G.J. F&#233;lix-Mart&#237;nez thanks CONACYT (Consejo Nacional de Ciencia y Tecnolog&#237;a, M&#233;xico) and the Department of Electrical Engineering of the Universidad Aut&#243;noma Metropolitana (M&#233;xico City) for the support given to this project. We thank Dr. Danh-Tai Hoang, Dr. Manami Hara, and Dr. Junghyo Jo for their outstanding work and generosity in sharing the islet architectures that made this work possible with the research community.

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).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63351/63351fig01.jpg" /> 
Figure 1: Flow diagram. A flow diagram describing the sequential order of the protocol as implemented in IsletLab. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63351/63351fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
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.
<ol>
	<li>Open an internet browser and go to&#160;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.</li>
	<li>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.</li>
	<li>Download and install the data science platform (see Table of Materials). Open a terminal and go to the IsletLab folder.</li>
	<li>Create a new environment by typing the following command in the terminal: 
	conda env create -f isletlabgui_v1.0.yml</li>
	<li>Activate the new environment by typing: 
	conda activate isletlab_v1.0</li>
	<li>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</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63351/63351fig02.jpg" /> 
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. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63351/63351fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Islet reconstruction
<ol>
	<li>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: &#593;-cell, 12: &#946;-cell, 13: &#948;-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).</li>
	<li>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.</li>
	<li>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: &#39;Error loading islet file&#39;. To correct this, repeat step 2.1.</li>
	<li>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).</li>
	<li>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.</li>
	<li>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).</li>
	<li>Click the Run button to start the reconstruction process. Monitor the optimization process until the message: &#39;Please close this window to continue&#39; 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).</li>
	<li>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).</li>
	<li>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 (&#62; 95% of experimental cells). See sections 5-7 of the Supplementary File 2 for further description of the reconstruction process and the related results.</li>
</ol>
3. Identifying cell-to-cell contacts
<ol>
	<li>Click the Reconstruction Settings button and set the&#160;Contact Tolerance&#160;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.</li>
	<li>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 &#593;-&#593;, &#946;-&#946;, &#948;-&#948;, &#593;-&#946;, &#593;-&#948;, &#946;-&#948; contacts) shown in the statistics panel (see the left column of Figure 4A-C).</li>
	<li>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.</li>
</ol>
4. Building the islet network
<ol>
	<li>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). 
	&#8203;NOTE: A further description of the results related to the islet network generated is given in section 9 of Supplementary File 2.</li>
</ol>
5. Functional simulation of the reconstructed islet
<ol>
	<li>Switch to the Simulation Tab of the configuration panel of the interface (Figure 5).</li>
	<li>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).</li>
	<li>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 2&#960; to all the oscillators. 
	NOTE: If a constant initial phase is selected, all the oscillators will be initialized in phase.</li>
	<li>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.</li>
	<li>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.</li>
	<li>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).</li>
	<li>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: &#39;Please close the window to continue&#39; is displayed. See section 12 of Supplementary File 2 for details about the simulation log.</li>
	<li>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.</li>
</ol>
6. Save the project (optional)
<ol>
	<li>Click File &#62; 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.</li>
	<li>Load an exported project by clicking File &#62; Load Project.</li>
</ol>
7. Save figures (optional)
<ol>
	<li>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. 
	&#8203;NOTE: Plots and islet visualizations can be modified using the icons available at the plot toolbar of the graphics panel.</li>
</ol>
8. Load project (optional)
<ol>
	<li>Click File &#62; Load Project in the main menu and select a project file saved previously as described in steps 6.1-6.2. 
	&#8203;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.</li>
</ol>
9. Restart the reconstruction process and analysis (optional)
<ol>
	<li>At any time the user can select File &#62; Restart to discard all the results and start a new reconstruction and analysis.</li>
</ol>

