This work was partially supported by grant NFSC 81170806. The authors would like to thank the micro-CT core facility of School of Stomatology, Wuhan University for helping scan and analyze the rat femurs.

Procedures involving animal subjects were conducted in accordance with the Guide for the Care and Use of Laboratory Animals (NIH publication, 8th edition, 2011), and have been reviewed and approved by the Institutional Animal Care and Use Committee of Wuhan University.
1. Software Installation
<ol>
	<li>Install ImageJ software. Download the Windows version of the ImageJ (version 1.51p) software bundled with 64-bit Java from https://imagej.nih.gov/ij/. Extract the downloaded software into a folder, which will be subsequently referred to as the &#34;ImageJ Directory&#34;. 
	NOTE: Trabecular analysis plugins require 64-bit java run time (version 1.8) and a 64-bit windows operating system, preferably a 64-bit Windows 7 operating system.</li>
	<li>Install trabecular analysis plugins. Request trabecular analysis plugins from http://www.bomomics.com, and unzip the contents into ImageJ&#39;s plugins directory, which is &#34;ImageJ Directory/plugins&#34;. 
	NOTE: The plugin can be obtained either as a free version, where the measurements for 5 specified adjacent slices are reported, or as a commercial version, where a range of slices can be specified and measured.</li>
</ol>
2. Prepare 3D Dataset for Trabecular Analysis
<ol>
	<li>Scan rat femurs with a micro-CT machine following the standard scanning protocol5 and then save the data in a format that can be imported into ImageJ, e.g., tiff format. If multiple bone samples were scanned simultaneously in a single sample tube, first import the data into ImageJ, and then separate each individual bone by cropping off other samples using ImageJ&#39;s image processing tools. Subsequently, save the resulting image in a format that can be imported into ImageJ later. 
	NOTE: A representative sample image file used in the analysis is included (Supplementary File 1).</li>
	<li>Simulate standard 2D and 3D objects.
	<ol>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Circle button, and enter 200 as the diameter in the popup window, then click OK to generate the simulated circle with a diameter of 200 pixels (Figure 3B). Save the generated circle in tiff format.</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Square button, and enter 200 as the side length in the popup window, then click OK to generate the simulated square with a side length of 200 pixels (Figure 3B). Save the generated square in tiff format.</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Rectangle button, and enter 200 as the width and 100 as the height in the popup window, then click OK to generate the simulated rectangle with a width of 200 pixels and a height of 100 pixels (Figure 3B). Save the generated rectangle in tiff format. 
		NOTE: A circle (diameter: 200 pixels), a square (side length: 200 pixels), and a rectangle (width: 200 pixels; height: 100 pixels) are saved for the subsequent analyses.</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Cube button, and enter 30 as the side length in the popup window, then click OK to generate the simulated cube. Finally, click the Plugins | 3D | Volume Viewer to view the generated cube and save it in tiff format (Figure 3C).</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Cuboid button, and enter 80 as the length, 40 as the width, and 30 as the height in the popup window, then click OK to generate the simulated cuboid. Finally, click the Plugins | 3D | Volume Viewer to view the generated cuboid and save it in tiff format (Figure 3C).</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Sphere button, and enter 30 as the diameter in the popup window, then click OK to generate the simulated sphere. Finally, click the Plugins | 3D | Volume Viewer to view the generated sphere and save it in tiff format (Figure 3C).</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Cylinder button, and enter 30 as the diameter and 100 as the height in the popup window, then click OK to generate the simulated cylinder. Finally, click the Plugins | 3D | Volume Viewer to view the generated cylinder and save it in tiff format (Figure 3C). 
		NOTE: A sphere (diameter: 30 pixels), a cube (side length: 30 pixels), a cuboid (length: 80 pixels; width: 40 pixels; height: 30 pixels), and a cylinder (diameter: 30 pixels; height: 100 pixels) are saved for the subsequent analyses.</li>
	</ol>
	</li>
</ol>
3. Profiling Analysis Parameters
<ol>
	<li>Open ImageJ software and open or import a scanned image.</li>
	<li>Slide the bottom scrollbar to choose one slice, then click the Image | Adjust | Threshold button. In the popup Threshold window, adjust the minimum and maximum threshold values by manual inspection to ensure that bones are well separated from the background, and record the minimal threshold value as the cortical bone threshold value. 
	NOTE: As different micro-CT manufacturers use different scaling factors for storing X-ray attenuation coefficients in the images, the actual threshold value should be determined empirically either by manual inspection or following the specific manufacturer&#39;s recommendations. In our practice, setting the minimum threshold for ISQ files produced by micro-CT machines to 6,000-7,000 can reliably separate bones from the scanning background.</li>
	<li>Click the Plugins | BoMomics | Trab Param Profiling button. In the popup window, set Slice Index to the position of the representative slice, and set the cortical bone (&#34;Cort Bone&#34;), range (&#34;Range&#34;), and step (&#34;Step&#34;) values for calculating a set of cortical thresholds for profiling segmentation parameters, where the cortical bone threshold value is acquired by step 3.2 (Figure 1). The default range and step values of 2,000 and 400 work for most micro-CT images. Set the Slice Index to 5 for the example dataset, and keep the default values for other settings. 
	Note: &#34;Cort Bone&#34; is the threshold for cortical bones, and the threshold value used for parameter profiling changes from the lowest threshold value to the highest threshold value with the increment of Step value, where the lowest threshold value is the value of Cort Bone - Range, and the highest threshold value is Cort Bone + Range. If the set values for Cort Bone, Range, and Step are 6,000, 1,000, and 500, respectively, then the lowest threshold value for cortical bones is 6,000-1,000=5,000, the highest threshold value is 6,000+1,000=7,000, and the thresholds used in the analyses are 5,000, 5,500, 6,000, 6,500, and 7,000.</li>
	<li>Set noise diameter (&#34;Noise Dia.&#34;), step (&#34;Step&#34;), and range (&#34;Range&#34;) values for specifying a set of noise values in the analyses, and the hole diameter (&#34;Hole Dia.&#34;), step (&#34;Step&#34;), and range (&#34;Range&#34;) values for calculating a set of hole values. In general, the default setting works for most micro-CT bone samples, where noise, step, and range are 5, 5, 2, respectively, and hole diameter, step, and range are 15, 5, 2, respectively. Keep the default settings for the example dataset. 
