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May 2019
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Method for Assessing Virulence of Colletotrichum higginsianum on Arabidopsis thaliana Leaves Using Automated Lesion Area Detection and Measurement
应用损伤面积的自动化检测和测量评价炭疽菌对拟南芥叶片致病性的方法   

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Abstract

The plant pathogenic fungus, Colletotrichum higginsianum is widely used to understand infection mechanisms, as it infects the model plant Arabidopsis thaliana. To determine the virulence of C. higginsianum, several methods have been developed, such as disease reaction scoring, lesion measurement, entry rate assays, and relative fungal biomass assays using real-time quantitative PCR. Although many studies have taken advantage of these methods, they have shortcomings in terms of objectivity, time, or cost. Here, we show a lesion area detection method applying ImageJ color thresholds to images of A. thaliana leaves infected by C. higginsianum. This method can automatically detect multiple lesions in a short time without the requirement for special equipment and measures lesion areas in a standardized way. This high throughput technique will aid better understanding of plant immunity and pathogenicity and contribute to reproducibility of assays.

Keywords: Colletotrichum higginsianum (菜炭疽病), Fungal phytopathogen (植物病原真菌), Arabidopsis thaliana (拟南芥), Infection assay (感染分析), ImageJ (ImageJ)

Background

Colletotrichum fungi cause anthracnose disease in a broad range of plants, including important crops, and have a serious economic impact (Cannon et al., 2012). Among Colletotrichum species, C. higginsianum infects Brassicaceae plants, including the model plant Arabidopsis thaliana (O’Connell et al., 2004). Thus, this pathogen has been used for various studies that have revealed molecular mechanisms of plant immunity and pathogenicity (Birker et al., 2009; Narusaka et al., 2009; Kleemann et al., 2012; Takahara et al., 2016; Plaumann et al., 2018). Moreover, recently released high-quality genome assemblies of C. higginsianum provide genomic resources that should increase the use of this fungus in functional studies by facilitating the identification of candidate genes of interest (Zampounis et al., 2016; Tsushima et al., 2019). In order to determine the virulence of C. higginsianum, initial studies employed disease reaction scores based on visual observation and subjective judgement (O’Connell et al., 2004; Narusaka et al., 2004). The necessity for more objective approaches has led to the development of alternative methods including lesion diameter measurement, entry rate assays, and measurement of relative fungal biomass using real-time quantitative PCR (Narusaka et al., 2010; Hiruma and Saijo, 2016). These methods are commonly employed in studies using C. higginsianum, yet they have disadvantages, such as the variability of lesion shapes leading to differences in measured values between observers, time-consuming steps, or the economic burden of purchasing reagents. ImageJ has been used as an open source tool for the analysis of scientific images (Schneider et al., 2012). In the field of plant pathology, many studies have utilized ImageJ to analyze pathogenicity or resistance, for instance by measuring lesion sizes (Dagdas et al., 2016; Kumakura et al., 2019). However, these procedures can be further automated. Therefore, we developed an objective, automated, and affordable method to detect and measure lesion areas from images of A. thaliana leaves infected by C. higginsianum using ImageJ. In this article, we describe how to perform infection assays to study the A. thaliana-C. higginsianum interaction and how to detect and measure lesions using ImageJ macros to record multiple lesion areas at one time. This method enables us to reproducibly perform infection assays with the A. thaliana-C. higginsianum pathosystem and may be adapted to other pathosystems. For example, ImageJ color thresholds were also used to measure lesion areas on C. shisoi-infected A. thaliana (Gan et al., 2019). This protocol will allow us to perform objective and high throughput analyses to gain further insights into plant-microbe interactions.

Materials and Reagents

  1. 50 ml conical tube (e.g., Falcon 50 ml Conical Centrifuge Tubes, Corning Inc., catalog number: 352070)
  2. Nylon mesh (100 μm pore size) cut into 11 x 11 cm square
  3. Surgical tape 
  4. 1.5 ml microcentrifuge tube (e.g., 1.5 ml Sampling Tubes, Round Bottom, FUKAE KASEI Co., Ltd., catalog number: 131-615C)
  5. Petri dish
  6. Paper towel
  7. Permanent marker
  8. A. thaliana plants grown at 22 °C with a 10-h photoperiod for 4 weeks
    Note: We recommend including genotypes known to show clear resistant and susceptible phenotypes as controls. In the case of infection assays using C. higginsianum MAFF 305635, Ws-2 and Ler-0 can be used as controls for resistance and susceptible phenotypes, respectively.
  9. C. higginsianum strains
  10. Potato dextrose agar (Potato Dextrose Agar, Nissui Pharmaceutical Co., Ltd., catalog number: 05709) prepared in Petri dishes
  11. Sterilized water

