Interphase Chromosomes in Replicative Senescence: Chromosome Positioning as a Senescence Biomarker and the Lack of Nuclear Motor-Driven Chromosome Repositioning in Senescent Cells.

This study demonstrates, and confirms, that chromosome territory positioning is altered in primary senescent human dermal fibroblasts (HDFs). The chromosome territory positioning pattern is very similar to that found in HDFs made quiescent either by serum starvation or confluence; but not completely. A few chromosomes are found in different locations. One chromosome in particular stands out, chromosome 10, which is located in an intermediate location in young proliferating HDFs, but is found at the nuclear periphery in quiescent cells and in an opposing location of the nuclear interior in senescent HDFs. We have previously demonstrated that individual chromosome territories can be actively and rapidly relocated, with 15 min, after removal of serum from the culture media. These chromosome relocations require nuclear motor activity through the presence of nuclear myosin 1ß (NM1ß). We now also demonstrate rapid chromosome movement in HDFs after heat-shock at 42°C. Others have shown that heat shock genes are actively relocated using nuclear motor protein activity via actin or NM1ß (Khanna et al., 2014; Pradhan et al., 2020). However, this current study reveals, that in senescent HDFs, chromosomes can no longer be relocated to expected nuclear locations upon these two types of stimuli. This coincides with a entirely different organisation and distribution of NM1ß within senescent HDFs.

Assessment of the Utility of Gene Positioning Biomarkers in the Stratification of Prostate Cancers.

There is a pressing need for additional clinical biomarkers to predict the aggressiveness of individual cancers. Here, we examine the potential usefulness of spatial genome organization as a prognostic tool for prostate cancer. Using fluorescence in situ hybridization on formalin-fixed, paraffin embedded human prostate tissue specimens, we compared the nuclear positions of four genes between clinically relevant subgroups of prostate tissues. We find that directional repositioning of SP100 and TGFB3 gene loci stratifies prostate cancers of differing Gleason scores. A more peripheral position of SP100 and TGFB3 in the nucleus, compared to benign tissues, is associated with low Gleason score cancers, whereas more internal positioning correlates with higher Gleason scores. Conversely, LMNA is more internally positioned in many non-metastatic prostate cancers, while its position is indistinguishable from benign tissue in metastatic cancer. The false positive rates were relatively low, whereas, the false negative rates of single or combinations of genes were high, limiting the clinical utility of this assay in its current form. Nevertheless, our findings of subtype-specific gene positioning patterns in prostate cancer provides proof-of-concept for the potential usefulness of spatial gene positioning for prognostic applications, and encourage further exploration of spatial gene positioning patterns to identify novel clinically relevant molecular biomarkers, which may aid treatment decisions for cancer patients.

Blank spots on the map: some current questions on nuclear organization and genome architecture.

The past decades have provided remarkable insights into how the eukaryotic cell nucleus and the genome within it are organized. The combined use of imaging, biochemistry and molecular biology approaches has revealed several basic principles of nuclear architecture and function, including the existence of chromatin domains of various sizes, the presence of a large number of non-membranous intranuclear bodies, non-random positioning of genes and chromosomes in 3D space, and a prominent role of the nuclear lamina in organizing genomes. Despite this tremendous progress in elucidating the biological properties of the cell nucleus, many questions remain. Here, we highlight some of the key open areas of investigation in the field of nuclear organization and genome architecture with a particular focus on the mechanisms and principles of higher-order genome organization, the emerging role of liquid phase separation in cellular organization, and the functional role of the nuclear lamina in physiological processes.

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.

Tissue-of-origin-specific gene repositioning in breast and prostate cancer.

Genes have preferential non-random spatial positions within the cell nucleus. The nuclear position of a subset of genes differ between cell types and some genes undergo repositioning events in disease, including cancer. It is currently unclear whether the propensity of a gene to reposition reflects an intrinsic property of the locus or the tissue. Using quantitative FISH analysis of a set of genes which reposition in cancer, we test here the tissue specificity of gene repositioning in normal and malignant breast or prostate tissues. We find tissue-specific organization of the genome in normal breast and prostate with 40 % of genes occupying differential positions between the two tissue types. While we demonstrate limited overlap between gene sets that repositioned in breast and prostate cancer, we identify two genes that undergo disease-related gene repositioning in both cancer types. Our findings indicate that gene repositioning in cancer is tissue-of-origin specific.

