The study is supported by US Public Health Service Grant DK-081527, DK-072473 and DK-20595 to the University of Chicago Diabetes Research and Training Center (Animal Models Core), and a gift from the Kovler Family Foundation.

1. Creating Virtual Slices of Immunohistochemically Stained Images
<ol>
<li>Open StereoInvestigator. Place the clean slide holding the sample in the microscope holder and visualize it in Stereo Investigator by clicking on "Acquisition" and then "Live Image" (Acquisition&#8594; Live Image). Determine exposure levels for each channel; in our specific example, Channel 2 is used for DAPI, Channel 3 for GFP, Channel 4 for RFP, Channel 5 for Cy5, and Channel 6 for Cy7. Use the "Video Histogram" window, which displays the intensity of the fluorescence, to determine the exposure level. Appropriate fluorescence intensities are reached when the intensity tails off at the right end of the Video Histogram. Exposure levels can be changed in the "Camera Settings" window. The Video Histogram can be focused with the microscope focus wheel.</li>
<li>Before creating a Virtual Slice, contour the sample by clicking on the screen at a point away from the sample, which will mark a reference point, and then clicking around the outline of the sample. When the contour is complete, right click and select "Close Contour" to connect the beginning and end points.</li>
<li>Once the contour is closed, capture a Virtual Slice for the sample (Acquisition&#8594; Acquire Virtual Slice). When the Virtual Slice window options open, select "High Speed Acquire" as well as automatic focusing by unchecking the box next to "Manual." Save the file.</li>
<li>The virtual slice prefocus must be calibrated by selecting (right click&#8594; Add to Focus Site List) and manually focusing several random sections, i.e., sites, in the Virtual Slice preview. When several sites have been focused, start the Virtual Slice (right click&#8594; Start Virtual Slice with Prefocus). After the Virtual Slice for the first sample is complete, switch to the next channel and adjust the exposure accordingly. Repeat steps 4 and 5 for each channel.</li>
</ol>
2. Computer-assisted Two-Dimensional Analysis 
Quantification of Islets
<ol>
<li>The images are then processed using an ImageJ macro named "IHCVS," which first prepares loaded images for analysis and then quantifies features of islets such as cellular composition (i.e. each area of beta-, alpha-, and delta-cell populations, and islet area at the sum value). The stack is split into its separate channels in ImageJ, where each individual channel shows a separate image based on the immunohistochemical staining. Each image is then automatically thresholded, after which an 8-bit black and white image, or a mask, of the thresholded image is produced. The separate channel images are then added together arithmetically into a composite mask, and ImageJ's built-in particle analysis is performed on the composite image.</li>
<li>ROIs are then identified while excluding particles smaller than one single beta cell (&lt;170 &mu;m2; area calculated using a diameter of ~15 &mu;m; Hara et al., 2003). The resultant ROIs are analyzed and quantified using ImageJ; quantification can include perimeter (a distance surrounding an area), circularity (a degree of roundness where the number 1.0 depicts a perfect circle), Feret's diameter (the longest distance within an area) and center coordinates for each analyzed region so that the islet distribution can be mathematically analyzed using various combinations of these parameters and plotted in a 3D graph, the data of which will be used for mathematical modeling. A single virtual slice image is analyzed in 30 seconds. Multiple images can be analyzed by embedding the image processing and analysis script into a loop syntax supported by ImageJ macro language. The loop opens all the files in a given directory, analyzes the images, and outputs results into a new location as an Excel file.</li>
<li>The aggregated results are saved into an Excel spreadsheet, storing data such as area, perimeter, circularity, Feret's diameter, and islet center for each islet, along with corresponding numbers labeled in the image.</li>
</ol>
Computational Analysis and Histogram Setup
<ol>
<li>Mathematica scripts, which run automatically once started, are used to process the spreadsheet data collected, amounting to hundreds of thousands of entries. One of the critical processes that are run is a frequency analysis, which constructs a histogram used to examine size distribution and also to determine outliers in the data. Under the script, total islet area is measured and compared to other parameters, particularly to time points or the age of the pancreas, and then accordingly displayed in a graph.</li>
</ol>
3. Three-Dimensional Reconstruction of Two Dimensional Immunohistochemically Stained Virtual Slice Images 
3D Reconstruction of Virtual Slices
<ol>
<li>Copy the script ("im_jp2_2_tiff") into the directory (i.e., folder) that contains the jp2 images. Run the script with the Linux shell by typing "./im_jp2_2_tiff" into the console and then pressing enter. Running this script will automatically convert all of the jp2 images in the directory into tiff files, which is the format used in the construction and quantification of three-dimensional stacks from the Virtual Slices. (Note: Type in "chmod+x im_jp2_2_tiff" if the script does not run, which will convert the text file into a script so that it can run properly.)</li>
<li>When all of the captured images have been converted from jp2 to tiff files, they are ready to be used for analysis in ImageJ.</li>
<li>With ImageJ, open all of the images for the first sample (five images in this case: DAPI, GFP, RFP, Cy5, Cy7) and merge them as one image (Image&#8594; Color&#8594; Merge Channels). Repeat this process for each of the samples. When all of the samples have been combined as single images, clean the images by selecting unwanted regions with the "Polygon Selections" tool and filling them in (Edit&#8594; Fill). (Resize the images to a smaller size, if necessary). Then convert each image into an RGB color image (Image&#8594; Type&#8594; RGB color). Finally, create a stack out of the images (Image&#8594; Stacks&#8594; Image to Stacks), after which they can further be aligned with the "Stack Reg" plugin, if necessary. Ultimately, merging the channels and then combining the images creates a 3D montage of all of the islets in the entire pancreas. To view the image, click on "Plugins" and then "3D viewer."</li>
