 DIMM(2 x 4 GB)RAM + 250 GB 7200 RPM 3.5硬盘,AMD Radeon HD 7450 DP)
  5. 带笔的平板电脑显示器(Wacom,型号:Cintiq 22HD)
  6. 带相机附件的立体显微镜(Leitz DMRB,Manta G-201B相机)
  7. 绿色LED灯(灯泡LED GU10点1 W = 10 W绿色)

    图1. PHENOPSIS表型分析平台。 :一种。生长室概述;公元前。带RBG相机和浇水管的机器人手臂可见; D.精密天平; E.控制平台的Optimalog软件截图。


  1. Microsoft Excel
  2. ImageJ v1.51( https://imagej.nih.gov/ij/ )(Schneider > et al。,2012)
  3. PHENOPSIS ImageJ宏( http://bioweb.supagro.inra.fr/phenopsis/MacroImageJ.php
  4. 应用于自动控制:
    OPTIMA PLC软件( https://optimalog.com/index.php?q=optimaplc_presentation
  5. 相机控制:ProsilicaGigE(RGB Camera,Allied Vision);可选:ThermaCAM研究员(IR相机,Flir),ImagingWin(荧光相机,Walz)
  6. R v3.4.1( https://www.r-project.org/
  7. 用于监测环境条件的软件:
    Campbell Logger Net( https://www.campbellsci.com/loggernet


  1. 准备实验
    1. 设计实验:实验条件(空气条件,如果考虑光周期和水分亏缺处理),实验持续时间,组织收获,平台中植物的总数以及生物学重复次数。一个示例实验可以是:21。在三种浇水方式(充分浇水,中度和重度缺水)中生长的拟南芥基因型,每种浇水条件下每基因型8个生物学重复。使用四个实验模块,每个模块会有两个重复项目(图2)。&nbsp;
    2. 创建一个随机化的实验设计,例如使用自定义的R脚本(图2)。盆坐标随机化是可取的,以减少生长室中位置的影响,这可能显着影响结果,例如生长速率和开花时间。可以将编号分配给试验室的不同区域以测试罐位置效应。将数字分配给底池并将数据输入到电子表格。

      图2.用PHENOPSIS进行的具有21 A的特定实验的示意性随机实验设计。拟南芥基因型,3种浇水方式和每种基因型在每种浇水条件下的8种生物学重复。每个点对应一个盆。形状对应于浇水制度和颜色与基因型。黑线将PHENOPSIS生长室中的托盘分开,灰色/白色阴影表示4个块。随机化可以减少生长室中壶的位置影响,块可以用来研究这种影响。

    3. 准备用胶带标记的两个系列盆(内部和外部)。如果需要,内盆用穿孔加长孔以增强土壤干燥过程(图3)。

      图3. PHENOPSIS实验中使用的花盆。内盆(左)具有延长的孔以增强土壤干燥(在盆的表面上可以看到两条线)。所有内锅的孔几乎完全相同,因此土壤干燥动态的差异很小。外盆(右)没有穿孔。资料来源: http://bioweb.supagro.inra.fr/phenopsis/
    4. 称重外部 ( )和内部 )花盆。
    5. 准备底物:将50%的农业土壤与50%的堆肥混合。
    6. 将内罐置于外罐中,并用混合基材填充内罐,用底板称重罐( Total ),并将该重量输入到电子表格中。
      每10盆收集一个小土壤样本(约10克)并称重( wet )。

    7. 在80°C下干燥土壤样品3天。
    8. 用10毫升营养液冲洗土壤表面(见食谱),以获得更好的种子吸收。
    9. 播种约5微米。使用湿牙签将拟南芥种子播种到土壤表面,并在黑暗中将盆放入生长室(20.5℃和70%相对空气湿度)两天。
    10. 将盆转移到PHENOPSIS生长室并调整环境条件(蒸气压不足,光周期,温度,PAR [最大值:200μmol])。
    11. 每天用去离子水喷洒所有盆中的土壤表面3次,直至萌发。
    12. 测量土壤样品的重量( s dry )并将它们包含在电子表格中。每个土壤样本将代表10个盆。按照以下步骤计算基材的初始 SWC :

