This research was funded by Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ; E-26/010.002643/2019 and E-26/201.335/2022), Coordena&#231;&#227;o de Aperfei&#231;oamento de Pessoal de N&#237;vel Superior -Brazil (CAPES)-001 Finance Code. Programa Institucional de bolsas de inicia&#231;&#227;o cient&#237;fica da Universidade Federal do Rio de Janeiro. National Council for Scientific and Technological Development (CNPq; FFB: 001. 306236/2022-2 TMO-C: 309339/2023-5). The authors acknowledge National Center for Structural Biology and Bioimaging (CENABIO)/ Universidade Federal do Rio de Janeiro for the use of its facilities, especially the microPET/SPECT/CT platform at the Small Animal Imaging Unit (UIPA). Supplemental&#160;Figure S1 was created with BioRender.com.

All methods were approved by the Institutional Animal Care and Use Committee of the Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro (IACUC - UFRJ; A16/23-025-20). Scans were performed on male C57BL/6 mice aged 6 and 22 months.
1. Animal preparation
<ol>
	<li>Anesthetize mice using 2% isoflurane in a mixture of oxygen delivered via vaporization. Use a precision vaporizer for both induction and maintenance with a mixture of 100% oxygen using a precision vaporizer.</li>
	<li>Position the anesthetized animal supine on the micro-CT scanning using a mouse bed. Secure the mouse with tape to minimize movement during the scan and then insert it into the gantry.</li>
	<li>Throughout the experiment, which lasts approximately 10 min, ensure that the animal wears a mask to keep it under the anesthetic plane while receiving the maintenance dose as needed.</li>
</ol>
2. Image acquisition and reconstruction
<ol>
	<li>Acquire body micro-CT scans using a high-resolution preclinical imaging system. Capture a total of 1,024 projections with an exposure time of 470 ms each, using a fly mode rotation at a voltage of 60 kV and a current of 480 &#181;A. Set the system to a 1.25 magnification, resulting in a 94.72 mm field of view (FOV) for a total acquisition time of 8.02 min. Capture images with a binning of 1 x 1, resulting in a resolution of 2,368 x 2,240 pixels. 
	NOTE: The equipment used in this manuscript for micro-CT images presents limitations in obtaining head images due to the overlap with the anesthetic mask. Additionally, the entire tail length is not captured. Thus, we excluded the head and tail analysis (see section 3).</li>
	<li>Perform a scan with the same parameters on an acrylic cylindrical phantom (diameter: 2.5 cm, length: 11.5 cm) half-filled with distilled water to determine the HU for air and water. Upon completion of the acquisitions, reconstruct the images using the acquisition software with settings optimized for noise reduction and the Filtered Back Projection (FBP) algorithm. Batch-reconstruct both animal and phantom images using a 147 &#181;m isotropic voxel size and a 640 matrix.</li>
	<li>Extract air and water HU from the phantom (raw data) using the referenced software. Convert the images into DICOM files and correct the HU values.</li>
</ol>
3. Image analysis
<ol>
	<li>Uploading a file 
	NOTE: Micro-CT is a technique that captures images temporally and spatially, generating an image sequence. Therefore, it is necessary to open the entire folder rather than a single image.
	<ol>
		<li>In the top left area of the interface, find the 3D Slicer menu, highlighted in a grayish-blue color. Click on Add Data (<img alt="Equation 1" src="/files/ftp_upload/67393/67393eq01.jpg" style="margin: auto;" />).</li>
		<li>When a window with two options appears, select the first option: Choose Directory to Add.</li>
		<li>Navigate to the folder containing the target animal DICOM images series and click on it. This action will open the folder within the software, allowing all the images to be viewed simultaneously.</li>
		<li>Observe the images displayed across three screens. Each screen represents a different anatomical plane: coronal, sagittal, and transverse, identified by the colors green, yellow, and red, respectively.</li>
	</ol>
	</li>
	<li>Segmentation
	<ol>
		<li>Locate the Segment Editor (<img alt="Equation 2" src="/files/ftp_upload/67393/67393eq02.jpg" style="margin: auto;" />) in the upper tab (Modules), and click on it.</li>
		<li>Click the green + Add button to create segments, defining the HU range.</li>
		<li>Create segments for bone, lean tissue, and adipose tissue by clicking 3x each.</li>
		<li>Double-click on each segment to name and color it according to the desired settings.</li>
		<li>Set the HU range for each segment using the Threshold function, located on the left side of the window. To do this, find the Threshold Range function <img alt="Equation 3" src="/files/ftp_upload/67393/67393eq03.jpg" style="margin: auto;" /> and enter the HU values for each tissue type: Lean tissue (-29 to 225 HU); adipose tissue (-190 to -30 HU); bone (500 to 5,000 HU). Click the Apply button. 
		NOTE: Refer to Supplemental Figure S1 for HU value comparisons.</li>
		<li>Repeat the segmentation process for each tissue type.</li>
		<li>Click Show 3D to generate a 3D rendering. 
		NOTE: The rendering will appear in the blue quadrant of the display.</li>
	</ol>
	</li>
	<li>Cleaning the image 
	NOTE: After segmentation and 3D rendering, extraneous items must be excluded to ensure they are not included in the volumetric measurements of lean tissue, adipose tissue, and bone. Removing these artifacts is essential to ensure accurate analysis.
	<ol>
		<li>Locate the &#34;Scissors&#34; tool (<img alt="Equation 4" src="/files/ftp_upload/67393/67393eq04.jpg" style="margin: auto;" />) in the Segmentation menu.</li>
		<li>Choose either the anatomical plane or the 3D rendering for a clearer view of the object to be removed.
		<ol>
			<li>If using an anatomical plane, click the maximize view button (<img alt="Equation 5" src="/files/ftp_upload/67393/67393eq05.jpg" style="margin: auto;" />) on the colored bar of the plane. Inside the anatomical plane window, use the mouse scroll button to navigate forward or backward through the CT scan. 
			NOTE: Each window can scroll independently.</li>
			<li>For 3D rendering, use the left mouse button to rotate along any axis and the right mouse button to zoom in. 
			NOTE: Arrow keys on the keyboard can also rotate the image.</li>
		</ol>
		</li>
		<li>With the Scissors tool selected, highlight the segmentation around the unwanted object and encircle it to remove it from the image. The software deletes the object automatically. 
		NOTE: Make sure the operation Erase Inside is selected. If the equipment used generates images for any nonbiological image, such as the bead, anesthetic, or surgical equipment, it is important to remove it. Additionally, if any anatomic area is not wholly obtained, it is fundamental to establish an anatomic reference to remove these areas that are not fully obtained to ensure repeatability between experiments. Unwanted objects must be removed in each plane.</li>
