Karen J. Meaburn

Cell biology: Chromosome territories

The natural habitat of eukaryotic genomes is the cell nucleus, where each chromosome is confined to a discrete region, referred to as a chromosome territory. This spatial organization is emerging as a crucial aspect of gene regulation and genome stability in health and disease.

The Meaning of Gene Positioning

There is no doubt that genomes are organized nonrandomly in the nucleus of higher eukaryotes. But what is the functional relevance of this nonrandomness? In this Essay, we explore the biological meaning of spatial gene positioning by examining the functional link between the activity of a gene and its radial position in the nucleus.

Locus-specific and activity-independent gene repositioning during early tumorigenesis

The mammalian genome is highly organized within the cell nucleus. The nuclear position of many genes and genomic regions changes during physiological processes such as proliferation, differentiation, and disease. It is unclear whether disease-associated positioning changes occur specifically or are part of more global genome reorganization events. Here, we have analyzed the spatial position of a defined set of cancer-associated genes in an established mammary epithelial three-dimensional cell culture model of the early stages of breast cancer. We find that the genome is globally reorganized during normal and tumorigenic epithelial differentiation. Systematic mapping of changes in spatial positioning of cancer-associated genes reveals gene-specific positioning behavior and we identify several genes that are specifically repositioned during tumorigenesis. Alterations of spatial positioning patterns during differentiation and tumorigenesis were unrelated to gene activity. Our results demonstrate the existence of activity-independent genome repositioning events in the early stages of tumor formation.

Gene deregulation and spatial genome reorganization near breakpoints prior to formation of translocations in anaplastic large cell lymphoma

Although the identification and characterization of translocations have rapidly increased, little is known about the mechanisms of how translocations occur in vivo. We used anaplastic large cell lymphoma (ALCL) with and without the characteristic t(2;5)(p23;q35) translocation to study the mechanisms of formation of translocations and of ALCL transformation. We report deregulation of several genes located near the ALCL translocation breakpoint, regardless of whether the tumor contains the t(2;5). The affected genes include the oncogenic transcription factor Fra2 (located on 2p23), the HLH protein Id2 (2p25), and the oncogenic tyrosine kinase CSF1-receptor (5q33.1). Their up-regulation promotes cell survival and repression of T cell-specific gene expression programs that are characteristic for ALCL. The deregulated genes are in spatial proximity within the nuclear space of t(2;5)-negative ALCL cells, facilitating their translocation on induction of double-strand breaks. These data suggest that deregulation of breakpoint-proximal genes occurs before the formation of translocations, and that aberrant transcriptional activity of genomic regions is linked to their propensity to undergo chromosomal translocations. Also, our data demonstrate that deregulation of breakpoint-proximal genes has a key role in ALCL.

A high-throughput system for segmenting nuclei using multiscale techniques.

Automatic segmentation of cell nuclei is critical in several high‐throughput cytometry applications whereas manual segmentation is laborious and irreproducible. One such emerging application is measuring the spatial organization (radial and relative distances) of fluorescence in situ hybridization (FISH) DNA sequences, where recent investigations strongly suggest a correlation between nonrandom arrangement of genes to carcinogenesis. Current automatic segmentation methods have varying performance in the presence of nonuniform illumination and clustering, and boundary accuracy is seldom assessed, which makes them suboptimal for this application. The authors propose a modular and model‐based algorithm for extracting individual nuclei. It uses multiscale edge reconstruction for contrast stretching and edge enhancement as well as a multiscale entropy‐based thresholding for handling nonuniform intensity variations. Nuclei are initially oversegmented and then merged based on area followed by automatic multistage classification into single nuclei and clustered nuclei. Estimation of input parameters and training of the classifiers is automatic. The algorithm was tested on 4,181 lymphoblast nuclei with varying degree of background nonuniformity and clustering. It extracted 3,515 individual nuclei and identified single nuclei and individual nuclei in clusters with 99.8 ± 0.3% and 95.5 ± 5.1% accuracy, respectively. Segmented boundaries of the individual nuclei were accurate when compared with manual segmentation with an average RMS deviation of 0.26 μm (∼2 pixels). The proposed segmentation method is efficient, robust, and accurate for segmenting individual nuclei from fluorescence images containing clustered and isolated nuclei. The algorithm allows complete automation and facilitates reproducible and unbiased spatial analysis of DNA sequences. Published 2008 Wiley‐Liss, Inc.

