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Corresponding author: Sudhir Kumar, Life Sciences Center A-351, Arizona State University, Tempe, AZ 85287-1501., s.kumar{at}asu.edu (E-mail)
Communicating editor: S. YOKOYAMA
 | ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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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.
PATTERNS of gene expression in the fruit fly Drosophila melanogaster have been extensively studied by visualizing the presence or absence of gene products or their markers in the developing embryo (visualization methods are reviewed in 
Familiarity with the wealth of images of gene expression patterns gathered over the past two decades is essential for discovering new genetic interactions. However, the burden of becoming familiar with extensive past literature and the rapidly increasing amount of information on gene expression patterns is an impediment to cross-laboratory endeavors and to building a global genetic framework for embryonic development. Computational tools to automatically identify images with similar gene expression patterns from a large collection of images and to predict potential genetic interactions using these images would greatly facilitate developmental biology research. Such tools will become increasingly important with the advent of large-scale in situ RNA hybridization studies (http://www.fruitfly.org). The need for a computational system is particularly acute in studies of Drosophila, as scientists move beyond studies of single genes or gene families to generate a global view of development. To address these issues, we have developed in silico approaches for automated comparison of gene expression pattern images that mimic some of the visual comparison techniques used by researchers in the laboratory. Our methods enable easy and efficient access to gene expression and interaction data and will likely facilitate new discoveries in developmental biology.
In this article, we describe methodologies for (a) standardizing gene expression pattern images as they are acquired under different illumination conditions and are published in a variety of sizes, orientations, and resolutions; (b) quantifying the amount of dissimilarity between two expression patterns by comparing images containing them; (c) identifying those images from a large collection that contain expression patterns similar to that contained in the given image; and (d) inferring genetic interactions between two genes by comparing the expression pattern of a particular gene in wild-type and mutant backgrounds. In the first phase of our efforts to develop computational methods, we have focused on early embryonic development in D. melanogaster. This focus reflects two relevant features of the field of Drosophila developmental genetics. First, there is an emphasis on understanding early developmental events that have global effects, such as the organization of the embryonic dorsal/ventral axis. Second, the deposition of the cuticle after roughly three-fourths of embryonic development prevents the use of several techniques commonly employed for the analysis of gene expression.
We present results showing the performance of our methodologies using a prototype collection of 982 image-searchable gene expression pattern images captured as whole embryos and retrieved from the literature (
| MATERIALS AND METHODS |
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| TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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To meaningfully compare gene expression images computationally, it is important to standardize them and to remove background information (reviewed in
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Embryo-enclosing algorithm:
This procedure starts with fitting the Drosophila embryo boundaries into the smallest possible rectangular area, a process referred to as "edge-fitting" (
For this step, we have developed an algorithm in which the boundaries of the initial rectangular image are moved inward until they touch the Drosophila embryo outline on all four sides. In this algorithm, we first consider an area of 5 x 5 pixels at the four corners of the image and then compute the mean (m) and standard error (s) of the average color intensities for pixels in the four corners of the image. Next, we find the topmost row in the image, where the upper boundary of the embryo is present. For this, we traverse top-to-bottom in each column to identify the pixels closest to the top boundary where the absolute difference between the pixel intensity (p) and m is significant (e.g., m - p is greater than twice the standard error under the assumption of normality). The row coordinate of the pixel identified closest to the top edge of the image is the top boundary of the embryo. To identify bottom, left, and right boundaries, we use the same algorithm; the only difference is that now we traverse from bottom to top in each column, left to right in each row, and right to left in each row, respectively. This results in an embryo enclosed in the smallest rectangular area possible. The embryo boundaries determined using this algorithm for A and B in Fig 1 are shown in C and D. Note that all images are standardized to an anterior (left)–posterior (right)–dorsal (top)–ventral (bottom) orientation.
Size standardization:
In the next step, we need to scale all the resultant images to the same size as they are often captured or published in very different sizes (compare Fig 1A and Fig B). We chose a size of 270 x 100 pixels, which was the average size of gene expression pattern images acquired from the published literature. For scaling, we perform a geometric transformation (simple scaling) followed by an interpolation to derive the pixel values of the new image. In these studies, we used a gray-level bilinear-interpolation scheme (
Expression pattern extraction:
The next step in the gene expression pattern image standardization is to eliminate background to focus on the foreground containing the expression pattern. This ensures that only actual patterns of gene expression are compared for biologically meaningful analysis. In Fig 1A, the gene expression pattern is the darkly stained region embedded in a lighter-color background. While this can be recognized easily by a trained eye, the process needs to be automated for large-scale data gathering. Therefore, the expression pattern (relevant visual content) first must be extracted from each image.