		<li>Click the restore view layout icon (<img alt="Equation 6" src="/files/ftp_upload/67393/67393eq06.jpg" style="margin: auto;" />) to return to the screen with the four windows, regardless of the window selected.</li>
		<li>Repeat the process for all windows where the object needs removal.</li>
	</ol>
	</li>
	<li>Segmentation volume
	<ol>
		<li>After segmentation, quantify the volumes of the segments using the quantification function found in the dropdown menu. 
		NOTE: All segmentations must be visible for this action.</li>
		<li>Navigate to Quantification | Segment Statistics. Click on Apply, and wait for the software to generate a table displaying values for each segmentation. The software provides two volume values for each segmentation: Labelmap (LM) and Closed Surface (CS). 
		NOTE: LM Value is generally suitable for calculating segment volumes, using the number of voxels multiplied by the volume of a single voxel. CS Value utilizes a smoothed surface model and integrates volume based on triangular surface elements. CS aligns more closely with the anatomical contours and is recommended for accurate measurements.</li>
		<li>To upload data from a new animal, save and close the scene at the file menu.</li>
	</ol>
	</li>
	<li>Corporal composition analysis
	<ol>
		<li>Use the volume measurements provided by the software in cm3 to convert these volumes to tissue mass and apply the appropriate density for each tissue type. Use the following tissue densities specified by the International Commission on Radiation Units and Measurements22: 0.95 g/cm&#179; (adipose tissue), 1.05 g/cm&#179; (lean tissue), and 1.92 g/cm&#179; (skeletal tissue)2.
		<ol>
			<li>To compute tissue mass, multiply the volume (cm&#179;) obtained from the software by the respective tissue density (g/cm&#179;): 
			Adipose Tissue Mass: Volumesegmentation X 0.95 = Massadipose 
			Lean Tissue Mass: Volumesegmentation X 1.05 = Masslean 
			Skeletal Tissue Mass: Volumesegmentation X 1.92 = Massskeletal 
			&#8203;Total Mass: Massadipose + Masslean + Massskeletal= MassTotal</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Bone length measurement 
	NOTE: To correct tissue mass by bone length for certain analyses, it is necessary to obtain specific bone lengths.
	<ol>
		<li>Return to the Segment Editor menu. Hide the adipose tissue and lean tissue segmentations by clicking on the eye icon (<img alt="Equation 7" src="/files/ftp_upload/67393/67393eq07.jpg" style="margin: auto;" />) next to each segment. 
		NOTE: Only the segmentation corresponding to skeletal tissue should remain visible.</li>
		<li>Select the Toggle Markups/Toolbar (<img alt="Equation 8" src="/files/ftp_upload/67393/67393eq08.jpg" style="margin: auto;" />) option in the upper corner of the main menu. This will open a secondary menu just below the main menu. Locate the create new line button (<img alt="Equation 9" src="/files/ftp_upload/67393/67393eq09.jpg" style="margin: auto;" />).</li>
		<li>After clicking on the create new line button, navigate to the 3D reconstruction and identify the bone to be measured. 
		NOTE: The recommended bones are the tibia or femur22.</li>
		<li>Once the bone is identified, left-click on one end of the bone and again on the other end of the tissue. This action allows the software to calculate the bone size. 
		NOTE: For consistent measurements, select the same anatomical region of the bone for each animal. For example, the femoral head is used as one end and the lateral condyle as the other end of the femur.</li>
	</ol>
	</li>
	<li>Data analysis and normalization 
	NOTE: The mass data can be obtained as explained in section 3.5 and used as it is. Alternatively, the lean, fat, and bone mass can be normalized in two ways.
	<ol>
		<li>Calculate the ratio between the tissue mass and the animal&#39;s weight on the day the images are obtained. 
		&#8203;Normalization to weight: Masslean &#247; Body weight</li>
		<li>Calculate the ratio between mass and bone size (for instance, femur or tibia). 
		Normalization to femur: Masslean &#247; Sizefemur 
		Normalization to tibia: Masslean &#247; Sizetibia 
		NOTE: The normalization parameters are chosen based on the experimental question. If changes in bone size are expected (for example: when comparing bone remodeling) the normalization by body weight is suggested (item 3.7.1). Additionally, if changes in body weight are expected in your experimental question, the long bone size is the suggested normalization parameter (item 3.7.2).</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Engelke, K., Museyko, O., Wang, L., Laredo, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+analysis+of+skeletal+muscle+by+computed+tomography+imaging-State+of+the+art.">Quantitative analysis of skeletal muscle by computed tomography imaging-State of the art.</a> J Orthop Translat. 15, 91 (2018).</li><li>Peixoto da Silva, S., Santos, J. M. O., Costa e Silva, M. P., Gil da Costa, R. M., Medeiros, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+cachexia+and+its+pathophysiology:+links+with+sarcopenia,+anorexia+and+asthenia.">Cancer cachexia and its pathophysiology: links with sarcopenia, anorexia and asthenia.</a> J Cachexia Sarcopenia Muscle. 11 (3), 619 (2020).</li><li>Lan, X. Q., 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+role+of+TGF-&#946;+signaling+in+muscle+atrophy,+sarcopenia+and+cancer+cachexia.">The role of TGF-&#946; signaling in muscle atrophy, sarcopenia and cancer cachexia.</a> Gen Comp Endocrinol. 353, 1-10 (2024).</li><li>Bloise, F. F., Oliveira, T. S., Cordeiro, A., Ortiga-Carvalho, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thyroid+hormones+play+role+in+sarcopenia+and+myopathies.">Thyroid hormones play role in sarcopenia and myopathies.</a> Front Physiol. 9, 560 (2018).</li><li>Yin, L., 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=Skeletal+muscle+atrophy:+From+mechanisms+to+treatments.">Skeletal muscle atrophy: From mechanisms to treatments.</a> Pharmacol Res. 172, 1-12 (2021).</li><li>Heymsfield, S. B., Adamek, M., Gonzalez, M. C., Jia, G., Thomas, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+skeletal+muscle+mass:+historical+overview+and+state+of+the+art.">Assessing skeletal muscle mass: historical overview and state of the art.</a> J Cachexia Sarcopenia Muscle. 5 (1), 9-18 (2014).</li><li>Cruz-Jentoft, A. J., 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=Sarcopenia:+revised+European+consensus+on+definition+and+diagnosis.">Sarcopenia: revised European consensus on definition and diagnosis.</a> Age Ageing. 48 (1), 16-31 (2019).</li><li>Faron, A., 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=Quantification+of+fat+and+skeletal+muscle+tissue+at+abdominal+computed+tomography:+associations+between+single-slice+measurements+and+total+compartment+volumes.">Quantification of fat and skeletal muscle tissue at abdominal computed tomography: associations between single-slice measurements and total compartment volumes.</a> Abdom Radiol (NY). 44 (5), 1907-1916 (2019).