Equipment

  1. Airtight transparent plastic container with a rubber seal lid
  2. Painting brush (autoclaved) (e.g., Nylon watercolor brush (brown hair) flat, Artec Co., Ltd., catalog number: 10629)
    Note: Any painting brush can be used. We autoclave painting brushes to eliminate any contaminants from previous experiments and reuse them.
  3. Funnels (autoclaved)
    Note: We autoclave funnels for the same reason of painting brushes.
  4. Centrifugal machine (e.g., High speed refrigerated micro centrifuge, TOMY SEIKO Co., Ltd., catalog number: MX-307)
  5. Incubator (dark condition, 24 °C) (e.g., Cool incubator, Mitsubishi Electric Corporation, catalog number: CN-25C)
  6. Growth chamber with BLB light (light/dark = 12 h/12 h, 24 °C) (e.g., Growth cabinet, SANYO Electric Co., Ltd., catalog number: MLR-350 and Blacklight blue, Hitachi, Ltd., catalog number: FL40SBLB)
  7. Hemacytometer (e.g., Reichert Bright-Line, Hausser Scientific Co., catalog number: 1492)
  8. Light microscope (e.g., Binocular Microscope, Olympus Corporation, catalog number: CX31)
  9. Pipette that can measure 5 μl (e.g., PIPETMAN Classic P20, Gilson Inc., catalog number: F123600)
  10. Spray bottle (500 ml)
  11. Digital camera (e.g., EOS Kiss X6i, Canon Inc., catalog number: 6557B001)
  12. Camera stand (e.g., Copy stand CS-A4, LPL Co., Ltd., catalog number: L18142)
  13. Scissors
  14. Forceps
  15. White plastic tray
  16. Scale

Software

1.ImageJ v1.52p (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, https://imagej.nih.gov/ij/, 1997-2018)

Procedure

  1. Preparation of A. thaliana plants and C. higginsianum culture
    1. Culture fungal strains on potato dextrose agar at 24 °C under 12-h black-light blue fluorescent bulb light/12-h dark conditions for 1 week (Figure 1A).
    2. Set up at least 8 replicate A. thaliana plants for each genotype to be tested in airtight transparent plastic containers with about 1 cm of water (Figure 1B).
      Note: The lid of this transparent plastic container should have a rubber seal to maintain 100% humidity conditions during infection.
    3. Mark 3 petioles of fully expanded leaves per plant using a permanent marker (Figure 1C).
      Note: We recommend completing all the steps given above before starting to prepare conidia suspensions.


      Figure 1. Preparation of A. thaliana plants and C. higginsianum culture. A. 7-day-old culture of C. higginsianum MAFF 305635 on potato dextrose agar incubated at 24 °C under 12-h black-light blue fluorescent bulb light/12-h dark conditions. B. Airtight transparent plastic container with A. thaliana plants. C. Marked petioles using a permanent marker (White arrows).

  2. Inoculation with C. higginsianum
    1. Place an autoclaved filter funnel in a 50 ml conical tube and fix a folded piece of nylon mesh to the funnel using a piece of surgical tape (Figure 2A).
    2. Add 10 ml of room temperature sterilized water to the fungal culture in a Petri dish and gently scrape the surface using an autoclaved painting brush to release conidia (Figure 2B).
    3. Pour the conidial suspension through the nylon mesh in the funnel to separate mycelia from conidia (Figure 2C).
      Note: Conidia should pass through the nylon mesh, but mycelia should not.
    4. After centrifugation at 4,500 x g for 5 min at room temperature, remove the supernatant by decanting (Figure 2D).
    5. Resuspend the conidial pellet in 2 ml of distilled water.
    6. Prepare a 1/100 diluted conidia suspension in distilled water in an Eppendorf® microcentrifuge tube and determine the concentration of the original suspension using a hemacytometer (Figure 2E).
      Note: Depending on the conidial concentration, you can prepare different dilution series to obtain ~100 conidia/mm2 on a hemacytometer.
    7. Prepare at least 1 ml of 5 x 105 conidia/ml suspension in an Eppendorf® microcentrifuge tube.
    8. Open the lid of the transparent plastic container and inoculate 5 μl of the conidia suspension per leaf on the surface of the selected leaves using a pipette (Figure 2F).
      Note: We recommend inoculating plants where they will be grown during pathotests to minimize the risk of the inoculated conidia suspension running off from the leaf surface during transportation.
    9. Spray tap water on the backside of a lid of a transparent plastic container using a spray bottle. Then, seal the lid of the container and carefully place the container in the growth chamber.
      Note: Do not open the lid until 5 days post-infection to maintain 100% humidity during infection.


