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/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/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>

<ol>
	<li>Chen, L., Pan, X., Zhang, Y. H., Huang, T., Cai, Y. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Gene+Expression+Differences+between+Different+Pancreatic+Cells.">Analysis of Gene Expression Differences between Different Pancreatic Cells.</a> ACS Omega. 4 (4), 6421-6435 (2019).</li><li>Longnecker, D. S., Gorelick, F., Thompson, E. D., Histology, H. G., Beger, A. L., Warshaw, R. H., Hruban, M. W., Buchler, M. M., Lerch, J. P., Neoptolemos, T., Shimosegawa, D. C., Whitcomb, C., GroB, <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anatomy,+Histology,+and+Fine+Structure+of+the+Pancreas.">Anatomy, Histology, and Fine Structure of the Pancreas.</a> The Pancreas. , (2018).</li><li>Liao, E. 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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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 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 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...

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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 ...

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1.5K Views.  Universidad Autónoma Metropolitana. In this protocol, the pancreatic islets are reconstructed and analyzed using computational algorithms implemented in a dedicated multiplatform application.

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

A Microfluidic-based Hydrodynamic Trap for Single Particles

The ability to confine and manipulate single particles in free solution is a key enabling technology for fundamental and applied science. Methods for particle trapping based on optical, magnetic, electrokinetic, and acoustic techniques have led to major advancements in physics and biology ranging from the molecular to cellular level. In this article, we introduce a new microfluidic-based technique for particle trapping and manipulation based solely on hydrodynamic fluid flow. Using this method, we demonstrate trapping of micro- and nano-scale particles in aqueous solutions for long time scales. The hydrodynamic trap consists of an integrated microfluidic device with a cross-slot channel geometry where two opposing laminar streams converge, thereby generating a planar extensional flow with a fluid stagnation point (zero-velocity point). In this device, particles are confined at the trap center by active control of the flow field to maintain particle position at the fluid stagnation point. In this manner, particles are effectively trapped in free solution using a feedback control algorithm implemented with a custom-built LabVIEW code. The control algorithm consists of image acquisition for a particle in the microfluidic device, followed by particle tracking, determination of particle centroid position, and active adjustment of fluid flow by regulating the pressure applied to an on-chip pneumatic valve using a pressure regulator. In this way, the on-chip dynamic metering valve functions to regulate the relative flow rates in the outlet channels, thereby enabling fine-scale control of stagnation point position and particle trapping. The microfluidic-based hydrodynamic trap exhibits several advantages as a method for particle trapping. Hydrodynamic trapping is possible for any arbitrary particle without specific requirements on the physical or chemical properties of the trapped object. In addition, hydrodynamic trapping enables confinement of a "single" target object in concentrated or crowded particle suspensions, which is difficult using alternative force field-based trapping methods. The hydrodynamic trap is user-friendly, straightforward to implement and may be added to existing microfluidic devices to facilitate trapping and long-time analysis of particles. Overall, the hydrodynamic trap is a new platform for confinement, micromanipulation, and observation of particles without surface immobilization and eliminates the need for potentially perturbative optical, magnetic, and electric fields in the free-solution trapping of small particles.

In this article, we present a microfluidic-based method for particle confinement based on hydrodynamic flow. We demonstrate stable particle trapping at a fluid stagnation point using a feedback control mechanism, thereby enabling confinement and micromanipulation of arbitrary particles in an integrated microdevice.

The ability to confine and manipulate single particles in free solution is a key enabling technology for fundamental and applied science.  Methods for ...

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific simulations of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. The objective of this project was to develop a methodology to estimate cardiac fiber orientations from in vivo images of patient heart geometries. An accurate representation of ventricular geometry and fiber orientations was reconstructed, respectively, from high-resolution ex vivo structural magnetic resonance (MR) and diffusion tensor (DT) MR images of a normal human heart, referred to as the atlas. Ventricular geometry of a patient heart was extracted, via semiautomatic segmentation, from an in vivo computed tomography (CT) image. Using image transformation algorithms, the atlas ventricular geometry was deformed to match that of the patient. Finally, the deformation field was applied to the atlas fiber orientations to obtain an estimate of patient fiber orientations. The accuracy of the fiber estimates was assessed using six normal and three failing canine hearts. The mean absolute difference between inclination angles of acquired and estimated fiber orientations was 15.4 &deg;. Computational simulations of ventricular activation maps and pseudo-ECGs in sinus rhythm and ventricular tachycardia indicated that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in personalized diagnosis and decisions regarding electrophysiological interventions.