</li><li>Marra, 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=Assessment+of+body+composition+in+health+and+disease+using+bioelectrical+impedance+analysis+(BIA)+and+dual+energy+X-ray+absorptiometry+(DXA):+A+critical+overview.">Assessment of body composition in health and disease using bioelectrical impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA): A critical overview.</a> Contrast Media Mol Imaging. 2019, 3548284 (2019).</li><li>Martin, L., 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=Cancer+cachexia+in+the+age+of+obesity:+skeletal+muscle+depletion+is+a+powerful+prognostic+factor,+independent+of+body+mass+index.">Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index.</a> J Clin Oncol. 31 (12), 1539-1547 (2013).</li><li>Schlaeger, S., 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=Association+of+paraspinal+muscle+water-fat+MRI-based+measurements+with+isometric+strength+measurements.">Association of paraspinal muscle water-fat MRI-based measurements with isometric strength measurements.</a> Eur Radiol. 29 (2), 599-608 (2019).</li><li>Kose, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physical+and+technical+aspects+of+human+magnetic+resonance+imaging:+present+status+and+50+years+historical+review.">Physical and technical aspects of human magnetic resonance imaging: present status and 50 years historical review.</a> Adv Phys: X. 6 (1), (2021).</li><li>Arnold, T. C., Freeman, C. W., Litt, B., Stein, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Low-field+MRI:+Clinical+promise+and+challenges.">Low-field MRI: Clinical promise and challenges.</a> J Magn Reson Imaging. 57 (1), 25-44 (2023).</li><li>Wang, H., 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=Preoperative+MRI-based+radiomic+machine-learning+nomogram+may+accurately+distinguish+between+benign+and+malignant+soft-tissue+lesions:+A+two-center+study.">Preoperative MRI-based radiomic machine-learning nomogram may accurately distinguish between benign and malignant soft-tissue lesions: A two-center study.</a> J Magn Reson Imaging. 52 (3), 873-882 (2020).</li><li>Brower, C., Rehani, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Radiation+risk+issues+in+recurrent+imaging.">Radiation risk issues in recurrent imaging.</a> Br J Radiol. 94 (1126), (2021).</li><li>Mainprize, J. G., Yaffe, M. J., Chawla, T., Glanc, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+ionizing+radiation+exposure+during+pregnancy.">Effects of ionizing radiation exposure during pregnancy.</a> Abdom Radiol (NY). 48 (5), 1564 (2023).</li><li>Geethanath, S., Vaughan, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accessible+magnetic+resonance+imaging:+A+review.">Accessible magnetic resonance imaging: A review.</a> J Magn Reson Imaging. 49 (7), e65-e77 (2019).</li><li>Ritman, E. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+status+of+developments+and+applications+of+micro-CT.">Current status of developments and applications of micro-CT.</a> Annu Rev Biomed Eng. 13, 531-552 (2011).</li><li>Khoury, B. 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=The+use+of+nano-computed+tomography+to+enhance+musculoskeletal+research.">The use of nano-computed tomography to enhance musculoskeletal research.</a> Connect Tissue Res. 56 (2), 106 (2015).</li><li>DenOtter, T. D., Schubert, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hounsfield+Unit.">Hounsfield Unit.</a> StatPearls [Internet]. , (2024).</li><li>Pinto, E. 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=Efficacy+of+Hounsfield+units+measured+by+lumbar+computer+tomography+on+bone+density+assessment:+A+systematic+review.">Efficacy of Hounsfield units measured by lumbar computer tomography on bone density assessment: A systematic review.</a> Spine (Phila Pa 1976). 47 (9), 702-710 (2022).</li><li>Beaucage, K. L., Pollmann, S. I., Sims, S. M., Dixon, S. J., Holdsworth, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+in+vivo+micro-computed+tomography+for+assessment+of+age-dependent+changes+in+murine+whole-body+composition.">Quantitative in vivo micro-computed tomography for assessment of age-dependent changes in murine whole-body composition.</a> Bone Rep. 5, 70-80 (2016).</li><li>Faot, F., Chatterjee, M., de Camargos, G. V., Duyck, J., Vandamme, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Micro-CT+analysis+of+the+rodent+jaw+bone+micro-architecture:+A+systematic+review.">Micro-CT analysis of the rodent jaw bone micro-architecture: A systematic review.</a> Bone Rep. 2, 14-24 (2015).</li><li>Cooper, D., Turinsky, A., Sensen, C., Hallgrimsson, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+voxel+size+on+3D+micro-CT+analysis+of+cortical+bone+porosity.">Effect of voxel size on 3D micro-CT analysis of cortical bone porosity.</a> Calcif Tissue Int. 80 (3), 211-219 (2007).</li><li>Mrzilkova, J., 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=Morphology+of+the+vasculature+and+blood+supply+of+the+brown+adipose+tissue+examined+in+an+animal+model+by+micro-CT.">Morphology of the vasculature and blood supply of the brown adipose tissue examined in an animal model by micro-CT.</a> Biomed Res Int. 2020 (1), 7502578 (2020).</li><li>Li, F. W., 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=Optimal+use+ratio+of+the+stromal+vascular+fraction+(SVF):+An+animal+experiment+based+on+micro-CT+dynamic+detection+after+large-volume+fat+grafting.">Optimal use ratio of the stromal vascular fraction (SVF): An animal experiment based on micro-CT dynamic detection after large-volume fat grafting.</a> Aesthet Surg J. 39 (6), NP213-NP224 (2019).</li><li>Berndt, S., 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=Microcomputed+tomography+technique+for+in+vivo+three-dimensional+fat+tissue+volume+evaluation+after+polymer+injection.">Microcomputed tomography technique for in vivo three-dimensional fat tissue volume evaluation after polymer injection.</a> Tissue Eng Part C Methods. 23 (12), 964-970 (2017).</li><li>Rother, L., 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=A+micro-CT-based+standard+brain+atlas+of+the+bumblebee.">A micro-CT-based standard brain atlas of the bumblebee.</a> Cell Tissue Res. 386 (1), 29-45 (2021).</li><li>Ijiri, T., Todo, H., Hirabayashi, A., Kohiyama, K., Dobashi, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digitization+of+natural+objects+with+micro+CT+and+photographs.">Digitization of natural objects with micro CT and photographs.</a> PLoS One. 13 (4), e0195852 (2018).</li><li>Fedorov, A., 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=3D+Slicer+as+an+image+computing+platform+for+the+Quantitative+Imaging+Network.">3D Slicer as an image computing platform for the Quantitative Imaging Network.</a> Magn Reson Imaging. 30 (9), 1323-1341 (2012).</li><li>Ponti, F., 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=Aging+and+imaging+assessment+of+body+composition:+From+fat+to+facts.">Aging and imaging assessment of body composition: From fat to facts.</a> Front Endocrinol (Lausanne). 10, 861 (2020).</li></ol>

