A quantitative spatiotemporal atlas of gene expression in the Drosophila blastoderm.

To fully understand animal transcription networks, it is essential to accurately measure the spatial and temporal expression patterns of transcription factors and their targets. We describe a registration technique that takes image-based data from hundreds of Drosophila blastoderm embryos, each costained for a reference gene and one of a set of genes of interest, and builds a model VirtualEmbryo. This model captures in a common framework the average expression patterns for many genes in spite of significant variation in morphology and expression between individual embryos. We establish the method's accuracy by showing that relationships between a pair of genes' expression inferred from the model are nearly identical to those measured in embryos costained for the pair. We present a VirtualEmbryo containing data for 95 genes at six time cohorts. We show that known gene-regulatory interactions can be automatically recovered from this data set and predict hundreds of new interactions.

A conserved developmental patterning network produces quantitatively different output in multiple species of Drosophila.

Differences in the level, timing, or location of gene expression can contribute to alternative phenotypes at the molecular and organismal level. Understanding the origins of expression differences is complicated by the fact that organismal morphology and gene regulatory networks could potentially vary even between closely related species. To assess the scope of such changes, we used high-resolution imaging methods to measure mRNA expression in blastoderm embryos of Drosophila yakuba and Drosophila pseudoobscura and assembled these data into cellular resolution atlases, where expression levels for 13 genes in the segmentation network are averaged into species-specific, cellular resolution morphological frameworks. We demonstrate that the blastoderm embryos of these species differ in their morphology in terms of size, shape, and number of nuclei. We present an approach to compare cellular gene expression patterns between species, while accounting for varying embryo morphology, and apply it to our data and an equivalent dataset for Drosophila melanogaster. Our analysis reveals that all individual genes differ quantitatively in their spatio-temporal expression patterns between these species, primarily in terms of their relative position and dynamics. Despite many small quantitative differences, cellular gene expression profiles for the whole set of genes examined are largely similar. This suggests that cell types at this stage of development are conserved, though they can differ in their relative position by up to 3-4 cell widths and in their relative proportion between species by as much as 5-fold. Quantitative differences in the dynamics and relative level of a subset of genes between corresponding cell types may reflect altered regulatory functions between species. Our results emphasize that transcriptional networks can diverge over short evolutionary timescales and that even small changes can lead to distinct output in terms of the placement and number of equivalent cells.

Transcription factors bind thousands of active and inactive regions in the Drosophila blastoderm.

Identifying the genomic regions bound by sequence-specific regulatory factors is central both to deciphering the complex DNA cis-regulatory code that controls transcription in metazoans and to determining the range of genes that shape animal morphogenesis. We used whole-genome tiling arrays to map sequences bound in Drosophila melanogaster embryos by the six maternal and gap transcription factors that initiate anterior-posterior patterning. We find that these sequence-specific DNA binding proteins bind with quantitatively different specificities to highly overlapping sets of several thousand genomic regions in blastoderm embryos. Specific high- and moderate-affinity in vitro recognition sequences for each factor are enriched in bound regions. This enrichment, however, is not sufficient to explain the pattern of binding in vivo and varies in a context-dependent manner, demonstrating that higher-order rules must govern targeting of transcription factors. The more highly bound regions include all of the over 40 well-characterized enhancers known to respond to these factors as well as several hundred putative new cis-regulatory modules clustered near developmental regulators and other genes with patterned expression at this stage of embryogenesis. The new targets include most of the microRNAs (miRNAs) transcribed in the blastoderm, as well as all major zygotically transcribed dorsal-ventral patterning genes, whose expression we show to be quantitatively modulated by anterior-posterior factors. In addition to these highly bound regions, there are several thousand regions that are reproducibly bound at lower levels. However, these poorly bound regions are, collectively, far more distant from genes transcribed in the blastoderm than highly bound regions; are preferentially found in protein-coding sequences; and are less conserved than highly bound regions. Together these observations suggest that many of these poorly bound regions are not involved in early-embryonic transcriptional regulation, and a significant proportion may be nonfunctional. Surprisingly, for five of the six factors, their recognition sites are not unambiguously more constrained evolutionarily than the immediate flanking DNA, even in more highly bound and presumably functional regions, indicating that comparative DNA sequence analysis is limited in its ability to identify functional transcription factor targets.

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data.

BACKGROUND: The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions. RESULTS: Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for eve mRNA pattern formation in the Drosophila melanogaster blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same cis-regulatory module depending on the factors' concentration, and implies different modes of activation and repression. CONCLUSIONS: Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.

NuMA influences higher order chromatin organization in human mammary epithelium.

