Surface landmark quantification of embryonic mouse craniofacial morphogenesis

Evo-Devo Mechanobiology: The Missing Link

While the modern framework of evolutionary development (evo-devo) has been decidedly genetic, historic analyses have also considered the importance of mechanics in the evolution of form. With the aid of recent technological advancements in both quantifying and perturbing changes in the molecular and mechanical effectors of organismal shape, how molecular and genetic cues regulate the biophysical aspects of morphogenesis is becoming increasingly well studied. As a result, this is an opportune time to consider how the tissue-scale mechanics that underlie morphogenesis are acted upon through evolution to establish morphological diversity. Such a focus will enable a field of evo-devo mechanobiology that will serve to better elucidate the opaque relations between genes and forms by articulating intermediary physical mechanisms. Here, we review how the evolution of shape is measured and related to genetics, how recent strides have been made in the dissection of developmental tissue mechanics, and how we expect these areas to coalesce in evo-devo studies in the future.

Automated landmarking via multiple templates

Manually collecting landmarks for quantifying complex morphological phenotypes can be laborious and subject to intra and interobserver errors. However, most automated landmarking methods for efficiency and consistency fall short of landmarking highly variable samples due to the bias introduced by the use of a single template. We introduce a fast and open source automated landmarking pipeline (MALPACA) that utilizes multiple templates for accommodating large-scale variations. We also introduce a K-means method of choosing the templates that can be used in conjunction with MALPACA, when no prior information for selecting templates is available. Our results confirm that MALPACA significantly outperforms single-template methods in landmarking both single and multi-species samples. K-means based template selection can also avoid choosing the worst set of templates when compared to random template selection. We further offer an example of post-hoc quality check for each individual template for further refinement. In summary, MALPACA is an efficient and reproducible method that can accommodate large morphological variability, such as those commonly found in evolutionary studies. To support the research community, we have developed open-source and user-friendly software tools for performing K-means multi-templates selection and MALPACA.

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

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 ).

A landmark-free morphometrics pipeline for high-resolution phenotyping: application to a mouse model of Down syndrome

Characterising phenotypes often requires quantification of anatomical shape. Quantitative shape comparison (morphometrics) traditionally uses manually located landmarks and is limited by landmark number and operator accuracy. Here, we apply a landmark-free method to characterise the craniofacial skeletal phenotype of the Dp1Tyb mouse model of Down syndrome and a population of the Diversity Outbred (DO) mouse model, comparing it with a landmark-based approach. We identified cranial dysmorphologies in Dp1Tyb mice, especially smaller size and brachycephaly (front-back shortening), homologous to the human phenotype. Shape variation in the DO mice was partly attributable to allometry (size-dependent shape variation) and sexual dimorphism. The landmark-free method performed as well as, or better than, the landmark-based method but was less labour-intensive, required less user training and, uniquely, enabled fine mapping of local differences as planar expansion or shrinkage. Its higher resolution pinpointed reductions in interior mid-snout structures and occipital bones in both the models that were not otherwise apparent. We propose that this landmark-free pipeline could make morphometrics widely accessible beyond its traditional niches in zoology and palaeontology, especially in characterising developmental mutant phenotypes.

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

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.

Aberrant cell segregation in the craniofacial primordium and the emergence of facial dysmorphology in craniofrontonasal syndrome