    13. 计算湿( PS wet )和干( ps )土壤重量如下:

    14. 使用单个(目标 单个 )和双精度来计算期望的 SWC (目标 double )。这里将SWC 定义为g H 2 -1 干土。


  2. 实验管理
    1. 在数据库中声明实验(PhenopsisDB, http://bioweb.supagro.inra.fr/phenopsis/ < / A>)。包括关于种子库存,浇水制度和实验的其他细节的信息。
    2. 请注意每个锅的发芽日期。
    3. 发芽后,调整Optimalog软件中的浇水条件。该软件控制机器人手臂的功能,包括罐体重量,浇水和成像的测量以及白天的周期。浇水可以使用电子表格进行编程,其中计算的锅重量用于期望的条件。每天设置1-2次称量/浇水周期。将目标 SWC 设置为0.35 g H 2 <-1> 干土。

      图4. zenithal玫瑰花图像示例 A.未处理的图像 A。 thaliana rosette; B.使用自定义ImageJ宏来处理图像,该图像将颜色阈值应用于图片。生成的二进制掩码是基于绿色像素创建的。资料来源:
      http://bioweb.supagro.inra.fr/phenopsis/ 。 >
    4. 在prosilicaGigE软件中调整天顶成像的属性。
    5. 每周进行三次物候阶段标记。符号采取 ca。 2小时2人,504盆,即,一个在室内看着植物的物候阶段,另一个在笔记本电脑上注意。应注意关键的发育阶段(Boyes等人,2001年),以及每个植物的叶片数量。符号最好存储在电子表格中。
    6. 在每个盆中分别确定的4叶阶段,移除过量的植物,每盆留下一个健康的植物。
    7. 如果实验涉及不同的 SWC 治疗,目标体重应该在外观上适当调整( single ))第一片真叶,外层盆应在土壤干燥期内除去。到达所需的 SWC 后,重新引入外罐并将目标重量调整为 double 。这需要为每个罐子单独完成。
    8. 在先前指定的生长阶段收获植物组织。如果考虑代谢组学和转录组学分析,建议在一天的特定时间收集组织以避免日间波动。夜间收获可以在绿光下完成,以避免激活光合作用过程和诱导光依赖性基因表达。

  3. 生物量分区和叶扫描
    1. 去除开花茎并在土壤表面附近切割莲座。测量莲座鲜重( FW R )。

    2. 将培养皿中的玫瑰花瓣放在培养皿中的湿纸巾之间,并将培养皿置于冷室中24小时。
    3. 用纸巾小心地去除花环上的水滴,然后测量玫瑰花结的水分饱和(turgid)重量( TW R )) 。
    4. 根据它们的等级,从玫瑰花结中除去所有叶片,并用双面胶带将它们粘在A4纸上。该表格的模板包括用于进一步分析的校准方块和用于样品描述的方框(图5)。在剥离叶片的玫瑰花结之后测量叶柄的水饱和重量( TW P ),并将叶柄粘在同一张纸上。


    5. 计算花环( LN )和茎生叶( L C N ),并记下这些数字。
    6. 用纸扫描纸页并收集每个植物的叶片和叶柄以分离袋子。
    7. 将袋子置于80℃3天,然后测量叶片( DW L )和叶柄( DW <子> P 的)。

  4. 气孔密度测量
    1. 从莲座上取下一片叶片(最大的叶子或特定的叶子等级)。用中间神经将它切成两半,用双面胶带将两半粘在一张纸上,两面朝上(图6A)。
    2. 用薄薄的透明指甲油覆盖叶片。指甲油不要太粘稠,应避免形成气泡。
    3. 干燥5分钟后,将单面胶带贴在指甲油层上,小心地将其从叶片上取下,并将其粘贴在标记的显微镜载玻片上。

      图6.表皮印记A.剥离指甲油后的叶片; B.表皮印记的示例显微照片。


  1. 叶子扫描
    使用自定义ImageJ宏分析叶子扫描( http://bioweb.supagro.inra.fr /phenopsis/MacroImageJ.php )。宏将提示您单击所有叶子,然后计算每个叶子的面积。每株植物的这些面积的总和是总叶面积( TLA )。