The authors declare no competing interests.

Evaluation through tomography is an effective and non-invasive method for obtaining detailed body composition information. Micro-CT, in particular, offers valuable outcome measures for preclinical studies. In the bone field, micro-CT has different uses, as analyzing the micro-architecture23 and bone remodeling24 are particularly interesting. Assessing the morphology of internal biological structure is also relevant in biomedical research, as analyzing the density of vasculature in specific tissue can assess its function and infer about the metabolism25. For endocrine and metabolic studies of the adipose mass, the use of micro-CT provides fundamental information in live small animals26,27. Additionally, micro-CT can be applied in other fields, such as zoology and botany, where it is used to evaluate the structures of small insects and plants28,29. 
A significant limitation of micro-CT is the high cost and limited access to image analysis software. Additionally, prior anatomical knowledge, especially of animal anatomy, is essential for correct measurements. This manuscript introduces the 3D slicer software capable of producing results comparable to commercial alternatives. This system also reduces the requirement for extensive anatomical knowledge, as it is semi-automatic30.
The images obtained through micro-CT and analyzed by the software demonstrated that the body composition of mice follows a similar trajectory to that of humans during aging, characterized by an increase in total adipose tissue and a concomitant decrease in lean mass31. These findings were evident when comparing adult mice (6 months) to elderly mice (22 months).
Due to the equipment's limitations, the anesthesia mask blocks the head acquisition. Thus, we could not capture and accurately segment the entire head. As a result, we established a convention to use the atlas bone (C1) as an anatomical reference point, focusing our analysis on the region below this point in the equipment used herein. Therefore, all images above the C1 were selected to be removed. The equipment did not obtain the full tail length. Thus, the tail was removed from the basal area in all analyses. We suggest the user establish anatomical points for select excluded areas to maintain reproducibility between experiments.
This article presents a method for analyzing tomographic images using an accessible and cost-effective protocol. The software successfully distinguished between three tissue types: lean mass, adipose tissue, and skeletal tissue. However, it was unable to distinguish skeletal muscle from visceral organs, a common challenge in murine body composition analysis.
Micro-CT lacks the capability to differentiate visceral tissue and skeletal muscle mass. Despite this limitation, our results demonstrate the software's value for studying diseases affecting body composition, particularly those impacting skeletal muscle, such as hypertrophy or atrophy. It can be repeated as a non-invasive test, enabling longitudinal body composition monitoring. This capability helps evaluate protective treatments and develop protocols to model many metabolic diseases.

Size, mass, and composition are crucial skeletal muscle properties for studying muscle-related and metabolic disease mechanisms1. Sarcopenia, cachexia, atrophy, and myopathies share common phenotypes: reduction in mass, alteration in composition, and impaired muscle function2,3,4,5. However, quantifying the body composition in a living animal remains highly complex and technically challenging6.
The primary methodologies for in vivo imaging and body composition analysis are dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI). These methods are primarily employed to screen and monitor diseases that lead to lean mass reduction7,8,9,10,11. DXA is the gold standard for body composition analysis due to its lower cost. However, DXA has a significant disadvantage compared to MRI and CT: its inability to spatially resolve muscle and adipose tissue1.
MRI uses strong magnetic fields and radio waves to generate detailed images of the body&#39;s internal structures12. One of its main advantages over CT is its superior contrast resolution, allowing excellent differentiation between distinct soft tissue types1,13,14. Unlike CT, MRI does not use ionizing radiation, making it safer for repeated use15,16. However, MRI is more expensive and less accessible, with longer scan times and higher maintenance costs13,17. Thus, MRI instruments adapted for small animal analysis are not usually available.
Micro-CT is similar to conventional CT but tailored for small structures and biomedical research18. Micro-CT is an advanced, non-invasive radiological assessment technique that enables detailed visualization of internal structures in small animal models. Micro-CT uses X-rays to create detailed images of the body&#39;s internal structures, relying on the differential attenuation of X-rays by various tissue types. During a micro-CT scan, the mouse lies on a table that moves slowly through a circular gantry. Inside the gantry, an X-ray tube rotates around the mouse, emitting X-rays from various angles. Detectors on the opposite side capture these X-rays after they pass through the body18.
The micro-CT scanner&#39;s software processes the data from these multiple angles to reconstruct two-dimensional cross-sectional images (slices) of the body. Through reconstruction, these slices can be combined to represent the internal anatomy comprehensively19. The images produced by micro-CT scans are based on the varying degrees of X-ray attenuation by different tissues within the body. This attenuation is quantified using Hounsfield Units (HU), a scale that standardizes radiodensity20,21. The HU scale is fundamental for segmentation, as each structure has a slightly different value.
In the present article, we used HU values to accurately differentiate between bone, lean tissue, and adipose tissue1. By referencing established HU ranges, we ensured precise analysis and comparison of body composition. Herein, we demonstrate how to acquire images using micro-CT and its application in visualizing and quantifying lean, fat, and skeletal mass.

