Analysis of 3D Cellular Organization of Fixed Plant Tissues Using a User-guided Platform for Image Segmentation

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The Plant Cell
Jun 2008



The advent of non-invasive, high-resolution microscopy imaging techniques and computational pipelines for high-throughput image processing has contributed to gain insights in plant organ morphogenesis at the cellular level. Confocal scanning laser microscopy (CSLM) allows the generation of three dimensional images constituted of serial optical sections reporting on stained subcellular structures. Fluorescent labels of cell walls or cell membranes, either chemically or through reporter proteins, are particularly useful for the analyses of tissue organization and cellular shapes in 3D. Image segmentation based on cell boundary signals is used as an input to generate 3D-segments representing cells. These digitalized, 3D objects provide quantitative data on cell shape, size, geometry, position or on (intercellular) intensity signals if additional reporters are used. Herein, we report a detailed, annotated workflow for image segmentation using microscopic data. We used it in the context of a study of tissue patterning during ovule primordium development in Arabidopsis thaliana. Whole carpels are stained for cell boundaries using a modified pseudo-Schiff propidium iodide (mPS-PI) protocol, 3D images are acquired at high resolution by CSLM, segmented and annotated for individual cell types using ImarisCell. This allows for quantitative analyses of cell shape and cell number that are relevant for tissue morphodynamic studies.

Keywords: High-resolution 3D imaging (高分辨率三维成像), Plant tissues (植物组织), Image segmentation (图像分割), Morphodynamics (地貌动力学), Tissue patterning (组织形态), Arabidopsis (拟南芥), Ovule primordium (胚珠原基)


Organ and tissue morphodynamic studies in plants rely on the analysis of the three-dimensional process of growth along development progression. The evolution of cell number, cell size and cell shape allows interpreting events of proliferation, cellular expansion and anisotropy, respectively (Roeder et al., 2011; Barbier de Reuille et al., 2015; Bassel and Smith, 2016; Coen and Rebocho, 2016). While time-lapse imaging appears in principle as the method of choice, it is not easily applicable to all plant organs, sometimes embedded in inaccessible structures, and image quality often compromise on robust quantitative analyses over a large number of samples and at the cellular level. A complementary, robust alternative is to stain and record three dimensional images of optically cleared tissue/organ at high-resolution, at consecutive time points in order to reconstruct a developmental progression. These 3D images can then be digitally segmented into individual cell objects, from which measurements of number, different descriptors of cell shape and cell size can be extracted. Whole-mount tissue clearing and staining of the cell wall using the modified pseudo-Schiff propidium iodide (mPS-PI) protocol (Truernit et al., 2008) is widely used in the plant community for 3D shape analyses (Sankar et al., 2014; Yoshida et al., 2014; Hervieux et al., 2016). It is described here with only minor modifications and specific notes enabling high-quality sample preparation for the delicate carpel structures. In addition, while plant tissue imaging using confocal scanning laser microscopy became common practice in many labs, specific knowledge on how to fine-tune the optical and software-controlled image recording remains elusive and often kept ‘in house’. Here we provide detailed recommendations aimed at guiding the user towards producing high-quality, high-resolution 3D images suitable for robust qualitative and quantitative analyses. For image segmentation, different open-source algorithms proved invaluable for tissue morphodynamic studies in plants and Drosophila, namely: MARS-ALT (Fernandez et al., 2010), MorphographX (Barbier de Reuille et al., 2015) and RACE (Stegmaier et al., 2016). Yet, these interfaces usually require computational skills to fine-tune segmentation parameters, correct manually for wrongly segmented objects, customize cell labelling when tissue models are not available in the software and export quantitative data for downstream analyses. An alternative solution for biologists lacking this expertise lies in the use of commercially available software with a streamlined user interface. We present here one option with Imaris, a software for 3D visualization and image processing. We report a detailed, annotated workflow applied to ovule tissue analyses but that is of broad application to analyze various plant tissues.

Materials and Reagents

  1. Microscope slides (76 x 26 mm) (Thermo Fisher Scientific, Thermo ScientificTM, catalog number: 10143562CEF )
  2. Microscope cover slips for confocal imaging: 18 x 18 mm, 0.17 ± 0.01 mm thickness (Hecht Assistant, catalog number: 41014509 )
  3. Dissecting needles (Tungsten, diameter: 0.75 and 0.35 mm)
  4. Round Petri dishes 35 mm (Greiner Bio One International, catalog number: 627102 )
  5. 2 ml micro tubes (SARSTEDT, catalog number: 72.695.500 )
  6. Dust-free paper
  7. Glass wool
  8. Plant material: Flowering Arabidopsis thaliana plants
  9. Sodium dodecyl sulfate, sodium salt (SDS) (Sigma-Aldrich, catalog number: 71729 )
  10. Sodium hydroxide (NaOH) (Sigma-Aldrich, catalog number: 71690 )
  11. Ethanol (70%, 80%) (Fisher Scientific, catalog number: 10428671 )
  12. Periodic acid (Sigma-Aldrich, catalog number: 375810 )
  13. Nail polish
  14. Mounting medium
  15. Methanol (Sigma-Aldrich, catalog number: 34860 )
  16. Acetic acid (Merck, catalog number: 100063 )
  17. Sodium metabisulphite (Na2S2O5) (Sigma-Aldrich, catalog number: 31448 )
  18. Hydrochloric acid fuming, 37% (HCl) (Carl Roth, catalog number: 4625.1 )
  19. Propidium iodide (PI) (Sigma-Aldrich, catalog number: P4864 )
  20. Chloral hydrate (Sigma-Aldrich, catalog number: 15307 )
  21. Glycerol (Carl Roth, catalog number: 3783.1 )
  22. Gum arabic (Sigma-Aldrich, catalog number: 51198 )
  23. Modified pseudo-Schiff propidium iodide (mPS-PI) solution (see Recipes)
    1. Fixative solution
    2. Pseudo-Schiff reagent with propidium iodide (PI)
    3. Chloral hydrate solution
    4. Hoyer’s solution


  1. Stereomicroscope (e.g., Leica Microsystems, model: Leica M60 )
  2. Incubator (e.g., Eppendorf, model: Thermomixer® C )
  3. Diamond- or carbide-tip pen (Sigma-Aldrich, catalog number: Z225568 )
  4. Confocal scanning laser microscope, resonant scanner with laser line 561 nm and APO PL objectives lenses 20x (NA 0.7) and 63x (NA 1.4) suitable for glycerol immersion (e.g., Leica Microsystems, model: Leica TCS SP5 )
  5. Computer ideally with high-end processor, memory and graphic environment as recommended ( Lower-end settings are possible but will result in slower processing speed


  1. Imaris 8.3.1 (, Oxford Instruments, UK)
  2. Data analysis software (e.g., R,


  1. Sample preparation
    1. Collect floral buds from A. thaliana apical inflorescences at the desired stage and place them on a glass slide with a drop of sterile water.
    2. Isolate the pistils under the stereomicroscope with dissecting needles.
    3. Transfer the pistils to a small Petri dish with 3 ml fixative solution (see Recipes).
      Note: Verify that the pistils are completely immersed in the fixative solution.
    4. You can keep the samples at 4 °C up to 1 month at this stage.
    5. The pseudo-Schiff PI staining follows a protocol previously published (Truernit et al., 2008; Yoshida et al., 2014) and detailed below.
    6. Transfer the pistils into a 2 ml micro tube with 1 ml solution of 1% SDS, 0.2 N NaOH for an overnight treatment at room temperature (RT).
    7. Discard the 1% SDS, 0.2 N NaOH solution and add 1 ml of 80% ethanol. Incubate at 80 °C at 350 rpm for 5 min. 
    Note: Gentle rotation is not necessary in the following steps.
    1. Remove the 80% solution leaving behind a minimum of 10 µl in the tube. Add 1 ml of fixative solution. Incubate at RT for 1 h.
    2. Rinse the tissues once with water and incubate in 1% periodic acid at RT for 1 h.
    3. Rinse the tissues once with water and incubate in the modified pseudo-Schiff PI solution (see Recipes) (10 µg/ml) at RT for 1 h or 2 h.
      Note: Tissues are stained if they appear reddish under eye inspection.
    4. Rinse three times with water and add 300 µl of chloral hydrate solution. Leave overnight at RT for further clarification.
      Note: Leave tissues in chloral hydrate solution at RT for a maximum of two days.