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P., Brass, B., Abelev, Z., Poretsky, L., Poretsky, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Endocrine+Pancreas.">Endocrine Pancreas.</a> Principles of Diabetes Mellitus. , (2017).</li><li>Noguchi, G. M., Huising, M. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+the+inputs+that+shape+pancreatic+islet+hormone+release.">Integrating the inputs that shape pancreatic islet hormone release.</a> Nature Metabolism. 1, 1189-1201 (2019).</li><li>P&#233;rez-Armendariz, E. 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L., 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=Beta-cell+hubs+maintain+Ca2++oscillations+in+human+and+mouse+islet+simulations.">Beta-cell hubs maintain Ca2+ oscillations in human and mouse islet simulations.</a> Islets. 10 (4), 151-167 (2018).</li><li>Watts, M., Ha, J., Kimchi, O., Sherman, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Paracrine+regulation+of+glucagon+secretion:+the+&#946;/&#945;/&#948;+model.">Paracrine regulation of glucagon secretion: the &#946;/&#945;/&#948; model.</a> American Journal of Physiology. Endocrinology and Metabolism. 310 (8), 597-611 (2016).</li><li>F&#233;lix-Mart&#237;nez, G. J., Mata, A., God&#237;nez-Fern&#225;ndez, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reconstructing+human+pancreatic+islet+architectures+using+computational+optimization.">Reconstructing human pancreatic islet architectures using computational optimization.</a> Islets. 12 (6), 121-133 (2020).</li><li>Hellman, B., Salehi, A., Gylfe, E., Dansk, H., Grapengiesser, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glucose+generates+coincident+insulin+and+somatostatin+pulses+and+antisynchronous+glucagon+pulses+from+human+pancreatic+islets.">Glucose generates coincident insulin and somatostatin pulses and antisynchronous glucagon pulses from human pancreatic islets.</a> Endocrinology. 150 (12), 5334-5340 (2009).</li><li>Hellman, B., Salehi, A., Grapengiesser, E., Gylfe, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolated+mouse+islets+respond+to+glucose+with+an+initial+peak+of+glucagon+release+followed+by+pulses+of+insulin+and+somatostatin+in+antisynchrony+with+glucagon.">Isolated mouse islets respond to glucose with an initial peak of glucagon release followed by pulses of insulin and somatostatin in antisynchrony with glucagon.</a> Biochemical and Biophysical Research Communications. 417 (4), 1219-1223 (2012).</li><li>F&#233;lix-Mart&#237;nez, G. 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The authors have nothing to disclose.

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 protocol have been previously described in detail12,19, a direct and user-friendly implementation was lacking due to their relative complexity, thus limiting their use as a complementary tool to the experimental and theoretical work.
Firstly, a recent algorithm proposed by F&#233;lix-Mart&#237;nez et al.19 is used to reconstruct the islet architectures from experimental data (e.g., nuclear coordinates and cell type). As a result, the user obtains an islet architecture composed of non-overlapping spherical cells with radii automatically assigned in accordance with the reported experimental distributions. In practice, the reconstruction algorithm is an iterative optimization procedure that becomes expensive from the computational standpoint as the number of cells in the islet increases. For this reason, it is highly recommended to use a multiprocessor system to take advantage of the parallel processing implementation of the algorithm described in this protocol. As described above, a key step for the reconstructing process is to define appropriate values for the parameters involved (i.e., iterations, acceptance factors, and initial temperature), since the computing time will be directly related to the number of iterations performed, in addition to the number of parallel processes used (i.e., threads parameter in the reconstruction settings). If computing time is not an issue, we strongly recommend using the highest values possible for the iterations and acceptance factors in order to increase the number of iterations performed.
The next steps of the protocol are the identification of cell-to-cell contacts and the generation of the islet network. Both steps are directly related to the reconstruction process and as such, the number of cells included in the reconstructed islets (and therefore the parameters involved), as well as the value of the contact tolerance used, is key to obtaining the best results possible.
Finally, if desired by the user, functional simulations can be performed through the implementation of the model of coupled oscillators proposed by Hoang et al.12 using the connectivity network derived from the reconstruction process to configure the oscillatory system. Given that the simulation process involves solving a system of hundreds or thousands of coupled differential equations (one for each cell in the islet), the simulation algorithm has been implemented taking advantage of the possibility of performing parallel computations using the GPU, thus allowing the user to simulate considerable long simulations in a relatively short computing time. Key steps in the simulation stage of the protocol are to determine the appropriate number of blocks and threads available in the computing platform settings section of the simulation panel, an aspect directly related to the characteristics of the hardware used. The other parameters involved (intrinsic frequency, initial phase, and interaction strengths in the simulation panel), although relevant for the simulation results, are mainly related to the problem under investigation and must be defined by the user after thoughtful consideration in order to represent the desired simulation scenario.
Despite the advantages offered by the protocol, some limitations must be acknowledged. Firstly, the parameters related to the reconstruction process and identification of the cell-to-cell contacts are not unique and might vary from case to case. For this reason, although a rule of thumb can be used to determine the value of the required parameters,&#160;a trial-and-error approach is still unavoidable. Another aspect that could limit the applicability of the protocol is the computational resources needed, particularly for the reconstruction and simulation stages of the protocol. Despite these limitations, the fact that programming knowledge is not needed for the implementation of the protocol allows researchers from diverse backgrounds to readily make use of the proposed algorithms that otherwise would remain obscure for the non-specialized user.
Potential uses of the proposed protocol include the visualization of experimental data, comparative analysis of normal and altered islets (e.g., in the presence of type 1 or 2 diabetes), or even comparison between islets from different species using quantitative morphological, structural, and network-based metrics23. Moreover, reconstructed islets using the protocol outlined here can be readily used to generate detailed functional mathematical models in which the connectivity and cell sizes determined by the reconstruction algorithm are complemented with detailed electrophysiological models of pancreatic cells to elucidate the functional implications of the intercellular communication within reconstructed islets.

