Regulatory analysis of the C. elegans genome with spatiotemporal resolution.

Discovering the structure and dynamics of transcriptional regulatory events in the genome with cellular and temporal resolution is crucial to understanding the regulatory underpinnings of development and disease. We determined the genomic distribution of binding sites for 92 transcription factors and regulatory proteins across multiple stages of Caenorhabditis elegans development by performing 241 ChIP-seq (chromatin immunoprecipitation followed by sequencing) experiments. Integration of regulatory binding and cellular-resolution expression data produced a spatiotemporally resolved metazoan transcription factor binding map. Using this map, we explore developmental regulatory circuits that encode combinatorial logic at the levels of co-binding and co-expression of transcription factors, characterizing the genomic coverage and clustering of regulatory binding, the binding preferences of, and biological processes regulated by, transcription factors, the global transcription factor co-associations and genomic subdomains that suggest shared patterns of regulation, and identifying key transcription factors and transcription factor co-associations for fate specification of individual lineages and cell types.

A microfluidic device and computational platform for high-throughput live imaging of gene expression.

To fully describe gene expression dynamics requires the ability to quantitatively capture expression in individual cells over time. Automated systems for acquiring and analyzing real-time images are needed to obtain unbiased data across many samples and conditions. We developed a microfluidics device, the RootArray, in which 64 Arabidopsis thaliana seedlings can be grown and their roots imaged by confocal microscopy over several days without manual intervention. To achieve high throughput, we decoupled acquisition from analysis. In the acquisition phase, we obtain images at low resolution and segment to identify regions of interest. Coordinates are communicated to the microscope to record the regions of interest at high resolution. In the analysis phase, we reconstruct three-dimensional objects from stitched high-resolution images and extract quantitative measurements from a virtual medial section of the root. We tracked hundreds of roots to capture detailed expression patterns of 12 transgenic reporter lines under different conditions.

Automatic annotation of spatial expression patterns via sparse Bayesian factor models.

Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D-4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors. We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database, and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions. The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories. On human-curated annotations, our sparse approach reaches similar or better classification of expression patterns at different developmental stages, when compared to other automatic image annotation methods using thousands of hard-to-interpret features. Our study therefore outlines a general framework for large microscopy data sets, in which both the generative model itself, as well as its application for analysis tasks such as automated annotation, can provide insight into biological questions.

Extraction and comparison of gene expression patterns from 2D RNA in situ hybridization images.

MOTIVATION: Recent advancements in high-throughput imaging have created new large datasets with tens of thousands of gene expression images. Methods for capturing these spatial and/or temporal expression patterns include in situ hybridization or fluorescent reporter constructs or tags, and results are still frequently assessed by subjective qualitative comparisons. In order to deal with available large datasets, fully automated analysis methods must be developed to properly normalize and model spatial expression patterns. RESULTS: We have developed image segmentation and registration methods to identify and extract spatial gene expression patterns from RNA in situ hybridization experiments of Drosophila embryos. These methods allow us to normalize and extract expression information for 78,621 images from 3724 genes across six time stages. The similarity between gene expression patterns is computed using four scoring metrics: mean squared error, Haar wavelet distance, mutual information and spatial mutual information (SMI). We additionally propose a strategy to calculate the significance of the similarity between two expression images, by generating surrogate datasets with similar spatial expression patterns using a Monte Carlo swap sampler. On data from an early development time stage, we show that SMI provides the most biologically relevant metric of comparison, and that our significance testing generalizes metrics to achieve similar performance. We exemplify the application of spatial metrics on the well-known Drosophila segmentation network. AVAILABILITY: A Java webstart application to register and compare patterns, as well as all source code, are available from: http://tools.genome.duke.edu/generegulation/image_analysis/insitu CONTACT: uwe.ohler@duke.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantification of transcription factor expression from Arabidopsis images.

MOTIVATION: Confocal microscopy has long provided qualitative information for a variety of applications in molecular biology. Recent advances have led to extensive image datasets, which can now serve as new data sources to obtain quantitative gene expression information. In contrast to microarrays, which usually provide data for many genes at one time point, these image data provide us with expression information for only one gene, but with the advantage of high spatial and/or temporal resolution, which is often lostin microarray samples. RESULTS: We have developed a prototype for the automatic analysis of Arabidopsis confocal images, which show the expression of a single transcription factor by means of GFP reporter constructs. Using techniques from image registration, we are able to address inherent problems of non-rigid transformation and partial mapping, and obtain relative expression values for 13 different tissues in Arabidopsis roots. This provides quantitative information with high spatial resolution, which accurately represents the underlying expression values within the organism. We validate our approach on a data set of 122 images depicting expression patterns of 30 transcription factors, both in terms of registration accuracy, as well as correlation with cell-sorted microarray data. Approaches like this will be useful to lay the groundwork to reconstruct regulatory networks on the level of tissues or even individual cells. AVAILABILITY: Upon request from the authors.

A high-fidelity cell lineage tracing method for obtaining systematic spatiotemporal gene expression patterns in Caenorhabditis elegans.

