Name: Morphometric Analyses of Shape: The Analysis Software Toolbox for Craniofacial Shape Quantification in Zebrafish (Video) | JoVE Journal
Uploaded: 2026-02-27T13:45:00.000+00:00
Duration: 9 min 3 s
Description: Learn landmark-based morphometric analysis to quantify subtle craniofacial shape changes in zebrafish larvae following ethanol exposure. This protocol detects defects that linear measurements miss.

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5 minutes

The research presented in this article was supported by a grant from the National Institutes of Health/National Institute on Alcohol Abuse (NIH/NIAAA) R01AA031043 to C.B.L.

All zebrafish larvae used in this procedure were raised and bred following established IACUC protocols approved by the University of Louisville23.
NOTE: The zebrafish strain bmp7aty68a24 and wild-type siblings (all in the AB background) were used in this study, with adult fish maintained at 28.5 &#176;C with a 14/10-h light / dark cycle. Use the appropriate lines per the desired experimental outcomes. Use water that was sterilized by reverse osmosis. Sex as a biological variable does not apply to the studied development stages, as sex is first detectable in zebrafish around 20-25 days post-fertilization (dpf)25, after all of the analyses.
1. Ethanol treatment
<ol>
	<li>Collect eggs from heterozygous crosses and stage embryos morphologically as described23.</li>
	<li>Sort embryos into sample groups of 100 and rear at 28.5 &#176;C to desired developmental time points. Incubate all groups in embryo media (EM)10,18.</li>
	<li>At 6 hours post fertilization (hpf), change EM to either fresh EM or EM containing 1% ethanol (v/v). At 24 hpf, wash out EM containing ethanol with three fresh changes of EM10,18.</li>
</ol>
2. Facial staining and imaging
<ol>
	<li>Fix zebrafish larvae at 5 dpf and stain with Alcian Blue, labeling cartilage (Figure 1) as previously described26.</li>
	<li>Take whole mount, ventral view, brightfield images of the viscerocranium, mounting larvae as previously described27.</li>
	<li>Briefly, mount fish, ventral surface up, in the 50% glycerol solution from the end of Alcian Blue staining. 
	NOTE: Settings on compound microscopes can vary; use settings appropriate to image structures desired. An Olympus BX53 compound microscope was used in this study to generate all images analyzed. Images were taken at 10x magnification.</li>
</ol>
3. Genotyping
<ol>
	<li>Remove the tails from 5 dpf larvae following Alcian Blue cartilage staining as previously described18.</li>
	<li>Separate the heads and tails of the larvae. Keep the heads in 50% glycerol storage for later imaging.</li>
	<li>Lyse tails with 5 &#181;L of Proteinase K (10 mg/mL) to extract gDNA.</li>
	<li>Perform PCR on the gDNA for each sample using primers targeting the gene of interest to amplify regions containing the SNP.</li>
	<li>Digest the PCR product using restriction endonuclease-based DNA digestion to differentiate mutant from wild-type alleles based on the SNP to identify the SNP. Divide the samples by genotype into pooled groups and image for analysis. 
	NOTE: For this work, the SNP was amplified in bmp7a and genotyped using BslI (NEB).</li>
</ol>
4. Software used for shape analyses
<ol>
	<li>Use tpsUtil and tpsDig221, and MorphoJ20 (https://sbmorphometrics.org) for morphometric analysis of alcian-stained larvae, and MorphoJ and Paleontological Statistics Software Package for Education and Data Analysis (PAST)22 analysis software (https://www.nhm.uio.no/english/research/resources/past/) for all the statistical analyses.&#160; 
	NOTE: The tpsUtil software can be found under the &#34;Utility programs&#34; link, the tpsDig2 software can be found under the &#34;data acquisition&#34; link, and MorphoJ can be found under the &#34;Comprehensive programs&#34; link on&#160;https://sbmorphometrics.org. Troubleshooting for each software can be readily found both freely online, through multiple platforms, and within the software. The Principal Component Analysis (PCA), Canonical Variate Analysis (CVA), Procrustes ANOVA, and wireframe graphs of facial variation were generated using MorphoJ, and a multivariate test of normality and a multivariate analysis of variance (MANOVA) were performed using PAST.&#160;</li>
</ol>
5. Imaging the facial skeleton consistently for all samples to be analyzed
<ol>
	<li>Ensure images of the facial skeleton are taken at identical microscope settings and saved as .tif files. 
	NOTE: Microscope settings vary depending on the setup and functionality of the microscope and the samples/tissues to be imaged. The key is a consistent microscope setup and sample preparation.</li>
	<li>Save files in a single folder labeled to fully identify the experiment.</li>
</ol>
6. Preparing images with the tps software
<ol>
	<li>Open Notepad and select Save As. From here, click all files at the bottom. Save the file as &#34;experiment-name.tps&#34; and save it in the folder containing all images to be analyzed.</li>
</ol>
7. Inputting images into the tps software
<ol>
	<li>Open tpsUtil software. Next, pull down Operation and select the Build tps file from the images tab.</li>
	<li>From this tab, click input and open the file containing the images. Click the first image in the file and select Open.</li>
	<li>Select the output tab, followed by the tps notepad file, and save. When prompted to replace the file, select yes.</li>
	<li>Next, click the set up tab and create. Go to the file to see if the images have been added.</li>
</ol>
8. Adding landmarks to images
