Tim Daley and Andrew Smith provided many useful computational discussions during the early days; Tim Daley wrote the program rotate_translate_cylindrical necessary for Protocol 5. Computational resources were provided by the High Performance Computing Cluster at the University of Southern California. This work was supported by NIH grant #GM098536 (MDD).

All procedures and personnel were approved by the University of Southern California&#39;s Institute for Animal Care and Use Committee (IACUC), protocol #11394.
1. Baculum Dissection and Preparation
<ol>
	<li>Euthanize a sexually mature male mouse via carbon dioxide over-exposure, according to protocols set forth by the relevant Institutional Animal Care and Use Committee (IACUC).</li>
	<li>Lay the animal in the supine position, and protract the penis by applying pressure with thumbs lateral to the preputial opening.</li>
	<li>Once the penis is protracted, extend the tissue through the prepuce as far as possible.</li>
	<li>With scissors, cut the penile body proximal to the glans penis where the baculum resides.</li>
	<li>Transfer the dissected penis to a 1.7 mL tube and add 200 &#181;L tap water. Make certain that the penis is fully submerged in the liquid.</li>
	<li>Incubate the tissue in water at ~50 &#176;C for 3-5 days.</li>
	<li>After proper incubation, remove the surrounding tissue from the baculum, using forceps under a dissection microscope. Gently squirt 70% ethanol to push off remaining tissue and clean the bone.</li>
	<li>Place the dissected baculum into a new microcentrifuge tube with the cap open. Leave cap open O/N to dry out bone.</li>
</ol>
2. MicroCT Scanning
<ol>
	<li>Press a microCT scan cylindrical holder into a brick of florist foam to create a cylinder of florist foam.</li>
	<li>Extract the cylinder of florist foam and cut slices ~2-5 cm thick.</li>
	<li>Push dried bacula into the florist foam, around the periphery of an individual slice to minimize interference during scanning. The precise orientation of bones should be remarked allowing for proper identification of individual specimens in Protocol 4.</li>
	<li>Gently place the slice with the embedded bones into the microCT holder.</li>
	<li>Acquire microCT scans. In the case of mouse bacula14, we used a uCT50 scanner (Scanco Medical AG, Bruttisellen, Switzerland) at the USC Molecular Imaging Center under the following settings: 90 kVp, 155 &#181;A, 0.5 mm Al filter, 750 projections per 180 (360 coverage), exposure time of 500 ms, and voxel size of 15.5 mm.</li>
</ol>
3. MicroCT Processing: Converting a .DCM Stack to a Single .xyz File
NOTE: Each microCT scan produces a stack of .DCM, or &#34;dicom&#34;, files that represent image slices taken through the object. All downstream computational geometry requires flat .xyz files, which is simply a text file that contains four columns &#8211; the x, y, and z coordinates of each pixel, and the intensity of the pixel, ranging from -5,000 (black) to +5,000 (white). A pixel threshold above 3,000 generally works well as a threshold for defining bones.
<ol>
	<li>Install Python (www.python.org) and the PYTHON modules COMMANDS, DICOM, PYLAB, SYS, and NUMPY.</li>
	<li>Open &#34;01_process_dicom.py&#34;{Figshare} with any text editor. Under the Variables section, change path, pixel thresholds, and directory names as necessary.</li>
	<li>Run &#34;python 01_process_dicom.py&#34;. Progress will be printed to screen. Within each directory named in step 3.2, a new file is made named; for example, directory_name.PT3000.xyz, where PT3000 indicates the pixel threshold indicated in step 3.2.</li>
</ol>
4. MicroCT processing: Segmenting-out Individual Specimen .xyz Files
<ol>
	<li>Install R (https://www.r-project.org/) with the library RGL.</li>
	<li>Open the file &#39;02_segment_dicoms.r&#39;{Figshare} with any text editor. Under the Variables section, change the path name to point to the .xyz file created in Protocol 3 above.</li>
	<li>From within R, run the command &#34;source(&#39;02_segment_dicoms.r&#39;)&#34; (without the double quotes).</li>
	<li>After the three dimensional image of the .xyz file created in Protocol 3 appears, enter the number of specimens in the overall .xyz file. Then label and select the points from each specimen using the scroll and zoom functions. 
	NOTE: In the background, separate .xyz files will be made for each specimen. These appear in a directory named, for example, XYZ_FILES_PT3000, where PT3000 indicates the pixel threshold used.</li>
</ol>
5. &#34;Aligning&#34; Specimen .xyz Files to Common Coordinates.
<ol>
	<li>Open the Python script &#34;03_transform.py&#34;{Figshare}, which requires the additional module mattdean_modules.py{Figshare}, as well as two self-standing applications: &#34;rotate_translate_cylindrical&#34; (https://github.com/timydaley/dean_cylindrical_tranform) and &#8220;qconvex&#8221; (www.qhull.org/html/qconvex.htm) that are used by this script.</li>
	<li>Under the Variables section, identify the full path names to mattdean_modules.py, rotate_path and qconvex_dir. In addition, identify the full path to the directory containing the individual .xyz files created in step 4.</li>
	<li>Run 03_transform.py, which creates a new file per specimen with the suffix .TRANSFORMED.xyz.</li>
</ol>
6. &#34;Slicing&#34; Aligned Specimen .xyz Files to Identify Semi-landmarks.
<ol>
	<li>Open and run the Python script &#34;04_identify_landmarks.py&#34;{Figshare}. In the Variables section, identify full path names to the directory containing the .TRANSFORMED.xyz files. This script identifies 802 semi-landmarks that can be used to quantify size and shape of the structure.</li>
</ol>