	Note: &#34;Noise Dia.&#34; is the diameter for the noise suppression filter, and the &#34;Hole Dia.&#34; is the diameter for holes within the cortical bones. The noise and hole values used for parameter profiling can be calculated similarly as cortical threshold values described above, using the specified noise/hole, range, and step values. Due to the variations in scanning parameters when acquiring the images, noise and hole parameters should be determined empirically according to the image quality, as no general ranges of noise and hole parameters are good for all scans. The lowest value for threshold, noise, or holes must be greater than or equal to 0, and if the calculated lowest value is negative using the supplied parameters, the specific lowest value is set to 0.</li>
	<li>Click OK to perform parameter profiling. Visually check the segmentation results in the Parameter Profiling Results window, and select a slice layer so that the bone outer boundary is outlined quite accurately (Figure 1B). Subsequently, retrieve the profiling parameters from the entry in the Parameter Profiling Results table corresponding to the chosen slice layer (Table 1).</li>
</ol>
4. Trabecular Analysis
<ol>
	<li>Segmentation of trabecular bones
	<ol>
		<li>Open ImageJ software, then open or import a scanned image.</li>
		<li>Click the Plugins | BoMomics | Trab Segmentation button, and fill in appropriate analysis parameters. Set &#34;Start&#34;, &#34;Outline Boundary&#34;, &#34;Trab. Bones&#34;, &#34;Noise Reduction Dia.&#34;, &#34;Hole Filling Dia.&#34;, and &#34;Cortical Thickness Dia.&#34; to 5, 7,200, 7,000, 6, 12 and 25, respectively. 
		NOTE: &#34;Start&#34; and &#34;End&#34; specify the selected slice range for segmentation of trabecular bones, &#34;Outline Boundary&#34; is corresponding to the profiled &#34;Cort Bone&#34; parameter, &#34;Noise Reduction Dia.&#34; to &#34;Noise Dia.&#34; Parameter, and &#34;Hole Filling Dia.&#34; to &#34;Hole Dia.&#34; parameter. &#34;Cortical Thickness Dia.&#34; is the specified thickness for excluding the outer cortical bones. &#34;Trab. Bones&#34; is the threshold for extracting trabecular bones (Figure 2), where the settings are determined using profiling analysis parameters command as described in step 3.5.</li>
		<li>Click OK to perform trabecular segmentation. Visually check the segmentation results in the Trab Segmentation Results window (Figure 2B). Save the extracted trabecular bones shown in the Segmented Trabecular Bones window in a tiff format (Figure 2B), which can be further analyzed by other software.</li>
	</ol>
	</li>
	<li>Analysis of trabecular bones.
	<ol>
		<li>Open ImageJ software, then open or import a scanned image.</li>
		<li>Click the Plugins | BoMomics | Trab Analysis button, and fill in appropriate analysis parameters, such as &#34;Start&#34;, &#34;Outline Boundary&#34;, &#34;Trab. Bones&#34;, &#34;Noise Reduction Dia.&#34;, &#34;Hole Filling Dia.&#34;, and &#34;Cortical Thickness Dia.&#34; as described above (Figure 3A), where the settings are determined using the profiling analysis parameters command as described in step 3.5. Set &#34;Start&#34;, &#34;Outline Boundary&#34;, &#34;Trab. Bones&#34;, &#34;Noise Reduction Dia.&#34;, &#34;Hole Filling Dia.&#34;, and &#34;Cortical Thickness Dia.&#34; to 5, 7,200, 7,000, 6, 12 and 25, respectively. 
		NOTE: In the free version of the plugin, five adjacent slices starting from the specified &#34;Start&#34; slice index are selected for measurement, while in the commercial version, an arbitrary number of slices can be specified by the user.</li>
		<li>Select one or more choices in Results Reporting section for the parameters to be measured, where the trabecular bone volume (BV), total volume (TV) of the selected region, and thicknesses measured either two-dimensionally (2D) or three-dimensionally (3D) are available for selection through three checkboxes, namely &#34;BV TV Only&#34;, &#34;2D&#34;, and &#34;3D&#34;. Select the checkboxes &#34;2D&#34; and &#34;3D&#34;, then click &#34;OK&#34; to perform trabecular analysis&#160;(Figure 3A, Table 2). 
		NOTE: When &#34;BV TV Only&#34; is checked, regardless of the selection statuses of &#34;2D&#34; and &#34;3D&#34;, raw measures of BV, TV, and Intensity are reported, and the segmented trabecular bones are extracted and displayed in a new window, which can be saved and further analyzed by other software. When &#34;2D&#34; is checked, raw measures of BV, TV, Intensity, and thicknesses measured two-dimensionally at each slice level using the plate model are reported. If &#34;3D&#34; is checked, three-dimensional thickness for each voxel is calculated directly without any model assumption, then raw measures of BV, TV, Intensity, and three-dimensional thicknesses sampled at each slice level are reported. However, if no checkbox is selected, trabecular bones are segmented using the above set of parameters, but no measurement is reported.</li>
	</ol>
	</li>
</ol>
5. Quantifying Simulated Objects
<ol>
	<li>Open ImageJ software, then open a simulated image. Here, open the simulated sphere with a diameter of 30 pixels as an example.</li>
	<li>Select the Plugins | BoMomics | Trab Analysis button and fill in appropriate analysis parameters as described previously. Keep the default values for &#34;Start&#34;, &#34;End&#34;, &#34;Outline Boundary&#34;, &#34;Trab. Bones&#34;, and set &#34;Noise Reduction Dia.&#34;, &#34;Noise Reduction Dia.&#34;, &#34;Hole Filling Dia.&#34;, and &#34;Cortical Thickness Dia.&#34; to 0 (Figure 3). 
	Note: For simulated objects, there is no unintended signal noise, and there may be no corresponding cortical shells. Therefore, parameters for such values should be set appropriately (default settings are to zero). For 3D quantifications, 30 slices before and after the specified &#34;Start&#34; slice are processed using the free version of the plugin, while only slices in the range specified by &#34;Start&#34; and &#34;End&#34; slices are analyzed by the commercial version of the plugin.</li>
	<li>In the Results Reporting section, select &#34;2D&#34; and &#34;3D&#34; for the parameters to be measured, and click OK to perform trabecular analysis for the simulated object (Table 3).</li>
</ol>
6. Calibration of Trabecular Measures and Data Presentation: Profile the Distributions of Trabecular Measures in the Selected Analyzing Region
<ol>
	<li>Get the calibration information from the scanned dataset, according to the micro-CT vendor&#39;s instructions. 
	NOTE: Only raw measures of BV, TV, Intensity, and thicknesses are reported by the plugin. To get measures comparable to reports generated by other software, result calibration is desired.</li>
	<li>Open Microsoft Excel, and open the reported results table. Calculate calibrated bone volume (BV), total volume (TV), bone mineral content (BMC), bone volume fraction (BV/TV), and bone mineral density (BMD) in new excel columns using the reported raw BV, TV, and Intensity values according to the following expressions.</li>
	<li>NOTE: Scanning resolution (resolution, &#181;m), gray scaling (scaling), density units (mg HA/cm3), density slope (slope), and density intercept (intercept) can be extracted from the scanned micro-CT image or meta files, such as the Scanco ISQ file. Therefore, the calibrated measures are calculated as follows: 
	Suppose BV, TV and Intensity are raw measures, BVc and TVc are calibrated values, resolution, scaling, slope, intercept are from calibration files of micro - CT images. 
	BVc = BV &#215; resolution3 [&#956;m3] 
	TVc = TV &#215; resolution3 [&#956;m3] 
	BV / TV = BV &#247; TV 
	BMC = (Intensity &#247; scaling &#215; slope - BV&#160;&#215; intercept) &#215; resolution3 &#215; 10-12 [mg HA] 
	BMD -&#160;BMC &#247; TVc &#215; 1012 [mg HA / cm3]</li>
	<li>Create an XY (Scatter) plot for calibrated measures (Y) against slice layers (X) using Microsoft Excel software (Figure 4).</li>
</ol>

