Transition through telomere crisis is thought to be a crucial event in the development of most breast carcinomas. Our goal in this study was to determine where this occurs in the context of histologically defined breast cancer progression. To this end, we assessed genome instability (using fluorescence in situ hybridization) and other features associated with telomere crisis in normal ductal epithelium, usual ductal hyperplasia, ductal carcinoma in situ and invasive cancer. We modeled this process in vitro by measuring these same features in human mammary epithelial cell cultures during ZNF217-mediated transition through telomere crisis and immortalization. Taken together, the data suggest that transition through telomere crisis and immortalization in breast cancer occurs during progression from usual ductal hyperplasia to ductal carcinoma in situ.The molecular events that enable normal epithelial cells to progress to invasive, metastatic disease are increasingly well understood 1,2 . Deregulation of the TP53 and RB1 pathways in most cancers enables extended proliferation. In breast cancer, deregulation of RB1 through inactivation of cyclin-dependent kinase inhibitor 2A (CDKN2A, also called p16 and INK4a) seems to be an early event 3 . Most epithelial cells
Segmentation of intact cell nuclei from three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D segmentation approach that combines the recognition capabilities of the human visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence-stained nuclei and unstained background. This includes a novel step, based on the Hough transform and an automatic focusing algorithm to estimate the size of nuclei. Then, using an interactive display, each nuclear region is shown to the analyst, who classifies it as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or debris. Next, automatic image analysis based on morphological reconstruction and the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst indicates which partial nuclei should be joined to form complete nuclei. The approach was assessed by calculating the fraction of correctly segmented nuclei for a variety of tissue types: Caenorhabditis elegans embryos (839 correct out of a total of 848), normal human skin (343/362), benign human breast tissue (492/525), a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335). Furthermore, due to the analyst's involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analyst's visual judgement is correct.
We present a new system for simultaneous morphological and molecular analysis of thick tissue samples. The system is composed of a computer assisted microscope and a JAVAbased image display, analysis and visualization program that allows acquisition, annotation, meaningful storage, three-dimensional reconstruction and analysis of structures of interest in thick sectioned tissue specimens. We describe the system in detail and illustrate its use by imaging, reconstructing and analyzing two complete tissue blocks which were differently processed and stained. One block was obtained from a ductal carcinoma in situ (DCIS) lumpectomy specimen and stained alternatively with Hematoxilyn and Eosin (H&E), and with a counterstain and fluorescence in situ hybridization (FISH) to the ERB-B2 gene. The second block contained a fully sectioned mammary gland of a mouse, stained for Histology with H&E.We show how the system greatly reduces the amount of interaction required for the acquisition and analysis and is therefore suitable for studies that require morphologically driven, wide scale (e.g. whole gland) analysis of complex tissue samples or cultures.2
A film-handling machine (robot) has been built which can, in conjunction with a commercially available film densitometer, exchange and digitize over 300 electron micrographs per day. Implementation of robotic film handling effectively eliminates the delay and tedium associated with digitizing images when data are initially recorded on photographic film. The modulation transfer function (MTF) of the commercially available densitometer is significantly worse than that of a high-end, scientific microdensitometer. Nevertheless, its signal-to-noise ratio (S/N) is quite excellent, allowing substantial restoration of the output to "near-to-perfect" performance. Due to the large area of the standard electron microscope film that can be digitized by the commercial densitometer (up to 10,000x13,680 pixels with an appropriately coded holder), automated film digitization offers a fast and inexpensive alternative to high-end CCD cameras as a means of acquiring large amounts of image data in electron microscopy.
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