<ol>
	<li>Slice, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Geometrics+morphometrics.">Geometrics morphometrics.</a> Annu. Rev. Anthropol. 36, 261-281 (2007).</li><li>Slice, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Modern morphometrics in physical anthropology. 6, (2005).</li><li>Zelditch, M. L., Swiderski, D. L., Sheets, H. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Geometric morphometrics for biologists: a primer. , (2012).</li><li>Bookstein, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Morphometric tools for landmark data: geometry and biology. , (1991).</li><li>Rohlf, F. J., Marcus, L. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Revolution+in+Morphometrics.">A Revolution in Morphometrics.</a> Trends. Ecol. Evol. 8 (4), 129-132 (1993).</li><li>Zelditch, M. L., Swiderski, D. L., Sheets, H. D., Fink, W. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Geometric morphometrics for biologists: a primer. , (2004).</li><li>Rohlf, F. J., Slice, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extensions+of+the+Procrustes+method+for+the+optimal+superimposition+of+landmarks.">Extensions of the Procrustes method for the optimal superimposition of landmarks.</a> Syst. Zool. 39 (1), 40-59 (1990).</li><li>Gunz, P., Mitteroecker, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Semilandmarks:+a+method+for+quantifying+curves+and+surfaces.">Semilandmarks: a method for quantifying curves and surfaces.</a> Hystrix. 24 (1), 103-109 (2013).</li><li>Gunz, P., Ramsier, M., Kuhrig, M., Hublin, J. J., Spoor, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+mammalian+bony+labyrinth+reconsidered,+introducing+a+comprehensive+geometric+morphometric+approach.">The mammalian bony labyrinth reconsidered, introducing a comprehensive geometric morphometric approach.</a> J. Anat. 220 (6), 529-543 (2012).</li><li>Mitteroecker, P., Gunz, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+geometric+morphometrics.">Advances in geometric morphometrics.</a> Evol. Biol. 36 (2), 235-247 (2009).</li><li>Bookstein, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Landmark+methods+for+forms+without+landmarks:+morphometrics+of+group+differences+in+outline+shape.">Landmark methods for forms without landmarks: morphometrics of group differences in outline shape.</a> Med. Im. Anal. 1 (3), 225-243 (1997).</li><li>Gunz, P., Mitteroecker, P., Bookstein, F., Slice, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Modern morphometrics in physical anthropology. , 73-98 (2005).</li><li>Oxnard, C., O'Higgins, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biology+Clearly+Needs+Morphometrics.+Does+Morphometrics+Need+Biology?.">Biology Clearly Needs Morphometrics. Does Morphometrics Need Biology?.</a> Biological Theory. 4 (1), 84-97 (2009).</li><li>Schultz, N. G., 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+genetic+basis+of+baculum+size+and+shape+variation+in+mice.">The genetic basis of baculum size and shape variation in mice.</a> G3. 6 (5), 1141-1151 (2016).</li><li>Schultz, N. G., Lough-Stevens, M., Abreu, E., Orr, T. J., Dean, M. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+baculum+was+gained+and+lost+multiple+times+during+mammalian+evolution.">The baculum was gained and lost multiple times during mammalian evolution.</a> Integr Comp Biol. 56 (4), 644-656 (2016).</li><li>Dines, J. P., 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=Sexual+selection+targets+cetacean+pelvic+bones.">Sexual selection targets cetacean pelvic bones.</a> Evolution. 68 (11), 3296-3306 (2014).</li><li>Adams, D. C., Ot&#225;rola-Castillo, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=geomorph:+an+R+package+for+the+collection+and+analysis+of+geometric+morphometric+shape+data.">geomorph: an R package for the collection and analysis of geometric morphometric shape data.</a> Methods Ecol. Evol. 4 (4), 393-399 (2013).</li></ol>

The authors declare that they have no competing financial interests.