<ol>
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A patent for the underlying algorithm of quantifying object thickness has been filed. The authors collaborated with others registered the bomomics.com website hosting the free version plugins, where consulting and quantifying services of image analysis are provided upon request.

This study describes an ImageJ plugin for analyzing trabecular bones, which is automatic, efficient, and user friendly. The plugin can also be used to quantify any 2D or 3D object for layer-by-layer measures of areas, volumes, and thicknesses. Currently, only a single measured value for each trabecular parameter is reported for each sample by standard micro-CT analysis, which cannot fully represent the characteristics of the measured entity in the selected analyzing region. The described plugin reports layer-by-layer quantities of each parameter for each sample, which fully retains the distribution information of the measured parameter in the selected analyzing region, and therefore more advanced and sensitive statistical approaches are applicable for analyzing such data.
With the standard, semi-automatic hand-contouring segmentation method, substantial variation between different hand-contouring slices were observed with operator bias8,9,10, warranting a more uniform and automatic method for trabecular segmentation. As the contrast is maximal between the scanning background and the bone outer boundary, outer-boundary assisted trabecular segmentation is performed by the plugin, where the bone&#39;s outer boundary can be automatically detected with accuracy when appropriate threshold, noise, and hole settings are provided. To facilitate the determination of segmentation parameters, a range of parameter combinations are profiled one by one using a representative slice from the analyzing region, and satisfactory segmentation results are checked visually. Subsequently, trabecular bones are analyzed with the profiled parameters. When appropriate segmentation parameters are set, and the plugin has been successfully applied to analyze trabecular bones from rat distal femurs treated with different bone anabolic agents5, reproducible results are reported for the same image without operator bias.
The plugin currently can only process one sample at a time. If multiple samples are present in the image, all samples are quantified as a single object without discrimination. Therefore, preprocessing is required if multiple samples are scanned simultaneously in a single sample tube. The quantification outputs are raw measures of pixel counts or gray values, which should be calibrated manually using appropriate calibration information.
The most critical step in this protocol is selecting appropriate parameters for trabecular analysis. Generally, groups of bone samples are analyzed using identical segmentation parameters to make the results comparable. In such a case, care should be taken to ensure that segmentation results for all samples are examined visually for potential errors before further analyses are conducted.
A major limitation of this technique is that the noise reduction settings are applied to cortical bones only for contouring a bone&#39;s outer boundary, but not on extracted trabecular bones. As various filtering strategies have been reported15 as optimal for trabecular analysis under different conditions, no single filter is good for all image samples. Therefore, there is no built-in filtration step before quantification of trabecular bones. To be compatible with the output by software with built-in image filtrations, extracted trabecular bones can be processed similarly first using various imaging tools, and then imported into ImageJ and quantified by the plugin subsequently. Moreover, different image formats are generated by different micro-CT vendors, and most of them cannot be imported or opened by ImageJ directly, thus modules for importing various image formats into ImageJ for further analysis are desired. Another limitation is that the 3D thicknesses in slices near the ends of the selection range are not accurate if part of the objects are outside of the selection range, which is also true for any available 3D quantification packages, therefore, a larger range of slices should be quantified, and only the middle portion of selected slices should be used for further analysis. Such an approach can only be available when measured quantities are reported layer-by-layer as shown in this plugin, but not for measurements that only a single value is reported for each quantified parameter in the selection range.

Micro-CT analysis of trabecular bones is the standard approach for tracking morphological changes of the bones in small animals under different bone loss conditions1,2,3, where several variables related to the structures of the bones are reported4. However, such parameters are not evenly distributed in the metaphysis of long bones5, and only a summarized or averaged value is reported for each structural variable of each sample by current commercial micro-CT machines6,7, though a single value cannot fully represent the characteristics of the measured parameter in the analyzing region. Layer-by-layer quantification of trabecular bones not only retains more information for each variable, but also enables the profiling of the distributions of such variables in the analyzing region, facilitating subsequent statistical analysis of structural changes under different conditions5. Therefore, the goal of this method is quantifying trabecular bones of micro-CT scans at each slice level, which is not available in any commercially available micro-CT analysis package currently.
To efficiently segment trabecular bones slice-by-slice, automatic segmentation methods are desirable. However, the current standard technique for micro-CT analysis is based on manual interactive contouring followed by semiautomatic interpolation to separate trabecular bones from the cortical compartments, which is labor intensive, error-prone, and associated with substantial operator bias8,9,10. Automatic segmentation methods11,12 were reported, but such methods are only optimal in regions with good separation between trabecular bones and cortical bones, but not in regions without clear separations. Moreover, different segmentation parameters are required for different samples12, and it is tedious to manually select satisfactory segmentation parameters applicable to groups of bone samples by trying various parameter combinations12, even though the segmentation process is automatic when all related parameters are set. As the bone outer boundary has the greatest contrast with the scanning background and the metaphyseal cortical shells of long bones show few changes in the chosen analyzing region, segmentation methods according to the outer-boundary contour of long bones can reliably and accurately separate trabecular bones from cortical shells. The advantage of such a segmentation method is that the segmentation is based on the difference between the background and the bone&#39;s outer boundary, but not on the differences between trabecular and cortical bones6,12,13, therefore it is generally easy to find a combination of segmentation parameters that is satisfactory for a group of bone samples, facilitating more reliable analysis of trabecular changes between different groups.
At each slice level, area, perimeter, and two-dimensional (2D) thickness are reported for 2D analysis, while volume, surface, and three-dimensional (3D) thickness are reported in 3D quantifications. Such information is generally not reported by current image analysis tools, indicating that the reported procedures can be applied to general images where such information is desired.