</ol>
4. Islet Mapping
Collecting Image Stacks
<ol>
<li>Situate the whole mount pancreatic islets under the microscope.</li>
<li>Open the Slidebook software. Access the "Capture and Focus Control" by pressing "Ctrl+Shift+E." In the "Focus Control" window, set the objective to 20X or 40X, depending on the size of the islet. Apply water if using a water immersion objective lens.</li>
<li>In order to use the microscope eyepiece to locate islets, set "Bin" to 2X and set the Filter Set to User1 in the "Focus Controls" window. "Emission Selection" should be set to 100% Eyes and Neutral Density to 1. Click "GFP ey" and then "Open Fluor."</li>
<li>To visualize the sample in Slidebook, return to the Focus Controls window and switch "Filter Set" to "Live" and click "GFP ds." Use the joystick to center the islet in the Camera 1 window as well as possible. Focus to a depth somewhere near the center of the islet, where the cells can be discerned easily.</li>
<li>In the "Focus Controls Window, " select the "Z tab." Click set in the upper left corner of the window to set the current depth as a reference point. Use the scope control to scroll to the uppermost depth of the islet in which features can be discerned clearly and click "Set Top." Scroll to the bottom of the islet and click "Set Bottom." Click "Go" to return to the reference point.</li>
<li>In the Capture window, set "Bin Factor" to "2X" and set "Filter Set" to "Live." Set the "Capture Type" to "3D" (and only 3D). On the right side of the window, click "Use Top and Bottom Positions." Click "Return to Reference Point After Capture." Set the "Step Size" to "3."</li>
<li>Beneath the "Filter Set Box," select DAPI dsu, GFP dsu, RFP dsu, and Cy5 Dsu. For each one, click "Find Best" under "Adjust Exposure," then click "Test" to make sure that the selected exposure is suitable. Click "Start" to begin the capture.</li>
<li>When the capture is completed, adjust the levels as necessary and change the displayed color for RFP to white. Save the slide and go to View&#8594;Export&#8594;TIFF series. Type in the name of the slide with a dash at the end and save it. Dozens of individual TIFF files are created, so it may be helpful to keep each islet's image stack in a separate folder.</li>
</ol>
Mapping Image Stacks
<ol>
<li>Open Stereo Investigator. Visualize the image by opening the image stacks (File&#8594; Image Stack &#8594; Image Stack Open). In the "Image Scaling menu," set "Distance Between Images" to 3.00 &mu;m. To correct for 2X binning in Slidebook, select "Override X and Y Scaling" and "User Specified for Source of X and Y" Scaling, and enter 0.65 for both X and Y.</li>
<li>Center the image by clicking in the middle of the islet of interest in the "Macro window." Mark at least one cell using the appropriate marker. Beta-cells will appear green, alpha cells will appear red, and delta cells will appear white; use Marker 2 (a hollow circle) to mark beta-cells, Marker 5 (a hollow triangle) to mark alpha cells, and Marker 6 (a hollow triangle) to delta cells. Cells should be marked in the center of their nucleus, which will appear blue if the sample has been stained with DAPI.</li>
<li>Once at least one cell has been marked, display the "Orthogonal View" using the icon in the top menu. Check "Z-Filter" and "Symmetric." The range should be set to 15.00. Starting at the 0.0 Z level, scroll through the islet using the mouse wheel and mark each cell with the appropriate marker. Each cell should be marked only once at the center of its depth. Upon finishing marking each of the Z-levels, the "Z-filter" checkbox can be unchecked. This will show all of the markers from all of the Z-levels.</li>
<li>When every cell has been marked, save the file as a "DAT" file, then go to File&#8594; Export Tracing. Save the tracing as a "TXT" file. Click "New Data File" to clear the workspace before attempting to map new islets.</li>
</ol>
5. Representative Results:
The preparation of Virtual Slices out of an immunohistochemically stained pancreas sample enables one to examine all of the endocrine cells (alpha, beta, and delta-cells) in the whole pancreas, both together as islets (Figure 1A) and individually in separate channels (Figure 1B). With the assistance of computer programs and scripts, a mass analysis of large-scale data can be performed on these Virtual Slices. Specifically, a particle analysis of composite masks (Figure 1C) is output as a statistics table containing such parameters as islet area, perimeter (the distance surrounding an area), circularity (a degree of roundness, where 1.0 represents a perfect circle), and Feret's diameter (the longest distance within an area) for each islet detected (Figure 1D). The large-scale analysis of these images results in the production of total islet number and size distribution histograms as well as a detailed comparison of alpha-, beta-, and delta-cell areas. In addition, each Virtual Slice is taken at a depth of approximately 5 &mu;m, and all of the individual 2D Virtual Slices are further stacked to create a 3D reconstruction of the entire pancreatic sample. Islet mapping demonstrates another instance not only of capturing islets in 3D, but also of detailed computer-assisted analysis. Islet mapping consists of the capture of distinct islets (Figure 2A) and the subsequent marking of alpha-, beta-, and delta-cells at various Z-planes (Figure 2B) to visualize an islet in 3D (Figure 2C, D). Automated mathematical analysis of mapped islets displays their cellular composition and architecture, including cell-cell distances (Figure 2E) and cumulative probabilities of cell-cell distance distributions (Figure 2F).

