Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis

Summary

In this protocol, the pancreatic islets are reconstructed and analyzed using computational algorithms implemented in a dedicated multiplatform application.

Abstract

Structural properties of pancreatic islets are key for the functional response of insulin, glucagon, and somatostatin-secreting cells, due to their implications in intraislet communication via electric, paracrine, and autocrine signaling. In this protocol, the three-dimensional architecture of a pancreatic islet is firstly reconstructed from experimental data using a novel computational algorithm. Next, the morphological and connectivity properties of the reconstructed islet, such as the number and percentages of the different type of cells, cellular volume, and cell-to-cell contacts, are obtained. Then, network theory is used to describe the connectivity properties of the islet through network-derived metrics such as average degree, clustering coefficient, density, diameter, and efficiency. Finally, all these properties are functionally evaluated through computational simulations using a model of coupled oscillators. Overall, here we describe a step-by-step workflow, implemented in IsletLab, a multiplatform application developed specifically for the study and simulation of pancreatic islets, to apply a novel computational methodology to characterize and analyze pancreatic islets as a complement to the experimental work.

Introduction

The pancreas is divided into regions referred to as head, neck, body, and tail, each having different structures, functions, and anatomical position^1,2. From a functional viewpoint, the pancreas can be divided into endocrine and exocrine systems with the former responsible for the secretion of hormones critically involved in the regulation of glucose homeostasis, while the latter contributes to food digestion via the secretion of enzymes into the duodenum¹. Pancreatic islets constitute the endocrine tissue of the pancreas and are responsible for the secretion of glucagon, insulin, and somatostatin, secreted from ɑ, β, and δ-cells, respectively³. In addition to their intrinsic regulatory mechanisms, these cells are regulated via direct electrical communication (between β-cells and likely β and δ-cells), and also by paracrine and autocrine signaling^4,5,6. Both mechanisms are highly dependent on the islet architecture (i.e., the composition and organization of the different types of cells within the islet)^7,8. Importantly, islet architecture is altered in the presence of diabetes, most likely disturbing intraislet communication as a result^9,10.

The study of pancreatic islets involves a wide range of experimental methodologies. Among these, the use of fluorescence techniques to determine the number, location, and type of the different cells in the islet has allowed to study the structural and morphological properties of pancreatic islets^11,12,13 and to gain a better understanding of the functional implications in health and disease. As a complement, computational models of pancreatic cells^14,15,16 and, more recently, pancreatic islets^12,17,18,19 have been used in the last decades to evaluate aspects difficult or even impossible to address experimentally.

In this protocol, we aim to bridge the gap between the experimental and computational work by outlining a methodology to reconstruct islet architectures, to analyze their morphological and connectivity properties through quantitative metrics, and to perform basic simulations to evaluate the functional implications of the islet properties.

The protocol described below is based on computational algorithms specifically designed for the study of pancreatic islets. In summary, in the first step of the protocol, the islet architecture is reconstructed from experimental data using the algorithm recently proposed by Félix-Martínez et al.¹⁹ in which nuclear positions obtained through 4′,6-diamidino-2-phenylindole (DAPI) staining and cellular types identified through immunofluorescence (as described in detail by Hoang et al.^11,12) are processed in an iterative optimization procedure. This leads to determining the optimal size and position of each cell and obtaining an islet composed of non-overlapping cells. Secondly, based on the reconstructed architecture, cell-to-cell contacts are identified to determine the connectivity properties and to generate the corresponding islet network which allows the user to obtain quantitative metrics to further describe the islet architecture (details about the reconstruction algorithm can be consulted in the original work on the subject¹⁹). Finally, basic functional simulations are performed using the modeling approach proposed by Hoang et al.¹² in which, based on the pulsatile nature of hormone secretion observed experimentally^20,21, each cell is treated as an oscillator, and therefore the islet is represented as a network of coupled oscillators following the connectivity properties of the reconstructed islet.

Given the computational complexity of the algorithms used in this protocol, all the steps involved have been implemented in a standalone application²² with the main objective of approaching these computational tools to all the interested readers regardless of their level of experience in the use of specialized software or programming languages.

Protocol

NOTE: A schematic diagram of the protocol is shown in Figure 1. A step-by-step description is given is as follows (see Supplementary File 1 for details about the control panels used at every step of the protocol).

Figure 1: Flow diagram. A flow diagram describing the sequential order of the protocol as implemented in IsletLab. Please click here to view a larger version of this figure.