Extraction of the gene expression pattern from the background requires the use of a threshold value of pixel intensity (
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Finally, we convert the color/gray-scale pixels into simple black-and-white patterns such that only pixels containing gene expression take on a black color and the rest of the image lacks any color (Fig 1G and Fig H). We do not convert original images directly into black-and-white images prior to conducting the above-mentioned size standardization and pattern extraction procedures because the information contained in differences in color intensities is valuable for reliably separating expression patterns from the background.
Digital representation of expression patterns:
The binary images representing gene expression patterns are processed to derive a vector of features describing the image content (gene expression patterns). We represent each image in the form of a string of 1's and 0's, where black pixels are denoted by a value of 1 and white pixels by a value of 0. For example, an expression pattern is represented as 0100111000...00000001111111. This is referred to as the binary sequence vector (BSV) representation. The BSV representation is particularly useful in quantifying image-to-image dissimilarity for finding images with similar or overlapping expression patterns, and it allows for localization of image similarity searches to any section of the embryo.
An alternative set of features can be derived using shape descriptors, which have proven to be effective in establishing the similarity between two images as well as in effecting image retrieval (
Finding best matching expression patterns with the basic expression search tool (BEST):
To find the best matching set of images for a given query image, we need to compute the extent of dissimilarity between gene expression patterns. We use the BSV representation for this purpose. The corresponding bit sequences are compared and the number of bits with different values are counted. We define the expression pattern distance (DE) to be equal to the number of differences between two images divided by the number of pixels depicting the expression pattern in at least one of the two images. In other words, we determine expression pattern similarity by focusing only on the pixels that show gene expression (have value 1) in either image. For example, two images, A and B, with bit sequences of length 30 given below have nine differences (underlined bits).
Therefore, DE = 9/18 = 0.5. Images showing the highest amount of match (i.e., lowest DE) with the query image are retrieved as the best matches. This simple metric showed excellent performance for early stage embryos in an analysis of a collection of 982 images (see below).
Inferring gene interactions:
Gene interactions are computed by comparing expression patterns of individual genes in wild-type and mutant backgrounds. Normally, investigators conduct this task by simple visual inspection and deduction. However, as the number of images increases, visual inspection becomes cumbersome. Therefore, we have devised an algorithm that uses gene expression pattern images, their genotypes, and their probes to infer the nature of the interaction for that pair of genes. This computational system works well for early stages of embryonic development because gene expression at these stages is often relatively broad, rather than highly localized as in later stage embryos. Also, we find that >80% of the available images are from early stage embryos. Therefore these methods are applicable for large amounts of existing data.
A flowchart for the two-gene case is shown in Fig 3, in which the expression patterns of gene B in the wild-type and mutant backgrounds for gene A are used. By design, these data shed light on how the expression of gene B is influenced by gene A. Fig 3 shows various paths starting from the gene expression images and proceeding to the decisions that correspond to whether or not the expression of gene B is only positively affected (A 
B), only negatively affected (A —|B), positively and negatively affected (A |B), or not affected (A x B) by gene A. Using the BSV representations of gene expression patterns, the actual algorithm works as follows. We begin by defining a new function, termed the gene expression function (gef), which evaluates whether a given expression pattern contains all 0's or all 1's or a combination of 0's and 1's in a given image (i.e., no expression, ubiquitous expression, or localized expression pattern, respectively). It produces 1 if the image contains all black pixels. If all pixels are white, then it produces a 0; otherwise, it returns a P (for partial). To analyze all possible interactions between the gene expression patterns, we derive new image patterns based on the two original images, for which the logical exclusive OR (xor) operation is used. This operator results in a 0 if the values of the corresponding pixels in the two images under inspection are the same (i.e., if they are both 0 or both 1) or results in a 1 if the values of the corresponding pixels in the two images are different. Outputs from gef function for this image as well as the original data are input to the flowchart diagram for inferring interactions.
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| RESULTS |
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| TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Performance of data standardization methods:
We examined the performance of our methodologies for embryo-enclosing and expression pattern extraction using 97 images from eight articles (
We evaluated the performance of our automatic approach for pattern extraction by again asking 16 independent evaluators to assign a score of 1–4 for each extracted pattern (1 being worst and 4 being best). This tests the performance of the above method to approximate what the human eye can easily recognize. The average success score for the combined region-growing and adaptive-thresholding methodology was found to be 3.5 (87.5%). The observed difficulty with automatic pattern extraction appears to relate to the lack of sufficient contrast between the expression pattern and the background. In addition, in the presence of multiple patterns of interest (with different illuminations) the automatic technique needs to be user guided to extract all biologically significant regions. For this reason, a semiautomatic system was developed in which different threshold values and/or new seed points for different regions of interest in the embryo can be specified manually. This improves data quality and makes the extraction biologically relevant.
