4D Light-sheet Imaging of Zebrafish Cardiac Contraction

Our research uses 4D light-sheet imaging to explore cardiac contractual function in zebrafish, aiming to understand the dynamics of cardiac contraction at a single cell level. One of the foremost experimental challenges is achieving high spatial terminal resolution dynamic imaging of cardiac function, while observing natural heart rhythms. Our study fills the gap in analyzing intricate 4D cardiac dynamic at single cell resolution.

We utilize GPU-based parallel computation, virtual reality, and advance cell tracking to interpret light-sheet imaging data. Our novel virtual reality platform offers an interactive approach to assess regional cardiac contractions, providing manipulative functionalities for more in-depth analysis. By facilitating user directed analysis of cardiac contraction, our protocol could lead to an enhanced understanding of cardiac development and disease.

To begin, turn on the stereo microscope to record zebrafish larvae development, between zero and seven days post fertilization. Prepare 0.8%low melting agarose, with 150 milligrams per liter tricaine. Once the agarose is cooled to room temperature, using a transfer pipette, move anesthetized zebrafish to the agarose.

Using another transfer pipette, mount the fish with agarose in a fluorinated ethylene propylene tube. Attach the tube, with the mounted zebrafish larvae, to the six axis motorized light-sheet microscope sample stage. Submerge the tube into the sample chamber, filled with E3 water.

Rotate the zebrafish larvae from the ventral side for better visualization of the heart. Connect the motorized sample stage to the workstation, and configure all necessary parameters, including the desired moving mode. Establish a connection between the sCMOS camera and the workstation.

Set the step size to one micrometer, the total number of frames to 300, and the exposure time to five milliseconds. Then, define the desired region of interest. Power on the laser, and initiate light sheet microscope imaging of the larva.

Record 300 frames as a 2D image sequence to cover three to five cardiac cycles of the larva. Record another image sequence of the new sample slice until the whole heart is covered. To begin, open the MATLAB.

Open the test_parallel. m file. In the baseDir variable, specify the raw image sequences folder location.

Assign the variables numbOfSlice with the total number of image sequences, and numbOfImage with the number of images in each sequence. Inspect the image sequence of the middle plane of the zebrafish heart. Identify the frame numbers of the first and fourth systoles in this sequence, and assign them to the variables systolicPoint_1st and systolicPoint_4th.

Click on run"to start the imaging reconstruction. Download the 3D cell tracker package, and set up the Python environment. Download and open the ITK-SNAP annotation software.

Manually label the 3D heart image at two time points, one, during ventricular distally, and the other, during ventricular systole, to create training and validation data sets. In Python, run the 3D cell tracker training program. In the training UNet 3D function, initialize the noise_level, folder_path, and model parameters to set the predefined 3D UNet model.

In MATLAB, use the image dim converter. m program, to convert, and rename, the training and validation dataset to the proper format for loading. In python, use the trainer.

load_dataset, and trainer. draw_dataset functions to load the training and validation data sets, respectively. Then run the first part of the 3D cell tracker training program, and define the imaging parameters for 3D cell segmentation.

Now, in MATLAB, use the image DIM converter. m program, to convert, and rename, all the 3D heart images to the proper format, and transfer them to the data folder. In Python, run the second part of the 3D cell tracker program to start segmentation.

Once the first 3D image is segmented, compare the segmentation result with the raw image. Move the corrected segmentation to the created manual volume one folder. In Python, run the third part of the 3D cell tracker program for segmenting all images.

Next, open the Amira software, and compare the positions of tracked cells with their corresponding raw images for visual assessment of tracking outcomes. Manually validate the cell tracking outcome data, and select cells with consistent image intensity across all volumes. In the 3D slicer software, using cell labels to obj.

ipymbScript, generate a surface mesh, and assign a unique color code to each cell. Export each 3D model as a single obj file, with multiple sub objects accompanied by a mtl file to describe the cell label. Import the 3D models into Unity, using the educational license.

Apply the customized scripts, consisting of functions written in the C#program, to the models, and user interface elements, for 4D visualization and interactive analysis.