The authors would like to acknowledge the National Science Foundation (NSF, award number NSF-CMMI-1662284 PI: T Ganesh), National Institute of Health (NIH - NIA P01 AG039355 PI: LF Bonewald) and (NIH/SIG S10OD021665 and S10RR027668 PI: SL Dallas), and the University of Missouri-Kansas City School of Graduate Studies Research Grant Program.

Animal experiments were carried out with the approval of the Institutional Animal Care and Use Committee at the University of Missouri, Kansas City (UMKC), and conformed to relevant federal guidelines.
1. Bone preparation process
<ol>
	<li>Collect femurs from 4-month-old and 22-month-old female C57BL6 mice and fix them in cold 4% paraformaldehyde in PBS for 24 h at 4 &#176;C with gentle rocking, then rinse them in PBS and store them in 70% ethanol before embedding. 
	NOTE: Fixative volume should be about 20x the tissue volume</li>
	<li>Embed bones quickly in a fast polymerizing acrylic (Table of Materials) following the manufacturer&#39;s instructions. 
	NOTE: It is important to use a fast polymerizing resin for this step (~10 min). The purpose is to support the bone tissue during sectioning using the diamond saw, but without the resin penetrating into the LCN, which would block the FITC stain from penetrating in.</li>
	<li>Cut thick 300 &#181;m thick transverse slices from a standardized site above the third trochanter using a diamond saw and store them at 4 &#176;C in 70% ethanol prior to FITC staining.</li>
	<li>Polish the sections using 600, 800, and then 1200 grit sandpaper to a final thickness of ~90-100 &#956;m. 
	NOTE: Attaining proper thickness was ensured using a digital caliper.</li>
	<li>Rinse the sections in 70%, 95%, and 100% ethanol for 5 min each.</li>
	<li>Stain them in 1% FITC in 100% ethanol for 4 h at room temperature (RT) in the dark with moderate shaking.</li>
	<li>Wash the sections in 100% ethanol for 30 min with gentle shaking in the dark. Then, air-dry them overnight in the dark.</li>
	<li>To mount, place the section into a drop of mounting media on a glass microscope slide. Use forceps to position the section as flat as possible against the slide, avoiding generating air bubbles and using the surrounding resin to manipulate the sample. Mount a coverslip on the specimen.</li>
</ol>
2. Confocal microscopy
<ol>
	<li>Use a confocal microscope for imaging of FITC-stained bone slices.</li>
	<li>Use a 100x 1.44NA oil objective with a digital zoom of 1.7 and a step size of 0.126 &#956;m to collect detailed Z-stacks of 400 Z-planes at 1024 x 1024 pixels, 0.089 &#956;m pixel resolution.</li>
	<li>Use a 488 nm laser for excitation, with an emission collection window of 496-596 nm. Collect the image stacks using compensation settings to correct for signal loss with increasing imaging depth.</li>
	<li>Increase the accuracy and resolution of the images by using image collection techniques such as oversampling and increased line averaging. Also, collect the images with a magnification of 5x, 20x, and 100x resolution of transverse sections of femur sections as shown in Figure 1. 
	NOTE: The low-resolution (5x) image in Figure 1 shows the complete cross-section area of the femur, with three regions selected for 100x imaging fields.</li>
	<li>Use the 100x Z-stacks for the computer modeling of osteocytes.</li>
</ol>
3. Computer modeling
<ol>
	<li>Import the collected 100x images, in TIFF&#160;format, to ImageJ software to build a sequence of images of the LCN in the Z-direction.</li>
	<li>Import the Z-stacks into the 3D image-based processing software to construct a mask of the LCN after defining the image orientation.</li>
	<li>Threshold the original image from the young and aged mice between 30,012-45,677 Hounsfield units and 15,000-46,701 Hounsfield units, respectively, to closely resemble the LCN. Adjust the Threshold in the section menu to change pixel intensity limits for inclusion in a mask.</li>
	<li>Crop one lacuna with its canaliculi as the region of interest (ROI) from the stack using the Crop Mask operation. Define the ROI so that it encircles the lacuna in the center of the cube, and all its connected canaliculi extend to the sides of the cube. Encase the lacuna in an imaginary bigger cube with side lengths of 21 &#181;m, 14 &#181;m, and 19 &#181;m.</li>
	<li>Since the model is created out of multiple parts, perform a region grow operation to select the connected pixel regions, remove noise and despeckle to generate a uniform LCN.</li>
	<li>Convert the lacunar-canalicular mask into an object using the Calculated Part operation in the 3D image-based processing software.</li>
	<li>Build the osteocyte and dendritic membranes by reducing the LCN volume using the Smoothing operation. Perform this operation several times to achieve a lacunar and canalicular space thickness of 0.75 &#181;m and 0.08 &#181;m, respectively9,18.</li>
	<li>Export the objects (STL format) as the last step in the 3D image-based processing software.</li>
	<li>Import two layers of the LCN and osteocyte-dendritic membranes into the 3D data optimization software to generate a volume mesh.</li>
	<li>Utilize the Fix Wizard tool in the software to identify mesh problems in each part. Check the mesh quality in the diagnostic section of Fix Wizard after each operation.</li>
	<li>Remove the inverted normal parts, intersecting triangles, and bad contours using the Auto-Fix operation in Fix Wizard.</li>
	<li>Replace the overlapping triangles manually by defining new ones or automatically via the Fill Hole Normal operation.</li>
	<li>Improve the mesh quality using operations including filtering sharp triangles, small edges, and small shells.</li>
	<li>After improving the mesh quality, combine two surfaces of the LCN and osteocyte-dendritic membranes into one surface (lacunar-canalicular fluidic space) that belongs to both parts using a non-manifold assembly.</li>
