Spatial gene expression quantification: a tool for analysis of in situ hybridizations in sea anemone Nematostella vectensis
© Botman and Kaandorp; licensee BioMed Central Ltd. 2012
Received: 15 March 2012
Accepted: 26 September 2012
Published: 5 October 2012
Spatial gene expression quantification is required for modeling gene regulation in developing organisms. The fruit fly Drosophila melanogaster is the model system most widely applied for spatial gene expression analysis due to its unique embryonic properties: the shape does not change significantly during its early cleavage cycles and most genes are differentially expressed along a straight axis. This system of development is quite exceptional in the animal kingdom.
In the sea anemone Nematostella vectensis the embryo changes its shape during early development; there are cell divisions and cell movement, like in most other metazoans. Nematostella is an attractive case study for spatial gene expression since its transparent body wall makes it accessible to various imaging techniques.
Our new quantification method produces standardized gene expression profiles from raw or annotated Nematostella in situ hybridizations by measuring the expression intensity along its cell layer. The procedure is based on digital morphologies derived from high-resolution fluorescence pictures. Additionally, complete descriptions of nonsymmetric expression patterns have been constructed by transforming the gene expression images into a three-dimensional representation.
We created a standard format for gene expression data, which enables quantitative analysis of in situ hybridizations from embryos with various shapes in different developmental stages. The obtained expression profiles are suitable as input for optimization of gene regulatory network models, and for correlation analysis of genes from dissimilar Nematostella morphologies. This approach is potentially applicable to many other metazoan model organisms and may also be suitable for processing data from three-dimensional imaging techniques.
Spatial gene expression assays are a substantial tool for verifying predicted regulatory interactions and for predicting properties of missing components in a regulation network [1, 2]. Still, their largest potential is in inferring numerical models of regulatory interaction networks, which is demonstrated for the embryonic development of fruit fly Drosophila melanogaster. To perform accurate simulations, the spatial gene expression patterns are digitally quantified and formatted to consistent profiles.
In any modeling approach, meaningful results require accurate reference data to initiate the simulation and to evaluate the simulation output. For gene regulation networks, this means that gene expression patterns should be quantified in a consistent manner. A digital quantification procedure has been created and validated for the fruit fly .
Gene expression in Drosophila is quantified along a straight line during superficial cleavage. In this stage, nuclei are dividing within a single cell membrane and the embryo’s outline does not change significantly. In most other animals however, nuclear division is coupled to cell division during cleavage and the early embryo displays rapid cell movement and morphological changes. This is why we developed a method for gene expression quantification that accounts for a complex and changing embryo morphology.
Over the past decade, Nematostella has become an important model organism in the field of evolutionary developmental biology . As a research object, the animal is easy to culture and its small size and transparent body wall make it suitable for all kinds of microscopy. Subsequent gene expression studies and the sequencing of the genome have shown that Nematostella, curiously, shares more genes with humans than either Drosophila melanogaster or C. elegans. Much work on Nematostella has been dedicated to the genetic regulation of development .
In this paper we discuss a geometric method for extracting quantitative spatio-temporal gene expression data from in situ hybridizations in the sea anemone Nematostella vectensis. We measure gene expression during gastrulation using a gene expression quantification tool developed by de Jong . We show in some preliminary results how this information can be analysed in a cluster analysis.
Gene expression quantification
In the following example, these graphical shapes are applied to quantify gene expression patterns with the GENEXP program (Additional file 1). Published Nematostella gene expression images are collected in the CnidBase  and Kahi Kai  databases, and Kahi Kai also contains expression images outside journal publications. Still, many Nematostella expression pictures are found in publications outside these databases.
Example 1: a 1D expression profile from a symmetrical pattern
The graph in Figure 4D contains features that do not represent the actual transcript concentration. The “en” and “ec” annotations cause a trough (orange arrow) and a ridge (green arrow), respectively, while an imperfect decomposition has shifted the peaks with regard to the center and introduced some noise (blue arrows). Moreover, the nonuniform background and nonsymmetric lighting cause an asymmetric baseline. The profile is exported to an editor for additional processing to correct for these features. To remove artefacts caused by annotations, the user selects this section of the graph and can choose among linear interpolation, cubic spline interpolation, piecewise cubic Hermite interpolation  and replacement with a specified constant. Noise from erroneous decomposition is smoothened with a lowpass filter (with filter coefficients equal to the reciprocal of the span), known as ‘moving average’ . The graph is lowered with a constant value to subtract the average background and regions without observed expression are put to zero. Both halves are averaged to cancel nonsymmetric influences. The final expression profile is plotted in Figure 4E.
Three-dimensional expression pattern reconstruction
For expression patterns that are radially symmetrical around the primary axis, a one-dimensional profile is a complete description. However, most signaling pathways involve genes that are asymmetrically expressed along the secondary axis. A three-dimensional representation is required to fully define the expression pattern of these genes. Depending on the data available for a gene expression pattern, a suitable method is determined for the approximation of this 3D pattern.
Example 2: a 3D representation from perpendicular embryo views
Figure 5A,B (adapted from ) shows the expression pattern of gene NvFoxB, which is concentrated on two spots on opposite sides of the oral end. Two sets of reference points are picked to scale and align both pictures. The height of the oral image is adjusted to match these points in the lateral image.
Elements for the 3D array that represents the volumetric expression pattern are calculated as the minimum expression from the associated pair of pixels (Figure 5C). A greyscale visualization of this array is displayed in Figure 5D. Two domains appear in the oral region as expected.