<table border="0" cellpadding="0" cellspacing="0" style="width:595px;" width="595">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ImageJ</td>
			<td style="width:93px;">NIH</td>
			<td style="width:119px;">imagej</td>
			<td style="width:167px;">Any version with a java 1.8 run time</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">trabecular analysis plugin</td>
			<td style="width:93px;">Bomomics</td>
			<td style="width:119px;">bomomics</td>
			<td>free or commercial version</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Micro CT scanner</td>
			<td style="width:93px;">Scanco</td>
			<td style="width:119px;">&#956;-50</td>
			<td>micro CT from any vendor</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Computer System</td>
			<td style="width:93px;">Lenovo</td>
			<td style="width:119px;"></td>
			<td>any brand</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Windows Operating System</td>
			<td style="width:93px;">Microsoft</td>
			<td style="width:119px;">Windows 7 x64</td>
			<td>any 64-bit Windows operating system&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Office Software</td>
			<td style="width:93px;">Microsoft</td>
			<td style="width:119px;">Office 2010</td>
			<td>any speadsheet software that has xy chart function</td>
		</tr>
	</tbody>
</table>

outer-boundary assisted segmentation and quantification of trabecular bones by an imagej plugin

Micro-computed tomography (micro-CT) is routinely used to assess bone quantity and trabecular microstructural properties in small animals under different bone loss conditions. However, the standard approach for trabecular analysis of micro-CT images is slice-by-slice semi-automatic hand-contouring, which is labor intensive and error prone. Described here is an efficient method for automatic segmentation of trabecular bones according to the bone's outer boundaries, where trabecular bones can be identified and segmented automatically with accuracy with less operator bias when appropriate segmentation parameters are set. To profile satisfactory segmentation parameters, an image stack of segmentation results is displayed, where all possible combinations of the segmentation parameters are changed one by one in sequence, and segmentation results with associated parameters can easily be visually checked. As a quality-control feature of the plugin, simulated standard objects are quantified where the measured quantities can be compared with theoretical values. Layer-by-layer quantification of trabecular properties and trabecular thicknesses are reported by such a plugin, and the distributions of such properties within the selected regions can be profiled easily. Although layer-by-layer quantification retains more information about trabecular bones and facilitates further statistical analysis of structural changes, such measures are unavailable from the output of current commercial software, where only a single quantified value for each parameter is reported for each sample. Therefore, the described workflows are better approaches for analyzing trabecular bones with accuracy and efficiency.