  2. Mounting for microscopy imaging
    1. Clean a slide and a coverslip with 70% ethanol using dust-free paper and let it dry.
    2. Cut a coverslip in 5-6 small pieces (ca. 3 x 18 mm per piece) using a diamond tip pen (Figures 1A and 1B).
    3. Place a drop of Hoyer’s solution or medium of choice on a slide between the two pieces of glass coverslip leaving a ca. 10-12 mm width.
    4. Place one pistil in the drop (Figure 1D).
    5. Place a coverslip on top without applying pressure (Figure 1E). This is very important to maintain the three dimensional structure of the pistil.
    6. Seal the borders of the coverslip with nail polish. Leave the slide positioned horizontally to allow the medium to solidify.

      Figure 1. Sample mounting for microscopy imaging. A. Cleaned coverslip; B. With a diamond tip pen, cut the coverslip in small pieces; C. Use two pieces as pillars, leaving a space in between; D. Place a pistil in a drop of mounting medium; E. Place a coverslip on top and seal its borders with nail polish. 

  3. Image acquisition
    1. Find the sample with the 20x objective using transmission light or epifluorescence (green excitation filter, red emission-long bandpass range) and then switch to higher magnification for acquisition.
    2. Set up parameters for image acquisition
      1. Excitation: 561 nm.
      2. Emission: 570-616 nm.
      3. Laser Transmission: 30-40%.
        Note: This can vary depending on laser type/age.
      4. Detection: ideally a new generation detector (e.g., HyD), Bright Contrast Gain mode.
        Note: The gain should be between 20 and 30% for good quality images.
      5. Scanning speed: Resonance mode, 8,000 Hz.
      6. Pinhole diameter: 1 AU (Airy Unit).
      7. Average frame: 4.
        Note: If the fluorescence signal is lower and laser transmission/gain have to be increased, higher averaging will increase image quality–this has to be tested.
      8. Objective: ideally 63x, plan + apochromatic corrected (PL APO), glycerol immersion lens (GLY), NA 1.4, working distance (WD) 0.3 mm.
        Note: Samples are mounted in a glycerol-based medium; hence a glycerol immersion lens allows for best match of the refractive index.
      9. Image format: 512 x 512 pixels.
      10. Image size: the field of view is cropped at around 80 to 85 µm to obtain x and y ≈ 85 nm.
        Note: This allows for a 2x oversampling using a 63x NA 1.3 objective at 561 nm (rx,y = 176 nm using rx,y = 0.4 x λexc/NA).
      11. Set the z-series to capture the entire pistil/object of interest (e.g., ovule).
        Note: Depending on the stage of ovule development, z-series range from 300 to 500 sections (0.02 to 0.04 GB with the image format described above, respectively).
      12. Set the z-steps to ~80 nm.
        Note: This allows for a near cubic voxel in the final image with sufficient over-sampling (Nyquist-Shannon theorem) for downstream image processing (deconvolution or segmentation).
    3. Acquire the image stack.
    4. Save the dataset. There is no need of a specific file format. Imaris can read files from most microscopy systems.
      Note: We strongly recommend to name the dataset according to a serial number determined by a hierarchical database (for instance Author_Initials_3or4digits, e.g., EM_023, EM_572, etc.). This allows for easy dataset calling and batch processing.
    5. Immediately update the image description in a customized database.
      Note: We strongly recommend to elaborate an image database (e.g., Excel spreadsheet or other specialized program) gathering information such as sample type (biological material, stage, genotype, species, staining batch, etc.), image type and user-based assessment of image/staining quality. This allows for efficient tracking and handling of replicate datasets for downstream processing.

  4. 3D reconstruction and image pre-processing
    For the basic usage of Imaris described here (3D view, section view, create contour surface/create mask), first users are referred to free online tutorials (
    1. Upload a lif file in Imaris 8.3.1.
      Note: Imaris Batch allows the user to convert all series in a lif file to ims files.
    2. Inspect the quality of the image (Figure 2 and Video 1) using the ‘Section’ mode in the tab ‘View’. Cell boundaries have to be clearly resolved in all dimensions (viewed in the XY, XZ, YZ panels).
      Note: Insufficient sampling in xy (image format too low) or along the z dimension (too little optical sections), unbalanced signal acquisition and/or refractive index in the mounting and immersion medium, unstable fluorescent probe and/or steep gradient of signal loss in depth will produce images with low signal to noise ratio impairing on robust segmentation.

      Figure 2. Image quality: the ugly, the bad and the good. Image dataset with a very low (A, B) and low signal to noise ratio (C, D). Weak contrast between the outer and inner cell space (B), interrupted and fuzzy boundary signals (D). Image dataset with a high signal-to-noise ratio (G) and clear cell boundaries (H). Scale bars = 10 µm. 

      Video 1. Short tutorial for membrane-based 3D image segmentation using Imaris Cell. Selected points of the protocol are presented as follows: D. 3D-reconstruction and image processing: 2. Inspect the image quality, 3. Down-sampling, 5. in silico dissection, 6. Free rotate; E. Image segmentation and labelling: 1. Automated segmentation, 4. Cell labelling.

    3. Down-sampling (optional) (Video 1): Edit/Resample 3D/Change the values in X, Y, Z and tick the option ‘Fixed ratio X/Y/Z’.
      Note: Segmentation speed depends on the computer environment. As an example, an 8-bit, grey scale, 0.1 GB image (512 x 512 x 385 pixels) was segmented in 4 min in Imaris 8.3.1 run on a PC with an Intel Xeon E5 v3 clocked at 3.20 GHz processor, Nvidia Geforce GTX Titan X graphics card and an SSD 850 PRO 1TB. Down-sampling by 2x in each dimension reduces the time to 2 min without quality loss in our case but this should be verified by the user.
    4. Crop the dataset to the informative region (optional): ‘Edit/Crop 3D’, move the sliders to create a 3D box capturing the region of interest (Figure 3).
      Note: This operation is recommended if the informative region represents a well-defined subset of the image. If the object has a complicated shape, in silico dissection (see step D5) is recommended instead.

      Figure 3. Image pre-processing–crop in 3D. The region of interest occupies a volume smaller than the original image. The image is cropped in 3D around the ovule primordia. A. Original image, 3D volume rendering, ‘Blend’ mode; B. In the ‘Edit’ tab, choose ‘Crop in 3D’; C. Adjust the three slider boxes in the pop-up window; D. Crop (Apply): the cropped stack is smaller and will be segmented faster. Scale bars = 10 µm.

    5. Select an object of interest by in silico dissection (optional) (Video 1):
      1. This step is relatively quick (few minutes) and highly recommended for images of high complexity, i.e., capturing several and/or intermingled objects and for images where only a subset is informative for downstream analyses. Typically, this step is particularly useful to dissect individual ovules away from neighboring ones and surrounding carpel tissue. It also allows resolving problems due to touching tissues that create inaccurate cell contours.
      2. If several objects are isolated using this procedure, name them for ease of downstream steps (click on the channel name in the Edit/Display adjustment tab).

