Characterizing the topography of membrane receptors and signaling molecules from spatial patterns obtained using nanometer-scale electron-dense probes and electron microscopy

The flow of information through a cell requires the constant remodeling of cell signaling networks. Thus, spatially and temporally resolved microscopy of signaling components is needed to understand the behavior of normal cells as well as to uncover abnormal behavior leading to human disease. Nanopr...

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Bibliographic Details
Published in:Micron (Oxford, England : 1993) England : 1993), 2006, Vol.37 (1), p.14-34
Main Authors: Zhang, Jun, Leiderman, Karin, Pfeiffer, Janet R., Wilson, Bridget S., Oliver, Janet M., Steinberg, Stanly L.
Format: Article
Language:English
Subjects:
Animals
Clustering
Co-clustering
Electron microscopy
Gold nanoprobe
Hopkins statistic
Humans
Image Processing, Computer-Assisted - methods
Membrane Proteins - metabolism
Microscopy, Electron - methods
Nanotechnology - methods
Receptors, Cytoplasmic and Nuclear - analysis
Receptors, Cytoplasmic and Nuclear - metabolism
Ripley statistic
Signal Transduction
Spatial statistics
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Summary:The flow of information through a cell requires the constant remodeling of cell signaling networks. Thus, spatially and temporally resolved microscopy of signaling components is needed to understand the behavior of normal cells as well as to uncover abnormal behavior leading to human disease. Nanoprobe labeling and transmission electron microscopy of cytoplasmic face-up sheets of cell membrane have been developed as a high-resolution approach to map the interactions of proteins and lipid during cell signaling. Membrane sheets are labeled with 3–15 nm electron-dense probes for receptors, signaling proteins and lipids and micrographs record the distributions of the probes relative to each other and to surface features. Here, we establish computational methods to extract spatial coordinates of probes from micrographs, to analyze and statistically validate the clustering and co-clustering of these probes and to integrate results between experiments in order to establish the relative spatial distributions of single and multiple probes. Our analyses, and the resulting programs for automating data collection and for carrying out statistical and clustering analyses provide toolboxes specialized for the spatiotemporal analysis and modeling of signal transduction pathways.
ISSN:0968-4328
1878-4291
DOI:10.1016/j.micron.2005.03.014