	<li>Create a volumetric model of the lacunar-canalicular space using the Remesh operation and then export it as an STL file. Adjust the object scale to be in micrometers in the export section.</li>
</ol>
4. Geometry modification technique in the 3D image-based processing software and 3D data optimization software
NOTE: The geometry modification technique is used to model changes in osteocyte morphology, such as canalicular density and diameter and lacunar-canalicular thickness owing to aging or bone disease.
<ol>
	<li>Choose the young osteocyte as the base model and modify it to build other distinct osteocyte models by applying morphological alterations.</li>
	<li>Generate an osteocyte model with different canalicular densities from the base model by changing the image thresholding in the 3D image-based processing software.
	<ol>
		<li>Select a lower threshold to reduce the light intensity of the image and obtain a lacuna with fewer canaliculi. The advantage of the thresholding technique is that the lacuna shape and size remain the same, and only the influence of canalicular density is studied. Figure 2 shows the simulated aged model generated from the young osteocyte using the geometry modification technique.</li>
	</ol>
	</li>
	<li>Develop osteocyte models with different lacunar-canalicular space thicknesses or dendrite/canalicular diameters in the 3D image-based processing software and 3D data optimization software. Build larger or smaller osteocyte models by Wrapping or Smoothing operations, respectively. Figure 3 shows six osteocyte models with altered geometry developed from the young osteocyte.</li>
</ol>
5. CFD analysis
NOTE: After generating the volumetric osteocyte models, several steps, including geometry, mesh, and setup, are conducted in the CFX module of the simulation software.
<ol>
	<li>Create a Fluid Flow in the simulation software to prepare the models for the CFD analysis.</li>
	<li>Import the developed confocal image-based geometries into the geometry section of CFX, known as ANSYS SpaceClaim (3D modeling tool). Set the unit dimensions to nanometers in the setting.</li>
	<li>Geometry appears as two facets of the LCN and osteocyte-dendritic processes. Click on Facet on the top menu and remove the geometrical errors such as intersections, sharp or overconnected edges, and vertices, openings, or holes for each facet.</li>
	<li>Click on Subtract on the Facet menu to reduce the smaller facet and osteocyte-dendritic processes from the larger facet, the LCN, to achieve a single body of lacunar-canalicular space. Then, right-click on the generated facet and convert it from facets to a solid domain without merging faces. Figure 4 depicts the cross-sectional area of the young osteocyte model, which represents the lacunar-canalicular space.</li>
	<li>Click on mesh and select linear tetrahedral elements using an element size of 0.06 &#181;m. Refine the mesh with a mesh convergence study to have enough elements in the tiny dendritic system to ensure the results are independent of the mesh size.</li>
	<li>Select the surface and choose the canaliculi on the top side of the imaginary cube as fluid inlets. Select the canaliculi on the other five faces as fluid outlets using Box select.</li>
	<li>Export the mesh (fluent file format) as it loads faster in the setup in the next step.</li>
	<li>Create another Fluid Flow in the simulation software and import the fluent mesh into the setup section of CFX. Define two boundary conditions of inlets and outlets for the faces preselected as inlets/outlets using the Insert Boundary option.</li>
	<li>To imitate physiological conditions, exert a fluid inlet pressure of 300 Pa and 0 Pa on the inlets and outlets, respectively19,20. Treat the remaining surfaces as walls with a no-slip condition in that fluid which has zero velocity at the interface of walls. Fluid flows from the inlets around the dendrites and osteocyte cell body and exits from the other canaliculi assigned as outlets.</li>
	<li>Treat the interstitial laminar fluid as water9, chosen from the material library. Set the Heat Transfer, Combustion, and Thermal Radiation sections to None, as no heat transfer is defined in the problem. Select the Turbulence mode as the fluid characteristic in the LCN, which is a laminar fluid9.</li>
	<li>Run the software using Double Precision and Direct Start as the submission type. Monitor mass and momentum until residuals drop and become constant. After solution convergence, measure FFSS data using the CFD-post section of the CFD software.</li>
</ol>
6. CFD post processing
<ol>
	<li>To depict FFSS experienced by osteocytes and their dendrites, insert a new contour in the results section of the CFD software. Create an FFSS contour by choosing the wall shear on the osteocyte-dendritic membranes as the variable at the domain. 
	NOTE: To better show high FFSS on dendritic membranes, the range of FFSS is set to User Specified to modify min/max values of FFSS.</li>
	<li>Insert a velocity streamlines contour inside the lacunar-canalicular domain starting from the inlets. Set the sampling to equally spaced and select the number of points as 2500. An animation section in the CFD software accurately displays in 3D how the fluid particles flow inside the lacunar-canalicular spaces using the velocity streamline graph.</li>
	<li>Use the Function Calculator tool in the CFD software for analyzing the magnitude of FFSS or velocity based on geometrical parameters, especially where there are various osteocyte models (i.e., young vs. aged). Measure the volume and surface area of the lacunar-canalicular space as geometrical parameters along with maximum, minimum, or average FFSS values.</li>
</ol>

