近红外光学投影层析成像技术在糖尿病研究β细胞质量分布的评估

The overall goal of this procedure is to perform optical projection tomography based assessments of the beta cell mass distribution in rodent, pancreat, or in tissues harboring eyelet grafts. Hereby the near infrared spectrum is used to enable studies of larger specimens and to allow for the simultaneous visualization of interacting or neighboring cell types. This is achieved by equipment, the OPT scanner with a near infrared sensitive camera, a high powered light source and appropriate filter sets.

The specimen is first isolated, fixed, and hormone immuno stain before it's embedded in aros and optically cleared. It is then attached to a custom made sample holder. A semi-automated positioning tool is utilized to ensure correct centering in the scanner after scanning.

The projection images are post-process to correct for artifacts introduced by the mechanical setup if required. An equalization algorithm is utilized to improve detection of low intensity objects. Finally, segmentation, visualization and quantification are performed using image processing software.

The insulin producing beta cells play a pivotal role in maintaining blood glucose hemostasis. Therefore, spatial and quantitative assessments of pancreatic beta cell mass distribution are key to many areas of diabetes research. In this video protocol, we describe an adaptation of optical reaction tomography or OPT that enables imaging in the near re near-infrared range and thereby the possibility to image larger bodies of pancreatic tissue.

It also enables greater range of cell types to be studied in a single specimen. We further present a compilation of computational tools developed optimized OPT for pancreas imaging in general, and for assessments Of B cell mass. In particular, the near infrared spectrum Is the most favorable window for light penetrance in the tissue.

To get access to this part of the spectrum in O PT imaging, a number of modifications were made to the original OPT scanner described by sharpen colleagues For single SH assessments of beta cell mass. In mouse pancrea, we routinely use Omic 3001 scan. The excitation light is applied by metal haa lamp that provides higher excitation energy than a mercury arc lamp at wavelengths about 650 nanometers.

The light is transferred through a liquid light guide before passing through the excitation filters. The filters that utilized in this protocol are shown in this table to evaluate channel separation antibodies. Conjugated with different Alexa floor dyes were immobilized on protein beads and embedded in separate planes of agros phantom.

The images demonstrate that there is no bleed through between the different channels. The emitted light is detected with a back illuminated CCD camera with a high quantum efficiency in the near infrared spectrum. To facilitate imaging of larger samples, the field of view is increased by incorporating a larger reflector mirror and vete into the scanner.

Setup seen above the vete is the stepper motor that rotates the sample during scanning. A lab U based program controls the camera and stepper motor during the scan, Harvest the pancreas and ice cold PBS to avoid protic degradation. Carefully remove surrounding fat and membranes that may otherwise interfere with the staining and scanning procedure.

Fix the sample in 4%PFA at four degrees Celsius for two to three hours. To avoid stabilizing the pancreas in a folded shape, spray it out in a Petri dish during fixation before whole mount immuno staining, wash the pancreas in excess PBS. The staining procedure has been described previously for the assessment of beta cell mass distribution.

We routinely rely on a combination of kidney pig, anti insulin, primary antibodies, and Alexa Fluor conjugated secondary antibodies of the staining. Separate the three main pancreatic lobes. This facilitates comparative assessment of the lobes and permit scanning at higher magnification.

Rinse the pancreas in water and soak it in agros. To remove any bubbles. Embed the pancreas in 1.5%low melting temperature, agros and keep on eyes until settled.

Cut the agros blocking, closing the pancreas. Make sure to leave approximately one centimeter spacer between the sample and the base of the agros. The spacer is required for securing the specimen into the sample holder.

Dehydrate the pancreas in a hundred percent methanol before optical clearing in B clearing solution, also known as Maurice.Clear. Clear the sample until transparent. Since B isol most plastic, A glass bottle is recommended.

Change B solution a couple of times to remove all traces of methanol. Now the sample is ready for scanning. During scanning, the sample is carried by a magnet attached to a step promoter.

This permits sliding of the sample along its X and set axis, which facilitates pre-scan alignment of the specimen. In most OPT protocols, RO block is glued to the sample holder, however most clues dissolving bulb, and this can result in unwanted movements of the sample. Therefore, a holder that omits the use of view was designed.

Position the clear sample in the holder and secure the sample by gently inserting two needles guided by the pre-drill holes in the holder flanges. Place The sample in the scanner and submerge it into the BAB solution. Shown here Is the graphical user interface for lab view based software that is used to control the camera exposure time, step promoter, and to input experimental parameters to log file parameters such as magnification.