      1. Create a Surface using the ‘Add new surfaces’ function and edit manually.
      2. In the wizard, select ‘Skip automatic creation, edit manually’. In the ‘Contour/Autofit’ tab, adjust the sliders to smooth shape and weak impact (leftmost position).
      3. Draw the contours: under the tab Contour/Mode select the drawing mode (not shown), under the tab Contour/Board, select the orthogonal plane, activate ‘Draw’ or Ctrl + Space bar.
      4. Only few contours are sufficient to create an approximate surface around the region of interest; take care to define precisely the beginning, end and median planes.
      5. In the wizard, proceed to ‘Create surface’, a surface object (yellow) is created (Figure 4B).
      6. In the ‘Edit’ tab, click on ‘Mask all’ and tick the box ‘Duplicate channel before applying mask’ and ‘Set voxels outside surface to 0.000’. A new channel (mask) is created comprising the voxels captured by the surface–this channel is used for segmentation.

        Figure 4. Image pre-processing–in silico dissection of an object of interest. A. The original stack is volume-rendered. B. The created surface is presented in yellow. C. A new channel (mask) is created comprising the voxels captured by the surface–this channel is used for segmentation. Scale bars = 10 µm.

    6. Rotate the sample in space to align the main growth axis along the YZ dimension (Figure 5 and Video 1).
      Note: This step is optional. It is however useful to have the sample aligned along one of the orthogonal axis for easier navigation with the slicer and pointer at later stages during cell identification and labelling.
      1. In the ‘Edit’ tab, click on ‘Free rotate’. A window shows the new coordinates automatically entered.
      2. Click on ‘Apply’ and the object is thus reframed. Repeat if necessary.
    7. Otherwise, choose another alignment in which tissue layers/cells of interest are in one of the orthogonal planes.

      Figure 5. Image pre-processing–Sample rotation. A. The object of interest is tilted to the right side. Sample rotation is performed using «Free rotate», the object is therefore aligned straight along the y z axis (B, C). D. An active clipping plane shows a mid-longitudinal section. Scale bars = 10 µm.

  5. Image segmentation and labelling
    1. Automated segmentation (Video 1)
      1. Choose the function ‘Add new cells’ to create a ‘Cell’ object. This opens a wizard guiding you through the steps of automated segmentation (Figures 6A and 6B). Each step must be validated by the blue arrow. The green arrow finalizes the process.
      2. In ‘Select detection type’, choose ‘Detect cell only’ (Figure 6C).
        Note: There is the possibility to segment only a region of interest as alternative to pre-processing steps (see step D4).
      3. Next, choose ‘Detect cell boundary from cell membrane’. Select the source channel (Figure 6D), i.e., the masked channel if relevant (see step D5).
      4. For the next step, it is needed to measure two parameters: the ‘cell smallest diameter’ and ‘membrane detail’ (corresponding to the membrane’s thickness): the software proposes default values that are usually good suggestions but real values should be measured: this can be done in the Slice View mode using the line measurement tool (drawn by positioning two crosses in two mouse clicks; values are indicated on the right panel) across the smallest cell aimed in segmentation (Figure 6E), and across the membrane (Figure 6F).

        Figure 6. Automated segmentation–parameters. The ‘Add new cells’ icon allows the user to start off the segmentation (A). The created cell will show up in the list of other parameters listed within Scene (B). At the same time, a panel will appear on the left bottom corner of the Imaris interface. Choose ‘Detect cell only’ and hit the blue button to proceed (C). Next, choose ‘Detect cell boundary from cell membrane’ and enter the optimal ‘Cell smallest diameter’ and ‘Membrane detail’ values (D), which can be obtained by clicking 2 points across the cell (E) or the membrane (F). Scale bar = 2 µm.

      5. With this information, adjust the parameters ‘Cell smallest diameter’ and ‘Membrane detail’ in the Cells creation wizard.
        Note: Values proposed by default are based on signal distribution and voxel size. They do not necessarily correspond to the optimal, biologically-relevant structures. When doing segmentation for the first time, it is advised to try different values. Reducing the smallest cell diameter or membrane detail allows capturing more cell segments with higher accuracy. Yet, too small or too large values result in over (Figure 7A) and under-(Figure 7B) segmentation, respectively.

        Figure 7. Effect of ‘Cell smallest diameter’ and ‘Membrane details’ on segmentation accuracy. ‘Cell smallest diameter’ (CSD) and ‘Membrane details’ (MD) are critical parameters for an optimal segmentation. Note that MD represents the thickness of the cell boundary signal but is not an actual biological measure of this cellular structure which cannot be resolved in conventional CSLM imaging. In this representative example CSD and MD measured values ranged from 1.5 to 3.36 µm, and 0.3 to 0.6 µm, respectively. At fixed MD (here 0.30 µm), smaller CSD values produce over-segmentation shown in yellow (A) whereas under-segmentation (yellow) occurs at higher MD values and fixed CSD (here 3.36 µm, B). In this example, optimal values are for CSD = 3.36 µm and MD = 0.30 µm. Scale bars = 10 µm.

      6. Select a ‘Filter type’ for the Cell membrane detection. ‘Local contrast’ proved robust for images created as described here.
        Note: This filter applies a pre-processing step prior to the segmentation algorithm. The smooth method applies a Gaussian filter whereas the local contrast method uses a combination of Gaussian smoothing and baseline subtraction (Imaris 8.3 reference manual). The latest uses more memory but provides more fine-tuning possibilities in downstream steps.
      7. The next step launches the segmentation algorithm. The processing time depends on the image size and computational environment (e.g., 4 min for a 100 MB in Imaris 8.3.1 on our PC, see step D3).
      8. The raw segmentation result is displayed for one section plane in a transparency mode showing the original image signal. Inspect the segmentation quality by moving the slicer along the image. Check it in other orthogonal planes or oblique planes depending on the orientation/shape of your object of interest.
      9. From this inspection, estimate if there is a typical failure, i.e., over- or under-segmentation and identify a few typical planes where this should be corrected.
      10. Adjust the merging thresholds using signal intensity and quality filter criteria (local contrast method) or only intensity (smooth method). Dynamically verify the segmentation accuracy on several planes until best-fit (Figures 8C and 8F).
        Note: This step aims at merging neighboring segments into realistic cell objects and takes into account the intensity of the small segments boundaries (‘intensity’) and of merged regions (‘quality’) hence the latest are interdependent (Imaris 8.3 reference manual).
      11. Proceed to the next step: 3D cell objects are created.

        Figure 8. Choosing a pre-processing method and fine-tuning merging thresholds. The local contrast filter possesses two values which can be adjusted by the user (intensity and quality values) whereas the smooth filter has only the latter one. Sliding the window to different positions (white and yellow arrows) give three different outputs: over-(A, D), under-(B, E) and best-fit segmentation (C, F). For both methods, yellow asterisks indicate over-segmentation of the background inside and outside the masked channel. They also pinpoint under-segmented cells. Scale bars = 10 µm.

      12. A last step (‘Classify cells’) allows removing outliers according to user-based criteria. (Figure 8). By default, ‘Cell number of voxels’ is chosen as a filter. This enables to rapidly remove small or large segments created around the object of interest and corresponding to intercellular space or tissue cavities.
        Note: The filtering mode can be customized to all ‘Cell statistics’ available in Imaris alone or in combinations. This includes ‘Cell position’ (absolute or relative to the image border), cell volume, area, and other geometric parameters. An example of filtering against cells close to the image border is shown in Figures 9E and 9F.
      13. Finalize the segmentation (green button ‘Finish’). The 3D segmentation can be visualized in 3D or along 2D slicers (‘Slicer display’ in the tab ‘Settings’). Cells can be rendered in different coloring modes and transparency (mapped color, random, statistics based, etc., see Imaris 8.3 reference manual).

        Figure 9. Classification and filtering for customized selection of cells. A. Pull-down menu showing the different cell statistics available for filtering. Click on + to add several filters in combination. B. In the slice view mode, all cells are selected (no filter applied); 3D coloring mode follows the filter type ‘Cell Volume’ (C). D. Cells are sorted against the filter ‘Cell volume’. E and F. Cells are sorted against the filter ‘Cell distance to image border XYZ’. Scale bars = 10 µm.

