Tulin Ersahin

Attributed Relational Graphs for Cell Nucleus Segmentation in Fluorescence Microscopy Images

More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms.

Inhibition of Akt signaling in hepatoma cells induces apoptotic cell death independent of Akt activation status

SummaryThe serine/threonine kinase Akt, a downstream effector of phosphatidylinositol 3-kinase (PI3K), is involved in cell survival and anti-apoptotic signaling. Akt has been shown to be constitutively expressed in a variety of human tumors including hepatocellular carcinoma (HCC). In this report we analyzed the status of Akt pathway in three HCC cell lines, and tested cytotoxic effects of Akt pathway inhibitors LY294002, Wortmannin and Inhibitor VIII. In Mahlavu human hepatoma cells Akt was constitutively activated, as demonstrated by its Ser473 phosphorylation, downstream hyperphosphorylation of BAD on Ser136, and by a specific cell-free kinase assay. In contrast, Huh7 and HepG2 did not show hyperactivation when tested by the same criteria. Akt enzyme hyperactivation in Mahlavu was associated with a loss of PTEN protein expression. Akt signaling was inhibited by the upstream kinase inhibitors, LY294002, Wortmannin, as well as by the specific Akt Inhibitor VIII in all three hepatoma cell lines. Cytotoxicity assays with Akt inhibitors in the same cell lines indicated that they were all sensitive, but with different IC50 values as assayed by RT-CES. We also demonstrated that the cytotoxic effect was through apoptotic cell death. Our findings provide evidence for its constitutive activation in one HCC cell line, and that HCC cell lines, independent of their Akt activation status respond to Akt inhibitors by apoptotic cell death. Thus, Akt inhibition may be considered as an attractive therapeutic intervention in liver cancer.

A multiplication-free framework for signal processing and applications in biomedical image analysis

A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1 norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix.

Image Classification of Human Carcinoma Cells Using Complex Wavelet-Based Covariance Descriptors

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-WT) coefficients and several morphological attributes are computed. Directionally selective DT-WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.

Liver cancer cells are sensitive to Lanatoside C induced cell death independent of their PTEN status.

BACKGROUND Hepatocellular carcinoma is the second deadliest cancer with limited treatment options. Loss of PTEN causes the P13K/Akt pathway to be hyperactive which contributes to cell survival and resistance to therapeutics in various cancers, including the liver cancer. Hence molecules targeting this pathway present good therapeutic strategies for liver cancer. HYPOTHESIS It was previously reported that Cardiac glycosides possessed antitumor activity by inducing apoptosis of multiple cancer cells through oxidative stress. However, whether Cardiac glycoside Lanatoside C can induce oxidative stress in liver cancer cells and induce cell death both in vitro and in vivo remains unknown. METHODS Cell viability was measured by SRB assay. Cell death analysis was investigated by propidium iodide staining with flow cytometry and PARP cleavage. DCFH-DA staining and cytometry were used for intracellular ROS measurement. Protein levels were analyzed by western blot analysis. Antitumor activity was investigated on mice xenografts in vivo. RESULTS In this study, we found that Cardiac glycosides, particularly Lanatoside C from Digitalis ferruginea could significantly inhibit PTEN protein adequate Huh7 and PTEN deficient Mahlavu human liver cancer cell proliferation by the induction of apoptosis and G2/M arrest in the cells. Lanatoside C was further shown to induce oxidative stress and alter ERK and Akt pathways. Consequently, JNK1 activation resulted in extrinsic apoptotic pathway stimulation in both cells while JNK2 activation involved in the inhibition of cell survival only in PTEN deficient cells. Furthermore, nude mice xenografts followed by MRI showed that Lanatoside C caused a significant decrease in the tumor size. In this study apoptosis induction by Lanatoside C was characterized through ROS altered ERK and Akt pathways in both PTEN adequate epithelial and deficient mesenchymal liver cancer cells. CONCLUSION The results indicated that Lanatoside C could be contemplated in liver cancer therapeutics, particularly in PTEN deficient tumors. This is due to Lanatoside Cs stress inducing action on ERK and Akt pathways through differential activation of JNK1 and JNK2 by GSK3β.

