Automatic Quantification of the Number of Intracellular Compartments in Arabidopsis thaliana Root Cells

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Nature Plants
Jun 2016



In the era of quantitative biology, it is increasingly required to quantify confocal microscopy images. If possible, quantification should be performed in an automatic way, in order to avoid bias from the experimenter, to allow the quantification of a large number of samples, and to increase reproducibility between laboratories. In this protocol, we describe procedures for automatic counting of the number of intracellular compartments in Arabidopsis root cells, which can be used for example to study endocytosis or secretory trafficking pathways and to compare membrane organization between different genotypes or treatments. While developed for Arabidopsis roots, this method can be used on other tissues, cell types and plant species.

Keywords: Arabidopsis (拟南芥), Endocytosis (内吞), Root (根), Compartment (区室), Confocal analysis (共聚焦分析), Image segmentation (图像分割), Spot detection (光斑检测)


Studies on plant intracellular trafficking have widely beneficiated from the identification and characterization of proteins that are localized to specific intracellular compartments. These proteins can serve in subsequent studies as compartment markers either using immunofluorescence when antibodies are available, or direct fusion with fluorescent proteins (Dettmer et al., 2006; Geldner et al., 2003; Jaillais et al., 2008; Jaillais et al., 2006). Typically, they can be used in co-localization experiments (Geldner et al., 2009; Simon et al., 2014), but also as reference points to characterize mutants, drugs or growth conditions that might affect intracellular trafficking pathways, such as exocytosis, endocytosis, autophagy, or secretory trafficking. The localization of these marker proteins may vary in different manner, including for example their number, size, shape, clustering or labeling intensity. For example, in root, the fungal toxin Brefeldin A (BFA), an inhibitor of protein recycling, degradation and secretion, induces the aggregation of multiple compartments in or around the so-called ‘BFA compartment’ (Geldner et al., 2003; Geldner et al., 2009). Wortmannin, an inhibitor of PI3 Kinase activity induces the fusion of late endosomal compartments (Jaillais et al., 2006; Tse et al., 2004), while concanamycin A induces TGN swelling (Dettmer et al., 2006). Accordingly similar effects on compartment numbers and/or morphology have been observed in trafficking mutants (Dettmer et al., 2006; Geldner et al., 2003; Jaillais et al., 2007; Sauer et al., 2013).

Automatic spot detection was spearheaded on leaf for the detection of endomembrane rearrangements induced by environmental changes such as dark, cold treatment or biotic stresses (Salomon et al., 2010). This technique was also applied to study the endocytosis of the FLAGELIN-INSENSITIVE2 (FLS2) receptor (Beck et al., 2012; Mbengue et al., 2016; Spallek et al., 2013). Here we described a protocol to computationally detect and count the number of intracellular compartments on Arabidopsis root image. This protocol relies on a macro that runs on the open source image analysis software ImageJ and that can work with a wide variety of images with different image-to-noise signals. In addition, it proposes two different modes of detection; a first one where the macro automatically finds the root area and another one that allows the selection of a user-defined region of interest (ROI). Finally, although this version of the macro is designed to count the number of spots, similar image segmentation can easily be used to measure spot size, to estimate signal intensity, to capture compartment morphology or to automatically quantify co-localization between two or more channels.


  1. Microscope: Plant imaging was performed on an inverted Zeiss microscope (Zeiss, model: AxioObserver Z1 ) equipped with a spinning disk module (Yokogawa, model: CSU-W1-T3 )
  2. Camera: ProEM+ 1024B camera (Princeton Instrument, model: ProEM+ 1024B)
  3. Objective: 63x Plan-Apochromat objective (numerical aperture 1.4, oil immersion).

Note: Images may be performed with any confocal microscope. We describe above the microscope setup used to take the sample images that can be downloaded below (see Software 7).


  1. ImageJ ( (Schneider et al., 2012) (see Note 1)
    Note: If link does not open, copy-paste the address in your browser.
  2. SiCE spot detector Macro for ImageJ
    Note: Download SiCE spot detector Macro for ImageJ. Go to, right click on SiCE ‘SpotDetector.ijm’ and choose ‘save link as’. Use the following name to save the file: ‘SiCE SpotDetectorV3.ijm’.
  3. Wavelet A Trou plugin for ImageJ (
    Note: If link does not open, copy-paste the address in your browser.
  4. FeatureJ plugin for ImageJ (
    Note: If link does not open, copy-paste the address in your browser.
  5. XLstat (Addinsoft,
  6. R (The R foundation,, Excel (Microsoft,
  7. (Optional) Download template images ( (see Note 2).