Locus-specific gene repositioning in prostate cancer.

Genes occupy preferred spatial positions within interphase cell nuclei. However, positioning patterns are not an innate feature of a locus, and genes can alter their localization in response to physiological and pathological changes. Here we screen the radial positioning patterns of 40 genes in normal, hyperplasic, and malignant human prostate tissues. We find that the overall spatial organization of the genome in prostate tissue is largely conserved among individuals. We identify three genes whose nuclear positions are robustly altered in neoplastic prostate tissues. FLI1 and MMP9 position differently in prostate cancer than in normal tissue and prostate hyperplasia, whereas MMP2 is repositioned in both prostate cancer and hyperplasia. Our data point to locus-specific reorganization of the genome during prostate disease.

Segmentation and quantitative analysis of individual cells in developmental tissues.

Image analysis is vital for extracting quantitative information from biological images and is used extensively, including investigations in developmental biology. The technique commences with the segmentation (delineation) of objects of interest from 2D images or 3D image stacks and is usually followed by the measurement and classification of the segmented objects. This chapter focuses on the segmentation task and here we explain the use of ImageJ, MIPAV (Medical Image Processing, Analysis, and Visualization), and VisSeg, three freely available software packages for this purpose. ImageJ and MIPAV are extremely versatile and can be used in diverse applications. VisSeg is a specialized tool for performing highly accurate and reliable 2D and 3D segmentation of objects such as cells and cell nuclei in images and stacks.

Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning.

BACKGROUND: Correct segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition settings often lead to segmentation errors. This manuscript introduces a ranked-retrieval approach using logistic regression to automate selection of accurately segmented nuclei from a set of candidate segmentations. The methodology is validated on an application of spatial gene repositioning in breast cancer cell nuclei. Gene repositioning is analyzed in patient tissue sections by labeling sequences with fluorescence in situ hybridization (FISH), followed by measurement of the relative position of each gene from the nuclear center to the nuclear periphery. This technique requires hundreds of well-segmented nuclei per sample to achieve statistical significance. Although the tissue samples in this study contain a surplus of available nuclei, automatic identification of the well-segmented subset remains a challenging task. RESULTS: Logistic regression was applied to features extracted from candidate segmented nuclei, including nuclear shape, texture, context, and gene copy number, in order to rank objects according to the likelihood of being an accurately segmented nucleus. The method was demonstrated on a tissue microarray dataset of 43 breast cancer patients, comprising approximately 40,000 imaged nuclei in which the HES5 and FRA2 genes were labeled with FISH probes. Three trained reviewers independently classified nuclei into three classes of segmentation accuracy. In man vs. machine studies, the automated method outperformed the inter-observer agreement between reviewers, as measured by area under the receiver operating characteristic (ROC) curve. Robustness of gene position measurements to boundary inaccuracies was demonstrated by comparing 1086 manually and automatically segmented nuclei. Pearson correlation coefficients between the gene position measurements were above 0.9 (p < 0.05). A preliminary experiment was conducted to validate the ranked retrieval in a test to detect cancer. Independent manual measurement of gene positions agreed with automatic results in 21 out of 26 statistical comparisons against a pooled normal (benign) gene position distribution. CONCLUSIONS: Accurate segmentation is necessary to automate quantitative image analysis for applications such as gene repositioning. However, due to heterogeneity within images and across different applications, no segmentation algorithm provides a satisfactory solution. Automated assessment of segmentations by ranked retrieval is capable of reducing or even eliminating the need to select segmented objects by hand and represents a significant improvement over binary classification. The method can be extended to other high-throughput applications requiring accurate detection of cells or nuclei across a range of biomedical applications.

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.

Supervised learning framework for screening nuclei in tissue sections.

Accurate segmentation of cell nuclei in microscope images of tissue sections is a key step in a number of biological and clinical applications. Often such applications require analysis of large image datasets for which manual segmentation becomes subjective and time consuming. Hence automation of the segmentation steps using fast, robust and accurate image analysis and pattern classification techniques is necessary for high throughput processing of such datasets. We describe a supervised learning framework, based on artificial neural networks (ANNs), to identify well-segmented nuclei in tissue sections from a multistage watershed segmentation algorithm. The successful automation was demonstrated by screening over 1400 well segmented nuclei from 9 datasets of human breast tissue section images and comparing the results to a previously used stacked classifier based analysis framework.