A methodology to estimate ventricular fiber orientations from in vivo images of patient heart geometries for personalized modeling is described. Validation of the methodology performed using normal and failing canine hearts demonstrate that that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.

Patient-specific  simulations of heart (dys)function aimed at personalizing cardiac therapy are hampered  by the absence of in vivo imaging technology for ...

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Cryo-electron tomography (cryoET) allows 3D visualization of cellular structures at molecular resolution in a close-to-physiological state1. However, direct visualization of individual viral complexes in their host cellular environment with cryoET is challenging2, due to the infrequent and dynamic nature of viral entry, particularly in the case of HIV-1. While time-lapse live-cell imaging has yielded a great deal of information about many aspects of the life cycle of HIV-13-7, the resolution afforded by live-cell microscopy is limited (~ 200 nm). Our work was aimed at developing a correlation method that permits direct visualization of early events of HIV-1 infection by combining live-cell fluorescent light microscopy, cryo-fluorescent microscopy, and cryoET. In this manner, live-cell and cryo-fluorescent signals can be used to accurately guide the sampling in cryoET. Furthermore, structural information obtained from cryoET can be complemented with the dynamic functional data gained through live-cell imaging of fluorescent labeled target.
In this video article, we provide detailed methods and protocols for structural investigation of HIV-1 and host-cell interactions using 3D correlative high-speed live-cell imaging and high-resolution cryoET structural analysis. HeLa cells infected with HIV-1 particles were characterized first by confocal live-cell microscopy, and the region containing the same viral particle was then analyzed by cryo-electron tomography for 3D structural details. The correlation between two sets of imaging data, optical imaging and electron imaging, was achieved using a home-built cryo-fluorescence light microscopy stage. The approach detailed here will be valuable, not only for study of virus-host cell interactions, but also for broader applications in cell biology, such as cell signaling, membrane receptor trafficking, and many other dynamic cellular processes.

We describe a correlative microscopy method that combines high-speed 3D live-cell fluorescent light microscopy and high-resolution cryo-electron tomography. We demonstrate the capability of the correlative method by imaging dynamic, small HIV-1 particles interacting with host HeLa cells.

Cryo-electron tomography (cryoET) allows 3D visualization of cellular structures at molecular resolution in a close-to-physiological state1. However, ...

Fluorescence-quenching of a Liposomal-encapsulated Near-infrared Fluorophore as a Tool for In Vivo Optical Imaging

Optical imaging offers a wide range of diagnostic modalities and has attracted a lot of interest as a tool for biomedical imaging. Despite the enormous number of imaging techniques currently available and the progress in instrumentation, there is still a need for highly sensitive probes that are suitable for in vivo imaging. One typical problem of available preclinical fluorescent probes is their rapid clearance in vivo, which reduces their imaging sensitivity. To circumvent rapid clearance, increase number of dye molecules at the target site, and thereby reduce background autofluorescence, encapsulation of the near-infrared fluorescent dye, DY-676-COOH in liposomes and verification of its potential for in vivo imaging of inflammation was done. DY-676 is known for its ability to self-quench at high concentrations. We first determined the concentration suitable for self-quenching, and then encapsulated this quenching concentration into the aqueous interior of PEGylated liposomes. To substantiate the quenching and activation potential of the liposomes we use a harsh freezing method which leads to damage of liposomal membranes without affecting the encapsulated dye. The liposomes characterized by a high level of fluorescence quenching were termed Lip-Q. We show by experiments with different cell lines that uptake of Lip-Q is predominantly by phagocytosis which in turn enabled the characterization of its potential as a tool for in vivo imaging of inflammation in mice models. Furthermore, we use a zymosan-induced edema model in mice to substantiate the potential of Lip-Q in optical imaging of inflammation in vivo. Considering possible uptake due to inflammation-induced enhanced permeability and retention (EPR) effect, an always-on liposome formulation with low, non-quenched concentration of DY-676-COOH (termed Lip-dQ) and the free DY-676-COOH were compared with Lip-Q in animal trials.