The pancreas is divided into regions referred to as head, neck, body, and tail, each having different structures, functions, and anatomical position1,2. 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,&#160;and somatostatin, secreted from &#593;, &#946;, and &#948;-cells, respectively3. In addition to their intrinsic regulatory mechanisms, these cells are regulated via direct electrical communication (between &#946;-cells and likely &#946; and &#948;-cells), and also by paracrine and autocrine signaling4,5,6. 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 result9,10.
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 islets11,12,13 and to gain a better understanding of the functional implications in health and disease. As a complement, computational models of pancreatic cells14,15,16 and, more recently, pancreatic islets12,17,18,19 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 F&#233;lix-Mart&#237;nez et al.19 in which nuclear positions obtained through 4&#8242;,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 experimentally20,21, 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.

<table><tbody><tr><td>CUDA-capable NVIDIA graphics card</td><td></td><td></td><td>Required for the functional simulations</td></tr><tr><td>IsletLab</td><td></td><td></td><td>https://github.com/gjfelix/IsletLab (Follow the instructions to download and install the application.)</td></tr></tbody></table>

computational reconstruction of pancreatic islets as a tool for structural and functional analysis

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.

&#160;The reconstruction of pancreatic islets using the methodology proposed by F&#233;lix-Mart&#237;nez 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 data is shown (509 out of 588 cells, initial temperature = 1, iterations factor = 1, acceptance factor = 1). When the initial temperature and the iteration and acceptance factors are increased (initial temperature = 10, iterations factor = 5, and acceptance factor = 5, Figure 3B), a higher percentage (93.37%) of initial cells were included in the reconstructed islets (i.e., 549 out of 588 cells). Even better results can be obtained if much higher values are used, particularly for the iteration and acceptance factors, as illustrated in Figure 3C (initial temperature = 10, iterations factor = 1000, acceptance factor = 500), where the reconstructed islet is composed of 99.15% of the initial cells (583 out of 588 cells). The convergence plots (right column in Figure 3A-C), showing the evolution of overlapped cells as a function of the temperature, must be evaluated to determine how the parameters affect the optimization process. As a rule of thumb, the interaction and acceptance factors must be increased when the reconstructed islet includes a low percentage of initial cells. Consequently, the computing time will inevitably increase, since these factors directly increase the number of iterations evaluated. For instance, the computing time of the first reconstruction described above was 6 s. In contrast, the computing times of the second and third reconstructions were 21 s and 24 min 6 s, respectively.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63351/63351fig03.jpg" /> 
Figure 3: Islet reconstruction using suboptimal sets of parameters in the reconstruction settings Using suboptimal sets of parameters could lead to a low percentage of experimental cells in the reconstructed islets. (A)&#160;Left: 86.6% of experimental cells were included in the reconstructed islet (initial temperature = 1, iterations factor = 1, acceptance factor = 1, computing time = 6 s). Right: convergence plot of the reconstruction process. (B)&#160;Left: 93.4% of experimental cells were included in the reconstructed islet (initial temperature = 10, iterations factor = 10, acceptance factor = 5, computing time = 21 s). Right: convergence plot of the reconstruction process. (C)&#160;Left: 99.15% of experimental cells were included in the reconstructed islet (initial temperature = 10, iterations factor = 1000, acceptance factor = 500, computing time = 24 min, 8 s). Right: convergence plot of the reconstruction process. The arrows in the convergence plots indicate the initial and final number overlapped cells of the reconstruction process (before the postprocessing phase of the reconstruction algorithm). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63351/63351fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Identification of cell-to-cell contacts from the reconstructed islet depends on the value of the contact tolerance parameter (defined in the reconstruction settings) as illustrated in Figure 4A-C, where the cell-to-cell contacts (represented by black lines), identified from the reconstructed architectures shown in Figure 3A-C, are presented. For instance, if a contact tolerance of 1 &#181;m is defined, as in Figure 4A, only 290 cell-to-cell contacts are identified. In contrast, if the contact tolerance is increased to 2 &#181;m, as in Figure 4B,C, the total contacts identified increased to 636 and 731, respectively (see the statistics panel in Figure 4A-C). These differences can also be noticed in the visual representation of the cell-to-cell contacts shown in the left column of Figure 4A-C, as the number of contacts between cells clearly increases as a higher value of the contact tolerance&#160;is used. It is worth highlighting that the number of contacts also depends on the number of cells included in the reconstructed islets, and therefore, the combination of the temperature parameter, iterations and acceptance factors, and contact tolerance ultimately determine the connectivity of the reconstructed islet, which is reflected on the islet networks formed and the corresponding network metrics, as shown in the right column of Figure 4A-C. The network plot allows the user to visualize how the different cells are connected. Quantitatively, connectivity properties of the islet are described in terms of the following network metrics: average degree, density, average clustering coefficient, efficiency, and diameter (details about these metrics can be consulted in section 9 of the Supplementary File 2).
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63351/63351fig04.jpg" /> 