The critical steps in the above protocol are 1) dissecting the bacula, 2) gathering the microCT images, 3) converting the microCT output to a flat file of x-y-z coordinates, 4) segmenting out each specimen&#39;s point cloud, 5) transforming each specimen to a standardized coordinate system, and 6) defining semi-landmarks. These steps are easily modified to accommodate different objects.
These methods can likely be applied to any object that is essentially &#34;rod-shaped&#34;, or at least not too curved. Objects that curve back on themselves to become &#34;u-shaped&#34; cannot be currently analyzed, because slicing (Figure 1G) would return points from different parts of the object. Such objects could be accommodated in the future by computationally straightening the object prior to slicing.
We have presented a general method for mathematically defining semi-landmarks from shapes that lack solid landmarks. These general methods have been modified to study the evolution of whale pelvic and rib bones16, which have very different shapes. Our computational methods for defining landmarks should be applicable to any series of x-y-z coordinates. We employed microCT scanning here, given the small size of mouse bacula14. For larger bones, such as the whale pelvic and rib bones, we employed a laser scanner that reconstructed the surface of the bones16. It is important to visually inspect all sets of semi-landmarks to verify the quality of the method. The main advantage of our computational methods is that they precisely quantify the size and shape variation, and preserve the correspondence between distinct regions of the object.

The field of morphometrics includes a diversity of methods to quantify the size and shape of the biological form, a fundamental step in scientific inquiry1,2,3,4,5,6. Traditionally, the statistical analysis of size and shape begins by identifying landmarks on a biological structure, and then measuring linear distances, angles and ratios, which could be analyzed in a multivariate framework. Landmark-based Geometric Morphometrics is an approach that retains the spatial position of landmarks, preserving geometric information from data collection through analysis and visualization5. Generalized Procrustes Analysis (GPA) can be applied to remove variation in location, scale, and rotation of landmarks to produce an alignment between specimens that minimizes their squared differences - what remains is shape dissimilarity7.
An important concept of any morphometric analysis is homology, or the idea that one can reliably identify landmarks representing biologically meaningful and discrete features that correspond between specimens or structures. For example, human skulls have homologous processes, foramina, sutures, and ducts that can enable morphometric analyses. Unfortunately, the identification of corresponding landmarks is difficult across many biological structures, especially those with smooth surfaces or curves8,9,10.
We approach this problem below using computational geometry. The general workflow is to generate a three dimensional scan of the object that can be represented as a cloud of points, and then rotate and transform that point cloud so that all specimens are oriented on a common coordinate system. Then we mathematically define semi-landmarks from specific regions of the object. Discrete semi-landmarks placed on such regions are biologically arbitrary11. Conducting GPA and subsequent statistical analyses can produce undesirable artifacts8,12 because arbitrarily placed landmarks may not be biologically homologous. Therefore, we allow these semi-landmarks to mathematically &#34;slide&#34;. This procedure minimizes the potential difference between structures. As argued elsewhere the sliding algorithm used here is appropriate to quantify similar anatomical regions lacking easily identified corresponding landmarks3,6,8,10,11,12. These methods have their limitations13, but should be adaptable to objects of different size and shape.
Here, we illustrate how this method was applied in a recent study of the mouse baculum14, a bone in the penis that has been gained and lost multiple independent times during mammalian evolution15. We discuss the dissection and preparation of a specific bone, the baculum (Protocol 1), the generation of microCT images (Protocol 2), and the conversion of these images to a format that enables all downstream computational geometry (Protocols 3 and 4). After these steps, each specimen is represented by ~100K x-y-z coordinates. We then walk through a series of transformations that effectively align all specimens into a common orientation (Protocol 5), then define semi-landmarks from aligned specimens (Protocol 6). Protocols 1-4 should be similar regardless of the object being analyzed. Protocol 5 and Protocol 6 are specifically designed for a baculum, but it is our hope that by detailing these steps, investigators can imagine modifications that would be relevant for their object of interest. For example, modifications of these methods were applied to study whale pelvic bones and rib bones16.

<table border="0" cellpadding="0" cellspacing="0" width="743">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:211px;">Dissecting scissors</td>
			<td style="width:287px;">VWR</td>
			<td style="width:79px;">470106-338</td>
			<td style="width:167px;">Most sizes should work</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:211px;">Dissecting Forceps, Fine Tip, Curved</td>
			<td style="width:287px;">VWR</td>
			<td style="width:79px;">82027-406</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:211px;">1.7 mL microcentrifuge tube</td>
			<td style="width:287px;">VWR</td>
			<td style="width:79px;">87003-294</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:211px;">Absolute Ethanol</td>
			<td style="width:287px;">Fisher Scientific</td>
			<td style="width:79px;">CAS 64-17-5</td>
			<td>To be diluted to 70% for dissections</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:211px;">Floral Foam</td>
			<td style="width:287px;">Wholesale Floral</td>
			<td style="width:79px;">6002-48-07</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">uCT50 scanner&#160;</td>
			<td colspan="2">Scanco Medical AG, Bruttisellen, Switzerland</td>
			<td></td>
		</tr>
	</tbody>
</table>

dissection, microct scanning and morphometric analyses of the baculum

Modern morphometrics provides powerful methods to quantify size and shape variation. A basic requirement is a list of coordinates that define landmarks; however such coordinates must represent homologous structures across specimens. While many biological objects consist of easily identified landmarks to satisfy the assumption of homology, many lack such structures. One potential solution is to mathematically place semi-landmarks on an object that represent the same morphological region across specimens. Here, we illustrate a recently developed pipeline to mathematically define semi-landmarks from the mouse baculum (penis bone). Our methods should be applicable to a wide range of objects.