Finding similar gene expression patterns computationally:
We explored the effectiveness of our computational approach in finding similar expression patterns using a prototype collection of 982 patterns from 49 published research articles (Fig 4). These tests were geared toward determining the sensitivity of our computational approach and the biological validity of the image-matching procedure. A computer program was written to automatically retrieve gene expression pattern images showing overlap with a given query pattern based solely on the similarities between images. We refer to the images retrieved by BEST as "BEST hits."
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Here we briefly summarize the characteristics of our 10 test-case expression patterns and then we describe BEST-hit results for each case. Of the 10 patterns, 9 are lateral views (Fig 4, A–E, G, and H) and one is a dorsal view (F). Expression patterns in some images differ considerably from each other (e.g., Fig 4A and Fig B), some differ only slightly (Fig 4B and Fig C), some have clearly overlapping domains of expression even though they are quite different overall (Fig 4D and Fig E and Fig G and Fig I), and others have multiple distinct regions of expression (Fig 4E, Fig F, Fig I, and Fig J). These query images were chosen without regard to their genotype, probe, or any other consideration except for diversity of expression pattern. Also, BEST hits were based solely on gene expression contained in the images; no prior knowledge of gene interactions was used. For each image, the program queried the entire collection of 982 images. The top five BEST hits for each image are shown in descending order below the query image in Fig 4 with the percentage of similarity (1 - DE) given in parentheses.
Fig 4A shows an image with expression restricted to the posterior 10% of the embryo (
Fig 4B shows an image with expression restricted to the anterior 25% of the embryo (
Fig 4C shows an image with expression of orthodenticle RNA restricted to the anterior 25% of the embryo, except that expression is absent at the anterior terminus (
Fig 4D shows an image with a single stripe of expression of short gastrulation RNA driven by the even skipped stripe 2 enhancer in a transgenic embryo (
Fig 4E shows an image of the two domains of hunchback RNA expression in a wild-type embryo. hunchback is expressed in a wide stripe toward the anterior and a narrow stripe toward the posterior of the embryo (
Fig 4F shows an image, in dorsal view, with three domains of expression. Expression is seen in the anterior 25% and in two stripes of expression toward the posterior of the embryo. Specifically, the image shows Race RNA expression in a goosecoid mutant embryo with short gastrulation expression driven by the even skipped stripe 2 enhancer (
Fig 4G shows an image with expression restricted to the ventral 10% of the embryo. The expression of snail RNA in an embryo derived from a groucho germline clone is shown (
The expression of decapentaplegic RNA is restricted to the dorsal 40% of a wild-type embryo as shown in Fig 4H (
Fig 4I shows an image with stripes of expression in ventral and lateral regions. Specifically, the image shows the expression of short gastrulation in a wild-type embryo (
Finally, we consider an embryo with seven stripes of expression, a pattern typical of pair-rule genes, for finding BEST hits (Fig 4J). Fig 4J shows the expression of runt RNA in an embryo expressing low levels of knirps under the control of the even skipped stripe 2 enhancer (
Overall these results suggest that the basic expression search tool is (i) able to successfully retrieve expression patterns of the same gene from the same article, (ii) sensitive to relatively small changes in expression pattern, (iii) able to retrieve expression patterns of different genes that have similar functions (e.g., dorsal/ventral patterning), and (iv) able to recover expression patterns of different genes within a spatially localized regulatory hierarchy (e.g., gap, pair-rule, and segment polarity genes). Our examples identify numerous known, biologically meaningful matches (e.g., short gastrulation and brinker) and several potentially new matches (e.g., orthodenticle and sloppy paired1) from a large data set. This is exactly what a researcher would do when comparing their images to those in the published literature manually by eye. The use of BEST hits will expedite the search for finding comparable RNA and protein expression patterns (as is the case for the BLAST search for molecular sequences). As when searching the literature for matching gene expression patterns, a BEST-hits user must determine the biological meaningfulness of matches retrieved by consulting the original articles.
Inferring gene interactions:
Here we present results from five examples in which genetic interactions were computed employing the flowchart diagram in Fig 3. Our algorithm compares the expression pattern of a gene in wild-type and mutant embryos. In each case, inferred interactions are expected to match those that an investigator would deduce on the basis of their visual inspection of the same images.