1. Installing IsletLab in Linux

NOTE: Follow the instructions given in sections 2 and 3 of the Supplementary File 2 to install IsletLab in windows or macOS.

1. Open an internet browser and go to https://github.com/gjfelix/IsletLab. Download and extract the IsletLab repository files.
   NOTE: The Supplementary File 3 includes a copy of the version of IsletLab used in this protocol.
2. Verify that the gcc and nvcc compilers are installed. Open a terminal and type the following commands:
   gcc --version
   nvcc --version
   Follow the instructions listed in section 1 of the Supplementary File 2 if any of these commands is not recognized by the system.
3. Download and install the data science platform (see Table of Materials). Open a terminal and go to the IsletLab folder.
4. Create a new environment by typing the following command in the terminal:
   conda env create -f isletlabgui_v1.0.yml
5. Activate the new environment by typing:
   conda activate isletlab_v1.0
6. Launch the IsletLab application by typing the following command in the terminal (see Figure 2 for a description of the main window):
   python isletlabgui_v1.0.py

Figure 2: The user interface of IsletLab. The interface is composed of three main panels: configuration (1), statistics (2), and graphics (3) panels. The graphics toolbar (4) is located at the bottom of the graphics panel. Please click here to view a larger version of this figure.

2. Islet reconstruction

1. Prepare the input data. Organize the input islet data (i.e., cell types and three-dimension (3D) coordinates) in a four-column file in which column 1 contains the cell type (coded as 11: ɑ-cell, 12: β-cell, 13: δ-cell) and columns 2 to 4 contain the x, y, and z coordinates, respectively (see an excerpt of an input file in section 5 of the Supplementary File 2 or the input test file included in the IsletLab repository).
2. Ensure that the input file does not include column headers. Use the example input file included in the IsletLab repository to follow the protocol if needed (Input_Islet_test.txt).
   NOTE: Input data used to develop the algorithms implemented in IsletLab was obtained experimentally as described in detail by Hoang et al.^11,12 using DAPI staining to determine the nuclear positions and immunofluorescence to identify the cellular types.
3. Click the Load Initial Islet button and select the file containing input data to generate an initial islet, the 3D representation, and the corresponding statistics. In case of error, the following message will be displayed: 'Error loading islet file'. To correct this, repeat step 2.1.
4. Configure the reconstruction process. Click the Reconstruction Settings button and modify the optimization parameters (see sections 4 and 5 of Supplementary File 2 for a brief description of the algorithm and the parameters shown in the reconstruction settings window).
5. Set the initial temperature = 1, iterations factor = 1, and acceptance factor = 1. Click the OK button to save the parameter values.
   NOTE: As a rule of thumb, an increase in the temperature parameter, as well as in the iterations and acceptance factors, will yield better reconstruction results in terms of the experimental cells included in the reconstructed islet at the expense of an increase in the computing time.
6. Click the Reconstruct Islet button to open the reconstruction log window (see section 6 of Supplementary File 2 for a description of the information provided in the reconstruction log).
7. Click the Run button to start the reconstruction process. Monitor the optimization process until the message: 'Please close this window to continue' is displayed. Close the reconstruction log window to generate the visual representation of the reconstructed islet and to calculate the related statistics (see the left column in Figure 3A-C).
8. Evaluate the results of the reconstruction process by analyzing the optimization statistics shown in the final islet tab of the statistics panel. Particularly, focus on maximizing the percentage of experimental cells included in the reconstructed islets (% of experimental) or equivalently, on minimizing the number of overlaps (also shown graphically in the convergence plot, right column in Figure 3A-C).
9. If the % of experimental statistic is considered low according to the user objectives, restart IsletLab as described in step 9.1, and increase the initial temperature, iterations factor and acceptance factor in the reconstruction settings and repeat steps 2.1-2.4 until satisfactory results are obtained (> 95% of experimental cells). See sections 5-7 of the Supplementary File 2 for further description of the reconstruction process and the related results.

3. Identifying cell-to-cell contacts

1. Click the Reconstruction Settings button and set the Contact Tolerance parameter to define the cell-to-cell contact tolerance and click OK to save the parameter values.
   NOTE: The contact tolerance parameter represents the maximal distance between cells in order to be considered as in contact.
2. Click the Cell-to-Cell Contacts button to identify the cells in close contact. In the contacts tab, check for cells in contact shown graphically (black lines) in the graphics panel and the corresponding statistics (i.e., total, homotypic and heterotypic contacts and ɑ-ɑ, β-β, δ-δ, ɑ-β, ɑ-δ, β-δ contacts) shown in the statistics panel (see the left column of Figure 4A-C).
3. Increase (decrease) the contact tolerance parameter to increase (decrease) the number of cell-to-cell contacts. See section 8 of Supplementary File 2 for details about the identification of cell-to-cell contacts and the associated results.