3D Modeling of Osteocytes for CFD Analysis to Study Mechanosensation in Bone

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The authors have nothing to disclose.

This protocol outlines a confocal imaging technique for visualization and computational modeling of the osteocytes. Before confocal imaging, the bone preparation process for sectioning and staining bone samples is performed. Confocal images of 100x magnification are imported into various software to develop computer models of osteocytes and the lacunar-canalicular space. A CFD analysis is conducted lastly on the confocal image-based models to model FFSS surrounding the osteocytes and dendritic membranes due to physical activity, which is technically challenging in vivo due to the inaccessibility of osteocytes in bone matrix. The provided protocol is also helpful in determining other stress or strains using a finite element or fluid-structure interaction (FSI) approach in multiphysics modeling.
Confocal imaging has been used to visualize the LCN7,9,24,25 owing to the high-resolution images that can be achieved using FITC staining of the bone. Although the diameter of the canaliculi is close to the resolution limit of the microscope, reliable confocal images of the canaliculi can be obtained, which can capture most of the canaliculi. Due to the smaller diameter of the osteocyte dendrites, it is technically more challenging to accurately capture images of the dendrites. A helpful technique was therefore developed to model the osteocyte and dendrites from confocal images of the LCN by reducing their volume using a smoothing operation in the software. Some other published studies used a similar approach to offset the LCN to generate the osteocyte-dendritic membrane9. The other benefit of the smoothing technique is in improving some imaging errors in CLSM, such as the elongation of canalicular diameter that occurs in the Z-axis due to the lower imaging resolution in the Z-axis, which is a feature of optical imaging. Deconvolution is also considered an alternative method to decrease the optical stretching in the Z-direction since it improves the signal-to-noise in the original data set. The lacunar-canalicular space is modeled from LCN and osteocyte-dendritic membranes to analyze the fluid profile inside the LCN.
Notably, a new technique was introduced with respect to existing methods to develop geometry-modified osteocyte models without sacrificing another mouse. The geometry modification technique also helps to simulate the alteration of bone microstructure due to aging or bone diseases such as osteoporosis, diabetes and conditions such as perlecan deficiency, etc. In this technique, a confocal image-based osteocyte model of a young mouse was used to model the morphology of aged or other bone-diseased osteocytes by modifying parameters such as canalicular density, lacunar-canalicular space, and dendrite diameter). This approach was validated by the similarity of the two aged osteocyte models, one from confocal image-based modeling of an actual osteocyte from a 22-month-old mouse and the other from geometry modification modeling of an osteocyte originally imaged from a 4-month-old mouse by reducing its canalicular density. These two aged osteocyte models showed a similar FFSS magnitude, which was the lowest among all osteocyte models. The geometry modification technique could have many applications in different biological&#160;fields as it can predict comparable results to osteocyte models generated using real osteocytes from an animal. Some of the limitations of this work include losing some of the detail of surface roughness due to the smoothing technique and the fact that the canalicular diameters are at the limit of resolution of the confocal microscopes, which could potentially lead to some inaccuracy for canaliculi with smaller diameters, and also fluid flow idealization instead of modeling using oscillatory flow.
To demonstrate the technique&#39;s functionality, osteocyte FFSS values were measured for distinct osteocyte models generated from a young and an aged mouse or using the geometry modification technique. One of the noteworthy findings using these computer models was that the volume of lacunar-canalicular space is directly proportional to the average FFSS experienced by the osteocytes. This new finding, which had not been investigated before, may have various applications in determining the osteocyte mechano-responsiveness as it can correlate the osteocyte network morphology to detected FFSS by the osteocytes and their processes.

Osteocytes are postulated to regulate bone mass in response to physical exercise1. Membrane deformation of osteocytes and their dendritic processes due to mechanical loading, subjects them to FFSS, which is detected by the osteocytes and triggers intracellular signaling2,3,4. Bone microstructure undergoes through deterioration or alterations in its lacunar-canalicular morphology due to aging or bone diseases such as osteoporosis and diabetes and in conditions such as perlecan deficiency that causes impaired mechano-responsiveness of osteocytes5,6. These changes in bone architecture cause osteocytes to experience different levels of FFSS and strains7,8. Importantly, FFSS experienced by osteocytes in response to mechanical loading is difficult to quantify in vivo because they are embedded in the calcified bone matrix.
Confocal image-based modeling is a powerful technique to overcome the limitations of studying inaccessible osteocytes in their natural environment by replicating computer models of the LCN9,10. Processing and modeling the interconnected network of LCN in 3D has been challenging. There are several imaging techniques, such as Transmission electron microscopy (TEM), scanning electron microscopy (SEM), serial block face sectioning, and serial focused ion beam scanning electron microscopy (FIB/SEM)2,11,12. A valuable technique was developed to visualize bone13,14,15 and generate 3D osteocyte models via confocal laser scanning microscopy (CLSM). CLSM was chosen here for computational modeling rather than other imaging techniques due to its ability to image all of the lacuna volume and most of the canaliculi in 3D16,17. The LCN geometry can be generated using CLSM for osteocyte Finite Element Analysis (FEA) to predict bone strains. However, fluid analysis to predict FFSS experienced by osteocytes is more complicated as it requires modeling of the cell membrane of the osteocyte and its dendrites within the LCN to enable modeling of the narrow lacunar-canalicular space, in which the interstitial fluid moves around18.
In this protocol, fluorescein isothiocyanate (FITC) dye is applied to undecalcified thick bone sections before confocal microscopy to label the LCN inside the bone, and osteocyte-dendritic membranes are modeled based on imaging data from the LCN. The lacunar-canalicular space is simulated using computational modeling, and physiological loading due to physical activity is modeled using a CFD approach. The osteocytes are subjected to a fluid pressure gradient in the CFD software to analyze the fluid profile inside the LCN and measure FFSS on the osteocyte and dendritic membranes. Furthermore, an FEA&#160;approach can measure osteocyte strains or stresses by applying compressive mechanical loading.
A geometry modification technique was also developed to modify the microstructures derived from images of young, healthy bone in order to simulate the altered lacunar-canalicular morphology in aged animals or those&#160;with bone disease. Alterations of the bone microstructure included reducing the number of canaliculi with aging, reducing the lacunar-canalicular space area to model what happens in perlecan deficiency and increasing it to model aging effects, and reducing the canalicular and dendritic wall area to model diabetic bone5,6. The geometry modification technique allows us to compare FFSS experienced by osteocytes in bone with different microstructures, such as young versus aged or bones in healthy versus diseased animals.
Overall, confocal image-based modeling is a valuable tool for simulating the morphology of osteocytes in healthy bone as well as in aging/disease-associated changes in osteocyte morphology. Furthermore, osteocyte morphological parameters, such as surface area and volume of the lacunar-canalicular space, can be measured and compared in various bones to predict cellular responses to mechanical strain.

