Tools to analyze the organization and formation of the germline cyst in zebrafish oogenesis

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Protocol for volume reconstruction in Imaris

1. From the Imaris homepage, open the working directory and import the labels in the Imaris Arena Tab.
2. Set the voxel size according to the raw image data.
- File>Image Preferences
3. In the Surpass Tree Item Menu, select the ‘Create New Surface’ tool from the Surpass Tree Item Menu.
4. In the Creation dialog box, uncheck the ‘Classify Surfaces’ and ‘Track Surface over time’ options.
5. Select ‘Absolute Intensity’ and select he area around the peak in the histogram to segment.
6. Set the desired color and transparency for the surface created in step 5 ( Fig. 2 A, right).
7. Repeat steps 3-6 for all surfaces of interest in the labeled image. Save the surface by exporting as ‘Scenes’.
8. Import the raw dataset in the Surpass workspace and the surfaces saved in step 7.
- File>Import Scenes
9. Add ‘Orthoslicer’ from the Surpass Tree Item Menu. Set the slice of choice.
10. Note: Uncheck ‘Volume’ in Surpass Tree Items to hide the raw dataset.
11. Go to the ‘3D Animation’ tab from the Surpass workspace menu.
12. Adjust the scene in the desired orientation.
13. Add the Animation option and the total number of frames.
14. Hit ‘Record’, and select the directory for saving the animation ( Fig. 2 B-G).

Deep learning-assisted instance segmentation

Sample preparation for live time-lapse imaging and immunostaining was previously described ( Elkouby and Mullins, 2017b ; Mytlis and Elkouby, 2021 ; Mytlis et al., 2022 ). Images were acquired on a Zeiss LSM 880 confocal microscope using a 40× lens. The acquisition setting was set between samples and experiments to: xy resolution=1104×1104 pixels, 12-bit, 2× sampling averaging, pixel dwell time=0.59 s, zoom=0.8×, pinhole adjusted to 1.1 μm of z thickness, increments between images in stacks 0.53 μm, laser power and gain set in an antibody-dependent manner to 7-11% and 400-650, respectively, and below saturation conditions.

Protocol for setting up the working environment

1. Install Anaconda environment manager ( https://www.anaconda.com/ ).
2. Create a virtual environment in Anaconda.
3. Install Jupyter Notebook.
4. Download the Jupyter notebooks for StarDist ( https://github.com/stardist/stardist.git ) and Cellpose ( https://github.com/MouseLand/cellpose.git ) from the respective GitHub repositories.
5. Launch the Anaconda Terminal.

Protocol for preparing the training dataset for StarDist

1. Open the images in Fiji. Using the crop tool, crop regions of 256×256 or 128×128.
2. Save the Crop regions in a directory named ‘Training Images’.
3. Open Training Images in Fiji and annotate all the structures of interest using Labkit ( Arzt et al., 2022 ) or TrakEM2 ( Cardona et al., 2012 ).
4. Export and save the labeled images in a directory named ‘Training Mask’.

Protocol for model training and predictions

1. From the Anaconda Terminal, activate the respective environment.
2. Launch Jupyter Notebook and browse to the Jupyter notebook downloaded earlier.
3. In the Notebook, enter the path to ‘Training Images’ and ‘Training Mask’ directories in the respective fields.
4. Enter the model name and the directory path in which to save it.
5. Train the model until the training curve plateaus.
6. Evaluate the quality of the model by looking at:
1. Inspection of loss function. The validation loss and training loss curves should converge at the end of training for successful model training. If the validation loss increases with the decrease of training loss, the model is overfitting and the training dataset should be increased.
2. ‘Intersection of Union (IOU)’. The closer to 1, the better the performance. (If IOU is less, the training dataset should be increased.)

Once the model is trained it can be further used to make predictions on unseen datasets.

7. Enter the path to unseen datasets in Jupyter Notebook ( Fig. 3 B). Choose the Custom Model.
8. Run the program to make predictions on the unseen dataset using the above created model ( Fig. 3 C).

Protocol for cell segmentation using Cellpose

1. Launch a new Jupyter browser in the Cellpose environment using the Anaconda environment manager.
2. Open a Cellpose notebook in the Jupyter notebook.
3. Provide the directory path for images to be predicted ( Fig. 3 B) and save results.
4. Choose the provided model.
5. Set do_3d=True for segmentation done using 3D image or set do_3d=False for 2D segmentation and stitching of labels.
6. Set the minimum diameter of cell in pixels, if do_3d=True in step 5.
7. Proceed to segmentation.

Protocol for features extraction from label images

1. Import the predicted label images into Fiji.
2. Set the voxel size to raw data voxel size from properties.
- Fiji>Image>Properties
3. Correct the labels for misidentification using the label editor from the MorphoLibJ plugin ( Legland et al., 2016 ).
4. Extract the features from the 3D label images using plugin ‘Analyze Regions 3D’ from MorphoLibJ ( Legland et al., 2016 ) ( Fig. 3 E).
5. Set the Glasbey colormap LUT on the predicted label image from LUT menu.
- Fiji>Image>Lookup Tables>Glasbey on Dark
6. Open 3D viewer from the Fiji Plugins menu.
- Fiji>Plugins>3D viewer
7. Select the filename from the drop-down menu in the import dialog box. Import as Volume.
8. Visualize the spatial arrangement of segmented structures in volume reconstruction.
9. Volume reconstruction can also be generated by importing the label images in to the Imaris Workspace followed by creating a surface using the ‘Surface Creation Tool’ ( Fig. 3 D).

