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  2. What is eHistology
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In Section 4 we describe new data-types now available from EMAGE including gene and regulatory expression patterns and the results of clonal analysis encoding fate maps and lineage and in section 5 the recent developments linking the eHistology resource to the high-resolution phenotype data now analysed and published by the Deciphering Mechanisms of Developmental Disorders DMDD on-line resource.

We have utilised this capability to provide an interactive 3D viewer showing a surface rendering of the defined anatomic components Fig. This supercedes the Java-based viewer we had implemented previously Feng et al. The new visualisation is controlled by the anatomy tree provided in the section browser Fig. There is the additional option to view in wireframe mode for the larger models, which may display slowly. We have enabled this functionality in all our embryo models and so provide 3D anatomy of the developing mouse from implantation, through gastrulation and neurulation, and into mid- and late-stage embryogenesis.

A Surface-rendered views of Theiler stage 21 top left , Theiler stage 24 top right , and Theiler stage 25 lower left and right mouse embryo models with delineated anatomy can be interactively explored using a web browser. Navigation controls are provided in the left panel and include: pan-and-zoom; translating the viewing plane distance ; and rotating the viewing plane in 3 dimensions pitch, yaw. The central panel shows a section through a 3D model with delineated anatomy shown in various colours.

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An anatomy tree in the right panel is used to visualise delineated anatomical domains. Clicking on 3D view top right opens up the selected anatomical domains as a surface rendered object in a separate window.

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Uniquely, the section viewer allows visualisation of anatomy on a section of arbitrary orientation through mouse embryos. Specific anatomical components can be toggled on or off through their selection in the anatomy tree or by clicking directly on a component displayed in the section of interest. Anatomy components selected in this way are automatically displayed in the 3D anatomy viewer including any colour selections.

An important feature of the section viewer is its ability to deliver sections through very large 3D volumes without the need for data download. This circumvents a common issue with many 3D viewer applications which require download of very large 3D volumes. IIP3D technology allows a single section through a 3D volume to be delivered and displayed, and consequently retrieving a section image is fast and can be accomplished on large volume 3D images Husz et al. The anatomy delineation process was undertaken by developmental biologists and anatomists and has involved delineation of visible anatomical components on a section-by-section basis through the 3D reconstruction of Theiler-staged mouse embryos.

To support the delineated anatomy, a mouse anatomy ontology was created that enabled the partonomic part-of relationships of mouse developmental anatomy to be rationalized and to be queried through computational means. To annotate the anatomy in the 3D reconstruction, each delineated anatomical component was assigned to a specific component in the anatomy ontology, and this enables the tree-view display that can be seen in the section viewer in the context of an interactive anatomy tree that displays the part-of relationships as sub-branches of the various anatomical components.

This process of delineation allows the 3D anatomy viewer to provide precise context for understanding mouse embryo development, and enables interoperability between the 3D atlas and the eHistology and EMAGE database resources such that an annotated component can be visualised in 3D alongside whole-mount and section data.

This interoperability allows for visualisation and communication of the relationship between gene expression and anatomy. A primary objective of the eMouseAtlas Project was to enable spatial mapping of whole embryo gene expression patterns to allow complex 3D gene expression patterns to be visualised, indexed, cross-compared and to deliver objective analysis using spatial analysis tools. The tools developed for internal use WoolzWarp use a novel 3D warping technique based on the constrained distance transform Hill and Baldock, , which allows interactive 3D mapping of the full image volume.

The mapping process utilises points of morphological equivalence that are defined on the source gene-expression data image and the stage-matched embryo model. These points define the spatial warp that maps the original data image into the space of the reference model. Having defined the transform, the signal in the original image is extracted and a representation of the original pattern is defined within the space of a 3D atlas model, without the constraint of anatomical boundaries.

Mapping gene expression patterns in this way provides an objective and accurate representation of the whole embryo expression patterns and additionally captures information on expression gradients.

Multiple mapped expression patterns can then be visualised and cross-compared in a common spatial framework Fig. A Volume rendered views of 19 E These visualisations can be used to cross-compare gene expression patterns in a spatial framework. B Point-cloud rendered views of spatially mapped E An advantage of the point cloud visualisation is that a user can see through the entire 3D volume, and can more accurately identify regions of expression. In Fig.