<table><tbody><tr><td>1,200 Grit sandpaper</td><td>Buehler</td><td>30-5170-012-100</td><td></td></tr><tr><td>3-Matic software</td><td>Materialise</td><td>https://www.materialise.com/en/industrial/software/3-matic</td><td>3D data optimization software</td></tr><tr><td>600 grit sandpaper</td><td>Buehler</td><td>30-5118-600-100</td><td></td></tr><tr><td>800 Grit sandpaper</td><td>Buehler</td><td>30-5170-800-100</td><td></td></tr><tr><td>ANSYS software</td><td>ANSYS</td><td>https://www.ansys.com/</td><td>simulation software</td></tr><tr><td>Fluorescein Isothiocyanate (FITC)</td><td>Sigma-Aldrich</td><td>F7250</td><td></td></tr><tr><td>ImageJ software</td><td></td><td>https://imagej.net/ij/</td><td></td></tr><tr><td>Immersion Oil for Microscopes</td><td>Leica Microsystems</td><td>195371-10-9</td><td></td></tr><tr><td>Leica TCS Sp5 II confocal microscope&amp;nbsp;</td><td>Leica Microsystems</td><td>TCS Sp5 II&amp;nbsp;</td><td></td></tr><tr><td>Leitz 1600 inner hole diamond saw</td><td>Leica&amp;nbsp;</td><td></td><td></td></tr><tr><td>MIMICS Innovation Suite software</td><td>Materialise</td><td>https://www.materialise.com/en/healthcare/mimics-innovation-suite</td><td>3D image-based processing software</td></tr><tr><td>Permount mount medium</td><td>Fisher scientific</td><td>SP15-500</td><td></td></tr><tr><td>Sampl-Kwick Fast Cure Acrylic Kit</td><td>Buehler</td><td>20-3560</td><td></td></tr><tr><td>Single Platform Laboratory Shaker</td><td>Reliable scientific INC</td><td>Model 55S</td><td></td></tr></tbody></table>

analysis and imaging of osteocytes

Osteocytes are the bone cells that are thought to respond to mechanical strains and fluid flow shear stress (FFSS) by activating various biological pathways in a process known as mechanotransduction. Confocal image-derived models of osteocyte networks are a valuable tool for conducting Computational Fluid Dynamics&#160;(CFD) analysis to evaluate shear stresses on the osteocyte membrane, which cannot be determined by direct measurement. Computational modeling using these high-resolution images of the microstructural architecture of bone was used to numerically simulate the mechanical loading exerted on bone and understand the load-induced stimulation of osteocytes.
This study elaborates on the methods to develop 3D single osteocyte models using confocal microscope images of the Lacunar-Canalicular Network&#160;(LCN) to perform CFD analysis utilizing various computational modeling software. Prior to confocal microscopy, the mouse bones are sectioned and stained with Fluorescein isothiocyanate (FITC) dye to label the LCN. At 100x resolution, Z-stack images are collected using a confocal microscope and imported into MIMICS software (3D image-based processing software) to construct a surface model of the LCN and osteocyte-dendritic processes.
These surfaces are then subtracted using a Boolean operation in 3-Matic software (3D data optimization software) to model the lacunar fluidic space around the osteocyte cell body and canalicular space around the dendrites containing lacunocanalicular fluid. 3D volumetric fluid geometry is imported into ANSYS software (simulation software) for CFD analysis. ANSYS CFX (CFD software) is used to apply physiological loading on the bone as fluid pressure, and the wall shear stresses on the osteocytes and dendritic processes are determined. The morphology of the LCN affects the shear stress values sensed by the osteocyte cell membrane and cell processes. Therefore, the details of how confocal image-based models are developed can be valuable in understanding osteocyte mechanosensation and can lay the groundwork for future studies in this area.