 <img src="/files/ftp_upload/2471/2471fig1.jpg" alt="Figure 1" /> 
Figure 1. Large-scale capture and analysis of islet distribution using Virtual Slice. A. Virtual slice view of a human pancreatic section. a. Immunohistochemical staining for insulin (green), glucagon (red), somatostatin (white) and DAPI (blue). b. Converted 8-bit mask after automatic thresholding. A boxed area is magnified in B. B. Views of each channel. a. delta-cells, b. beta-cells, c. alpha-cells, and d. merged composite image. C. Particle analysis performed upon composite mask. Note that each islet structure including small cell clusters is numbered (blue highlight). D. Statistics table of various parameters measured for individual structures, which have IDs that correspond to tags shown in C.
 <img src="/files/ftp_upload/2471/2471fig2.jpg" alt="Figure 1" /> 
Figure 2. Analysis of Immunohistochemical Virtual Slice. A. 3D scatter plot of Figure 1 showing size and shape distribution of each islet by parameters such as area, circularity and feret's diameter. B. 3D scatter plot of Figure 1 showing cellular islet composition and size. C. Islet size distribution of the whole human section analysis from Figure 1 fitted to a lognormal probability density distribution. D. Mathematical analysis of cellular composition ratios (beta-cells in green, alpha-cells in red and delta-cells in blue) for each islet effective diameter bin of Figure 1. E. a. Islet size distribution of random sampling immunohistochemical analysis (left). Islet size distribution of virtual slice analysis (right). b. Log-normal plot comparison of random sampling immunohistochemical analysis (red) and virtual slice (blue).
 <img src="/files/ftp_upload/2471/2471fig3.jpg" alt="Figure 3" /> 
Figure 3. Islet mapping and mathematical analysis of cellular composition and architecture. A: Screen-capture showing a single focal plane from a 3D reconstructed stack of images of a human islet uploaded into Stereo-Investigator (beta-cells in green, alpha-cells in red, delta-cells in white, and nuclei in blue). B: Fluorescence images (left) and corresponding mapped data (right) in three different focal planes shown at an interval of 10 &mu;m. C: Representative view of 3D reconstructed islet mapping data. D: 3D reconstruction of the quarter-sliced islet based on the coordinates obtained by islet mapping. E: Mathematical analysis of cellular composition and architecture. Left. Relative frequency of cell-cell distances between two cells in a single cell population. Right. Relative frequency of cell-cell distances between two different cell populations. F: Kolmogorov-Smirnov (K-S) test. Left. Cumulative probabilities of cell-to-cell distance distributions for alpha-to-alpha, beta-to-beta, and delta-to-delta cells. Right. K-S distances for the corresponding three cumulative probabilities.

