Name: computational reconstruction of pancreatic islets as a tool for structural and functional analysis
Uploaded: 2022-03-09T13:30:00
Description: Watch this Scientific Journal Video about Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis at JoVE.com

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 or...

<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. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Connexin+36,+a+key+element+in+pancreatic+beta+cell+function.">Connexin 36, a key element in pancreatic beta cell function.</a> Neuropharmacology. 75, 557-566 (2013).</li><li>Briant, 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=&#948;-cells+and+&#946;-cells+are+electrically+coupled+and+regulate+&#945;-cell+activity+via+somatostatin.">&#948;-cells and &#946;-cells are electrically coupled and regulate &#945;-cell activity via somatostatin.</a> The Journal of Physiology. 596 (2), 197-215 (2018).</li><li>Arrojoe Drigo, 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=New+insights+into+the+architecture+of+the+islet+of+Langerhans:+a+focused+cross-species+assessment.">New insights into the architecture of the islet of Langerhans: a focused cross-species assessment.</a> Diabetologia. 58 (10), 2218-2228 (2015).</li><li>Cabrera, 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=The+unique+cytoarchitecture+of+human+pancreatic+islets+has+implications+for+islet+cell+function.">The unique cytoarchitecture of human pancreatic islets has implications for islet cell function.</a> Proceedings of the National Academy of Sciences of the United States of America. 103 (7), 2334-2339 (2006).</li><li>Folli, F., 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=Pancreatic+islet+of+Langerhans'+cytoarchitecture+and+ultrastructure+in+normal+glucose+tolerance+and+in+type+2+diabetes+mellitus.">Pancreatic islet of Langerhans' cytoarchitecture and ultrastructure in normal glucose tolerance and in type 2 diabetes mellitus.</a> Diabetes, Obesity &amp; Metabolism. 20, 137-144 (2018).</li><li>Kilimnik, G., 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=Altered+islet+composition+and+disproportionate+loss+of+large+islets+in+patients+with+type+2+diabetes.">Altered islet composition and disproportionate loss of large islets in patients with type 2 diabetes.</a> PloS One. 6 (11), 27445 (2011).</li><li>Hoang, D. T., 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+Conserved+Rule+for+Pancreatic+Islet+Organization.">A Conserved Rule for Pancreatic Islet Organization.</a> PloS One. 9 (10), 110384 (2014).</li><li>Hoang, D. T., Hara, M., Jo, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+Principles+of+Pancreatic+Islets:+Glucose-Dependent+Coordination+of+Hormone+Pulses.">Design Principles of Pancreatic Islets: Glucose-Dependent Coordination of Hormone Pulses.</a> PloS One. 11 (4), 0152446 (2016).</li><li>Brissova, 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=Assessment+of+human+pancreatic+islet+architecture+and+composition+by+laser+scanning+confocal+microscopy.">Assessment of human pancreatic islet architecture and composition by laser scanning confocal microscopy.</a> The Journal of Histochemistry and Cytochemistry: Official Journal of the Histochemistry Society. 53 (9), 1087-1097 (2005).</li><li>F&#233;lix-Martinez, G. J., 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=Mathematical+models+of+electrical+activity+of+the+pancreatic+&#946;-cell:+a+physiological+review.">Mathematical models of electrical activity of the pancreatic &#946;-cell: a physiological review.</a> Islets. 6 (3), 949195 (2014).</li><li>F&#233;lix-Mart&#237;nez, G. J., Gonz&#225;lez-V&#233;lez, V., God&#237;nez-Fern&#225;ndez, J. R., Gil, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electrophysiological+models+of+the+human+pancreatic+&#948;-cell:+From+single+channels+to+the+firing+of+action+potentials.">Electrophysiological models of the human pancreatic &#948;-cell: From single channels to the firing of action potentials.</a> International Journal for Numerical Methods in Biomedical Engineering. 36 (2), 3296 (2020).</li><li>Watts, M., 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=Modeling+the+pancreatic+&#945;-cell:+dual+mechanisms+of+glucose+suppression+of+glucagon+secretion.">Modeling the pancreatic &#945;-cell: dual mechanisms of glucose suppression of glucagon secretion.</a> Biophysical Journal. 106 (3), 741-751 (2014).</li><li>Lei, C. 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. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IsletLab:+an+application+to+reconstruct+and+analyze+islet+architectures.">IsletLab: an application to reconstruct and analyze islet architectures.</a> Islets. 14 (1), 36-39 (2022).</li><li>F&#233;lix-Mart&#237;nez, G. J., 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=Comparative+analysis+of+reconstructed+architectures+from+mice+and+human+islets.">Comparative analysis of reconstructed architectures from mice and human islets.</a> Islets. 14 (1), 23-35 (2022).</li></ol>

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...

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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 i...

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 ...

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.

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