This protocol describes how to develop confocal-derived osteocyte models to investigate the amount of fluid flow shear stress an osteocyte and its dendritic processes are subjected to due to mechanical loading. An aged and a young C57BL6 mouse were selected to build young and aged confocal image-based osteocyte models. Six other simulated osteocyte models were generated from the same young osteocyte model using the geometry modification technique to study the alteration of LCN morphology due to aging or bone disease. Geometry-modified parameters included the lacunar-canalicular thickness, canalicular density, and diameter. Modified osteocyte models were developed by changing one parameter at a time while holding the other parameters the same. Therefore, the effect of each of the morphological parameters on FFSS is examined in isolation. The results revealed that the amount of FFSS experienced by an osteocyte was different in these osteocyte models, showing the dependency of FFSS on LCN morphology. FFSS values were predicted on various parts of the osteocyte model, such as the osteocyte cell body or dendritic membranes. Dendrites in all models experienced greater FFSS values than the osteocyte cell body membrane.
Figure 5&#160;shows the FFSS contours for young vs. aged osteocyte models generated directly from confocal FITC image stacks and six other osteocyte models developed from Model 1 using the geometry modification technique. The average FFSS on osteocyte and dendritic membranes in the young osteocyte model (Model 1) was 0.42 Pa, which is more significant than the average FFSS of 0.13 Pa in the aged osteocyte model (Model 2). The obtained data show an increase in average FFSS in osteocytes with larger lacunar-canalicular space (Model 7 and Model 8) and a decrease in FFSS in osteocytes with lower canalicular density (Model 3 and Model 4), while FFSS did not significantly change with changing dendrite diameter in Model 5 and Model 6. Model 2 and Model 4 had the lowest FFSS values of 0.19 Pa and 0.13 Pa, respectively. The magnitude of FFSS in Model 8 is greater than in the other osteocyte models. Not only are most of the dendrites in red color in Model 8 but&#160;the osteocyte cell body also experiences higher FFSS than in other models.
FFSS experienced by osteocytes was also presented based on the lacunar-canalicular space volume and surface area, which were obtained by post-processing in ANSYS CFX to find out the correlation between FFSS and the altered structure of the LCN. It was also possible to measure the surface area of each model exposed to FFSS greater than 0.8 Pa, which is thought to be the level of FFSS that causes bone formation21,22. The osteocyte modeling showed a correlation between the average FFSS and the lacunar-canalicular space volume or surface area for all osteocyte models, as shown in Table 1. The aged osteocyte (Model 2) and simulated middle-aged osteocyte (Model 4) with the lowest canalicular density had the lowest lacunar-canalicular surface area of 1372.7 &#181;m2 and 1122.6 &#181;m2. Model 2 and Model 4 also had the lowest lacunar-canalicular space volume of 229.4 &#181;m3 and 203.5 &#181;m3, respectively, which can be correlated to their low amounts of FFSS. Model 6, with the largest dendrite diameter, had the largest lacunar-canalicular surface area. Model 8 had the largest lacunar-canalicular space volume and the greatest FFSS values.
<img alt="Figure 1" src="/files/ftp_upload/64699/64699fig01.jpg" /> 
Figure 1: Single Z-plane confocal images of a transverse section of a FITC-stained femur taken with 5x, 20x, and 100x oil objectives. The Lacunar-canalicular network is visible in the image taken with the 100x objective. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64699/64699fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/64699/64699fig02.jpg" /> 
Figure 2: Confocal image-based models of a young (Model 1) and an aged (Model 2) osteocyte generated from the FITC-stained femur of 4-month-old and 22-month-old C57BL6 mouse. The magnified image shows the lacunar-canalicular (LC) thickness for young osteocyte and the same for the aged osteocyte. Model 1 image has been modified with permission from Niroobakhsh et al.23. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64699/64699fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/64699/64699fig03.jpg" /> 
Figure 3: Six osteocyte models developed from Model 1 using the geometry modification technique. Canalicular density was reduced to generate Model 3 (simulated middle-aged) and Model 4 (simulated aged), while dendrite diameter and LC thickness were the same as Model 1. Model 5 and Model 6 were developed with the same canalicular density and LC thickness as Model 1, whereas their dendrite diameter was two and four times greater than Model 1. Model 7 and Model 8 had two times and four times greater lacunar-canalicular thickness, respectively, while canalicular density and dendrite diameter remained the same as in Model 1. Model 1 image has been modified with permission from Niroobakhsh et al.23. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64699/64699fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/64699/64699fig04.jpg" /> 
Figure 4: 3D view confocal image-based young osteocyte model. 3D view of the cross-sectional area of the confocal image-based young osteocyte model depicting the modeling of the lacunar-canalicular space, which is used for fluid analysis around the osteocyte and dendrites. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64699/64699fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/64699/64699fig05.jpg" /> 
Figure 5: FFSS distribution for all osteocyte models shows the dependence of FFSS magnitude on osteocyte morphology. Red regions show the highest FFSS values, especially on the dendritic membranes rather than the osteocyte cell body. This image has been modified with permission from Niroobakhsh et al.23. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64699/64699fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Models</td>
			<td>Lacunar-canalicular surface area (&#181;m2)</td>
			<td>Lacunar-canalicular space volume (&#181;m3)</td>
			<td>Ave FFSS (Pa)</td>
		</tr>
		<tr>
			<td rowspan="4">Confocal image-based osteocyte models</td>
			<td>Model 1</td>
			<td rowspan="2">2652.9</td>
			<td rowspan="2">261</td>
			<td rowspan="2">0.42</td>
		</tr>
		<tr>
			<td>Young (4-month-old)</td>
		</tr>
		<tr>
			<td>Model 2</td>
			<td rowspan="2">1372.7</td>
			<td rowspan="2">229.4</td>
			<td rowspan="2">0.19</td>
		</tr>
		<tr>
			<td>Aged (22-month-old)</td>
		</tr>
		<tr>
			<td rowspan="12">Simulated osteocyte models generated using geometry modification technique</td>
			<td>Model 3</td>
			<td rowspan="2">1530.4</td>
			<td rowspan="2">242.6</td>
			<td rowspan="2">0.27</td>
		</tr>
		<tr>
			<td>(Simulated middle-aged)</td>
		</tr>
		<tr>
			<td>Model 4</td>
			<td rowspan="2">1122.6</td>
			<td rowspan="2">203.5</td>
			<td rowspan="2">0.13</td>
		</tr>
		<tr>
			<td>(Simulated aged)</td>
		</tr>
		<tr>
			<td>Model 5</td>
			<td rowspan="2">3724.9</td>
			<td rowspan="2">302.9</td>
			<td rowspan="2">0.49</td>
		</tr>
		<tr>
			<td>(2 x dendrite diameter)</td>
		</tr>
		<tr>
			<td>Model 6</td>
			<td rowspan="2">4791</td>
			<td rowspan="2">337</td>
			<td rowspan="2">0.57</td>
		</tr>
		<tr>
			<td>(4 x dendrite diameter)</td>
		</tr>
		<tr>
			<td>Model 7</td>
			<td rowspan="2">3116.3</td>
			<td rowspan="2">440.6</td>
			<td rowspan="2">1.17</td>
		</tr>
		<tr>
			<td>(2 x LC thickness)</td>
		</tr>
		<tr>
			<td>Model 8</td>
			<td rowspan="2">3542.2</td>
			<td rowspan="2">658.2</td>
			<td rowspan="2">1.89</td>
		</tr>
		<tr>
			<td>(4 x LC thickness)</td>
		</tr>
	</tbody>
</table>
Table 1: Comparison of osteocyte models. Comparison of average FFSS, lacunar-canalicular space volume, and surface area in eight distinct osteocyte models, including two confocal image-based models and six simulated models generated from Model 1 using the geometry modification technique.