Figure 4: Effect of the contact tolerance parameter in the identification of cell-to-cell contacts. (A-C) Left: cell-to-cell contacts identified from the reconstructed islets shown in Figure 3A-C (290, 636, and 731 total contacts in panels A, B, and C, respectively). Values used for the contact tolerance&#160;parameter were 1 &#181;m (A) and 2 &#181;m (B and C). Note that the number of cells included in the reconstructed islets also affects the number of cell-to-cell contacts identified. Right: networks generated from the cell-to-cell contacts are shown in the corresponding left column. Note the impact of the connectivity on the network metrics is highlighted in the statistics panel. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63351/63351fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Finally, once the islet has been reconstructed and the cell-to-cell contacts have been identified, a functional simulation can be performed (only when a compatible GPU is available). Typical simulation results are shown in Figure 5, including the summed oscillations of the different cell populations (&#593;, &#946;, and &#948;-cells) and the whole islet (upper plot of the graphics panel in Figure 5).&#160;This figure shows the phase differences over time between the different cell populations as a result of the connectivity and interaction properties and allows the user to determine the contribution of each cell population (red, green, and blue lines) to the oscillatory behavior of the whole islet (black line). For instance, the upper panel of Figure 5 suggests that, at the population level, &#593; and &#946;-cells oscillate completely out of phase, while &#948;-cells oscillate out of phase with &#593; and &#946;-cells. Moreover, according to the simulation, the oscillatory behavior of the islet is dominated by the oscillations of the &#593;-cells, although the effect of the other cell populations can be also noticed. Note that the oscillatory signals of all the islet cells are saved automatically in a data file (see Table 1 and section 13 in Supplementary File 2), thus allowing the user to perform a detailed analysis of the simulation results. As a complement, the islet synchronization index, which reflects the phase coherence of the oscillations, is also calculated and displayed (bottom plot of the graphics panel in Figure 5). Note that the synchronization index ranges from 0 to 1, where 0 and 1 indicate a null and total synchronization between all the cells in the islet, respectively. The synchronization index plot can be therefore interpreted as a visualization of how the synchronicity between islet cells varies over time as a result of the connectivity and interaction properties of the reconstructed islet. Since the simulation performed is based on the idea of coupled oscillators12 and heavily depends on the connectivity of the reconstructed islet, it is key to reach an acceptable islet reconstruction and cell-to-cell connectivity before performing a functional simulation.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63351/63351fig05.jpg" /> 
Figure 5: The simulation parameters are defined in the configuration panel of the simulation tab. The results of the simulation are shown in the simulation tab of the graphics panel where the summed oscillatory behavior of the different populations of cells (&#593;, &#946;, and &#948;) and the whole islet are shown (top). The synchronization index, a measure of phase coherence between the islet cells, is also shown (bottom). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63351/63351fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
It is worth mentioning that practically at every step of the process, data files are generated. A description of the data files generated can be found in Table 1 and throughout Supplementary File 2.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>File</td>
			<td>Description</td>
		</tr>
		<tr>
			<td>IsletFileName</td>
			<td>Input data (given by the user)</td>
		</tr>
		<tr>
			<td>IsletFileName_initial.txt</td>
			<td>Initial islet architecture proposed by the algorithm to as initial step of the reconstruction</td>
		</tr>
		<tr>
			<td>IsletFileName_reconstructed.txt</td>
			<td>Reconstructed islet (not posptrocessed)</td>
		</tr>
		<tr>
			<td>IsletFileName_postprocessed_islet.txt</td>
			<td>Final reconstructed islet and postrocessed islet</td>
		</tr>
		<tr>
			<td>IsletFileName_processlog.txt</td>
			<td>Reconstruction log (optimization algorithm)</td>
		</tr>
		<tr>
			<td>IsletFileName_overlapped_cells.txt</td>
			<td>Overlapped cells at the end of the reconstruction process (postprocessing)</td>
		</tr>
		<tr>
			<td>IsletFileName_all_contacts.txt</td>
			<td>Adjancency matrix of all contacts</td>
		</tr>
		<tr>
			<td>IsletFileName_aa_contacts.txt</td>
			<td>Adjancency matrix of &#593;-&#593; contacts</td>
		</tr>
		<tr>
			<td>IsletFileName_ab_contacts.txt</td>
			<td>Adjancency matrix of &#593;-&#946; contacts</td>
		</tr>
		<tr>
			<td>IsletFileName_ad_contacts.txt</td>
			<td>Adjancency matrix of &#593;-&#948; contacts</td>
		</tr>
		<tr>
			<td>IsletFileName_bbbd_contacts.txt</td>
			<td>Adjancency matrix of &#946;-&#946; and &#946;-&#948; contacts</td>
		</tr>
		<tr>
			<td>IsletFileName_bb_contacts.txt</td>
			<td>Adjancency matrix of &#946;-&#946; contacts</td>
		</tr>
		<tr>
			<td>IsletFileName_bd_contacts.txt</td>
			<td>Adjancency matrix of &#946;-&#948; contacts</td>
		</tr>
		<tr>
			<td>IsletFileName_dd_contacts.txt</td>
			<td>Adjancency matrix of &#948;-&#948; contacts</td>
		</tr>
		<tr>
			<td>IsletFileName_Kmat.txt</td>
			<td>Interaction matrix used in the simulation</td>
		</tr>
		<tr>
			<td>IsletFileName_kuramoto_angles.txt</td>
			<td>Results of the Kuramoto simulation</td>
		</tr>
	</tbody>
</table>
Table 1: Description of files saved as a part of the project file. Note that the file name used to save the project files are automatically defined by the initial data file selected by the user.
Supplementary File 1: Graphical description of the protocol using the control panels of IsletLab. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63351/Supplementary_File_1.pdf" target="_blank">Please click here to download this File.</a>
Supplementary File 2: IsletLab documentation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63351/Supplementary_File_2_R2.pdf" target="_blank">Please click here to download this File.</a>
Supplementary File 3: Includes all the files needed to install IsletLab. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63351/IsletLab_JOVE.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis at JoVE.com

