We express our gratitude to Dr. Caroline Burns at Boston Children&#39;s Hospital for generously sharing the transgenic zebrafish. We thank Ms. Elizabeth Ibanez for her help in husbanding zebrafish at UT Dallas. We also appreciate all the constructive comments provided by D-incubator members at UT Dallas. This work was supported by NIH R00HL148493 (Y.D.), R01HL162635 (Y.D.), and UT Dallas STARS program (Y.D.).

Approval for this study was granted by the Institutional Animal Care and Use Committee (IACUC) of the University of Texas at Dallas, under protocol number #20-07. Tg(myl7:nucGFP) transgenic zebrafish larvae12 were used for the present study. All data acquisition and image post-processing were carried out using open-source software or platforms with research or educational licenses. The resources are available from the authors upon reasonable request.
1. Zebrafish breeding and embryo microinjection
Timing: 2 days
<ol>
	<li>Maintain and breed adult zebrafish through standard care and breed procedures. For detailed methods, refer to previous report13.</li>
	<li>Perform microinjection following established protocol14. For the present study, microinjection was performed at the 1-2 cell (zygote) stage. 
	&#8203;NOTE: It is crucial to accurately identify this stage for effective microinjection and to ensure developmental consistency. During the one-cell stage, a small dome forms atop the yolk, and this stage typically lasts approximately 12 min. Upon progressing to the two-cell stage, two adjacent domes become visible, and this stage endures for about 45 min post-fertilization15. More detailed information of system construction can be found in other reports or protocols12,16,17.&#160;</li>
</ol>
2. Zebrafish embryos/larvae preparation and mounting
Timing: 7 days
<ol>
	<li>Transfer embryos at 1 day post fertilization (dpf) to E3 water with 0.2 mM 1-Phenyl-2-thiourea (PTU) (see Table of Materials) to prevent pigment formation.</li>
	<li>Replace the medium with fresh E3 water with PTU every day until LSM imaging.</li>
	<li>Image embryos or larvae with the stereo microscope to record their development at the desired timepoints between 0 and 7 dpf.</li>
	<li>Prepare segmented fluorinated ethylene propylene (FEP) tubes with 2 mm inner diameter for embedding zebrafish larvae under the LSM system12. 
	NOTE: The FEP tube is utilized to secure the sample with refractive index matching materials that closely resemble the surrounding medium, such as water.</li>
	<li>Starting from 3 dpf, transfer fish to a 150 mg/L tricaine (MS-222) solution (see Table of Materials) to immobilize fish before imaging.</li>
	<li>Prepare 0.8 % low-melting agarose with 150 mg/L tricaine and use a transfer pipette to move anesthetized fish to the agarose once it has cooled to room temperature18.</li>
	<li>Use another transfer pipette to mount the fish with agarose in FEP tubes (Figure 1). 
	NOTE: To ascertain the minimal stress in the developing embryos, pre- and post-fixation examinations of cardiac activity, such as heart rate, were conducted under microscopic observation. These assessments aimed to identify any significant differences before and after tricaine anesthesia. An additional examination of the skin and musculature for irregularities in cardiac structure, such as edema, could also be conducted post-fixation. The concentration of tricaine also plays a crucial role in cardiac imaging19, and therefore the 150 mg/L concentration tricaine solution was utilized for the imaging of contraction in zebrafish larvae in this project. While the present retrospective synchronization allows us to address the irregular heartbeats20, a comprehensive solution for the 4D imaging of cardiac arrhythmias and long-term imaging in zebrafish is still under development.</li>
</ol>
3. Light-sheet imaging system setup and configuration
Timing: 3-14 days
<ol>
	<li>Construct an in-house LSM system based on a cylindrical lens using continuous-wave diode-pumped solid-state (DPSS) laser systems at specific wavelengths such as 473 nm and 532 nm as illumination sources (Figure 2A).</li>
	<li>Customize LabVIEW (see Table of Materials) control codes for the synchronization of translational sample stage, light-sheet illumination, and exposure of sCMOS camera to capture image sequences. 
	&#8203;NOTE: More detailed information of system construction can be found in other reports or protocols12,16,17. We encourage research groups to seek collaborative opportunities with laboratories that possess established expertise in optical imaging during system construction.</li>
</ol>
4. Zebrafish imaging preparation and data collection
Timing: 1 day
<ol>
	<li>Calibrate LSM system spatial resolution and minimize opacity variation at varying depths by measuring fluorescence beads.
	<ol>
		<li>First, dilute fluorescent beads (see Table of Materials) having the diameter of 0.53 &#181;m to a concentration of 1: 1.5 x 105 using 0.8 % low-melting agarose and transfer the solution to the segmented FEP tube.</li>
		<li>Second, use the LSM system to image the beads and measure the point spread function (PSF) across the entire sample, validating the spatial resolution and system alignment12. 
		NOTE: In this system, the analysis of representative results for the full width at half maximum (FWHM) indicates lateral and axial resolutions of 1.26 &#177; 0.15 and 2.48 &#177; 0.15 &#181;m (n = 60 beads), respectively. This suggests a consistent spatial resolution throughout the imaging depth.</li>
	</ol>
	</li>
	<li>Attach the FEP tube with mounted zebrafish larva securely to the 6-axis (x, y, z, pitch, yaw, and roll) motorized sample stage, and submerge the tubes into the sample chamber filled with E3 water. To avoid the light scattering by the yolk sac and better locate the position of the heart, rotate the zebrafish larva to take image from the ventral side (Figure 2B). In this case, most captured images would include both the ventricle and atrium (Figure 2C). 
	NOTE: Given that the current imaging procedures typically lasted less than one hour per fish and were conducted in a controlled room temperature environment, the chamber temperature control and oxygen level regulation are deemed optional for this project. However, for longer-term time-lapse imaging studies, particularly those focused on developmental processes, continuous temperature monitoring and regulation at 27 &#176;C within the sample chamber is suggested to ensure the physiological environment of the zebrafish and to prevent any potential developmental abnormalities.</li>
	<li>Connect the motorized sample stage to the workstation and configure all necessary parameters, including the desired moving mode.</li>
	<li>Establish a connection between the sCMOS camera and the workstation. Adjust all essential settings, such as an exposure time of 5 ms and the desired region of interest.</li>
	<li>Configure the number of image sequences and frames within the customized LabVIEW control program.</li>
	<li>Power on the laser and initiate LSM imaging of the sample. Maintain the process until all image sequences have been successfully recorded.</li>
	<li>Record 300 frames as a 2D image sequence to cover 3-5 cardiac cycles of the larva with a framerate of 200 frames/second. The recording captures cardiac contractility at the selective plane illuminated by the light-sheet.</li>
	<li>Move the larva at the step size of 1 &#181;m across the light-sheet illumination to virtually slice the sample. 
	NOTE: The step size of the sample stage should be set based on the axial resolution of the light-sheet system, following the Nyquist-Shannon sampling theorem12.</li>
	<li>Repeat step 7 to record another image sequence of the new sample slice until the whole heart is covered. Generally, 100-200 image sequences with the step size of 1 &#181;m ensure the coverage of the entire heart of a zebrafish larva (Figure 2D). 