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Direct 3D visualisation of the expression profiles of all 19 Wnt family genes highlights the complexity of specific compartments of the embryo in terms of Wnt coexpression. Tools are needed to isolate coexpression domains in these complex 3D models, and these represent an important future development for this aspect of the project. In addition, there are key advantages in being able to sequentially order gene expression in compartments of the developing mouse embryo in these rich spatial datasets. For example, tools that would enable nested sets of gene expression and other hitherto poorly understood spatial relationships to be identified in these 3D models of gene expression may help us understand the regulation of Wnt gene expression.

From a visualisation perspective, mapped gene expression has additional complexities that we do not encounter with anatomical domains. In contrast to the 3D anatomy, where computational measures were used to ensure that overlaps did not occur, the molecular anatomical gene expression profiles in general overlap in 3D voxel-space and our core measure of putative coexpression can be calculated using the Jaccard index Armit et al.

To be able to identify whether genes are coexpressed overlap or show adjacent expression domains non-overlap we have developed a viewer that enables point cloud visualisation of the 3D molecular anatomy as this enables both overlapping and non-overlapping gene expression domains to be explored, and overcomes unwanted colour-mixing effects that are a feature of surface-rendered overlapping domains.

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In this example, it can be seen that a major advantage of using the point cloud visualisation is the ability to see through the entire mapped 3D volume. From a data mining perspective, we are additionally exploring computational measures that can be used to define adjacency, and to determine whether non-overlapping domains are coupled in other ways, for example whether they track one another longitudinally along the alar and basal plates of the developing CNS.

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It is anticipated that the 3D modelling and 3D spatial analysis approach outlined here will enable a deeper understanding of the role of genes in development, and may deliver powerful new tools that will allow researchers to explore the cross-regulation of key signal transduction pathways such as Wnt , Shh, Notch, and Fgf signaling pathways in the developing mouse embryo. The 3D atlas framework is a resource describing and defining the developmental anatomy of the mouse and was originally developed as a spatio-temporal framework for capturing spatially organised image data, specifically in situ gene-expression.

This is the basis of the Edinburgh Mouse Atlas Gene Expression EMAGE Database and provided a mechanism to index and collate spatial in situ patterns in an unbiased form and independent of the constraints of the underlying anatomical structures.

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All of these data are directly mapped to the 2D and 3D embryo models to enable spatial indexing, query, cross comparison and analysis. The mapping process involves accurate stage selection of mouse embryo models and spatial warping of raw image data onto a stage-matched model described in Section 3 and reviewed in Armit et al. This mapping process generates standardised representations of gene expression patterns that can be archived in the EMAGE database, and queried through a web interface.

New discoveries can be extracted using this web interface, and in Fig. This example query identified Foxa1 , Serpin6b , Slc35b4 , Nkx , and Tbx4 as the top 5 genes that show spatially mapped expression in the lung. Importantly, the first gene in this list — Foxa1 — was not text-annotated by the authors and so it would be impossible to find this gene through a text-based query for genes expressed in the lung. An additional advantage of this system is that expression patterns can be analysed spatially in relation to each other, allowing gene expression patterns to be ranked and clustered through computational measures of spatial similarity.

Each EMAGE entry details the expression pattern of a single gene at a single stage of development, and may include multiple images of an embryo specimen. A use case scenario in which an EMAGE spatial query is used to identify gene expression patterns in the developing lung. A A user-defined region pink on an embryo model is delineated using the Embryo Space paint query tool. The gene symbol entity detected , spatial annotation expression region , text annotation structures , and similarity score similarity to query region are all shown in the results table.

This example query identified Foxa1 , Serpin6b , Slc35b4 , Nkx , and Tbx4 as the top 5 genes that show spatially mapped expression in the E The first gene in this list, Foxa1 , was not text-annotated by the authors but was spatially mapped by the EMAGE Editorial Office and so could be retrieved by spatial query.