      Figure 2. Inoculation with C. higginsianum. A. Setup to separate mycelia from conidia. B. Scraping the fungal culture using a painting brush to obtain conidia suspension. C. Filtering the conidia suspension through nylon mesh. D. Conidial pellet after centrifugation. E. Conidia on a hemacytometer (Black arrows). Bar = 100 μm. F. Inoculation of conidia on the leaf surface using a pipette.

  3. Preparing images of symptoms
    1. Set up a digital camera on a camera stand and set magnification, aperture, shutter speed, and ISO.
      Note: Adjust your camera and exposure settings to minimize shadows and reflections in the images. Make sure to use the same settings for reproducibility when you repeat experiments. For this reason, we also recommend taking photos in a room without windows to avoid the effect of sunshine.
    2. Open the lid of the transparent plastic container. If necessary, gently wipe water droplets on the leaf surface using a paper towel to avoid capturing water-reflected light.
    3. Cut the marked petioles, put leaves on a white tray and take photos.
      Note: The white tray provides a uniform white background for the images. Make sure to include a ruler in images of inoculated leaves.

Data analysis

Note: For data analysis using ImageJ, you can also refer to the video tutorials provided in this article (Videos 1 and 2).

Video 1. How to analyze images of infected leaves using ImageJ

Video 2. How to visualize obtained data in a beeswarm boxplot using R


  1. Install ImageJ in your environment following the instructions at https://imagej.nih.gov/ij/download.html.
    Note: The procedure described below has been tested under Windows10 version 1903 and macOS Mojave 10.14.5. 
  2. Download ImageJ macros lesion.ijm and lesion_loop.ijm and save them in the same directory.
  3. Open a photo of infected leaves in ImageJ. File > Open > Select your photo or drag-and-drop your image file on to the ImageJ main menu bar.
  4. Open the ROI Manager. Analyze > Tools > ROI Manager.
  5. Set each leaf as a region of interest (ROI) on the photo using the rectangle tool and add this ROI by pressing “Add” in the ROI Manager (Figure 3A).
    Note: To add each ROI to the ROI Manager, you can use the short cut key. (In default settings, it is [t].)
  6. Save ROIs as a .zip file in your preferred directory. In the ROI Manager: More >Save > Select the destination directory.
    Note: You can load the ROIs by drag-and-drop of your .zip file on to the ImageJ main menu bar.
  7. Open the Threshold Color menu and set your color space as HSB. Image > Adjust > Color Threshold > Select the color space as HSB.
  8. Find the thresholds to define lesions in the photo by changing HSB thresholds (Figure 3B). After confirming the appropriate thresholds, click “Original” and close the Threshold Color menu.
    Note: In this step, you can define your thresholds by referring to images of control genotypes that are known to show resistance and susceptible phenotypes. The settings used in Tsushima et al. (2019) are as follows: hue, 0-255; saturation, 110-140; and brightness, 0-255. We normally adjust only the saturation values from the default settings to define lesion areas. After customizing the settings that work for your images, make sure to use the same settings when you repeat analyses.
  9. Open lesion.ijm in a text editor, edit the thresholds according to the settings identified in Step 8 and save. In lesion.ijm, min[0] and max[0], min[1] and max[1], and min[2] and max[2] indicate the minimum and maximum thresholds for hue, saturation, and brightness, respectively (Figure 3C).
  10. Install lesion.ijm and lesion_loop.ijm in your ImageJ. Plugins > Macros > Install > Select lesion.ijm or lesion_loop.ijm
  11. Measure 10 mm on the scale in your photo by using the straight tool. Then, set the scale in the dialog box. Analyze > Set Scale > Enter a known distance (10) and the unit of length (mm).
    Note: If you take your photos with the same magnification, you can tick “Global” in the dialog box and apply this scale to all subsequent images analyzed.
  12. Open the “Set Measurements” dialog box and tick “Area”, “Mean gray value” and “Limit to threshold”. Analyze > Set Measurements > tick in the dialog box.
  13. Run the ImageJ macro. Plugins > Macros > Run > Select lesion_loop.ijm.
    Note: You can assign a short cut key for running the macro. Plugins > Shortcuts > Add Shortcut.
  14. Save results as a comma-delimited (.csv) file. In the Results: More > Save > Select the directory where the results should be saved.
  15. Repeat to open the next photo, set ROIs, run lesion_loop.ijm and save the data until you have processed all images. 
  16. Analyze the detected lesion areas, for example, using R or Excel. The following describes an example of how to visualize the obtained data in a beeswarm boxplot using R.
    Note: We provide sample files in sample_files.zip to test all data analysis steps. This includes images of C. higginsianum-infected A. thaliana leaves (Ler-0_ChWT.JPG and Ws-2_ChWT.JPG), ROI files (Ler-0_ChWT.zip and Ws-2_ChWT.zip), raw data of detected lesion areas obtained from ImageJ (Ler-0_ChWT.csv and Ws-2_ChWT.csv), lesion area data reformatted for use as input for R analysis (Ws_Ler.csv), R code to generate the beeswarm plots in Figure 3D (beeswarm.Rmd), and the expected output of running the R code (Lesion_area.pdf).
  17. Prepare a .csv file with the lesion area data.
    Note: Please format the file as in Ws_Ler.csv
  18. Open RStudio and install “beeswarm” and “ggsci” packages by typing install.packages ("beeswarm", dependencies = TRUE) and install.packages ("ggsci", dependencies = TRUE).
    Note: Once you install these two packages, you do not need to install them from next time.
  19. Set the directory containing the .csv file as your working directory by typing setwd ("PATH_TO_DIRECTORY") in the console.
  20. In RStudio, open beeswarm.Rmd included in sample_files.zip. and change the .csv file name in Line 21 if necessary. Then, click “Run Current Chunk”. This will create the “Lesion_area.pdf” file in your working directory. Figure 3D shows the expected result using the sample files provided in this article.