<ol>
	<li>Open tpsDigs2 and open the following tabs in order: Input source, file, then the saved tps Notepad file.</li>
	<li>Next, click options to visualize the Image tools option. Here, make sure to set the reference length to 100 &#181;m. Press set scale. 
	NOTE: Do not scale relative to the scale on the image.</li>
	<li>When setting the scale, press ok to set parameters, then exit image tools.</li>
	<li>At this point, add landmarks to each image to be analyzed for proper morphology analysis by clicking the aim symbol.</li>
	<li>Ensure landmarks are placed in the same order for each image. Landmarks were placed on the following joints between the cartilage elements (See Figure 1E, E&#39;):
	<ol>
		<li>Midline joint between the Meckel&#39;s cartilages (Landmark #1, Figure 1E, E&#39;).</li>
		<li>The bilateral joints between Meckel&#39;s and the palatoquadrate cartilages (Landmarks #2, Figure 1E, E&#39;).</li>
		<li>Midline joint between the ceratohyal cartilages (Landmark #3, Figure 1E, E&#39;).</li>
		<li>The bilateral joints between the palatoquadrate and ceratohyal cartilages (Landmarks #4, Figure 1E, E&#39;).</li>
		<li>The distal end of the hyomandibular cartilages (Landmarks #5, Figure 1E, E&#39;) .</li>
	</ol>
	</li>
	<li>After placing landmarks on an image, click files for the dropdown menu. Follow this pathway to save data and then overwrite.</li>
	<li>Exit tpsDigs2.</li>
</ol>
9. Using MorphoJ to analyze shape by Procrustes ANOVA
<ol>
	<li>Open the MorphoJ software.</li>
	<li>Under preliminaries, create and name the dataset. Select create new dataset and name the dataset. Then, click tps and select the notepad with the new data points added from step 8.4. Create the dataset.</li>
	<li>The images are ready to be statistically viewed using Procrustes fit. Select the tab reading project tree and click on the dataset. Select the following buttons in this pathway: preliminaries, new Procrustes fit, Align by principal axes, and Perform Procrustes fit.</li>
	<li>Select generate covariance matrix under preliminaries; the software will say Procrustes coordinates. Execute the function when prompted.</li>
	<li>Select create or edit wireframe under preliminaries and link the points on the images. Then select link points and accept or create the image. Edit classifiers.</li>
	<li>Open a spreadsheet while keeping MorphoJ open. Add all necessary information, for example: Genotype, Treatment, Genotype and Treatment, Experiment, etc. Save the spreadsheet as a CSV file.</li>
	<li>In MorphoJ, click file and import classifier variables to select the CSV file. After opening the file, go back to the project tree and click the dataset.</li>
	<li>Once the dataset is opened, click preliminaries and edit classifiers. This will ensure all images are added.</li>
	<li>Next, click project tree, Covmatrix, then Variation at the top.</li>
	<li>Select Principal Component Analysis to see PC scores. Click PC scores to see the generated graph.</li>
	<li>To add colors to the graph, right-click and press confidence ellipsis to add the desired classifier. Press color data points to add the colors, and ok to accept these changes. 
	NOTE: Once the PC score tab is closed and reopened following the instructions in step 9.10, the colors should be updated.</li>
	<li>To change the wireframe colors, go to Preliminaries. Click set options for the shape graph, located at the bottom of the screen. Select wireframe graphs to change the colors of the target shape, starting shape, and numbers.</li>
	<li>Select Variation, then Procrustes ANOVA.</li>
	<li>Export the Procrustes ANOVA results to the spreadsheet file from step 9.7.</li>
</ol>
10. Canonical variate analysis of the dataset
<ol>
	<li>In MorphoJ, select the original dataset or project. To begin Canonical Variate Analysis (CVA), select Comparison followed by Canonical Variate Analysis. Select the classifier variable(s) - select Genotype &#38; Treatment and execute the function.</li>
	<li>To export CVA, click the results tab and right-click on the now-open results page. Select Export to File and save the information.</li>
	<li>Click Project Tree and select CVA, then Scores. At the top left of the page, select File. Once the File tab has opened, choose Export Dataset and select the data type and Genotype &#38; Treatment. Save CVA scores as a .txt file.</li>
	<li>To prepare the file for PAST software, open the saved CVA scores in Notepad or an equivalent software. Change ID in the top left corner to Label so as not to confuse PAST software. Save the edited CVA Scores or equivalent.</li>
</ol>
11. MANOVA in PAST software
<ol>
	<li>To import the CVA scores in PAST, select File, then Open to access the saved CVA scores from Notepad or equivalent. When the Import text file window opens, select Names, Data for row and column, and tab for a separator. Select Import.</li>
	<li>Under the Show tab, select Column Attributes. Next to the Type button, open the dropdown menu and select Group for the first column that should have the classifier variable(s).</li>
	<li>Highlight the PC data or CV data. Select Multivariate, Tests, and Multivariate Normality to run the normality test. Run this test on PC and CV separately.</li>
	<li>Click the gray empty cell in the top left corner, above type, to select the entire dataset. Select Multivariate, Tests, and MANOVA to process the MANOVA. Export the MANOVA results to the spread sheet file from step 9.7. 
	NOTE: Both Summary and Pairwise results will be given by the software. Export or save both.</li>
</ol>