The x-y-z coordinates of the semi-landmarks produced in Protocol 6 can be directly imported into any landmark-based geometric morphometrics analysis17. The computational pipeline above has been applied to study mouse bacula14, as well as whale pelvic and rib bones16. More details on the computational definition of semi-landmarks are presented here, in an attempt to help researchers visualize steps that might be modified to accommodate their particular object of interest. The baculum contains several unique features that were exploited to automate certain transformations. For example, after computationally cutting the bone into two halves along a proximal-distal axis, we identified the proximal half simply by comparing the total number of points (the proximal has more). As long as there exist unique features such as this, our methods should be adaptable to any object. In addition, it should be emphasized that we empirically determined certain thresholds, such as &#34;10% proximal&#34; that performed well in our baculum studies, but most certainly need to be reassessed for other objects.
Beginning in Protocol 5, the first computational step is to calculate the convex hull (the smallest set of points that contains all other points in a sample) to identify the two points that are furthest apart from each other. These two points (red spheres, Figure 1C) begin to define a new z-axis (red line, Figure 1C) that runs proximal-distal through the bone. In the case of the baculum, the half of the point cloud that contains more points is defined as the proximal end.
Second, the entire point cloud is transformed so that the proximal point takes on the x,y,z coordinates of 0,0,0 and the distal point takes on the x,y,z coordinates of 0,0,+z, where +z is some positive value dependent on the size of the bone. At the end of this step, a z-axis passes through the length of the bone. For procedures below, the length from the minimum to maximum z-coordinates will be referred to as Zlength.
Third, to correct for variance associated with the exact placement of the proximal and distal points identified above, the 10% most proximal and 10% most distal points are sampled separately (Figure 1D), their respective centroids identified (red spheres, Figure 1E) and the point cloud transformed such that the proximal centroid is 0,0,0, and the distal centroid is 0,0,+z, with a new z-axis that passes through the center of the specimen (red line, Figure 1E).
Fourth, the point cloud is rotated around the z-axis by first taking a slice of points in the proximal 15 to 15.25% Zlength of the structure (blue points, Figure 1E). This slice of points is flattened in the z dimension (i.e.&#160;z-coordinates are simply ignored), the convex hull taken, and the minimum bounding rectangle (the smallest rectangle that contains all other points) calculated. Imagine a line connecting the midpoints of the two short sides of this minimum bounding rectangle. We rotate the point cloud until these two midpoints become -x,0,+z and +x,0,+z, respectively, thus this line becomes a new x-axis. After transformation, the distance between the maximal and minimal x values are referred to as Xlength. A new file is created from specimen.xyz to specimen.TRANSFORMED.xyz.
Fifth, points within 1% Xlength of the z-axis are sliced out (blue points, Figure 1F), and the single most proximal and single most distal point identified from this central slice and labeled DISTAL and PROXIMAL, respectively. These are the first two semi-landmarks identified.
Sixth, 50 evenly spaced slices of points are sampled along the z-axis (red points, Figure 1G). Each slice is a thickness of 1% Zlength. Each slice is then flattened in the z dimension, and divided equally by 7 vertical lines (red lines, Figure 1H). Points within 2% Xlength of each line are kept (red points, Figure 1H), then the points with the maximum and minimum y-coordinate are kept, projected onto each respective line, and labeled VENTRAL and DORSAL, respectively. In addition, labels contain the slice number and the line number, for example P15_VENTRAL4 is the ventral point sampled from the 4th vertical line of the 15th slice. Importantly, every point labeled, for example, P15_VENTRAL4, occurs once and only once across all specimens, preserving correspondence. In addition to the ventral and dorsal points of each of the 7 lines (14 semi-landmarks total), the points with the maximum and minimum x-value are sampled and labeled LEFT and RIGHT, respectively. The y and z coordinates of LEFT and RIGHT are smoothed using the lowess function in R. For the baculum, a total of 16 semi-landmarks are defined per slice (red spheres, Figure 1H); with 50 slices plus the PROXIMAL and DISTAL semi-landmarks defined above, 802 semi-landmarks are sampled per specimen (green spheres, Figure 1I). All other points from the original microCT scan are discarded.
It should be noted that although ventral/dorsal and proximal/distal polarity was determined mathematically, all specimen alignments were visually confirmed and manually adjusted as required. In our sample of 369 bacula, approximately 10 had to be manually adjusted.
<img alt="Figure 1" src="/files/ftp_upload/55342/55342fig1.jpg" /> 
Figure 1: Visual Representation of the Computational Workflow (Protocol 4-6). (A) A screenshot from the 02_segment_dicoms.r script (Protocol 4), showing the assignment of distinct point clouds to individual specimens. (B) Enlarged view of one baculum, represented by a cloud of ~100K x-y-z points. (C) Identification of the two points furthest apart from each other (red spheres), used to define a new z-axis that runs proximal-distal through the bone (red line). (D) Sampling the proximal-most 10% and distal-most 10% of points (red points) provides a means to adjust for slight variance in the exact placement of the z-axis. (E) The centroids of the proximal-most 10% and distal-most 10% (red spheres) are used to define a new z-axis (red line). Then, a slice of points falling between 15.00-15.25% of this new z-axis (blue points) is taken to calculate the minimum bounding rectangle. The point cloud is rotated until the long side of the minimum bounding rectangle is parallel to a new x-axis. F) a slice of points running along the midline (blue points) is sampled and the proximal-most and distal-most point defined as a semi-landmark. G) 50 evenly spaced slices of points are sampled (red points), with H) showing one such slice. 16 points (red circles) are defined to capture the outline of each slice. I) When repeated across all slices, a total of 802 semi-landmarks (green spheres) define the structure and are used in all downstream morphometric applications. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55342/55342fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum at JoVE.com

