Invading Basement Membrane Matrix Is Sufficient for MDA-MB-231 Breast Cancer Cells to Develop a Stable In Vivo Metastatic Phenotype

1 - ArticleIntroduction: The poor efficacy of various anti-cancer treatments against metastatic cells has focused attention on the role of tumor microenvironment in cancer progression. To understand the contribution of the extracellular matrix (ECM) environment to this phenomenon, we isolated ECM surrogate invading cell populations from MDA-MB-231 breast cancer cells and studied their genotype and malignant phenotype. Methods: We isolated invasive subpopulations (INV) from non invasive populations (REF) using a 2D-Matrigel assay, a surrogate of basal membrane passage. INV and REF populations were investigated by microarray assay and for their capacities to adhere, invade and transmigrate in vitro, and to form metastases in nude mice. Results: REF and INV subpopulations were stable in culture and present different transcriptome profiles. INV cells were characterized by reduced expression of cell adhesion and cell-cell junction genes (44% of down regulated genes) and by a gain in expression of anti-apoptotic and pro-angiogenic gene sets. In line with this observation, in vitro INV cells showed reduced adhesion and increased motility through endothelial monolayers and fibronectin. When injected into the circulation, INV cells induced metastases formation, and reduced injected mice survival by up to 80% as compared to REF cells. In nude mice, INV xenografts grew rapidly inducing vessel formation and displaying resistance to apoptosis. Conclusion: Our findings reveal that the in vitro ECM microenvironment per se was sufficient to select for tumor cells with a stable metastatic phenotype in vivo characterized by loss of adhesion molecules expression and induction of proangiogenic and survival factors

A Framework for 4-D Biomedical Image Processing, Visualization and Analysis

Today, studies on biological systems are often realized acquiring, processing and analyzing 3D-time lapse images. Different structures of the organism can be simultaneously observed by acquiring multi-channel image datasets. In this paper we present a software framework that aims at providing support for managing these kinds of multidimensional images, designing and validating new image processing algorithms, and analyzing processed images through different visualization techniques. We present a real scenario where the framework has been used for the detection and segmentation of biological cell membranes and nuclei imaged from live zebrafish embryos

CELLS SHAPE RECONSTRUCTION FROM 3-D+TIME LSM IMAGES OF EARLY ZEBRAFISH EMBRYOGENESIS

We design a chain of image processing methods to automatically reconstruct the shape of membranes and nuclei from time lapse Multi Photon Laser Scanning Microscopy images, taken throughout early animal embryogenesis. This strategy is a prerequisite for an integrated understanding of morphogenetic processes during organogenesis. In order to produce high contrast images, the embryo is labelled through the expression of fluorescent proteins, the eGFP (enhanced Green Fluorescent Protein) and the mcherry (Red Fluorescent Protein), addressed to membranes and nuclei. The two channels are acquired separately but simultaneously. The noise intrinsically related to the images is removed using the geodesic mean curvature flow, an edge-preserving filtering method which has been proven to be the best suitable for this kind of data. Cells are recognized and located either applying the so-called advection-diffusion equations or the generalized 3D Hough transform on nuclei images. The segmentation of cellular structures is then achieved using variational level set techniques