	&#8203;NOTE: Step 4.7-4.9 are controlled by the LabVIEW program and automatically performed by the LSM system (Figure 3). Given the extensive volume of image data generated, a standardized naming convention is recommended for image sequences. For example, designate folders with names such as &#34;z-1&#34;, &#34;z-2&#34;, etc., to categorize data across various image sequences. Within each sequence, the images should be named sequentially (e.g., &#34;1&#34;, &#34;2&#34;, ...) to denote distinct time points. The total time for mounting fish, adjusting the imaging&#160;angle, and collecting all the data for one zebrafish larva is around 1 h.</li>
</ol>
5. 4D image&#160;reconstruction with parallel computation
Timing: 1 day 
NOTE: The 4D reconstruction algorithm developed by our group and sample data are publicly accessible21. This method allows one to reconstruct the 4D heart image from the image sequences collected in previous steps (Table 1).
<ol>
	<li>Open the file test_Parallel.m in MATLAB (see Table of Materials). Specify the folder location where the raw image sequences are stored in the variable &#34;baseDir&#34;.</li>
	<li>Assign the variable &#34;numOfSlice&#34; with the total number of image sequences (typically 100-150) and the variable &#34;numOfImage&#34; with the number of images (typically 300-500) in each sequence.</li>
	<li>Inspect the image sequence that shows the middle plane of the zebrafish heart (for example, the 51st sequence out of 101 total sequences). Identify the frame numbers of the first and fourth systoles in this sequence and assign them to the variables systolicPoint_1st and systolicPoint_4th.</li>
	<li>Click on Run to start the process. 
	NOTE: The length of the cardiac cycle in each image sequence will first be identified by the MATLAB program through a comparison of the similarity (sum of squared differences) between images at different time points. Subsequently, the cardiac cycle will be estimated using the average of the cycle lengths from all image sequences. Next, the starting points of image sequences at different axial locations will be aligned by the program through an iterative comparison of the image similarity from the first image sequence to the last image sequence.</li>
	<li>Check the results in the output folder. The program will save the images as &#34;.tif&#34; files with time stamps. Each &#34;.tif&#34; file is a 3D heart image at a specific time point. The time interval between two 3D images is equal to the exposure time that is set. 
	&#8203;NOTE: Reconstruct the acquired images from the previous steps to depict the 4D (3D spatial + 1D temporal) cardiac contractions. To enhance proficiency in high-throughput investigations during the retrospective synchronization, this approach enables simultaneous computational operations on a GPU by leveraging multiple CPU cores through the MATLAB parallel computing toolbox and transforming images into the gpuArray format.</li>
</ol>
6. 3D cell segmentation and cell tracking
Timing: 1 day
<ol>
	<li>Download 3DeeCellTracker package (see Table of Materials) and set up the Python environment22.</li>
	<li>Download ITK-SNAP annotation software23 (see Table of Materials) and use it to manually label the 3D heart image in two selected timepoints, one at the ventricular diastole and the other at the ventricular systole, to create the training and validation datasets. 
	NOTE: Other labeling software may also be applicable.</li>
	<li>In Python, run the 3DeeCellTracker training program and initialize the noise_level(100 in this case), folder_path and model parameters in TrainingUNet3D function to set the predefined 3D U-Net model.</li>
	<li>In MATLAB, use imageDimConverter.m program to convert and rename the training and validation dataset to the proper format for loading.</li>
	<li>In Python, load the training and validation datasets by using trainer.load_dataset() and trainer.draw_dataset() functions.</li>
	<li>In Python, run the first part of 3DeeCellTracker tracking program and initialize the parameters. 
	NOTE: This includes defining image parameters to characterize image dimensions, segmentation parameters to select the machine learning model applied for cell segmentation, and tracking parameters to select the pre-trained deep neural network models employed for cell registration. For the first time use, employing the U-Net model for cell segmentation and FFN+ PR-GLS for cell registration is recommended. It is needed to store data, model, and results in folders that will be automatically created in this step.</li>
	<li>In MATLAB, use imageDimConverter.m program to convert and rename all the 3D heart images to the proper format and transfer them to the data folder created in the last step.</li>
	<li>In Python, run the second part of 3DeeCellTracker program to start segmentation.</li>
	<li>Once the first 3D image is segmented, compare the segmentation result with the raw image, and perform manual correction if any incorrect segmentation is found. Move the corrected segmentation to the created &#34;/manual_vol1&#34; folder.</li>
	<li>In Python, run the third part of 3DeeCellTracker program to segment all the images.</li>
	<li>Use Amira (see Table of Materials) to perform a visual assessment of tracking outcomes by comparing the positions of tracked cells with their corresponding raw images. If test results are not satisfying, recommence the procedure from step 6.6, employ an alternative machine learning model for segmentation or cell registration, and then re-initiate the tracking process. 
	&#8203;NOTE: After the segmentation, the data of cell labels is stored as an 8-bit image (0 to 255) by default, which means only 256 scales are provided as labels for cell classification. There are two solutions for addressing this issue when additional classes are needed. One solution is to set the format of data of cell labels as a 16-bit image in the tracking program, which will cause an exponential increase in the processing time, more than two days in the present case. The other solution is to use the &#34;separateTrackingResults.py&#34; program to post-process the cell labels, which will separate cells with the same labels based on their physical location and assign each cell a different label. This process takes around 10 min in this case.</li>
</ol>
7. Cardiac contractility analysis in the virtual reality mode
Timing: 1 day
<ol>
	<li>Acquire the cell tracking outcome from previous steps.</li>
	<li>Manually validate the data and select cells with consistent image intensity across all volumes (about 500 cells out of approximately 600).</li>
	<li>Use cellLabelsToObj.ipynb script21 to generate a surface mesh for each individual cell through 3D Slicer software24 (see Table of Materials) and assign a unique color code to each cell.</li>
	<li>Export each 3D model, consisting of all cells in a single time point, as a single .obj file consisting of multiple sub-objects accompanied by a .mtl file to describe the cell label.</li>
	<li>Import the models into Unity using the educational license, a development engine used for extended reality.</li>
	<li>Apply the customized scripts consisting of functions written in C# to the models and user-interface elements to allow for 4D visualization and interactive analysis. Perform VR interaction following step 7.7.</li>
	<li>Interact with the models in virtual reality (VR) using the following functions (Figure 4):
	<ol>
		<li>Cell selection: Select up to two cells at a time and view their trajectories, velocities, volumes, surface areas, and relative distances.</li>
		<li>Time point selection: Choose a specific time point in the data to analyze the outputs of the selected cells, such as at t = 0 ms during diastole, or at t = 200 ms during systole.</li>
		<li>Time pause: Freeze the dynamic model to focus on the selected cells and their outputs.</li>
		<li>Contrast: Modulate the contrast between the selected cells and surrounding cells in the model.</li>
	</ol>
	</li>
</ol>