<table><tbody><tr><td>3DSlicer, version 5.6.2</td><td>3D Slicer platform</td><td></td><td>A free and open-source software for image analysis and scientific visualization.</td></tr><tr><td>Amide, version 1.0.1</td><td>Amide platform</td><td></td><td>A free and open-source software used for correcting the Hounsfield Unit values in the processed DICOM images.</td></tr><tr><td>Amira, version 4.1</td><td>Thermo Fisher Scientific</td><td></td><td>Used to extract air and water Hounsfield Unit values from the phantom&#039;s raw data and to convert images into DICOM files.</td></tr><tr><td>Isoforine</td><td>Crist&amp;aacute;lia</td><td></td><td>Isoflurane is a non-flammable liquid anesthetic agent for use in general inhalation anesthesia by vaporization.</td></tr><tr><td>Triumph XO subsystem</td><td>Gamma Medica-Ideas Flex</td><td></td><td>Advanced imaging subsystem designed for preclinical small animal imaging, offering high-resolution CT and PET capabilities for quantitative and qualitative analysis.</td></tr></tbody></table>

non-invasive skeletal muscle quantification in small animals using micro-computed tomography

Skeletal muscle size, mass, and composition are critical properties for studying metabolic and muscle-related diseases, as they directly impact the understanding of disease progression and treatment outcomes. Quantifying a live animal's lean, adipose, and skeletal mass is important in metabolic, physiology, pharmacologic, and geroscience studies. However, obtaining accurate body composition measurements, especially of lean mass, remains challenging due to the inherent limitations of conventional assessment techniques. Micro-computed tomography (micro-CT) is a non-invasive radiological technique that enables high-resolution visualization of internal structures in small animal models. A standardized micro-CT method can significantly enhance translational research with more reliable and impactful results, particularly during aging studies or at different time points within the same animal. Despite its potential, the lack of standardization in image acquisition and analysis methods significantly hinders the comparability of results across different studies. Herein, we present a comprehensive and detailed low-cost protocol for lean mass analysis using micro-CT to address these challenges and promote consistency in research involving small animal models.