    Figure 3. Data analysis using color thresholds in ImageJ. A. Setting regions of interest (ROIs) on a photo. Yellow rectangles indicate ROIs that will be measured with color thresholds. Arrow heads indicate the rectangle tool and the straight tool. B. The Threshold Color menu of ImageJ. Red-colored parts in ROIs will be detected as lesions. C. Lesion.ijm opened in a text editor indicating the lines specifying the minimum and maximum thresholds of hue, saturation, and brightness, respectively. D. An expected result using the sample files provided in this article.

Acknowledgments

This protocol was derived from our published work (Tsushima et al., 2019). This work was supported in part by JSPS Grant-in-Aid for JSPS Research Fellow to A.T. (17J02983) and KAKENHI (17H06172 to K.S. and 19K15846 to P.G.).

Competing interests

The authors declare no competing interests.

References

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  2. Cannon, P. F., Damm, U., Johnston, P. R. and Weir, B. S. (2012). Colletotrichum - current status and future directions. Stud Mycol 73: 181-213.
  3. Dagdas, Y. F., Beihaj, K., Maqbool, A., Chaparro-Garcia, A., Pandey, P., Petre, B., Tabassum, N., Cruz-Mireles, N., Hughes, R. K., Sklenar, J., Win, J., Menke, F., Findlay, K., Banfield, M. J., Kamoun, S. and Bozkurt, T. O. (2016). An effector of the irish potato famine pathogen antagonizes a host autophagy cargo receptor. ELife 5: e10856.
  4. Gan, P., Tsushima, A., Hiroyama, R., Narusaka, M., Takano, Y., Narusaka, Y., Kawaradani, M., Damm, U. and Shirasu, K. (2019). Colletotrichum shisoi sp. nov., an anthracnose pathogen of Perilla frutescens in Japan: molecular phylogenetic, morphological and genomic evidence. Sci Rep 9(1): 13349.
  5. Hiruma, K. and Saijo, Y. (2016). Plant inoculation with the fungal leaf pathogen Colletotrichum higginsianum. Methods Mol Biol 1398: 313-318.
  6. Kleemann, J., Rincon-Rivera, L. J., Takahara, H., Neumann, U., van Themaat, E. V. L., van der Does, H. C., Hacquard, S., Stüber, K., Will, I., Schmalenbach, W., Schmelzer, E. and O’Connell, R. J. (2012). Sequential delivery of host-induced virulence effectors by appressoria and intracellular hyphae of the phytopathogen Colletotrichum higginsianum. PLoS Pathog 8(4): e1002643.
  7. Kumakura, N., Ueno, A. and Shirasu, K. (2019). Establishment of a selection marker recycling system for sequential transformation of the plant-pathogenic fungus Colletotrichum orbiculare. Mol Plant Pathol 20(3): 447-459.
  8. Narusaka, M., Shiraishi, T., Iwabuchi, M. and Narusaka, Y. (2010). Monitoring fungal viability and development in plants infected with Colletotrichum higginsianum by quantitative reverse transcription-polymerase chain reaction. J General Plant Pathol 76(1): 1-6.
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简介