The coiled-coil protein NuMA is an important contributor to mitotic spindle formation and stabilization. A potential role for NuMA in nuclear organization or gene regulation is suggested by the observations that its pattern of nuclear distribution depends upon cell phenotype and that it interacts and/or colocalizes with transcription factors. To date, the precise contribution of NuMA to nuclear function remains unclear. Previously, we observed that antibody-induced alteration of NuMA distribution in growth-arrested and differentiated mammary epithelial structures (acini) in three-dimensional culture triggers the loss of acinar differentiation. Here, we show that in mammary epithelial cells, NuMA is present in both the nuclear matrix and chromatin compartments. Expression of a portion of the C terminus of NuMA that shares sequence similarity with the chromatin regulator HPC2 is sufficient to inhibit acinar differentiation and results in the redistribution of NuMA, chromatin markers acetyl-H4 and H4K20m, and regions of deoxyribonuclease I-sensitive chromatin compared with control cells. Short-term alteration of NuMA distribution with anti-NuMA C-terminus antibodies in live acinar cells indicates that changes in NuMA and chromatin organization precede loss of acinar differentiation. These findings suggest that NuMA has a role in mammary epithelial differentiation by influencing the organization of chromatin.

Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis.

BACKGROUND: The distribution of chromatin-associated proteins plays a key role in directing nuclear function. Previously, we developed an image-based method to quantify the nuclear distributions of proteins and showed that these distributions depended on the phenotype of human mammary epithelial cells. Here we describe a method that creates a hierarchical tree of the given cell phenotypes and calculates the statistical significance between them, based on the clustering analysis of nuclear protein distributions. RESULTS: Nuclear distributions of nuclear mitotic apparatus protein were previously obtained for non-neoplastic S1 and malignant T4-2 human mammary epithelial cells cultured for up to 12 days. Cell phenotype was defined as S1 or T4-2 and the number of days in cultured. A probabilistic ensemble approach was used to define a set of consensus clusters from the results of multiple traditional cluster analysis techniques applied to the nuclear distribution data. Cluster histograms were constructed to show how cells in any one phenotype were distributed across the consensus clusters. Grouping various phenotypes allowed us to build phenotype trees and calculate the statistical difference between each group. The results showed that non-neoplastic S1 cells could be distinguished from malignant T4-2 cells with 94.19% accuracy; that proliferating S1 cells could be distinguished from differentiated S1 cells with 92.86% accuracy; and showed no significant difference between the various phenotypes of T4-2 cells corresponding to increasing tumor sizes. CONCLUSION: This work presents a cluster analysis method that can identify significant cell phenotypes, based on the nuclear distribution of specific proteins, with high accuracy.

Automatic channel unmixing for high-throughput quantitative analysis of fluorescence images.

Laser-scanning microscopy allows rapid acquisition of multi-channel data, paving the way for high-throughput, high-content analysis of large numbers of images. An inherent problem of using multiple fluorescent dyes is overlapping emission spectra, which results in channel cross-talk and reduces the ability to extract quantitative measurements. Traditional unmixing methods rely on measuring channel cross-talk and using fixed acquisition parameters, but these requirements are not suited to high-throughput processing. Here we present a simple automatic method to correct for channel cross-talk in multi-channel images using image data only. The method is independent of the acquisition parameters but requires some spatial separation between different dyes in the image. We evaluate the method by comparing the cross-talk levels it estimates to those measured directly from a standard fluorescent slide. The method is then applied to a high-throughput analysis pipeline that measures nuclear volumes and relative expression of gene products from three-dimensional, multi-channel fluorescence images of whole Drosophila embryos. Analysis of images before unmixing revealed an aberrant spatial correlation between measured nuclear volumes and the gene expression pattern in the shorter wavelength channel. Applying the unmixing algorithm before performing these analyses removed this correlation.

Enhancing image quality in cleared tissue with adaptive optics.

Our ability to see fine detail at depth in tissues is limited by scattering and other refractive characteristics of the tissue. For fixed tissue, we can limit scattering with a variety of clearing protocols. This allows us to see deeper but not necessarily clearer. Refractive aberrations caused by the bulk index of refraction of the tissue and its variations continue to limit our ability to see fine detail. Refractive aberrations are made up of spherical and other Zernike modes, which can be significant at depth. Spherical aberration that is common across the imaging field can be corrected using an objective correcting collar, although this can require manual intervention. Other aberrations may vary across the imaging field and can only be effectively corrected using adaptive optics. Adaptive optics can also correct other aberrations simultaneously with the spherical aberration, eliminating manual intervention and speeding imaging. We use an adaptive optics two-photon microscope to examine the impact of the spherical and higher order aberrations on imaging and contrast the effect of compensating only for spherical aberration against compensating for the first 22 Zernike aberrations in two tissue types. Increase in image intensity by 1.6× and reduction of root mean square error by 3× are demonstrated.

Building quantitative, three-dimensional atlases of gene expression and morphology at cellular resolution.