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</ol>

The authors have nothing to disclose.

FASD is characterized by a wide range of developmental defects, including craniofacial defects such as jaw hypoplasia. Zebrafish are a strong model for FASD due to their genetic conservation with humans, high fecundity, translucent larvae, and external fertilization. Zebrafish have been used for decades to study both the formation of the craniofacial skeleton and the impact of ethanol on development 6,12,13,<sup class...

Fetal Alcohol Spectrum Disorders (FASD) are characterized by a broad range of developmental defects, including behavioral, neurological, and physical1,2. Included in these physical defects are craniofacial defects, such as jaw hypoplasia3,4,5,6. While timing and dosage of ethanol exposure contribute to the complex etiology of FASD, genetic contribution plays a significant role in FASD etiology6,7,8,9,10,11,12. This combination of factors makes studying facial defects in human cohorts challenging. To study the impact of prenatal ethanol exposure on development, we use the zebrafish model. Zebrafish serve as a strong model for FASD as they share 70% gene orthologs with humans, 82% of known disease-causing genes13,14,15,16. In addition to genetic conservation, zebrafish: (i) have high fecundity, allowing for several zebrafish larvae to be produced at a time, (ii) undergo external fertilization, which allows direct study of ethanol-sensitive developmental processes, (iii) have highly conserved and stereotyped vertebrate craniofacial development, and (iv) are translucent embryos/larvae allowing for visualization of skeletal structures17. We have previously shown that mutations in many different zebrafish genes sensitize embryos to ethanol-induced facial defects, and these defects can be subtle and difficult to identify by eye10,18. In addition, we observed that ethanol-treated wild-type larvae also have slight changes in the craniofacial region compared to untreated wild-type larvae, though again these changes are difficult to identify by eye10. Although these changes go undetected upon visual observation, we were able to show these ethanol-induced changes in facial shape using morphometric analyses available on 2D images of the viscerocranium10.
The face is a complex 3D structure that is difficult to measure using conventional linear measurements. These single linear measures do not account for the relative position of each structure measured to, and their impact on, the other measured structures of the face. Morphometric analyses address these shortcomings by using shape configurations via landmarks that account for the relative position of all structures measured, even controlling for overall differences in size18,19,20. This approach results in data that is more accurate, has greater resolution, and is much easier to visualize, which can yield results that may not be observed using conventional linear measurements18,19,20. In addition, morphometric approaches can make direct comparisons between and within groups using multivariate statistical methods. Here we describe the use of the freeware morphometric software, tpsUtil, tpsDigs221, MorphoJ20, and Paleontological Statistics (PAST)22, in combination with 2D images of the viscerocranium in untreated and ethanol-treated wild-type and larvae heterozygous for the Bone Morphogenetic Protein (Bmp) gene bmp7a. While these programs have been utilized mainly in paleontology and ecology-focused fields, when used in combination, they provide a thorough analysis of facial shape. Ultimately, in this protocol, we show how to use these freely available software applications to quantify craniofacial cartilage shape changes in the zebrafish model.