Analysis of Zebrafish Cardiac Contraction with Virtual Reality Interaction

<ol>
	<li>Power, R. M., Huisken, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+guide+to+light-sheet+fluorescence+microscopy+for+multiscale+imaging.">A guide to light-sheet fluorescence microscopy for multiscale imaging.</a> Nat. Methods. 14 (4), 360-373 (2017).</li><li>Liu, J., Stainier, D. Y. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Zebrafish+in+the+study+of+early+cardiac+development.">Zebrafish in the study of early cardiac development.</a> Circ. Res. 110 (6), 870 (2012).</li><li>Ding, Y., Bu, H., Xu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+inherited+cardiomyopathies+in+adult+zebrafish+for+precision+medicine.">Modeling inherited cardiomyopathies in adult zebrafish for precision medicine.</a> Front. Physiol. 11, 599244 (2020).</li><li>Giardoglou, P., Beis, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+zebrafish+disease+models+and+matters+of+the+heart.">On zebrafish disease models and matters of the heart.</a> Biomedicines. 7 (1), 15 (2019).</li><li>Zhang, X., Alexander, R. V., Yuan, J., Ding, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+analysis+of+cardiac+contractile+function.">Computational analysis of cardiac contractile function.</a> Curr. Cardiol. Rep. 24 (12), 1983-1994 (2022).</li><li>Weber, M., Huisken, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vivo+imaging+of+cardiac+development+and+function+in+zebrafish+using+light+sheet+microscopy.">In vivo imaging of cardiac development and function in zebrafish using light sheet microscopy.</a> Swiss Med. Wkly. 145 (51), w14227 (2015).</li><li>Vedula, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+to+quantify+mechanobiologic+forces+during+zebrafish+cardiac+development+using+4-D+light+sheet+imaging+and+computational+modeling.">A method to quantify mechanobiologic forces during zebrafish cardiac development using 4-D light sheet imaging and computational modeling.</a> PLOS Comput. Biol. 13 (10), e1005828 (2017).</li><li>Yalcin, H. C., Amindari, A., Butcher, J. T., Althani, A., Yacoub, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart+function+and+hemodynamic+analysis+for+zebrafish+embryos.">Heart function and hemodynamic analysis for zebrafish embryos.</a> Dev. Dyn. 246 (11), 868-880 (2017).</li><li>Baek, K. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advanced+microscopy+to+elucidate+cardiovascular+injury+and+regeneration:+4D+light-sheet+imaging.">Advanced microscopy to elucidate cardiovascular injury and regeneration: 4D light-sheet imaging.</a> Prog. Biophys. Mol. Biol. 138, 105-115 (2018).</li><li>Salman, H. E., Yalcin, H. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advanced+blood+flow+assessment+in+Zebrafish+via+experimental+digital+particle+image+velocimetry+and+computational+fluid+dynamics+modeling.">Advanced blood flow assessment in Zebrafish via experimental digital particle image velocimetry and computational fluid dynamics modeling.</a> Micron. 130, 102801 (2020).</li><li>Sodimu, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Light+sheet+imaging+and+interactive+analysis+of+the+cardiac+structure+in+neonatal+mice.">Light sheet imaging and interactive analysis of the cardiac structure in neonatal mice.</a> J. Biophotonics. 16 (5), e202200278 (2023).</li><li>Zhang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=4D+light-sheet+imaging+and+interactive+analysis+of+cardiac+contractility+in+zebrafish+larvae.">4D light-sheet imaging and interactive analysis of cardiac contractility in zebrafish larvae.</a> APL Bioeng. 7 (2), 026112 (2023).</li><li>Westerfield, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Zebrafish+Book.">The Zebrafish Book.</a> Eugene. , (2000).</li><li>Rosen, J. N., Sweeney, M. F., Mably, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microinjection+of+zebrafish+embryos+to+analyze+gene+function.">Microinjection of zebrafish embryos to analyze gene function.</a> J Vis Exp. 25, e1115 (2009).</li><li>Kimmel, C. B., Ballard, W. W., Kimmel, S. R., Ullmann, B., Schilling, T. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stages+of+embryonic+development+of+the+zebrafish.">Stages of embryonic development of the zebrafish.</a> Dev Dyn. 203 (3), 253-310 (1995).</li><li>Girkin, J. M., Carvalho, M. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+light-sheet+microscopy+revolution.">The light-sheet microscopy revolution.</a> J. Opt. 20 (5), 053002 (2018).</li><li>Gualda, E. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OpenSpinMicroscopy:+an+open-source+integrated+microscopy+platform.">OpenSpinMicroscopy: an open-source integrated microscopy platform.</a> Nat. Methods. 10 (7), 599-600 (2013).</li><li>Messerschmidt, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Light-sheet+fluorescence+microscopy+to+capture+4-dimensional+images+of+the+effects+of+modulating+shear+stress+on+the+developing+zebrafish+heart.">Light-sheet fluorescence microscopy to capture 4-dimensional images of the effects of modulating shear stress on the developing zebrafish heart.