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Many biological structures lack easily definable landmarks, making it difficult to apply modern morphometric methods. Here we illustrate methods to study the mouse baculum (a bone in the penis), including dissection and microCT scanning, followed by computational methods to define semi-landmarks that are used to quantify size and shape variation.

Modern morphometrics provides powerful methods to quantify size and shape variation. A basic requirement is a list of coordinates that define landmarks; ...

dissection-microct-scanning-and-morphometric-analyses-of-the-baculum

Nanyang Technological University

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JoVE Journal

Bioengineering

7.5K Views.  University of Southern California (USC). Many biological structures lack easily definable landmarks, making it difficult to apply modern morphometric methods. Here we illustrate methods to study the mouse baculum (a bone in the penis), including dissection and microCT scanning, followed by computational methods to define semi-landmarks that are used to quantify size and shape variation.

Video: Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

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

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

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

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

Video-rate Scanning Confocal Microscopy and Microendoscopy

Confocal microscopy has become an invaluable tool in biology and the biomedical sciences, enabling rapid, high-sensitivity, and high-resolution optical sectioning of complex systems. Confocal microscopy is routinely used, for example, to study specific cellular targets1, monitor dynamics in living cells2-4, and visualize the three dimensional evolution of entire organisms5,6. Extensions of confocal imaging systems, such as confocal microendoscopes, allow for high-resolution imaging in vivo7 and are currently being applied to disease imaging and diagnosis in clinical settings8,9.
Confocal microscopy provides three-dimensional resolution by creating so-called "optical sections" using straightforward geometrical optics. In a standard wide-field microscope, fluorescence generated from a sample is collected by an objective lens and relayed directly to a detector. While acceptable for imaging thin samples, thick samples become blurred by fluorescence generated above and below the objective focal plane. In contrast, confocal microscopy enables virtual, optical sectioning of samples, rejecting out-of-focus light to build high resolution three-dimensional representations of samples.
Confocal microscopes achieve this feat by using a confocal aperture in the detection beam path. The fluorescence collected from a sample by the objective is relayed back through the scanning mirrors and through the primary dichroic mirror, a mirror carefully selected to reflect shorter wavelengths such as the laser excitation beam while passing the longer, Stokes-shifted fluorescence emission. This long-wavelength fluorescence signal is then passed to a pair of lenses on either side of a pinhole that is positioned at a plane exactly conjugate with the focal plane of the objective lens. Photons collected from the focal volume of the object are collimated by the objective lens and are focused by the confocal lenses through the pinhole. Fluorescence generated above or below the focal plane will therefore not be collimated properly, and will not pass through the confocal pinhole1, creating an optical section in which only light from the microscope focus is visible. (Fig 1). Thus the pinhole effectively acts as a virtual aperture in the focal plane, confining the detected emission to only one limited spatial location.
Modern commercial confocal microscopes offer users fully automated operation, making formerly complex imaging procedures relatively straightforward and accessible. Despite the flexibility and power of these systems, commercial confocal microscopes are not well suited for all confocal imaging tasks, such as many in vivo imaging applications. Without the ability to create customized imaging systems to meet their needs, important experiments can remain out of reach to many scientists.
In this article, we provide a step-by-step method for the complete construction of a custom, video-rate confocal imaging system from basic components. The upright microscope will be constructed using a resonant galvanometric mirror to provide the fast scanning axis, while a standard speed resonant galvanometric mirror will scan the slow axis. To create a precise scanned beam in the objective lens focus, these mirrors will be positioned at the so-called telecentric planes using four relay lenses. Confocal detection will be accomplished using a standard, off-the-shelf photomultiplier tube (PMT), and the images will be captured and displayed using a Matrox framegrabber card and the included software.