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

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

Structural properties of pancreatic islets are key for the functional response of insulin, glucagon, and somatostatin-secreting cells, due to their ...

computational-reconstruction-pancreatic-islets-as-tool-for-structural

Nanyang Technological University

Research

JoVE Journal

Bioengineering

1.5K Views.  Universidad Autónoma Metropolitana. Dit protocol is belangrijk omdat het een computationele werklast beschrijft om eilandjesarchitecturen te reconstrueren, hun morfologische en connectiviteitseigenschappen te analyseren en hun functionele implicaties te evalueren via computationele simulaties. Het belangrijkste voordeel is dat het een manier biedt om high-performance computing-algoritmen te implementeren als aanvulling op het theoretische en experimentele werk op het gebied van eilandjesonderzoek. Deze methodologie kan bijzonde...

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

Staining Protocols for Human Pancreatic Islets

Estimates of islet area and numbers and endocrine cell composition in the adult human pancreas vary from several hundred thousand to several million and beta mass ranges from 500 to 1500 mg 1-3. With this known heterogeneity, a standard processing and staining procedure was developed so that pancreatic regions were clearly defined and islets characterized using rigorous histopathology and immunolocalization examinations. 
Standardized procedures for processing human pancreas recovered from organ donors are described in part 1 of this series. The pancreas is processed into 3 main regions (head, body, tail) followed by transverse sections. Transverse sections from the pancreas head are further divided, as indicated based on size, and numbered alphabetically to denote subsections. This standardization allows for a complete cross sectional analysis of the head region including the uncinate region which contains islets composed primarily of pancreatic polypeptide cells to the tail region.
The current report comprises part 2 of this series and describes the procedures used for serial sectioning and histopathological characterization of the pancreatic paraffin sections with an emphasis on islet endocrine cells, replication, and T-cell infiltrates. Pathology of pancreatic sections is intended to characterize both exocrine, ductular, and endocrine components. The exocrine compartment is evaluated for the presence of pancreatitis (active or chronic), atrophy, fibrosis, and fat, as well as the duct system, particularly in relationship to the presence of pancreatic intraductal neoplasia4. Islets are evaluated for morphology, size, and density, endocrine cells, inflammation, fibrosis, amyloid, and the presence of replicating or apoptotic cells using H&amp;E and IHC stains. 
The final component described in part 2 is the provision of the stained slides as digitized whole slide images. The digitized slides are organized by case and pancreas region in an online pathology database creating a virtual biobank. Access to this online collection is currently provided to over 200 clinicians and scientists involved in type 1 diabetes research. The online database provides a means for rapid and complete data sharing and for investigators to select blocks for paraffin or frozen serial sections.

This video demonstrates procedures for characterization of human pancreatic islets using hematoxylin and eosin (H&E) and immunohistochemistry (IHC). Pancreatic sections from head, body, and tail regions are stained by both H&amp;E and IHC to determine islet endocrine composition (insulin, glucagon, and pancreatic polypeptide), cell replication (Ki67), and inflammatory infiltrates (H&E, CD3). The uncinate region is localized using IHC for pancreatic polypeptide.

Estimates of islet area and numbers and endocrine cell composition in the adult human pancreas vary from several hundred thousand to several million and ...