<ol>
	<li>Steiner, D. J., Kim, A., Miller, K., Hara, M. <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+plasticity+-+Interspecies+comparison+of+islet+architecture+and+composition.">Pancreatic islet plasticity - Interspecies comparison of islet architecture and composition.</a> ISLETS. 2, 135-145 (2010).</li><li>Kim, A., Miller, K., Jo, J., Wojcik, P. l., Kilimnik, G., Hara, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Islet+architecture+-+a+comparative+study.">Islet architecture - a comparative study.</a> ISLETS. 1, 129-136 (2009).</li><li>Kilimnik, G., Kim, A., Jo, J., Miller, K., Hara, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+pancreatic+islet+distribution+in+situ+in+mice.">Quantification of pancreatic islet distribution in situ in mice.</a> Am J Physiol Endocrinol Metab. 297, E1331-E1338 (2009).</li><li>Hara, M., Dizon, R. F., Glick, B. S., Lee, C. S., Kaestner, K. H., Piston, D. W., Bindokas, V. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+pancreatic+beta-cells+in+the+intact+pancreas.">Imaging pancreatic beta-cells in the intact pancreas.</a> Am J Physiol Endocrinol Metab. 290, E1041-E1047 (2006).</li><li>Hara, M., Wang, X., Kawamura, T., Bindokas, V. P., Dizon, R. F., Alcoser, S. Y., Magnuson, M. A., Bell, G. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transgenic+mice+with+green+fluorescent+protein-labeled+pancreatic+beta+-cells.">Transgenic mice with green fluorescent protein-labeled pancreatic beta -cells.</a> Am J Physiol Endocrinol Metab. 284, E177-E183 (2003).</li></ol>

No conflicts of interest declared.

The computer-assisted large-scale visualization and quantification presented here afford four key points in pancreatic islet studies: (1) A large-scale analysis of pancreatic specimens provides a comprehensive view of overall islet size distribution and islet architecture. (2) The 3D reconstruction and mathematical analysis of cellular composition and architecture further facilitate the examination of the spatial arrangement of endocrine cells within an islet. (3) Striking islet plasticity among different species and in ...

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">	
		
		<div>
		<table border="1">
		<thead>
		<tr>
		<th>Material Name</th>
		<th>Type</th>
		<th>Company</th>
		<th>Catalogue Number</th>
		<th>Comment</th>
		</tr>
		</thead>
		<tbody>
		
<tr>
<td>Fluorescent microscope</td>
<td>Microscope</td>
<td>Olympus </td>
<td>IX-81
</td>
<td>&nbsp;</td>
<tr>
<td>Stereo Investigator</td>
<td>Program</td>
<td>MicroBrightField</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>MIP-GFP mice</td>
<td>Mice</td>
<td>Jackson Laboratory</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>Mathematica</td>
<td>Program</td>
<td>Wolfram</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>Image J</td>
<td>Program</td>
<td>NIH</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>Slidebook</td>
<td>&nbsp;</td>
<td>Program</td>
<td>Olympus</td>
<td>&nbsp;</td>		</tbody>
		</table>
		</div>

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.

