Osteoclasts control reactivation of dormant myeloma cells by remodelling the endosteal niche. Nature communications. Neuropeptide y attenuates stress-induced bone loss through suppression of noradrenaline circuits. Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research. Neuropeptide Y modulates fracture healing through Y1 receptor signaling.
Journal of orthopaedic research : official publication of the Orthopaedic Research Society. Cell Metab. Neuropeptide Y is a critical modulator of leptin's regulation of cortical bone. J Bone Miner Res.
Peptide YY regulates bone remodeling in mice: A link between gut and skeletal biology. PLoS One ; 7:e Neuropeptide Y Y1 receptor antagonism increases bone mass in mice. Bone ; Osteoblast specific Y1 receptor deletion enhances bone mass. Neuropeptide Y knockout mice reveal a central role of NPY in the coordination of bone mass to bodyweight.
PLoS One ; 4 e Within the bone marrow, hematopoiesis is critically dependent on the support of the microenvironment, which includes cells of the osteoblast lineage. From mesenchymal stem cells to fully mature osteoblasts, cells at each stage of differentiation play unique roles in supporting hematopoietic development.
Research studies have demonstrated the clinical relevance of bone health to hematopoiesis. This webinar will provide an overview of the mechanisms by which the osteoblast lineage can influence hematopoiesis, examples of reciprocal interactions between bone health and blood counts, and future research directions at the intersection of hematology and bone biology.
Join us for our Annual Meeting October 1 - 4, ! Now, the scale of the image is visible on the top left of the image. Repeat steps 5—6 until the results are obtained. However, the long manual process of histomorphometry as shown above can be replaced by the automated TWS script discussed in this manuscript. The procedure to carry out the automated histomorphometry is as shown below:.
The next prompt window asks for the user input to define stack size. The area percentage and area in defined scale values will be saved automatically at the end after all images are analyzed.
The working directory was defined by the user in the previous session. The trabecular bone appears in black and other as white portion. Th and Tb. Sp [adapted from 14 ]. A pop-up window asks user to select for thickness and spacing. The result window will provide the values of Tb. Sp in the user-defined scale. Additionally, graphical results can be saved. Repeat steps 2—9 until all images are analyzed. Figure 3. Application of BoneJ in the measurement of Tb.
Movat pentachrome stain classified overview of sheep iliac crest biopsy was used to obtain Tb. Left to right. The classified overview was uploaded and scale was set. The cortical bone and cartilage area was cleaned out to measure trabecular parameters. The image was then converted into binary using ImageJ toolbox. Trabecular bone appears as black and other as white.
However, the time consuming manual protocol for Tb. Sp measurements as shown above can be replaced by the automated BoneJ script discussed in this manuscript. The procedure to carry out the automated Tb. Sp measurements are as shown below:. Run the macro once it is installed. The script will by default downsize the image to 0. Here, the user can define the region of interest ROI; trabecular bone to measure Tb.
The user has to define the ROI for each classified image once. The script will run in the background. The result excel file will be created at the end with the name of the sample and their respective Tb.
Sp values. Osteoclast activity is mainly defined by the count of osteoclast and the length of ruffled border. Repeat step 3 and 4 until all images are done. Figure 4. Rat vertebral sample was used to perform TRAP enzyme histochemistry.
Osteoclasts are identified as multi-nucleated TRAP positive cells near the bone surface. The scale was set up before proceeding with measurements. The length of ruffled borders arrows govern the osteoclast activity. The length of ruffled border was measured using free-hand line tool after the scale was set. TWS is a machine learning based tool that uses manual annotation to train a classifier and automatically segment the remaining data. TWS can make use of predefined image features.
The color based image segmentation plays a critical role in the quantitative evaluation of bone parameters like mineralized and non-mineralized bone matrix area Tables 1 , 2. This protocol using TWS resulted in the area percentage distribution of mineralized and non-mineralized bone matrix in an osteoporotic sheep sample. These area measurements will help in understanding the bone loss.