Proper anatomical positioning of the sedated animal on the imaging table ensures consistent and reproducible scan outcomes, highlighting the data acquisition effectiveness in achieving reliable results. Proper animal sedation throughout imaging, including specialized gas delivery systems and vaporizers, is fundamental for precise anatomical assessments (Figure 1).
Figure 2&#160;illustrates segmentation and rendering in different anatomical planes, presenting sequential images in the coronal, sagittal, and transverse planes. These images demonstrate the gradual segmentation of skeletal, adipose, and lean tissue, culminating in a thoroughly segmented representation that integrates all tissue types, highlighting a clear distinction between tissues and demonstrating region-specific markings based on the identified tissue types. Despite equipment limitations precluding whole-body capture, the method provides detailed information on tissue distribution and composition across various anatomical planes.
Figure 3&#160;showcases 3D renderings emphasizing tissue types-bone (blue), adipose tissue (yellow), and lean tissue (red)-offering a comprehensive visualization of anatomical structures. This visualization underscores the protocol&#39;s effectiveness in generating detailed 3D models essential for anatomical studies.
Table 1 details normalized tissue masses (lean mass, body fat, and skeletal mass) relative to the animal&#39;s weight on imaging day. The values presented result from multiplying the tissue volume (in cm&#179;) by the corresponding tissue density, as detailed in step 3.5.1.1 of the methodology.
Table 2 provides absolute body composition values, showing each animal&#39;s mass distribution and lean, body fat, and skeletal tissue percentage. It allows us to assess the total amount of each tissue type and how it contributes to each specimen&#39;s overall body composition.
Supplemental Table S1 shows the time each analyst takes to complete the body composition analysis for each animal and the variability in the results across analysts. It assesses whether there is significant variability between experienced (Analysts 1, 2, and 5) and inexperienced operators (Analysts 3 and 4) in measuring body composition metrics such as lean mass, body fat mass, and skeletal mass.
Figure 4&#160;presents a comparative body composition analysis between adult and elderly subjects. It reveals a reduction in lean mass percentage and increased body fat, which aligns with typical aging patterns. These findings collectively highlight the imaging protocol&#39;s robustness and precision in delivering critical anatomical data, which is essential for advancing preclinical research methodologies.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/67393/67393fig01.jpg" /> 
Figure 1: Animal preparation. (A) The apparatus used for animal sedation throughout the imaging process, such as gas delivery systems and vaporizers; (B) the micro-CT scanningsystem. (C) The anesthetized animal is lying supine on a mouse bed. Proper positioning is critical to obtain consistent and reproducible results. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67393/67393fig01large.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/67393/67393fig02.jpg" /> 
Figure 2: Segmentation and visualization of slices in different anatomical planes. (A) Coronal plane, (B) sagittal plane, and (C) transverse plane. From left to right, the images show an unsegmented scan (gray), bone segmentation (blue), body fat segmentation (yellow), lean tissue segmentation (red), and finally, complete segmentation, including all segmentations. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67393/67393fig02large.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/67393/67393fig03.jpg" /> 
Figure 3: 3D renderings. From left to right, the images display the 3D rendering of the subject, highlighting different tissue types: bone in blue, body fat in yellow, lean tissue in red, and the final image shows a comprehensive view combining bone, adipose tissue, and lean tissue. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67393/67393fig03large.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/67393/67393fig04.jpg" /> 
Figure 4: Comparison of body composition between adult and elderly mice. The bar graph illustrates the body composition percentages of skeletal tissue (adult: 10.5 &#177; 0.5%; elderly: 9.9 &#177; 1.0%, p = 0.19), body fat (adult: 17.2 &#177; 5.7%; elderly: 27.9 &#177; 4.4%, p = 0.03), and lean mass (adult: 72.3 &#177; 5.7%; elderly: 62.3 &#177; 3.6%, p = 0.03). The data are presented as MEAN &#177; SD from the animals (adult: n = 3; elderly: n = 3). Statistics were calculated using an unpaired t-test with a one-tail p value (* p &#60; 0.05). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67393/67393fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="4">LEAN</td>
		</tr>
		<tr>
			<td>Animal ID</td>
			<td>Lean mass (g)</td>
			<td>Weight (g)</td>
			<td>Normalization (Weight)</td>
		</tr>
		<tr>
			<td>Elderly 1</td>
			<td>15.1</td>
			<td>37.4</td>
			<td>0.4</td>
		</tr>
		<tr>
			<td>Elderly 2</td>
			<td>14.4</td>
			<td>44.4</td>
			<td>0.3</td>
		</tr>
		<tr>
			<td>Elderly 3</td>
			<td>14.3</td>
			<td>37.7</td>
			<td>0.4</td>
		</tr>
		<tr>
			<td>Adult 1</td>
			<td>12.3</td>
			<td>29.1</td>
			<td>0.4</td>
		</tr>
		<tr>
			<td>Adult 2</td>
			<td>16.5</td>
			<td>30.4</td>
			<td>0.5</td>
		</tr>
		<tr>
			<td>Adult 3</td>
			<td>16.1</td>
			<td>31.7</td>
			<td>0.5</td>
		</tr>
		<tr>
			<td colspan="4">BODY FAT</td>
		</tr>
		<tr>
			<td>Animal ID</td>
			<td>Body fat mass (g)</td>
			<td>Weight (g)</td>
			<td>Normalization (Weight)</td>
		</tr>
		<tr>
			<td>Elderly 1</td>
			<td>6.4</td>
			<td>37.4</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>Elderly 2</td>
			<td>8.2</td>
			<td>44.4</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>Elderly 3</td>
			<td>5.3</td>
			<td>37.7</td>
			<td>0.1</td>
		</tr>
		<tr>
			<td>Adult 1</td>
			<td>4.4</td>
			<td>29.1</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>Adult 2</td>
			<td>2.7</td>
			<td>30.4</td>
			<td>0.1</td>
		</tr>
		<tr>
			<td>Adult 3</td>
			<td>3.4</td>
			<td>31.7</td>
			<td>0.1</td>
		</tr>
		<tr>
			<td colspan="4">SKELETAL</td>
		</tr>
		<tr>
			<td>Animal ID</td>
			<td>Skeletal mass (g)</td>
			<td>Weight (g)</td>
			<td>Normalization (Weight)</td>
		</tr>
		<tr>
			<td>Elderly 1</td>
			<td>2.3</td>
			<td>37.4</td>
			<td>0.06</td>
		</tr>
		<tr>
			<td>Elderly 2</td>
			<td>2.3</td>
			<td>44.4</td>
			<td>0.05</td>
		</tr>
		<tr>
			<td>Elderly 3</td>
			<td>2.4</td>
			<td>37.7</td>
			<td>0.06</td>
		</tr>
		<tr>
			<td>Adult 1</td>
			<td>2.0</td>
			<td>29.1</td>
			<td>0.07</td>
		</tr>
		<tr>
			<td>Adult 2</td>
			<td>2.3</td>
			<td>30.4</td>
			<td>0.08</td>
		</tr>
		<tr>
			<td>Adult 3</td>
			<td>2.2</td>
			<td>31.7</td>
			<td>0.07</td>
		</tr>
	</tbody>
</table>
Table 1: Normalization of masses by animal weight on the day of imaging. As explained in Section 3.7, the mass of the tissue can be normalized by the animal&#39;s weight on the day of the examination. This normalization has been applied to lean, adipose, and bone tissue based on the animal&#39;s weight.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="5">Body Composition (g)</td>
		</tr>
		<tr>
			<td></td>
			<td>Segmentation Volume X 1.05</td>
			<td>Segmentation Volume X 0.95</td>
			<td>Segmentation Volume X 1.92</td>
			<td>Lean + Adipose + Skeletal</td>
		</tr>
		<tr>
			<td>Animal ID</td>
			<td>Lean mass (g)</td>
			<td>Body fat mass (g)</td>
			<td>Skeletal mass (g)</td>
			<td>Total mass (g)</td>
		</tr>
		<tr>
			<td>Elderly 1</td>
			<td>15.1</td>
			<td>6.4</td>
			<td>2.3</td>
			<td>23.8</td>
		</tr>
		<tr>
			<td>Elderly 2</td>
			<td>14.3</td>
			<td>8.2</td>
			<td>2.3</td>
			<td>24.9</td>
		</tr>
		<tr>
			<td>Elderly 3</td>
			<td>14.3</td>
			<td>5.3</td>
			<td>2.4</td>
			<td>22.0</td>
		</tr>
		<tr>
			<td>Adult 1</td>
			<td>12.3</td>
			<td>4.4</td>
			<td>2.0</td>
			<td>18.6</td>
		</tr>
		<tr>
			<td>Adult 2</td>
			<td>16.5</td>
			<td>2.7</td>
			<td>2.3</td>
			<td>21.5</td>
		</tr>
		<tr>
			<td>Adult 3</td>
			<td>16.1</td>
			<td>3.4</td>
			<td>2.2</td>
			<td>21.7</td>
		</tr>
		<tr>
			<td colspan="5">Body Composition (%)</td>
		</tr>
		<tr>
			<td>Animal ID</td>
			<td>Lean mass (%)</td>
			<td>Body fat mass (%)</td>
			<td>Skeletal mass (%)</td>
			<td>Total mass (%)</td>
		</tr>
		<tr>
			<td>Elderly 1</td>
			<td>63.6</td>
			<td>26.8</td>
			<td>9.6</td>
			<td>100</td>
		</tr>
		<tr>
			<td>Elderly 2</td>
			<td>58.1</td>
			<td>32.8</td>
			<td>9.0</td>
			<td>100</td>
		</tr>
		<tr>
			<td>Elderly 3</td>
			<td>64.9</td>
			<td>24.1</td>
			<td>11.0</td>
			<td>100</td>
		</tr>
		<tr>
			<td>Adult 1</td>
			<td>65.9</td>
			<td>23.5</td>
			<td>10.6</td>
			<td>100</td>
		</tr>
		<tr>
			<td>Adult 2</td>
			<td>76.8</td>
			<td>12.4</td>
			<td>10.9</td>
			<td>100</td>
		</tr>
		<tr>
			<td>Adult 3</td>
			<td>74.3</td>
			<td>15.7</td>
			<td>9.9</td>
			<td>100</td>
		</tr>
	</tbody>
</table>
Table 2: Body composition. (A) The body composition of the animals in grams; (B) the percentage of body composition.
Supplemental Table S1: Body composition evaluated by different operators. Lean mass, body fat mass, skeletal mass, and the time taken for analysis (in minutes) were recorded for each evaluation. Analysts 1, 2, and 5 were experienced operators, while Analysts 3 and 4 were inexperienced and followed only the protocol described in the study. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67393/Supplementary table 1 R1.xlsx" target="_blank">Please click here to download this File.</a>
Supplemental Figure S1: Density range of different biological and non-biological materials. The figure illustrates the density ranges of various biological tissues and non-biological substances. The values were obtained from the literature and adjusted to achieve optimal tissue differentiation. Bones exhibit the highest density, followed by lean tissue, while body fat shows a significantly lower density. The device was calibrated with water as the reference at a density of 0. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67393/Supplementary Figure.pdf" target="_blank">Please click here to download this File.</a>

This protocol uses micro-computed tomography to enable the cost-effective quantification of lean mass, including skeletal muscle and visceral tissue, adipose tissue, and skeletal tissue in small animals. It distinguishes between lean and adipose tissue, which offers significant advantages for biomedical research, particularly in translational research in small animals.