Fluorescence-quenching of a Liposomal-encapsulated Near-infrared Fluorophore as a Tool for In Vivo Optical Imaging

The use of fluorophores for in vivo imaging can be greatly limited by opsonization, rapid clearance, low detection sensitivity and cytotoxic effects on the host. Encapsulation of fluorophores in liposomes by film hydration and extrusion leads to fluorescence quenching and protection which enables in vivo imaging with high detection sensitivity.

Optical imaging offers a wide range of diagnostic modalities and has attracted a lot of interest as a tool for biomedical imaging. Despite the enormous ...

The Synthesis of RGD-functionalized Hydrogels as a Tool for Therapeutic Applications

The use of polymers as biomaterials has provided significant advantages in therapeutic applications. In particular, the possibility to modify and functionalize polymer chains with compounds that are able to improve biocompatibility, mechanical properties, or cell viability allows the design of novel materials to meet new challenges in the biomedical field. With the polymer functionalization strategies, click chemistry is a powerful tool to improve cell-compatibility and drug delivery properties of polymeric devices. Similarly, the fundamental need of biomedicine to use sterile tools to avoid potential adverse-side effects, such as toxicity or contamination of the biological environment, gives rise to increasing interest in the microwave-assisted strategy.
The combination of click chemistry and the microwave-assisted method is suitable to produce biocompatible hydrogels with desired functionalities and improved performances in biomedical applications. This work aims to synthesize RGD-functionalized hydrogels. RGD (arginylglycylaspartic acid) is a tripeptide that can mimic cell adhesion proteins and bind to cell-surface receptors, creating a hospitable microenvironment for cells within the 3D polymeric network of the hydrogels. RGD functionalization occurs through Huisgen 1,3-dipolar cycloaddition. Some PAA carboxyl groups are modified with an alkyne moiety, whereas RGD is functionalized with azido acid as the terminal residue of the peptide sequence. Finally, both products are used in a copper catalyzed click reaction to permanently link the peptide to PAA. This modified polymer is used with carbomer, agarose and polyethylene glycol (PEG) to synthesize a hydrogel matrix. The 3D structure is formed due to an esterification reaction involving carboxyl groups from PAA and carbomer and hydroxyl groups from agarose and PEG through microwave-assisted polycondensation. The efficiency of the gelation mechanism ensures a high degree of RGD functionalization. In addition, the procedure to load therapeutic compounds or biological tools within this functionalized network is very simple and reproducible.

We present a protocol for the synthesis of RGD-functionalized hydrogels as devices for cell and drug delivery. The procedure involves copper catalyzed alkyne-azide cycloaddition (CuAAC) between alkyne-modified polyacrylic acid (PAA) and a RGD-azide derivative. The hydrogels are formed using microwave-assisted polycondensation and their physicochemical properties are investigated.

The use of polymers as biomaterials has provided significant advantages in therapeutic applications. In particular, the possibility to modify and ...

Production of Metal Nanoparticles by Pulsed Laser-ablation in Liquids: A Tool for Studying the Antibacterial Properties of Nanoparticles

The emergence of multidrug-resistant bacteria is a global clinical concern leading some to speculate about our return to a "pre-antibiotics" era of medicine. In addition to efforts to identify novel small-molecule antimicrobial drugs, there has been great interest in the use of metal nanoparticles as coatings for medical devices, wound dressings, and consumer packaging, due to their antimicrobial properties. The wide variety of methods available for nanoparticle synthesis results in a broad spectrum of chemical and physical properties which can affect antibacterial efficacy. This manuscript describes the pulsed laser-ablation in liquids (PLAL) method to create nanoparticles. This approach allows for the fine tuning of nanoparticle size, composition, and stability using post-irradiation methods as well as the addition of surfactants or volume excluders. By controlling particle size and composition, a large range of physical and chemical properties of metal nanoparticles can be explored which may contribute to their antimicrobial efficacy thereby opening new avenues for antibacterial development.