The trabecular analysis plugin is designed to automatically segment and quantify trabecular bones with accuracy. Initially, bone outer boundary is detected and delineated followed by a hole-filling operation where any holes within bone outer cortical shells are filled. Then an erosion operation is performed to exclude the outer cortical bones and get the segmented trabecular bones. Finally, measures of trabecular bones in the segmented region are quantified.
As micro-CT images are inherently noisy, segmentations using predefined arbitrary parameters often fail to accurately identify the bone&#39;s outer boundaries. It is tedious and labor intensive to try a lot of parameter combinations for selecting satisfactory segmentation parameters. Therefore, a parameter profiling plugin is provided for changing the parameters one by one in the set range automatically to assist in selecting satisfactory parameter combinations, which also facilitates selecting a common set of parameters for a group of bone samples. Figure 1A shows the settings used for profiling good segmentation parameters. When the parameters for cortical bone threshold (Cort Bone), range of threshold to be profiled (Range), and increment amount for threshold (Step) at each step are specified, a series of thresholds to be profiled are generated. Subsequently, a series of noise and hole values are generated similarly by setting the corresponding parameters. Finally, trabecular bones are segmented by changing the parameters one at a time for all the possible parameter combinations. Figure 1B shows the representative segmentation results for different parameter combinations. Obviously, some parameter combinations are better than others at delineating a bone&#39;s outer boundaries, and more than one parameter combination shows satisfactory segmentation results. After visually checking the segmentation results, parameter values for satisfactory segmentations can be retrieved from the profiling results table (Table 1).
To quantify measures of trabecular bones, trabecular segmentation and analysis are performed. Figure 2A shows the setting dialog for trabecular segmentation, where trabecular bones in the selected region are segmented and extracted (Figure 2B), and segmentation results using the supplied parameters can be checked slice-by-slice visually. Subsequently, trabecular bones are analyzed with satisfactory parameters (Figure 3A). Depending on the selection statuses of reporting options, raw quantifications of bone volume (BV), total volume (TV), sum of gray values (Intensity), and thicknesses measured either two dimensionally or three dimensionally (Table 2) are reported. Finally, calibration information is extracted from the scanned micro-CT dataset and calibrated measures of BV, TV, BMC, BV/TV, and BMD are calculated followed by profiling their distributions in the selected analyzing region layer-by-layer against the layer positions (Figure 4).
As a quality-control feature of the plugin, quantification of simulated objects is supported. Simulated standard objects with known dimensions are quantified by the plugin for comparison with the theoretical values or measures from other software, such as commercial software supplied with micro-CT machines or BoneJ14, a free open source plugin for bone image analysis. For analyzing simulated objects, the noise settings are set to zero, as simulated images are considered high quality images without any noise. Objects with various thicknesses were simulated and results are shown (Figure 3, Table 3). For simulated standard 2D objects, such as circles, squares and rectangles, exact values for area (TV or BV) and thickness are reported (Table 3). For 3D objects, exact thickness measures for cubes, spheres, and cuboids are reported, however, the thicknesses for cylinders are not accurate for voxels near cylinder&#39;s both ends, while the voxel thicknesses in the middle slices of the cylinders are exactly as predicted. This is a feature of the underlying thickness measurement algorithm, wherein the voxel&#39;s thickness for each object is determined by the diameter of the largest sphere, or the side length of the largest cube, which contains this voxel and is completely inside the object. Therefore, thicknesses of objects made of different spheres and cubes can be measured accurately, while cylinders can only be measured accurately in the middle slice&#39;s radius-distance away from both ends.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57178/57178fig1.jpg" /> 
Figure 1: Representative results of parameter profiling analysis. (A) The parameters setting page. (B) Representative results of parameter profiling analysis. Some parameter combinations are better than others for detecting the outer boundaries of bones. <a href="https://cloudflare.jove.com/files/ftp_upload/57178/57178fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57178/57178fig2.jpg" /> 
Figure 2: Representative results of trabecular segmentation analysis. (A) The parameters setting page. (B) Representative segmentation results of trabecular bones at different layers. <a href="https://cloudflare.jove.com/files/ftp_upload/57178/57178fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57178/57178fig3.jpg" /> 
Figure 3: Trabecular analysis. (A) The parameters setting page. (B) Representative results of simulated 2D objects. (C) Representative results of simulated 3D objects visualized by 3D volume viewer. <a href="https://cloudflare.jove.com/files/ftp_upload/57178/57178fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57178/57178fig4.jpg" /> 
Figure 4: Distributions of trabecular bone measures in the selected analyzing region. The horizontal axis represents the relative distance to the starting slice layer in the analyzing region. Values in the Y-axis are calibrated trabecular measures in the analyzing region. <a href="https://cloudflare.jove.com/files/ftp_upload/57178/57178fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Slice</td>
			<td>Outline Boundary</td>
			<td>Noise Reduction Dia.</td>
			<td>Hole Filling Dia.</td>
		</tr>
		<tr>
			<td>4</td>
			<td>5200</td>
			<td>0</td>
			<td>10</td>
		</tr>
		<tr>
			<td>147</td>
			<td>5200</td>
			<td>2</td>
			<td>20</td>
		</tr>
		<tr>
			<td>361</td>
			<td>7200</td>
			<td>6</td>
			<td>16</td>
		</tr>
		<tr>
			<td>539</td>
			<td>8000</td>
			<td>10</td>
			<td>20</td>
		</tr>
	</tbody>
</table>
Table 1: Representative results of parameter profiling analysis.
<img alt="Table 2" src="/files/ftp_upload/57178/57178table2.jpg" /> 
Table 2: Representative results of trabecular analysis.
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Object</td>
			<td>Dimension</td>
			<td>Volumeb</td>
			<td>Surfacec</td>
			<td>Thickness</td>
		</tr>
		<tr>
			<td>Square</td>
			<td>200 X 200</td>
			<td>40000</td>
			<td>796</td>
			<td>200</td>
		</tr>
		<tr>
			<td>Rectangle</td>
			<td>200 X 100</td>
			<td>20000</td>
			<td>596</td>
			<td>100</td>
		</tr>
		<tr>
			<td>Circle</td>
			<td>Dia: 200</td>
			<td>31428</td>
			<td>796</td>
			<td>200</td>
		</tr>
		<tr>
			<td>Cube</td>
			<td>30 X 30 X 30</td>
			<td>27000</td>
			<td>5048</td>
			<td>30</td>
		</tr>
		<tr>
			<td>Cuboid</td>
			<td>80 X 40 X 30</td>
			<td>96000</td>
			<td>13008</td>
			<td>30</td>
		</tr>
		<tr>
			<td>Sphere</td>
			<td>Dia: 30</td>
			<td>14328</td>
			<td>3944</td>
			<td>30</td>
		</tr>
		<tr>
			<td>Cylinderd</td>
			<td>Dia:30; H: 100</td>
			<td>71600</td>
			<td>12800</td>
			<td>27.84</td>
		</tr>
		<tr>
			<td>Cylindere</td>
			<td>Dia:30; H: 100</td>
			<td>51552</td>
			<td>9552</td>
			<td>30</td>
		</tr>
		<tr>
			<td colspan="5">a: Results are in raw voxels. Dia: diameter; H: height.</td>
		</tr>
		<tr>
			<td colspan="5">b: Volume (3D) or area (2D).</td>
		</tr>
		<tr>
			<td colspan="5">c: Surface (3D) or perimeter (2D).</td>
		</tr>
		<tr>
			<td colspan="5">d: Measurs from slice 1 to slice 100 are used for analysis.</td>
		</tr>
		<tr>
			<td colspan="5">e: Measurs from slice 15 to slice 85 are used for analysis.</td>
		</tr>
	</tbody>
</table>
Table 3: Quantification results for simulated objects.
<img alt="Supplementary File 1" src="/files/ftp_upload/57178/57178supfig1.jpg" /> 
Supplementary File 1. Sample bone. <a href="https://www.jove.com/files/ftp_upload/57178/SampleBone.tif.crdownload" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin at JoVE.com

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

We present a workflow for segmenting and quantifying trabecular bones for 2D and 3D images based on the bone&#39;s outer boundary using an ImageJ plugin. This approach is more efficient and accurate than the current manual hand-contouring approach, and provides layer-by-layer quantifications, which are not available in current commercial software.

Micro-computed tomography (micro-CT) is routinely used to assess bone quantity and trabecular microstructural properties in small animals under different ...

outer-boundary-assisted-segmentation-quantification-trabecular-bones

Nanyang Technological University

Research

JoVE Journal

Bioengineering

9.2K Views.  Wuhan University. We present a workflow for segmenting and quantifying trabecular bones for 2D and 3D images based on the bone's outer boundary using an ImageJ plugin. This approach is more efficient and accurate than the current manual hand-contouring approach, and provides layer-by-layer quantifications, which are not available in current commercial software.