Craniofrontonasal syndrome (CFNS) is a rare X-linked disorder characterized by craniofacial, skeletal, and neurological anomalies and is caused by mutations in EFNB1. Heterozygous females are more severely affected by CFNS than hemizygous males, a phenomenon called cellular interference that results from EPHRIN-B1 mosaicism. In Efnb1 heterozygous mice, mosaicism for EPHRIN-B1 results in cell sorting and more severe phenotypes than Efnb1 hemizygous males, but how craniofacial dysmorphology arises from cell segregation is unknown and CFNS etiology therefore remains poorly understood. Here, we couple geometric morphometric techniques with temporal and spatial interrogation of embryonic cell segregation in mouse mutant models to elucidate mechanisms underlying CFNS pathogenesis. By generating EPHRIN-B1 mosaicism at different developmental timepoints and in specific cell populations, we find that EPHRIN-B1 regulates cell segregation independently in early neural development and later in craniofacial development, correlating with the emergence of quantitative differences in face shape. Whereas specific craniofacial shape changes are qualitatively similar in Efnb1 heterozygous and hemizygous mutant embryos, heterozygous embryos are quantitatively more severely affected, indicating that Efnb1 mosaicism exacerbates loss of function phenotypes rather than having a neomorphic effect. Notably, neural tissue-specific disruption of Efnb1 does not appear to contribute to CFNS craniofacial dysmorphology, but its disruption within neural crest cell-derived mesenchyme results in phenotypes very similar to widespread loss. EPHRIN-B1 can bind and signal with EPHB1, EPHB2, and EPHB3 receptor tyrosine kinases, but the signaling partner(s) relevant to CFNS are unknown. Geometric morphometric analysis of an allelic series of Ephb1; Ephb2; Ephb3 mutant embryos indicates that EPHB2 and EPHB3 are key receptors mediating Efnb1 hemizygous-like phenotypes, but the complete loss of EPHB1-3 does not fully recapitulate the severity of CFNS-like Efnb1 heterozygosity. Finally, by generating Efnb1+/Δ; Ephb1; Ephb2; Ephb3 quadruple knockout mice, we determine how modulating cumulative receptor activity influences cell segregation in craniofacial development and find that while EPHB2 and EPHB3 play an important role in craniofacial cell segregation, EPHB1 is more important for cell segregation in the brain; surprisingly, complete loss of EPHB1-EPHB3 does not completely abrogate cell segregation. Together, these data advance our understanding of the etiology and signaling interactions underlying CFNS dysmorphology.

Nonlinear gene expression-phenotype relationships contribute to variation and clefting in the A/WySn mouse

BACKGROUND

Cleft lip and palate is one of the most common human birth defects, but the underlying etiology is poorly understood. The A/WySn mouse is a spontaneously occurring model of multigenic clefting in which 20% to 30% of individuals develop an orofacial cleft. Recent work has shown altered methylation at a specific retrotransposon insertion downstream of the Wnt9b locus in clefting animals, which results in decreased Wnt9b expression.

RESULTS

Using a newly developed protocol that allows us to measure morphology, gene expression, and DNA methylation in the same embryo, we relate gene expression in an individual embryo directly to its three-dimensional morphology for the first time. We find that methylation at the retrotransposon relates to Wnt9b expression and morphology. IAP methylation relates to shape of the nasal process in a manner consistent with clefting. Embryos with low IAP methylation exhibit increased among-individual variance in facial shape.

CONCLUSIONS

Methylation and gene expression relate nonlinearly to nasal process morphology. Individuals at one end of a continuum of phenotypic states display a clinical phenotype and increased phenotypic variation. Variable penetrance and expressivity in this model is likely determined both by among-individual variation in methylation and changes in phenotypic robustness along the underlying liability distribution for orofacial clefting.

The effect of automated landmark identification on morphometric analyses

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.

Developmental nonlinearity drives phenotypic robustness

Robustness to perturbation is a fundamental feature of complex organisms. Mutations are the raw material for evolution, yet robustness to their effects is required for species survival. The mechanisms that produce robustness are poorly understood. Nonlinearities are a ubiquitous feature of development that may link variation in development to phenotypic robustness. Here, we manipulate the gene dosage of a signaling molecule, Fgf8, a critical regulator of vertebrate development. We demonstrate that variation in Fgf8 expression has a nonlinear relationship to phenotypic variation, predicting levels of robustness among genotypes. Differences in robustness are not due to gene expression variance or dysregulation, but emerge from the nonlinearity of the genotype–phenotype curve. In this instance, embedded features of development explain robustness differences. How such features vary in natural populations and relate to genetic variation are key questions for unraveling the origin and evolvability of this feature of organismal development.

Developmental processes often involve nonlinearities, but the consequences for translating genotype to phenotype are not well characterized. Here, Green et al. vary Fgf8 signaling across allelic series of mice and show that phenotypic robustness in craniofacial shape is explained by a nonlinear effect of Fgf8 expression.