Analysis and Imaging of Osteocytes

This study outlines the method to visualize and develop three-dimensional (3D) models of osteocytes within the lacunar-canalicular network (LCN) for computational fluid dynamics (CFD) analysis. The generated models using this method help to understand osteocyte mechanosensation in healthy or diseased bones.

Osteocytes are the bone cells that are thought to respond to mechanical strains and fluid flow shear stress (FFSS) by activating various biological ...

Nanyang Technological University

Research

JoVE Journal

Bioengineering

632 Views.  University of Missouri-Kansas City. Vloeistofstroomschuifspanning wordt verondersteld een mechanositimulator van osteocyten te zijn. Aangezien directe meting geen optie is, zijn confocale beeldafgeleide modellen van osteocyten een waardevol hulpmiddel voor het uitvoeren van computationele vloeistofdynamica-analyse om de enorme spanningen van de vloeistofstroom op de dendritische membranen van de osteocyten te evalueren. Een deel van het werk dat in ons laboratorium wordt gedaan, komt overeen me...

Video: Analyse en beeldvorming van osteocyten

Methods for Analyzing the Impacts of Natural Uranium on In Vitro Osteoclastogenesis

Uranium has been shown to interfere with bone physiology and it is well established that this metal accumulates in bone. However, little is known about the effect of natural uranium on the behavior of bone cells. In particular, the impact of uranium on osteoclasts, the cells responsible for the resorption of the bone matrix, is not documented. To investigate this issue, we have established a new protocol using uranyl acetate as a source of natural uranium and the murine RAW 264.7 cell line as a model of osteoclast precursors. Herein, we detailed all the assays required to test uranium cytotoxicity on osteoclast precursors and to evaluate its impact on the osteoclastogenesis and on the resorbing function of mature osteoclasts. The conditions we have developed, in particular for the preparation of uranyl-containing culture media and for the seeding of RAW 264.7 cells allow to obtain reliable and highly reproductive results. Moreover, we have optimized the use of software tools to facilitate the analysis of various parameters such as the size of osteoclasts or the percentage of resorbed matrix.

Methods for Analyzing the Impacts of Natural Uranium on In Vitro Osteoclastogenesis

Uranium is known to affect bone metabolism. Here, we present a protocol aimed at investigating the effect of natural uranium exposure on the viability, the differentiation, and the function of osteoclasts, the cells in charge of bone resorption.

Uranium has been shown to interfere with bone physiology and it is well established that this metal accumulates in bone. However, little is known about ...

Developmental Biology

Analysis of Minerals Produced by hFOB 1.19 and Saos-2 Cells Using Transmission Electron Microscopy with Energy Dispersive X-ray Microanalysis

This video presents the use of transmission electron microscopy with energy dispersive X-ray microanalysis (TEM-EDX) to compare the state of minerals in vesicles released by two human bone cell lines: hFOB 1.19 and Saos-2. These cell lines, after treatment with ascorbic acid (AA) and &#946;-glycerophosphate (&#946;-GP), undergo complete osteogenic transdifferentiation from proliferation to mineralization and produce matrix vesicles (MVs) that trigger apatite nucleation in the extracellular matrix (ECM).
Based on Alizarin Red-S (AR-S) staining and analysis of the composition of minerals in cell lysates using ultraviolet (UV) light or in vesicles using TEM imaging followed by EDX quantitation and ion mapping, we can infer that osteosarcoma Saos-2 and osteoblastic hFOB 1.19 cells reveal distinct mineralization profiles. Saos-2 cells mineralize more efficiently than hFOB 1.19 cells and produce larger mineral deposits that are not visible under UV light but are similar to hydroxyapatite (HA) in that they have more Ca and F substitutions.
The results obtained using these techniques allow us to conclude that the process of mineralization differs depending on the cell type. We propose that, at the cellular level, the origin and properties of vesicles predetermine the type of minerals.

We present a protocol to compare the state of minerals in vesicles released by two human bone cell lines: hFOB 1.19 and Saos-2. Their mineralization profiles were analyzed by Alizarin Red-S (AR-S) staining, ultraviolet (UV) light visualization, transmission electron microscopy (TEM) imaging and energy dispersive X-ray microanalysis (EDX).

This video presents the use of transmission electron microscopy with energy dispersive X-ray microanalysis (TEM-EDX) to compare the state of minerals in ...

Chemistry

Hybrid &#181;CT-FMT imaging and image analysis

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to assess the biodistribution of novel probes and to assess disease progression using established molecular probes or reporter genes. The combination with an anatomical modality, e.g., micro computed tomography (&#181;CT), is beneficial for image analysis and for fluorescence reconstruction. We describe a protocol for multimodal &#181;CT-FMT imaging including the image processing steps necessary to extract quantitative measurements. After preparing the mice and performing the imaging, the multimodal data sets are registered. Subsequently, an improved fluorescence reconstruction is performed, which takes into account the shape of the mouse. For quantitative analysis, organ segmentations are generated based on the anatomical data using our interactive segmentation tool. Finally, the biodistribution curves are generated using a batch-processing feature. We show the applicability of the method by assessing the biodistribution of a well-known probe that binds to bones and joints.

Hybrid  &#181;CT-FMT imaging and image analysis

We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (&#181;CT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to ...