    2. Manual Curation–Delete, Merge and Split function
      The automated segmentation is generally robust with 6-15% inaccurate segmentation on image datasets as produced according to the protocol described here. Over- and under-segmentation largely results from heterogeneous boundary signal throughout the image. Some cells will remain over- or under-segmented despite merging threshold adjustments. A set of methods is described below that allow for a rapid curation.
      1. Inspection of the segmentation is best achieved in the slicer mode. Use orthogonal or oblique planes.
        Note: If the image has been rotated (see step D6), the axis with the lowest resolution is no longer along the z axis as in the original image.
      2. Curate the erroneous regions using the ‘Delete’, ‘Merge’ or ‘Split’ functions in the ‘Edit’ tab to correct for irrelevant, under- and over-segmentation, respectively. For instance, to correct an over-segmented region, select the two erroneous segments belonging to the same cell and use the ‘Merge’ function. By contrast, to correct an under-segmented region, select the segment capturing two (or more cells) and re-segment using the ‘Split’ function. This action often solves the issue. If it generates over-segmented fragments; however, those can quickly be merged again as before (Figure 10). If this action does not correct the problem, follow step 3.

        Figure 10. Manual curation–merge and split functions. Scale bars = 10 µm.

    3. Manual Curation–Re-segmentation of a small region of interest (ROI)
      This approach is particularly useful when split and merge functions fail to correct the segmentation. The idea is to re-segment locally a difficult region, for instance with cell boundaries of disrupted or varying signal intensity. For this, a new cell object is created. The new (correct) cells can, however, not be directly imported into the first Cell object to curate (at least in Imaris 8.3). They first have to be exported as Surface object. The procedure is described below. The user is advised to check the latest software version, as future improvements may provide a direct Cell import option.
      1. Delete the badly segmented cells (select in the slicer mode, ‘Edit/Delete’).
      2. Create a new Cell Object (‘Add new cells’).
      3. Choose ‘Segment only a region of interest’ and define the box around the region to be re-segmented. To better locate the ROI, keep the former Cell object visible in the background (Slicer mode).
      4. Follow the wizard for automated segmentation but try different parameters. Decreasing the membrane details or smallest cell diameter usually solves the problem.
      5. If the ROI also captured cells that were previously properly corrected, delete them (see step E2b). Or, specifically select the new cells to be imported into the first segmentation object for correction.
      6. Export the selected, correct Cells: Tab ‘Edit/Export cells to surfaces’. Check the box ‘Selection’. A Surface object ‘Cell Export’ is created.
      7. Import the Surface object into the first Cells object to be curated: ‘Edit/Import surface to cell’. Follow the wizard. Verify the result of the import in the slicer display–the badly segmented region is now corrected.
    4. Cell Labelling–create tissue or cell type-specific categories (Video 1)
      In order to analyze the statistics of the cells (size, geometric description, position, etc.) according to their identity, or position in tissue layers, it is possible to give cells a label, corresponding to a category defined by the user. For instance, a tissue layer (epidermis, cortex, L1, L2, etc.), cell type (guard cell, root hair, precursor, etc.) or position relative to an axis (radial, apico-basal, etc.).
      1. Select the Cell object, which contains the segmented region. To tag a subset of cells, go to the panel on the right of the main display frame. First, delete the default labels if any.
      2. Create a new label group by clicking on +. All created labels will appear in the drop-down menu.
      3. A given label group can comprise different labels. For that, select the label group and click on ‘Add a label’ (Figure 11A). Enter the name of the new label and edit the color if needed.
      4. Once all labels groups and their corresponding labels are created, select the cells belonging to one category using either the slicer or volume display in the ‘Settings’ tab (for peripheral cells). Double-click to assign a label to the cell subset. To remove the current label, double-click again.
      5. In the ‘Color’ tab, choose ‘Labels’ and tick off all or selected labels groups to be visualized (Figure 11E).

        Figure 11. Customized cell labelling. Scale bars = 10 µm.

Data analysis

  1. Quantitative data: visualization and export
    Cell labelling allows classifying the quantitative data associated with each cell object per category. The variables can be selectively exported (one txt file per variable) or in batch (one txt file for all variables per cell). Common cell variables include area, volume, sphericity, ellipticity (oblate and prolate), x, y, z coordinates (center of mass). The full list includes approximately 100 variables (Imaris 8.3 reference manual). The data can be plotted and visualized using a third-source data analysis software (e.g., R, or Vantage in Imaris as described below.
    1. Visualization using Vantage Plot
      1. Select the Cell object to plot.
      2. Go to the ‘Vantage’ plot application (Top Menu).
      3. Choose the viewing mode (e.g., ‘1D View’), select the variables to plot (‘Type’ in the drop-down menu).
      4. Choose the color display in the ‘Draw style’ drop-down menu. An example is presented in Figures 12A and 12B for two variables.

        Figure 12. Plot generation with Vantage. Boxplots generated for cell volume (A) and cell sphericity (B) from segmented and labelled cells.

    2. Export various descriptors of cell segments
      1. In ‘File/Preferences/Statistics’, choose the statistic values to be exported. Otherwise, all statistics will be included.
        Note: The term ‘Statistics’ in ImarisCell refers to different quantitative descriptions associated with each cell object (e.g., area, volume, sphericity, fluorescence intensity etc.). It however does not relate to a statistical treatment of the data.
      2. In the ‘Statistics’ tab of the cell object, select the category ‘Detailed’. In the drop-down menu, choose all values, average or specific values.
      3. Export all values by clicking on the floppy disk icon ‘Export statistics on tab display to file’. Browse the location in which the data will be stored.
      4. By default, the file name is saved as .csv. However, it is also possible to save as .xls and .xml.
      5. Exported data can be used to plot specific cell categories and quantitative measurements in third-source data analysis software (e.g., R,
        Note: Each study has its own requirement in terms of cellular and tissue layer comparison. Data plotting and statistical analyses can be done using standard software and approaches recommended in biological sciences ( One recommendation, however, is to base the study on a minimum of fifteen replicate images for the same sample stage. This number takes into account the technical difficulty and time required to process images, data and variability between sample preparations.


  1. This protocol is applicable to a wide variety of tissues. We successfully imaged and segmented tissues from maize, rice, petunia, wheat and Marchantia (data not shown).
  2. Reproducibility of the staining quality depends on the health state and age of the plant. Stressed and/or old plant tissue will stain poorly and show higher light scattering (hence noise).
  3. The robustness of the segmentation strongly depends on the homogeneity of the signal throughout the 3D image, as commented in the section ‘segmentation’.
  4. The variability in quantitative measurements on cell objects depends on the quality of sample preparation: if the tissue is slightly squashed under the coverslip and does not show a native 3D shape, the image should not be used for segmentation.


  1. Modified pseudo-Schiff propidium iodide reagent
    Note: Prepare the following solutions under the fume hood. 
    1. Fixative
      50% (v/v) methanol-water
      10% (v/v) acetic acid-water
    2. Pseudo-Schiff reagent with propidium iodide (PI)
      100 mM sodium metabisulphite (Na2S2O5)
      0.15 M HCl
      10 µg/ml PI
    3. Chloral hydrate solution
      4 g chloral hydrate
      1 ml glycerol
      2 ml distilled water
    4. Hoyer’s solution
      30 g gum arabic
      50 ml distilled water
      200 g chloral hydrate
      20 ml glycerol
      Dissolve the gum arabic in water at 30 °C by stirring overnight. Add the chloral hydrate; dissolve by stirring further, and finally add 20 ml glycerol. Filter the solution through glass wool. Store at RT and protect from the light in a tightly sealed glass bottle


This work was supported and funded by the Commission for Technology and Innovation (CTI grant 16997), and the Baugarten Stiftung Zürich, the University of Zürich and the Swiss National Science Foundation (SNF grants to CB and UG). We acknowledge Peter Majer and Xuan Zhang (Bitplane R&D, Zürich) for insightful discussions and technical assistance with ImarisCell, Professor Ueli Grossniklaus (UG) for scientific support and insightful discussions and technical assistance of the department for organisational support and assistance with microscopy imaging (Christoph Eichenberger, Valeria Gagliardini, Arturo Bolaños, Peter Kopf). The staining protocol was performed based on previously published work of Truernit et al. (2008) and Yoshida et al. (2014).