The antimicrobial properties of metals such as copper and silver have been recognized for centuries. This protocol describes pulsed laser-ablation in liquids, a method of synthesizing metal nanoparticles that provides the ability to fine tune the properties of these nanoparticles to optimize their antimicrobial effects.

The emergence of multidrug-resistant bacteria is a global clinical concern leading some to speculate about our return to a "pre-antibiotics" era of ...

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers

Mechanical properties of complex, polymer-based soft matter, such as cells or biopolymer networks, can be understood in neither the classical frame of flexible polymers nor of rigid rods. Underlying filaments remain outstretched due to their non-vanishing backbone stiffness, which is quantified via the persistence length (lp), but they are also subject to strong thermal fluctuations. Their finite bending stiffness leads to unique, non-trivial collective mechanics of bulk networks, enabling the formation of stable scaffolds at low volume fractions while providing large mesh sizes. This underlying principle is prevalent in nature (e.g., in cells or tissues), minimizing the high molecular content and thereby facilitating diffusive or active transport. Due to their biological implications and potential technological applications in biocompatible hydrogels, semiflexible polymers have been subject to considerable study. However, comprehensible investigations remained challenging since they relied on natural polymers, such as actin filaments, which are not freely tunable. Despite these limitations and due to the lack of synthetic, mechanically tunable, and semiflexible polymers, actin filaments were established as the common model system. A major limitation is that the central quantity lp cannot be freely tuned to study its impact on macroscopic bulk structures. This limitation was resolved by employing structurally programmable DNA nanotubes, enabling controlled alteration of the filament stiffness. They are formed through tile-based designs, where a discrete set of partially complementary strands hybridize in a ring structure with a discrete circumference. These rings feature sticky ends, enabling the effective polymerization into filaments several microns in length, and display similar polymerization kinetics as natural biopolymers. Due to their programmable mechanics, these tubes are versatile, novel tools to study the impact of lp on the single-molecule as well as the bulk scale. In contrast to actin filaments, they remain stable over weeks, without notable degeneration, and their handling is comparably straightforward.

Semiflexible polymers display unique mechanical properties that are extensively applied by living systems. However, systematic studies on biopolymers are limited since properties such as polymer rigidity are inaccessible. This manuscript describes how this limitation is circumvented by programmable DNA nanotubes, enabling experimental studies on the impact of filament rigidity.

Mechanical properties of complex, polymer-based soft matter, such as cells or biopolymer networks, can be understood in neither the classical frame of ...

The Organoid Reconstitution Assay (ORA) for the Functional Analysis of Intestinal Stem and Niche Cells

The intestinal epithelium is characterized by an extremely rapid turnover rate. In mammals, the entire epithelial lining is renewed within 4 - 5 days. Adult intestinal stem cells reside at the bottom of the crypts of Lieberk&#252;hn, are earmarked by expression of the Lgr5 gene, and preserve homeostasis through their characteristic high proliferative rate1. Throughout the small intestine, Lgr5+ stem cells are intermingled with specialized secretory cells called Paneth cells. Paneth cells secrete antibacterial compounds (i.e., lysozyme and cryptdins/defensins) and exert a controlling role on the intestinal flora. More recently, a novel function has been discovered for Paneth cells, namely their capacity to provide niche support to Lgr5+ stem cells through several key ligands as Wnt3, EGF, and Dll12.
When isolated ex vivo and cultured in the presence of specific growth factors and extracellular matrix components, whole intestinal crypts give rise to long-lived and self-renewing 3D structures called organoids that highly resemble the crypt-villus epithelial architecture of the adult small intestine3. Organoid cultures, when established from whole crypts, allow the study of self-renewal and differentiation of the intestinal stem cell niche, though without addressing the contribution of its individual components, namely the Lgr5+ and Paneth cells.
Here, we describe a novel approach to the organoid assay that takes advantage of the ability of Paneth and Lgr5+ cells to associate and form organoids when co-cultured. This approach, here referred to as &#34;organoid reconstitution assay&#34; (ORA), allows the genetic and biochemical modification of Paneth or Lgr5+ stem cells, followed by reconstitution into organoids. As such, it allows the functional analysis of the two main components of the intestinal stem cell niche.

