Biomedical subjects

Robert F Murphy

Publications and source records attributed to Robert F Murphy.

22 records · Page 2Linked to original sources

PubMed2003

Dispersal of Golgi matrix proteins during mitotic Golgi disassembly.

During mitosis, the mammalian Golgi disassembles into numerous vesicles and larger membrane structures referred to as clusters or remnants. Following mitosis, the vesicles and clusters reassemble to form an intact Golgi in each daughter cell. One model of Golgi biogenesis states that Golgi matrix proteins remain assembled in mitotic clusters and then serve as a template for Golgi reassembly. To test this idea, we performed a 3D-computational analysis of mitotic cells to determine the extent to which these proteins remain in mitotic clusters. As a control we used brefeldin A-induced Golgi disassembly which causes dispersal of Golgi enzymes, but leaves matrix proteins in remnant structures. Unlike brefeldin A-treated cells, in which matrix proteins were clearly sorted from non-matrix proteins, we observed extensive dispersal of matrix proteins in metaphase cells with no evidence of differential sorting of these proteins from other Golgi proteins. The extensive disassembly of matrix proteins argues against their participation in a stable template and supports a self-assembly mode of Golgi biogenesis.

Animals↗

PubMed2002

Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools.

Cellular behavior is complex. Successfully understanding systems at ever-increasing complexity is fundamental to advances in modern science and unraveling the functional details of cellular behavior is no exception. We present a collection of prospectives to provide a glimpse of the techniques that will aid in collecting, managing and utilizing information on complex cellular processes via molecular imaging tools. These include: 1) visualizing intracellular protein activity with fluorescent markers, 2) high throughput (and automated) imaging of multilabeled cells in statistically significant numbers, and 3) machine intelligence to analyze subcellular image localization and pattern. Although not addressed here, the importance of combining cell-image-based information with detailed molecular structure and ligand-receptor binding models cannot be overlooked. Advanced molecular imaging techniques have the potential to impact cellular diagnostics for cancer screening, clinical correlations of tissue molecular patterns for cancer biology, and cellular molecular interactions for accelerating drug discovery. The goal of finally understanding all cellular components and behaviors will be achieved by advances in both instrumentation engineering (software and hardware) and molecular biochemistry.

Animals↗

PubMed2002

Objective evaluation of differences in protein subcellular distribution.

Research on the cell and molecular biology of proteins involved in membrane traffic often requires determination of the effects of various experimental conditions on the subcellular distributions of those proteins. This is most often accomplished by acquiring fluorescence microscope images and visually comparing these images. While this approach is quite suitable for detecting major changes in distributions, it is not sensitive to small changes and does not permit a quantitative and objective analysis. We therefore describe the application of pattern analysis methods to the comparison of sets of fluorescence microscope images. This approach provides a high throughput and reproducible technique to determine whether image distributions differ within a specified statistical confidence, and is shown to resolve image sets indistinguishable by visual inspection.

Animals↗

PubMedDate not supplied

From quantitative microscopy to automated image understanding.

Quantitative microscopy has been extensively used in biomedical research and has provided significant insights into structure and dynamics at the cell and tissue level. The entire procedure of quantitative microscopy is comprised of specimen preparation, light absorption/reflection/emission from the specimen, microscope optical processing, optical/electrical conversion by a camera or detector, and computational processing of digitized images. Although many of the latest digital signal processing techniques have been successfully applied to compress, restore, and register digital microscope images, automated approaches for recognition and understanding of complex subcellular patterns in light microscope images have been far less widely used. We describe a systematic approach for interpreting protein subcellular distributions using various sets of subcellular location features (SLF), in combination with supervised classification and unsupervised clustering methods. These methods can handle complex patterns in digital microscope images, and the features can be applied for other purposes such as objectively choosing a representative image from a collection and performing statistical comparisons of image sets.

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