  1. Barbier de Reuille, P., Routier-Kierzkowska, A. L., Kierzkowski, D., Bassel, G. W., Schupbach, T., Tauriello, G., Bajpai, N., Strauss, S., Weber, A., Kiss, A., Burian, A., Hofhuis, H., Sapala, A., Lipowczan, M., Heimlicher, M. B., Robinson, S., Bayer, E. M., Basler, K., Koumoutsakos, P., Roeder, A. H., Aegerter-Wilmsen, T., Nakayama, N., Tsiantis, M., Hay, A., Kwiatkowska, D., Xenarios, I., Kuhlemeier, C. and Smith, R. S. (2015). MorphoGraphX: A platform for quantifying morphogenesis in 4D. Elife 4: 05864.
  2. Bassel, G. W. and Smith, R. S. (2016). Quantifying morphogenesis in plants in 4D. Curr Opin Plant Biol 29: 87-94.
  3. Coen, E. and Rebocho, A. B. (2016). Resolving conflicts: modeling genetic control of plant morphogenesis. Dev Cell 38(6): 579-583.
  4. Fernandez, R., Das, P., Mirabet, V., Moscardi, E., Traas, J., Verdeil, J. L., Malandain, G. and Godin, C. (2010). Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution. Nat Methods 7(7): 547-553.
  5. Hervieux, N., Dumond, M., Sapala, A., Routier-Kierzkowska, A-L., Kierzkowski, D., Roeder, A.H.K., Smith, R.S., Boudaoud, A. and Hamant, O. (2016). A mechanical feedback restricts sepal growth and shape in Arabidopsis. Curr Biol 26(8): 1019-1028.
  6. Imaris 8.3 reference manual.
  7. Roeder, A. H., Tarr, P. T., Tobin, C., Zhang, X., Chickarmane, V., Cunha, A. and Meyerowitz, E. M. (2011). Computational morphodynamics of plants: integrating development over space and time. Nat Rev Mol Cell Biol 12(4): 265-273.
  8. Sankar, M., Nieminen, K., Ragni, L., Xenarios, I. and Hardtke, C. S. (2014). Automated quantitative histology reveals vascular morphodynamics during Arabidopsis hypocotyl secondary growth. Elife 3: e01567.
  9. Stegmaier, J., Amat, F., Lemon, W. C., McDole, K., Wan, Y., Teodoro, G., Mikut, R. and Keller, P. J. (2016). Real-time three-dimensional cell segmentation in large-scale microscopy data of developing embryos. Dev Cell 36(2): 225-240.
  10. Truernit, E., Bauby, H., Dubreucq, B., Grandjean, O., Runions, J., Barthelemy, J. and Palauqui, J. C. (2008). High-resolution whole-mount imaging of three-dimensional tissue organization and gene expression enables the study of Phloem development and structure in Arabidopsis. Plant Cell 20(6): 1494-1503.
  11. Yoshida, S., Barbier de Reuille, P., Lane, B., Bassel, G. W., Prusinkiewicz, P., Smith, R. S. and Weijers, D. (2014). Genetic control of plant development by overriding a geometric division rule. Dev Cell 29(1): 75-87.


【背景】植物器官和组织动力学研究依赖于沿着发育进程的三维生长过程的分析。细胞数量,细胞大小和细胞形态的演变允许分别解释增殖,细胞扩增和各向异性的事件(Roeder等,2011; Barbier de Reuille等,2015; Bassel和Smith,2016; Coen和Rebocho, 2016)。虽然延时成像在原理上作为选择的方式出现,但并不容易适用于所有植物器官,有时嵌入在不可接近的结构中,并且图像质量通常在大量样品和细胞水平上对强大的定量分析造成损害。互补,健壮的替代方案是在连续的时间点以高分辨率染色和记录光学清除的组织/器官的三维图像,以重建发育进展。然后可以将这些3D图像数字地分割成单独的单元对象,从中可以提取数字的测量,单元格形状和单元大小的不同描述符。使用修改的假希夫碘化丙啶(mPS-PI)方案(Truernit等人,2008),全部组织清除和染色细胞壁,被广泛用于植物群落的3D形状分析(Sankar等, 2014; Yoshida等,2014; Hervieux等,2016)。这里描述的只有微小的修改和具体的说明,使精细的心皮结构的高品质样品准备。另外,虽然使用共焦扫描激光显微镜的植物组织成像在许多实验室中成为常见的实践,但是关于如何微调光学和软件控制的图像记录的具体知识仍然难以捉摸,并且经常保持在内部。在这里,我们提供详细的建议,旨在指导用户生产适合强大的定性和定量分析的高质量,高分辨率的3D图像。对于图像分割,不同的开源算法被证明对植物和果蝇的组织动力学研究是非常有价值的,即:MARS-ALT(Fernandez等,2010),MorphographX(Barbier de Reuille等,2015)和RACE(Stegmaier et al。等等,2016)。然而,这些接口通常需要计算技巧来微调分割参数,手动纠正错误分割的对象,当组织模型在软件中不可用时导出细胞标记,并为下游分析输出定量数据。缺乏这一专业知识的生物学家的替代解决方案在于使用具有简化用户界面的市售软件。我们在这里介绍一个用于3D可视化和图像处理的软件Imaris的选项。我们报告了一个详细的,注释的工作流程应用于胚珠组织分析,但这是广泛应用于分析各种植物组织。

关键字:高分辨率三维成像, 植物组织, 图像分割, 地貌动力学, 组织形态, 拟南芥, 胚珠原基


  1. 显微镜载玻片(76 x 26 mm)(Thermo Fisher Scientific,Thermo Scientific TM,目录号:10143562CEF)
  2. 显微镜盖片用于共聚焦成像:18 x 18 mm,0.17±0.01 mm厚度(Hecht Assistant,目录号:41014509)
  3. 解剖针(钨,直径:0.75和0.35 mm)
  4. 圆形培养皿35毫米(Greiner Bio One International,目录号:627102)
  5. 2毫升微管(SARSTEDT,目录号:72.695.500)
  6. 无尘纸
  7. 玻璃棉
  8. 植物材料:开花拟南芥植物
  9. 十二烷基硫酸钠,钠盐(SDS)(Sigma-Aldrich,目录号:71729)
  10. 氢氧化钠(NaOH)(Sigma-Aldrich,目录号:71690)
  11. 乙醇(70%,80%)(Fisher Scientific,目录号:10428671)
  12. 周期酸(Sigma-Aldrich,目录号:375810)
  13. 指甲油
  14. 安装介质
  15. 甲醇(Sigma-Aldrich,目录号:34860)
  16. 乙酸(Merck,目录号:100063)
  17. 偏亚硫酸氢钠(Na 2 S 2 O 5 O 5)(Sigma-Aldrich,目录号:31448)
  18. 盐酸发烟,37%(HCl)(Carl Roth,目录号:4625.1)
  19. 碘化丙啶(PI)(Sigma-Aldrich,目录号:P4864)
  20. 水合氯仿(Sigma-Aldrich,目录号:15307)
  21. 甘油(Carl Roth,目录号:3783.1)
  22. 阿拉伯胶(Sigma-Aldrich,目录号:51198)
  23. 改性假席夫碘化丙啶(mPS-PI)溶液(参见食谱)
    1. 固定解决方案
    2. 带有碘化丙啶(PI)的伪希夫试剂
    3. 水合氯解决方案
    4. 霍耶的解决方案


