Comprehensive Characterization of Tissue Mineralization in an Ex Vivo Model

Our research focuses on understanding and ameliorating bone regeneration. Here we aim to extensively characterize tissue mineralization using various and complementary technical approaches to facilitate the early identification of promising candidates for bone regeneration therapies. To evaluate the quality and structure of newly mineralized tissue, we use complementary approaches including high resolution 3D imaging with micro-CT associated with deep learning analysis, nano indentation, histological examinations, backscattering scanning electron microscopy mapping, and Raman spectroscopy.

Repair of bone defects is a major challenge in regenerative medicine. We developed explant models that facilitate the evaluation of bone regeneration and the monitoring of mineralization over time. To begin, use an osteochondral autograph transfer system to create a 4.75-millimeter diameter defect in the lateral condyle of a euthanized sheep.

Using an orthopedic hammer, create a defect 10 millimeters in depth and retrieve the osteochondral explant from the condyle. Prepare the calcium phosphate cement supplemented with 40 micrograms per milliliter of BMP-2, and load the cement paste into a 3-milliliter syringe. Then inject the cement into the explant defect with an 18-gauge needle.

Turn on the micro tomography x-ray machine and place the explant tube on the sample holder. Set the resolution to 10.7 micrometers and exposure time to 1, 200 milliseconds with a 1-millimeter aluminum filter at 80 kilovolts and 125 microamperes. Average three images for each 0.45 degree rotation increment to enhance the signal-to-noise ratio.

To conduct image segmentation, use the integrated segmentation wizard to train a deep learning model for distinguishing between bone and cement. Select a representative zone containing bone, cement, and background from the reconstructed micro computed tomography images, and segment this first frame. Now, in the model tab, generate a deep learning model and select the 3D U-Net routine.

Then right click on the generated model and set the experimental parameters as the depth of 5, patch size 32 by 32, Adadelta algorithm, stride ratio of 0.25, and 10X data. Use the segmented frame to train the deep learning model by clicking the train button. Once the training is complete, define a second frame and automatically segment it using the predict function.

Then click on export to publish the trained model and apply it to the entire micro computed tomography dataset by selecting segment, exported model, segment full dataset. To begin, dehydrate the osteochondral bone explant collected from sheep in a 40-milliliter glass vial containing 25 milliliters of dehydration solution. Place the vials on a rotating wheel for one hour at room temperature.

After the last wash, replace the dehydration solution with 25 milliliters of xylene. Place the glass vials on the rotating wheel for one hour at room temperature. Next, add 10%benzoyl peroxide, 10%dibutyl phthalate, and 450 microliters of N, N-Dimethylaniline diluted 1 to 20 in propanol and place the glass vial at minus 20 degrees Celsius overnight.

Now place the explant into a medium-sized embedding mold in a plastic box. Pour the methylmethacrylate benzoyl peroxide, dibutyl phthalate, N, N-aniline solution into the mold. Ventilate the mold with nitrogen flow for five minutes.

Close the box hermetically and place it at four degrees Celsius for 48 hours for MMA polymerization and hardening. After two days, remove the resin containing the explant from the mold. Cut the polymethyl methacrylate block containing the explant using a diamond saw along its long axis to generate a 1.5 millimeter thick section at 3, 000 RPM with a speed of 3 millimeters per minute.

Grind the section with silicon carbide paper with ascending numbers. Carbon coat the thick section with a 10-nanometer carbon film. Mount the section on an aluminum stub, then create a silver paint bridge between the top of the section and the stub to allow for electron charging evacuation to the ground.

Place the stub on the scanning electron microscopy or SEM stage. Close the chamber and initiate a vacuum. Turn on the electron beam and adjust the SEM settings to operate in backscattered electron mode.

Set the gray level standards to 25 for carbon, 225 for aluminum, and 253 for silicon. After calibrating the SEM with the standards, acquire images of the specimen in backscattered electron mode. Use the backscattered electron image of the specimen to convert the gray levels into calcium content.

Then plot the calcium distribution content of the image. Place the section on the stage of the Raman micro spectrometer. Calibrate the wave number for accuracy and align the laser prior to measurement.

To collect the Raman spectra, use a 785-nanometer laser at 30 milliwatts. Set the time of integration to 20 seconds. Repeat three times and use a spectral range of 350 to 1, 800 per centimeter with a grading of 1, 200 lines per millimeter.

Once the nano indentation system is calibrated, place the sample in the optical system and identify the location for the nano indentation. Then move the sample under the indentation device. Set the indentation depth to 900 nanometers with a loading unloading speed of 40 millimeters per minute and a 15-second pause between loading and unloading.

Set the Poisson coefficient for bone tissue to 0.3 and start the nano indentation.