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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M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Degeneration+of+the+osteocyte+network+in+the+C57BL/6+mouse+model+of+aging.">Degeneration of the osteocyte network in the C57BL/6 mouse model of aging.</a> Aging (Albany NY). 9 (10), 2190-2208 (2017).</li><li>Lai, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+dependences+of+osteocyte+network+on+bone+compartment,+age,+and+disease.">The dependences of osteocyte network on bone compartment, age, and disease.</a> Bone Res. 3 (1), 15009 (2015).</li><li>Schurman, C. A., Verbruggen, S. 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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 a...

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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&#160;</td>
			<td>Leica Microsystems</td>
			<td>TCS Sp5 II&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>Leitz 1600 inner hole diamond saw</td>
			<td>Leica&#160;</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. Geo...

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 ...

Research

JoVE Journal

Bioengineering

437 Views.  University of Missouri-Kansas City. 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.

Video: Analysis and Imaging of Osteocytes

Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at submicrometer resolution. One of the first such instruments producing whole-brain-scale data is the Knife-Edge Scanning Microscope (KESM)7, 5, 9, developed and hosted in the authors' lab. KESM has been used to section and image whole mouse brains at submicrometer resolution, revealing the intricate details of the neuronal networks (Golgi)1, 4, 8, vascular networks (India ink)1, 4, and cell body distribution (Nissl)3. The use of KESM is not restricted to the mouse nor the brain. We have successfully imaged the octopus brain6, mouse lung, and rat brain. We are currently working on whole zebra fish embryos. Data like these can greatly contribute to connectomics research10; to microcirculation and hemodynamic research; and to stereology research by providing an exact ground-truth. 
In this article, we will describe the pipeline, including specimen preparation (fixing, staining, and embedding), KESM configuration and setup, sectioning and imaging with the KESM, image processing, data preparation, and data visualization and analysis. The emphasis will be on specimen preparation and visualization/analysis of obtained KESM data. We expect the detailed protocol presented in this article to help broaden the access to KESM and increase its utilization.

The full process from brain specimen preparation to serial sectioning imaging using the Knife-Edge Scanning Microscope, to data visualization and analysis is described. This technique is currently used to acquire mouse brain data, but it is applicable to other organs, other species.

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at ...

Self-reporting Scaffolds for 3-Dimensional Cell Culture

Culturing cells in 3D on appropriate scaffolds is thought to better mimic the in vivo microenvironment and increase cell-cell interactions. The resulting 3D cellular construct can often be more relevant to studying the molecular events and cell-cell interactions than similar experiments studied in 2D. To create effective 3D cultures with high cell viability throughout the scaffold the culture conditions such as oxygen and pH need to be carefully controlled as gradients in analyte concentration can exist throughout the 3D construct. Here we describe the methods of preparing biocompatible pH responsive sol-gel nanosensors and their incorporation into poly(lactic-co-glycolic acid) (PLGA) electrospun scaffolds along with their subsequent preparation for the culture of mammalian cells. The pH responsive scaffolds can be used as tools to determine microenvironmental pH within a 3D cellular construct. Furthermore, we detail the delivery of pH responsive nanosensors to the intracellular environment of mammalian cells whose growth was supported by electrospun PLGA scaffolds. The cytoplasmic location of the pH responsive nanosensors can be utilized to monitor intracellular pH (pHi) during ongoing experimentation.

Biocompatible pH responsive sol-gel nanosensors can be incorporated into poly(lactic-co-glycolic acid) (PLGA) electrospun scaffolds. The produced self-reporting scaffolds can be used for in&#160;situ monitoring of microenvironmental conditions whilst culturing cells upon the scaffold. This is beneficial as the 3D cellular construct can be monitored in real-time without disturbing the experiment.

Culturing cells in 3D on appropriate scaffolds is thought to better mimic the in vivo microenvironment and increase cell-cell interactions. The resulting ...

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Optical sensors of ultrasound are a promising alternative to piezoelectric techniques, as has been recently demonstrated in the field of optoacoustic imaging. In medical applications, one of the major limitations of optical sensing technology is its susceptibility to environmental conditions, e.g. changes in pressure and temperature, which may saturate the detection. Additionally, the clinical environment often imposes stringent limits on the size and robustness of the sensor. In this work, the combination of pulse interferometry and fiber-based optical sensing is demonstrated for ultrasound detection. Pulse interferometry enables robust performance of the readout system in the presence of rapid variations in the environmental conditions, whereas the use of all-fiber technology leads to a mechanically flexible sensing element compatible with highly demanding medical applications such as intravascular imaging. In order to achieve a short sensor length, a pi-phase-shifted fiber Bragg grating is used, which acts as a resonator trapping light over an effective length of 350 &#181;m. To enable high bandwidth, the sensor is used for sideway detection of ultrasound, which is highly beneficial in circumferential imaging geometries such as intravascular imaging. An optoacoustic imaging setup is used to determine the response of the sensor for acoustic point sources at different positions.

