Branislav Stojkovic

Cell Type Specific Alterations in Interchromosomal Networks across the Cell Cycle

The interchromosomal organization of a subset of human chromosomes (#1, 4, 11, 12, 16, 17, and 18) was examined in G1 and S phase of human WI38 lung fibroblast and MCF10A breast epithelial cells. Radial positioning of the chromosome territories (CTs) was independent of gene density, but size dependent. While no changes in radial positioning during the cell cycle were detected, there were stage-specific differences between cell types. Each CT was in close proximity (interaction) with a similar number of other CT except the gene rich CT17 which had significantly more interactions. Furthermore, CT17 was a member of the highest pairwise CT combinations with multiple interactions. Major differences were detected in the pairwise interaction profiles of MCF10A versus WI38 including cell cycle alterations from G1 to S. These alterations in interaction profiles were subdivided into five types: overall increase, overall decrease, switching from 1 to ≥2 interactions, vice versa, or no change. A global data mining program termed the chromatic median determined the most probable overall association network for the entire subset of CT. This probabilistic interchromosomal network was nearly completely different between the two cell lines. It was also strikingly altered across the cell cycle in MCF10A, but only slightly in WI38. We conclude that CT undergo multiple and preferred interactions with other CT in the nucleus and form preferred -albeit probabilistic- interchromosomal networks. This network of interactions is altered across the cell cycle and between cell types. It is intriguing to consider the relationship of these alterations to the corresponding changes in the gene expression program across the cell cycle and in different cell types.

Wide-scale Alterations in Interchromosomal Organization in Breast Cancer Cells: Defining a Network of Interacting Chromosomes

The interchromosomal spatial positionings of a subset of human chromosomes was examined in the human breast cell line MCF10A (10A) and its malignant counterpart MCF10CA1a (CA1a). The nine chromosomes selected (#1, 4, 11, 12, 15, 16, 18, 21 and X) cover a wide range in size and gene density and compose ∼40% of the total human genome. Radial positioning of the chromosome territories (CT) was size dependent with certain of the CT more peripheral in CA1a. Each CT was in close proximity (interaction) with a similar number of other CT except the inactive CTXi. It had lower levels of interchromosomal partners in 10A which increased strikingly in CA1a. Major alterations from 10A to CA1a were detected in the pairwise interaction profiles which were subdivided into five types of altered interaction profiles: overall increase, overall decrease, switching from 1 to ≥2, vice versa or no change. A global data mining program termed the chromatic median calculated the most probable overall association network for the entire subset of CT. This interchromosomal network was drastically altered in CA1a with only 1 of 20 shared connections. We conclude that CT undergo multiple and preferred interactions with other CT in the cell nucleus and form preferred-albeit probabilistic-interchromosomal networks. This network of interactions is highly altered in malignant human breast cells. It is intriguing to consider the relationship of these alterations to the corresponding changes in the gene expression program of these malignant cancer cells.

Gauging Association Patterns of Chromosome Territories via Chromatic Median

Computing accurate and robust organizational patterns of chromosome territories inside the cell nucleus is critical for understanding several fundamental genomic processes, such as co-regulation of gene activation, gene silencing, X chromosome inactivation, and abnormal chromosome rearrangement in cancer cells. The usage of advanced fluorescence labeling and image processing techniques has enabled researchers to investigate interactions of chromosome territories at large spatial resolution. The resulting high volume of generated data demands for high-throughput and automated image analysis methods. In this paper, we introduce a novel algorithmic tool for investigating association patterns of chromosome territories in a population of cells. Our method takes as input a set of graphs, one for each cell, containing information about spatial interaction of chromosome territories, and yields a single graph that contains essential information for the whole population and stands as its structural representative. We formulate this combinatorial problem as a semi-definite programming and present novel techniques to efficiently solve it. We validate our approach on both artificial and real biological data, the experimental results suggest that our approach yields a near-optimal solution, and can handle large-size datasets, which are significant improvements over existing techniques.

Non‐Random Patterns in the Distribution of NOR‐Bearing Chromosome Territories in Human Fibroblasts: A Network Model of Interactions

We present a 3‐D mapping in WI38 human diploid fibroblast cells of chromosome territories (CT) 13,14,15,21, and 22, which contain the nucleolar organizing regions (NOR) and participate in the formation of nucleoli. The nuclear radial positioning of NOR‐CT correlated with the size of chromosomes with smaller CT more interior. A high frequency of pairwise associations between NOR‐CT ranging from 52% (CT13–21) to 82% (CT15–21) was detected as well as a triplet arrangement of CT15–21‐22 (72%). The associations of homologous CT were significantly lower (24–36%). Moreover, singular contacts between CT13–14 or CT13–22 were found in the majority of cells, while CT13–15 or CT13–21 predominantly exhibited multiple interactions. In cells with multiple nucleoli, one of the nucleoli (termed “dominant”) always associated with a higher number of CT. Moreover, certain CT pairs more frequently contributed to the same nucleolus than to others. This nonrandom pattern suggests that a large number of the NOR‐chromosomes are poised in close proximity during the postmitotic nucleolar recovery and through their NORs may contribute to the formation of the same nucleolus. A global data mining program termed the chromatic median determined the most probable interchromosomal arrangement of the entire NOR‐CT population. This interactive network model was significantly above randomized simulation and was composed of 13 connections among the NOR‐CT. We conclude that the NOR‐CT form a global interactive network in the cell nucleus that may be a fundamental feature for the regulation of nucleolar and other genomic functions. J. Cell. Physiol. 230: 427–439, 2015.