Animals comprise dynamic three-dimensional arrays of cells that express gene products in intricate spatial and temporal patterns that determine cellular differentiation and morphogenesis. A rigorous understanding of these developmental processes requires automated methods that quantitatively record and analyze complex morphologies and their associated patterns of gene expression at cellular resolution. Here we summarize light microscopy-based approaches to establish permanent, quantitative datasets-atlases-that record this information. We focus on experiments that capture data for whole embryos or large areas of tissue in three dimensions, often at multiple time points. We compare and contrast the advantages and limitations of different methods and highlight some of the discoveries made. We emphasize the need for interdisciplinary collaborations and integrated experimental pipelines that link sample preparation, image acquisition, image analysis, database design, visualization, and quantitative analysis.

Volumetric Semantic Segmentation using Pyramid Context Features.

We present an algorithm for the per-voxel semantic segmentation of a three-dimensional volume. At the core of our algorithm is a novel "pyramid context" feature, a descriptive representation designed such that exact per-voxel linear classification can be made extremely efficient. This feature not only allows for efficient semantic segmentation but enables other aspects of our algorithm, such as novel learned features and a stacked architecture that can reason about self-consistency. We demonstrate our technique on 3D fluorescence microscopy data of Drosophila embryos for which we are able to produce extremely accurate semantic segmentations in a matter of minutes, and for which other algorithms fail due to the size and high-dimensionality of the data, or due to the difficulty of the task.

Three-dimensional morphology and gene expression mapping for the Drosophila blastoderm.

To properly understand the transcriptional network of animals, we must have full quantitative comprehension of the spatial and temporal expression patterns of transcription factors and their targets. Visual inspection of embryos stained to reveal the patterns of genes shows levels of expression that change from cell to cell in a complex manner. With our current wealth of knowledge regarding the basic biology of animal genomes and the components of their transcriptional regulatory networks, combined with current technologies in optical microscopy, computing, and image and vision analysis, we should be able to capture quantitative, three-dimensional (3D) information about the transcriptional network (all factors and targets) for an entire animal at cellular resolution. It should also be possible to assemble these data into a single computationally analyzable database--an atlas--that could be the basis for uncovering new biology governing regulatory gene networks. This article describes progress toward realizing these goals, with the focus on Drosophila melanogaster. It describes a suite of high-throughput methods that have been used to create the first quantitative 3D description of gene expression and morphology at cellular resolution in a whole animal, and it presents some of the new biology that has been revealed by this quantitative atlas.

Integrating data clustering and visualization for the analysis of 3D gene expression data.

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex data sets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss 1) the integration of data clustering and visualization into one framework, 2) the application of data clustering to 3D gene expression data, 3) the evaluation of the number of clusters k in the context of 3D gene expression clustering, and 4) the improvement of overall analysis quality via dedicated postprocessing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data.

Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies -such as efficient data management- supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.

Visual exploration of three-dimensional gene expression using physical views and linked abstract views.

During animal development, complex patterns of gene expression provide positional information within the embryo. To better understand the underlying gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed methods that support quantitative computational analysis of three-dimensional (3D) gene expression in early Drosophila embryos at cellular resolution. We introduce PointCloudXplore (PCX), an interactive visualization tool that supports visual exploration of relationships between different genes' expression using a combination of established visualization techniques. Two aspects of gene expression are of particular interest: 1) gene expression patterns defined by the spatial locations of cells expressing a gene and 2) relationships between the expression levels of multiple genes. PCX provides users with two corresponding classes of data views: 1) Physical Views based on the spatial relationships of cells in the embryo and 2) Abstract Views that discard spatial information and plot expression levels of multiple genes with respect to each other. Cell Selectors highlight data associated with subsets of embryo cells within a View. Using linking, these selected cells can be viewed in multiple representations. We describe PCX as a 3D gene expression visualization tool and provide examples of how it has been used by BDTNP biologists to generate new hypotheses.

Developmental roles of 21 Drosophila transcription factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions.

BACKGROUND: We previously established that six sequence-specific transcription factors that initiate anterior/posterior patterning in Drosophila bind to overlapping sets of thousands of genomic regions in blastoderm embryos. While regions bound at high levels include known and probable functional targets, more poorly bound regions are preferentially associated with housekeeping genes and/or genes not transcribed in the blastoderm, and are frequently found in protein coding sequences or in less conserved non-coding DNA, suggesting that many are likely non-functional. RESULTS: Here we show that an additional 15 transcription factors that regulate other aspects of embryo patterning show a similar quantitative continuum of function and binding to thousands of genomic regions in vivo. Collectively, the 21 regulators show a surprisingly high overlap in the regions they bind given that they belong to 11 DNA binding domain families, specify distinct developmental fates, and can act via different cis-regulatory modules. We demonstrate, however, that quantitative differences in relative levels of binding to shared targets correlate with the known biological and transcriptional regulatory specificities of these factors. CONCLUSIONS: It is likely that the overlap in binding of biochemically and functionally unrelated transcription factors arises from the high concentrations of these proteins in nuclei, which, coupled with their broad DNA binding specificities, directs them to regions of open chromatin. We suggest that most animal transcription factors will be found to show a similar broad overlapping pattern of binding in vivo, with specificity achieved by modulating the amount, rather than the identity, of bound factor.