Laser-induced ablation of cyst cell organelles

Ovary mounting and culture

Ovaries were isolated as described previously ( Elkouby and Mullins, 2017b ; Mytlis et al., 2022 ) ( Fig. 4 A1,2). A dissecting dish [made in-house by casting plastic Petri dishes with animal-proof nontoxic silicone for reusable dishes or 2-3% agarose in Hank's solution (0.137 M NaCl, 5.4 mM KCl, 0.25 mM Na ₂ HPO ₄ , 0.44 mM KH ₂ PO ₄ , 1.3 mM CaCl ₂ , 1.0 mM MgSO ₄ , 4.2 mM NaHCO ₃ ) for single-use dishes], micro-scissors and Forceps #5 (Sigma-Aldrich) were used for ovary dissection (for details, see Mytlis and Elkouby (2021) . Dissected ovaries were kept in a glass 9-well plate at 28°C until mounting.

In a glass-bottom dish (60 µ, ibidi), ∼150 µl of mounting solution was added (agarose layer 1) and left until it started to solidify. Ovaries were transferred carefully to the mounting solution (agarose layer 1) in the glass-bottom dish using forceps ( Fig. 4 A3). The ovaries were gently pushed to the bottom of the dish, avoiding curls, as described previously ( Elkouby and Mullins, 2017b ; Mytlis et al., 2022 ), and allowed to rest until the agar solidified ( Fig. 4 A3). Once the agar had solidified, more mounting solution was added (agarose layer 2) until it covered the solidified mounting solution containing the ovaries ( Fig. 4 A3), and then allowed to rest for the agar to solidify properly. An adequate volume (∼1.5 ml) of HL-15 medium (60% Hanks, 40% L-15, 1:100 GlutaMAX) was added to the cell culture dish ( Fig. 4 A3). Note that HL-15 should be stored at 4°C. L-15 (2× stock) was used without L-glutamine and Phenol Red. L-glutamine is not stable and should be added fresh from a stock (GlutaMAX 100×; Gibco, 35050-061; store at room temperature). Mounted ovaries were kept at 28°C until use. Agarose layers 1 and 2 were prepared by mixing 500 µl of Mounting Solution A (1% low-melt agarose in Hank's Solution; store at 4°C) with 500 µl of Mounting Solution B [490 µl of 2× L-15 (no L-glutamine, no Phenol Red) and 10 µl GlutaMax; equivalent to a 2× HL-15 solution; make fresh and keep at 28°C] to make a final solution of 0.5% low-melt agarose in 1× HL-15 (gelling temperature, 27.4°C).

Laser-induced ablation

Laser excisions were performed using a Leica TCS SP8 MP two-photon microscope with a 25× objective and equipped with an incubation chamber set to 28°C. The glass-bottom dish with the cultured ovaries was mounted on the microscope stage inside the incubator chamber. The region of interest (ROI) was located using a 25× objective. The desired zoomed view of the ROI was obtained using ‘Digital Zoom’ and ‘Capture a Live View’. and draw The ROI for ablation was drawn on the above acquired image using ‘ROI tools’. Once the ROI was marked, the imaging time parameters were set as follows: pre-ablation timelapse acquisition 60 s; laser stimulation of the ROI 60 s at laser power 2.0-8.0% out of a power source of ∼3 W; post-ablation timelapse acquisition 600 s. These steps were repeated for each ROI. Note that only one ablation per cyst should be performed to avoid cell and tissue damage. Ovaries (4-6 wpf) should be mounted in the cell culture dish towards the center, leaving enough space for the lens to move around.

Software

Fiji was used for the preprocessing of image datasets and post processing of labeled images. Anaconda, an open-source distribution of Python was used specifically to maintain a dedicated virtual environment with the desired versions of Python packages installed. Jupyter Notebook, a web-based interactive computational environment for creating and sharing documents, was used to run deep-learning algorithms. Cellpose ( Stringer et al., 2021 ) is an anatomical segmentation algorithm written in Python3. StarDist ( Weigert et al., 2020 ; Schmidt et al., 2018 ) is a deep learning based-algorithm for star-convex object detection for 2D and 3D images. Imaris is a commercial microscopy image analysis software.

Supplementary Material

Supplementary information

Click here for additional data file. ^{(1.7M, pdf)}

Acknowledgements

We thank A. Beckett, from the Biomedical EM unit at the University of Liverpool, for her help in processing our samples for SBF-SEM imaging. We also thank Z. Manevich, from the Faculty of Medicine Microscopy Core at the Hebrew University, and Y. Addadi, from the core facility at the Weizmann Institute, for their support of our multi-photon microscopy operation in our laser ablation experiments.

Footnotes

Author contributions

Conceptualization: V.K., Y.M.E.; Methodology: V.K., Y.M.E.; Validation: V.K.; Formal analysis: V.K.; Investigation: V.K., Y.M.E.; Resources: V.K.; Data curation: V.K.; Writing - original draft: V.K.; Writing - review & editing: V.K., Y.M.E.; Visualization: V.K.; Supervision: Y.M.E.; Funding acquisition: Y.M.E.

Funding

This work was supported by the Israel Science Foundation (3291/19 and 558/19 to Y.E.) and a Hebrew University of Jerusalem (HUJI) International PhD Talent Scholarship (V.K.). Open Access funding provided by Hebrew University of Jerusalem. Deposited in PMC for immediate release.

Data availability

All relevant data can be found within the article and its supplementary information .

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