Quantifying three-dimensional morphology and RNA from individual embryos

Quantitative analysis of morphogenesis aids our understanding of developmental processes by providing a method to link changes in shape with cellular and molecular processes. Over the last decade, many methods have been developed for 3D imaging of embryos using microCT scanning to quantify the shape of embryos during development. These methods generally involve a powerful, cross-linking fixative such as paraformaldehyde to limit shrinkage during the CT scan. However, the extended time frames that these embryos are incubated in such fixatives prevent use of the tissues for molecular analysis after microCT scanning. This is a significant problem because it limits the ability to correlate variation in molecular data with morphology at the level of individual embryos. Here we outline a novel method that allows RNA, DNA, or protein isolation following CT scan while also allowing imaging of different tissue layers within the developing embryo. We show shape differences early in craniofacial development (E11.5) between common mouse genetic backgrounds, and demonstrate that we are able to generate RNA from these embryos after CT scanning that is suitable for downstream real time PCR (RT-PCR) and RNAseq analyses. Developmental Dynamics 246:431-436, 2017. © 2017 Wiley Periodicals, Inc.

Rapid automated landmarking for morphometric analysis of three-dimensional facial scans

Automated phenotyping is essential for the creation of large, highly standardized datasets from anatomical imaging data. Such datasets can support large-scale studies of complex traits or clinical studies related to precision medicine or clinical trials. We have developed a method that generates three-dimensional landmark data that meet the requirements of standard geometric morphometric analyses. The method is robust and can be implemented without high-performance computing resources. We validated the method using both direct comparison to manual landmarking on the same individuals and also analyses of the variation patterns and outlier patterns in a large dataset of automated and manual landmark data. Direct comparison of manual and automated landmarks reveals that automated landmark data are less variable, but more highly integrated and reproducible. Automated data produce covariation structure that closely resembles that of manual landmarks. We further find that while our method does produce some landmarking errors, they tend to be readily detectable and can be fixed by adjusting parameters used in the registration and control-point steps. Data generated using the method described here have been successfully used to study the genomic architecture of facial shape in two different genome-wide association studies of facial shape.

Morphometrics, 3D Imaging, and Craniofacial Development

Recent studies have shown how volumetric imaging and morphometrics can add significantly to our understanding of morphogenesis, the developmental basis for variation, and the etiology of structural birth defects. On the other hand, the complex questions and diverse imaging data in developmental biology present morphometrics with more complex challenges than applications in virtually any other field. Meeting these challenges is necessary in order to understand the mechanistic basis for variation in complex morphologies. This chapter reviews the methods and theory that enable the application of modern landmark-based morphometrics to developmental biology and craniofacial development, in particular. We discuss the theoretical foundations of morphometrics as applied to development and review the basic approaches to the quantification of morphology. Focusing on geometric morphometrics, we discuss the principal statistical methods for quantifying and comparing morphological variation and covariation structure within and among groups. Finally, we discuss the future directions for morphometrics in developmental biology that will be required for approaches that enable quantitative integration across the genotype-phenotype map.

Regulating Craniofacial Development at the 3' End: MicroRNAs and Their Function in Facial Morphogenesis

Defects in craniofacial development represent a majority of observed human birth defects, occurring at a rate as high as 1:800 live births. These defects often occur due to changes in neural crest cell (NCC) patterning and development and can affect non-NCC-derived structures due to interactions between NCCs and the surrounding cell types. Proper craniofacial development requires an intricate array of gene expression networks that are tightly controlled spatiotemporally by a number of regulatory mechanisms. One of these mechanisms involves the action of microRNAs (miRNAs), a class of noncoding RNAs that repress gene expression by binding to miRNA recognition sequences typically located in the 3' UTR of target mRNAs. Recent evidence illustrates that miRNAs are crucial for vertebrate facial morphogenesis, with changes in miRNA expression leading to facial birth defects, including some in complex human syndromes such as 22q11 (DiGeorge Syndrome). In this review, we highlight the current understanding of miRNA biogenesis, the roles of miRNAs in overall craniofacial development, the impact that loss of miRNAs has on normal development and the requirement for miRNAs in the development of specific craniofacial structures, including teeth. From these studies, it is clear that miRNAs are essential for normal facial development and morphogenesis, and a potential key in establishing new paradigms for repair and regeneration of facial defects.