  1. Images acquisition
    The macro described below can work with any type of confocal images whether taken with a laser scanning confocal microscope or with a spinning disk confocal microscope. You should therefore define acquisition parameter according to your microscope set-up. Note that for quantitative imaging, pictures should be taken with detector settings optimized for low background and no pixel saturation (Simon et al., 2016). For comparison purposes, we recommend using similar confocal settings for all images. Exposure time must be defined by taking into account compartments movement during acquisition. Too long exposure might lead to abnormal shape or multiple counts of the same intracellular particles.
    As an example, using the spinning disk microscope described in the equipment section above, we typically use the following settings: image acquisition time 200 msec, laser power 30%, EM gain 100 and image size 1024 x 1024 pixels.
    Note: The settings used might vary greatly depending on the microscope set-up and the strength of the marker line.

  2. Plugin installation (Video 1)
    1. Copy Wavelet_A_Trou file and FeatureJ plugins in ImageJ/plugins folder.
    2. Copy SiCE Spot DetectorV3.ijm file in ImageJ/macros folder.
    3. Install the SiCE spot detector macro by selecting it in Plugins > Macros > Install. SiCE SpotDetectorV3 should appear in the Plugins > Macros > lower panel.

      Video 1. Procedure to install the Sice Spotdetector Macro and related Plugins

  3. Setting up parameters for single image analysis
    The purpose of single image analysis (Procedure C) is to find the best parameters possible to segment your image correctly (i.e., best parameters to find all [or most] of the compartments in the image with as little false positive as possible) and then use these parameters in batch mode (Procedure D). Every marker lines have different signal strength, particle size and signal-to-noise ratio. It is impossible to predict which will be the best parameters to use for a specific marker line. Therefore, these parameters have to be determined empirically for each marker lines. The procedure in C allows testing a range of parameters to confront the segmentation results with your input image (step C10). Mostly, the use of these parameters are empirical and we advise you to explore different ones in order to find the best setting for your image (below, we provide some numbers that we find often work well for counting endosome numbers in Arabidopsis root). However some of these parameters are directly dependent on the type of particles you want to analyze, notably the size and circularity. Particle size can be estimated using the optional step C3, but you can also try different size parameters empirically as well and by-pass step C3.
    1. Open a confocal image with ImageJ (or Fiji) software in which you want to quantify the number of intracellular compartments or template pictures (see Note 2). It is possible to use a projected 3D-stack. However, we do not recommend using projections that might yield artifacts and/or mask the presence of some particles and we recommend instead quantifying all the images in the Z-stack using the batch mode (see Procedure D).
    2. If you want to use scaled images, ‘Set Scale’ in the ‘Analyze Tab’ according to your microscope set-up, otherwise proceeds directly to step C3.
    3. Estimate particle size with ImageJ (Optional STEP, Video 2). Use straight-line tool to draw a line through several compartments. Then, use the plot profile tool (Analyze > Plot profile) and measure the size of the structures within the profile by using again the straight-line tool. Measure several compartments small and big ones approximately at the base of the peaks (Figure 1). It will help you to determine maximum size, minimum size and sigma parameters, which will be useful for detection step (see below step C8). As an example, see Figure 2, which shows the typical size of various compartments obtained with the microscope set-up described above (see Equipment section). 

      Figure 1. Snapshot from FIJI program showing manual measurement of individual spot-like structures using plot-profile and straight-line tools (step C3). A. FIJI main toolbar; B. Test image window; C. Enlargement of the dashed-square in (B), showing the red line that is drawn across several compartments using the straight-line tool of ImageJ. D. Fluorescence plot along the red line. Compartments are identified as peaks and are measured by drawing lines at their base. E. Peak width measurements.

      Video 2. Procedure to estimate the size of the compartments of interest

      Figure 2. Snapshot from ImageJ program showing the size of different intracellular compartments as captured by spinning disk microscopy. A. FIJI main toolbar; B. Golgi apparatus, marker line is Wave18 (W18) (Geldner et al., 2009). C. Post-Golgi endosomal compartments, marker line is Wave25 (W25) (Geldner et al., 2009). D. Late endosomal compartments, marker line is Wave7 (W7) (Geldner et al., 2009). E. Wortmannin compartments. Late endosomes (W7) fuse into the so-called ‘Wortmannin compartment’ following treatment with the PI3Kinase/PI4Kinase inhibitor Wortmannin (Wm, 30 µM for 60 min) (Jaillais et al., 2006; Simon et al., 2014; Simon et al., 2016). F. BFA compartments. Endosomes labeled by the endocytic tracer FM4-64 aggregates into the so-called ‘BFA compartment’ following treatment with the fungal toxin BrefeldinA (BFA, 50 µM for 60 min) (Geldner et al., 2003; Geldner et al., 2009). G. Summary table of peak width measurements for the marker shown in A to F.