植物病原真菌 Colletotrichum higginsianum 被广泛用于了解感染机理,因为它感染了模型植物 Arabidopsis thaliana 。确定 C的毒力。 higginsianum ,已经开发了多种方法,例如疾病反应评分,病灶测量,进入率测定和使用实时定量PCR的相对真菌生物量测定。尽管许多研究都利用了这些方法,但是它们在客观性,时间或成本方面都有不足。在这里,我们展示了一种将ImageJ颜色阈值应用于 A图像的病变区域检测方法。拟南芥叶片感染了 C。 higginsianum 。这种方法可以在短时间内自动检测出多个病变,而无需特殊设备,并且可以标准化方式测量病变区域。这种高通量技术将有助于更好地了解植物的免疫力和致病性,并有助于测定的可重复性。
【背景】炭疽病真菌在多种植物(包括重要农作物)中引起炭疽病,并具有严重的经济影响(Cannon等,2012)。在 Colletotrichum 物种中, C。 higginsianum 感染了十字花科植物,包括模型植物 Arabidopsis thaliana (O'Connell et al。,2004)。因此,这种病原体已用于各种研究中,揭示了植物免疫力和致病性的分子机制(Birker等,2009; Narusaka等,2009; Kleemann et al。,2012; Takahara et al。,2016; Plaumann et al。,2018)。此外,最近发布了 C的高质量基因组组件。 higginsianum 提供了基因组资源,应通过促进感兴趣的候选基因的鉴定来增加这种真菌在功能研究中的使用(Zampounis等人,2016; Tusshima等人。 ,2019年)。为了确定 C的毒力。 higginsianum ,最初的研究采用了基于视觉观察和主观判断的疾病反应评分(O'Connell等,2004; Narusaka等,2004)。 。采取更客观的方法的必要性导致了替代方法的发展,包括病变直径测量,进入率测定以及使用实时定量PCR测量相对真菌生物量的方法(Narusaka et al。,2010; Hiruma和Saijo,2016年)。这些方法通常用于使用 C的研究中。 higginsianum ,但是它们具有缺点,例如病变形状的可变性会导致观察者之间的测量值差异,耗时的步骤或购买试剂的经济负担。 ImageJ已被用作分析科学图像的开源工具(Schneider et al。,2012)。在植物病理学领域,许多研究已利用ImageJ分析病原性或耐药性,例如通过测量病灶大小来进行分析(Dagdas等人,2016年; Kumakura等人。 ,2019)。但是,这些过程可以进一步自动化。因此,我们开发了一种客观,自动化且价格合理的方法,可以从 A图像中检测和测量病变区域。拟南芥叶片感染了 C。 higginsianum 使用ImageJ。在本文中,我们描述了如何进行感染检测以研究 A。拟南芥- C。 higginsianum 交互以及如何使用ImageJ宏一次检测和测量病变以记录多个病变区域。这种方法使我们能够可重复地使用 A进行感染检测。拟南芥- C。 higginsianum 病理系统,可能适用于其他病理系统。例如,ImageJ颜色阈值还用于测量C上的病变区域。被shisoi 感染的 A。 Thaliana (Gan et al。,2019)。该协议将使我们能够进行客观和高通量的分析,以进一步了解植物与微生物之间的相互作用。

关键字:菜炭疽病, 植物病原真菌, 拟南芥, 感染分析, ImageJ

材料和试剂

  1. 50 ml锥形管(例如,Falcon 50 ml锥形离心管,康宁公司,目录号:352070)
  2. 尼龙网(孔径为100μm)切成11 x 11 cm正方形
  3. 手术胶带
  4. 1.5 ml微量离心管(例如,1.5 ml采样管,圆底,FUKAE KASEI Co.,Ltd.,目录号:131-615C)
  5. 培养皿
  6. 纸巾
  7. 永久标记
  8. A。拟南芥植物在22°C和10小时光照下生长4周
    注意:我们建议将已知具有明显抗性和易感表型的基因型作为对照。在使用 C进行感染检测的情况下。 higginsianum MAFF 305635,Ws-2和Ler-0可以分别用作抗药性和易感表型的对照。
  9. C。 Higginsianum 菌株
  10. 在陪替氏培养皿中制备的马铃薯葡萄糖琼脂(尼苏制药有限公司的马铃薯葡萄糖琼脂,目录号:05709)
  11. 消毒水