Video: Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Quantitative cardiac function assessment remains a challenge for physiologists and clinicians.&#160;Although historically invasive methods have comprised the only means available, the development of noninvasive imaging modalities (echocardiography, MRI, CT) having high temporal and spatial resolution provide a new window for quantitative diastolic function assessment. Echocardiography is the agreed upon standard for diastolic function assessment, but indexes in current clinical use merely utilize selected features of chamber dimension (M-mode) or blood/tissue motion (Doppler) waveforms without incorporating the physiologic causal determinants of the motion itself.&#160;The recognition that all left ventricles (LV) initiate filling by serving as mechanical suction pumps allows global diastolic function to be assessed based on laws of motion that apply to all chambers. What differentiates one heart from another are the parameters of the equation of motion that governs filling. Accordingly, development of the Parametrized Diastolic Filling (PDF) formalism&#160;has shown that the entire range of clinically observed early transmitral flow (Doppler E-wave) patterns are extremely well fit by the laws of damped oscillatory motion.&#160;This permits analysis of individual E-waves in accordance with a causal mechanism (recoil-initiated suction) that yields three (numerically) unique lumped parameters whose physiologic analogues are chamber stiffness (k), viscoelasticity/relaxation (c), and load (xo).&#160;The recording of transmitral flow (Doppler E-waves) is standard practice in clinical cardiology and, therefore, the echocardiographic recording method is only briefly reviewed. Our focus is on determination of the PDF parameters from routinely recorded E-wave data.&#160;As the highlighted results indicate, once the PDF parameters have been obtained from a suitable number of load varying E-waves, the investigator is free to use the parameters or construct indexes from the parameters (such as stored energy 1/2kxo2, maximum A-V pressure gradient kxo, load independent index of diastolic function, etc.) and select the aspect of physiology or pathophysiology to be quantified.

Accurate, causality-based quantification of global diastolic function has been achieved by kinematic modeling-based analysis of transmitral flow via the Parametrized Diastolic Filling (PDF) formalism. PDF generates unique stiffness, relaxation,&#160;and load parameters and elucidates &#39;new&#39; physiology while providing sensitive and specific indexes of dysfunction.

Quantitative cardiac function assessment remains a challenge for physiologists and clinicians.&#160;Although historically invasive methods have comprised ...

Biomembrane Fabrication by the Solvent-assisted Lipid Bilayer (SALB) Method

In order to mimic cell membranes, the supported lipid bilayer (SLB) is an attractive platform which enables in vitro investigation of membrane-related processes while conferring biocompatibility and biofunctionality to solid substrates. The spontaneous adsorption and rupture of phospholipid vesicles is the most commonly used method to form SLBs. However, under physiological conditions, vesicle fusion (VF) is limited to only a subset of lipid compositions and solid supports. Here, we describe a one-step general procedure called the solvent-assisted lipid bilayer (SALB) formation method in order to form SLBs which does not require vesicles. The SALB method involves the deposition of lipid molecules onto a solid surface in the presence of water-miscible organic solvents (e.g., isopropanol) and subsequent solvent-exchange with aqueous buffer solution in order to trigger SLB formation. The continuous solvent exchange step enables application of the method in a flow-through configuration suitable for monitoring bilayer formation and subsequent alterations using a wide range of surface-sensitive biosensors. The SALB method can be used to fabricate SLBs on a wide range of hydrophilic solid surfaces, including those which are intractable to vesicle fusion. In addition, it enables fabrication of SLBs composed of lipid compositions which cannot be prepared using the vesicle fusion method. Herein, we compare results obtained with the SALB and conventional vesicle fusion methods on two illustrative hydrophilic surfaces, silicon dioxide and gold. To optimize the experimental conditions for preparation of high quality bilayers prepared via the SALB method, the effect of various parameters, including the type of organic solvent in the deposition step, the rate of solvent exchange, and the lipid concentration is discussed along with troubleshooting tips. Formation of supported membranes containing high fractions of cholesterol is also demonstrated with the SALB method, highlighting the technical capabilities of the SALB technique for a wide range of membrane configurations.

Biomembrane Fabrication by the Solvent-assisted Lipid Bilayer SALB Method

We present an experimental protocol to form a supported lipid bilayer on solid substrates without using lipid vesicles. We demonstrate a one-step method to form a lipid bilayer on silicon dioxide and gold as well as supported membranes with cholesterol-enriched domain for various biological applications.

In order to mimic cell membranes, the supported lipid bilayer (SLB) is an attractive platform which enables in vitro investigation of membrane-related ...

Whole-body Mass Spectrometry Imaging by Infrared Matrix-assisted Laser Desorption Electrospray Ionization (IR-MALDESI)

Ambient ionization sources for mass spectrometry (MS) have been the subject of much interest in the past decade. Matrix-assisted laser desorption electrospray ionization (MALDESI) is an example of such methods, where features of matrix-assisted laser desorption/ionization (MALDI) (e.g., pulsed nature of desorption) and electrospray ionization (ESI) (e.g., soft-ionization) are combined. One of the major advantages of MALDESI is its inherent versatility. In MALDESI experiments, an ultraviolet (UV) or infrared (IR) laser can be used to resonantly excite an endogenous or exogenous matrix. The choice of matrix is not analyte dependent, and depends solely on the laser wavelength used for excitation. In IR-MALDESI experiments, a thin layer of ice is deposited on the sample surface as an energy-absorbing matrix. The IR-MALDESI source geometry has been optimized using statistical design of experiments (DOE) for analysis of liquid samples as well as biological tissue specimens. Furthermore, a robust IR-MALDESI imaging source has been developed, where a tunable mid-IR laser is synchronized with a computer controlled XY translational stage and a high resolving power mass spectrometer. A custom graphical user interface (GUI) allows user selection of the repetition rate of the laser, number of shots per voxel, step-size of the sample stage, and the delay between the desorption and scan events for the source. IR-MALDESI has been used in variety of applications such as forensic analysis of fibers and dyes and MSI of biological tissue sections. Distribution of different analytes ranging from endogenous metabolites to exogenous xenobiotics within tissue sections can be measured and quantified using this technique. The protocol presented in this manuscript describes major steps necessary for IR-MALDESI MSI of whole-body tissue sections.

Whole-body Mass Spectrometry Imaging by Infrared Matrix-assisted Laser Desorption Electrospray Ionization IR-MALDESI

A mass spectrometry imaging (MSI) source operated at atmospheric pressure was developed by coupling mid-infrared laser desorption and electrospray post-ionization. Exogenous ice matrix was used as the energy-absorbing matrix to facilitate resonant desorption of tissue-related material. This manuscript provides a step-by-step protocol for performing IR-MALDESI MSI of whole-body neonatal mouse.