Focal adjustments and changing of filter sets are carried out manually on the microscope. Stop by selecting the magnification. Illuminate the sample using the GFP filter and display a preview image using the lab use software.

Rotate the specimen to find its widest part note when comparing a series of pancreat, use the same magnification for all specimens. This factor set based on the larger sample in the series. Next, change to the filter set that displays the specific signal and adjust the focus.

Note, the exposure time should be chosen to highest signal to noise ratio possible without saturating any part of the specimen. For assessments of pancreatic beta cell mass, the entire specimen is normally the region of interest and therefore the samples center of mass com should be precisely positioned at its axis of rotation are to obtain optima results. The commar algorithm calculates the sample center of mass and provides a reference image that facilitates aligning the center of mass at the axis of rotation.

To calculate the X coordinates, start by acquiring a snapshot of the GFP channel at zero degrees. Then switch the filter and acquire a snapshot of the specific signal at the same position. Next, apply an expectation maximization algorithm on the GFP image to threshold the region of interest apply a center of mass calculation on the threshold image superimpose a vertical line passing through the found center of mass point on the image of the specific signal.

These steps are repeated for the sample rotated 90 degrees to calculate the Z coordinates. Finally used acquired images delineating the center of mass points as a reference to move the sample so that the center line of the field of view passes through the found center of mass points of the sample. When the scan is completed, a post alignment value is assigned to the projections to fine tune the images position along the axis of rotation prior to reconstruction.

However, a small aberration in the angle of the camera towards the optical axis can cause geometric distortions during reconstruction. To avoid such distortions a discreet free transform based alignment, the FDA is used after scanning a specimen. Two projections are retrieved from the dataset to compute the FDA.

These projections are F the specific signal at zero degree and G, the specific signal at 180 degrees. The image D is transposed so that it matches the orientation of F.The DFT function is used to register eight pixel height blocks throughout the image from the two projections, and the measure shifts along the X axis to calculate the necessary rotation angle, reuse the same parameters to correct all channels acquired through the same scan. More insights into the foundation of the algorithms COMMAR and the FTA can be found in AL To facilitate iLet segmentation.

A contrast limited adaptive histogram equalization claw is applied on the projection images. In this protocol, the mat built-in function adapt hiss egg was used and applied with the default clip limit of 0.01 and a lytes of 256. The optimal tire size, however, needs to be tested empirically and can vary depending on the specimen analyzed more details on the algorithm and examples can be found in home blood and shadow.

At add. The alignment compensated and normalized data can now be reconstructed into tomographic sections. To visualize and quantify the stack of virtual sections obtained, generate 3D ISO surfaces using suitable image processing software such as S or velocity.

This figure shows one projection view of a pancreatic lobe stained for insulin with a cocktail of fluorochrome conjugated secondary antibodies, including Alexa Fluor 4 88, 5 94, 6 80, and seven 50. The signal tono ratio dramatically increase as we move towards the near infrared part of the spectrum. This is further illustrated in this graph, showing the mean signal to noise ratio for each signal channel by getting access to a broader part of the spectrum.

Near OPT imaging increase the range of cell types that may be analyzed in a single specimen. This video shows the pancreas from the non-obese diabetic model for type one diabetes. In the video, the insulin producing islas of Langerhans are blue smooth muscle alpha actin expressing blood vessels are red and CD three labeled infiltrating T lymphocytes.

Green theacrine parenchyma in gray is reconstructed based on endogenous tissue autofluorescence by taking advantage of the increased life penetrance into tissue in the near infrared range. O PT imaging enables assessments of beta cell mass distribution in much larger specimens than previously possible. This figure shows the ISO surface reconstruction of the eyelet distribution in the splenic lobe of a SER fat rat labeled for insulin using ER erdi six 80 as the secondary antibody.

A mouse splenic globe is shown for precise reference. These images correspond to two different isosurface reconstructions of the beta cell mass distribution in an individual pancreatic globe as acid appears without claw processing in green, and when the algorithm has been applied in red. By overlaying the two reconstructions, it's demonstrated that claw facilitates the segmentation also for low signal intensity eyelets.

These are seen as red only in the overlay image. We have just shown You how takes extract spatial and quantitative information of the beta cell mass throughout the volume of the neuron pancreas and down to the level of individual eyelets of Langerhans. These protocols are not limited to the assessments of beta cells or the pancreas at near impaired OPT as well as the computational tools presented should be possible to translate to other areas of investigation.

Good luck. Good luck with your experiments.