设备

  1. 带有橡胶密封盖的气密透明塑料容器
  2. 油漆刷(高压灭菌)(例如,尼龙平头水彩笔(棕发),Artec Co.,Ltd.,目录号:10629)
    注意:可以使用任何画笔。我们对油漆刷进行高压灭菌,以消除先前实验中的任何污染物,然后将其重复使用。
  3. 漏斗(高压灭菌)
    注意:出于相同的原因,我们对漏斗进行高压灭菌。
  4. 离心机(例如,高速冷冻微型离心机,TOMY SEIKO Co.,Ltd.,目录号:MX-307)
  5. 培养箱(黑暗条件下,24°C)( e.g。,Cool培养箱,三菱电机公司,目录号:CN-25C)
  6. 带BLB灯的生长室(明/暗= 12 h / 12 h,24°C)( eg ),生长柜,三洋电机株式会社,目录号:MLR-350和黑光蓝,日立有限公司,目录号:FL40SBLB)
  7. 血细胞计数器(例如,Reichert Bright-Line,Hausser Scientific Co.,目录号:1492)
  8. 光学显微镜(例如,双目显微镜,奥林巴斯公司,目录号:CX31)
  9. 可以测量5μl的移液器(例如,PIPETMAN Classic P20,Gilson Inc.,目录号:F123600)
  10. 喷雾瓶(500毫升)
  11. 数码相机(例如,EOS Kiss X6i,佳能公司,目录号:6557B001)
  12. 相机架(例如,复印架CS-A4,LPL Co.,Ltd.,目录号:L18142)
  13. 剪刀
  14. 钳子
  15. 白色塑料托盘
  16. 规模

软件

1. ImageJ v1.52p(Rasband,WS,ImageJ,美国国立卫生研究院,贝塞斯达,马里兰,美国, https ://imagej.nih.gov/ij/ ,1997-2018年)

程序

  1. 制备 A。拟南芥植物和 C。希金斯人文化
    1. 在12个小时的黑光蓝色荧光灯/ 12小时的黑暗条件下于24°C在马铃薯葡萄糖琼脂上培养真菌菌株1周(图1A)。
    2. 设置至少8个复制 A。在密封的透明塑料容器中用约1厘米的水(图1B)测试每种基因型的拟南芥植物。
      注意:此透明塑料容器的盖子上应有橡胶密封条,以在感染期间保持100%的湿度条件。
    3. 使用永久性标记物标记每个植物的3个叶柄的完全展开的叶子(图1C)。
      注意:我们建议您在开始准备分生孢子悬浮液之前先完成上述所有步骤。


      图1. A的制备。拟南芥 植物和 C。 higginsianum 文化。 A. 7天大的 C文化。马铃薯葡萄糖琼脂上的higginsianum MAFF 305635在24°C下在12小时黑光蓝色荧光灯光照/ 12小时黑暗条件下孵育。 B.带有 A的气密透明塑料容器。拟南芥植物。 C.使用永久标记标记的叶柄(白色箭头)。

  2. 接种 C。希金斯菊属
    1. 将高压灭菌的过滤漏斗放在50 ml的锥形管中,并用一块手术胶带将折叠的尼龙网片固定在漏斗上(图2A)。
    2. 在培养皿中的真菌培养物中加入10 ml室温无菌水,并用高压灭菌的涂刷轻轻刮擦表面以释放分生孢子(图2B)。
    3. 将分生孢子悬浮液倒入漏斗中的尼龙网,将菌丝体和分生孢子分开(图2C)。
      注意:分生孢子应该穿过尼龙网,但菌丝体不能穿过。
    4. 在室温下以4,500 x g 离心5分钟后,通过倾析除去上清液(图2D)。
    5. 将分生孢子沉淀重悬于2 ml蒸馏水中。
    6. 在Eppendorf ?微量离心管中准备1/100稀释的分生孢子在蒸馏水中的悬浮液,并使用血细胞计数器测定原始悬浮液的浓度(图2E)。
      注意:根据分生孢子浓度,您可以准备不同的稀释系列,以在血细胞计数器上获得〜100分生孢子/ mm2。
    7. 在Eppendorf ?微量离心管中准备至少1 ml 5 x 10 5 分生孢子/ ml悬浮液。
    8. 打开透明塑料容器的盖子,并用移液器在所选叶片的表面上每片叶片接种5μl分生孢子悬浮液(图2F)。
      注意:我们建议对病原体进行种植的植物进行接种,以最大程度减少运输过程中接种的分生孢子悬浮液从叶表面流失的风险。
    9. 用喷雾瓶将自来水喷在透明塑料容器盖的背面。然后,密封容器的盖子并将容器小心地放入生长室中。
      注意:感染后5天才打开盖子,以在感染期间保持100%的湿度。


