计算机辅助大型胰岛质量，粒度分布和结构的可视化和量化

The overall goal of this procedure is to collect unbiased representative data within a limited sample and to precisely analyze the complex three dimensional structure of the tissue. This is accomplished by first capturing virtual slices of immunohisto, chemically stained pancreatic sections. Then the virtual slices are quantified with the IHC virtual slice macro in image J software, and the data is analyzed with scripts written for Mathematica.

Next, A 3D reconstruction of the virtual slices is performed using Image J.The final step of the procedure is to capture a stack of individual islet images using the slide book software and to map the image stacks manually in three dimensions using stereo investigator. Ultimately precise eyelet architecture with 3D coordinates for each eyelet cell can be obtained through a combined method of 3D imaging and manual mapping. This method can help answer key questions in the field of obesity and diabetes studies such as how physiological and path of physiological conditions affect pancreatic, beta cell mass eyelet distribution, and architecture.

The implications of this technique extend toward diagnosis or therapy of obesity and diabetes Melitis because a better understanding of the changes in beta cell mass will facilitate the development and evaluation of therapeutic interventions. Though the methods described in the present study specifically provide insight into the dynamic immunohistochemical analysis of the pancreas can also be applied to an array of other organisms and tissues. Visual demonstration of this method is critical as the computer assisted analysis steps are difficult to learn.

Because of the complexity of analyzing such large data sets in two and three dimensions. Begin this procedure by placing a clean slide holding an entire section of an immunohisto, chemically stained pancreas in the holder of a fluorescent microscope. Open the stereo investigator software and visualize the sample by clicking on acquisition.

And then live image. Determine the exposure levels for each channel using the video histogram window, which displays the intensity of the fluorescence. In this example, channel two is used for DAP P channel three for GFP channel four for RFP, channel five for sci five and channel six for SCI seven.

Using the camera settings window, adjust the exposure level so that fluorescence intensity tails off. At the right end of the video histogram, the image can be focused with the microscope focus wheel. 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.

Then click around the outline of the sample when the contour is complete. Right click and select close contour to connect the beginning and end points once the contour is closed, capture a virtual slice for the sample by selecting acquisition, and then acquire virtual slice. When the virtual slice window options open, select high speed acquire, as well as disabling manual.

Focusing by unchecking the box next to manual. Save the file. Calibrate the prefo by right clicking and selecting add to focus site list.

Manually focus several random sections in the virtual slice preview When several sites have been focused, start the virtual slice by right clicking and selecting. Start virtual slice with prefo after the virtual slice is complete for the first sample, switch to the next channel and adjust the exposure accordingly. This procedure is repeated for each channel to perform quantification of eyelets process the images using an image J macro named I-H-C-V-S, which first prepares loaded images for analysis and then quantifies features of eyelets such as cellular composition.

The I-H-C-V-S macros splits the stack into its respective color channels. In image J converts each image to an eight bit black and white mask. After automatic intensity, thresholding and adds the separate channel images together arithmetically into a composite mask.

Image J'S built-in particle analysis on the composite image will then identify regions of interest or ROIs while excluding particles smaller than one beta cell. The quantification of ROIs. Using image J produces a spreadsheet of cell parameters.

Save the aggregated results into an Excel spreadsheet. Storing data such as area perimeter circularity, fer diameter, and eyelet center for each eyelet, along with corresponding numbers labeled in the image. Computational analysis of these parameters can be performed as described in the written protocol To reveal information including eyelet distribution and frequency analysis, perform 3D reconstruction of virtual slices by running a script that converts all of the JP two images in the directory into TIF files, which is the format used in the construction and quantification of three dimensional stacks from the virtual slices.

Here, a script called I MJ two two tiff is used. Copy the script into the directory that contains the JP two images. Run the script with the Linux shell by typing dot slash I am JP two two TIFF into the console and then pressing enter.

When all of the captured images have been converted from JP two to TIFF files, they're ready to be used for analysis in image J.Using image J, open all of the images for the first sample, which in this case are from a human fetal pancreas and merge them as one by selecting image, color, and then 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.

Fill them by selecting edit, and then fill. Convert each image into an RGB color image. Finally, create a stack out of the images after which they can be further aligned with the stack reg plugin.

Ultimately, merging the channels and combining the images creates a 3D montage of all the eyelets in the entire pancreas. To collect image stacks, situate the whole mount pancreatic eyelets under the microscope. Apply water if using a water immersion objective lens.

Open the slide book software, access the capture and focus control by pressing control plus shift plus E in the focus control window. Set the objective to 20 times or 40 times depending on the size of the eyelet. To use the microscope eyepiece to locate the eyelets set bin to two times and set the filter set to user one.

In the focus control window, emission selection should be set to 100%Eyes in neutral density should be set to one click GFP ey, and then open floor to visualize the sample in slide book, return to the focus control window and switch filter set to fix. Then click G-F-P-D-S. Use the joystick to center the eyelet in the camera one window as well as possible.