Advances in microscopy and fluorescent reporters have allowed us to detect the onset of gene expression on a cell-by-cell basis in a systemic fashion. This information, however, is often encoded in large repositories of images, and developing ways to extract this spatiotemporal expression data is a difficult problem that often uses complex domain-specific methods for each individual data set. We present a more unified approach that incorporates general previous information into a hierarchical probabilistic model to extract spatiotemporal gene expression from 4D confocal microscopy images of developing Caenorhabditis elegans embryos. This approach reduces the overall error rate of our automated lineage tracing pipeline by 3.8-fold, allowing us to routinely follow the C. elegans lineage to later stages of development, where individual neuronal subspecification becomes apparent. Unlike previous methods that often use custom approaches that are organism specific, our method uses generalized linear models and extensions of standard reversible jump Markov chain Monte Carlo methods that can be readily extended to other organisms for a variety of biological inference problems relating to cell fate specification. This modeling approach is flexible and provides tractable avenues for incorporating additional previous information into the model for similar difficult high-fidelity/low error tolerance image analysis problems for systematically applied genomic experiments.

The pervasive presence of fluctuating oxygenation in tumors.

Tumor hypoxia is a persistent obstacle for traditional therapies in solid tumors. Strategies for mitigating the effects of hypoxic tumor cells have been developed under the assumption that chronically hypoxic tumor cells were the central cause of treatment resistance. In this study, we show that instabilities in tumor oxygenation are a prevalent characteristic of three tumor lines and previous characterization of tumor hypoxia as being primarily diffusion-limited does not accurately portray the tumor microenvironment. Phosphorescence lifetime imaging was used to measure fluctuations in vascular pO(2) in rat fibrosarcomas, 9L gliomas, and R3230 mammary adenocarcinomas grown in dorsal skin-fold window chambers (n = 6 for each tumor type) and imaged every 2.5 minutes for a duration of 60 to 90 minutes. O(2) delivery to tumors is constantly changing in all tumors, resulting in continuous reoxygenation events throughout the tumor. Vascular pO(2) maps show significant spatial heterogeneity at each time point, as well as between time points. The fluctuations in oxygenation occur with a common periodicity within and between tumors, suggesting a common mechanism, but have tumor type-dependent spatial patterns. The widespread presence of fluctuations in tumor oxygenation has broad ranging implications for tumor progression, stress response, and signal transduction, which are altered by oxygenation/reoxygenation events.

Cell identity mediates the response of Arabidopsis roots to abiotic stress.

Little is known about the way developmental cues affect how cells interpret their environment. We characterized the transcriptional response to high salinity of different cell layers and developmental stages of the Arabidopsis root and found that transcriptional responses are highly constrained by developmental parameters. These transcriptional changes lead to the differential regulation of specific biological functions in subsets of cell layers, several of which correspond to observable physiological changes. We showed that known stress pathways primarily control semiubiquitous responses and used mutants that disrupt epidermal patterning to reveal cell-layer-specific and inter-cell-layer effects. By performing a similar analysis using iron deprivation, we identified common cell-type-specific stress responses and revealed the crucial role the environment plays in defining the transcriptional outcome of cell-fate decisions.

A high-resolution root spatiotemporal map reveals dominant expression patterns.

Transcriptional programs that regulate development are exquisitely controlled in space and time. Elucidating these programs that underlie development is essential to understanding the acquisition of cell and tissue identity. We present microarray expression profiles of a high-resolution set of developmental time points within a single Arabidopsis root and a comprehensive map of nearly all root cell types. These cell type-specific transcriptional signatures often predict previously unknown cellular functions. A computational pipeline identified dominant expression patterns that demonstrate transcriptional similarity between disparate cell types. Dominant expression patterns along the root's longitudinal axis do not strictly correlate with previously defined developmental zones, and in many cases, we observed expression fluctuation along this axis. Both robust co-regulation of gene expression and potential phasing of gene expression were identified between individual roots. Methods that combine these profiles demonstrate transcriptionally rich and complex programs that define Arabidopsis root development in both space and time.

Transcriptional and posttranscriptional regulation of transcription factor expression in Arabidopsis roots.

Understanding how the expression of transcription factor (TF) genes is modulated is essential for reconstructing gene regulatory networks. There is increasing evidence that sequences other than upstream noncoding can contribute to modulating gene expression, but how frequently they do so remains unclear. Here, we investigated the regulation of TFs expressed in a tissue-enriched manner in Arabidopsis roots. For 61 TFs, we created GFP reporter constructs driven by each TF's upstream noncoding sequence (including the 5'UTR) fused to the GFP reporter gene alone or together with the TF's coding sequence. We compared the visually detectable GFP patterns with endogenous mRNA expression patterns, as defined by a genome-wide microarray root expression map. An automated image analysis method for quantifying GFP signals in different tissues was developed and used to validate our visual comparison method. From these combined analyses, we found that (i) the upstream noncoding sequence was sufficient to recapitulate the mRNA expression pattern for 80% (35/44) of the TFs, and (ii) 25% of the TFs undergo posttranscriptional regulation via microRNA-mediated mRNA degradation (2/24) or via intercellular protein movement (6/24). The results suggest that, for Arabidopsis TFs, upstream noncoding sequences are major contributors to mRNA expression pattern establishment, but modulation of transcription factor protein expression pattern after transcription is relatively frequent. This study provides a systematic overview of regulation of TF expression at a cellular level.