    4. Launch SICE-Spot Detector Macro from Plugins > Macro > SpotDetectorMacroV3 (Video 3).

      Video 3. Procedure to find the best parameters to segment intracellular particles with the SiCE spot detector macro

    5. Uncheck Batch mode to start single image analysis.
    6. Select if you want to use scaled images or un-scaled images, by checking/unchecking the ‘scaled image’ box. Scaled image will provide results expressed in number of spots/µm2, while un-scalded images will provide result expressed in number of spot/pixel2
    7. Uncheck Automatic ROI if you want to manually draw region of interest (Note 3).
    8. Select parameters (Figure 2)
      First three parameters directly arise from step C3. Min Particle Size, Max Particle Size and Circularity will be used in the Built-in Macro Function Analyse Particles ( Particles with size outside the range defined are ignored. Values are expressed either in square µm or square pixels. Circularity depends on the shape of the compartments and range from 0 (infinitely elongated polygon) to 1 (perfect circle). Classically, intracellular compartments in Arabidopsis roots, such as endosomes, can be detected using 0.5-5 µm2 and 0.5-1 circularity (but other parameters may be used to quantify more elongated particles, or bigger particles such as BFA compartments).
      1. In confocal images Intracellular compartments of interest will appear as more or less round shaped objects with higher fluorescent signal in the center. Image convolution by a Gaussian kernel is generally sufficient for segmenting them. Sigma parameter corresponds to standard deviation of the distribution used in the Gaussian filtering method (Lowe, 2004). Adjusting Sigma will depend on the background within cells and your object size and has to be determined empirically.
      2. The Gaussian filtering that will be applied to your image can be chosen from Difference of Gaussian ‘DoG’ or Laplacian of Gaussian. Classically, Laplacian of Gaussian filtering gives the sharpest boundaries of the objects allowing size measurement, while DoG filtering will be more efficient in findings structures of different sizes or in a higher background.
      3. Auto-threshold corresponds to the method/algorithm that will be applied to your filtered image (Triangle, Otsu and Huang) (see more details about this step at the following address Again, you need to try these different methods to find the best one empirically.

        Figure 3. Snapshot from ImageJ program showing selection of parameters step. A. FIJI main toolbar; B. Test image window; C. Select Parameters selection windows of the SICE spot detector.

    9. Once you have chosen all your parameters, click ‘OK’ to proceed with the analysis.
      1. If you chose manual mode type OK and define a Region of Interest (ROI). Compartments within the defined area will be segmented according to the parameters, methods and modes chosen previously in step C8.
      2. If you chose automatic ROI (step C7), the macro will automatically define the portion of the image that corresponds to the root tissue with the highest signal (see red line in Figure 4) (see Note 3) and will discard the rest of the image for subsequent analyses.
    10. A merge image between segmented compartments boundaries and original fluorescent image will be displayed to evaluate the efficiency of the process (Figure 4); i.e., it will help you to evaluate if the parameters you used are the best to find the intracellular compartments of interest. ROI will be displayed on the original image as a red surrounded region. By clicking ‘OK’ you can return to step C8 to modify parameters and make new analyses.
      1. In manual ROI selection mode, you will be able to test a new set of parameters on the same manually defined ROI (ROI defined previously in step C9a). You will also have the option to define a new ROI.
      2. Automatic ROI allows you to switch from automatic to manual ROI selection of vice-versa.
    11. Reiterate steps C6-C10 until you find the best parameters to segment intracellular compartments in your image and then proceed to step C12.
    12. Uncheck the ‘unhappy’ box to display a ‘Summary’ table with ‘Picture name’, ‘Cell Area’, ‘Spot number’ and parameters used. Once you determined optimum parameters for your data set, you can relaunch the macro for batch mode analysis (see Procedure D).