CD8 Lineage-specific Regulation of Interleukin-7 Receptor Expression by the Transcriptional Repressor Gfi1

Background: Expression of the IL-7Rα gene is up-/down-regulated during T/B-lymphocyte development. Results: IL-7Rα gene transcription is repressed by the transcription factor Gfi1, specifically in CD8+ T-lymphocytes. Conclusion: Treatment by dexamethasone down-regulates Gfi1, which contributes to glucocorticoid receptor mediated up-regulation of IL-7R expression. Significance: The mechanism by which the IL-7R gene gets turned on and off during development is a critical issue in biology. Interleukin-7 receptor α (IL-7Rα) is essential for T cell survival and differentiation. Glucocorticoids are potent enhancers of IL-7Rα expression with diverse roles in T cell biology. Here we identify the transcriptional repressor, growth factor independent-1 (Gfi1), as a novel intermediary in glucocorticoid-induced IL-7Rα up-regulation. We found Gfi1 to be a major inhibitory target of dexamethasone by microarray expression profiling of 3B4.15 T-hybridoma cells. Concordantly, retroviral transduction of Gfi1 significantly blunted IL-7Rα up-regulation by dexamethasone. To further assess the role of Gfi1 in vivo, we generated bacterial artificial chromosome (BAC) transgenic mice, in which a modified Il7r locus expresses GFP to report Il7r gene transcription. By introducing this BAC reporter transgene into either Gfi1-deficient or Gfi1-transgenic mice, we document in vivo that IL-7Rα transcription is up-regulated in the absence of Gfi1 and down-regulated when Gfi1 is overexpressed. Strikingly, the in vivo regulatory role of Gfi1 was specific for CD8+, and not CD4+ T cells or immature thymocytes. These results identify Gfi1 as a specific transcriptional repressor of the Il7r gene in CD8 T lymphocytes in vivo.

PATZ1 Is a DNA Damage-Responsive Transcription Factor That Inhibits p53 Function

ABSTRACT Insults to cellular health cause p53 protein accumulation, and loss of p53 function leads to tumorigenesis. Thus, p53 has to be tightly controlled. Here we report that the BTB/POZ domain transcription factor PATZ1 (MAZR), previously known for its transcriptional suppressor functions in T lymphocytes, is a crucial regulator of p53. The novel role of PATZ1 as an inhibitor of the p53 protein marks its gene as a proto-oncogene. PATZ1-deficient cells have reduced proliferative capacity, which we assessed by transcriptome sequencing (RNA-Seq) and real-time cell growth rate analysis. PATZ1 modifies the expression of p53 target genes associated with cell proliferation gene ontology terms. Moreover, PATZ1 regulates several genes involved in cellular adhesion and morphogenesis. Significantly, treatment with the DNA damage-inducing drug doxorubicin results in the loss of the PATZ1 transcription factor as p53 accumulates. We find that PATZ1 binds to p53 and inhibits p53-dependent transcription activation. We examine the mechanism of this functional inhibitory interaction and demonstrate that PATZ1 excludes p53 from DNA binding. This study documents PATZ1 as a novel player in the p53 pathway.

Iterative h‐minima‐based marker‐controlled watershed for cell nucleus segmentation

Automated microscopy imaging systems facilitate high‐throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker‐controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h‐minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts.

Large‐scale Automated Function Prediction of Protein Sequences and an Experimental Case Study Validation on PTEN Transcript Variants

Recent advances in computing power and machine learning empower functional annotation of protein sequences and their transcript variations. Here, we present an automated prediction system UniGOPred, for GO annotations and a database of GO term predictions for proteomes of several organisms in UniProt Knowledgebase (UniProtKB). UniGOPred provides function predictions for 514 molecular function (MF), 2909 biological process (BP), and 438 cellular component (CC) GO terms for each protein sequence. UniGOPred covers nearly the whole functionality spectrum in Gene Ontology system and it can predict both generic and specific GO terms. UniGOPred was run on CAFA2 challenge target protein sequences and it is categorized within the top 10 best performing methods for the molecular function category. In addition, the performance of UniGOPred is higher compared to the baseline BLAST classifier in all categories of GO. UniGOPred predictions are compared with UniProtKB/TrEMBL database annotations as well. Furthermore, the proposed tools ability to predict negatively associated GO terms that defines the functions that a protein does not possess, is discussed. UniGOPred annotations were also validated by case studies on PTEN protein variants experimentally and on CHD8 protein variants with literature. UniGOPred protein functional annotation system is available as an open access tool at http://cansyl.metu.edu.tr/UniGOPred.html.

Microscopic image classification via ℂWT-based covariance descriptors using Kullback-Leibler distance

In this paper, we present a novel method for classification of cancer cell line images using complex wavelet-based region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. ℂWT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method.