More than a decade of developmental gene expression atlases: where are we now?

To unravel regulatory networks of genes functioning during embryonic development, information on in situ gene expression is required. Enormous amounts of such data are available in literature, where each paper reports on a limited number of genes and developmental stages. The best way to make these data accessible is via spatio-temporal gene expression atlases. Eleven atlases, describing developing vertebrates and covering at least 100 genes, were reviewed. This review focuses on: (i) the used anatomical framework, (ii) the handling of input data and (iii) the retrieval of information. Our aim is to provide insights into both the possibilities of the atlases, as well as to describe what more than a decade of developmental gene expression atlases can teach us about the requirements of the design of the ‘ideal atlas’. This review shows that most ingredients needed to develop the ideal atlas are already applied to some extent in at least one of the discussed atlases. A review of these atlases shows that the ideal atlas should be based on a spatial framework, i.e. a series of 3D reference models, which is anatomically annotated using an ontology with sufficient resolution, both for relations as well as for anatomical terms.     

Integrating technologies for comparing 3D gene expression domains in the developing chick limb

Chick embryos are good models for vertebrate development due to their accessibility and manipulability. Recent large increases in available genomic data from both whole genome sequencing and EST projects provide opportunities for identifying many new developmentally important chicken genes. Traditional methods of documenting when and where specific genes are expressed in embryos using wholemount and section in-situ hybridisation do not readily allow appreciation of 3-dimensional (3D) patterns of expression, but this can be accomplished by the recently developed microscopy technique, Optical Projection Tomography (OPT). Here we show that OPT data on the developing chick wing from different labs can be reliably integrated into a common database, that OPT is efficient in capturing 3D gene expression domains and that such domains can be meaningfully compared. Novel protocols are used to compare 3D expression domains of 7 genes known to be involved in chick wing development. This reveals previously unappreciated relationships and demonstrates the potential, using modern genomic resources, for building a large scale 3D atlas of gene expression. Such an atlas could be extended to include other types of data, such as fate maps, and the approach is also more generally applicable to embryos, organs and tissues.     

BEST: a novel computational approach for comparing gene expression patterns from early stages of Drosophila melanogaster development

Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data. Therefore, we have developed computational approaches to automate the comparison of gene expression patterns contained in images of early stage Drosophila melanogaster embryos (prior to the beginning of germ-band elongation); similarities and differences in gene expression patterns in these early stages have extensive developmental effects. Here we describe a basic expression search tool (BEST) to retrieve best matching expression patterns for a given query expression pattern and a computational device for gene interaction inference using gene expression pattern images and information on the associated genotypes and probes. Analysis of a prototype collection of Drosophila gene expression pattern images is presented to demonstrate the utility of these methods in identifying biologically meaningful matches and inferring gene interactions by direct image content analysis. In particular, the use of BEST searches for gene expression patterns is akin to that of BLAST searches for finding similar sequences. These computational developmental biology methodologies are likely to make the great wealth of embryonic gene expression pattern data easily accessible and to accelerate the discovery of developmental networks.     

SPI: a tool for incorporating gene expression data into a four-dimensional database of Caenorhabditis elegans embryogenesis

MOTIVATION: A comprehensive gene expression database is essential for computer modeling and simulation of biological phenomena, including development. Development is a four-dimensional (4D; 3D structure and time course) phenomenon. We are constructing a 4D database of gene expression for the early embryogenesis of the nematode Caenorhabditis elegans. As a framework of the 4D database, we have constructed computer graphics (CG), into which we will incorporate the expression data of a number of genes at the subcellular level. However, the assignment of 3D distribution of gene products (protein, mRNA), of embryos at various developmental stages, is both difficult and tedious. We need to automate this process. For this purpose, we developed a new system, named SPI after superimposing fluorescent confocal microscopic data onto a CG framework. RESULTS: The scheme of this system comprises the following: (1) acquirement of serial sections (40 slices) of fluorescent confocal images of three colors (4',6'-diamino-2-phenylindole (DAPI) for nuclei, indodicarbocyanine (Cy-3) for the internal marker, which is a germline-specific protein POS-1 and indocarbocyanine (Cy-5) for the gene product to be examined); (2) identification of several features of the stained embryos, such as contour, developmental stage and position of the internal marker; (3) selection of CG images of the corresponding stage for template matching; (4) superimposition of serial sections onto the CG; (5) assignment of the position of superimposed gene products. The Snakes algorithm identified the embryo contour. The detection accuracy of embryo contours was 92.1% when applied to 2- to 28-cell-stage embryos. The accuracy of the developmental stage prediction method was 81.2% for 2- to 8-cell-stage embryos. We manually judged only the later stage embryos because the accuracy for embryos at the later stages was unsatisfactory due to experimental noise effects. Finally, our system chose the optimal CG and performed the superposition and assignment of gene product distribution. We established an initial 4D gene expression database with 56 maternal gene products. AVAILABILITY: This system is available at http://anti.lab.nig.ac.jp/spi/ and http://anti.lab.nig.ac.jp/4ddb/     