Optical detection of ultrasound is impractical in many imaging scenarios because it often requires stable environmental conditions. We demonstrate an optical technique for ultrasound sensing in volatile environments with miniaturization and sensitivity levels appropriate for optoacoustic imaging in restrictive scenarios, e.g. intravascular applications.

Optical sensors of ultrasound are a promising alternative to piezoelectric techniques, as has been recently demonstrated in the field of optoacoustic ...

High-resolution Spatiotemporal Analysis of Receptor Dynamics by Single-molecule Fluorescence Microscopy

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., kinetics, coexistence of different states and populations, transient interactions), which are typically hidden in ensemble measurements, such as those obtained with standard biochemical or microscopy methods. Thus, dynamic events, such as receptor-receptor interactions, can be followed in real time in a living cell with high spatiotemporal resolution. This protocol describes a method based on labeling with small and bright organic fluorophores and total internal reflection fluorescence (TIRF) microscopy to directly visualize single receptors on the surface of living cells. This approach allows one to precisely localize receptors, measure the size of receptor complexes, and capture dynamic events such as transient receptor-receptor interactions. The protocol provides a detailed description of how to perform a single-molecule experiment, including sample preparation, image acquisition and image analysis. As an example, the application of this method to analyze two G-protein-coupled receptors, i.e., &#946;2-adrenergic and &#947;-aminobutyric acid type B (GABAB) receptor, is reported. The protocol can be adapted to other membrane proteins and different cell models, transfection methods and labeling strategies.

This protocol describes how to use total internal reflection fluorescence microscopy to visualize and track single receptors on the surface of living cells and thereby analyze receptor lateral mobility, size of receptor complexes as well as to visualize transient receptor-receptor interactions. This protocol can be extended to other membrane proteins.

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., ...

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  &amp;#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 ...

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

Recent fiber-bundle microendoscopy techniques enable non-invasive analysis of in vivo tissue using either imaging techniques or a combination of spectroscopy techniques. Combining imaging and spectroscopy techniques into a single optical probe may provide a more complete analysis of tissue health. In this article, two dissimilar modalities are combined, high-resolution fluorescence microendoscopy imaging and diffuse reflectance spectroscopy, into a single optical probe. High-resolution fluorescence microendoscopy imaging is a technique used to visualize apical tissue micro-architecture and, although mostly a qualitative technique, has demonstrated effective real-time differentiation between neoplastic and non-neoplastic tissue. Diffuse reflectance spectroscopy is a technique which can extract tissue physiological parameters including local hemoglobin concentration, melanin concentration, and oxygen saturation. This article describes the specifications required to construct the fiber-optic probe, how to build the instrumentation, and then demonstrates the technique on in vivo human skin. This work revealed that tissue micro-architecture, specifically apical skin keratinocytes, can be co-registered with its associated physiological parameters. The instrumentation and fiber-bundle probe presented here can be optimized as either a handheld or endoscopically-compatible device for use in a variety of organ systems. Additional clinical research is needed to test the viability of this technique for different epithelial disease states.

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

The assembly and use of a multimodal microendoscope is described which can co-register superficial tissue image data with tissue physiological parameters including hemoglobin concentration, melanin concentration, and oxygen saturation. This technique can be useful for evaluating tissue structure and perfusion, and can be optimized for individual needs of the investigator.

Recent fiber-bundle microendoscopy techniques enable non-invasive analysis of in vivo tissue using either imaging techniques or a combination of ...

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

Currently, many clinical diagnostic procedures are complex, costly, inefficient, and inaccessible to a large population in the world. The requirements for specialized equipment and trained personnel require that many diagnostic tests be performed at remote, centralized clinical laboratories. Magnetic levitation is a simple yet powerful technique and can be applied to levitate cells, which are suspended in a paramagnetic solution and placed in a magnetic field, at a position determined by equilibrium between a magnetic force and a buoyancy force. Here, we present a versatile platform technology designed for point-of-care diagnostics which uses magnetic levitation coupled to microscopic imaging and automated analysis to determine the density distribution of a patient's cells as a useful diagnostic indicator. We present two platforms operating on this principle: (i) a smartphone-compatible version of the technology, where the built-in smartphone camera is used to image cells in the magnetic field and a smartphone application processes the images and to measures the density distribution of the cells and (ii) a self-contained version where a camera board is used to capture images and an embedded processing unit with attached thin-film-transistor (TFT) screen measures and displays the results. Demonstrated applications include: (i) measuring the altered distribution of a cell population with a disease phenotype compared to a healthy phenotype, which is applied to sickle cell disease diagnosis, and (ii) separation of different cell types based on their characteristic densities, which is applied to separate white blood cells from red blood cells for white blood cell cytometry. These applications, as well as future extensions of the essential density-based measurements enabled by this portable, user-friendly platform technology, will significantly enhance disease diagnostic capabilities at the point of care.