</a> J Vis Exp. 138, e57763 (2018).</li><li>Kaufmann, A., Mickoleit, M., Weber, M., Huisken, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multilayer+mounting+enables+long-term+imaging+of+zebrafish+development+in+a+light+sheet+microscope.">Multilayer mounting enables long-term imaging of zebrafish development in a light sheet microscope.</a> Development. 139 (17), 3242-3247 (2012).</li><li>Lee, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=4-Dimensional+light-sheet+microscopy+to+elucidate+shear+stress+modulation+of+cardiac+trabeculation.">4-Dimensional light-sheet microscopy to elucidate shear stress modulation of cardiac trabeculation.</a> J Clin Invest. 126 (5), 1679-1690 (2016).</li><li>Zhang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=4D+light-sheet+imaging+and+interactive+analysis+of+cardiac+contractility+in+Zebrafish+larvae.">4D light-sheet imaging and interactive analysis of cardiac contractility in Zebrafish larvae.</a> Zenodo. , (2023).</li><li>Wen, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3deecelltracker,+a+deep+learning-based+pipeline+for+segmenting+and+tracking+cells+in+3d+time+lapse+images.">3deecelltracker, a deep learning-based pipeline for segmenting and tracking cells in 3d time lapse images.</a> Elife. 10, e59187 (2021).</li><li>Yushkevich, P. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=User-guided+3D+active+contour+segmentation+of+anatomical+structures:+significantly+improved+efficiency+and+reliability.">User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability.</a> Neuroimage. 31 (3), 1116-1128 (2006).</li><li>Fedorov, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+Slicer+as+an+image+computing+platform+for+the+quantitative+imaging+network.">3D Slicer as an image computing platform for the quantitative imaging network.</a> Magn. Reson. Imaging. 30 (9), 1323-1341 (2012).</li><li>Naderi, A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+learning-based+framework+for+cardiac+function+assessment+in+embryonic+zebrafish+from+heart+beating+videos.">Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos.</a> Comput. Biol. Med. 135, 104565 (2021).</li><li>Mickoleit, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+reconstruction+of+the+beating+zebrafish+heart.">High-resolution reconstruction of the beating zebrafish heart.</a> Nat. Methods. 11 (9), 919-922 (2014).</li><li>Lee, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=4-Dimensional+light-sheet+microscopy+to+elucidate+shear+stress+modulation+of+cardiac+trabeculation.">4-Dimensional light-sheet microscopy to elucidate shear stress modulation of cardiac trabeculation.</a> J. Clin. Invest. 126 (5), 1679-1690 (2016).</li><li>Ding, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiscale+light-sheet+for+rapid+imaging+of+cardiopulmonary+system.">Multiscale light-sheet for rapid imaging of cardiopulmonary system.</a> JCI Insight. 3 (16), e121396 (2018).</li><li>Choe, C. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transgenic+fluorescent+zebrafish+lines+that+have+revolutionized+biomedical+research.">Transgenic fluorescent zebrafish lines that have revolutionized biomedical research.</a> Lab. Anim. Res. 37 (1), 1-29 (2021).</li><li>Coelho-Filho, O. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+cardiomyocyte+hypertrophy+by+cardiac+magnetic+resonance:+implications+on+early+cardiac+remodeling.">Quantification of cardiomyocyte hypertrophy by cardiac magnetic resonance: implications on early cardiac remodeling.</a> Circulation. 128 (11), 1225 (2013).</li><li>Zhang, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+segmentation+and+cardiac+mechanics+analysis+of+evolving+zebrafish+using+deep+learning.">Automatic segmentation and cardiac mechanics analysis of evolving zebrafish using deep learning.</a> Front. Cardiovasc. Med. 8, 675291 (2021).</li><li>Ding, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+light-sheet+imaging+with+virtual+reality+to+recapitulate+developmental+cardiac+mechanics.">Integrating light-sheet imaging with virtual reality to recapitulate developmental cardiac mechanics.</a> JCI Insight. 2 (22), e97180 (2017).</li><li>Koger, C. R., Hassan, S. S., Yuan, J., Ding, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virtual+reality+for+interactive+medical+analysis.">Virtual reality for interactive medical analysis.</a> Front. Virtual Real. 3, 782854 (2022).</li><li>Yuan, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extended+reality+for+biomedicine.">Extended reality for biomedicine.</a> Nat. Rev. Methods Prim. 3 (1), 1-1 (2023).</li></ol>

The authors have no conflict of interest to disclose.

The integration of the zebrafish model with engineering methods holds immense potential for the in vivo exploration of myocardial infarction, arrhythmias, and congenital heart defects. Leveraging its optical transparency, regenerative capacity, and genetic and physiological similarities to humans, zebrafish embryos and larvae have become extensively utilized in research1,2,4. The superior spatiotemporal resolution, mini...