      Figure 4. Snapshot from ImageJ program showing the results for single image analysis. A. FIJI main toolbar; B. Test image window. Region of Interest is represented as a red line. C. Result of spot detection using the following parameters: MinSize 0.5 MaxSize 99 circMin 0.5 circMax 1 Sigma 3 Filtering Method Difference of Gaussian Threshold Triangle; D. Result of spot detection using the following parameters:  MinSize 1 MaxSize 99 circMin 0.5 circMax 1 Sigma 3 Filtering Method Difference of Gaussian Threshold Triangle; D. ‘Result summary’ table showing the quantification of the number of spot in the different condition tested, using the parameters defined as in C for line 1 and as in D for line 2. 

  4. Batch images analysis
    1. Launch SICE-Spot Detector Macro from Plugins > Macro > SpotDetectorMacroV3.
    2. Choose Source directory.
    3. Choose Destination directory where the merged images will be saved (see step C10) (these files allow you to check that segmentation was correctly performed in all images).
    4. Select the previously optimized parameters that will be used for analyzing your images.
    5. Macro will perform Spot detection on all images contained in the Source directory and collect results in the Summary table. This table can be directly saved as an Excel file or copied and pasted in a pre-existing table.

Data analysis

When manually counting the number of spots per cells, 100 cells are typically counted on 10 to 20 different roots (Simon et al., 2016). We suggest keeping a similar number of independent roots (10 to 20) when using automatic spot detection, which will ultimately represent around 500 to 1,000 cells. However, because variability may vary according to the type of spots and markers used, we advise to first estimate sample size. For this purpose, quantify the number of spots on a small numbers of samples and estimate sample size using the following formula:
n = (t x t) x (s x s)/(d x d)
s is standard error of the mean,
t is the statistical confidence level (1.96 as deduced from the standard normal distribution for a confidence interval of 95%),
d is the margin of error (0.05).
Biological triplicates are suggested. For statistical analyses, choose parametric or non-parametric test depending on sample distribution (Gaussian distribution or not, respectively). Parametric test such as Tukey’s honestly significant difference test or non parametric test such as Kruskal-wallis test or Dwass-Steel-Critchlow-Fligner procedure for multiple comparison between samples can be performed with XLstat software ( or R software (, in order to find if means are significantly different from each other. Graphic representation can be done using either R software or Excel software (Microsoft,


  1. The SiCE SpotDetector macro works with the current distribution of ImageJ (as of November 2016). The only required plugins are Wavelet_A_Trou and FeatureJ. If you have an older version of ImageJ and encounter problems with the macro, re-install the newest version of ImageJ (
  2. To test the macro, you can download sample images ( (set scale at distance in pixels: 4.8473, known distance: 1, pixel aspect ratio 1.0, Unit of length: µm).
  3. This method will automatically detect the root in your picture using wavelet-based image segmentation. It will either select the entire root in the image, or the part of the root that has the highest signal. The macro selects only one continuous area per image. This will allow the macro to quantify compartments only in the area with highest signal in the case of uneven labeling along the root. If you want to quantify several areas in the same root with significant difference in labeling intensity, crop your pictures according to these different areas or use the normal mode and manually define ROI.


We thank Laia Armengot (RDP, ENS Lyon) for critical comments on the manuscript, Erik Meijering (Erasmus University Medical Center, Rotterdam, The Netherlands) for developing FeatureJ. Y.J. is funded by ERC No. 3363360-APPL under FP/2007-2013.