      图2.用 C接种。 higginsianum 。 A。设置分离菌丝体和分生孢子。 B.使用油漆刷刮擦真菌培养物以获得分生孢子悬浮液。 C.通过尼龙网过滤分生孢子悬浮液。 D.离心后的分生孢子沉淀。 E.血细胞计数仪上的分生孢子(黑色箭头)。棒=100μm。 F.用移液管将分生孢子接种在叶片表面。

  3. 准备症状图像
    1. 将数码相机设置在相机支架上,并设置放大倍率,光圈,快门速度和ISO。
      注意:调整相机和曝光设置以最大程度地减少图像中的阴影和反射。重复实验时,请确保使用相同的设置以提高可重复性。因此,我们还建议您在没有窗户的房间里拍照,以免受到阳光的影响。
    2. 打开透明塑料容器的盖子。如有必要,请使用纸巾轻轻擦拭叶子表面的水滴,以免捕获水反射光。
    3. 切下标记的叶柄,将叶子放在白色托盘上并拍照。
      注意:白色托盘为图像提供统一的白色背景。确保在接种叶片的图像中包括标尺。

数据分析

注意:有关使用ImageJ进行数据分析的信息,您还可以参考本文提供的视频教程(视频1和2)。


视频1.如何使用ImageJ分析感染叶片的图像


视频2.如何使用R在蜂巢箱图中可视化获得的数据

  1. 按照 https://imagej.nih.gov/ij/download上的说明,在您的环境中安装ImageJ。 html 。
    注意:下面描述的过程已经在Windows10 1903版和macOS Mojave 10.14.5下进行了测试。
  2. 下载ImageJ宏 lesion.ijm和lesion_loop.ijm 并保存它们在同一目录中。
  3. 在ImageJ中打开受感染叶子的照片。文件>打开>选择您的照片或将图像文件拖放到ImageJ主菜单栏上。
  4. 打开ROI管理器。分析>工具>投资回报率经理。
  5. 使用矩形工具将每片叶子设置为照片上的关注区域(ROI),然后通过在ROI Manager中按“添加”来添加此ROI(图3A)。
    注意:要将每个ROI添加到ROI Manager,您可以使用快捷键。 (默认设置为[t]。)
  6. 将ROI以.zip文件格式保存在您的首选目录中。在ROI管理器中:更多>保存>选择目标目录。
    注意:您可以通过将.zip文件拖放到ImageJ主菜单栏上来加载ROI。
  7. 打开“阈值颜色”菜单,然后将颜色空间设置为HSB。图像>调整>颜色阈值>选择颜色空间作为HSB。
  8. 通过更改HSB阈值找到阈值以定义照片中的病变(图3B)。确认适当的阈值后,单击“原始”,然后关闭“阈值颜色”菜单。
    注意:在此步骤中,您可以参考已知具有抗性和易感表型的对照基因型图像来定义阈值。 Tsushima 等 (2019)中使用的设置如下:色相,0-255;饱和度110-140;和亮度0-255。通常,我们仅从默认设置中调整饱和度值以定义病变区域。自定义适用于图像的设置后,请确保在重复分析时使用相同的设置。
  9. 在文本编辑器中打开lesion.ijm,根据步骤8中确定的设置编辑阈值并保存。在lesion.ijm中,min [0]和max [0],min [1]和max [1]以及min [2]和max [2]分别指示色调,饱和度和亮度的最小和最大阈值(图3C)。
  10. 在ImageJ中安装lesion.ijm和lesion_loop.ijm。插件>宏>安装>选择lesion.ijm或lesion_loop.ijm
  11. 使用直线工具在照片的刻度上测量10毫米。然后,在对话框中设置比例。分析>设置比例尺>输入已知距离(10)和长度单位(mm)。
    注意:如果您以相同的放大倍数拍摄照片,则可以在对话框中勾选“全局”,并将此比例尺应用于所有后续分析的图像。
  12. 打开“设置度量”对话框,然后勾选“面积”,“平均灰度值”和“限制阈值”。分析>设置度量>在对话框中打勾。
  13. 运行ImageJ宏。插件>宏>运行>选择lesion_loop.ijm。
    注意:您可以为运行宏分配一个快捷键。插件>快捷方式>添加快捷方式。
  14. 将结果另存为逗号分隔(.csv)文件。在结果中:更多>保存>选择要保存结果的目录。