The zebrafish (Danio rerio) is a widely used model organism for studying cardiac development, physiology, and repair due to its optical transparency, genetic tractability, and regenerative capacity1,2,3,4. Upon myocardial infarction, while structural and functional changes impact the cardiac ejection and hemodynamics, technical limitations continue to hinder the ability to investigate the dynamic process during cardiac regeneration with the high spatiotemporal resolution. For example, conventional imaging methods, such as confocal microscopy, have limitations in terms of imaging depth, temporal resolution, or phototoxicity for capturing the dynamic changes and assessing cardiac contractile function during multiple cardiac cycles5.
Light-sheet microscopy represents a state-of-the-art imaging method that successfully addresses these issues by quickly sweeping the laser across the heart&#39;s ventricle and atrium, achieving detailed images with enhanced spatiotemporal resolution and negligible photo-bleaching and photo-toxic effects6,7,8,9,10,11.
This protocol introduces a comprehensive imaging strategy that includes LSM system construction, 4D image reconstruction, 3D cell tracking, and interactive analysis to capture and analyze the dynamics of cardiomyocytes across the entire heart during multiple cardiac cycles12. The customized imaging system and computational methodology allow&#160;one to track the myocardial microstructure and contractile function at the single-cell level in transgenic Tg(myl7:nucGFP) zebrafish larvae. Furthermore, small molecule compounds were delivered into the embryos using microinjection to assess drug-induced cardiac injury and subsequent regeneration. This holistic strategy provides an entry point to in vivo investigate structural, functional, and mechanical properties of myocardium at the single-cell level during cardiac development and regeneration.

<table>
	<tbody>
		<tr>
			<td>RESOURCE</td>
			<td>SOURCE/Reference</td>
			<td>IDENTIFIER</td>
		</tr>
		<tr>
			<td>Animal models</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Tg(myl7:nucGFP) transgenic zebrafish</td>
			<td>Burns Lab in Boston Children&#39;s Hospital</td>
			<td>ZDB-TGCONSTRCT-070117-49</td>
		</tr>
		<tr>
			<td>Software and algorithms</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>The MathWorks Inc.</td>
			<td>R2023a</td>
		</tr>
		<tr>
			<td>LabVIEW</td>
			<td>National Instruments Corporation</td>
			<td>2017 SP1</td>
		</tr>
		<tr>
			<td>HCImage Live</td>
			<td>Hamamatsu Photonics</td>
			<td>4.6.1.2</td>
		</tr>
		<tr>
			<td>Python</td>
			<td>The Python Software Foundation</td>
			<td>3.9.0</td>
		</tr>
		<tr>
			<td>Fiji-ImageJ</td>
			<td>Schneider et al.18</td>
			<td>1.54f</td>
		</tr>
		<tr>
			<td>3DeeCellTracker</td>
			<td>Chentao Wen et al.15</td>
			<td>v0.5.2</td>
		</tr>
		<tr>
			<td>Unity</td>
			<td>Unity Software Inc.</td>
			<td>2020.3.2f1</td>
		</tr>
		<tr>
			<td>Amira</td>
			<td>Thermo Fisher Scientific</td>
			<td>2021.2</td>
		</tr>
		<tr>
			<td>3D Slicer</td>
			<td>Andriy Fedorov et al.17</td>
			<td>5.2.1</td>
		</tr>
		<tr>
			<td>ITK SNAP</td>
			<td>Paul A Yushkevich et al.16</td>
			<td>4</td>
		</tr>
		<tr>
			<td>Light-sheet system</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Cylindrical lens</td>
			<td>Thorlabs</td>
			<td>ACY254-050-A</td>
		</tr>
		<tr>
			<td>4X Illumination objective</td>
			<td>Nikon</td>
			<td>MRH00045</td>
		</tr>
		<tr>
			<td>20X Detection objective</td>
			<td>Olympus</td>
			<td>1-U2M585</td>
		</tr>
		<tr>
			<td>sCMOS camera</td>
			<td>Hamamatsu</td>
			<td>C13440-20CU</td>
		</tr>
		<tr>
			<td>Motorized XYZ stage</td>
			<td>Thorlabs</td>
			<td>PT3/M-Z8</td>
		</tr>
		<tr>
			<td>Two-axis tilt stage</td>
			<td>Thorlabs</td>
			<td>GN2/M</td>
		</tr>
		<tr>
			<td>Rotation stepper motor</td>
			<td>Pololu</td>
			<td>1474</td>
		</tr>
		<tr>
			<td>Fluorescent beads</td>
			<td>Spherotech</td>
			<td>FP-0556-2</td>
		</tr>
		<tr>
			<td>473nm DPSS Laser</td>
			<td>Laserglow</td>
			<td>R471003GX</td>
		</tr>
		<tr>
			<td>532nm DPSS laser</td>
			<td>Laserglow</td>
			<td>R531003FX</td>
		</tr>
		<tr>
			<td>Microinjector and vacuum pump</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Microinjector</td>
			<td>WPI</td>
			<td>PV850</td>
		</tr>
		<tr>
			<td>Vacuum pump</td>
			<td>Welch</td>
			<td>2522B-01</td>
		</tr>
		<tr>
			<td>Pre-Pulled Glass Pipettes</td>
			<td>WPI</td>
			<td>TIP10LT</td>
		</tr>
		<tr>
			<td>Capillary tip for gel loading</td>
			<td>Bio-Rad</td>
			<td>2239912</td>
		</tr>
		<tr>
			<td>Virtual reality hardware</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>VR headset</td>
			<td>Meta</td>
			<td>Quest 2</td>
		</tr>
		<tr>
			<td>30mg/L PTU solution</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>PTU</td>
			<td>Sigma-Aldrich</td>
			<td>P7629</td>
		</tr>
		<tr>
			<td>1X E3 working solution</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>1% Agarose</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>Low-melt agarose</td>
			<td>Thermo Fisher</td>
			<td>16520050</td>
		</tr>
		<tr>
			<td>Deionized water</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>10g/L Tricaine stock solution</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Tricaine</td>
			<td>Syndel</td>
			<td>SYNC-M-GR-US02</td>
		</tr>
		<tr>
			<td>Deionized water</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>Sodium bicarbonate</td>
			<td>Sigma-Aldrich</td>
			<td>S6014</td>
		</tr>
		<tr>
			<td>150mg/L Tricaine working solution</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>10g/L Tricaine stock solution</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>Deionized water</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>60X E3 stock solution</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium Chloride</td>
			<td>Lab Animal Resource Center (LARC), The University of Texas at Dallas</td>
			<td>NaCl</td>
		</tr>
		<tr>
			<td>Potassium Chloride</td>
			<td>-</td>
			<td>KCL</td>
		</tr>
		<tr>
			<td>Calcium Chloride Dihydrate</td>
			<td>-</td>
			<td>CaCL2 x 2H2O</td>
		</tr>
		<tr>
			<td>Magnesium Sulfate Heptahydrate</td>
			<td>-</td>
			<td>MgSO4 x 7H2O</td>
		</tr>
		<tr>
			<td>RO Water</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
		</tr>
		<tr>
			<td>1X E3 working solution</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>60X E3 stock solution</td>
			<td>Lab Animal Resource Center (LARC), The University of Texas at Dallas</td>
			<td>-</td>
		</tr>
		<tr>
			<td>RO Water</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>1% Methylene Blue (optional)&#160;</td>
			<td>-</td>
			<td>C16H18ClN3S</td>
		</tr>
	</tbody>
</table>

