Quantification of gene expression patterns to reveal the origins of abnormal morphogenesis

Quantifying the relationship between cell proliferation and morphology during development of the face

Morphogenesis requires highly coordinated, complex interactions between cellular processes: proliferation, migration, and apoptosis, along with physical tissue interactions. How these cellular and tissue dynamics drive morphogenesis remains elusive. Three dimensional (3D) microscopic imaging poses great promise, and generates elegant images. However, generating even moderate through-put quantified images is challenging for many reasons. As a result, the association between morphogenesis and cellular processes in 3D developing tissues has not been fully explored. To address this critical gap, we have developed an imaging and image analysis pipeline to enable 3D quantification of cellular dynamics along with 3D morphology for the same individual embryo. Specifically, we focus on how 3D distribution of proliferation relates to morphogenesis during mouse facial development. Our method involves imaging with light-sheet microscopy, automated segmentation of cells and tissues using machine learning-based tools, and quantification of external morphology via geometric morphometrics. Applying this framework, we show that changes in proliferation are tightly correlated to changes in morphology over the course of facial morphogenesis. These analyses illustrate the potential of this pipeline to investigate mechanistic relationships between cellular dynamics and morphogenesis during embryonic development.

Combined method of whole mount and block-face imaging: Acquisition of 3D data of gene expression pattern from conventional in situ hybridization

Visualization of spatiotemporal expression of a gene of interest is a fundamental technique for analyzing the involvements of genes in organ development. In situ hybridization (ISH) is one of the most popular methods for visualizing gene expression. When conventional ISH is performed on sections or whole-mount specimens, the gene expression pattern is represented in 2-dimensional (2D) microscopic images or in the surface view of the specimen. To obtain 3-dimensional (3D) data of gene expression from conventional ISH, the "serial section method" has traditionally been employed. However, this method requires an extensive amount of time and labor because it requires researchers to collect a tremendous number of sections, label all sections by ISH, and image them before 3D reconstruction. Here, we proposed a rapid and low-cost 3D imaging method that can create 3D gene expression patterns from conventional ISH-labeled specimens. Our method consists of a combination of whole-mount ISH and Correlative Microscopy and Blockface imaging (CoMBI). The whole-mount ISH-labeled specimens were sliced using a microtome or cryostat, and all block-faces were imaged and used to reconstruct 3D images by CoMBI. The 3D data acquired using our method showed sufficient quality to analyze the morphology and gene expression patterns in the developing mouse heart. In addition, 2D microscopic images of the sections can be obtained when needed. Correlating 2D microscopic images and 3D data can help annotate gene expression patterns and understand the anatomy of developing organs. These results indicated that our method can be useful in the field of developmental biology.

4D reconstruction of murine developmental trajectories using spherical harmonics

Normal organogenesis cannot be recapitulated in vitro for mammalian organs, unlike in species including Drosophila and zebrafish. Available 3D data in the form of ex vivo images only provide discrete snapshots of the development of an organ morphology. Here, we propose a computer-based approach to recreate its continuous evolution in time and space from a set of 3D volumetric images. Our method is based on the remapping of shape data into the space of the coefficients of a spherical harmonics expansion where a smooth interpolation over time is simpler. We tested our approach on mouse limb buds and embryonic hearts. A key advantage of this method is that the resulting 4D trajectory can take advantage of all the available data while also being able to interpolate well through time intervals for which there are little or no data. This allows for a quantitative, data-driven 4D description of mouse limb morphogenesis.

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Computer-based method recreating a 3D plus time evolution of a set of volumetric images

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Technique based on the interpolation of the coefficients of spherical harmonics

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Data-driven quantitative 4D description of limb and heart morphogenesis

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Quantitatively reliable baseline description of organ development

A diffusion MRI-based spatiotemporal continuum of the embryonic mouse brain for probing gene-neuroanatomy connections

We established an ultra high-resolution diffusion MRI atlas of the embryonic mouse brains from E10.5 to E15.5, which characterizes the continuous changes of brain morphology and microstructures at mesoscopic scale. By integrating gene-expression data into the spatiotemporal continuum, we can navigate the evolving landscape of gene expression and neuroanatomy across both spatial and temporal dimensions to visualize their interactions in normal and abnormal embryonic brain development. We also identified regional clusters with distinct developmental trajectories and identified gene-expression profiles that matched to these regional domains. The diffusion MRI–based continuum of the embryonic brain and the computational techniques presented in this study offer a valuable tool for systematic study of the genetic control of brain development.

The embryonic mouse brain undergoes drastic changes in establishing basic anatomical compartments and laying out major axonal connections of the developing brain. Correlating anatomical changes with gene-expression patterns is an essential step toward understanding the mechanisms regulating brain development. Traditionally, this is done in a cross-sectional manner, but the dynamic nature of development calls for probing gene–neuroanatomy interactions in a combined spatiotemporal domain. Here, we present a four-dimensional (4D) spatiotemporal continuum of the embryonic mouse brain from E10.5 to E15.5 reconstructed from diffusion magnetic resonance microscopy (dMRM) data. This study achieved unprecedented high-definition dMRM at 30- to 35-µm isotropic resolution, and together with computational neuroanatomy techniques, we revealed both morphological and microscopic changes in the developing brain. We transformed selected gene-expression data to this continuum and correlated them with the dMRM-based neuroanatomical changes in embryonic brains. Within the continuum, we identified distinct developmental modes comprising regional clusters that shared developmental trajectories and similar gene-expression profiles. Our results demonstrate how this 4D continuum can be used to examine spatiotemporal gene–neuroanatomical interactions by connecting upstream genetic events with anatomical changes that emerge later in development. This approach would be useful for large-scale analysis of the cooperative roles of key genes in shaping the developing brain.