Watch this Scientific Journal Video about Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture at JoVE.com

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

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), ...

computer-assisted-large-scale-visualization-quantification-pancreatic

Research

JoVE Journal

Biology

12.2K Görüntüleme.  University of Chicago. Immünohistokimyasal lekeli pankreas örneklerin büyük ölçekli satın alma ve analiz Roman bilgisayar destekli yöntemler açıklanmıştır: (1) Sanal Dilim yakalama bölümün tamamını, büyük ölçekli veri (2) Kütle analiz 2D Sanal Dilimleri (3) İmar (4) 3D adacık haritalama ve (5) matematiksel analizi.

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

Large-Scale Screens of Metagenomic Libraries

Metagenomic libraries archive large fragments of contiguous genomic sequences from microorganisms without requiring prior cultivation. Generating a streamlined procedure for creating and screening metagenomic libraries is therefore useful for efficient high-throughput investigations into the genetic and metabolic properties of uncultured microbial assemblages. Here, key protocols are presented on video, which we propose is the most useful format for accurately describing a long process that alternately depends on robotic instrumentation and (human) manual interventions. First, we employed robotics to spot library clones onto high-density macroarray membranes, each of which can contain duplicate colonies from twenty-four 384-well library plates. Automation is essential for this procedure not only for accuracy and speed, but also due to the miniaturization of scale required to fit the large number of library clones into highly dense spatial arrangements. Once generated, we next demonstrated how the macroarray membranes can be screened for genes of interest using modified versions of standard protocols for probe labeling, membrane hybridization, and signal detection. We complemented the visual demonstration of these procedures with detailed written descriptions of the steps involved and the materials required, all of which are available online alongside the video.

Metagenomic libraries archive large fragments of contiguous genomic sequences from microorganisms without requiring prior cultivation.  Generating a ...

Murine Pancreatic Islet Isolation

Human Pancreatic Islet Isolation: Part I: Digestion and Collection of Pancreatic Tissue

Management of Type 1 diabetes is burdensome, both to the individual and society, costing over 100 billion dollars annually. Despite the widespread use of glucose monitoring and new insulin formulations, many individuals still develop devastating secondary complications. Pancreatic islet transplantation can restore near normal glucose control in diabetic patients 1, without the risk of serious hypoglycemic episodes that are associated with intensive insulin therapy. Providing sufficient islet mass is important for successful islet transplantation. However, donor characteristic, organ procurement and preservation affect the isolation outcome 2. At University of Illinois at Chicago (UIC) we have developed a successful isolation protocol with an improved purification gradient 3. The program started in January 2004, and more than 300 isolations were performed up to November 2008. The pancreata were sent in cold preservation solutions (UW, University of Wisconsin or HTK, Histidine-Tryptophan Ketoglutarate) 4-7 to the Cell Isolation Laboratory at UIC for islet isolation. Pancreatic islets were isolated using the UIC method, which is a modified version of the method originally described by Ricordi et al 8. Briefly, after cleaning the pancreas from the surrounding tissue, it was perfused with enzyme solution (Serva Collagenase + Neutral Protease or Sigma V enzyme). The distended pancreas was then transferred to the Ricordi digestion chamber, connected to a modified, closed circulation tubing system, and warmed up to 37&deg;C. During the digestion, the chamber was shaken gently. Samples were taken continuously to monitor the digestion progress. Once free islets were detected under the microscope, the digestion was stopped by flushing cold (4&deg;C) RPMI dilution solution (Mediatech, Herndon, VA) into the circulation system to dilute the enzyme. After being collected and washed in M199 media supplemented with human albumin, the tissue was sampled for pre-purification count and incubated with UW solution before purification. Purification process will be described in Part II: Purification and Culture of Human Islets.

Achieving high quality and appropriate quantity of human islets is one of the prominent prerequisites for successful islet transplantation. In this video, we describe step by step the procedures for human pancreatic islet isolation (part I: digestion and collection of pancreatic tissue) using a modified automated method.

Management of Type 1 diabetes is burdensome, both to the individual and society, costing over 100 billion dollars annually. Despite the widespread use of ...

