Stephen J. Lockett

In situ analyses of genome instability in breast cancer

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.

Segmentation of confocal microscope images of cell nuclei in thick tissue sections

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 analysts involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analysts visual judgement is correct.

Segmentation of nuclei and cells using membrane related protein markers

Segmenting individual cell nuclei from microscope images normally involves volume labelling of the nuclei with a DNA stain. However, this method often fails when the nuclei are tightly clustered in the tissue, because there is little evidence from the images on where the borders of the nuclei are. In this paper we present a method which solves this limitation and furthermore enables segmentation of whole cells. Instead of using volume stains, we used stains that specifically label the surface of nuclei or cells: lamins for the nuclear envelope and alpha‐6 or beta‐1 integrins for the cellular surface. The segmentation is performed by identifying unique seeds for each nucleus/cell and expanding the boundaries of the seeds until they reach the limits of the nucleus/cell, as delimited by the lamin or integrin staining, using gradient‐curvature flow techniques. We tested the algorithm using computer‐generated objects to evaluate its robustness against noise and applied it to cells in culture and to tissue specimens. In all the cases that we present the algorithm gave accurate results.

Intensity-based energy transfer measurements in digital imaging microscopy.

Abstract Investigation of protein-protein associations is important in understanding structure and function relationships in living cells. Using Förster-type resonance energy transfer between donor and acceptor labeled monoclonal antibodies we can assess the cell surface topology of membrane proteins against which the antibodies were raised. In our current work we elaborated a quantitative image microscopic technique based on the measurement of fluorescence intensities to calculate the energy transfer efficiency on a pixel-by-pixel basis. We made use of the broad excitation and emission spectrum of cellular autofluorescence for background correction of images. In addition to the reference autofluorescence images (UV background) we recorded three fluorescent images (donor, acceptor and energy transfer signal) of donor-acceptor double labeled samples, and corrected for spectral spillage of the directly excited donor and acceptor fluorescence into the energy transfer image. After careful image registration we were able to calculate the energy transfer efficiency on a pixel-by-pixel basis. In this paper, we also present a critical comparison between results obtained with this method and other approaches (photobleaching and flow cytometric energy transfer measurements).

EFFICIENT, INTERACTIVE, AND THREE-DIMENSIONAL SEGMENTATION OF CELL NUCLEI IN THICK TISSUE SECTIONS

Segmentation of intact cell nuclei in three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. Because automatic algorithms do not correctly segment all nuclei in tissue sections, interactive algorithms may be preferable for some applications. Existing interactive segmentation algorithms require the analyst to draw a border around the nucleus under consideration in all successive two-dimensional (2D) planes of the 3D image. The present paper describes an algorithm with two main advantages over the existing method. First, the analyst draws borders only in 2D planes that cut approximately through the center of the nucleus under consideration so that the nuclear borders generally are most distinct. Second, the analyst draws only five borders around each nucleus, and then the algorithm interpolates the entire surface. The algorithm results in segmented objects that correspond to individual, visually identifiable nuclei. The segmented surfaces, however, may not exactly represent the true nuclear surface. An optional, automatic surface optimization algorithm can be applied to reduce this error.

Analysis of the 3D spatial organization of cells and subcellular structures in tissue

Advancements in image analysis shave recently made it possible to segment the cells and nuclei, of a wide variety of tissues, from 3D images collected using fluorescence confocal microscopy. This has made it possible to analyze the spatial organization of individual cells and nuclei within the natural tissue context. We present here a spatial statistical method which examines an arbitrary 3D distribution of cells of two different types and determines the probability that the cells are randomly mixed, cells of one type are clustered, or cells of different types are preferentially associated. Beginning with a segmented 3D image of cells, the Voronoi diagram is calculated to indicate the nearest neighbor relationships of the cells. Then, in a test image of the same topology, cells are randomly assigned a type in the same proportions as in the actual specimen and the ratio of cells with nearest neighbors of the same type versus the other types is calculated. Repetition of this random assignment is used to generate a distribution function which is specific for the tissue image. Comparison of the ratios for the actual sample to this distribution assigns probabilities for the conditions defined above. The technique is being used to analyze the organization of genetically normal versus abnormal cells in cancer tissue.