Computed Tomography and Optical Imaging of Osteogenesis-angiogenesis Coupling to Assess Integration of Cranial Bone Autografts and Allografts

A major parameter determining the success of a bone-grafting procedure is vascularization of the area surrounding the graft. We hypothesized that implantation of a bone autograft would induce greater bone regeneration by abundant blood vessel formation. To investigate the effect of the graft on neovascularization at the defect site, we developed a micro&#8211;computed tomography (&#181;CT) approach to characterize newly forming blood vessels, which involves systemic perfusion of the animal with a polymerizing contrast agent. This method enables detailed vascular analysis of an organ in its entirety. Additionally, blood perfusion was assessed using fluorescence imaging (FLI) of a blood-borne fluorescent agent. Bone formation was quantified by FLI using a hydroxyapatite-targeted probe and &#181;CT analysis. Stem cell recruitment was monitored by bioluminescence imaging (BLI) of transgenic mice that express luciferase under the control of the osteocalcin promoter. Here we describe and demonstrate preparation of the allograft, calvarial defect surgery, &#181;CT scanning protocols for the neovascularization study and bone formation analysis (including the in vivo perfusion of contrast agent), and the protocol for data analysis.
The 3D high-resolution analysis of vasculature demonstrated significantly greater angiogenesis in animals with implanted autografts, especially with respect to arteriole formation. Accordingly, blood perfusion was significantly higher in the autograft group by the 7th day after surgery. We observed superior bone mineralization and measured greater bone formation in animals that received autografts. Autograft implantation induced resident stem cell recruitment to the graft-host bone suture, where the cells differentiated into bone-forming cells between the 7th and 10th postoperative day. This finding means that enhanced bone formation may be attributed to the augmented vascular feeding that characterizes autograft implantation. The methods depicted may serve as an optimal tool to study bone regeneration in terms of tightly bounded bone formation and neovascularization.

Implantation of autologous and allogeneic bone grafts constitute accepted approaches to treat major craniofacial bone loss. Yet the effect of graft composition on the interplay among neovascularization, cell differentiation and bone formation is unclear. We present a multimodal imaging protocol aimed to elucidate the angiogenesis-osteogenesis interdependence at the graft proximity.

A major parameter determining the success of a bone-grafting procedure is vascularization of the area surrounding the graft. We hypothesized that ...

A Fluorescent Intravital Imaging Approach to Study Load-Induced Calcium Signaling Dynamics in Mouse Osteocytes

Bone tissue is exquisitely sensitive to differences in mechanical load magnitude. Osteocytes, dendritic cells that form a syncytium throughout the bone, are responsible for the mechanosensory function of bone tissue. Studies employing histology, mathematical modeling, cell culture, and ex vivo bone organ cultures have greatly advanced the understanding of osteocyte mechanobiology. However, the fundamental question of how osteocytes respond to and encode mechanical information at the molecular level in vivo is not well understood. Intracellular calcium concentration fluctuations in osteocytes offer a useful target for learning more about acute bone mechanotransduction mechanisms. Here, we report a method for studying osteocyte mechanobiology in vivo, combining a mouse strain with a fluorescently genetically encoded calcium indicator expressed in osteocytes with an in vivo loading and imaging system to directly detect osteocyte calcium levels during loading. This is achieved with a three-point bending device that can deliver well-defined mechanical loads to the third metatarsal of living mice while simultaneously monitoring fluorescently indicated calcium responses of osteocytes using two-photon microscopy. This technique allows for direct in vivo observation of osteocyte calcium signaling events in response to whole bone loading and is useful in the endeavor to reveal mechanisms in osteocyte mechanobiology.

The current article describes performing an intravital imaging approach to observe mechanically induced calcium signaling of embedded osteocytes in vivo in real-time in response to tissue-level mechanical loading of the mouse third metatarsal.

Bone tissue is exquisitely sensitive to differences in mechanical load magnitude. Osteocytes, dendritic cells that form a syncytium throughout the bone, ...

Application of Retinoic Acid to Obtain Osteocytes Cultures from Primary Mouse Osteoblasts

The need for osteocyte cultures is well known to the community of bone researchers; isolation of primary osteocytes is difficult and produces low cell numbers. Therefore, the most widely used cellular system is the osteocyte-like MLO-Y4 cell line.
The method here described refers to the use of retinoic acid to generate a homogeneous population of ramified cells with morphological and molecular osteocyte features.
After isolation of osteoblasts from mouse calvaria, all-trans retinoic acid (ATRA) is added to cell medium, and cell monitoring is conducted daily under an inverted microscope. First morphological changes are detectable after 2 days of treatment and differentiation is generally complete in 5 days, with progressive development of dendrites, loss of the ability to produce extracellular matrix, down-regulation of osteoblast markers and up-regulation of osteocyte-specific molecules.
Daily cell monitoring is needed because of the inherent variability of primary cells, and the protocol can be adapted with minimal variation to cells obtained from different mouse strains and applied to transgenic models. 
The method is easy to perform and does not require special instrumentation, it is highly reproducible, and rapidly generates a mature osteocyte population in complete absence of extracellular matrix, allowing the use of these cells for unlimited biological applications.

Treatment of primary mouse osteoblasts with retinoic acid produces a homogeneous population of ramified cells bearing morphological and molecular features of osteocytes. The method overcomes the difficulty of obtaining and maintaining primary osteocytes in culture, and can be advantageous to study cells derived from transgenic models.&#160;

The need for osteocyte cultures is well known to the community of bone researchers; isolation of primary osteocytes is difficult and produces low cell ...

Biology

Osteoclast Derivation from Mouse Bone Marrow

Osteoclasts are highly specialized cells that are derived from the monocyte/macrophage lineage of the bone marrow. Their unique ability to resorb both the organic and inorganic matrices of bone means that they play a key role in regulating skeletal remodeling. Together, osteoblasts and osteoclasts are responsible for the dynamic coupling process that involves both bone resorption and bone formation acting together to maintain the normal skeleton during health and disease.
As the principal bone-resorbing cell in the body, changes in osteoclast differentiation or function can result in profound effects in the body. Diseases associated with altered osteoclast function can range in severity from lethal neonatal disease due to failure to form a marrow space for hematopoiesis, to more commonly observed pathologies such as osteoporosis, in which excessive osteoclastic bone resorption predisposes to fracture formation.
An ability to isolate osteoclasts in high numbers in vitro has allowed for significant advances in the understanding of the bone remodeling cycle and has paved the way for the discovery of novel therapeutic strategies that combat these diseases. 
Here, we describe a protocol to isolate and cultivate osteoclasts from mouse bone marrow that will yield large numbers of osteoclasts.