  1. 立体显微镜(例如,Leica Microsystems,型号:Leica M60)
  2. 孵化器(例如,Eppendorf,型号:Thermomixer C)
  3. 钻石或碳化物笔(Sigma-Aldrich,目录号:Z225568)
  4. 共焦扫描激光显微镜,激光线561nm的共振扫描仪和适用于甘油浸渍的APO PL物镜20x(NA 0.7)和63x(NA 1.4)(徕卡微系统,型号:徕卡TCS SP5)
  5. 计算机理想地具有推荐的高端处理器,内存和图形环境( http: / )。低端设置是可能的,但会导致处理速度较慢


  1. Imaris 8.3.1( ,英国牛津仪器)
  2. 数据分析软件(例如,R, www.r-


  1. 样品制备
    1. 从 A收集花蕾。在拟合阶段将顶端花序放置在玻璃载片上,并放入一滴无菌水中。
    2. 用解剖针将立体显微镜下的雌蕊分离开。
    3. 将雌蕊转移到具有3ml固定液的小培养皿(参见食谱)。
    4. 在这个阶段,您可以将样品保存在4°C至多1个月。
    5. 假Schiff PI染色遵循先前公布的方案(Truernit等人,2008;吉田等,2014),并详细说明。
    6. 将雌蕊转移到1ml的1%SDS,0.2N NaOH溶液的2ml微管中,在室温(RT)下进行过夜处理。
    7. 弃去1%SDS,0.2N NaOH溶液,加入1ml 80%乙醇。在80℃下以350rpm孵育5分钟。 
    1. 取出80%溶液,在管中留下最少10μl。加入1 ml固定液。在室温下孵育1小时。
    2. 用水冲洗组织一次,并在室温下在1%的高碘酸中孵育1小时
    3. 用水冲洗组织一次,并在室温下在改良的假Schiff PI溶液(见食谱)(10μg/ml)中孵育1小时或2小时。
    4. 用水冲洗三次,加入300μl水合氯醛水溶液。在室内过夜进一步澄清。

  2. 安装用于显微镜成像
    1. 使用无尘纸清洁幻灯片和70%乙醇的盖玻片,让其干燥。
    2. 使用金刚石尖笔,将5-6个小块(约3 x 18毫米每片)切成盖玻片(图1A和1B)。
    3. 将一滴Hoyer的溶液或介质选择在两片玻璃盖玻片之间的幻灯片上,留下一个ca.宽度为10-12毫米
    4. 将一个雌蕊放在落下(图1D)。
    5. 将盖玻片放在顶部而不施加压力(图1E)。这对于保持雌蕊的三维结构非常重要
    6. 用指甲油密封盖玻片的边框。将滑块置于水平位置,以使介质固化。

      图1.用于显微镜成像的样品安装。 A.清洁的盖玻片; B.用金刚石笔,将盖玻片切成小块; C.用两块作为支柱,在它们之间留一个空间; D.将雌蕊放在一滴安装介质中; E.将盖玻片放在上面并用指甲油密封其边界。 

  3. 图像采集
    1. 使用传输光或落射荧光(绿色激发滤光片,红色发射 - 长带通范围),使用20x物镜查找样品,然后切换到更高的放大倍率进行采集。
    2. 设置图像采集参数
      1. 激发:561 nm
      2. 发射:570-616 nm
      3. 激光传输:30-40%。
      4. 检测:理想地是新一代检测器(例如,HyD),亮对比度增益模式。
      5. 扫描速度:共振模式,8,000 Hz。
      6. 针孔直径:1 AU(Airy Unit)。
      7. 平均帧:4.
        注意:如果荧光信号较低,激光传输/增益必须增加,则较高的平均值将提高图像质量 - 必须进行测试。
      8. 目标:理想的63x,计划+复消色差校正(PL APO),甘油浸没透镜(GLY),NA 1.4,工作距离(WD)0.3 mm。
      9. 图像格式:512 x 512像素。
      10. 图像尺寸:视野在80到85μm之间被裁剪得到x和y≈85nm。
        注意:这允许使用63×NA 1.3物镜在561nm(r x x,y z = 176nm,使用r x,y 0 = 0.4×x)进行2x过采样//NA)
      11. 设置z系列以捕获感兴趣的整个雌蕊/物体(例如,胚珠)。
        注意:根据胚珠发育的阶段,z系列范围为300至500个部分(分别为0.02至0.04 GB,分别为上述图像格式)。
      12. 将z步骤设置为〜80 nm。
        注意:这允许最终图像中的近似立方体素具有足够的过采样(奈奎斯特 - 香农定理)用于下游图像处理(去卷积或分割)。
    3. 获取图像堆栈。
    4. 保存数据集。不需要特定的文件格式。 Imaris可以从大多数显微系统读取文件。
    5. 立即更新自定义数据库中的图像描述。

  4. 3D重建和图像预处理
    对于这里描述的Imaris的基本用法(3D视图,截面视图,创建轮廓曲面/创建蒙版),第一个用户可以参考免费在线教程( )。
    1. 在Imaris 8.3.1中上传一个文件。
      注意:Imaris Batch允许用户将lif文件中的所有系列转换为ims文件。
    2. 使用"视图"选项卡中的"部分"模式检查图像的质量(图2和视频1)。细胞边界必须在所有维度上得到明确的解决(在XY,XZ,YZ面板中查看)。

      Video 1. Short tutorial for membrane-based 3D image segmentation using Imaris Cell. Selected points of the protocol are presented as follows: D. 3D-reconstruction and image processing: 2. Inspect the image quality, 3. Down-sampling, 5. in silico

      To play the video, you need to install a newer version of Adobe Flash Player.

      Get Adobe Flash Player

    3. 下采样(可选)(视频1):编辑/重新采样3D /更改X,Y,Z中的值,并勾选"固定比X/Y/Z"选项。 注意:细分速度取决于计算机环境。例如,在Imaris 8.3.1中,4分钟内分割了一个8位,灰度级,0.1 GB的图像(512 x 512 x 385像素),在带有3.20 GHz处理器的Intel Xeon E5 v3的PC上运行,Nvidia Geforce GTX Titan X显卡和SSD 850 PRO 1TB。在我们的情况下,在每个维度下采样2次,将时间缩短到2分钟,而不会造成质量损失,但这应由用户验证。
    4. 将数据集裁剪到信息区域(可选):"编辑/裁剪3D",移动滑块以创建捕获感兴趣区域的3D框(图3)。

      图3. 3D中的图像预处理 - 裁剪。感兴趣的区域占据比原始图像小的体积。图像在胚珠原基下围绕三维裁剪。 A.原始图像,3D体积渲染,"混合"模式; B.在"编辑"选项卡中,选择"裁剪3D"; C.调整弹出窗口中的三个滑块; D.裁剪(应用):裁剪堆栈较小,分段速度更快。比例尺=10μm
    5. 以 解剖(可选)(视频1):
      选择感兴趣的对象 注意:
      1. 此步骤相对较快(几分钟),并且强烈建议用于高复杂度的图像,即捕获多个和/或混合对象以及对于其中仅一个子集为下游分析提供信息的图像。通常,该步骤特别有助于从邻近的周围和周围的心皮组织分离单个的胚珠。它还允许解决由于触摸组织造成不准确的细胞轮廓的问题。
      2. 如果使用此过程隔离了几个对象,请将其命名为便于下游步骤(单击"编辑/显示调整"选项卡中的通道名称)。

      1. 使用"添加新曲面"功能创建曲面并手动编辑。
      2. 在向导中,选择"跳过自动创建,手动编辑"。在"轮廓/自动调整"选项卡中,调整滑块的平滑形状和弱影响(最左侧的位置)。
      3. 绘制轮廓:在"轮廓/模式"下,选择绘图模式(未显示),在"轮廓/板"选项卡下,选择正交平面,激活"绘制"或Ctrl +空格键。
      4. 只有很少的轮廓足以在感兴趣的区域周围产生一个近似的表面;注意准确地定义开始,结束和中间平面。
      5. 在向导中,进入"创建曲面",创建曲面对象(黄色)(图4B)。
      6. 在"编辑"选项卡中,单击"全部掩码",然后勾选"应用蒙版之前的复制通道"框和"将表面外的体素设置为0.000"。创建一个新的通道(掩码),包括由表面捕获的体素,该通道用于分割。