Human Pancreatic Islet Isolation: Part II: Purification and Culture of Human Islets

Management of Type 1 diabetes is burdensome, both to the individual and society, costing over 100 billion dollars annually. Despite the widespread use of glucose monitoring and new insulin formulations, many individuals still develop devastating secondary complications. Pancreatic islet transplantation can restore near normal glucose control in diabetic patients 1, without the risk of serious hypoglycemic episodes that are associated with intensive insulin therapy. Providing sufficient islet mass is important for successful islet transplantation. However, donor characteristics, organ procurement and preservation affect the isolation outcome 2. At University of Illinois at Chicago (UIC) we developed a successful isolation protocol with an improved purification gradient 3. The program started in January 2004 and more than 300 isolations were performed up to November 2008. The pancreata were sent in cold preservation solutions (UW, University of Wisconsin or HTK, Histidine-Tryptophan Ketoglutarate) 4-7 to the Cell Isolation Laboratory at UIC for islet isolation. Pancreatic islets were isolated using the UIC method, which is a modified version of the method originally described by Ricordi et al 8. As described in Part I: Digestion and Collection of Pancreatic Tissue, human pancreas was trimmed, cannulated, perfused, and digested. After collection and at least 30 minutes of incubation in UW solution, the tissue was loaded in the cell separator (COBE 2991, Cobe, Lakewood, CO) for purification 3. Following purification, islet yield (expressed as islet equivalents, IEQ), tissue volume, and purity was determined according to standard methods 9. Isolated islets were cultured in CMRL-1066 media (Mediatech, Herndon, VA), supplemented with 1.5% human albumin, 0.1% insulin-transferrin-selenium (ITS), 1 ml of Ciprofloxacin, 5 ml o f 1M HEPES, and 14.5 ml of 7.5% Sodium Bicarbonate in T175 flasks at 37&deg;C overnight culture before islets were transplanted or used for research.

Achieving high quality and appropriate quantity of human islets is one of the prominent prerequisites for successful islet transplantation. In this video, we describe step by step the procedures for human pancreatic islet isolation (part II: purification and culture of human islets) using a modified automated method.

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 ...

A Method for Mouse Pancreatic Islet Isolation and Intracellular cAMP Determination

Uncontrolled glycemia is a hallmark of diabetes mellitus and promotes morbidities like neuropathy, nephropathy, and retinopathy. With the increasing prevalence of diabetes, both immune-mediated type 1 and obesity-linked type 2, studies aimed at delineating diabetes pathophysiology and therapeutic mechanisms are of critical importance. The &#946;-cells of the pancreatic islets of Langerhans are responsible for appropriately secreting insulin in response to elevated blood glucose concentrations. In addition to glucose and other nutrients, the &#946;-cells are also stimulated by specific hormones, termed incretins, which are secreted from the gut in response to a meal and act on &#946;-cell receptors that increase the production of intracellular cyclic adenosine monophosphate (cAMP). Decreased &#946;-cell function, mass, and incretin responsiveness are well-understood to contribute to the pathophysiology of type 2 diabetes, and are also being increasingly linked with type 1 diabetes. The present mouse islet isolation and cAMP determination protocol can be a tool to help delineate mechanisms promoting disease progression and therapeutic interventions, particularly those that are mediated by the incretin receptors or related receptors that act through modulation of intracellular cAMP production. While only cAMP measurements will be described, the described islet isolation protocol creates a clean preparation that also allows for many other downstream applications, including glucose stimulated insulin secretion, [3H]-thymidine incorporation, protein abundance, and mRNA expression.

Assaying in vitro &#946;-cell function using isolated mouse islets of Langerhans is an important component in the study of diabetes pathophysiology and therapeutics. While many&#160;downstream applications are available, this protocol specifically describes the measurement of intracellular cyclic adenosine monophosphate (cAMP) as an essential parameter determining &#946;-cell function.

Uncontrolled glycemia is a hallmark of diabetes mellitus and promotes morbidities like neuropathy, nephropathy, and retinopathy. With the increasing ...