The complete construction of a custom, real-time confocal scanning imaging system is described. This system, which can be readily used for video-rate microscopy and microendoscopy, allows for an array of imaging geometries and applications not accessible using standard commercial confocal systems, at a fraction of the cost.

Confocal microscopy has become an invaluable tool in biology and the biomedical sciences, enabling rapid, high-sensitivity, and high-resolution optical ...

Evaluation of Biomaterials for Bladder Augmentation using Cystometric Analyses in Various Rodent Models

Renal function and continence of urine are critically dependent on the proper function of the urinary bladder, which stores urine at low pressure and expels it with a precisely orchestrated contraction. A number of congenital and acquired urological anomalies including posterior urethral valves, benign prostatic hyperplasia, and neurogenic bladder secondary to spina bifida/spinal cord injury can result in pathologic tissue remodeling leading to impaired compliance and reduced capacity1. Functional or anatomical obstruction of the urinary tract is frequently associated with these conditions, and can lead to urinary incontinence and kidney damage from increased storage and voiding pressures2. Surgical implantation of gastrointestinal segments to expand organ capacity and reduce intravesical pressures represents the primary surgical treatment option for these disorders when medical management fails3. However, this approach is hampered by the limitation of available donor tissue, and is associated with significant complications including chronic urinary tract infection, metabolic perturbation, urinary stone formation, and secondary malignancy4,5.
Current research in bladder tissue engineering is heavily focused on identifying biomaterial configurations which can support regeneration of tissues at defect sites. Conventional 3-D scaffolds derived from natural and synthetic polymers such as small intestinal submucosa and poly-glycolic acid have shown some short-term success in supporting urothelial and smooth muscle regeneration as well as facilitating increased organ storage capacity in both animal models and in the clinic6,7. However, deficiencies in scaffold mechanical integrity and biocompatibility often result in deleterious fibrosis8, graft contracture9, and calcification10, thus increasing the risk of implant failure and need for secondary surgical procedures. In addition, restoration of normal voiding characteristics utilizing standard biomaterial constructs for augmentation cystoplasty has yet to be achieved, and therefore research and development of novel matrices which can fulfill this role is needed.
In order to successfully develop and evaluate optimal biomaterials for clinical bladder augmentation, efficacy research must first be performed in standardized animal models using detailed surgical methods and functional outcome assessments. We have previously reported the use of a bladder augmentation model in mice to determine the potential of silk fibroin-based scaffolds to mediate tissue regeneration and functional voiding characteristics.11,12 Cystometric analyses of this model have shown that variations in structural and mechanical implant properties can influence the resulting urodynamic features of the tissue engineered bladders11,12. Positive correlations between the degree of matrix-mediated tissue regeneration determined histologically and functional compliance and capacity evaluated by cystometry were demonstrated in this model11,12. These results therefore suggest that functional evaluations of biomaterial configurations in rodent bladder augmentation systems may be a useful format for assessing scaffold properties and establishing in vivo feasibility prior to large animal studies and clinical deployment. In the current study, we will present various surgical stages of bladder augmentation in both mice and rats using silk scaffolds and demonstrate techniques for awake and anesthetized cystometry.

Surgical stages of bladder augmentation are described using 3-D scaffolds in murine and rat models. To test the efficacy of biomaterial configurations for use in bladder augmentation, techniques for both awake and anesthetized cystometry are presented.

Renal function and continence of urine are critically dependent on the proper function of the urinary bladder, which stores urine at low pressure and ...

Aplysia Ganglia Preparation for Electrophysiological and Molecular Analyses of Single Neurons

A major challenge in neurobiology is to understand the molecular underpinnings of neural circuitry that govern a specific behavior. Once the specific molecular mechanisms are identified, new therapeutic strategies can be developed to treat abnormalities in specific behaviors caused by degenerative diseases or aging of the nervous system. The marine snail Aplysia californica is well suited for the investigations of cellular and molecular basis of behavior because neural circuitry underlying a specific behavior could be easily determined and the individual components of the circuitry could be easily manipulated. These advantages of Aplysia have led to several fundamental discoveries of neurobiology of learning and memory. Here we describe a preparation of the Aplysia nervous system for the electrophysiological and molecular analyses of individual neurons. Briefly, ganglion dissected from the nervous system is exposed to protease to remove the ganglion sheath such that neurons are exposed but retain neuronal activity as in the intact animal. This preparation is used to carry out electrophysiological measurements of single or multiple neurons. Importantly, following the recording using a simple methodology, the neurons could be isolated directly from the ganglia for gene expression analysis. These protocols were used to carry out simultaneous electrophysiological recordings from L7 and R15 neurons, study their response to acetylcholine and quantitating expression of CREB1 gene in isolated single L7, L11, R15, and R2 neurons of Aplysia.