        图4.图像预处理对于感兴趣的对象的剖析 A.原始堆栈是体积渲染的。 B.创建的表面呈黄色。 C.创建一个新的通道(掩码),包括由表面捕获的体素,该通道用于分割。比例尺=10μm
    6. 在空间中旋转样品以使主增长轴沿YZ尺寸对准(图5和视频1)。
      1. 在"编辑"选项卡中,单击"自由旋转"。一个窗口显示自动输入的新坐标。
      2. 点击"应用",然后重构对象。必要时重复。
    7. 否则,选择另一个对齐,其中感兴趣的组织层/细胞在正交平面之一中。

      图5.图像预处理 - 样品旋转。 A.感兴趣的对象倾斜到右侧。样品旋转使用"自由旋转"进行,因此物体沿着y z轴(B,C)直线对齐。 D.主动剪取平面显示中纵向部分。比例尺=10μm
  5. 图像分割和标记
    1. 自动细分(视频1)
      1. 选择功能"添加新单元格"以创建"单元格"对象。这将打开一个向导,指导您完成自动分割的步骤(图6A和6B)。每个步骤必须由蓝色箭头验证。绿色箭头完成了这个过程。
      2. 在"选择检测类型"中,选择"仅检测单元格"(图6C)。
      3. 接下来,选择"从细胞膜检测细胞边界"。选择源通道(图6D),即,即,相关的屏蔽通道(参见步骤D5)。
      4. 对于下一步,需要测量两个参数:"细胞最小直径"和"膜细节"(对应于膜的厚度):软件提出通常是良好建议的默认值,但应测量实际值:可以在Slice View模式中使用线测量工具(通过在两个鼠标点击中定位两个十字点来绘制图形;值在右侧面板上显示),跨越目标分割的最小单元格(图6E)和跨膜(图6F)

        图6.自动分割参数。"添加新单元格"图标允许用户开始分割(A)。创建的单元格将显示在场景(B)中列出的其他参数列表中。同时,面板将出现在Imaris界面的左下角。选择"仅检测单元格",然后点击蓝色按钮继续(C)。接下来,选择"从细胞膜检测细胞边界",并输入最佳的"细胞最小直径"和"膜细节"值(D),可以通过点击细胞(E)或膜(F)上的2个点获得。比例尺= 2μm
      5. 使用此信息,在"单元格创建向导"中调整参数"单元格最小直径"和"膜细节"。
        注意:默认值是基于信号分布和体素大小。它们不一定对应于最佳的生物相关结构。首次做细分时,建议尝试不同的值。减小最小的细胞直径或膜细节允许以更高的精度捕获更多的细胞片段。然而,太小或太大的值分别导致(图7A)和下 - (图7B)细分。

        图7."细胞最小直径"和"膜细节"对分割精度的影响。"细胞最小直径"(CSD)和"膜细节"(MD)是最佳分割的关键参数。注意,MD表示细胞边界信号的厚度,但不是在常规CSLM成像中不能解决的该细胞结构的实际生物学测量。在该代表性示例中,CSD和MD测量值分别为1.5至3.36μm和0.3至0.6μm。在固定的MD(这里为0.30μm),较小的CSD值产生黄色(A)的过分割,而分割较低(黄色)则发生在较高的MD值和固定的CSD(这里为3.36μm,B)。在这个例子中,CSD =3.36μm和MD =0.30μm的最佳值。比例尺=10μm
      6. 选择"过滤器类型"进行细胞膜检测。 "本地对比度"对于如此所述创建的图像而言是强大的。
        注意:此过滤器在分割算法之前应用预处理步骤。平滑方法应用高斯滤波器,而局部对比方法使用高斯平滑和基线减法的组合(Imaris 8.3参考手册)。最新的使用更多的内存,但在下游步骤中提供更多的微调可能性。
      7. 下一步启动分割算法。处理时间取决于图像大小和计算环境(例如,在我们的PC上,Imaris 8.3.1中为100 MB的4分钟,见步骤D3)。
      8. 原始分割结果以显示原始图像信号的透明度模式显示一个截面平面。通过沿着图像移动切片器来检查分割质量。根据您感兴趣的对象的方向/形状,在其他正交平面或倾斜平面中进行检查。
      9. 从这个检查中,估计是否存在典型的故障,即,过分或欠分割,并且识别几个典型的平面,这些应该被更正。
      10. 使用信号强度和质量过滤标准(局部对比度法)或仅强度(平滑方法)调整合并阈值。动态地验证几个平面上的分割精度,直到最佳拟合(图8C和8F)。
        注意:此步骤旨在将相邻段合并成逼真的单元格对象,并考虑到小段边界("强度")和合并区域("质量")的强度,因此最新的是相互依赖的(Imaris 8.3参考手册)。
      11. 继续下一步:创建3D单元格对象。

        图8.选择预处理方法和微调合并阈值。局部对比度滤镜具有两个可由用户调整的值(强度和质量值),而平滑滤镜只有后一个。将窗口滑动到不同的位置(白色和黄色箭头)给出三个不同的输出:过(A,D),下 - (B,E)和最佳拟合分割(C,F)。对于这两种方法,黄色星号表示屏蔽通道内外的背景的过度分割。他们还精确定位细分细胞。比例尺=10μm
      12. 最后一步("分类单元")允许根据用户标准去除异常值。 (图8)。默认情况下,选择"单元格体素"作为过滤器。这使得能够快速去除围绕感兴趣对象产生并对应于细胞间隙或组织腔的小或大片段。
      13. 完成分割(绿色按钮"完成")。 3D分割可以3D或2D切片器("切片机显示"在"设置"选项卡)中可视化。单元格可以呈现为不同的着色模式和透明度(映射颜色,随机,基于统计信息,等,参见Imaris 8.3参考手册)。

        图9.用于自定义选择单元格的分类和过滤。 A.下拉菜单,显示可用于过滤的不同单元格统计信息。点击+添加几个过滤器组合。 B.在切片视图模式下,选择所有单元格(不应用滤镜); 3D着色模式遵循过滤器类型"细胞体积"(C)。 D.细胞按照过滤器"细胞体积"进行分选。 E和F.根据滤波器"细胞到图像边界XYZ"的距离对细胞进行分类。比例尺=10μm
    2. 手动清除,合并和拆分功能
      1. 在切片器模式下最好实现分割的检查。使用正交或斜面。
      2. 使用"编辑"选项卡中的"删除","合并"或"拆分"功能来策略错误的区域,以分别对不相关,欠分割和过度分割进行纠正。例如,要纠正过分区域,请选择属于同一单元格的两个错误段,并使用"合并"功能。相反,要纠正欠分段区域,请选择使用"拆分"功能重新分割两个(或多个单元格)的段。这个行动经常解决这个问题。如果它生成过分片段;然而,这些可以像以前一样再次合并(图10)。如果此操作无法解决问题,请按照步骤3.