  2. 气孔特征
    1. 将幻灯片放在立体显微镜下。从每个表皮印记中取出至少3张质量良好的代表性图像,图像区域可见50-200个气孔( area )(例如,图6B)。
    2. 在每个图像计数气孔的数量( num )和路面单元的数量( pav <子> NUM )。可以使用在线提供的自定义ImageJ宏来执行气孔计数( http://bioweb.supagro。 inra.fr/phenopsis/MacroImageJ.php )。所提供的链接还包含足够的文档,并清楚地说明如何执行细胞计数。

  3. Rosette预测
    1. 收集要在分析中使用的全景图像。为了成功进行曲线拟合,每4天需要一个图像。
    2. 使用自定义ImageJ宏提取投影玫瑰花区( PRA )( http: //bioweb.supagro.inra.fr/phenopsis/MacroImageJ.php )。

  4. 计算特征
    1. 计算性状值:玫瑰果干重(DW),叶片/叶柄比(DW)水分含量(WC),相对含水量(RWC ),叶干物质含量( ), > LDMC )和每个区域的叶片质量( LMA ):

    2. 计算气孔密度(S dens )和气孔指数( S I > ):

    3. 使用LOESS或曲线拟合(图7A)估计叶形成的动态(Vile等人,2012):

      num 是测量时的叶号,
      a 是形成第一片真叶之后的天数,
      0 是 LN / 2叶开发的时间,
      LN 是最大叶数,
      b 是叶片生产的最大速率。
    4. 用曲线拟合法预测玫瑰花结面积数据(图7B)估计生长速率参数(Bac-Molenaar等人,2015)。根据数据集的复杂程度,可以使用不同的函数,如简单的指数函数:

      r 是增长率;或更复杂的S形函数(Gompertz函数):

      是最终花环大小,而 b 是沿着时间轴的位置。

      图7. A的动态增长测量。 (蓝色),中度(红色)和严重(黑色)水分亏缺的拟南芥Col-0加入。A.莲座叶数的变化。数据来自物候阶段符号。线条和阴影分别对应于LOESS和标准误差。 B.预测莲座区面积的变化。线条对应于Gompertz曲线。



    1. 可以使用双向方差分析(ANOVA)来计算基因型(G),环境(E)和G×E相互作用的影响。多变量效应可以通过多变量ANOVA(MANOVA)进行分析。对于这些统计方法,需要额外的数据分析软件。
      在 A。 拟南芥种质中,基因型效应趋于强烈,这使得直接观察植物反应变得困难。 因此,除了绝对特质值的分析外,还应该计算和分析Rymaszewski等人(2017年)所做的对不同条件的响应比率。 应答比率是处理条件下的平均特性值与对照条件下的平均特性值的对数化比率。
    2. 处理多种基因型和多种性状时,使用主成分分析(PCA)等方法降低维度通常是有益的。 这些方法也可用于在进行深入分析之前可视化并更好地理解数据结构。



  1. 营养液(150升)
    60 ml of 28.76 g/L stock
    120 ml of 88.45 g/L stock
    Microelement solution
    60 ml
     Fe (E.D.D.H.A)
    7.2 g
    Adjust pH to 5.2-5.8 with HNO3
  2. Microelement solution (5 L)
    1.85 g
    1.90 g
    0.03 g
    1.44 g
    0.06 g


由于欧盟FP7研究基础设施项目资助的欧洲植物现象分析网络(EPPN,赠款协议第284443号)的资金允许访问PHENOPSIS平台。这项研究还得到了来自波兰国家科学中心的PRELUDIUM(2012/05 / N / NZ9 / 01396)资助,授予WR PHENOPSIS开发项目得到了FEDER-FSE-IEJ 2014-2020朗格多克 - 鲁西荣APSEVIR和PHENOPSIS的支持2.0项目。作者声明不存在利益冲突。