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Skeletal muscle size, mass, and composition are critical properties for studying metabolic and muscle-related diseases, as they directly impact the ...

non-invasive-skeletal-muscle-quantification-small-animals-using-micro

Nanyang Technological University

Research

JoVE Journal

Biology

327 Views.  Universidade Federal do Rio de Janeiro. microCT is een kosteneffectieve, niet-invasieve techniek voor de analyse van de lichaamssamenstelling. Het is voordelig in spierfysiologische studies, waardoor het mogelijk is tijdens interventies. microCT levert meerdere keren gegevens van hetzelfde dier en verbetert de analysekwaliteit en vermindert het gebruik van dieren.Röntgendetectoren voor het tellen van fotonen zijn een innovatie in microCT die de kwantitatieve differentiatie van weefsels moge...

Video: Niet-invasieve kwantificering van skeletspieren bij kleine dieren met behulp van microcomputertomografie

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

Obesity is associated with increased morbidity and mortality as well as reduced metrics in quality of life.1 Both environmental and genetic factors are associated with obesity, though the precise underlying mechanisms that contribute to the disease are currently being delineated.2,3 Several small animal models of obesity have been developed and are employed in a variety of studies.4 A critical component to these experiments involves the collection of regional and/or total animal fat content data under varied conditions.
Traditional experimental methods available for measuring fat content in small animal models of obesity include invasive (e.g. ex vivo measurement of fat deposits) and non-invasive (e.g. Dual Energy X-ray Absorptiometry (DEXA), or Magnetic Resonance (MR)) protocols, each of which presents relative trade-offs. Current invasive methods for measuring fat content may provide details for organ and region specific fat distribution, but sacrificing the subjects will preclude longitudinal assessments. Conversely, current non-invasive strategies provide limited details for organ and region specific fat distribution, but enable valuable longitudinal assessment. With the advent of dedicated small animal X-ray computed tomography (CT) systems and customized analytical procedures, both organ and region specific analysis of fat distribution and longitudinal profiling may be possible. Recent reports have validated the use of CT for in vivo longitudinal imaging of adiposity in living mice.5,6 Here we provide a modified method that allows for fat/total volume measurement, analysis and visualization utilizing the Carestream Molecular Imaging Albira CT system in conjunction with PMOD and Volview software packages.

Fat content analysis is routinely conducted in studies utilizing murine obesity models. Emerging methods in small animal CT imaging and analysis are providing for longitudinal detail rich fat content analysis. Here we detail step by step procedures for performing small animal CT imaging, analysis, and visualization.

Obesity is associated with increased morbidity and mortality as well as reduced metrics in quality of life.1 Both environmental and genetic factors are ...

Medicine

Tracking Dynamics of Muscle Engraftment in Small Animals by In Vivo Fluorescent Imaging

Muscular dystrophies are a group of degenerative muscle diseases characterized by progressive loss of contractile muscle cells. Currently, there is no curative treatment available. Recent advances in stem cell biology have generated new hopes for the development of effective cell based therapies to treat these diseases. Transplantation of various types of stem cells labeled with fluorescent proteins into muscles of dystrophic animal models has been used broadly in the field. A non-invasive technique with the capability to track the transplanted cell fate longitudinally can further our ability to evaluate muscle engraftment by transplanted cells more accurately and efficiently.
In the present study, we demonstrate that in vivo fluorescence imaging is a sensitive and reliable method for tracking transplanted GFP (Green Fluorescent Protein)-labeled cells in mouse skeletal muscles. Despite the concern about background due to the use of an external light necessary for excitation of fluorescent protein, we found that by using either nude mouse or eliminating hair with hair removal reagents much of this problem is eliminated. Using a CCD camera, the fluorescent signal can be detected in the tibialis anterior (TA) muscle after injection of 5 x 105 cells from either GFP transgenic mice or eGFP transduced myoblast culture. For more superficial muscles such as the extensor digitorum longus (EDL), injection of fewer cells produces a detectable signal. Signal intensity can be measured and quantified as the number of emitted photons per second in a region of interest (ROI). Since the acquired images show clear boundaries demarcating the engrafted area, the size of the ROI can be measured as well. If the legs are positioned consistently every time, the changes in total number of photons per second per muscle and the size of the ROI reflect the changes in the number of engrafted cells and the size of the engrafted area. Therefore the changes in the same muscle over time are quantifiable. In vivo fluorescent imaging technique has been used primarily to track the growth of tumorogenic cells, our study shows that it is a powerful tool that enables us to track the fate of transplanted stem cells.

Tracking Dynamics of Muscle Engraftment in Small Animals by In Vivo Fluorescent Imaging

We describe an in vivo fluorescence imaging protocol to monitor muscle regeneration by GFP-labeled myoblasts after transplantation into skeletal muscles of both healthy and dystrophic mice. This protocol can be adapted to study muscle regeneration by transplantation of other types of cells and in other muscular conditions as well.

Muscular dystrophies are a group of degenerative muscle diseases characterized by progressive loss of contractile muscle cells. Currently, there is no ...

In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography

The use of Micro-Computed Tomography (MicroCT) for in vivo studies of small animals as models of human disease has risen tremendously due to the fact that MicroCT provides quantitative high-resolution three-dimensional (3D) anatomical data non-destructively and longitudinally. Most importantly, with the development of a novel preclinical iodinated contrast agent called eXIA160, functional and metabolic assessment of the heart became possible. However, prior to the advent of commercial MicroCT scanners equipped with X-ray flat-panel detector technology and easy-to-use cardio-respiratory gating, preclinical studies of cardiovascular disease (CVD) in small animals required a MicroCT technologist with advanced skills, and thus were impractical for widespread implementation. The goal of this work is to provide a practical guide to the use of the high-speed Quantum FX MicroCT system for comprehensive determination of myocardial global and regional function along with assessment of myocardial perfusion, metabolism and viability in healthy mice and in a cardiac ischemia mouse model induced by permanent occlusion of the left anterior descending coronary artery (LAD).

In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography

This method outlines the use of Quantum Micro-Computed Tomography (MicroCT) to assess cardiac morphology, function, perfusion, metabolism and viability with iodinated contrast agent in mice with experimentally-induced myocardial ischemia. The technique can be applied for non-destructive high-throughput longitudinal in vivo imaging of various animal models of human heart disease.

The use of Micro-Computed Tomography (MicroCT) for in vivo studies of small animals as models of human disease has risen tremendously due to the fact that ...