  1. Beck, M., Zhou, J., Faulkner, C., MacLean, D. and Robatzek, S. (2012). Spatio-temporal cellular dynamics of the Arabidopsis flagellin receptor reveal activation status-dependent endosomal sorting. Plant Cell 24(10): 4205-4219.
  2. Dettmer, J., Hong-Hermesdorf, A., Stierhof, Y. D. and Schumacher, K. (2006). Vacuolar H+-ATPase activity is required for endocytic and secretory trafficking in Arabidopsis. Plant Cell 18(3): 715-730.
  3. Geldner, N., Anders, N., Wolters, H., Keicher, J., Kornberger, W., Muller, P., Delbarre, A., Ueda, T., Nakano, A. and Jurgens, G. (2003). The Arabidopsis GNOM ARF-GEF mediates endosomal recycling, auxin transport, and auxin-dependent plant growth. Cell 112(2): 219-230.
  4. Geldner, N., Denervaud-Tendon, V., Hyman, D. L., Mayer, U., Stierhof, Y. D. and Chory, J. (2009). Rapid, combinatorial analysis of membrane compartments in intact plants with a multicolor marker set. Plant J 59(1): 169-178.
  5. Jaillais, Y., Fobis-Loisy, I., Miege, C. and Gaude, T. (2008). Evidence for a sorting endosome in Arabidopsis root cells. Plant J 53(2): 237-247.
  6. Jaillais, Y., Fobis-Loisy, I., Miege, C., Rollin, C. and Gaude, T. (2006). AtSNX1 defines an endosome for auxin-carrier trafficking in Arabidopsis. Nature 443(7107): 106-109.
  7. Jaillais, Y., Santambrogio, M., Rozier, F., Fobis-Loisy, I., Miege, C. and Gaude, T. (2007). The retromer protein VPS29 links cell polarity and organ initiation in plants. Cell 130(6): 1057-1070.
  8. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2): 91-110.
  9. Mbengue, M., Bourdais, G., Gervasi, F., Beck, M., Zhou, J., Spallek, T., Bartels, S., Boller, T., Ueda, T., Kuhn, H. and Robatzek, S. (2016). Clathrin-dependent endocytosis is required for immunity mediated by pattern recognition receptor kinases. Proc Natl Acad Sci U S A 113(39): 11034-11039.
  10. Salomon, S., Grunewald, D., Stuber, K., Schaaf, S., MacLean, D., Schulze-Lefert, P. and Robatzek, S. (2010). High-throughput confocal imaging of intact live tissue enables quantification of membrane trafficking in Arabidopsis. Plant Physiol 154(3): 1096-1104.
  11. Sauer, M., Delgadillo, M. O., Zouhar, J., Reynolds, G. D., Pennington, J. G., Jiang, L., Liljegren, S. J., Stierhof, Y. D., De Jaeger, G., Otegui, M. S., Bednarek, S. Y. and Rojo, E. (2013). MTV1 and MTV4 encode plant-specific ENTH and ARF GAP proteins that mediate clathrin-dependent trafficking of vacuolar cargo from the trans-Golgi network. Plant Cell 25(6): 2217-2235.
  12. 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.
  13. Simon, M. L., Platre, M. P., Assil, S., van Wijk, R., Chen, W. Y., Chory, J., Dreux, M., Munnik, T. and Jaillais, Y. (2014). A multi-colour/multi-affinity marker set to visualize phosphoinositide dynamics in Arabidopsis. Plant J 77(2): 322-337.
  14. Simon, M. L., Platre, M. P., Marques-Bueno, M. M., Armengot, L., Stanislas, T., Bayle, V., Caillaud, M. C. and Jaillais, Y. (2016). A PtdIns(4)P-driven electrostatic field controls cell membrane identity and signalling in plants. Nat Plants 2: 16089.
  15. Spallek, T., Beck, M., Ben Khaled, S., Salomon, S., Bourdais, G., Schellmann, S. and Robatzek, S. (2013). ESCRT-I mediates FLS2 endosomal sorting and plant immunity. PLoS Genet 9(12): e1004035.
  16. Tse, Y. C., Mo, B., Hillmer, S., Zhao, M., Lo, S. W., Robinson, D. G. and Jiang, L. (2004). Identification of multivesicular bodies as prevacuolar compartments in Nicotiana tabacum BY-2 cells. Plant Cell 16(3): 672-693.



背景 关于植物细胞内运输的研究广泛地从鉴定和表征定位于特定细胞内区室的蛋白质中获益。这些蛋白质可以在随后的研究中作为室标记物使用,当抗体可用时使用免疫荧光,或与荧光蛋白直接融合(Dettmer等人,2006; Geldner等人)。 ,2003; Jaillais等人,2008; Jaillais等人,2006)。通常,它们可以用于共定位实验(Geldner等人,2009; Simon等人,2014),而且也作为表征突变体的参考点,可能影响细胞内运输途径的药物或生长条件,例如胞吐作用,胞吞作用,自噬或分泌性运输。这些标记蛋白的定位可以以不同的方式变化,包括例如它们的数目,大小,形状,聚集或标记强度。例如,在根中,真菌毒素Brefeldin A(BFA)是蛋白质回收,降解和分泌的抑制剂,诱导所谓的“BFA隔室”内或周围的多个隔室的聚集(Geldner等人,2003; Geldner等人,2009)。渥曼青霉素PI3激酶活性的抑制剂诱导晚期内体区的融合(Jaillais等人,2006; Tse等人,2004),而伴侣霉素A诱导TGN肿胀(Dettmer等人,2006)。因此,在贩运突变体中观察到对隔室数量和/或形态的类似影响(Dettmer等人,2006; Geldner等人,2003; Jaillais et al。 al。,2007; Sauer等人,2013)。
 自动斑点检测是在叶片上引导的,用于检测由环境变化(如黑暗,冷处理或生物胁迫)引起的内膜重排(Salomon等,2010)。该技术也用于研究FLAGELIN-INSENSITIVE2(FLS2)受体的内吞作用(Beck等人,2012; Mbengue等人,2016; Spallek et al。,2013)。在这里,我们描述了计算检测和计数拟南芥根瘤形成细胞内区室数量的方案。该协议依赖于在开源图像分析软件ImageJ上运行的宏,并且可以与具有不同图像到噪声信号的各种图像一起工作。此外,它提出了两种不同的检测方式:第一个宏自动找到根区域,另一个允许选择用户定义的感兴趣区域(ROI)。最后,虽然这个版本的宏被设计为对点数进行计数,但类似的图像分割可以很容易地用于测量斑点尺寸,估计信号强度,捕获隔室形态或自动量化两个或多个通道之间的共定位。