Fig 5A shows the effect of bicoid on Krüppel (Kr) expression. On the left, we show Krüppel RNA expression in a wild-type embryo (
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Fig 5B shows the effect of bicoid on tailless (tll) expression by comparing tailless RNA expression in wild-type and bcd- embryos (
In Fig 5C, we examine the interaction between caudal and knirps (kni). For this purpose, we use images containing knirps RNA expression in a wild-type embryo (
The posterior refinement of giant expression by dCtBP-mediated repression is shown in the next example (Fig 5D). Here giant RNA expression in a wild-type embryo and a dCtBP germline clone mutant embryo (dCtBP-;
Finally, we consider the effect of bicoid on orthodenticle (otd) expression (Fig 5E). The algorithm compares the expression patterns of Orthodenticle protein in a wild-type embryo (
These examples illustrate the ability of our computational techniques to infer genetic interactions using image data in a manner similar to that employed visually by researchers. The simplicity of the approach is that the entire process can be described by a few logic functions and is therefore easily automated for analyzing all available pairs of images that are suitable for inferring genetic interactions (results from those analyses will be published elsewhere).
| DISCUSSION |
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| TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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A major problem facing biologists today is that data generation has consistently outpaced advancements in computational techniques for developing biological insights and generating hypotheses. This problem has plagued molecular sequence data for over a decade. With the advent of large-scale high-throughput in situ efforts, we are headed toward biological data overload in developmental biology as well. For example, Berkeley Drosophila Genome Project has released a large collection of images (>30,000) generated with a standardized RNA in situ hybridization method using whole embryos and cDNA probes from the Drosophila UNIGENE set (http://www.fruitfly.org). About 15,000 of these images are expected to depict gene expression from early stage embryos. These data are released in raw form to the public. How will we view these data efficiently? How will we find images with similar expression patterns, other than by manually browsing through each and every image? How will we compare the newly acquired knowledge to all existing knowledge published in the literature? How will the images be standardized? How can we use all the data to generate a global view of the early stages of Drosophila embryonic development? No computational frameworks that will facilitate efficient analyses of these new images or integrate these new images with existing image data from the literature exist.
We have tackled this challenge by developing methods and algorithms for standardization, extraction, and similarity search of gene expression patterns. We have demonstrated their utility in finding similar and overlapping expression patterns for a wide variety of genes from early stages of Drosophila embryonic development. These bioinformatic methodologies are likely to pave the way for establishing a public computational resource to find similar expression patterns based on the visual content of the query image, a facility that will parallel the BLAST search (
We have also proposed a method for elucidating genetic interactions computationally by comparing expression patterns of a given gene in wild-type and mutant backgrounds. This method will provide researchers for the first time with a tool to exhaustively examine every possible pair of genes for which wild-type and mutant expression patterns are available. It therefore will facilitate construction of developmental networks and help explore the combinatorial nature of genetic interactions that provide the exquisite specificity and diversity of gene expression patterns. These algorithms provide specific predictions for researchers, who can then test the validity of these in silico inferences. In the future, we plan to develop similar algorithms for simultaneous analysis of multiple genes.
In summary, methods presented in this article are efforts to fulfill the need for computational technology for large-scale analysis of gene expression data. For instance, our approach will facilitate the construction of similarity-based clusters of expression pattern images. Examination of similarities and differences in their spatial and temporal contexts and of the genetic background of images within each similarity cluster will likely reveal new genetic interactions and thus accelerate the discovery of hidden links in developmental networks. Therefore, an accelerated, expanded understanding of the interconnected nature of gene function may emerge from in silico analysis.
| ACKNOWLEDGMENTS |
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We thank Drs. Tom Brody, David Capco, Alan Filipski, Manfred Laubichler, Alan Rawls, Koichiro Tamura, and Jeanne Wilson-Rawls for invaluable comments on an early draft of this article. We greatly appreciate the efforts of Emily Davenport, Veena Ganeshan, Eric Herbig, and Aaron Johnson in image data collection and pattern extraction, and Dr. Sankar Subramanian's technical assistance. This research was supported in part by funds from the Center for Evolutionary Functional Genomics (S.K.) and research grants from the National Institutes of Health (S.K., S.J.N.) and National Science Foundation (S.P.).
Manuscript received August 2, 2002; Accepted for publication September 30, 2002.
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test whether the expression pattern in the image on the left is contained completely in the expression pattern of the image on the right.