Aplysia Ganglia Preparation for Electrophysiological and Molecular Analyses of Single Neurons

Marine snail Aplysia californica has been widely used as a neurobiology model for the studies on cellular and molecular basis of behavior. Here a methodology is described for exploring the nervous system of Aplysia for the electrophysiological and molecular analyses of single neurons of identified neural circuitry.

A major challenge in neurobiology is to understand the molecular underpinnings of neural circuitry that govern a specific behavior. Once the specific ...

Large-area Scanning Probe Nanolithography Facilitated by Automated Alignment and Its Application to Substrate Fabrication for Cell Culture Studies

Scanning probe microscopy has enabled the creation of a variety of methods for the constructive (&#39;additive&#39;) top-down fabrication of nanometer-scale features. Historically, a major drawback of scanning probe lithography has been the intrinsically low throughput of single probe systems. This has been tackled by the use of arrays of multiple probes to enable increased nanolithography throughput. In order to implement such parallelized nanolithography, the accurate alignment of probe arrays with the substrate surface is vital, so that all probes make contact with the surface simultaneously when lithographic patterning begins. This protocol describes the utilization of polymer pen lithography to produce nanometer-scale features over centimeter-sized areas, facilitated by the use of an algorithm for the rapid, accurate, and automated alignment of probe arrays. Here, nanolithography of thiols on gold substrates demonstrates the generation of features with high uniformity. These patterns are then functionalized with fibronectin for use in the context of surface-directed cell morphology studies.

Here we present a protocol for wide-area scanning probe nanolithography enabled by the iterative alignment of probe arrays, as well as the utilization of lithographic patterns for cell-surface interaction studies.

Scanning probe microscopy has enabled the creation of a variety of methods for the constructive (&#39;additive&#39;) top-down fabrication of ...

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own experiments. In this article, we describe a protocol that can be used by those with access to a widefield high-content analysis system (WHCAS) to image any slide-mounted tissue, with options for customization within pre-built modules found in the associated software. Not originally intended for slide scanning, the steps detailed in this article make it possible to acquire slide scanning images in the WHCAS which can be imported into the associated software. In this example, the automated segmentation of brain tumor slides is demonstrated, but the automated segmentation of any fluorescently-labeled nuclear or cytoplasmic marker is possible. Furthermore, there are a variety of other quantitative software modules including assays for protein localization/translocation, cellular proliferation/viability/apoptosis, and angiogenesis that can be run. This technique will save researchers time and effort and create an automated protocol for slide analysis.

Here we describe a protocol for the automated segmentation of fluorescently labeled tissues on slides using a widefield high-content analysis system (WHCAS). This protocol has wide-ranging applications in any field which involves the quantitation of fluorescent markers in biological tissues including the biological sciences, medical engineering, and health sciences.

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. ...

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (oSLO) and Optical Coherence Tomography (OCT)

While fluorescence imaging is widely used in ophthalmology, a large field of view (FOV) three-dimensional (3D) fluorescence retinal image is still a big challenge with the state-of-the-art retinal imaging modalities because they would require z-stacking to compile a volumetric dataset. Newer optical coherence tomography (OCT) and OCT angiography (OCTA) systems overcome these restrictions to provide three-dimensional (3D) anatomical and vascular images, but the dye-free nature of OCT cannot visualize leakage indicative of vascular dysfunction. This protocol describes a novel oblique scanning laser ophthalmoscopy (oSLO) technique that provides 3D volumetric fluorescence retinal imaging. The setup of the imaging system generates the oblique scanning by a dove tail slider and aligns the final imaging system at an angle to detect fluorescent cross-sectional images. The system uses the laser scanning method, and therefore, allows an easy incorporation of OCT as a complementary volumetric structural imaging modality. In vivo imaging on rat retina is demonstrated here. Fluorescein solution is intravenously injected to produce volumetric fluorescein angiography (vFA).

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Here, we present a protocol to get a large field of view (FOV) three-dimensional (3D) fluorescence and OCT retinal image by using a novel imaging multimodal platform. We will introduce the system setup, the method of alignment, and the operational protocols. In vivo imaging will be demonstrated, and representative results will be provided.

While fluorescence imaging is widely used in ophthalmology, a large field of view (FOV) three-dimensional (3D) fluorescence retinal image is still a big ...