Efficient algorithms for segmenting globally optimal and smooth multi-surfaces

Despite extensive studies in the past, the problem of segmenting globally optimal single and multiple surfaces in 3D volumetric images remains challenging in medical imaging. The problem becomes even harder in highly noisy and edge-weak images. In this paper we present a novel and highly efficient graph-theoretical iterative method with bi-criteria of global optimality and smoothness for both single and multiple surfaces. Our approach is based on a volumetric graph representation of the 3D image that incorporates curvature information. To evaluate the convergence and performance of our method, we test it on a set of 14 3D OCT images. Our experiments suggest that the proposed method yields optimal (or almost optimal) solutions in 3 to 5 iterations. To the best of our knowledge, this is the first algorithm that utilizes curvature in objective function to ensure the smoothness of the generated surfaces while striving for achieving global optimality. Comparing to the best existing approaches, our method has a much improved running time, yields almost the same global optimality but with much better smoothness, which makes it especially suitable for segmenting highly noisy images.

Faster segmentation algorithm for optical coherence tomography images with guaranteed smoothness

This paper considers the problem of segmenting an accurate and smooth surface from 3D volumetric images. Despite extensive studies in the past, the segmentation problem remains challenging in medical imaging, and becomes even harder in highly noisy and edge-weak images. In this paper we present a highly efficient graph-theoretical approach for segmenting a surface from 3D OCT images. Our approach adopts an objective function that combines the weight and the smoothness of the surface so that the resulting segmentation achieves global optimality and smoothness simultaneously. Based on a volumetric graph representation of the 3D images that incorporates curvature information, our approach first generates a set of 2D local optimal segmentations, and then iteratively improves the solution by fast local computation at regions where significant improvement can be achieved. It can be shown that our approach monotonically improves the quality of solution and converges rather quickly to the global optimal solution. To evaluate the convergence and performance of our method, we test it on both artificial data sets and a set of 14 3D OCT images. Our experiments suggest that the proposed method yields optimal (or almost optimal) solutions in 3 to 5 iterations. Comparing to the existing approaches, our method has a much improved running time, yields almost the same global optimality but with much better smoothness, which makes it especially suitable for segmenting highly noisy images. Our approach can be easily generalized to multi-surface detection.

Computing maximum association graph in microscopic nucleus images

In this paper, we study the problem of finding organization patterns of chromosomes inside the cell nucleus from microscopic nucleus images. Emerging evidence from cell biology research suggests that global chromosome organization has a vital role in fundamental cell processes related to gene expression and regulation. To understand how chromosome territories are neighboring (or associated) to each other, in this paper we present a novel technique for computing a common association pattern, represented as a Maximum Association Graph (MAG), from the nucleus images of a population of cells. Our approach is based on an interesting integer linear programming formulation of the problem and utilizes inherent observations of the problem to yield optimal solutions. A two-stage technique is also introduced for producing near optimal approximations for large data sets.

Efficient searching of globally optimal and smooth multisurfaces with shape priors

Despite extensive studies in the past, the problem of segmenting globally optimal multiple surfaces in 3D volumetric images remains challenging in medical imaging. The problem becomes even harder in highly noisy and edge-weak images. In this paper we present a novel and highly efficient graph-theoretical iterative method based on a volumetric graph representation of the 3D image that incorporates curvature and shape prior information. Compared with the graph-based method, applying the shape prior to construct the graph on a specific preferred shape model allows easy incorporation of a wide spectrum of shape prior information. Furthermore, the key insight that computation of the objective function can be done independently in the x and y directions makes local improvement possible. Thus, instead of using global optimization technique such as maximum flow algorithm, the iteration based method is much faster. Additionally, the utilization of the curvature in the objective function ensures the smoothness. To the best of our knowledge, this is the first paper to combine the shape-prior penalties with utilizing curvature in objective function to ensure the smoothness of the generated surfaces while striving for achieving global optimality. To evaluate the performance of our method, we test it on a set of 14 3D OCT images. Comparing to the best existing approaches, our experiments suggest that the proposed method reduces the unsigned surface positioning errors form 5.44 ± 1.07(μm) to 4.52 ± 0.84(μm). Moreover, our method has a much improved running time, yields almost the same global optimality but with much better smoothness, which makes it especially suitable for segmenting highly noisy images. The proposed method is also suitable for parallel implementation on GPUs, which could potentially allow us to segment highly noisy volumetric images in real time.

Chromatic kernel and its applications

In this paper, we study the following Chromatic kernel (CK) problem: given an