关键字:拟南芥, 内吞, 根, 区室, 共聚焦分析, 图像分割, 光斑检测


  1. 显微镜:在装有旋转盘模块(Yokogawa,型号:CSU-W1-T3)的倒立式蔡司显微镜(Zeiss,型号:AxioObserver Z1)上进行植物成像
  2. 相机:ProEM + 1024B相机(普林斯顿仪器,型号:ProEM + 1024B)
  3. 目标:63x计划 - 消色差目标(数值孔径1.4,油浸)



  1. ImageJ( (Schneider等人,2012)(见注1)
  2. 用于ImageJ的SiCE斑点检测器
    注意:下载用于ImageJ的SiCE斑点检测器Macro。转到 http://www.ens-lyon。 fr/RDP/SiCE/METHODS.html ,右键单击SiCE的SpotDetector.ijm,然后选择"将链接另存为"。使用以下名称保存文件:"SiCE SpotDetectorV3.ijm"。
  3. ImageJ的小波A Trou插件(
  4. ImageJ的FeatureJ插件(
  5. XLstat(Addinsoft, )。
  6. R(R基金会,,Excel(Microsoft,。
  7. (可选)下载模板图片( http://www.ens )(见注2)。


  1. 图像采集
    下面描述的宏可以与任何类型的共焦图像一起使用,无论是用激光扫描共聚焦显微镜还是用旋转盘共聚焦显微镜。因此,您应该根据显微镜设置定义采集参数。请注意,对于定量成像,应使用针对低背景优化的检测器设置和无像素饱和度(Simon& et al。,2016)拍摄照片。为了比较的目的,我们建议对所有图像使用类似的共焦设置。曝光时间必须通过考虑到采集过程中的舱室运动来定义。太长的暴露可能会导致异常形状或多个相同细胞内颗粒的计数 作为示例,使用上述设备部分中描述的旋转圆盘显微镜,我们通常使用以下设置:图像采集时间为200毫秒,激光功率为30%,EM增益为100,图像尺寸为1024 x 1024像素。

  2. 插件安装(视频1)
    1. 在ImageJ/plugins文件夹中复制Wavelet_A_Trou文件和FeatureJ插件。
    2. 在ImageJ /宏文件夹中复制SiCE Spot DetectorV3.ijm文件。
    3. 通过在插件>中选择它来安装SiCE点检测器宏。宏>>安装。 SiCE SpotDetectorV3应该出现在插件>宏>>下图。

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      视频1.安装Sice Spotdetector宏和相关插件的步骤
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  3. 设置单个图像分析的参数
    单次图像分析(程序C)的目的是找到可以正确分割图像的最佳参数( ie ,最佳参数,以图像中的所有[或大多数]隔室尽可能少的假阳性),然后在批处理模式下使用这些参数(程序D)。每个标记线具有不同的信号强度,粒度和信噪比。不可能预测什么是用于特定标记线的最佳参数。因此,这些参数必须根据经验确定每个标记线。 C中的过程允许测试一系列参数以使用输入图像来对抗分割结果(步骤C10)。大多数情况下,使用这些参数是经验性的,我们建议您探索不同的参数,以便为您的图像找到最佳设置(下面,我们提供一些我们发现的数字通常适用于计算拟南芥中的内体数量 root)。但是,这些参数中的一些直接取决于要分析的粒子类型,特别是大小和圆形度。可以使用可选步骤C3来估计粒子大小,但您也可以根据经验和旁路步骤C3尝试不同大小的参数。
    1. 用ImageJ(或斐济)软件打开一个共焦图像,您可以在其中量化细胞内区室或模板图片的数量(见注2)。可以使用投影3D堆栈。但是,我们不建议使用可能产生伪像和/或掩盖某些粒子的存在的投影,因此我们建议使用批处理模式对Z-stack中的所有图像进行量化(参见过程D)。
    2. 如果要使用缩放图像,请根据显微镜设置在"分析选项卡"中的"设置缩放",否则直接进入步骤C3。
    3. 使用ImageJ估算粒度(可选STEP,视频2)。使用直线工具通过几个隔间画一条线。然后,使用绘图轮廓工具(Analyze>绘图轮廓),并再次使用直线工具测量轮廓内的结构尺寸。测量几个小区和大座的区域大约在峰的底部(图1)。它将帮助您确定最大尺寸,最小尺寸和西格玛参数,这将对检测步骤有用(见下文步骤C8)。例如,参见图2,其示出了用上述显微镜装置获得的各个隔室的典型尺寸(参见设备部分)。 