Nonetheless, analysis of immunostainings using TWS helped in monitoring the osteoblast activity across the study data not shown. Besides the measurement of area, the investigation of trabecular thinning using BoneJ provided a comprehensive overview in our study.
Such parameters helps in correlating 2D analysis with 3D analysis in bone research. Table 2. Both the optimized scripts were designed to facilitate quantitative evaluation of histological stained samples and prevent the manual work and time taken for analysis.
Although the scripts rely on the latest version of java and Fiji based ImageJ, an unavoidable limitation is the dependence of evaluation time on the computer hardware system. The evaluation time might increase based on the size of sample images and computer capacity. Nonetheless, the automated scripts save high amount of manual work.
The slight degree of differences between manual and semi-automated measurements can occur due to user based pixel identification procedure as in TWS. Parameters like Tb. Therefore, slight chances of deviations are present in these measurements by different users.
The problem can be resolved by saving the ROI and re-applying it for trabecular measurements from same sample. The classifier file obtained from TWS can be applied only to the same magnification images. However, the classifier file obtained from large magnification pictures can be applied to the smaller magnification.
User might come across few errors or failure messages while working with the ImageJ. Following are the expected errors and possible solution to them:. Image too big to import and simultaneous hanging of ImageJ. Restart the ImageJ after memory assignment. The computer system takes a long time or fails to analyze the whole overview images of sample. The number of stacks should be made in direct proportion of computer memory.
TWS fails to differentiate between two closely related colors and gives false results. In certain cases, ImageJ results in java based errors in between the trainable weka segmentation.
Trou bleshooting : The established script works with the latest version of java and ImageJ. Therefore, update it regularly. BoneJ error: could not find zip file for the installation of 3D libraries. Trou bleshooting : User can install 3D libraries manually and copy to plugins folder of ImageJ. Restart the ImageJ and run BoneJ. The conversion of classified image to binary results in bone as white and other as black. This will give the false results.
The script used in this protocol is used routinely to successfully quantify the bone parameters. However, there are a few limitations to the application of program as listed below:.
There is no possibility to analyze the whole image at one time using TWS without creating the stacks. In case of histological stain like Toluidine Blue, the bone and the bone marrow are visualized in the same color which makes it difficult for the program to differentiate.
Therefore, the bone marrow must be cleared out prior to the TWS. The optimized BoneJ script fail to provide Tb. Sp in case of non-homogenous bone sections for example major cracks. This might result in an outlier. The optimized scripts fail to automatically count the cells like osteocytes, osteoblast. Bone histomorphometry following ASBMR standards provide quantitative information on metabolic bone diseases and fracture healing 1 , Histomorphometry is grouped into: static and dynamic histomorphometry.
Static histomorphometry involves evaluation of bone parameters at a particular time point while dynamic histomorphometry involves evaluation of bone structure during time series experiment Further, static histomorphometry includes evaluation of parameters like osteoblast, osteoclast activity. While, dynamic histomorphometry includes evaluation of bone mineralization from fluorochrome labeled samples. The standards for both static and dynamic histomorphometry are well-defined.
Although micro-computed tomography micro-CT and DXA are the gold standards in bone research, histomorphometry is essential to get cellular insight. This will indeed help in bridging a gap between 2D and 3D analysis of bone samples. Nonetheless, histology and histomorphometry provides additional information related to the biomarkers activity IHC and bone mineralization.
Therefore, histomorphometry is one of the building block in bone research. The global application of common histomorphometry methods to analyze different set of images are much needed. Previous studies reported different concerns about application of semi-automated or automated software for bone histomorphometry The need of standardized algorithm which prevents any interference with quantification procedure during analysis is needed.
However, the use of complicated algorithm and protocols makes it difficult for routine use in preclinical and clinical research. Hence, there is an urgent need of setting up an easier and user-friendly bone histomorphometry method. Our study focused on establishing an automated easily accessible scripts linked to ImageJ to perform bone histomorphometry.
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