4d light-sheet imaging of zebrafish cardiac contraction

Zebrafish is an intriguing model organism known for its remarkable cardiac regeneration capacity. Studying the contracting heart in vivo is essential for gaining insights into structural and functional changes in response to injuries. However, obtaining high-resolution and high-speed 4-dimensional (4D, 3D spatial + 1D temporal) images of the zebrafish heart to assess cardiac architecture and contractility remains challenging. In this context, an in-house light-sheet microscope (LSM) and customized computational analysis are&#160;used to overcome these technical limitations. This strategy, involving LSM system construction, retrospective synchronization, single cell tracking, and user-directed analysis, enables one to investigate the micro-structure and contractile function across the entire heart at the single-cell resolution in the transgenic Tg(myl7:nucGFP) zebrafish larvae. Additionally, we are able to further incorporate microinjection of small molecule compounds to induce cardiac injury in a precise and controlled manner. Overall, this framework allows one to track physiological and pathophysiological changes, as well as the regional mechanics at the single-cell level during cardiac morphogenesis and regeneration.

The current protocol consists of three main steps: zebrafish preparation and microinjection, light-sheet imaging and 4D image reconstruction, and cell tracking and VR interaction. Adult zebrafish were allowed to mate, the fertilized eggs were collected, and performed microinjection as needed for the proposed experiments (Figure 1). This step provides an entry point to explore zebrafish applications in the investigation of cardiac development and regeneration, and it also plays a crucial role...

Watch this Scientific Journal Video about 4D Light-sheet Imaging of Zebrafish Cardiac Contraction at JoVE.com

Author Spotlight: High-Resolution 4D Light-Sheet Imaging and Virtual Reality in Zebrafish for Single-Cell Analysis of Heart Function

This protocol utilizes light-sheet imaging to investigate cardiac contractile function in zebrafish larvae and gain insights into cardiac mechanics through cell tracking and interactive analysis.

4D Light-sheet Imaging of Zebrafish Cardiac Contraction

Zebrafish is an intriguing model organism known for its remarkable cardiac regeneration capacity. Studying the contracting heart in vivo is essential for ...

4d-light-sheet-imaging-of-zebrafish-cardiac-contraction

Research

JoVE Journal

Bioengineering

1.1K Views.  The University of Texas at Dallas. This protocol utilizes light-sheet imaging to investigate cardiac contractile function in zebrafish larvae and gain insights into cardiac mechanics through cell tracking and interactive analysis.

Video: Author Spotlight: High-Resolution 4D Light-Sheet Imaging and Virtual Reality in Zebrafish for Single-Cell Analysis of Heart Function

Preparation of Complaint Matrices for Quantifying Cellular Contraction

The regulation of cellular adhesion to the extracellular matrix (ECM) is essential for cell migration and ECM remodeling. Focal adhesions are macromolecular assemblies that couple the contractile F-actin cytoskeleton to the ECM. This connection allows for the transmission of intracellular mechanical forces across the cell membrane to the underlying substrate. Recent work has shown the mechanical properties of the ECM regulate focal adhesion and F-actin morphology as well as numerous physiological processes, including cell differentiation, division, proliferation and migration. Thus, the use of cell culture substrates has become an increasingly prevalent method to precisely control and modulate ECM mechanical properties.
To quantify traction forces at focal adhesions in an adherent cell, compliant substrates are used in conjunction with high-resolution imaging and computational techniques in a method termed traction force microscopy (TFM). This technique relies on measurements of the local magnitude and direction of substrate deformations induced by cellular contraction. In combination with high-resolution fluorescence microscopy of fluorescently tagged proteins, it is possible to correlate cytoskeletal organization and remodeling with traction forces.
Here we present a detailed experimental protocol for the preparation of two-dimensional, compliant matrices for the purpose of creating a cell culture substrate with a well-characterized, tunable mechanical stiffness, which is suitable for measuring cellular contraction. These protocols include the fabrication of polyacrylamide hydrogels, coating of ECM proteins on such gels, plating cells on gels, and high-resolution confocal microscopy using a perfusion chamber. Additionally, we provide a representative sample of data demonstrating location and magnitude of cellular forces using cited TFM protocols.

In this video, we demonstrate the experimental techniques used to fabricate compliant, extracellular matrix (ECM) coated substrates suitable for cell culture, and which are amenable to traction force microscopy and observing effects of ECM stiffness on cell behavior.

The regulation of cellular adhesion to the extracellular matrix (ECM) is essential for cell migration and ECM remodeling.  Focal adhesions are ...

