David W. Knowles

In situ analyses of genome instability in breast cancer

Transition through telomere crisis is thought to be a crucial event in the development of most breast carcinomas. Our goal in this study was to determine where this occurs in the context of histologically defined breast cancer progression. To this end, we assessed genome instability (using fluorescence in situ hybridization) and other features associated with telomere crisis in normal ductal epithelium, usual ductal hyperplasia, ductal carcinoma in situ and invasive cancer. We modeled this process in vitro by measuring these same features in human mammary epithelial cell cultures during ZNF217-mediated transition through telomere crisis and immortalization. Taken together, the data suggest that transition through telomere crisis and immortalization in breast cancer occurs during progression from usual ductal hyperplasia to ductal carcinoma in situ.

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

BackgroundWe 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.ResultsHere 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.ConclusionsIt 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.

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

BackgroundTo 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.ResultsHere 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.ConclusionThe 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.

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.

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.

Membrane Dynamics of the Water Transport Protein Aquaporin-1 in Intact Human Red Cells

Aquaporin-1 (AQP1) is the prototype integral membrane protein water channel. Although the three-dimensional structure and water transport function of the molecule have been described, the physical interactions between AQP1 and other membrane components have not been characterized. Using fluorescein isothiocyanate-anti-Co3 (FITC-anti-Co3), a reagent specific for an extracellular epitope on AQP1, the fluorescence photobleaching recovery (FPR) and fluorescence imaged microdeformation (FIMD) techniques were performed on intact human red cells. By FPR, the fractional mobility of fluorescently labeled AQP1 (F-alphaAQP1) in the undeformed red cell membrane is 66 +/- 10% and the average lateral diffusion coefficient is (3.1 +/- 0.5) x 10(-11) cm2/s. F-alphaAQP1 fractional mobility is not significantly affected by antibody-induced immobilization of the major integral proteins band 3 or glycophorin A, indicating that AQP1 does not exist as a complex with these proteins. FIMD uses pipette aspiration of individual red cells to create a constant but reversible skeletal density gradient. F-alphaAQP1 distribution, like that of lipid-anchored proteins, is not at equilibrium after microdeformation. Over time, approximately 50% of the aspirated F-alphaAQP1 molecules migrate toward the membrane portion that had been maximally dilated, the aspirated cap. Based on the kinetics of migration, the F-alphaAQP1 lateral diffusion coefficient in the membrane projection is estimated to be 6 x 10(-10) cm2/s. These results suggest that AQP1 lateral mobility is regulated in the unperturbed membrane by passive steric hindrance imposed by the spectrin-based membrane skeleton and/or by skeleton-linked membrane components, and that release of these constraints by dilatation of the skeleton allows AQP1 to diffuse much more rapidly in the plane of the membrane.

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.

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.

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

BackgroundThe 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.ResultsNuclear 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.ConclusionThis work presents a cluster analysis method that can identify significant cell phenotypes, based on the nuclear distribution of specific proteins, with high accuracy.

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.