Fluorescence in situ hybridization on 3D cultures of tumor cells.

Genomes are spatially highly organized within interphase nuclei. Spatial genome organization is increasingly linked to genome function. Fluorescence in situ hybridization (FISH) allows the visualization of specific regions of the genome for spatial mapping. While most gene localization studies have been performed on cultured cells, genome organization is likely to be different in the context of tissues. Three-dimensional (3D) culture model systems provide a powerful tool to study the contribution of tissue organization to gene expression and organization. However, FISH on 3D cultures is technically more challenging than on monocultures. Here, we describe an optimized protocol for interphase DNA FISH on 3D cultures of the breast epithelial cell line MCF-10A.B2, which forms breast acini and can be used as a model for early breast cancer.

Disease-specific gene repositioning in breast cancer.

Genomes are nonrandomly organized within the three-dimensional space of the cell nucleus. Here, we have identified several genes whose nuclear positions are altered in human invasive breast cancer compared with normal breast tissue. The changes in positioning are gene specific and are not a reflection of genomic instability within the cancer tissue. Repositioning events are specific to cancer and do not generally occur in noncancerous breast disease. Moreover, we show that the spatial positions of genes are highly consistent between individuals. Our data indicate that cancer cells have disease-specific gene distributions. These interphase gene positioning patterns may be used to identify cancer tissues.

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.

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.

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.

Positioning of human chromosomes in murine cell hybrids according to synteny.

Chromosomes occupy non-random spatial positions in interphase nuclei. It remains unclear what orchestrates this high level of organisation. To determine how the nuclear environment influences the spatial positioning of chromosomes, we utilised a panel of stable mouse hybrid cell lines carrying a single, intact human chromosome. Eleven of 22 human chromosomes revealed an alternative location in hybrid nuclei compared to that of human fibroblasts, with the majority becoming more internally localised. Human chromosomes in mouse nuclei position according to neither their gene density nor size, but rather the position of human chromosomes in hybrid nuclei appears to mimic that of syntenic mouse chromosomes. These results suggest that chromosomes adopt the behaviour of their host species chromosomes and that the nuclear environment is an important determinant of the interphase positioning of chromosomes.

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.

Primary laminopathy fibroblasts display altered genome organization and apoptosis.

A number of diseases associated with specific tissue degeneration and premature aging have mutations in the nuclear envelope proteins A-type lamins or emerin. Those diseases with A-type lamin mutation are inclusively termed laminopathies. Due to various hypothetical roles of nuclear envelope proteins in genome function we investigated whether alterations to normal genomic behaviour are apparent in cells with mutations in A-type lamins and emerin. Even though the distributions of these proteins in proliferating laminopathy fibroblasts appear normal, there is abnormal nuclear positioning of both chromosome 18 and 13 territories, from the nuclear periphery to the interior. This genomic organization mimics that found in normal nonproliferating quiescent or senescent cells. This finding is supported by distributions of modified pRb in the laminopathy cells. All laminopathy cell lines tested and an X-linked Emery-Dreifuss muscular dystrophy cell line also demonstrate increased incidences of apoptosis. The most extreme cases of apoptosis occur in cells derived from diseases with mutations in the tail region of the LMNA gene, such as Dunningan-type familial partial lipodystrophy and mandibuloacral dysplasia, and this correlates with a significant level of micronucleation in these cells.

Spatial genome organization in the formation of chromosomal translocations.

Chromosomal translocations and genomic instability are universal hallmarks of tumor cells. While the molecular mechanisms leading to the formation of translocations are rapidly being elucidated, a cell biological understanding of how chromosomes undergo translocations in the context of the cell nucleus in vivo is largely lacking. The recent realization that genomes are non-randomly arranged within the nuclear space has profound consequences for mechanisms of chromosome translocations. We review here the emerging principles of spatial genome organization and discuss the implications of non-random spatial genome organization for the genesis and specificity of cancerous chromosomal translocations.