Bioengineering
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Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biology
Chapters
Summary April 18th, 2018
This protocol outlines a novel method to create a spatially detailed finite element model of the intracellular architecture of cardiomyocytes from electron microscopy and confocal microscopy images. The power of this spatially detailed model is demonstrated using case studies in calcium signaling and bioenergetics.
Transcript
The overall goal of this procedure is to create a spatially detailed finite element model of the intracellular architecture of a heart cell. This model is created from electron and confocal microscopy data. Such models can be used to study the role of cellular architecture on cardiac cell function.
This method can help answer key questions about how change to internal organization of the cell and health and disease conditions can affect cardiac cell systems'biology. The main advantage of this method is that it allows you to integrate complementary information from electron microscopy and confocal microscopy in order to create a spatially detailed model of the cellular architecture. Though this method can provide insight into cardiac cell system biology, but this general overall approach, it can also be used on other cells where we need to understand the effect of the cellular structure on the function of the cell.
Visual demonstration of this method is critical, as the steps to simulate and map the ion channels onto the finer element mesh is so new. After preparing left ventricular free wall tissue sections and imaging them using electron tomography according to the text protocol, execute the IMOD program 3dmod. Within the graphical user interface, enter the address of the rec or mrc file that contains the 3D reconstructed image data set of the cell into the entry box labeled Image Files, then press Okay.
Next, under the file menu, select New Model. Then, using the Save Model As menu item, save the file with an appropriate name. Under the Special menu, select Drawing Tools, then choose the Sculpt option and move the mouse over to the image window.
A circular contour centered on the mouse pointer will appear. While holding the middle mouse button down over a mitochondrion, which is a darker region of the image files, drag the perimeter of the circle contour into the shape of its boundary. Once contouring of the mitochondrion boundary is complete, release the middle button and repeat this process for each mitochondrion in the data.
Each contour will automatically be recognized by IMOD as a new contour within the same object. Under the Edit Object menu, select New to create a new object. This will automatically increment the total number of objects by one and assign this number to the new object.
Use the Sculpt option to segment and save myofibril contours. Also segment the cell boundary, then under the File menu save the model file. After downloading iso2mesh according to the text protocol, download the source codes and data to simulate RyR clusters on the mesh from the GitHub website shown here.
Start the Cardiac Cell Mesh Generator MATLAB application. Then, using the three Push buttons on the upper left hand side of the GUI, load the different organelle component masks into MATLAB. To create another binary image stack that demarcates gaps between myofibrils and mitochondria, open Image J, then, using the File Open dialogue, load the myofibrils and mitochondria TIFF stacks into the program.
Initiate the Image Addition plug-in by selecting Process Calculator Plus. Select the myofibril image stack as I1, the mitochondria image stack as I2, and choose the Add Operator, then click Okay. After a new image stack representing the result appears, select Edit Invert to produce an image stack.
Load the file containing the binary image stack of the gaps between myofibrils and mitochondria by pushing the RyR Gaps file button on the Cardiac Cell Mesh Generator program, then push Generate Mesh on the GUI. Generate the necessary inputs for the RyR-Simulator by pushing the button labeled Generate RyR-Simulator Inputs on the GUI. After the function executes, check that the following files have been created within the directory specified as the OUT_DIR path, the d_axial_micron.
txt file, which represents the axial distance between the position of the Z-disc and the remainder of the pixels in the image stack, the d_radial_micron. txt file, which represents the Euclidean distance excluding the axial component from each pixel in the set of possible RyR cluster locations to the pixels on the Z-disc plane, the W_micro. txt file, which represents the list of spatial coordinates of all the available positions for RyR clusters to be present.
The remaining three files in the folder contain the suffix pixel rather than micron to denote that the values within these files have been written out in pixel-coordinate form. To simulate RyR cluster distributions on the binary image stack of myofibrils, push the button labeled Open RyR-Simulator in R to initiate the R program. On the R GUI, select File Open and within the RyR-Simulator package, find the file settings.
R.Refer to the text protocol for detailed explanations of the parameters in this field and the default values. Users should examine the simulated distribution of RyR clusters in the R window and adjust the settings to ensure that the clusters are located near the Z-discs and evenly spread across the entire cross-section of the cell. To map points as spatial densities onto a computational model using the Cardiac Cell Mesh Generator, select the button labeled Select RyR Points File and choose a simulated RyR cluster distribution text file from those that were output by the RyR-Simulator.
Finally, on the GUI in MATLAB, execute the RyR Density Mapper. Users should check visually that the density values are distributed such that the neighborhoods of high density values are similar to the size of RyR clusters in experimental data. These bright field images reveal how cells appear when oriented longitudinally, obliquely, and cross-sectionally with respect to the cutting plane.
Oblique and longitudinal samples exhibit striations, while cross-sectional samples do not. The capillaries also appear more circular in cross-sectional views than in oblique views. This panel represents a good quality tomogram stack that can be acquired when the tissue preparation portion of this protocol is followed.
Care and experience are necessary to ensure that the tissue blocks are sufficiently stained and that there is an even distribution of colloidal gold particles through the tissue volume. Shown here is a segmented 3D model of myofibrils and mitochondria. This 3D rendering of the tetrahedral mesh is produced by iso2mesh.
As long as the original image stack and segmentation tasks are of good quality, this step is fairly robust. Finally, these panels show three examples of simulated RyR clusters on the same mesh topology after using the spherical kernel intensity estimator algorithm. Notice the variation in organization of the RyR clusters.
Once mastered and if it's performed properly, a structurally realistic finite element model of a heart cell, it can be generated within a couple of hours by using our technique. Following this procedure, finite element simulations of calcium signaling, cardiac bioenergetics, and cellular mechanics, it can be performed to answer questions about the role of cellular architecture on the functioning of cardiomyocytes.
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