Interactive algorithms for rapid enumeration of hybridization signals in individual whole-cell nuclei inside intact-tissue specimens

Fluorescence in situ hybridization (FISH) is useful for analyzing specific nucleic acid sequences in individual cells. Its application to tissue sections has been limited however because of the difficulties of performing the hybridization and analysis in sections that are thick enough to contain intact nuclei. Recent improvements in FISH permit hybridization with chromosome-specific, centromeric probes throughout 20 micrometers formalin fixed, paraffin- embedded sections, which do contain many intact nuclei. This paper describes software to facilitate analysis of these 3D hybridizations. We have developed two algorithms for analyzing 3D, confocal images of thick sections. One displays 2D, maximum-intensity, projection images through the original 3D image at different angles. When projections are viewed sequentially, the 3D image appears semi-transparent and rotates. The second algorithm allows interactive enumeration of FISH signals. Each signal is marked by the analyst. Then, for each pair of marked signals, a 2D slice image along the line connecting both marked signals and parallel to the z (depth) axis is displayed. From this slice, the analyst decides if the signals are in the same or different nuclei, or if the signals should be rejected because they are in a nucleus truncated by the upper or lower surface of the section. After consideration of all pairs of signals, the algorithm produces a map of the tissue section showing the numbers of signals in each of the intact nucleus. The algorithms enable analysis of small, premalignant and early malignant lesions and infiltrative lesions that cannot be analyzed by other molecular techniques and permit the direct correlation of FISH information with histology/cytology.

Interactive system for registering adjacent tissue sections

The molecular and structural analysis of cells within their tissue context helps us understand disease mechanisms, such as carcinogenesis. Standard analysis of cutting specimens into thin (4 micrometer) sections, followed by labeling and visual microscopic analysis, has the limitation that tissue properties can only be studied within the section plane, and not perpendicular to the plane. We solved these limitations by building a system for registering images of adjacent sections. In addition, the system enables analysis of many molecular markers in a specific tissue volume, by labeling different sections with different markers, followed by using the system to locate the relevant tissue volume in each section. The system has three stages. First, it automatically images each entire section and two fiducial markers per slide. After this stage, the slides can be removed from the microscope. In stage two, pairs of images of adjacent sections are registered. This is done by interactively marking several points that are common to both images, which are used to calculate the translation and rotation of the images relative to each other. Different registrations can be performed on different parts of the images to account for differential stretching, tearing and folding of sections. In stage three, a slide is placed on the microscope stage and the analyst can bring a specific location into the field of view by referring to it in the previously acquired image. Accuracy is approximately equal to 10 micrometers.

Three‐Dimensional Image Visualization and Analysis

This unit introduces the concepts of 3D image analysis and visualization as applied in cytometry. The author discusses the nature of 3D data sets and describes the techniques for visualization and analysis of 3D images. Discussions of noise removal, depth attenuation, and correction and segmentation are also included, as is a brief introduction to 3D analysis options and deconvolution prinicples. This commentary unit is a good way to begin an understanding of the application of 3D data sets.

Measurement of genetic instability in breast cancer by confocal microscopy and 3D image analysis

Genetic instability (GI) is frequently associated with solid tumors including cancers of the breast. We hypothesize that the degree of GI, as evidenced by the cell-to-cell variations in the copy number of specific DNA loci, can be used as a marker for cancer prognosis. We present a computer-aided method for GI measurement in thick tissue sections based on confocal microscopy and 3D image analysis and visualization. The results, based on the analysis of breast tissue sections show considerable cell-to-cell variability in the copy number of the centromere of chromosome 1 and the 20q13 locus in invasive cancer but the normal 2 copies per cell in histologically normal tissue.