Bioengineering

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative magnetic resonance imaging (qMRI) describes the development and use of MRI to quantify physical, chemical, and/or biological properties of living systems. Neuromuscular diseases often exhibit a temporally varying, spatially heterogeneous, and multi-faceted pathology. The goal of this protocol is to characterize this pathology using qMRI methods. The MRI acquisition protocol begins with localizer images (used to locate the position of the body and tissue of interest within the MRI system), quality control measurements of relevant magnetic field distributions, and structural imaging for general anatomical characterization. The qMRI portion of the protocol includes measurements of the longitudinal and transverse relaxation time constants (T1 and T2, respectively). Also acquired are diffusion-tensor MRI data, in which water diffusivity is measured and used to infer pathological processes such as edema. Quantitative magnetization transfer imaging is used to characterize the relative tissue content of macromolecular and free water protons. Lastly, fat-water MRI methods are used to characterize fibro-adipose tissue replacement of muscle. In addition to describing the data acquisition and analysis procedures, this paper also discusses the potential problems associated with these methods, the analysis and interpretation of the data, MRI safety, and strategies for artifact reduction and protocol optimization.

Neuromuscular diseases often exhibit a temporally varying, spatially heterogeneous, and multi-faceted pathology. The goal of this protocol is to characterize this pathology using non-invasive magnetic resonance imaging methods.

Quantitative magnetic resonance imaging (qMRI) describes the development and use of MRI to quantify physical, chemical, and/or biological properties of ...

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 ...

Real Time and Repeated Measurement of Skeletal Muscle Growth in Individual Live Zebrafish Subjected to Altered Electrical Activity

A number of methods can be used to visualize individual cells throughout the body of live embryonic, larval or juvenile zebrafish. We show that live fish with fluorescently-marked plasma membranes can be scanned in a confocal laser scanning microscope in order to determine the volume of muscle tissue and the number of muscle fibers present. Efficient approaches for the measurement of cell number and size in live animals over time are described and validated against more arduous segmentation methods. Methods are described that permit the control of muscle electrical, and thus contractile, activity. Loss of skeletal muscle contractile activity greatly reduced muscle growth. In larvae, a protocol is described that allows reintroduction of patterned electrical-evoked contractile activity. The described methods minimize the effect of inter-individual variability and will permit analysis of the effect of electrical, genetic, drug, or environmental stimuli on a variety of cellular and physiological growth parameters in the context of the living organism. Long-term follow-up of the measured effects of a defined early-life intervention on individuals can subsequently be performed.

Optical clarity is a major advantage for cell biological and physiological work in zebrafish. Robust methods for measurement of cell growth in individual animals are described that permit novel insights into how growth of skeletal muscle and neighboring tissues are integrated with whole body growth.

A number of methods can be used to visualize individual cells throughout the body of live embryonic, larval or juvenile zebrafish. We show that live fish ...

Developmental Biology

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.

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 ...

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

The developmental goal of 3D ultrasound imaging (3DUS) is to engineer a modality to perform 3D morphological ultrasound analysis of human muscles. 3DUS images are constructed from calibrated freehand 2D B-mode ultrasound images, which are positioned into a voxel array. Ultrasound (US) imaging allows quantification of muscle size, fascicle length, and angle of pennation. These morphological variables are important determinants of muscle force and length range of force exertion. The presented protocol describes an approach to determine volume and fascicle length of m.&#160;vastus lateralis and m.&#160;gastrocnemius medialis. 3DUS facilitates standardization using 3D anatomical references. This approach provides a fast and cost-effective approach for quantifying 3D morphology in skeletal muscles. In healthcare and sports, information on the morphometry of muscles is very valuable in diagnostics and/or follow-up evaluations after treatment or training.

3D ultrasound imaging (3DUS) allows fast and cost-effective morphometry of musculoskeletal tissues. We present a protocol to measure muscle volume and fascicle length using 3DUS.

The developmental goal of 3D ultrasound imaging (3DUS) is to engineer a modality to perform 3D morphological ultrasound analysis of human muscles. 3DUS ...

Sample Preparation for Computed Tomography-based Three-dimensional Visualization of Murine Hind-limb Vessels

Blood vessels are complex networks with tree-like structures, and vascular networks are essential for maintaining both circulation and maintaining organ function. Clarifying the mechanism of blood vessel formation is therefore extremely useful for elucidating developmental processes and pathological mechanisms. Murine hind-limb vessels are often used as a model for physiological and pathological angiogenesis. Evaluation is mainly performed via a two-dimensional method using tissue sections. However, methods for evaluating three-dimensional (3D) vascular morphology are particularly limited. This paper introduces a method for visualizing murine hind-limbs using computed tomography (CT). Radiation-opaque resin is injected through the descending aorta, and whole vessels are filled with dye. By adjusting the time of dye injection, arterial-specific filling is also possible, and samples can be obtained with any micro-X-ray CT device. This contrast method provides a basic technique for the 3D evaluation of murine blood vessels in the lower extremities. Furthermore, this method can be used to visualize all blood vessels below the diaphragm and evaluate blood vessels in the abdominal organs.

Here, we describe a visualization and quantification method for murine hind-limb vessels using micro-X-ray computed tomography.

Blood vessels are complex networks with tree-like structures, and vascular networks are essential for maintaining both circulation and maintaining organ ...

In Vivo Measurement of Hindlimb Dorsiflexor Isometric Torque from Pig

Reliable assessment of skeletal muscle strength is arguably the most important outcome measure in neuromuscular and musculoskeletal disease and injury studies, particularly when evaluating regenerative therapies&#39; efficacy. Additionally, a critical aspect of translating many regenerative therapies is the demonstration of scalability and effectiveness in a large animal model. Various physiological preparations have been established to evaluate intrinsic muscle function properties in basic science studies, primarily in small animal models. The practices may be categorized as: in vitro (isolated fibers, fiber bundles, or whole muscle), in situ (muscle with intact vascularization and innervation but distal tendon attached to a force transducer), and in vivo (structures of the muscle or muscle unit remain intact). There are strengths and weaknesses to each of these preparations; however, a clear advantage of in vivo strength testing is the ability to perform repeated measurements in the same animal. Herein, the materials and methods to reliably assess isometric torque produced by the hindlimb dorsiflexor muscles in vivo in response to standard peroneal electrical stimulation in anesthetized pigs are presented.

In Vivo Measurement of Hindlimb Dorsiflexor Isometric Torque from Pig

The present protocol describes concise experimental details on the evaluation and interpretation of in vivo torque data obtained via electrical stimulation of the common peroneal nerve in anesthetized pigs.

Reliable assessment of skeletal muscle strength is arguably the most important outcome measure in neuromuscular and musculoskeletal disease and injury ...

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video