  15. 重复以打开下一张照片,设置ROI,运行lesion_loop.ijm并保存数据,直到处理完所有图像为止。
  16. 例如,使用R或Excel分析检测到的病变区域。下面描述了一个示例,该示例如何使用R在蜂巢箱图中可视化获取的数据。
    注意:我们在 中提供示例文件> sample_files.zip 来测试所有数据分析步骤。这包括 C的图像。被希金斯虫 感染的 A。拟南芥 叶子(Ler-0_ChWT.JPG和Ws-2_ChWT.JPG),ROI文件(Ler-0_ChWT.zip和Ws-2_ChWT.zip),从ImageJ获得的检测到的病变区域的原始数据(Ler -0_ChWT.csv和Ws-2_ChWT.csv),病变区域数据的格式重新设置为用作R分析的输入(Ws_Ler.csv),R代码以生成图3D中的蜂群图(beeswarm.Rmd),以及预期的输出运行R代码(Lesion_area.pdf)。
  17. 准备包含病变区域数据的.csv文件。
    注意:请按照Ws_Ler.csv 中的格式设置文件
  18. 打开RStudio并通过键入install.packages(“ beeswarm”,依赖关系= TRUE)和install.packages(“ ggsci”,依赖关系= TRUE)来安装“ beeswarm”和“ ggsci”软件包。
    注意:一旦安装了这两个软件包,则无需从下一次开始安装它们。
  19. 通过在控制台中键入setwd(“ PATH_TO_DIRECTORY”),将包含.csv文件的目录设置为工作目录。
  20. 在RStudio中,打开sample_files.zip中包含的beeswarm.Rmd。并在第21行中更改.csv文件名。然后,单击“运行当前块”。这将在您的工作目录中创建“ Lesion_area.pdf”文件。图3D使用本文提供的示例文件显示了预期的结果。


    图3.使用ImageJ中的颜色阈值进行数据分析。 A.在照片上设置关注区域(ROI)。黄色矩形表示将使用颜色阈值测量的ROI。箭头指示矩形工具和直线工具。 B. ImageJ的“阈值颜色”菜单。 ROI中的红色部分将被检测为病变。 C. Lesion.ijm在文本编辑器中打开,指示分别指定色调,饱和度和亮度的最小和最大阈值的行。 D.使用本文提供的示例文件得到的预期结果。

致谢

该协议源自我们已发表的工作(Tsushima et al。,2019年)。这项工作得到了A.T的JSPS研究学者的部分资助。 (17J02983)和KAKENHI(K.S.的17H06172和P.G.的19K15846)。

利益争夺

作者宣称没有利益冲突。

参考文献

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Copyright: © 2019 The Authors; exclusive licensee Bio-protocol LLC.
引用:Tsushima, A., Gan, P. and Shirasu, K. (2019). Method for Assessing Virulence of Colletotrichum higginsianum on Arabidopsis thaliana Leaves Using Automated Lesion Area Detection and Measurement. Bio-protocol 9(22): e3434. DOI: 10.21769/BioProtoc.3434.
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如果您对本实验方案有任何疑问/意见, 强烈建议您发布在此处。我们将邀请本文作者以及部分用户回答您的问题/意见。为了作者与用户间沟通流畅(作者能准确理解您所遇到的问题并给与正确的建议),我们鼓励用户用图片的形式来说明遇到的问题。

如果您对本实验方案有任何疑问/意见, 强烈建议您发布在此处。我们将邀请本文作者以及部分用户回答您的问题/意见。为了作者与用户间沟通流畅(作者能准确理解您所遇到的问题并给与正确的建议),我们鼓励用户用图片的形式来说明遇到的问题。