Medicine

Generation of Scaffold-free, Three-dimensional Insulin Expressing Pancreatoids from Mouse Pancreatic Progenitors In Vitro

The pancreas is a complex organ composed of many different cell types that work together to regulate blood glucose homeostasis and digestion. These cell types include enzyme-secreting acinar cells, an arborized ductal system responsible for the transportation of enzymes to the gut, and hormone-producing endocrine cells. 
Endocrine beta-cells are the sole cell type in the body that produce insulin to lower blood glucose levels. Diabetes, a disease characterized by a loss or the dysfunction of beta-cells, is reaching epidemic proportions. Thus, it is essential to establish protocols to investigate beta-cell development that can be used for screening purposes to derive the drug and cell-based therapeutics. While the experimental investigation of mouse development is essential, in vivo studies are laborious and time-consuming. Cultured cells provide a more convenient platform for screening; however, they are unable to maintain the cellular diversity, architectural organization, and cellular interactions found in vivo. Thus, it is essential to develop new tools to investigate pancreatic organogenesis and physiology.
Pancreatic epithelial cells develop in the close association with mesenchyme from the onset of organogenesis as cells organize and differentiate into the complex, physiologically competent adult organ. The pancreatic mesenchyme provides important signals for the endocrine development, many of which are not well understood yet, thus difficult to recapitulate during the in vitro culture. Here, we describe a protocol to culture three-dimensional, cellular complex mouse organoids that retain mesenchyme, termed pancreatoids. The e10.5 murine pancreatic bud is dissected, dissociated, and cultured in a scaffold-free environment. These floating cells self-assemble with mesenchyme enveloping the developing pancreatoid and a robust number of endocrine beta-cells developing along with the acinar and the duct cells. This system can be used to study the cell fate determination, structural organization, and morphogenesis, cell-cell interactions during organogenesis, or for the drug, small molecule, or genetic screening.

Generation of Scaffold-free, Three-dimensional Insulin Expressing Pancreatoids from Mouse Pancreatic Progenitors In Vitro

Here, we present a protocol to generate insulin expressing 3D murine pancreatoids from free-floating e10.5 dissociated pancreatic progenitors and the associated mesenchyme.

The pancreas is a complex organ composed of many different cell types that work together to regulate blood glucose homeostasis and digestion. These cell ...

Developmental Biology

In situ Quantification of Pancreatic Beta-cell Mass in Mice

Tracing changes of specific cell populations in health and disease is an important goal of biomedical research. The process of monitoring pancreatic beta-cell proliferation and islet growth is particularly challenging. We have developed a method to capture the distribution of beta-cells in the intact pancreas of transgenic mice with fluorescence-tagged beta-cells with a macro written for ImageJ (rsb.info.nih.gov/ij/). Following pancreatic dissection and tissue clearing, the entire pancreas is captured as a virtual slice, after which the GFP-tagged beta-cells are examined. The analysis includes the quantification of total beta-cell area, islet number and size distribution with reference to specific parameters and locations for each islet and for small clusters of beta-cells. The entire distribution of islets can be plotted in three dimensions, and the information from the distribution on the size and shape of each islet allows a quantitative and qualitative comparison of changes in overall beta-cell area at a glance.

In situ Quantification of Pancreatic Beta-cell Mass in Mice

The following protocol outlines the process of pancreatic dissection for virtual slice imaging, and the subsequent quantification of all GFP-tagged beta-cells in the entire pancreas.

Tracing changes of specific cell populations in health and disease is an important goal of biomedical research. The process of monitoring pancreatic ...

Biology

High Resolution 3D Imaging of the Human Pancreas Neuro-insular Network

Using traditional histological methods, researchers are hampered in their ability to image whole tissues or organs in large-scale 3D. Histological sections are generally limited to &#60;20 &#181;m as formalin fixed paraffin section on glass slides or &#60;500 &#181;m for free-floating fixed sections. Therefore, extensive efforts are required for serial sectioning and large-scale image reconstruction methods to recreate 3D for samples &#62;500 &#181;m using traditional methods. In addition, light scatters from macromolecules within tissues, particularly lipids, prevents imaging to a depth &#62;150 &#181;m with most confocal microscopes. To reduce light scatter and to allow for deep tissue imaging using simple confocal microscopy, various optical clearing methods have been developed that are relevant for rodent and human tissue samples fixed by immersion. Several methods are related and use protein crosslinking with acrylamide and tissue clearing with sodium dodecyl sulfate (SDS). Other optical clearing techniques used various solvents though each modification had various advantages and disadvantages. Here, an optimized passive optical clearing method is described for studies of the human pancreas innervation and specifically for interrogation of the innervation of human islets.

Here, we present a protocol to image human pancreas sections in three dimensions (3D) using optimized passive clearing methods. This manuscript demonstrates these procedures for passive optical clearing followed by multiple immunofluorescence staining to identify key elements of the autonomic and sensory neural networks innervating human islets.

Using traditional histological methods, researchers are hampered in their ability to image whole tissues or organs in large-scale 3D. Histological ...