Focus to a depth somewhere near the center of the eyelet where the cells can be discerned easily. In the focus control window, select the zab. Use the scope control to scroll to the uppermost depth of the eyelet in which features can be discerned clearly, and click set top.

Scroll to the bottom of the eyelet and click set. Bottom click go. To return to the reference point in the capture window, click on advanced, and then on alternate to channel mode.

Set bin factor to two times and the capture type to 3D on the right side of the window, click use top and bottom positions and return to center volume after capture. Set the step size to three. Set the filter set to live beneath the filter set box.

Select DAP, E-D-S-U-G-F-P-D-S-U-R-F-P-D-S-U and SCI five 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. 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 export TIFF series.

Type in the name of the slide with a dash at the end and save it. Dozens of individual TIF files are created, so it may be helpful to keep each eyelets image stack in a separate folder. To map image stacks, open the stereo investigator software.

Visualize the image by opening the image stacks in the image scaling menu. Set distance between images to three microns to correct for two times bending. In slide book select override X and Y scaling and use a specified for source of x and Y scaling.

Enter 0.65 for both X and Y center and zoom in on the image by clicking on the zoom button, and then in the middle of the ilis of interest in the macro window market least one cell. Using the appropriate marker beta cells will appear green. Alpha cells will appear red and delta cells will appear white.

Mark the cells in the center of their nucleus, which will appear blue if the sample has been stained with DPI use Marker two to mark beta cells. Marker five to mark alpha cells, and marker six to mark delta cells. 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.Scroll through the eyelets using the mouse wheel and mark each cell with the appropriate marker mark each cell only once at the center of its depth. Upon finishing marking each of the Z levels, the Z filter check box can be unchecked.

This will show all of the markers from all of the Z levels. When every cell has been marked, save the file as a DAT file. Then go to file export tracing.

Save the tracing as a TXT file. Finally, click new data file to clear the workspace before attempting to map new eyelets. The preparation of virtual slices out of an immunohistochemical stained pancreas sample allows for the examination of the alpha, beta and delta endocrine cells in the whole pancreas together as islets immunohistochemical staining for insulin can be seen in green, glucagon in red, somatostatin in white, and dap in blue.

The images then converted to the eight bit mask after automatic thresholding. Shown here is the merge composite image. However, eyelet distribution using virtual slice also enables individual analysis in separate channels.

Here, views of each channel are shown to reveal the delta cells, beta cells, and alpha cells. Particle analysis with performed upon composite mask showing each eyelet structure numbered and highlighted in blue. The particle analysis of composite masks is output as a data table containing such parameters as Islas area perimeter circularity and various diameter for each eyelet detected, which have IDs that correspond to the numbered tags on the composite mask.

The large scale analysis of these images results in the production of total eyelet number and size distribution histograms, as well as detailed comparisons of alpha, beta and delta cell areas. Eyelet mapping and mathematical analysis of cellular composition and architecture can reveal a single focal plane from a 3D reconstructed stack of images of a human eyelets uploaded into stereo investigator. Here, beta cells are in green alpha cells in red delta cells in white and nuclei in blue.

Subsequent to the capture of distinct eyelets, alpha, beta and delta cells are marked at various sea planes here, fluorescent images and the corresponding maps data are shown for three different focal planes at an interval of 10 microns. Once the alpha, beta and delta cells have been marked at various planes, the eyelet can be visualized in 3D. Shown here is a 3D reconstruction of the quarter sliced eyelet based on coordinates obtained by eyelet mapping.

Furthermore, automated mathematical analysis of mapped eyelets can display the cellular composition and architecture. Here, the relative frequency of cell cell distances between two cells in a single cell population is shown. Also shown is the relative frequency of cell cell distances between two different cell populations.

Also shown are the cumulative probabilities of cell to cell distance distributions for alpha to alpha, beta to beta and delta to delta cells. The cole OV smirnov or KS test can be performed on these probabilities to obtain the corresponding KS distances. Once mastered large scale imaging and analysis of an entire tissue section can be performed in four hours.

3D reconstruction of a block of tissue can be done in 30 minutes. Finally, 3D imaging and manual mapping can be completed in four hours if performed properly. While attempting this procedure, it's important to start with a high quality immunohistochemical staining of the sections.

The accuracy of the subsequent analysis will all depend on the raw data. Also, make sure your computers have at least eight gigabytes of RAM to process such large scale analysis After its development. This technique paved the way for researchers, not only in the field of obesity and diabetes, but widely in many other fields that use the standard pathological analysis.

It may be particularly useful when the availability of samples is limited. After watching this video, you should have a good understanding of how to collect unbiased representative data within a limited sample size using standard techniques. Selecting only a specific region may not be representative of the whole picture, as we have shown using our pancreatic eyelet distribution study.