The Organoid Reconstitution Assay ORA for the Functional Analysis of Intestinal Stem and Niche Cells

Intestinal organoid cultures are established from whole crypts and do not allow the analysis of self-renewal and differentiation in a cell-specific fashion. This protocol describes reconstitution of sorted stem (Lgr5+) and niche (Paneth) cells, which give rise to organoids while enabling their prior biochemical and genetic modification and functional analysis.

The intestinal epithelium is characterized by an extremely rapid turnover rate. In mammals, the entire epithelial lining is renewed within 4 - 5 days. ...

Surface Engineering of Pancreatic Islets with a Heparinized StarPEG Nanocoating

Cell surface engineering can protect implanted cells from host immune attack. It can also reshape cellular landscape to improve graft function and survival post-transplantation. This protocol aims to achieve surface engineering of pancreatic islets using an ultrathin heparin-incorporated starPEG (Hep-PEG) nanocoating. To generate the Hep-PEG nanocoating for pancreatic islet surface engineering, heparin succinimidyl succinate (Heparin-NHS) was first synthesized by modification of its carboxylate groups using N-(3-dimethylamino propyl)-N'-ethyl carbodiimide hydrochloride (EDC) and N-hydroxysuccinimide (NHS). The Hep-PEG mixture was then formed by crosslinking of the amino end-functionalized eight-armed starPEG (starPEG-(NH2)8) and Heparin-NHS. For islet surface coating, mouse islets were isolated via collagenase digestion and gradient purification using Histopaque. Isolated islets were then treated with ice cold Hep-PEG solution for 10 min to allow covalent binding between NHS and the amine groups of islet cell membrane. Nanocoating with the Hep-PEG incurs minimal alteration to islet size and volume and heparinization of the islets with Hep-PEG may also reduce instant blood-mediated inflammatory reaction during islet transplantation. This "easy-to-adopt" approach is mild enough for surface engineering of living cells without compromising cell viability. Considering that heparin has shown binding affinity to multiple cytokines, the Hep-PEG nanocoating also provides an open platform that enables incorporation of unlimited functional biological mediators and multi-layered surfaces for living cell surface bioengineering.

This protocol aims to achieve surface engineering of pancreatic islets using a heparin-incorporated starPEG nanocoating via pseudo-bioorthogonal chemistry between the N-hydroxysuccinimide groups of the nanocoating and the amine groups of islet cell membrane.

Cell surface engineering can protect implanted cells from host immune attack. It can also reshape cellular landscape to improve graft function and ...

Tissue-Engineered Graft for Circumferential Esophageal Reconstruction in Rats

The use of biocompatible materials for circumferential esophageal reconstruction is a technically challenging task in rats and requires an optimal implant technique with nutritional support. Recently, there have been many attempts at esophageal tissue engineering, but the success rate has been limited due to difficulty in early epithelization in the special environment of peristalsis. Here, we developed an artificial esophagus that can improve the regeneration of the esophageal mucosa and muscle layers through a two-layered tubular scaffold, a mesenchymal stem cell-based bioreactor system, and a bypass feeding technique with modified gastrostomy. The scaffold is made of polyurethane (PU) nanofibers in a cylindrical shape with a three-dimensional (3D) printed polycaprolactone strand wrapped around the outer wall. Prior to transplantation, human-derived mesenchymal stem cells were seeded into the lumen of the scaffold, and bioreactor cultivation was performed to enhance cellular reactivity. We improved the graft survival rate by applying surgical anastomosis and covering the implanted prosthesis with a thyroid gland flap, followed by temporary nonoral gastrostomy feeding. These grafts were able to recapitulate the findings of initial epithelialization and muscle regeneration around the implanted sites, as demonstrated by histological analysis. In addition, increased elastin fibers and neovascularization were observed in the periphery of the graft. Therefore, this model presents a potential new technique for circumferential esophageal reconstruction.

Esophageal reconstruction is a challenging procedure, and development of a tissue-engineered esophagus that enables regeneration of esophageal mucosa and muscle and that can be implanted as an artificial graft is necessary. Here, we present our protocol to generate an artificial esophagus, including scaffold manufacturing, bioreactor cultivation, and various surgical techniques.

The use of biocompatible materials for circumferential esophageal reconstruction is a technically challenging task in rats and requires an optimal implant ...