4. Building the islet network

1. Click the Build Network button to generate the islet network and to calculate the associated network metrics (see the right column in Figure 4A-C).
   ​NOTE: A further description of the results related to the islet network generated is given in section 9 of Supplementary File 2.

5. Functional simulation of the reconstructed islet

1. Switch to the Simulation Tab of the configuration panel of the interface (Figure 5).
2. Select the desired mode of intrinsic frequency - Constant or Random - and click the Configure Intrinsic Frequency button to define the oscillators frequency (in Hz). If a random intrinsic frequency is selected, define the mean and deviation (in Hz) to generate normally distributed random frequencies (see section 11 of the Supplementary File 2 for a description of the simulation parameters).
3. Select the desired mode of the initial phase - Constant or Random. If a Constant initial phase is selected, click the Configure Initial Phase button to define the phase of the oscillators (in radians). If random initial phase is selected, system will assign random phases between 0 and 2π to all the oscillators.
   NOTE: If a constant initial phase is selected, all the oscillators will be initialized in phase.
4. Click the Configure Interactions button to define the cell-to-cell interaction parameters in the interaction strength window. For a description of the interaction parameters, see section 11 of Supplementary File 2.
5. Configure the simulation by defining the total simulation time (in s), time step (in s), and save factor (numbers of steps between saved data points). In the simulation shown in Figure 5 the total time was 20000 s, with a time step of 0.1 s and a save factor of 500.
6. Define the number of blocks, threads, and computing platform capability available to perform the simulation. Define these parameters in accordance with the specific characteristics of the graphics processing unit (GPU) available.
   NOTE: In the example shown in Figure 5, 36 blocks and 64 threads per block were used (2304 computing platform cores) since a GPU with 36 multiprocessors and 64 computing platform cores per multiprocessors was used. The value of the computing platform capability parameter used was 75 since the hardware computing platform capability was 7.5 (see section 10 of Supplementary File 2 for details about these parameters).
7. 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. See section 12 of Supplementary File 2 for details about the simulation log.
8. Close the simulation log window to observe the simulation results (see Figure 5). Details about the simulation results are given in section 13 of Supplementary File 2.

6. Save the project (optional)

1. Click File > Export Project in the menu bar. Select the directory in which the project file will be saved and click the OK button.
   NOTE: The name of the project file is determined automatically based on the name of the initial data file. If the project is not saved, all the results and related files will be deleted automatically.
2. Load an exported project by clicking File > Load Project.

7. Save figures (optional)

1. Click the Save Plot Icon located in the plot toolbar to save the current visualization to an image file. Repeat this step for all the figures created throughout the protocol.
   ​NOTE: Plots and islet visualizations can be modified using the icons available at the plot toolbar of the graphics panel.

8. Load project (optional)

1. Click File > Load Project in the main menu and select a project file saved previously as described in steps 6.1-6.2.
   ​NOTE: A project will not be loaded correctly if the project file has been modified externally. If a project is loaded successfully, only the simulation capabilities will be available.

9. Restart the reconstruction process and analysis (optional)

1. At any time the user can select File > Restart to discard all the results and start a new reconstruction and analysis.

Results

 The reconstruction of pancreatic islets using the methodology proposed by Félix-Martínez et al.¹⁹ is highly dependent on the parameters given to the optimization algorithm (defined in the reconstruction settings). An example of this is shown visually in Figure 3 where reconstructed islets obtained using different sets of parameters are shown. First, in Figure 3A, a reconstruction that included 86.6% of the cells included in the initial d...

Discussion

The above protocol outlines a practical approach to reconstruct and analyze pancreatic islet architectures using novel computational algorithms. The main objective of this work is to enable the islet research community to derive quantitative metrics to characterize the morphological and connectivity properties of pancreatic islet architectures and to evaluate the possible functional implications of such properties via computational simulations.

While the algorithms adopted in this pro...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CUDA-capable NVIDIA graphics card			Required for the functional simulations
IsletLab			https://github.com/gjfelix/IsletLab (Follow the instructions to download and install the application.)