      图1. FIJI程序的快照,显示使用绘图轮廓和直线工具手动测量各个点状结构(步骤C3)。 A. FIJI主工具栏; B.测试图像窗口; C.放大(B)中的虚线方块,显示使用ImageJ的直线工具绘制在几个隔间上的红线。 D.沿红线的荧光图。隔室被识别为峰值,并通过在其底部的线条测量。峰值测量。

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      图2.来自ImageJ程序的快照显示通过旋转磁盘显微镜捕获的不同细胞内区室的大小。 A. FIJI主工具栏; B.高尔基体,标记线是Wave18(W18)(Geldner等人,2009)。 C.高尔基体内室,标记线是Wave25(W25)(Geldner等人,2009)。 D.晚期内体区室,标记线是Wave7(W7)(Geldner等人,2009)。渥曼青霉素隔室使用PI3Kinase/PI4Kinase抑制剂渥曼青霉素(Wm,30μM,60分钟)治疗后,晚期内体(W7)融合到所谓的"渥曼青霉素区"(Jaillais等人,2006; Simon& em,et al。,2014; Simon等人,2016)。 F.BFA隔间。用真菌毒素BrefeldinA(BFA,50μM,60分钟)处理后,由内吞示踪剂FM4-64标记的内体聚集成所谓的"BFA隔室"(Geldner等人,2003; Geldner等人,2009)。 G.对于A至F所示标记的峰宽测量的汇总表。

    4. 从插件启动SICE-Spot检测器宏>宏> SpotDetectorMacroV3(视频3)。

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    5. 取消选中批量模式开始单一图像分析。
    6. 选择是否要使用缩放图像或未缩放图像,方法是选中/取消选中"缩放图像"框。缩放图像将提供以斑数/μm 2表示的结果,而非烫伤图像将提供以点/像素数量 2
    7. 如果要手动绘制感兴趣区域,请取消选中自动ROI(注3)。
    8. 选择参数(图2)
      前三个参数直接来自步骤C3。最小粒径,最大粒径和圆形将用于内置宏函数分析粒子( )。尺寸超出所定义范围的粒子将被忽略。值以平方μm或正方形像素表示。圆形取决于隔间的形状,范围从0(无限长拉长多边形)到1(正圆)。典型地,可以使用0.5-5μm2和0.5-1圆形度检测拟南芥根系中的细胞内区室,例如内体,但是可以使用其他参数来量化更多细长的颗粒或更大的颗粒如BFA隔室)。
      1. 在共焦图像中,感兴趣的细胞内区室将在中心出现或多或少的具有较高荧光信号的圆形物体。通过高斯核的图像卷积通常足以分割它们。 Sigma参数对应于在高斯滤波方法中使用的分布的标准偏差(Lowe,2004)。调整Sigma将取决于单元格中的背景和对象大小,并且必须根据经验确定。
      2. 将应用于您的图像的高斯滤波可以从高斯"DoG"或高斯拉普拉斯算子的差异中选择。经典地,高斯滤波的拉普拉斯算子给出了允许大小测量的对象的最清晰的边界,而DoG过滤将在不同大小的发现结构或更高背景下更有效。
      3. 自动阈值对应于将应用于过滤图像(Triangle,Otsu和Huang)的方法/算法(有关此步骤的更多详细信息,请访问以下地址 )。再次,您需要尝试这些不同的方法,从经验上找到最好的方法