Cleavage pattern and survivin expression in porcine embryos by somatic cell nuclear transfer

Mammalian embryos produced in vitro show a high rate of early developmental failure. Numerous somatic cell nuclear transfer (SCNT) embryos undergo arrest and show abnormal gene expression in the early developmental stages. The purpose of this study was to analyze porcine SCNT embryo development and investigate the cause of porcine SCNT embryo arrest. The temporal cleavage pattern of porcine SCNT embryos was analyzed first, and the blastocyst origin at early developmental stage was identified. To investigate markers of arrest in the cleavage patterns of preimplantation SCNT embryos, the expression of survivin—the smallest member of the inhibitor of apoptosis (IAP) gene family, which suppresses apoptosis and regulates cell division—was compared between embryos showing normal cleavage and arrested embryos.A total of 511 SCNT embryos were used for cleavage pattern analysis. Twenty-four hours post activation (hpa), embryos were classified into five groups based on the cleavage stage as follows; 1-cell, 2-cell, 4-cell, 8-cell and fragmentation (frag). In addition, 48 hpa embryos were more strictly classified into 15 groups based on the cleavage stage of 24 hpa; 1-1 cell (24 hpa-48 hpa), 1-2 cell, 1-4 cell, 1-8 cell, 1 cell-frag, 2-2 cell, 2-4 cell, 2-8 cell, 2 cell-frag, 4-4 cell, 4-8 cell, 4 cell-frag, 8-8 cell, 8 cell-frag, and frag-frag. These groups were cultured until 7 d post activation, and were evaluated for blastocyst formation. At 24 hpa, the proportion of 2-cell stage was significantly higher (44.5%) than those in the other cleavage stages (1-cell: 13.4%; 4-cell: 17.9%; 8-cell: 10.3%; and frag: 13.9%). At 48 hpa, the proportion of embryos in the 2-4 cell stage was significantly higher (32.4%) than those in the other cleavage stages (2-8 cell: 8.2%; 4-8 cell: 12.1%; and frag-frag: 13.9%). Some embryos arrested at 48 hpa (1-1 cell: 5.8%; 2-2 cell: 2.8%; 4-4 cell: 3.8%; 8-8 cell: 6.5%; and total arrested embryos: 18.9%). Blastocyst formation rates were higher in 2-4 cell cleavage group (20.2%) than in other groups. SCNT embryos in 2-4 cell stage showed stable developmental competence. In addition, we investigated survivin expression in porcine SCNT embryos during the early developmental stages. The levels of survivin mRNA in 2-cell, 4-cell stage SCNT embryos were significantly higher than those of arrested embryos. Survivin protein expression showed a similar pattern to that of survivin mRNA. Normally cleaving embryos showed higher survivin protein expression levels than arrested embryos. These observations suggested that 2-4 cell cleaving embryos at 48 hpa have high developmental competence, and that embryonic arrest, which may be influenced by survivin expression in porcine SCNT embryos.     

The embryonic expression patterns of zebrafish genes encoding LysM-domains

The function and structure of LysM-domain containing proteins are very diverse. Although some LysM domains are able to bind peptidoglycan or chitin type carbohydrates in bacteria, in fungi and in plants, the function(s) of vertebrate LysM domains and proteins remains largely unknown. In this study we have identified and annotated the six zebrafish genes of this family, which encode at least ten conceptual LysM-domain containing proteins. Two distinct sub-families called LysMD and OXR were identified and shown to be highly conserved across vertebrates. The detailed characterization of LysMD and OXR gene expression in zebrafish embryos showed that all the members of these sub-families are strongly expressed maternally and zygotically from the earliest stages of a vertebrate embryonic development. Moreover, the analysis of the spatio-temporal expression patterns, by whole mount and fluorescent in situ hybridizations, demonstrates pronounced LysMD and OXR gene expression in the zebrafish brain and nervous system during stages of larval development. None of the zebrafish LysMD or OXR genes was responsive to challenge with bacterial pathogens in embryo models of Salmonella and Mycobacterium infections. In addition, the expression patterns of the OXR genes were mapped in a zebrafish brain atlas.     

