Texture-Based Segmentation and Finite Element Mesh Generation for Heterogeneous Biological Image Data

The design, analysis, and control of bio-systems remain an engineering challenge. This is mainly due to the material heterogeneity, boundary irregularity, and nonlinear dynamics associated with these systems. The recent developments in imaging techniques and stochastic upscaling methods provides a window of opportunity to more accurately assess these bio-systems than ever before. However, the use of image data directly in upscaled stochastic framework can only be realized by the development of certain intermediate steps. The goal of the research presented in this dissertation is to develop a texture-segmentation method and a unstructured mesh generation for heterogeneous image data. The following two new techniques are described and evaluated in this dissertation: 1. A new texture-based segmentation method, using the stochastic continuum concepts and wavelet multi-resolution analysis, is developed for characterization of heterogeneous materials in image data. The feature descriptors are developed to efficiently capture the micro-scale heterogeneity of macro-scale entities. The materials are then segmented at a representative elementary scale at which the statistics of the feature descriptor stabilize. 2. A new unstructured mesh generation technique for image data is developed using a hierarchical data structure. This representation allows for generating quality guaranteed finite element meshes. The framework for both the methods presented in this dissertation, as such, allows them for extending to higher dimensions. The experimental results using these methods conclude them to be promising tools for unifying data processing concepts within the upscaled stochastic framework across biological systems. These are targeted for inclusion in decision support systems where biological image data, simulation techniques and artificial intelligence will be used conjunctively and uniformly to assess bio-system quality and design effective and appropriate treatments that restore system health

Disease-specific gene repositioning in breast cancer

The nuclear repositioning of specific genes may be a novel diagnostic strategy to distinguish malignant from normal tissue

Allele-specific nuclear positioning of the monoallelically expressed astrocyte marker GFAP

Chromosomes and genes are nonrandomly arranged within the mammalian cell nucleus. However, the functional significance of nuclear positioning in gene expression is unclear. Here we directly probed the relationship between nuclear positioning and gene activity by comparing the location of the active and inactive copies of a monoallelically expressed gene in single cell nuclei. We demonstrate that the astrocyte-specific marker GFAP (glial fibrillary acidic protein) is monoallelically expressed in cortical astrocytes. Selection of the active allele occurs in a stochastic manner and is generally maintained through cell division. Taking advantage of the monoallelic expression of GFAP, we show that the functionally distinct alleles occupy differential radial positions within the cell nucleus and differentially associate with intranuclear compartments. In addition, coordinately regulated astrocyte-specific genes on distinct chromosomes spatially associate in their inactive state and dissociate upon activation. These results provide direct evidence for function-related differential positioning of individual gene alleles within the interphase nucleus

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

Abstract 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 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.</p

Ceramide transfer protein deficiency compromises organelle function and leads to senescence in primary cells.

Ceramide transfer protein (CERT) transfers ceramide from the endoplasmic reticulum (ER) to the Golgi complex. Its deficiency in mouse leads to embryonic death at E11.5. CERT deficient embryos die from cardiac failure due to defective organogenesis, but not due to ceramide induced apoptotic or necrotic cell death. In the current study we examined the effect of CERT deficiency in a primary cell line, namely, mouse embryonic fibroblasts (MEFs). We show that in MEFs, unlike in mutant embryos, lack of CERT does not lead to increased ceramide but causes an accumulation of hexosylceramides. Nevertheless, the defects due to defective sphingolipid metabolism that ensue, when ceramide fails to be trafficked from ER to the Golgi complex, compromise the viability of the cell. Therefore, MEFs display an incipient ER stress. While we observe that ceramide trafficking from ER to the Golgi complex is compromised, the forward transport of VSVG-GFP protein is unhindered from ER to Golgi complex to the plasma membrane. However, retrograde trafficking of the plasma membrane-associated cholera toxin B to the Golgi complex is reduced. The dysregulated sphingolipid metabolism also leads to increased mitochondrial hexosylceramide. The mitochondrial functions are also compromised in mutant MEFs since they have reduced ATP levels, have increased reactive oxygen species, and show increased glutathione reductase activity. Live-cell imaging shows that the mutant mitochondria exhibit reduced fission and fusion events. The mitochondrial dysfunction leads to an increased mitophagy in the CERT mutant MEFs. The compromised organelle function compromise cell viability and results in premature senescence of these MEFs