Ambient ionization sources for mass spectrometry (MS) have been the subject of much interest in the past decade. Matrix-assisted laser desorption ...

A Novel Surgical Technique As a Foundation for In Vivo Partial Liver Engineering in Rat

Organ engineering is a novel strategy to generate liver organ substitutes that can potentially be used in transplantation. Recently, in vivo liver engineering, including in vivo organ decellularization followed by repopulation, has emerged as a promising approach over ex vivo liver engineering. However, postoperative survival was not achieved. The aim of this study is to develop a novel surgical technique of in vivo selective liver lobe perfusion in rats as a prerequisite for in vivo liver engineering. We generate a circuit bypass only through the left lateral lobe. Then, the left lateral lobe is perfused with heparinized saline. The experiment is performed with 4 groups (n = 3 rats per group) based on different perfusion times of 20 min, 2 h, 3 h, and 4 h. Survival, as well as the macroscopically visible change of color and the histologically determined absence of blood cells in the portal triad and the sinusoids, is taken as an indicator for a successful model establishment. After selective perfusion of the left lateral lobe, we observe that the left lateral lobe, indeed, turned from red to faint yellow. In a histological assessment, no blood cells are visible in the branch of the portal vein, the central vein, and the sinusoids. The left lateral lobe turns red after reopening the blocked vessels. 12/12 rats survived the procedure for more than one week. We are the first to report a surgical model for in vivo single liver lobe perfusion with a long survival period of more than one week. In contrast to the previously published report, the most important advantage of the technique presented here is that perfusion of 70% of the liver is maintained throughout the whole procedure. The establishment of this technique provides a foundation for in vivo partial liver engineering in rats, including decellularization and recellularization.

A Novel Surgical Technique As a Foundation for In Vivo Partial Liver Engineering in Rat

We establish a novel surgical technique for an in vivo single liver lobe perfusion model in rat as a prerequisite for further studying in vivo partial liver engineering in the future.

Organ engineering is a novel strategy to generate liver organ substitutes that can potentially be used in transplantation. Recently, in vivo liver ...

Detection of Endotoxin in Nano-formulations Using Limulus Amoebocyte Lysate (LAL) Assays

When present in pharmaceutical products, a Gram-negative bacterial cell wall component endotoxin (often also called lipopolysaccharide) can cause inflammation, fever, hypo- or hypertension, and, in extreme cases, can lead to tissue and organ damage that may become fatal. The amounts of endotoxin in pharmaceutical products, therefore, are strictly regulated. Among the methods available for endotoxin detection and quantification, the Limulus Amoebocyte Lysate (LAL) assay is commonly used worldwide. While any pharmaceutical product can interfere with the LAL assay, nano-formulations represent a particular challenge due to their complexity. The purpose of this paper is to provide a practical guide to researchers inexperienced in estimating endotoxins in engineered nanomaterials and nanoparticle-formulated drugs. Herein, practical recommendations for performing three LAL formats including turbidity, chromogenic and gel-clot assays are discussed. These assays can be used to determine endotoxin contamination in nanotechnology-based drug products, vaccines, and adjuvants.

Detection of Endotoxin in Nano-formulations Using Limulus Amoebocyte Lysate LAL Assays

Detection of endotoxins in engineered nanomaterials represents one of the grand challenges in the field of nanomedicine. Here, we present a case study that describes the framework composed of three different LAL formats to estimate potential endotoxin contamination in nanoparticles.

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Screening and Identification of Small Peptides Targeting Fibroblast Growth Factor Receptor2 using a Phage Display Peptide Library

The human Fibroblast Growth Factor Receptor (FGFR) family comprises four members, namely, FGFR1, FGFR2, FGFR3, and FGFR4, that are involved in various biological activities including cell proliferation, survival, migration and differentiation. Several aberrations in the FGFR signaling pathway, owing to mutations or gene amplification events, have been identified in various types of cancers. Hence, recent research has focused on developing strategies involving therapeutic targeting of FGFRs. Current FGFR inhibitors at various stages of pre-clinical and clinical development include either small molecule inhibitors of tyrosine kinases or monoclonal antibodies, with only a few peptide- based inhibitors in the pipeline. Here, we provide a protocol using phage display technology to screen small peptides as antagonists of FGFR2. Briefly, a library of phage-displayed peptides was incubated in a plate coated with FGFR2. Subsequently, unbound phage was washed off by TBST (TBS + 0.1% [v/v] Tween-20), and bound phage was eluted with 0.2 M glycine-HCl buffer (pH 2.2). The eluted phage was further amplified and used as input for the next round of biopanning. Following three rounds of biopanning, the peptide sequences of individual phage clones were identified by DNA sequencing. Finally, the screened peptides were synthesized and analyzed for affinity and biological activity.

Herein, we present a detailed protocol for screening small peptides that bind to FGFR2 using a phage display peptide library. We further analyze the affinity of the selected peptides toward FGFR2 in vitro and its ability to suppress cell proliferation.

The human Fibroblast Growth Factor Receptor (FGFR) family comprises four members, namely, FGFR1, FGFR2, FGFR3, and FGFR4, that are involved in various ...

Simultaneous Quantification of Selected Kynurenines Analyzed by Liquid Chromatography-Mass Spectrometry in Medium Collected from Cancer Cell Cultures

The kynurenine pathway and the tryptophan catabolites called kynurenines have received increased attention for their involvement in immune regulation and cancer biology. An in vitro cell culture assay is often used to learn about the contribution of different tryptophan catabolites in a disease mechanism and for testing therapeutic strategies. Cell culture medium that is rich in secreted metabolites and signaling molecules reflects the status of tryptophan metabolism and other cellular events. New protocols for the reliable quantification of multiple kynurenines in the complex cell culture medium are desired to allow for a reliable and quick analysis of multiple samples. This can be accomplished with liquid chromatography coupled with mass spectrometry. This powerful technique is employed in many clinical and research laboratories for the quantification of metabolites and can be used for measuring kynurenines.
Presented here is the use of liquid chromatography coupled with single quadrupole mass spectrometry (LC-SQ) for the simultaneous determination of four kynurenines, i.e., kynurenine, 3-hydroxykynurenine, 3-hydroxyanthranilic, and xanthurenic acid in the medium collected from in vitro cultured cancer cells. SQ detector is simple to use and less expensive compared to other mass spectrometers. In the SQ-MS analysis, multiple ions from the sample are generated and separated according to their specific mass-to-charge ratio (m/z), followed by the detection using a Single Ion Monitoring (SIM) mode.
This paper draws the attention on the advantages of the reported method and indicates some weak points. It is focused on critical elements of LC-SQ analysis including sample preparation along with chromatography and mass spectrometry analysis. The quality control, method calibration conditions and matrix effect issues are also discussed. We described a simple application of 3-nitrotyrosine as one analog standard for all target analytes. As confirmed by experiments with human ovary and breast cancer cells, the proposed LC-SQ method&#160;generates reliable results and can be further applied to other in vitro cellular models.