Morphometric Protocol for the Objective Assessment of Blastocyst Behavior During Vitrification and Warming Steps

This article describes the noninvasive method of blastocyst morphometry based on time-lapse microphotography for the accurate monitoring of a blastocyst&#39;s volume changing during individual phases before and after vitrification. The method can be useful in searching for the most optimal timing of blastocyst exposure to different concentrations of cryoprotectants by observing blastocyst shrinkage and re-expansion in different pre- and post-vitrification phases. With this methodology, the blastocyst vitrification protocol can be optimized. For a better demonstration of the usefulness of this morphometric method, two different blastocyst preparation protocols for vitrification are compared; one with using an artificial blastocoel collapsing and one without this intervention before vitrification. Both blastocysts&#39; volume changes are followed by time-lapse microphotography and measured by photo-editing software tools. The measurements are taken every 20 seconds in previtrification phases and every 5 minutes in the post-warming period. The changes of the blastocyst dimensions per time unit are presented graphically in line diagrams. The results show a long equilibration previtrification phase in which the intact blastocyst first shrinks and then slowly refills the blastocoel, entering vitrification with a fluid-filled blastocoel. The artificially collapsed blastocyst remains in its shrunken stage through the entire equilibration phase. During the vitrification phase, it also does not change its volume. Since the blastocyst morphometry shows a constant volume of the artificially collapsed blastocysts during the previtrification step, it seems that this stage could be shorter. The described protocol provides many additional comparative parameters of blastocyst behavior during and after cryopreservation on the basis of the speed and intensity of the volume changes, the number of partial blastocoel contractions or total blastocyst collapses, and the time to a total blastocoel re-expansion or the time to hatching.

Here we present a time-lapse morphometric protocol to follow the intensity of blastocyst shrinkage and re-expansion during previtrification interventions and post-warming recovery. The application of the protocol is possible in in vitro fertilization laboratories equipped with time-lapse microscopes and is recommended in the development of an optimal blastocyst vitrification method.

This article describes the noninvasive method of blastocyst morphometry based on time-lapse microphotography for the accurate monitoring of a ...

Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many parameters that efficiently measure photosynthesis. Leaf reflectance analysis provides several vegetation indices in ecology and agriculture, including the photochemical reflectance index (PRI), which can be used as an indicator of thermal energy dissipation during photosynthesis because it correlates with non-photochemical quenching (NPQ). However, since NPQ is a composite parameter, its validation is required to understand the nature of the PRI parameter. To obtain physiological evidence for evaluation of the PRI parameter, we simultaneously measured chlorophyll fluorescence and leaf reflectance in xanthophyll cycle defective mutant (npq1) and wild-type Arabidopsis plants. Additionally, the qZ parameter, which likely reflects the xanthophyll cycle, was extracted from the results of chlorophyll fluorescence analysis by monitoring relaxation kinetics of NPQ after switching the light off. These simultaneous measurements were carried out using a pulse-amplitude modulation (PAM) chlorophyll fluorometer and a spectral radiometer. The fiber probes from both instruments were positioned close to each other to detect signals from the same leaf position. An external light source was used to activate photosynthesis, and the measuring lights and saturated light were provided from the PAM instrument. This experimental system enabled us to monitor light-dependent PRI in the intact plant and revealed that light-dependent changes in PRI differ significantly between the wild type and npq1 mutant. Furthermore, PRI was strongly correlated with qZ, meaning that qZ reflects the xanthophyll cycle. Together, these measurements demonstrated that simultaneous measurement of leaf reflectance and chlorophyll fluorescence is a valid approach for parameter evaluation.

We describe a new technical approach to study photosynthetic responses in higher plants involving simultaneous measurements of chlorophyll a fluorescence and leaf reflectance using a PAM and a spectral radiometer for the detection of signals from the same leaf area in Arabidopsis.

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many ...

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Neutrophil swarming is a cooperative process by which neutrophils seal off a site of infection and promote tissue reorganization. Swarming has classically been studied in vivo in animal models showing characteristic patterns of cell migration. However, in vivo models have several limitations, including intercellular mediators that are difficult to access and analyze, as well as the inability to directly analyze human neutrophils. Because of these limitations, there is a need for an in vitro platform that studies swarming with human neutrophils and provides easy access to the molecular signals generated during swarming. Here, a multistep microstamping process is used to generate a bioparticle microarray that stimulates swarming by mimicking an in vivo infection. The bioparticle microarray induces neutrophils to swarm in a controlled and stable manner. On the microarray, neutrophils increase in speed and form stable swarms around bioparticle clusters. Additionally, supernatant generated by the neutrophils was analyzed and 16 proteins were discovered to have been differentially expressed over the course of swarming. This in vitro swarming platform facilitates direct analysis of neutrophil migration and protein release in a reproducible, spatially controlled manner.

This protocol generates bioparticle microarrays that provide spatially controlled neutrophil swarming. It provides easy access to the mediators that neutrophils release during migration and allows for quantitative imaging analysis.

Neutrophil swarming is a cooperative process by which neutrophils seal off a site of infection and promote tissue reorganization. Swarming has classically ...