The 3D array is an intermediate step in the quantification process that is completed for the next example.
Example 3: a 2D expression landscape from a single embryo view
Figure 7A (based on ) shows the expression of Nvvas2 in the early gastrula stage. The expression domain covers the embryo’s lower half and its future mouth. A line is drawn on the image that divides the expression domain. Each element in the 3D array is the weighted average of the image pixels at both ends of a circular arc around this line (Figure 7B,C).
This discrete volumetric expression array is not yet suited to be compared to other patterns, because their shapes do not match or their cell layers are located at different Cartesian positions. To arrive at consistent profiles, slices are cut through the primary axis and decomposed. The primary axis is drawn on the original image and slices of the 3D array through this axis are constructed (Figure 7D,E). These slices are overlaid with a geometry that fits the native image (Figure 7F) and through the decomposition procedure described in the one-dimensional example, a set of profiles is produced. These profiles are stacked in a 2D array and displayed as a landscape in Figure 7G.
Visualization and clustering
From the cluster tree, the patterns are divided in three main groups, and five individuals with little similarity. In green, all four asymmetric expression patterns are included. The purple patterns are restricted to the endoderm. The yellow group is the largest, containing genes that are expressed in the presumptive pharynx and mouth. The remaining genes are expressed in ectoderm away from the mouth.
The clustering displayed in Figure 8 might be somewhat artificial as the expression domains clearly overlap. Moreover, some regions are elongating faster during gastrulation than others and even after pinning the estimated endoderm-ectoderm boundary, stationary patterns such as NvFoxA seem to migrate. Still, the comparison is very helpful in observing correlations and proposing hypotheses. For example, the three main groups may indicate regulatory modules. More specific, the patterns with broad boundaries belong to embryos in a relatively early stage, indicating a regulatory cascade in which fuzzy domain boundaries are sharpened, comparable to the Drosophila gap genes. An extended and systematic set of profiles would enable an inference of the developmental gene regulation network in Nematostella, based on the modeling techniques and analyses that established many properties of the Drosophila gap gene network [20–22].
Our quantification procedure provides a standardized format for the most diverse spatial visualization techniques. In this paper, hybridized mRNA has been quantified, but the method can be applied to any specific molecular entity. Potential examples include native proteins visualized with antibody staining and overexpressed proteins fused to a fluorescent agent [11, 23].
Current limitations arise from the strong assumptions imposed on the images that are used to construct 3D representations. For a rotated pattern, it is formally assumed that only expression in the plane of dissection is visible, while in fact observed expression is not restricted to this plane.
More serious is the requirement that the embryos used for the reverse projection are completely transparent, while the endoderm and aboral ectoderm are hidden on most oral images. Sometimes, only the cumulative signal on the periphery of an expression region is detected, as in Figure 5A (the speckled region at the arrow and on the opposite side).
The embryo is also assumed to be viewed from exactly perpendicular angles, but the sample is often rotated imperfectly. Additionally, slight deformations can occur during rotation, causing small domains with granular expression to overlap improperly and thus to be misrepresented. These issues are observed in Figure 5 as well.
With the advance of direct three-dimensional imaging the volumetric array construction may become superfluous, and these limitations will be removed. Confocal laser scanning microscopy has already been applied to zebrafish , Drosophila and sea urchin  embryos. This method may provide quantitative, spatial expression data for Nematostella as well. Conversely, general methods for mapping these data to the embryo’s morphology should be useful for comparison and analysis in these other organisms.
An integrated method has been presented that combines geometry extraction and gene expression quantification. The basic concept is that gene expression is conveniently measured along the cell layer in a morphology that can be viewed as a continuous sheet of cells. This straightforward approach can be applied generally to embryos across the animal kingdom. As confocal laser scans with high three-dimensional spatial resolution are widely applied, application of this method is not limited to symmetrical body shapes.
We have shown how to extract quantified gene expression profiles from Nematostella in situ hybridizations, and how a preliminary comparison and cluster analysis lead to new insights. The next step is to estimate parameters that describe interactions among genes in the Nematostella regulatory network. The powerful methods designed for parameter inference [4, 5, 20] and network analysis [21, 22] in Drosophila can now be applied to the standardized gene expression profiles of Nematostella vectensis and other model species in genetics.
Currently, a database of published images is processed into 1D arrays, annotated with roughly estimated development times based on comparison with high resolution micrographs. This comparison is very subjective, as our designation often differs from the developmental stage originally claimed. Moreover, the embryos change very subtly in the hollow sphere stage (Figure 2G) and between invagination and septa formation (Figure 2I), so timestamps are highly ambiguous. If a gene or a combination of genes is found with continuously changing expression patterns, we can derive labelling protocols to determine the exact developmental time. (Registration techniques like this have already been described and proved useful for Drosophila.)
Spatial gene expression quantification can be combined with modern quantitative polymerase chain reaction (qPCR) techniques . The absolute total amount of transcripts measured with qPCR coupled to the spatial distribution from quantified in situ images should enable the calculation of absolute local mRNA concentrations.
Availability and requirements
BioPreDyn (new bioinformatics methods and tools for data-driven predictive dynamic modelling in biotechnological applications)
Project home page
Linux, Windows, MacOS.
Any restrictions to use by non-academics
Daniel Botman was funded by the FP7 project BioPreDyn. We would like to thank Mark Martindale (Kewalo Marine Laboratory, University of Hawaii) for allowing us to use the pictures shown in Figure 3.
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