Computational Topology Based Quantification Of Hepatocytes Nuclei In Lipopolysaccharide-Induced Liver Injury In Mice

INTRODUCTION / BACKGROUND: Automated high resolution scanning microscopes digitize large sets of histological samples and access the an anatomical features of cells and tissues from the mm range down to a resolution of .25mpp microns per pixel). The high quality of the scans allows for the collection of the quantitative morphotopological features of cells and tissues from different samples, which can be coupled to functional information through, e.g., concomitant immunostaining. The basis for robust and accurate quantification of structural and functional features is the segmentation of regions of interest (ROIs) which define different elements within the scans. Due to acquisition artifacts and the diversity and variance of possible tar- gets, the characterization and segmentation of ROIs in histological samples is difficult and challenging. In recent years, computational algebraic topology, a field of mathematics, has established a robust and versatile way to obtain qualitative information from data. The most fundamental qualitative description of an object is given by the study of its topology, how the object is connected, how many holes it has, and of what type. That allows characterizing data sets according to their structure, increasing our understanding of their properties. AIMS: We propose a method for the robust segmentation of hepatocyte nuclei based on the principles of persistent homology, a tool of algebraic topology. We show the application of our technique in histopathological, whole slide images obtained from liver sections of lipopolysaccharide (LPS)-treated mice. The robustness is achieved by the introduction of persistent homology to characterize the hepatocyte nuclei. Its stability proves the usefulness of persistent homology; variations in the properties of the ROIs induce small changes in the resulting characterization. By means of this representation for the hepatocyte nuclei, the resulting segmentation is less sensitive to acquisition artifacts and natural variations of the images across batches of slides. METHODS: The sample space of this study consists of 856 cropped images of 616x616 pixels each, obtained from three specimens. Each image was fragmented into connected components at different scales. Persistent homology is used to study the inclusion relations between connected components. The outcome of such process is a persistence diagram that provides a low-dimensional projection of the image structure. From that representation, it is possible to use conventional statistical methods for segmenting hepatocyte nuclei. After the segmentation, we assess the performance in comparison to a gold standard segmentation validated by experts. RESULTS: The computational topology approach proposed successfully detected hepatocyte cells under several natural variations. We evaluated on a per-pixel basis how the segmentation performs on: i) all nuclei in the images, ii) big round nuclei considered belonging to hepatocytes cells (accuracy 87.2%, recall 80.3%), and iii) nuclei regarded to non-parenchymal cells

Bone morphogenetic protein-9 induces epithelial to mesenchymal transition in hepatocellular carcinoma cells

Dermatology-oncolog

Epigenetic silencing of tumor suppressor candidate 3 confers adverse prognosis in early colorectal cancer.

Colorectal cancer (CRC) is a biologically and clinically heterogeneous disease. Even though many recurrent genomic alterations have been identified that may characterize distinct subgroups, their biological impact and clinical significance as prognostic indicators remain to be defined. The tumor suppressor candidate-3 (TUSC3/N33) locates to a genomic region frequently deleted or silenced in cancers. TUSC3 is a subunit of the oligosaccharyltransferase (OST) complex at the endoplasmic reticulum (ER) which catalyzes bulk N-glycosylation of membrane and secretory proteins. However, the consequences of TUSC3 loss are largely unknown. Thus, the aim of the study was to characterize the functional and clinical relevance of TUSC3 expression in CRC patients' tissues (n=306 cases) and cell lines. TUSC3 mRNA expression was silenced by promoter methylation in 85 % of benign adenomas (n=46 cases) and 35 % of CRCs (n =74 cases). Epidermal growth factor receptor (EGFR) was selected as one exemplary ER-derived target protein of TUSC3-mediated posttranslational modification. We found that TUSC3 inhibited EGFR-signaling and promoted apoptosis in human CRC cells, whereas TUSC3 siRNA knock-down increased EGFR-signaling. Accordingly, in stage I/II node negative CRC patients (n=156 cases) loss of TUSC3 protein expression was associated with poor overall survival. In sum, our data suggested that epigenetic silencing of TUSC3 may be useful as a molecular marker for progression of early CRC.This work was supported by grants to ME from the State of Baden-Württemberg for “Center of Geriatric Biology and Oncology (ZOBEL) - Perspektivförderung” and “Biology of Frailty - Sonderlinie Medizin”. EB received funding from the Deutsche Krebshilfe (#108287, #111086), the Deutsche Forschungsgemeinschaft (DFG, BU2285) and the German Cancer Research Center (DKFZ-MOST, Ca158)