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture cellular behaviors involved in developmental processes.&#160; The ability to track and follow cell movements is limited by sample movements that occur due to drift of the sample or, in some cases, growth during image acquisition. Tracking cells in datasets affected by drift and/or growth will incorporate these movements into any analysis of cell position. This may result in the apparent movement of static structures within the sample. Therefore prior to cell tracking, any sample drift should be corrected. Using the open source Fiji distribution 1 &#160;of ImageJ 2,3 and the incorporated LOCI tools 4, we developed the Correct 3D drift plug-in to remove erroneous sample movement in confocal datasets. This protocol effectively compensates for sample translation or alterations in focal position by utilizing phase correlation to register each time-point of a four-dimensional confocal datasets while maintaining the ability to visualize and measure cell movements over extended time-lapse experiments.

Time-lapse microscopy allows the visualization of developmental processes. Growth or drift of samples during image acquisition reduces the ability to accurately follow and measure cell movements during development. We describe the use of open source image processing software to correct for three dimensional sample drift over time.

The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture ...

Automated Contraction Analysis of Human Engineered Heart Tissue for Cardiac Drug Safety Screening

Cardiac tissue engineering describes techniques to constitute three dimensional force-generating engineered tissues. For the implementation of these procedures in basic research and preclinical drug development, it is important to develop protocols for automated generation and analysis under standardized conditions. Here, we present a technique to generate engineered heart tissue (EHT) from cardiomyocytes of different species (rat, mouse, human). The technique relies on the assembly of a fibrin-gel containing dissociated cardiomyocytes between elastic polydimethylsiloxane (PDMS) posts in a 24-well format. Three-dimensional, force-generating EHTs constitute within two weeks after casting. This procedure allows for the generation of several hundred EHTs per week and is technically limited only by the availability of cardiomyocytes (0.4-1.0 x 106/EHT). Evaluation of auxotonic muscle contractions is performed in a modified incubation chamber with a mechanical interlock for 24-well plates and a camera placed on top of this chamber. A software controls a camera moved on an XYZ axis system to each EHT. EHT contractions are detected by an automated figure recognition algorithm, and force is calculated based on shortening of the EHT and the elastic propensity and geometry of the PDMS posts. This procedure allows for automated analysis of high numbers of EHT under standardized and sterile conditions. The reliable detection of drug effects on cardiomyocyte contraction is crucial for cardiac drug development and safety pharmacology. We demonstrate, with the example of the hERG channel inhibitor E-4031, that the human EHT system replicates drug responses on contraction kinetics of the human heart, indicating it to be a promising tool for cardiac drug safety screening.

Here, we show the generation of human engineered heart tissue from induced pluripotent stem cells (hiPSC)-derived cardiomyocytes. We present a method to analyze contraction force and exemplary alteration of contraction pattern by the hERG channel inhibitor E-4031. This method shows high level of robustness and suitability for cardiac drug screening.

Cardiac tissue engineering describes techniques to constitute three dimensional force-generating engineered tissues. For the implementation of these ...

Light-sheet Fluorescence Microscopy to Capture 4-Dimensional Images of the Effects of Modulating Shear Stress on the Developing Zebrafish Heart

The hemodynamic forces experienced by the heart influence cardiac development, especially trabeculation, which forms a network of branching outgrowths from the myocardium. Genetic program defects in the Notch signaling cascade are involved in ventricular defects such as Left Ventricular Non-Compaction Cardiomyopathy or Hypoplastic Left Heart Syndrome. Using this protocol, it can be determined that shear stress driven trabeculation and Notch signaling are related to one another. Using Light-sheet Fluorescence Microscopy, visualization of the developing zebrafish heart was possible. In this manuscript, it was assessed whether hemodynamic forces modulate the initiation of trabeculation via Notch signaling and thus, influence contractile function occurs. For qualitative and quantitative shear stress analysis, 4-D (3-D+time) images were acquired during zebrafish cardiac morphogenesis, and integrated light-sheet fluorescence microscopy with 4-D synchronization captured the ventricular motion. Blood viscosity was reduced via gata1a-morpholino oligonucleotides (MO) micro-injection to decrease shear stress, thereby, down-regulating Notch signaling and attenuating trabeculation. Co-injection of Nrg1 mRNA with gata1a MO rescued Notch-related genes to restore trabeculation. To confirm shear stress driven Notch signaling influences trabeculation, cardiomyocyte contraction was further arrested via tnnt2a-MO to reduce hemodynamic forces, thereby, down-regulating Notch target genes to develop a non-trabeculated myocardium. Finally, corroboration of the expression patterns of shear stress-responsive Notch genes was conducted by subjecting endothelial cells to pulsatile flow. Thus, the 4-D light-sheet microscopy uncovered hemodynamic forces underlying Notch signaling and trabeculation with clinical relevance to non-compaction cardiomyopathy.

Here, we present a protocol to visualize developing hearts in zebrafish in 4-Dimensions (4-D). 4-D imaging, via light-sheet fluorescence microscopy (LSFM), takes 3-Dimensional (3-D) images over time, to reconstruct developing hearts. We show qualitatively and quantitatively that shear stress activates endocardial Notch signaling during chamber development, which promotes cardiac trabeculation.

The hemodynamic forces experienced by the heart influence cardiac development, especially trabeculation, which forms a network of branching outgrowths ...

Light-sheet Fluorescence Microscopy for the Study of the Murine Heart

Light-sheet fluorescence microscopy has been widely used for rapid image acquisition with a high axial resolution from micrometer to millimeter scale. Traditional light-sheet techniques involve the use of a single illumination beam directed orthogonally at sample tissue. Images of large samples that are produced using a single illumination beam contain stripes or artifacts and suffer from a reduced resolution due to the scattering and absorption of light by the tissue. This study uses a dual-sided illumination beam and a simplified CLARITY optical clearing technique for the murine heart. These techniques allow for deeper imaging by removing lipids from the heart and produce a large field of imaging, greater than 10 x 10 x 10 mm3. As a result, this strategy enables us to quantify the ventricular dimensions, track the cardiac lineage, and localize the spatial distribution of cardiac-specific proteins and ion-channels from the post-natal to adult mouse hearts with sufficient contrast and resolution.