μMatch: 3D Shape Correspondence for Biological Image Data

Modern microscopy technologies allow imaging biological objects in 3D over a wide range of spatial and temporal scales, opening the way for a quantitative assessment of morphology. However, establishing a correspondence between objects to be compared, a first necessary step of most shape analysis workflows, remains challenging for soft-tissue objects without striking features allowing them to be landmarked. To address this issue, we introduce the μMatch 3D shape correspondence pipeline. μMatch implements a state-of-the-art correspondence algorithm initially developed for computer graphics and packages it in a streamlined pipeline including tools to carry out all steps from input data pre-processing to classical shape analysis routines. Importantly, μMatch does not require any landmarks on the object surface and establishes correspondence in a fully automated manner. Our open-source method is implemented in Python and can be used to process collections of objects described as triangular meshes. We quantitatively assess the validity of μMatch relying on a well-known benchmark dataset and further demonstrate its reliability by reproducing published results previously obtained through manual landmarking.

Label-free optical imaging in developmental biology [Invited]

Application of optical imaging in developmental biology marks an exciting frontier in biomedical optics. Optical resolution and imaging depth allow for investigation of growing embryos at subcellular, cellular, and whole organism levels, while the complexity and variety of embryonic processes set multiple challenges stimulating the development of various live dynamic embryonic imaging approaches. Among other optical methods, label-free optical techniques attract an increasing interest as they allow investigation of developmental mechanisms without application of exogenous markers or fluorescent reporters. There has been a boost in development of label-free optical imaging techniques for studying embryonic development in animal models over the last decade, which revealed new information about early development and created new areas for investigation. Here, we review the recent progress in label-free optical embryonic imaging, discuss specific applications, and comment on future developments at the interface of photonics, engineering, and developmental biology.

Evolutionary Integration and Modularity in the Archosaur Cranium

Complex structures, like the vertebrate skull, are composed of numerous elements or traits that must develop and evolve in a coordinated manner to achieve multiple functions. The strength of association among phenotypic traits (i.e., integration), and their organization into highly-correlated, semi-independent subunits termed modules, is a result of the pleiotropic and genetic correlations that generate traits. As such, patterns of integration and modularity are thought to be key factors constraining or facilitating the evolution of phenotypic disparity by influencing the patterns of variation upon which selection can act. It is often hypothesized that selection can reshape patterns of integration, parceling single structures into multiple modules or merging ancestrally semi-independent traits into a strongly correlated unit. However, evolutionary shifts in patterns of trait integration are seldom assessed in a unified quantitative framework. Here, we quantify patterns of evolutionary integration among regions of the archosaur skull to investigate whether patterns of cranial integration are conserved or variable across this diverse group. Using high-dimensional geometric morphometric data from 3D surface scans and computed tomography scans of modern birds (n = 352), fossil non-avian dinosaurs (n = 27), and modern and fossil mesoeucrocodylians (n = 38), we demonstrate that some aspects of cranial integration are conserved across these taxonomic groups, despite their major differences in cranial form, function, and development. All three groups are highly modular and consistently exhibit high integration within the occipital region. However, there are also substantial divergences in correlation patterns. Birds uniquely exhibit high correlation between the pterygoid and quadrate, components of the cranial kinesis apparatus, whereas the non-avian dinosaur quadrate is more closely associated with the jugal and quadratojugal. Mesoeucrocodylians exhibit a slightly more integrated facial skeleton overall than the other grades. Overall, patterns of trait integration are shown to be stable among archosaurs, which is surprising given the cranial diversity exhibited by the clade. At the same time, evolutionary innovations such as cranial kinesis that reorganize the structure and function of complex traits can result in modifications of trait correlations and modularity.

It's about Time: Ossification Center Formation in C57BL/6 Mice from E12⁻E16

The establishment of precise, high-resolution temporal sequences for morphogenetic events in laboratory mice remains a vexing issue in developmental biology. Mouse embryos collected at the same period of gestation, even those from the same litter, show wide variation in individual levels of progress along their developmental trajectory. Therefore, age at harvest does not provide sufficient information about developmental progress to serve as the basis for forming samples for the study of rapidly or near-simultaneously occurring events such as the sequence of ossification center formation. Here, we generate two measures of individual developmental progress (developmental age) for a large sample of mouse embryos using crown⁻rump lengths that measures size, and limbstaging ages produced by the embryonic Mouse Ontogenetic Staging System (eMOSS) that measure shape. Using these measures, we establish fine-grained sequences of ossification center appearance for mouse embryos. The two measures of developmental progress generate slightly different sequences of ossification center formation demonstrating that despite their tight correlation throughout the developmental period, size and shape are aspects of form that are at least partially dissociated in development.

The developmental-genetics of canalization

Canalization, or robustness to genetic or environmental perturbations, is fundamental to complex organisms. While there is strong evidence for canalization as an evolved property that varies among genotypes, the developmental and genetic mechanisms that produce this phenomenon are very poorly understood. For evolutionary biology, understanding how canalization arises is important because, by modulating the phenotypic variation that arises in response to genetic differences, canalization is a determinant of evolvability. For genetics of disease in humans and for economically important traits in agriculture, this subject is important because canalization is a potentially significant cause of missing heritability that confounds genomic prediction of phenotypes. We review the major lines of thought on the developmental-genetic basis for canalization. These fall into two groups. One proposes specific evolved molecular mechanisms while the other deals with robustness or canalization as a more general feature of development. These explanations for canalization are not mutually exclusive and they overlap in several ways. General explanations for canalization are more likely to involve emergent features of development than specific molecular mechanisms. Disentangling these explanations is also complicated by differences in perspectives between genetics and developmental biology. Understanding canalization at a mechanistic level will require conceptual and methodological approaches that integrate quantitative genetics and developmental biology.