Infinium Assay for Large-scale SNP Genotyping Applications

Genotyping variants in the human genome has proven to be an efficient method to identify genetic associations with phenotypes. The distribution of variants within families or populations can facilitate identification of the genetic factors of disease. Illumina&#39;s panel of genotyping BeadChips allows investigators to genotype thousands or millions of single nucleotide polymorphisms (SNPs) or to analyze other genomic variants, such as copy number, across a large number of DNA samples. These SNPs can be spread throughout the genome or targeted in specific regions in order to maximize potential discovery. The Infinium assay has been optimized to yield high-quality, accurate results quickly. With proper setup, a single technician can process from a few hundred to over a thousand DNA samples per week, depending on the type of array. This assay guides users through every step, starting with genomic DNA and ending with the scanning of the array. Using propriety reagents, samples are amplified, fragmented, precipitated, resuspended, hybridized to the chip, extended by a single base, stained, and scanned on either an iScan or Hi Scan high-resolution optical imaging system. One overnight step is required to amplify the DNA. The DNA is denatured and isothermally amplified by whole-genome amplification; therefore, no PCR is required. Samples are hybridized to the arrays during a second overnight step. By the third day, the samples are ready to be scanned and analyzed. Amplified DNA may be stockpiled in large quantities, allowing bead arrays to be processed every day of the week, thereby maximizing throughput.

A protocol is described that uses Illumina&#39;s Infinium assays to perform large-scale genotyping. These assays can reliably genotype millions of SNPs across hundreds of individual DNA samples in three days. Once generated, these genotypes can be used to check for associations with a variety of different diseases or phenotypes.

Genotyping variants in the human genome has proven to be an efficient method to identify genetic associations with phenotypes. The distribution of ...

An Innovative Method for Exosome Quantification and Size Measurement

Although the biological importance of exosomes has recently gained an increasing amount of scientific and clinical attention, much is still unknown about their complex pathways, their bioavailability and their diverse functions in health and disease. Current work focuses on the presence and the behavior of exosomes (in vitro as well as in vivo) in the context of different human disorders, especially in the fields of oncology, gynecology and cardiology.
Unfortunately, neither a consensus regarding a gold standard for exosome isolation exists, nor is there an agreement on such a method for their quantitative analysis. As there are many methods for the purification of exosomes and also many possibilities for their quantitative and qualitative analysis, it is difficult to determine a combination of methods for the ideal approach. 
Here, we demonstrate nanoparticle tracking analysis (NTA), a semi-automated method for the characterization of exosomes after isolation from human plasma by ultracentrifugation. The presented results show that this approach for isolation, as well as the determination of the average number and size of exosomes, delivers reproducible and valid data, as confirmed by other methods, such as scanning electron microscopy (SEM).

A method for isolation of exosomes from whole blood and further analysis by nanoparticle tracking using a semi-automatic instrument is presented in this article. The presented technology provides an extremely sensitive method for visualizing and analyzing particles in liquid suspension.

Although the biological importance of exosomes has recently gained an increasing amount of scientific and clinical attention, much is still unknown about ...

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.&#160; Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. Utilizing normal-phased liquid chromatography coupled to high-resolution mass spectrometry with polarity switching and a rapid duty cycle, we describe a protocol to analyze the polar metabolic composition of biological material with high sensitivity, accuracy, and resolution.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting ...

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Growth phenotypes of microorganisms are a strong indicator of their underlying genetic fitness and can be segregated into 3 growth regimes: lag-phase, log-phase, and stationary-phase. Each growth phase can reveal different aspects of fitness that are related to various environmental and genetic conditions. High-resolution and quantitative measurements of all 3 phases of growth are generally difficult to obtain. Here we present a detailed method to characterize all 3 growth phases on solid media using an assay called One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). ODELAY quantifies growth phenotypes of individual cells growing into colonies on solid media using time-lapse microscopy. This method can directly observe population heterogeneity with each growth parameter in genetically identical cells growing into colonies. This population heterogeneity offers a unique perspective for understanding genetic and epigenetic regulation, and responses to genetic and environmental perturbations. While the ODELAY method is demonstrated using yeast, it can be utilized on any colony forming microorganism that is visible by bright field microscopy.

We present a method for quantifying growth phenotypes of individual yeast cells as they grow into colonies on solid media using time-lapse microscopy termed, One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). Population heterogeneity of genetically identical cells growing into colonies can be directly observed and quantified.

Growth phenotypes of microorganisms are a strong indicator of their underlying genetic fitness and can be segregated into 3 growth regimes: lag-phase, ...