  1. Bac-Molenaar,J.A.,Granier,C.,Keurentjes,J.J。和Vreugdenhil,D。(2016)。 拟南芥中时间依赖性生长反应与中度干旱胁迫的全基因组关联作图 Plant Cell Environ 39(1):88-102。
  2. Bac-Molenaar,J.A.,Vreugdenhil,D.,Granier,C。和Keurentjes,J.J。(2015)。 生长动态的全基因组关联图谱检测时间特异性和一般数量性状基因座。 J Exp Bot 66(18):5567-5580。
  3. Baerenfaller,K.,Massonnet,C.,Walsh,S.,Baginsky,S.,Buhlmann,P.,Hennig,L.,Hirsch-Hoffmann,M.,Howell,KA,Kahlau,S.,Radziejwoski,A. ,Russenberger,D.,Rutishauser,D.,Small,I.,Stekhoven,D.,Sulpice,R.,Svozil,J.,Wuyts,N.,Stitt,M.,Hilson,P.,Granier,C.和Gruissem,W(2012)。 对拟南芥叶生长的系统分析表明适应水分亏缺。 Mol Syst Biol 8:606.
  4. Boyes,D.C.,Zayed,A.M.,Ascenzi,R.,McCaskill,A.J.,Hoffman,N.E.,Davis,K.R。和Gorlach,J。(2001)。 基于生长阶段的拟南芥表型分析:高通量模型功能基因组学在植物中的应用 Plant Cell 13(7):1499-1510。
  5. Fiorani,F。和Schurr,U。(2013)。 植物表型的未来场景 Annu Rev Plant Biol 64:267-291。
  6. Granier,C.,Aguirrezabal,L.,Chenu,K.,Cookson,SJ,Dauzat,M.,Hamard,P.,Thioux,JJ,Rolland,G.,Bouchier-Combaud,S.,Lebaudy, Muller,B.,Simonneau,T和Tardieu,F。(2006)。 PHENOPSIS,一种植物对拟南芥中土壤水分亏缺的植物响应可重复表型分型的自动化平台允许识别对土壤水分亏缺敏感度低的种质。新植物酚 169(3):623-635。
  7. Granier,C.和Vile,D。(2014)。 表型及其他:模拟特征之间的关系。 Curr Opin Plant Biol 18:96-102。
  8. Rymaszewski,W.,Vile,D.,Bediee,A.,Dauzat,M.,Luchaire,N.,Kamrowska,D.,Granier,C。和Hennig,J。(2017)。 与压力有关的基因表达反映了对水分亏缺的形态生理反应。植物生理学 174(3):1913-1930。
  9. Schneider,C.A.,Rasband,W.S。和Eliceiri,K.W。(2012)。 NIH Image to ImageJ:25年的图像分析 Nat Methods 9(7):671-675。
  10. Tisné,S.,Schmalenbach,I.,Reymond,M.,Dauzat,M.,Pervent,M.,Vile,D。和Granier,C。(2010)。 在干旱条件下继续生长:利用重组自交系对玫瑰花区进行反应的遗传和发育基础人口。 Plant Cell Environ 33(11):1875-1887。
  11. Vile,D.,Pervent,M.,Belluau,M.,Vasseur,F.,Bresson,J.,Muller,B.,Granier,C。和Simonneau,T。(2012)。 Arabidopsis 在长时间高温和缺水情况下生长:独立或互动效应? Plant Cell Environ 35(4):702-718。
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Copyright: © 2018 The Authors; exclusive licensee Bio-protocol LLC.
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Rymaszewski, W., Dauzat, M., Bédiée, A., Rolland, G., Luchaire, N., Granier, C., Hennig, J. and Vile, D. (2018). Measurement of Arabidopsis thaliana Plant Traits Using the PHENOPSIS Phenotyping Platform. Bio-protocol 8(4): e2739. DOI: 10.21769/BioProtoc.2739.
  2. Rymaszewski, W., Vile, D., Bediee, A., Dauzat, M., Luchaire, N., Kamrowska, D., Granier, C. and Hennig, J. (2017). Stress-related gene expression reflects morphophysiological responses to water deficit. Plant Physiol 174(3): 1913-1930.