VISTA is a resource of experimentally validated human and mouse non-coding genomic DNA fragments with gene enhancer activity as assessed in transgenic mice Visel et al. This resource utilises Sleeping Beauty lacZ transposons that have been randomly integrated into the mouse genome. Hundreds of insertions have been mapped to specific genomic positions, and the corresponding regulatory potential is documented through lacZ imaging of E Through spatial mapping of the lacZ expression patterns, the EMAGE gene expression database enables rapid identification of cis -regulatory elements that are expressed in a region of interest in the mouse embryo.

Additional query capabilities - such as the find similar query - enable co-localisation and co-expression of regulatory elements to be explored computationally and allow the VISTA and TRACER datasets to be spatially compared with ISH gene expression profiles. Lineage tracing is a powerful means of describing recruitment of cells to various tissue compartments reviewed in Wilson and Lawson Importantly, studies of clonal analysis that utilise labelling of individual cells enrich our understanding of developmental processes and can reveal cryptic boundaries that may exist within the developing embryo.

We have collaborated with Drs Val Wilson and Kirstie Lawson of the University of Edinburgh to develop a clonal analysis database that allows researchers to explore patterns of genetic mosaicism that have been uncovered in the mid-gestation mouse embryo. Dr Val Wilson's group have utilised a single-cell labelling method that relies on the spontaneous reversion of an inactive lacZ gene laacZ carrying a sequence duplication to an active lacZ reporter Tzouanacou et al. In addition, the size of the clones allow for estimation of the time at which the progenitor cells were labelled.

In addition, a mapping between the annotator's in-house scoring criteria and the EMAGE nomenclature was used to describe whether clones are present or not detected in a specific anatomical component. In future developments we wish to develop clonal maps with the aim of capturing clone distribution in a spatial model. Dr Val Wilson's group have captured this spatial information as part of their annotation process Fig. A spatial model for capturing clonal analysis data. These entries include text-annotation provided by the original authors.

B Clonal analysis maps used by the authors as part of their annotation process can additionally be incorporated into EMAGE entries and used to capture clone distribution.

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Reprinted from Fig. S4 in Developmental Cell 17, Tzouanacou et al. The eHistology resource provides online access to cellular-resolution images of the histology sections used in Kaufman's The Atlas of Mouse Development Graham et al. The original publication of this book by Kaufman is the definitive work for mouse developmental anatomy. The eHistology resource has now been extended to include the annotated coronal sections published in Kaufman's Atlas of Mouse Development Supplement Price et al.

This dataset catered for a specific demand from the neuroscience community for annotated coronal sections that could be used as a reference atlas, and includes detailed annotation of the brain at E11, E These sections include some original annotations from M. Kaufman supplemented by more detailed brain annotations. The DMDD interface provides orthogonal views through the embryo data and the eHistology web-service API can be used to discover a closely matching annotated view of the high-resolution histology Fig. We anticipate that this interface will provide a key service for the similar International Mouse Phenotyping Consortium IMPC resource and expression atlases.

B The supporting eHistology image for the section plane shown in A highlights multiple anatomical components that researchers should additionally consider when evaluating phenotype image data. The web tool we have delivered enables image matching between DMDD and eHistology resources, such that for any given section through a DMDD image volume the closest match from the eHistology Kaufman atlas can be retrieved.

We have recently developed a new eLearning resource that provides short and interactive vignettes in embryo primarily vertebrate development, from gametogenesis through to organogenesis Fig.

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The resource describes the development of various anatomical systems including the cardiovascular system, nervous system, and musculo-skeletal system, and additionally introduces core developmental biology concepts such as gastrulation, placentation, and the formation of the germ layers. A An animated tutorial is presented in the central panel. The accordion selection tool to the right of the page links to accompanying 3D visualisations. B The eLearning 3D viewer shows a 3D surface reconstruction of an embryo model, combined with a section through the 3D volume.

In this example, the developing nervous system is delineated. By doing so, this viewer enables the detail provided on section to be shown in the context of the 3D anatomy. The navigation tools allow a user to change the section plane. The resource combines illustrative animations of embryonic development with interactive 3D visualisations of mouse developmental anatomy.