We present a magnetic levitation technique coupled with automated imaging and analysis in both a smartphone-compatible device and a device with embedded imaging and processing. This is applied to measure the density distribution of cells with two demonstrated biomedical applications: sickle cell disease diagnosis and separating white and red blood cells.

Currently, many clinical diagnostic procedures are complex, costly, inefficient, and inaccessible to a large population in the world. The requirements for ...

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Osteoporosis-related vertebral compression fractures (OVCFs) are a common and clinically unmet need with increasing prevalence as the world population ages. Animal OVCF models are essential to the preclinical development of translational tissue engineering strategies. While a number of models currently exist, this protocol describes an optimized method for inducing multiple highly reproducible vertebral defects in a single nude rat. A novel longitudinal semiautomated microcomputed tomography (&#181;CT)-based quantitative structural analysis of the vertebral defects is also detailed. Briefly, rats were imaged at multiple time points post-op. The day 1 scan was reoriented to a standard position, and a standard volume of interest was defined. Subsequent &#181;CT scans of each rat were automatically registered to the day 1 scan so the same volume of interest was then analyzed to assess for new bone formation. This versatile approach can be adapted to a variety of other models where longitudinal imaging-based analysis could benefit from precise 3D semiautomated alignment. Taken together, this protocol describes a readily quantifiable and easily reproducible system for osteoporosis and bone research. The suggested protocol takes 4 months to induce osteoporosis in nude ovariectomized rats and between 2.7 and 4 h to generate, image, and analyze two vertebral defects, depending on tissue size and equipment.

The goal of this protocol is to generate a nude rat osteoporosis-related vertebral compression fracture model that can be longitudinally evaluated in vivo using a semiautomated microcomputed tomography-based quantitative structural analysis.

Osteoporosis-related vertebral compression fractures (OVCFs) are a common and clinically unmet need with increasing prevalence as the world population ...

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in live-cell imaging and high-throughput microscopy techniques. This led to a constant increase in the amount and complexity of the microscopy data for a single experiment. Because manual analysis of microscopy data is very time consuming, subjective, and prohibits quantitative analyses, automation of bioimage analysis is becoming almost unavoidable. We built an informatics workflow called Substructure Analyzer to fully automate signal analysis in bioimages from fluorescent microscopy. This workflow is developed on the user-friendly open-source platform Icy and is completed by functionalities from ImageJ. It includes the pre-processing of images to improve the signal to noise ratio, the individual segmentation of cells (detection of cell boundaries) and the detection/quantification of cell bodies enriched in specific cell compartments. The main advantage of this workflow is to propose complex bio-imaging functionalities to users without image analysis expertise through a user-friendly interface. Moreover, it is highly modular and adapted to several issues from the characterization of nuclear/cytoplasmic translocation to the comparative analysis of different cell bodies in different cellular sub-structures. The functionality of this workflow is illustrated through the study of the Cajal (coiled) Bodies under oxidative stress (OS) conditions. Data from fluorescence microscopy show that their integrity in human cells is impacted a few hours after the induction of OS. This effect is characterized by a decrease of coilin nucleation into characteristic Cajal Bodies, associated with a nucleoplasmic redistribution of coilin into an increased number of smaller foci. The central role of coilin in the exchange between CB components and the surrounding nucleoplasm suggests that OS induced redistribution of coilin could affect the composition and the functionality of Cajal Bodies.

We present a freely available workflow built for rapid exploration and accurate analysis of cellular bodies in specific cell compartments in fluorescence microscopy images. This user-friendly workflow is designed on the open-source software Icy and also uses ImageJ functionalities. The pipeline is affordable without knowledge in image analysis.

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in ...

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental technique and image analysis protocol presented here can also be applied for imaging microscopic bubbles in other fields of research; therefore, it has a wide range of applications. We apply this to image cavitation around dental ultrasonic scalers. It is important to image cavitation to characterize it and to understand how it can be exploited for various applications. Cavitation occurring around dental ultrasonic scalers can be used as a novel method of dental plaque removal, which would be more effective and cause less damage than current periodontal therapy techniques. We present a method for imaging the cavitation bubble clouds occurring around dental ultrasonic scaler tips using a high-speed camera and a zoom lens. We also calculate the area of cavitation using machine learning image analysis. Open source software is used for image analysis. The image analysis presented is easy to replicate, does not require programming experience, and can be modified easily to suit the application of the user.

Cavitation microbubbles are imaged using a high-speed camera attached to a zoom lens. The experimental setup is explained, and image analysis is used to calculate the area of the cavitation. Image analysis is done using ImageJ.

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental ...