micro-ct-imaging-and-morphometric-analysis-of-mouse-neonatal-brains

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Neuroimages are a valuable tool for studying brain morphology in experiments using animal models. Magnetic resonance imaging (MRI) has become the standard ...

University of Calgary

Morphometric analysis of anatomical landmarks allows researchers to identify specific morphological differences between natural populations or experimental groups, but manually identifying landmarks is time-consuming. We compare manually and automatically generated adult mouse skull landmarks and subsequent morphometric analyses to elucidate how switching from manual to automated landmarking will impact morphometric analysis results for large mouse (Mus musculus) samples (n = 1205) that represent a wide range of 'normal' phenotypic variation (62 genotypes). Other studies have suggested that the use of automated landmarking methods is feasible, but this study is the first to compare the utility of current automated approaches to manual landmarking for a large dataset that allows the quantification of intra- and inter-strain variation. With this unique sample, we investigated how switching to a non-linear image registration-based automated landmarking method impacts estimated differences in genotype mean shape and shape variance-covariance structure. In addition, we tested whether an initial registration of specimen images to genotype-specific averages improves automatic landmark identification accuracy. Our results indicated that automated landmark placement was significantly different than manual landmark placement but that estimated skull shape covariation was correlated across methods. The addition of a preliminary genotype-specific registration step as part of a two-level procedure did not substantially improve on the accuracy of one-level automatic landmark placement. The landmarks with the lowest automatic landmark accuracy are found in locations with poor image registration alignment. The most serious outliers within morphometric analysis of automated landmarks displayed instances of stochastic image registration error that are likely representative of errors common when applying image registration methods to micro-computed tomography datasets that were initially collected with manual landmarking in mind. Additional efforts during specimen preparation and image acquisition can help reduce the number of registration errors and improve registration results. A reduction in skull shape variance estimates were noted for automated landmarking methods compared with manual landmarking. This partially reflects an underestimation of more extreme genotype shapes and loss of biological signal, but largely represents the fact that automated methods do not suffer from intra-observer landmarking error. For appropriate samples and research questions, our image registration-based automated landmarking method can eliminate the time required for manual landmarking and have a similar power to identify shape differences between inbred mouse genotypes.

The effect of automated landmark identification on morphometric analyses.

Allometry refers to the ways in which organismal shape is associated with size. It is a special case of integration, or the tendency for traits to covary, in that variation in size is ubiquitous and evolutionarily important. Allometric variation is so commonly observed that it is routinely removed from morphometric analyses or invoked as an explanation for evolutionary change. In this case, familiarity is mistaken for understanding because rarely do we know the mechanisms by which shape correlates with size or understand their significance. As with other forms of integration, allometric variation is generated by variation in developmental processes that affect multiple traits, resulting in patterns of covariation. Given this perspective, we can dissect the genetic and developmental determinants of allometric variation. Our work on the developmental and genetic basis for allometric variation in craniofacial shape in mice and humans has revealed that allometric variation is highly polygenic. Different measures of size are associated with distinct but overlapping patterns of allometric variation. These patterns converge in part on a common genetic basis. Finally, environmental modulation of size often generates variation along allometric trajectories, but the timing of genetic and environmental perturbations can produce deviations from allometric patterns when traits are differentially sensitive over developmental time. These results question the validity of viewing allometry as a singular phenomenon distinct from morphological integration more generally.

Integration and the Developmental Genetics of Allometry.

The major milestones in mouse placental development are well described, but our understanding is limited to how the placenta can adapt to damage or changes in the environment. By using stereology and expression of cell cycle markers, we found that the placenta grows under normal conditions not just by hyperplasia of trophoblast cells but also through extensive polyploidy and cell hypertrophy. In response to feeding a low protein diet to mothers prior to and during pregnancy, to mimic chronic malnutrition, we found that this normal program was altered and that it was influenced by the sex of the conceptus. Male fetuses showed intrauterine growth restriction (IUGR) by embryonic day (E) 18.5, just before term, whereas female fetuses showed IUGR as early as E16.5. This difference was correlated with differences in the size of the labyrinth layer of the placenta, the site of nutrient and gas exchange. Functional changes were implied based on up-regulation of nutrient transporter genes. The junctional zone was also affected, with a reduction in both glycogen trophoblast and spongiotrophoblast cells. These changes were associated with increased expression of Phlda2 and reduced expression of Egfr. Polyploidy, which results from endoreduplication, is a normal feature of trophoblast giant cells (TGC) but also spongiotrophoblast cells. Ploidy was increased in sinusoidal-TGCs and spongiotrophoblast cells, but not parietal-TGCs, in low protein placentas. These results indicate that the placenta undergoes a range of changes in development and function in response to poor maternal diet, many of which we interpret are aimed at mitigating the impacts on fetal and maternal health.

Complex patterns of cell growth in the placenta in normal pregnancy and as adaptations to maternal diet restriction.

Geometric morphometrics is the statistical analysis of landmark-based shape variation and its covariation with other variables. Over the past two decades, the gold standard of landmark data acquisition has been manual detection by a single observer. This approach has proven accurate and reliable in small-scale investigations. However, big data initiatives are increasingly common in biology and morphometrics. This requires fast, automated, and standardized data collection. We combine techniques from image registration, geometric morphometrics, and deep learning to automate and optimize anatomical landmark detection. We test our method on high-resolution, micro-computed tomography images of adult mouse skulls. To ensure generalizability, we use a morphologically diverse sample and implement fundamentally different deformable registration algorithms. Compared to landmarks derived from conventional image registration workflows, our optimized landmark data show up to a 39.1% reduction in average coordinate error and a 36.7% reduction in total distribution error. In addition, our landmark optimization produces estimates of the sample mean shape and variance-covariance structure that are statistically indistinguishable from expert manual estimates. For biological imaging datasets and morphometric research questions, our approach can eliminate the time and subjectivity of manual landmark detection whilst retaining the biological integrity of these expert annotations.

A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics.

Bones in the vertebrate cranial base and limb skeleton grow by endochondral ossification, under the control of growth plates. Mechanisms of endochondral ossification are conserved across growth plates, which increases covariation in size and shape among bones, and in turn may lead to correlated changes in skeletal traits not under direct selection. We used micro-CT and geometric morphometrics to characterize shape changes in the cranium of the Longshanks mouse, which was selectively bred for longer tibiae. We show that Longshanks skulls became longer, flatter, and narrower in a stepwise process. Moreover, we show that these morphological changes likely resulted from developmental changes in the growth plates of the Longshanks cranial base, mirroring changes observed in its tibia. Thus, indirect and non-adaptive morphological changes can occur due to developmental overlap among distant skeletal elements, with important implications for interpreting the evolutionary history of vertebrate skeletal form.

Selection for increased tibia length in mice alters skull shape through parallel changes in developmental mechanisms.

Brain and skull tissues interact through molecular signalling and mechanical forces during head development, leading to a strong correlation between the neurocranium and the external brain surface. Therefore, when brain tissue is unavailable, neurocranial endocasts are often used to approximate brain size and shape. Evolutionary changes in brain morphology may have resulted in secondary changes to neurocranial morphology, but the developmental and genetic processes underlying this relationship are not well understood. Using automated phenotyping methods, we quantified the genetic basis of endocast variation across large genetically varied populations of laboratory mice in two ways: (1) to determine the contributions of various genetic factors to neurocranial form and (2) to help clarify whether a neurocranial variation is based on genetic variation that primarily impacts bone development or on genetic variation that primarily impacts brain development, leading to secondary changes in bone morphology. Our results indicate that endocast size is highly heritable and is primarily determined by additive genetic factors. In addition, a non-additive inbreeding effect led to founder strains with lower neurocranial size, but relatively large brains compared to skull size; suggesting stronger canalization of brain size and/or a general allometric effect. Within an outbred sample of mice, we identified a locus on mouse chromosome 1 that is significantly associated with variation in several positively correlated endocast size measures. Because the protein-coding genes at this locus have been previously associated with brain development and not with bone development, we propose that genetic variation at this locus leads primarily to variation in brain volume that secondarily leads to changes in neurocranial globularity. We identify a strain-specific missense mutation within Akt3 that is a strong causal candidate for this genetic effect. Whilst it is not appropriate to generalize our hypothesis for this single locus to all other loci that also contribute to the complex trait of neurocranial skull morphology, our results further reveal the genetic basis of neurocranial variation and highlight the importance of the mechanical influence of brain growth in determining skull morphology.

The genetic basis of neurocranial size and shape across varied lab mouse populations.

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase ( www.facebase.org , https://doi.org/10.25550/3-HXMC ) and GitHub ( https://github.com/jaydevine/MusMorph ).

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses.

Placental abnormalities have been sporadically implicated as a source of developmental heart defects. Yet it remains unknown how often the placenta is at the root of congenital heart defects (CHDs), and what the cellular mechanisms are that underpin this connection. Here, we selected three mouse mutant lines, Atp11a, Smg9 and Ssr2, that presented with placental and heart defects in a recent phenotyping screen, resulting in embryonic lethality. To dissect phenotype causality, we generated embryo- and trophoblast-specific conditional knockouts for each of these lines. This was facilitated by the establishment of a new transgenic mouse, Sox2-Flp, that enables the efficient generation of trophoblast-specific conditional knockouts. We demonstrate a strictly trophoblast-driven cause of the CHD and embryonic lethality in one of the three lines (Atp11a) and a significant contribution of the placenta to the embryonic phenotypes in another line (Smg9). Importantly, our data reveal defects in the maternal blood-facing syncytiotrophoblast layer as a shared pathology in placentally induced CHD models. This study highlights the placenta as a significant source of developmental heart disorders, insights that will transform our understanding of the vast number of unexplained congenital heart defects.

Article	Year
The effect of automated landmark identification on morphometric analyses. Journal of anatomy\| PubMed ID: 30901082	2019
Integration and the Developmental Genetics of Allometry. Integrative and comparative biology\| PubMed ID: 31199435	2019
Complex patterns of cell growth in the placenta in normal pregnancy and as adaptations to maternal diet restriction. PloS one\| PubMed ID: 31917811	2020
A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics. Evolutionary biology\| PubMed ID: 33583965	2020
Selection for increased tibia length in mice alters skull shape through parallel changes in developmental mechanisms. eLife\| PubMed ID: 33899741	2021
The genetic basis of neurocranial size and shape across varied lab mouse populations. Journal of anatomy\| PubMed ID: 35357006	2022
MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses. Scientific data\| PubMed ID: 35614082	2022
Defects in placental syncytiotrophoblast cells are a common cause of developmental heart disease. Nature communications\| PubMed ID: 36859534	2023

Jay Devine

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