The control of tissue architecture over nuclear organization is crucial for epithelial cell fate.

The remodeling of nuclear organization during differentiation and the dramatic alteration of nuclear organization associated with cancer development are well documented. However, the importance of tissue architecture in the control of nuclear organization remains to be determined. Differentiation of mammary epithelial cells into functional tissue structures, in three-dimensional culture, is characterized by a specific tissue architecture (i.e. a basoapical polarity axis), cell cycle exit and maintenance of cell survival. Here we show that induction of partial differentiation (i.e. basal polarity only, cell cycle exit and cell survival) by epigenetic mechanisms in malignant breast cells is sufficient to restore features of differentiation-specific nuclear organization, including perinucleolar heterochromatin, large splicing factor speckles, and distinct nuclear mitotic apparatus protein (NuMA) foci. Upon alteration of nuclear organization using an antibody against NuMA, differentiated non-neoplastic cells undergo apoptosis, whereas partially differentiated malignant cells enter the cell cycle. Non-neoplastic cells cultured under conditions that prevent the establishment of apical polarity also enter the cell cycle upon NuMA antibody treatment. These findings demonstrate that the differentiation status rather than the non-neoplastic or neoplastic origin of cells controls nuclear organization and suggest a link between nuclear organization and epigenetic mechanisms dictated by tissue architecture for the control of cell behavior.

Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype.

The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently stained nuclear protein NuMA in different mammary phenotypes obtained using 3D cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from 3D confocal images. Prominent features of fluorescently stained NuMA were detected by using a previously undescribed local bright feature analysis technique, and their normalized spatial density was calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features when nonneoplastic cells underwent phenotypically normal acinar morphogenesis. Conversely, we did not detect any reorganization of NuMA during formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating nonneoplastic from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues.

Three-dimensional morphology and gene expression in the Drosophila blastoderm at cellular resolution II: dynamics.

BACKGROUND: To accurately describe gene expression and computationally model animal transcriptional networks, it is essential to determine the changing locations of cells in developing embryos. RESULTS: Using automated image analysis methods, we provide the first quantitative description of temporal changes in morphology and gene expression at cellular resolution in whole embryos, using the Drosophila blastoderm as a model. Analyses based on both fixed and live embryos reveal complex, previously undetected three-dimensional changes in nuclear density patterns caused by nuclear movements prior to gastrulation. Gene expression patterns move, in part, with these changes in morphology, but additional spatial shifts in expression patterns are also seen, supporting a previously proposed model of pattern dynamics based on the induction and inhibition of gene expression. We show that mutations that disrupt either the anterior/posterior (a/p) or the dorsal/ventral (d/v) transcriptional cascades alter morphology and gene expression along both the a/p and d/v axes in a way suggesting that these two patterning systems interact via both transcriptional and morphological mechanisms. CONCLUSION: Our work establishes a new strategy for measuring temporal changes in the locations of cells and gene expression patterns that uses fixed cell material and computational modeling. It also provides a coordinate framework for the blastoderm embryo that will allow increasingly accurate spatio-temporal modeling of both the transcriptional control network and morphogenesis.

Three-dimensional morphology and gene expression in the Drosophila blastoderm at cellular resolution I: data acquisition pipeline.

BACKGROUND: To model and thoroughly understand animal transcription networks, it is essential to derive accurate spatial and temporal descriptions of developing gene expression patterns with cellular resolution. RESULTS: Here we describe a suite of methods that provide the first quantitative three-dimensional description of gene expression and morphology at cellular resolution in whole embryos. A database containing information derived from 1,282 embryos is released that describes the mRNA expression of 22 genes at multiple time points in the Drosophila blastoderm. We demonstrate that our methods are sufficiently accurate to detect previously undescribed features of morphology and gene expression. The cellular blastoderm is shown to have an intricate morphology of nuclear density patterns and apical/basal displacements that correlate with later well-known morphological features. Pair rule gene expression stripes, generally considered to specify patterning only along the anterior/posterior body axis, are shown to have complex changes in stripe location, stripe curvature, and expression level along the dorsal/ventral axis. Pair rule genes are also found to not always maintain the same register to each other. CONCLUSION: The application of these quantitative methods to other developmental systems will likely reveal many other previously unknown features and provide a more rigorous understanding of developmental regulatory networks.