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), and delta-cells (somatostatin) that are embedded in the exocrine tissues and comprise 1-2% of the entire pancreas. There is a close correlation between body and pancreas weight. Total beta-cell mass also increases proportionately to compensate for the demand for insulin in the body. What escapes this proportionate expansion is the size distribution of islets. Large animals such as humans share similar islet size distributions with mice, suggesting that this micro-organ has a certain size limit to be functional. The inability of large animal pancreata to generate proportionately larger islets is compensated for by an increase in the number of islets and by an increase in the proportion of larger islets in their overall islet size distribution. Furthermore, islets exhibit a striking plasticity in cellular composition and architecture among different species and also within the same species under various pathophysiological conditions. In the present study, we describe novel approaches for the analysis of biological image data in order to facilitate the automation of analytic processes, which allow for the analysis of large and heterogeneous data collections in the study of such dynamic biological processes and complex structures. Such studies have been hampered due to technical difficulties of unbiased sampling and generating large-scale data sets to precisely capture the complexity of biological processes of islet biology. Here we show methods to collect unbiased "representative" data within the limited availability of samples (or to minimize the sample collection) and the standard experimental settings, and to precisely analyze the complex three-dimensional structure of the islet. Computer-assisted automation allows for the collection and analysis of large-scale data sets and also assures unbiased interpretation of the data. Furthermore, the precise quantification of islet size distribution and spatial coordinates (i.e. X, Y, Z-positions) not only leads to an accurate visualization of pancreatic islet structure and composition, but also allows us to identify patterns during development and adaptation to altering conditions through mathematical modeling. The methods developed in this study are applicable to studies of many other systems and organisms as well.

Novel computer-assisted methods of large-scale procurement and analysis of immunohistochemically stained pancreatic specimens are described: (1) Virtual Slice capture of the entire section; (2) Mass analysis of large-scale data; (3) Reconstruction of 2D Virtual Slices; (4) 3D islet mapping; and (5) Mathematical analysis.

The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), ...

Observing Islet Function and Islet-Immune Cell Interactions in Live Pancreatic Tissue Slices

Live pancreatic tissue slices allow for the study of islet physiology and function in the context of an intact islet microenvironment. Slices are prepared from live human and mouse pancreatic tissue embedded in agarose and cut using a vibratome. This method allows for the tissue to maintain viability and function in addition to preserving underlying pathologies such as type 1 (T1D) and type 2 diabetes (T2D). The slice method enables new directions in the study of the pancreas through the maintenance of the complex structures and various intercellular interactions that comprise the endocrine and exocrine tissues of the pancreas. This protocol demonstrates how to perform staining and time-lapse microscopy of live endogenous immune cells within pancreatic slices along with assessments of islet physiology. Further, this approach can be refined to discern immune cell populations specific for islet cell antigens using major histocompatibility complex-multimer reagents.

This study presents the application of live pancreatic tissue slices to the study of islet physiology and islet-immune cell interactions.

Live pancreatic tissue slices allow for the study of islet physiology and function in the context of an intact islet microenvironment. Slices are prepared ...

Immunology and Infection

Visualization of Endogenous Mitophagy Complexes In Situ in Human Pancreatic Beta Cells Utilizing Proximity Ligation Assay

Mitophagy is an essential mitochondrial quality control pathway, which is crucial for pancreatic islet beta cell bioenergetics to fuel glucose-stimulated insulin release. Assessment of mitophagy is challenging and often requires genetic reporters or multiple complementary techniques not easily utilized in tissue samples, such as primary human pancreatic islets. Here we demonstrate a robust approach to visualize and quantify formation of key endogenous mitophagy complexes in primary human pancreatic islets. Utilizing the sensitive proximity ligation assay technique to detect interaction of the mitophagy regulators NRDP1 and USP8, we are able to specifically quantify formation of essential mitophagy complexes in situ. By coupling this approach to counterstaining for the transcription factor PDX1, we can quantify mitophagy complexes, and the factors that can impair mitophagy, specifically within beta cells. The methodology we describe overcomes the need for large quantities of cellular extracts required for other protein-protein interaction studies, such as immunoprecipitation (IP) or mass spectrometry, and is ideal for precious human islet&#160;samples generally not available in sufficient quantities for these approaches. Further, this methodology obviates the need for flow sorting techniques to purify beta cells from a heterogeneous islet population for downstream protein applications. Thus, we describe a valuable protocol for visualization of mitophagy highly compatible for use in heterogeneous and limited cell populations.

This protocol outlines a method for quantitative analysis of mitophagy protein complex formation specifically in beta cells from primary human islet samples. This technique thus allows analysis of mitophagy from limited biological material, which are crucial in precious human pancreatic beta cell samples.

Mitophagy is an essential mitochondrial quality control pathway, which is crucial for pancreatic islet beta cell bioenergetics to fuel glucose-stimulated ...