        图3.来自ImageJ程序的快照显示参数步骤的选择。 A. FIJI主工具栏; B.测试图像窗口; C.选择SICE点检仪的参数选择窗口。

    9. 选择所有参数后,单击"确定"继续进行分析。
      1. 如果您选择手动模式类型确定并定义感兴趣区域(ROI)。根据之前在步骤C8中选择的参数,方法和模式,将对定义区域内的隔间进行分段。
      2. 如果您选择了自动ROI(步骤C7),则宏将自动定义与根部组织相对应的具有最高信号的图像部分(参见图4中的红线)(见注3),并将丢弃其余部分图像进行后续分析。
    10. 将显示分段隔间边界和原始荧光图像之间的合并图像,以评估过程的效率(图4); ,它将帮助您评估您使用的参数是否最适合找到感兴趣的细胞内区室。 ROI将作为红色包围区域显示在原始图像上。单击"确定",您可以返回到步骤C8修改参数并进行新的分析。
      1. 在手动投资回报率选择模式下,您将能够在相同的手动定义ROI(以前在步骤C9a中定义的ROI)中测试一组新参数。您还可以选择定义新的投资回报率。
      2. 自动投资回报率允许您从自动切换到手动ROI选择,反之亦然。
    11. 重申步骤C6-C10,直到您找到最佳参数来分割图像中的细胞内区室,然后继续执行步骤C12。
    12. 取消选中"不快乐"框,显示"图片名称","单元格区域","现货号码"和使用的参数的"摘要"表格。确定数据集的最佳参数之后,您可以重新启动批量模式分析的宏(见过程D)。

      图4.来自ImageJ程序的快照,显示单个图像分析的结果。 A. FIJI主工具栏; B.测试图像窗口。兴趣区域表示为红线。 C.使用以下参数的斑点检测结果:MinSize 0.5 MaxSize 99 circMin 0.5 circMax 1 Sigma 3滤波方法高斯阈值三角形的差异; D.使用以下参数进行斑点检测的结果:MinSize 1 MaxSize 99 circMin 0.5 circMax 1 Sigma 3滤波方法高斯阈值三角形的差异; D."结果摘要"表,显示了在不同条件下测试的斑点数量的量化,使用了定义在第1行的C和第2行的D中的参数。 

  4. 批量图像分析
    1. 从插件启动SICE-Spot检测器宏>宏> SpotDetectorMacroV3。
    2. 选择源目录
    3. 选择要保存合并图像的目标目录(参见步骤C10)(此文件允许您检查所有图像中的分段是否正确执行)。
    4. 选择将用于分析图像的以前优化的参数。
    5. 宏将对源目录中包含的所有图像执行Spot检测,并在Summary表中收集结果。该表格可以直接保存为Excel文件,也可以复制并粘贴到预先存在的表格中。


n =(t x t)x(s x s)/(d x d)
建议生物一式三份。对于统计分析,根据样本分布(分别为高斯分布或不分配)选择参数或非参数测试。参数测试如Tukey的真实显着差异测试或非参数测试,如Kruskal-wallis测试或Dwass-Steel-Critchlow-Fligner程序,用于样品之间的多重比较可以使用XLstat软件(或R软件(,以便找出是否有明显的差异。图形表示可以使用R软件或excel软件(Microsoft, https://产品)完成。。


  1. SiCE SpotDetector宏与目前的ImageJ分布(截至2016年11月)一起使用。唯一需要的插件是Wavelet_A_Trou和FeatureJ。如果您有较旧版本的ImageJ并遇到宏问题,请重新安装最新版本的ImageJ(。
  2. 要测试宏,可以下载示例图像( )(以像素为单位设置缩放比例:4.8473,已知距离:1,像素长宽比1.0,单位长度:μm)。
  3. 该方法将使用基于小波的图像分割来自动检测图片中的根。它将选择图像中的整个根,或选择具有最高信号的根部分。宏选择每个图像只有一个连续区域。这将允许宏只在具有最高信号的区域中量化隔离区,在根部标记不均匀的情况下。如果要量化相同根部的几个区域,标记强度有显着差异,请根据这些不同的区域裁剪图片,或使用正常模式并手动定义投资回报率。


感谢Laia Armengot(RDP,ENS里昂)对手稿的批评性意见,Erik Meijering(伊拉斯姆斯大学医学中心,荷兰鹿特丹)开发FeatureJ。 Y.。。。。。。。。。。。。。。。。。。。。。。


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引用:Bayle, V., Platre, M. P. and Jaillais, Y. (2017). Automatic Quantification of the Number of Intracellular Compartments in Arabidopsis thaliana Root Cells. Bio-protocol 7(4): e2145. DOI: 10.21769/BioProtoc.2145.