<table><tbody><tr><td>tpsUtil, tpsDig2, MorphoJ</td><td>&lt;a href=&quot;https://sbmorphometrics.org/&quot;&gt;https://sbmorphometrics.org&lt;/a&gt;</td><td></td><td>Freeware Software - may require administrative permissions to download</td></tr><tr><td>PAST</td><td>&lt;a href=&quot;https://www.nhm.uio.no/english/research/resources/past/&quot;&gt;https://www.nhm.uio.no/english/research/resources/past/&lt;/a&gt;</td><td></td><td>Freeware Software - may require administrative permissions to download</td></tr><tr><td>Compound Brightfield Microscope</td><td>Olympus</td><td>BX53</td><td></td></tr></tbody></table>

morphometric analyses of shape: the analysis software toolbox for craniofacial shape quantification in zebrafish

Morphometric Analyses of Shape: The Analysis Software Toolbox for Craniofacial Shape Quantification in Zebrafish

Fetal alcohol spectrum disorders (FASD) are characterized by a varying set of physical, cognitive, and behavioral disabilities caused by prenatal ethanol exposure, including those affecting the facial skeleton. Ethanol-sensitive genetic loci contribute to this high degree of variation in FASD, which complicates analyses of facial shape. We have previously shown that we can analyze these changes in facial shape from gene-ethanol interactions in zebrafish. Zebrafish are an ideal model to analyze this variation for several reasons; (i) 70% of genes are orthologs between humans and zebrafish; (ii) external fertilization allows researchers to control timing and dosage of ethanol treatments; (iii) the structure of their facial skeletal is conserved with vertebrates; (iv) translucent larvae enable direct viewing of changes to the craniofacial skeletal structure during development. However, analyzing the shape of the craniofacial skeleton can be difficult to fully assess through simple linear measures, as these do not capture overall changes in shape. In addition, changes in head size can complicate data interpretation. To address this, we undertook a morphometric approach, analyzing overall facial shape through principal component analyses via freeware software, tpsDigs2, MorphoJ, and PAST. The combination of this software allows for pairwise comparison of overall facial morphology. Here, we outline our approach and analysis of facial shape in ethanol-treated zebrafish mutants using these programs to conduct a series of complementary multivariate statistical analyses.

To identify ethanol-induced facial shape changes in zebrafish, we combined freely available software applications to generate and quantify morphological data of the facial skeleton. Embryos from bmp7a heterozygous adult carrier fish were generated and treated with ethanol from 6-24 hpf. Larvae from these crosses were fixed at 5 dpf, and facial cartilages were stained with Alcian Blue. Images of the viscerocranium were taken for each larva in each genotype and treatment group (Figure 1A</stro...

This work describes a protocol for quantifying craniofacial cartilage shape using free software (tpsDigs2, MorphoJ, and PAST) to measure changes in facial structure in zebrafish larvae.

Fetal alcohol spectrum disorders (FASD) are characterized by a varying set of physical, cognitive, and behavioral disabilities caused by prenatal ethanol ...