Automated pipeline for atlas-based annotation of gene expression patterns: Application to postnatal day 7 mouse brain

Massive amounts of image data have been collected and continue to be generated for representing cellular gene expression throughout the mouse brain. Critical to exploiting this key effort of the post-genomic era is the ability to place these data into a common spatial reference that enables rapid interactive queries, analysis, data sharing, and visualization. In this paper, we present a set of automated protocols for generating and annotating gene expression patterns suitable for the establishment of a database. The steps include imaging tissue slices, detecting cellular gene expression levels, spatial registration with an atlas, and textual annotation. Using high-throughput in situ hybridization to generate serial sets of tissues displaying gene expression, this process was applied toward the establishment of a database representing over 200 genes in the postnatal day 7 mouse brain. These data using this protocol are now well-suited for interactive comparisons, analysis, queries, and visualization.     

Multi-target Chromogenic Whole-mount In Situ Hybridization for Comparing Gene Expression Domains in Drosophila Embryos

To analyze gene regulatory networks active during embryonic development and organogenesis it is essential to precisely define how the different genes are expressed in spatial relation to each other in situ. Multi-target chromogenic whole-mount in situ hybridization (MC-WISH) greatly facilitates the instant comparison of gene expression patterns, as it allows distinctive visualization of different mRNA species in contrasting colors in the same sample specimen. This provides the possibility to relate gene expression domains topographically to each other with high accuracy and to define unique and overlapping expression sites. In the presented protocol, we describe a MC-WISH procedure for comparing mRNA expression patterns of different genes in Drosophila embryos. Up to three RNA probes, each specific for another gene and labeled by a different hapten, are simultaneously hybridized to the embryo samples and subsequently detected by alkaline phosphatase-based colorimetric immunohistochemistry. The described procedure is detailed here for Drosophila, but works equally well with zebrafish embryos.     

WormGUIDES: an interactive single cell developmental atlas and tool for collaborative multidimensional data exploration

Background

Imaging and image analysis advances are yielding increasingly complete and complicated records of cellular events in tissues and whole embryos. The ability to follow hundreds to thousands of cells at the individual level demands a spatio-temporal data infrastructure: tools to assemble and collate knowledge about development spatially in a manner analogous to geographic information systems (GIS). Just as GIS indexes items or events based on their spatio-temporal or 4D location on the Earth these tools would organize knowledge based on location within the tissues or embryos. Developmental processes are highly context-specific, but the complexity of the 4D environment in which they unfold is a barrier to assembling an understanding of any particular process from diverse sources of information. In the same way that GIS aids the understanding and use of geo-located large data sets, software can, with a proper frame of reference, allow large biological data sets to be understood spatially. Intuitive tools are needed to navigate the spatial structure of complex tissue, collate large data sets and existing knowledge with this spatial structure and help users derive hypotheses about developmental mechanisms.

Results

Toward this goal we have developed WormGUIDES, a mobile application that presents a 4D developmental atlas for Caenorhabditis elegans. The WormGUIDES mobile app enables users to navigate a 3D model depicting the nuclear positions of all cells in the developing embryo. The identity of each cell can be queried with a tap, and community databases searched for available information about that cell. Information about ancestry, fate and gene expression can be used to label cells and craft customized visualizations that highlight cells as potential players in an event of interest. Scenes are easily saved, shared and published to other WormGUIDES users. The mobile app is available for Android and iOS platforms.

Conclusion

WormGUIDES provides an important tool for examining developmental processes and developing mechanistic hypotheses about their control. Critically, it provides the typical end user with an intuitive interface for developing and sharing custom visualizations of developmental processes. Equally important, because users can select cells based on their position and search for information about them, the app also serves as a spatially organized index into the large body of knowledge available to the C. elegans community online. Moreover, the app can be used to create and publish the result of exploration: interactive content that brings other researchers and students directly to the spatio-temporal point of insight. Ultimately the app will incorporate a detailed time lapse record of cell shape, beginning with neurons. This will add the key ability to navigate and understand the developmental events that result in the coordinated and precise emergence of anatomy, particularly the wiring of the nervous system.