Segmentation of Whole Cells and Cell Nuclei From 3-D Optical Microscope Images Using Dynamic Programming

Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection.

Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images.

Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100–200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large‐scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization‐labeled genes of interest. Segmentation was performed by a multistage watershed‐based algorithm and screening by an artificial neural network‐based pattern recognition engine. The performance of the workflow was quantified in terms of the fraction of automatically selected nuclei that were visually confirmed as well segmented and by the boundary accuracy of the well‐segmented nuclei relative to a 2D dynamic programming‐based reference segmentation method. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all four normal cases, and all five noncancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach.

Spatial Genome Organization and Its Emerging Role as a Potential Diagnosis Tool

In eukaryotic cells the genome is highly spatially organized. Functional relevance of higher order genome organization is implied by the fact that specific genes, and even whole chromosomes, alter spatial position in concert with functional changes within the nucleus, for example with modifications to chromatin or transcription. The exact molecular pathways that regulate spatial genome organization and the full implication to the cell of such an organization remain to be determined. However, there is a growing realization that the spatial organization of the genome can be used as a marker of disease. While global genome organization patterns remain largely conserved in disease, some genes and chromosomes occupy distinct nuclear positions in diseased cells compared to their normal counterparts, with the patterns of reorganization differing between diseases. Importantly, mapping the spatial positioning patterns of specific genomic loci can distinguish cancerous tissue from benign with high accuracy. Genome positioning is an attractive novel biomarker since additional quantitative biomarkers are urgently required in many cancer types. Current diagnostic techniques are often subjective and generally lack the ability to identify aggressive cancer from indolent, which can lead to over- or under-treatment of patients. Proof-of-principle for the use of genome positioning as a diagnostic tool has been provided based on small scale retrospective studies. Future large-scale studies are required to assess the feasibility of bringing spatial genome organization-based diagnostics to the clinical setting and to determine if the positioning patterns of specific loci can be useful biomarkers for cancer prognosis. Since spatial reorganization of the genome has been identified in multiple human diseases, it is likely that spatial genome positioning patterns as a diagnostic biomarker may be applied to many diseases.

Automatic nuclei segmentation and spatial FISH analysis for cancer detection

Spatial analysis of gene localization using fluorescent in-situ hybridization (FISH) labeling is potentially a new method for early cancer detection. Current methodology relies heavily upon accurate segmentation of cell nuclei and FISH signals in tissue sections. While automatic FISH signal detection is a relatively simpler task, accurate nuclei segmentation is still a manual process which is fairly time consuming and subjective. Hence to use the methodology as a clinical application, it is necessary to automate all the steps involved in the process of spatial FISH signal analysis using fast, robust and accurate image processing techniques. In this work, we describe an intelligent framework for analyzing the FISH signals by coupling hybrid nuclei segmentation algorithm with pattern recognition algorithms to automatically identify well segmented nuclei. Automatic spatial statistical analysis of the FISH spots was carried out on the output from the image processing and pattern recognition unit. Results are encouraging and show that the method could evolve into a full fledged clinical application for cancer detection.

Locus-specific gene repositioning in prostate cancer

The spatial organization of the genome is altered in prostate cancer compared to normal tissue in a gene-specific manner. The repositioning of two genes, FLI1 and MMP9, is specific to cancer, and the positioning patterns of these genes may serve as diagnostic biomarkers.