This study uses a dual-sided illumination light-sheet fluorescence microscopy (LSFM) technique combined with optical clearing to study the murine heart.

Light-sheet fluorescence microscopy has been widely used for rapid image acquisition with a high axial resolution from micrometer to millimeter scale. ...

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Three dimensional&#160;models can be a valuable tool for surgeons as they develop surgical plans and medical fellows as they learn about complex cases. In particular, 3D models can play an important role in the field of cardiology, where complex congenital heart diseases occur. While many 3D printers can provide anatomically correct and detailed models, existing 3D printing materials fail to replicate myocardial tissue properties and can be extremely costly. This protocol aims to develop a process for the creation of patient-specific models of complex congenital heart defects using a low-cost silicone that more closely matches cardiac muscle properties. With improved model fidelity, actual surgical procedural training could occur in advance of the procedure. Successful creation of cardiac models begins with the segmentation of radiologic images to generate a virtual blood pool (blood that fills the chambers of the heart) and myocardial tissue mold. The blood pool and myocardial mold are 3D printed in acrylonitrile butadiene styrene (ABS), a plastic dissolvable in acetone. The mold is assembled around the blood pool, creating a negative space simulating the myocardium. Silicone with a shore hardness of 2A is poured into the negative space and allowed to cure. The myocardial mold is removed, and the remaining silicone/blood pool model is submerged in acetone. The described process results in a physical model in which all cardiac features, including intra-cardiac defects, are represented with more realistic tissue properties and are more closely approximated than a direct 3D printing approach. The successful surgical correction of a model with a ventricular septal defect (VSD) using a GORE-TEX patch (standard surgical intervention for defect) demonstrates the utility of the method.

Patient-specific models improve surgeon and fellow confidence when developing or learning surgical plans. Three-dimensional (3D) printers generate adequate detail for surgical preparation, but fail to replicate tissue haptic fidelity. A protocol is presented detailing the creation of patient-specific, silicone cardiac models, combining 3D printing precision with simulated silicone tissue.

Three dimensional&#160;models can be a valuable tool for surgeons as they develop surgical plans and medical fellows as they learn about complex cases. In ...

Developing Drosophila melanogaster Models for Imaging and Optogenetic Control of Cardiac Function

Using Drosophila melanogaster (fruit fly) as a model organism has ensured significant progress in many areas of biological science, from cellular organization and genomic investigations to behavioral studies. Due to the accumulated scientific knowledge, in recent years, Drosophila was brought to the field of modeling human diseases, including heart disorders. The presented work describes the experimental system for monitoring and manipulating the heart function in the context of a whole live organism using red light (617 nm) and without invasive procedures. Control over the heart was achieved using optogenetic tools. Optogenetics combines the expression of light-sensitive transgenic opsins and their optical activation to regulate the biological tissue of interest. In this work, a custom integrated optical coherence tomography (OCT) imaging and optogenetic stimulation system was used to visualize and modulate the functioning D. melanogaster heart at the 3rd instar larval and early pupal developmental stages. The UAS/GAL4 dual genetic system was employed to express halorhodopsin (eNpHR2.0) and red-shifted channelrhodopsin (ReaChR), specifically in the fly heart. Details on preparing D. melanogaster for live OCT imaging and optogenetic pacing are provided. A lab-developed integration software processed the imaging data to create visual presentations and quantitative characteristics of Drosophila heart function. The results demonstrate the feasibility of initiating cardiac arrest and bradycardia caused by eNpHR2.0 activation and performing heart pacing upon ReaChR activation.

Developing Drosophila melanogaster Models for Imaging and Optogenetic Control of Cardiac Function

The present protocol describes the generation of Drosophila melanogaster expressing eNpHR2.0 or ReaChR opsins in the heart for OCT imaging and optogenetic heart pacing. Detailed instructions for Drosophila OCT imaging and heart beating modulation, including the simulation of restorable heart arrest, bradycardia, and tachycardia in live animals at different developmental stages, are reported.

Using Drosophila melanogaster (fruit fly) as a model organism has ensured significant progress in many areas of biological science, from cellular ...

Light-Sheet Microscopy and Tissue Clearing for Comprehensive Cardiac Imaging

Light-Sheet Imaging to Reveal Cardiac Structure in Rodent Hearts

Light-sheet microscopy (LSM) plays a pivotal role in comprehending the intricate three-dimensional (3D) structure of the heart, providing crucial insights into fundamental cardiac physiology and pathologic responses. We hereby delve into the development and implementation of the LSM technique to elucidate the micro-architecture of the heart in mouse models. The methodology integrates a customized LSM system with tissue clearing techniques, mitigating light scattering within cardiac tissues for volumetric imaging. The combination of conventional LSM with image stitching and multiview deconvolution approaches allows for the capture of the entire heart. To address the inherent trade-off between axial resolution and field of view (FOV), we further introduce an axially swept light-sheet microscopy (ASLM) method to minimize out-of-focus light and uniformly illuminate the heart across the propagation direction. In the meanwhile, tissue clearing methods such as iDISCO enhance light penetration, facilitating the visualization of deep structures and ensuring a comprehensive examination of the myocardium throughout the entire heart. The combination of the proposed LSM and tissue clearing methods presents a promising platform for researchers in resolving cardiac structures in rodent hearts, holding great potential for the understanding of cardiac morphogenesis and remodeling.

Author Spotlight: Customized Light-Sheet Imaging for Investigating Myocardial Structures in Rodent Hearts

The protocol utilizes advanced light-sheet microscopy along with adapted tissue clearing methods to investigate intricate cardiac structures in rodent hearts, holding great potential for the understanding of cardiac morphogenesis and remodeling.

Light-sheet microscopy (LSM) plays a pivotal role in comprehending the intricate three-dimensional (3D) structure of the heart, providing crucial insights ...