Osteoclasts are the principal bone-resorbing cell in the body. An ability to isolate osteoclasts in large numbers has resulted in significant advances in the understanding of osteoclast biology. In this protocol, we describe a method for isolation, cultivating and quantifying osteoclast activity in vitro.

Osteoclasts are highly specialized cells that are derived from the monocyte/macrophage lineage of the bone marrow. Their unique ability to resorb both the ...

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious projects to disrupt every gene in the mouse genome. The research community needs rapid and inexpensive means to phenotype tissues via histology. Histological analyses of skeletal tissues are often time consuming and semi-quantitative at best, regularly requiring subjective interpretation of slides from trained individuals. Here, we present a cryohistological paradigm for efficient and inexpensive phenotyping of mineralized tissues. First, we present a novel method of tape-stabilized cryosectioning that preserves the morphology of mineralized tissues. These sections are then adhered rigidly to glass slides and imaged repeatedly over several rounds of staining. The resultant images are then aligned either manually or via computer software to yield composite stacks of several layered images. The protocol allows for co-localization of numerous molecular signals to specific cells within a given section. In addition, these fluorescent signals can be quantified objectively via computer software. This protocol overcomes many of the shortcomings associated with histology of mineralized tissues and can serve as a platform for high-throughput, high-content phenotyping of musculoskeletal tissues moving forward.

In this manuscript, we present a high-throughput, semi-automated cryohistology platform to produce aligned composite images of multiple response measures from several rounds of fluorescent imaging on frozen sections of mineralized tissues.

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious ...

HE Staining and Micro-CT to Evaluate Murine Trabecular Bone Microarchitecture

Trabecular Bone Microarchitecture Evaluation in an Osteoporosis Mouse Model

Bone microstructure refers to the arrangement and quality of bone tissue at the microscopic level. Understanding the bone microstructure of the skeleton is crucial for gaining insight into the pathophysiology of osteoporosis and improving its treatment. However, handling bone samples can be complex due to their hard and dense properties. Secondly, specialized software makes image processing and analysis difficult. In this protocol, we present a cost-effective and easy-to-use solution for trabecular bone microstructure analysis. Detailed steps and precautions are provided. Micro-CT is a non-destructive three-dimensional (3D) imaging technique that provides high-resolution images of trabecular bone structure. It allows for the objective and quantitative evaluation of bone quality, which is why it is widely regarded as the gold standard method for bone quality assessment. However, histomorphometry remains indispensable as it offers crucial cellular-level parameters, bridging the gap between two-dimensional (2D) and 3D assessments of bone specimens. As for the histologic techniques, we chose to decalcify the bone tissue and then perform traditional paraffin embedding. In summary, combining these two methods can provide more comprehensive and accurate information on bone microstructure.

Author Spotlight: An Economic and Efficient Method for Quantitative Evaluation of Bone Microarchitecture in a Murine Osteoporosis Model 

This protocol presents an economical and efficient method for quantitative evaluation of bone microarchitecture in a mouse model of osteoporosis by combining Hematoxylin-Eosin (HE) staining and micro-computed tomography (Micro-CT) techniques.

Bone microstructure refers to the arrangement and quality of bone tissue at the microscopic level. Understanding the bone microstructure of the skeleton ...

Medicine

A Sectioning, Coring, and Image Processing Guide for High-Throughput Cortical Bone Sample Procurement and Analysis for Synchrotron Micro-CT

Bone is a dynamic and mechanically active tissue that changes in structure over the human lifespan. The products of the bone remodeling process have been studied substantially using traditional two-dimensional techniques. Recent advancements in X-ray imaging technology via desktop micro-computed tomography (&#181;CT) and synchrotron radiation micro-computed tomography (SR&#181;CT) have allowed for the acquisition of high-resolution three-dimensional (3D) scans of a larger field of view (FOV) than other 3D imaging techniques (e.g., SEM) providing a more complete picture of microscopic structures within human cortical bone. The specimen should be accurately centered within the FOV, however, to limit the appearance of streak artifacts known to impact data analysis. Previous studies have reported procurement of irregularly shaped rectilinear bone blocks that result in imaging artifacts due to uneven edges or image truncation. We have applied a geological sampling protocol (coring) to procure consistently sized cortical bone core specimens for SR&#181;CT experiments from the anterior aspect of human femora. This coring method is efficient and minimally destructive to tissue. It creates uniform cylindrical samples that decrease imaging artifacts by nature of being isometric during rotation and providing a uniform path length for X-ray beams throughout scanning. Image processing of X-ray tomographic data of cored and irregularly shaped samples confirms the potential of the technique to improve visualization and analysis of cortical bone microarchitecture. A goal of this protocol is to deliver a reliable and repeatable method for the extraction of cortical bone cores that is adaptable for various types of high-resolution bone imaging experiments. An overarching goal of the work is to create a standardized cortical bone procurement for SR&#181;CT that is affordable, consistent, and straightforward. This procedure may further be adapted by researchers in related fields who commonly evaluate hard composite materials such as in biological anthropology, geosciences, or material sciences.

We employed a geological (coring) sampling protocol to procure cortical bone specimens of uniform size for SR&#181;CT experiments from the anterior aspect of human femora. This method is minimally destructive, efficient, results in cylindrical specimens that minimize imaging artifacts from irregular sample shapes and improves microarchitectural visualization and analysis.

Bone is a dynamic and mechanically active tissue that changes in structure over the human lifespan. The products of the bone remodeling process have been ...

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