Our research uses morphometric software to quantify whether ethanol and bmp7a interact to cause subtle craniofacial defects in zebrafish. Linear measures miss subtle changes. With this method, we use landmarks to quantify shape changes while controlling for size differences.To begin, open the tpsDigs2 software. Click Input source, then File, and select the saved TPS Notepad file. Click Options to visualize the Image tools option.Set the reference length to 100 micrometers and press on Set scale. Click OK to confirm the scale parameters, then exit the Image tools window. Then, click the aim symbol and place the first landmark at the midline joint between the Meckel's cartilages.Place the second landmarks at the bilateral joints between Meckel's and the palatoquadrate cartilages. Place the third landmark at the midline joint between the ceratohyal cartilages and the fourth landmark at the bilateral joints between the palatoquadrate and ceratohyal cartilages. Then, place the fifth landmarks at the distal end of the hyomandibular cartilages, ensuring landmarks are placed in the same order for every image.After placing landmarks, click File to open the dropdown menu. Select Save data, and then Overwrite to save the updated file. Exit the tpsDigs2 software.Launch the MorphoJ software. Click File, select Create New Dataset, and assign a name to the dataset. Click TPS and select the Notepad file containing the newly added data points.Then, create the dataset. In Project Tree, click the dataset. Select Preliminaries, then New Procrustes Fit.Choose Align by principal axes and click Perform Procrustes Fit. Under Preliminaries, select Generate Covariance Matrix to obtain procrustes coordinates. Execute the function when prompted.Now, select Create or Edit Wireframe and link the points on the images. Click linked points and accept or create the image, then edit classifiers as needed. Open a spreadsheet program while keeping MorphoJ open.Enter classifier information such as GENOTYPE, TREATMENT, and GENOTYPE TREATMENT, Then, save the file as a CSV file. In MorphoJ, click File and select Import Classifier Variables to import the CSV file. Return to Project Tree and click the dataset.Under Preliminaries, select Edit Classifiers to confirm all images are included. In Project Tree, select CovMatrix, then click Variation at the top of the page. Select Principal Component Analysis to calculate principal component scores.Click PC scores to display the generated graph. Right-click on the graph and select Confidence Ellipsis to add the desired classifier. Select Color Data Points, assign colors, and click OK to apply changes.Under Preliminaries, select Set Options for the Shape Graph located at the bottom of the screen. Choose Wireframe Graphs and modify the colors of the target shape, starting shape, and numbers. Select variation, then choose procrustes ANOVA.Export the procrustes ANOVA results to the previously created spreadsheet file. In MorphoJ, Select the original dataset in Project Tree. Click Comparison, then Canonical Variate Analysis.Select the classifier variables GENOTYPE TREATMENT and execute the function. To export the results, click the Results tab and right-click on the results page. Select Export to File and save the results.In Project Tree, select Canonical Variate Analysis, then scores. Click File, choose Export Dataset, select the data type and GENOTYPE TREATMENT. Save the CVA scores as a TXT file.To prepare the file for PAST software, open the saved CVA scores in a text editor. Replace the term id in the top-left corner with Label and save the edited file. Open the PAST software and click File, then open to select the edited CVA scores file.In the import window. Select Names, data for row and column and Tab as the separator, then click Import. Under the Show tab, select Column attributes.In the dropdown menu next to Type, assign Group to the first column containing classifier variables. Highlight the principal component or canonical variate data columns. Select Multivariate, then Tests, and choose Multivariate normality to run the normality test separately for principal component and canonical variate data.Click the gray empty cell in the top-left corner above Type to select the entire dataset. Select Multivariate, then Tests, and choose multivariate analysis of variants to perform the analysis. Export the MANOVA results to the previously created spreadsheet file.Images of the viscerocranium were acquired for each larva in each genotype and treatment group. Cartilages of the viscerocranium were labeled in a representative image, and landmarks were placed on each image to generate landmark-labeled data sets. Principal component 1 accounted for approximately 34%of total shape variation and principal component 2 accounted for 20%of total shape variation.Each principal component showed specific variation in viscerocranial shape relative to the average shape of all viscerocrania. The principal component analysis plot showed overlapping means among the genotype and treatment groups with no distinct clustering. Ethanol-treated wild-type larvae exhibited a large 95%confidence ellipse of the mean due to the low sample size.Canonical variate 1 represented a subtle shortening or lengthening of the ceratohyal joint in the magenta wireframe relative to the average black wireframe. Canonical variate 2 showed a shift medially only on one side of the viscerocranium at the joints between the Meckel's-palatoquadrate and the palatoquadrate-ceratohyal. Ethanol-treated wild-type larvae exhibited a large 95%confidence ellipse of the mean that overlapped all other groups.Multivariate analysis of variants revealed a significant overall effect of genotype and treatment. Consistent sample mounting is the biggest challenge. Tilted larva can cause inaccurate measurements and alter results.This protocol adapts to different anatomical structures, analyzes 3D data, generates linear measures, and helps determine experimental sample sizes. Future studies will investigate other genotype-ethanol sensitivities, expand sample sizes, and apply this versatile toolbox to various anatomical structures.

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JoVE Journal

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Learn landmark-based morphometric analysis to quantify subtle craniofacial shape changes in zebrafish larvae following ethanol exposure. This protocol detects defects that linear measurements miss.

Morphometric Analyses of Shape: The Analysis Software Toolbox for Craniofacial Shape Quantification in Zebrafish

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