        图10.手动合并和拆分功能。比例尺= 10μm。

    3. 手工修复 - 重新分割小区域(ROI)
      当拆分和合并函数无法更正分割时,此方法特别有用。这个想法是在本地重新分割一个困难的区域,例如,具有干扰或变化的信号强度的单元格边界。为此,创建一个新的单元对象。但是,新的(正确的)单元格可以直接导入到第一个Cell对象中(至少在Imaris 8.3中)。它们首先必须导出为Surface对象。该过程如下所述。建议用户检查最新的软件版本,因为未来的改进可能会提供直接的单元格导入选项。
      1. 删除细分割的单元格(在切片器模式中选择"编辑/删除")。
      2. 创建一个新的单元格对象('添加单元格')。
      3. 选择"仅区隔感兴趣区域",并定义要重新分段的区域周围的框。为了更好地定位ROI,请保留前一个Cell对象在后台可见(切片器模式)。
      4. 按照向导自动分割,但尝试不同的参数。降低膜细节或最小的细胞直径通常会解决问题
      5. 如果ROI也捕获了以前正确更正的单元格,请删除它们(请参阅步骤E2b)。或者,具体选择要导入到第一个分割对象中的新单元格进行更正。
      6. 导出所选的,正确的单元格:选项卡"编辑/导出单元格到曲面"。选中"选择"框。创建表面对象"单元格导出"。
      7. 将Surface对象导入要进行策划的第一个Cells对象:'编辑/导入表面到单元格'。按照向导。验证切片器显示中导入的结果 - 现在更正了严重分段的区域。
    4. 细胞标记 - 创建组织或细胞类型特定类别(视频1)
      1. 选择包含分段区域的单元格对象。要标记单元格的子集,请转到主显示框右侧的面板。首先,删除默认标签(如果有的话)。
      2. 点击+创建一个新的标签组。所有创建的标签将显示在下拉菜单中。
      3. 给定的标签组可以包括不同的标签。为此,选择标签组,然后单击"添加标签"(图11A)。输入新标签的名称,并根据需要编辑颜色。
      4. 创建所有标签组及其相应标签后,使用"设置"选项卡中的切片器或音量显示选择属于一个类别的单元格(对于外围设备)。双击将标签分配给单元格子集。要删除当前标签,请再次双击。
      5. 在"颜色"选项卡中,选择"标签",勾选要显示的全部或所选标签组(图11E)。

        图11.自定义单元格标签比例尺= 10μm。


  1. 定量数据:可视化和导出
    细胞标签可以分类与每个细胞对象相关的定量数据。可以有选择地导出变量(每个变量一个txt文件)或批量(每个单元格的所有变量一个txt文件)。常见的细胞变量包括面积,体积,球形度,椭圆率(扁平和长圆形),x,y,z坐标(质心)。完整列表包括大约100个变量(Imaris 8.3参考手册)。可以使用第三源数据分析软件(例如,R, )或在Imaris中的Vantage,如下所述。
    1. 使用Vantage Plot进行可视化
      1. 选择要绘制的单元格对象。
      2. 转到"Vantage"剧情应用程序(Top Menu)。
      3. 选择查看模式(例如,,'1D View'),选择要绘制的变量(下拉菜单中的"类型")。
      4. 在"绘制样式"下拉菜单中选择颜色显示。图12A和12B中有两个变量的例子


    2. 导出单元格段的各种描述符
      1. 在"文件/首选项/统计"中,选择要导出的统计值。否则,将包括所有统计资料。
      2. 在单元格对象的"统计"选项卡中,选择"详细"类别。在下拉菜单中,选择所有值,平均值或特定值。
      3. 通过单击软盘图标"导出选项卡显示的统计信息到文件"导出所有值。浏览数据的存储位置。
      4. 默认情况下,文件名保存为.csv。但是,也可以另存为.xls和.xml。
      5. 导出的数据可用于绘制第三源数据分析软件中特定的单元格类别和定量测量(例如。,R, )。
        注意:每项研究在细胞和组织层比较方面都有自己的要求。数据绘制和统计分析可以使用生物科学推荐的标准软件和方法进行( )。然而,一项建议是将该研究的基础用于相同样本阶段的至少十五个重复图像。这个数字考虑了处理样品制备过程中图像,数据和变异性所需的技术难度和时间。


  1. 该方案适用于各种组织。我们成功地对来自玉米,水稻,矮牵牛,小麦和马来西亚的组织进行成像和分割(数据未显示)。
  2. 染色质量的重现性取决于植物的健康状况和年龄。胁迫和/或老植物组织会染色不良,表现出较高的光散射(因此产生噪音)
  3. 分割的鲁棒性强烈地取决于整个3D图像中的信号的均匀性,如"分割"部分所述。
  4. 细胞对象的定量测量的变异性取决于样品制备的质量:如果组织在盖玻片下略微压扁,并且不显示原生3D形状,则图像不应用于分割。


  1. 改性假希夫碘化丙啶试剂
    1. 固定
      50%(v/v)甲醇 - 水
      10%(v/v)乙酸 - 水
    2. 带有碘化丙啶(PI)的伪希夫试剂 100mM偏亚硫酸氢钠(Na 2 S 2 O 5 O 5)
      0.15 M HCl
      10μg/ml PI
    3. 水合氯解决方案
    4. 霍伊的解决方案


这项工作由技术与创新委员会(CTI授权16997)和苏黎世百慕大基金会,苏黎世大学和瑞士国家科学基金会(SNF授予CB和UG)支持和资助。我们承认Peter Majer和Xuan Zhang(Bitplane R& D,苏黎世)与ImarisCell教授,Ueli Grossniklaus教授(UG)进行了深入的讨论和技术协助,为科学支持和有见地的讨论和技术支持部门组织支持和协助显微成像(Christoph Eichenberger,Valeria Gagliardini,ArturoBolaños,Peter Kopf)。染色方案基于先前发表的Truernit等人的工作进行。 (2008)和吉田等人。 (2014)。


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  2. Bassel,GW和Smith,RS(2016)。量化在4D植物中的形态发生。 Curr Opin Plant Biol 29:87-94。
  3. Coen,E.和Rebocho,AB(2016)。解决冲突:模拟植物形态发生的遗传控制。 Dev Cell 38(6):579-583。
  4. Fernandez,R.,Das,P.,Mirabet,V.,Moscardi,E.,Traas,J.,Verdeil,JL,Malandain,G。和Godin,C。(2010)。< a class = -insertfile"href =""target ="_ blank"> 4D中的成像植物生长:细胞分辨率下的强化组织重建和分类。 Nat Methods 7(7):547-553。
  5. Hervieux,N.,Dumond,M.,Sapala,A.,Routier-Kierzkowska,AL。,Kierzkowski,D.,Roeder,AHK,Smith,RS,Boudaoud,A.and Hamant,O。(2016) 机械反馈限制了拟南芥中的萼片生长和形状。 Curr Biol 26(8):1019-1028。
  6. Imaris 8.3参考手册。
  7. Roeder,AH,Tarr,PT,Tobin,C.,Zhang,X.,Chickarmane,V.,Cunha,A。和Meyerowitz,EM(2011)。  植物的计算动力学:整合空间和时间的发展。 Nat Rev Mol Cell Biol 12(4):265-273。
  8. Sankar,M.,Nieminen,K.,Ragni,L.,Xenarios,I. and Hardtke,CS(2014)。 
  9. Stegmaier,J.,Amat,F.,Lemon,WC,McDole,K.,Wan,Y.,Teodoro,G.,Mikut,R.and Keller,PJ(2016)。< a class = insertfile"href =""target ="_ blank">发展中的胚胎的大规模显微镜数据中的实时三维细胞分割。 > Dev Cell 36(2):225-240。
  10. Truernit,E.,Bauby,H.,Dubreucq,B.,Grandjean,O.,Runions,J.,Barthelemy,J.and Palauqui,JC(2008)。< a class ="ke-insertfile"href = ""target ="_ blank">三维组织组织和基因表达的高分辨率整体成像使得能够研究韧皮部的发育和结构拟南芥。植物细胞 20(6):1494-1503。
  11. Yoshida,S.,Barbier de Reuille,P.,Lane,B.,Bassel,GW,Prusinkiewicz,P.,Smith,RS和Weijers,D。(2014)。< a class ="ke-insertfile"href =""target ="_ blank">通过覆盖几何划分规则进行植物发育的遗传控制。 Dev Cell 29(1):75-87。
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Copyright: © 2017 The Authors; exclusive licensee Bio-protocol LLC.
引用:Mendocilla Sato, E. and Baroux, C. (2017). Analysis of 3D Cellular Organization of Fixed Plant Tissues Using a User-guided Platform for Image Segmentation. Bio-protocol 7(12): e2355. DOI: 10.21769/BioProtoc.2355.