A Simple High Efficiency Protocol for Pancreatic Islet Isolation from Mice

Pancreatic islets, also called the Islets of Langerhans, are a cluster of endocrine cells which produces hormones for glucose regulation and other important biological functions. The islets primarily consist of five types of hormone-secreting cells: &#945; cells secrete glucagon, &#946; cells secrete insulin, &#948; cells secrete somatostatin, &#949; cells secrete ghrelin, and PP cells secrete pancreatic polypeptide. Sixty to 80% of the cells in the islets are &#946; cells, which are the most important cell population to study insulin secretion. Pancreatic islets are a crucial model system to study ex vivo insulin secretion. Acquiring high quality islets is of great importance for diabetes research. Most islet isolation procedures require technically difficult to access site of collagenase injection, harsh and complex digestion procedures, and multiple density gradient purification steps. This paper features a simple high yield mouse islet isolation method with detailed descriptions and realistic demonstrations, showing the following specific steps: 1) injection of collagenase P at the ampulla of Vater, a small area joining the pancreatic duct and the common bile duct, 2) enzymatic digestion and mechanical separation of the exocrine pancreas, and 3) a single gradient purification step. The advantages of this method are the injection of digestive enzyme using the more accessible ampulla of Vater, more complete digestion using combination of enzymatic and mechanical approaches, and a simpler single gradient purification step. This protocol produces approximately 250&#8212;350 islets per mouse; and islets are suitable for various ex vivo studies. Possible caveats of this procedure are potentially damaged islets due to enzymatic digestion and/or prolonged gradient incubation, all of which can be largely avoided by careful ad justification of incubation time.

This islet isolation protocol described a novel route of collagenase injection to digest the exocrine tissue and a simplified gradient procedure to purify the islets from mice. It involves enzymatic digestion, gradient separation/purification, and islet hand-picking. Successful isolation can yield 250&#8211;350 high quality and fully functional islets per mouse.

Pancreatic islets, also called the Islets of Langerhans, are a cluster of endocrine cells which produces hormones for glucose regulation and other ...

Pancreatic Islet Embedding for Paraffin Sections

Experiments using isolated pancreatic islets are important for diabetes research, but islets are expensive and of limited abundance. Islets contain a mixed cell population in a structured architecture that impacts function, and human islets are widely variable in cell type composition. Current frequently used methods to study cultured islets include molecular studies performed on whole islets, lumping disparate islet cell types together, or microscopy or molecular studies on dispersed islet cells, disrupting islet architecture. For in vivo islet studies, paraffin-embedded pancreas sectioning is a powerful technique to assess cell-specific outcomes in the native pancreatic environment. Studying post-culture islets by paraffin sectioning would offer several advantages: detection of multiple outcomes on the same islets (potentially even the exact-same islets, using serial sections), cell-type-specific measurements, and maintaining native islet cell-cell and cell-substratum interactions both during experimental exposure and for analysis. However, existing techniques for embedding isolated islets post-culture are inefficient, time consuming, prone to loss of material, and generally produce sections with inadequate islet numbers to be useful for quantifying outcomes. Clinical pathology laboratory cell block preparation facilities are inaccessible and impractical for basic research laboratories. We have developed an improved, simplified bench-top method that generates sections with robust yield and distribution of islets. Fixed islets are resuspended in warm histological agarose gel and pipetted into a flat disc on a standard glass slide, such that the islets are distributed in a plane. After standard dehydration and embedding, multiple (10+) 4 - 5 &#181;m sections can be cut from the same islet block. Using this method, histological&#160;and immunofluorescent analyses can be performed on mouse, rat, and human islets. This is an effective, inexpensive, time-saving approach to assess cell-type-specific, intact-architecture outcomes from cultured islets.

Ex vivo pancreatic islet studies are important for diabetes research. Existing techniques to study cultured islets in their native 3-dimensional architecture are time consuming, inefficient, and infrequently used. This work describes a new, simple, and efficient method for generating high-quality paraffin sections of whole cultured islets.

Experiments using isolated pancreatic islets are important for diabetes research, but islets are expensive and of limited abundance. Islets contain a ...

Confocal Laser Scanning Microscopy of Calcium Dynamics in Acute Mouse Pancreatic Tissue Slices

The acute mouse pancreatic tissue slice is a unique in situ preparation with preserved intercellular communication and tissue architecture that entails significantly fewer preparation-induced changes than isolated islets, acini, ducts, or dispersed cells described in typical in vitro studies. By combining the acute pancreatic tissue slice with live-cell calcium imaging in confocal laser scanning microscopy (CLSM), calcium signals can be studied in a large number of endocrine and exocrine cells simultaneously, with a single-cell or even subcellular resolution. The sensitivity permits the detection of changes and enables the study of intercellular waves and functional connectivity as well as the study of the dependence of physiological responses of cells on their localization within the islet and paracrine relationship with other cells. Finally, from the perspective of animal welfare, recording signals from a large number of cells at a time lowers the number of animals required in experiments, contributing to the 3R-replacement, reduction, and refinement-principle.

We present the preparation of acute pancreatic tissue slices and their use in confocal laser scanning microscopy to study calcium dynamics simultaneously in a large number of live cells, over long time periods, and with high spatiotemporal resolution.

The acute mouse pancreatic tissue slice is a unique in situ preparation with preserved intercellular communication and tissue architecture that entails ...

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

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