Materials and Reagents

Microscope slides (76 x 26 mm) (Thermo Fisher Scientific, Thermo Scientific^TM, catalog number: 10143562CEF )
Microscope cover slips for confocal imaging: 18 x 18 mm, 0.17 ± 0.01 mm thickness (Hecht Assistant, catalog number: 41014509 )
Dissecting needles (Tungsten, diameter: 0.75 and 0.35 mm)
Round Petri dishes 35 mm (Greiner Bio One International, catalog number: 627102 )
2 ml micro tubes (SARSTEDT, catalog number: 72.695.500 )
Dust-free paper
Glass wool
Plant material: Flowering Arabidopsis thaliana plants
Sodium dodecyl sulfate, sodium salt (SDS) (Sigma-Aldrich, catalog number: 71729 )
Sodium hydroxide (NaOH) (Sigma-Aldrich, catalog number: 71690 )
Ethanol (70%, 80%) (Fisher Scientific, catalog number: 10428671 )
Periodic acid (Sigma-Aldrich, catalog number: 375810 )
Nail polish
Mounting medium
Methanol (Sigma-Aldrich, catalog number: 34860 )
Acetic acid (Merck, catalog number: 100063 )
Sodium metabisulphite (Na₂S₂O₅) (Sigma-Aldrich, catalog number: 31448 )
Hydrochloric acid fuming, 37% (HCl) (Carl Roth, catalog number: 4625.1 )
Propidium iodide (PI) (Sigma-Aldrich, catalog number: P4864 )
Chloral hydrate (Sigma-Aldrich, catalog number: 15307 )
Glycerol (Carl Roth, catalog number: 3783.1 )
Gum arabic (Sigma-Aldrich, catalog number: 51198 )
Modified pseudo-Schiff propidium iodide (mPS-PI) solution (see Recipes)

Fixative solution
Pseudo-Schiff reagent with propidium iodide (PI)
Chloral hydrate solution
Hoyer’s solution

Equipment

Stereomicroscope (e.g., Leica Microsystems, model: Leica M60 )
Incubator (e.g., Eppendorf, model: Thermomixer^® C )
Diamond- or carbide-tip pen (Sigma-Aldrich, catalog number: Z225568 )
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 )
Computer ideally with high-end processor, memory and graphic environment as recommended (http://www.bitplane.com/systemrequirements.aspx). Lower-end settings are possible but will result in slower processing speed

Software

Imaris 8.3.1 (www.bitplane.com, Oxford Instruments, UK)
Data analysis software (e.g., R, www.r-project.org)

Procedure

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.
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.
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 (r_x,y = 176 nm using r_x,y = 0.4 x λ_exc/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.
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 (http://www.bitplane.com/learning).
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):
  Notes:
  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.
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.
Image segmentation and labelling
1. Automated segmentation (Video 1)
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.).

Data analysis

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, www.r-project.org) or Vantage in Imaris as described below.
1. Visualization using Vantage Plot
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, www.r-project.org).
    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 (http://www.nature.com/collections/qghhqm). 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.

Notes

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).
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).
The robustness of the segmentation strongly depends on the homogeneity of the signal throughout the 3D image, as commented in the section ‘segmentation’.
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.

Recipes

Modified pseudo-Schiff propidium iodide reagent
Note: Prepare the following solutions under the fume hood.

Fixative
50% (v/v) methanol-water
10% (v/v) acetic acid-water
Pseudo-Schiff reagent with propidium iodide (PI)
100 mM sodium metabisulphite (Na₂S₂O₅)
0.15 M HCl
10 µg/ml PI
Chloral hydrate solution
4 g chloral hydrate
1 ml glycerol
2 ml distilled water
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

Acknowledgments

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).

References

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