Described here is a protocol for the determination of four different tryptophan metabolites generated in the kynurenine pathway (kynurenine, 3-hydroxykynurenine, xanthurenic acid, 3-hydroxyanthranilic acid) in the medium collected from cancer cell cultures analyzed by liquid chromatography coupled with a single quadrupole mass spectrometry.

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Injectable biomaterials are becoming increasingly popular for the minimally invasive delivery of drugs and cells. These materials are typically more viscous than traditional aqueous injections and may be semi-solid, therefore, their injectability cannot be assumed. This protocol describes a method to objectively assess the injectability of these materials using a standard mechanical tester. The syringe plunger is compressed by the crosshead at a set rate, and the force is measured. The maximum or plateau force value can then be used for comparison between samples, or to an absolute force limit. This protocol can be used with any material, and any syringe and needle size or geometry. The results obtained may be used to make decisions about formulations, syringe and needle sizes early in the translational process. Further, the effects of altering formulations on injectability may be quantified, and the optimum time to inject temporally changing materials determined. This method is also suitable as a reproducible way to examine the effects of injection on a material, to study phenomena such as self-healing and filter pressing or study the effects of injection on cells. This protocol is faster and more directly applicable to injectability than rotational rheology, and requires minimal post processing to obtain key values for direct comparisons.

Presented here is a protocol for quantitatively evaluating the injectability of a material through a syringe-needle system using a standard mechanical testing rig.

Injectable biomaterials are becoming increasingly popular for the minimally invasive delivery of drugs and cells. These materials are typically more ...

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Mouse models have contributed significantly to understanding genetic and physiological factors involved in healthy cardiac function, how perturbations result in pathology, and how myocardial diseases may be treated. Cardiovascular magnetic resonance imaging (CMR) has become an indispensable tool for a comprehensive in vivo assessment of cardiac anatomy and function. This protocol shows detailed measurements of mouse heart left ventricular function, myocardial strain, and hemodynamic forces using 7-Tesla CMR. First, animal preparation and positioning in the scanner are demonstrated. Survey scans are performed for planning imaging slices in various short- and long-axis views. A series of prospective ECG-triggered short-axis (SA) movies (or CINE images) are acquired covering the heart from apex to base, capturing end-systolic and end-diastolic phases. Subsequently, single-slice, retrospectively gated CINE images are acquired in a midventricular SA view, and in 2-, 3-, and 4-chamber views, to be reconstructed into high-temporal resolution CINE images using custom-built and open-source software. CINE images are subsequently analyzed using dedicated CMR image analysis software.
Delineating endomyocardial and epicardial borders in SA end-systolic and end-diastolic CINE images allows for the calculation of end-systolic and end-diastolic volumes, ejection fraction, and cardiac output. The midventricular SA CINE images are delineated for all cardiac time frames to extract a detailed volume-time curve. Its time derivative allows for the calculation of the diastolic function as the ratio of the early filling and atrial contraction waves. Finally, left ventricular endocardial walls in the 2-, 3-, and 4-chamber views are delineated using feature-tracking, from which longitudinal myocardial strain parameters and left ventricular hemodynamic forces are calculated. In conclusion, this protocol provides detailed in vivo quantification of the mouse cardiac parameters, which can be used to study temporal alterations in cardiac function in various mouse models of heart disease.

This study describes a comprehensive cardiovascular magnetic resonance imaging (CMR) protocol to quantify the left ventricular functional parameters of the mouse heart. The protocol describes the acquisition, post-processing, and analysis of the CMR images as well as assessment of different cardiac functional parameters.

Mouse models have contributed significantly to understanding genetic and physiological factors involved in healthy cardiac function, how perturbations ...

Affordable Oxygen Microscopy-Assisted Biofabrication of Multicellular Spheroids

Multicellular spheroids are important tools for studying tissue and cancer physiology in 3D and are frequently used in tissue engineering as tissue assembling units for biofabrication. While the main power of the spheroid model is in mimicking physical-chemical gradients at the tissue microscale, the real physiological environment (including dynamics of metabolic activity, oxygenation, cell death, and proliferation) inside the spheroids is generally ignored. At the same time, the effects of the growth medium composition and the formation method on the resulting spheroid phenotype are well documented. Thus, characterization and standardization of spheroid phenotype are required to ensure the reproducibility and transparency of the research results. The analysis of average spheroid oxygenation and the value of O2 gradients in three dimensions (3D) can be a simple and universal way for spheroid phenotype characterization, pointing at their metabolic activity, overall viability, and potential to recapitulate in vivo tissue microenvironment. The visualization of 3D oxygenation can be easily combined with multiparametric analysis of additional physiological parameters (such as cell death, proliferation, and cell composition) and applied for continuous oxygenation monitoring and/or end-point measurements. The loading of the O2 probe is performed during the stage of spheroid formation and is compatible with various protocols of spheroid generation. The protocol includes a high-throughput method of spheroid generation with introduced red and near-infrared emitting ratiometric fluorescent O2 nanosensors and the description of multi-parameter assessment of spheroid oxygenation and cell death before and after bioprinting. The experimental examples show comparative O2 gradients analysis in homo- and hetero-cellular spheroids as well as spheroid-based bioprinted constructs. The protocol is compatible with a conventional fluorescence microscope having multiple fluorescence filters and a light-emitting diode as a light source.

The protocol describes high-throughput spheroid generation for bioprinting using the multi-parametric analysis of their oxygenation and cell death on a standard fluorescence microscope. This approach can be applied to control the spheroids viability and perform standardization, which is important in modeling 3D tissue, tumor microenvironment, and successful (micro)tissue biofabrication.

Multicellular spheroids are important tools for studying tissue and cancer physiology in 3D and are frequently used in tissue engineering as tissue ...