This protocol is significant because it describes a computational workload to reconstruct islet architectures, to analyze their morphological and connectivity properties, and to evaluate their functional implications via computational simulations. The main advantage is that it provides a way to implement high-performance computing algorithms to complement the theoretical and experimental work in the field of islet research. This methodology can be particularly useful to compare the morphological and connectivity properties of healthy and altered islets or to compare islet architectures from different animal species.
To begin, verify that the GCC and NVCC compilers are installed. Open a terminal by executing the commands and follow the instructions provided in the text if any of these commands are not recognized by the system. Open a terminal and go to the IsletLab folder.
Create a new environment by executing the command in the terminal. Activate the new environment by executing the command. Launch the IsletLab application by executing the command in the terminal.
To prepare the input data, organize the input islet data in a four-column file in which first column is the cell type and the second, third, and fourth columns are the X, Y, and Z coordinates of the input cells, respectively, ensuring that the input file does not include column headers. Click the Load initial islet button and select the file containing input data to generate an initial islet, the three-dimensional representation, and the corresponding statistics. To configure the reconstruction process, click the Reconstruction settings button and modify the optimization parameters.
Click the OK button to save the parameter values. Click the Reconstruct islet button to open the Reconstruction Log window. Click the Run button to start the reconstruction process.
Evaluate the results of the reconstruction process by analyzing the optimization statistics shown in the Final Islet tab of the statistics panel by focusing on maximizing the percentage of experimental cells included in the reconstructed islets or, equivalently, on minimizing the number of overlaps. If the percent of experimental statistics is considered low according to the user objectives, click the File menu and select Restart. Click the Load initial islet button and select the file containing input data to generate an initial islet.
Then increase the Initial temperature, Iterations factor, and Acceptance factor in the Reconstruction settings, and repeat the islet reconstruction process described previously until satisfactory results are obtained. Click the Reconstruction settings and select Contact tolerance to define the cell-to-cell contact tolerance. Click OK to save the parameter values.
Click the Cell-to-cell contacts button to identify the cells in close contact. Click the Build Network button to generate the islet network and to calculate the associated network matrix. Switch to the Simulation tab of the configuration panel of the interface.
Select the desired mode of intrinsic frequency, Constant or Random. Click the Configure intrinsic frequency button to define the oscillator's frequency in hertz. Select the desired mode of the initial phase, Constant or Random.
Click the Configure interactions button to define the cell-to-cell interaction parameters in the Interaction strength window. Configure the simulation by defining the total simulation time, time step, and save factor. Define the number of blocks, threads, and computing platform capability available to perform the simulation.
Click the Run Simulation button to open the Simulation log window. Click the Run button to start the simulation and monitor the process until the legend Please close the window to continue is displayed. Then close the Simulation log window to observe the simulation results.
Click File and select the Export Project in the menu bar. Select the directory in which the project file will be saved and click the OK button. Islet reconstruction using suboptimal sets of parameters in the reconstruction settings was obtained, including 86.6%of the initial cells.
Increasing the initial temperature, iteration, and acceptance factors resulted in a higher percentage of initial cells as 93.37 and 99.15%in the reconstructed islets. The convergence plots of the reconstruction process were obtained, demonstrating the evolution of overlapped cells as a function of temperature. Identification of cell-to-cell contacts from the reconstructed islet architectures depends on the value of the contact tolerance parameter.
Only 290 cell-to-cell contacts were identified for the value of one micrometer, while for two micrometers, the total context identified increased to 636 and 731, respectively. The network plot was obtained which provides a visual representation of the cell-to-cell contacts that are connected together. Simulation results demonstrated that alpha and beta cells oscillate completely out of phase, while delta cells oscillate out-phase with alpha and beta cells.
The oscillatory behavior of the islet was dominated by the oscillations of the alpha cells with a minor contribution of the other cell population. The islet synchronization index plot was obtained which provides the measure of phase coherence of the oscillations between the islet cells and variation of the synchronicity between islet cells over time. All the derived metrics and the functional simulations depend on the reconstruction process, so it is key to achieve the highest percentage of initial cells in the reconstructed islets.
Reconstructed islets obtained from this procedure can be further used to develop more realistic computational models of pancreatic islets by including a detailed biophysical description of islet cells.
