CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2010 vol.7 Issue No.01 - January-March
Issue No.01 - January-March (2010 vol.7)
Ju Han , Lawrence Berkeley National Laboratory, Berkeley
Hang Chang , Lawrence Berkeley National Laboratory, Berkeley
Kumari Andarawewa , University of Virginia, Charlottesville
Paul Yaswen , Lawrence Berkeley National Laboratory, Berkeley
Mary Helen Barcellos-Hoff , New York University Langone School of Medicine
Bahram Parvin , Lawrence Berkeley National Laboratory, Berkeley
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.134
Cell membrane proteins play an important role in tissue architecture and cell-cell communication. We hypothesize that segmentation and multidimensional characterization of the distribution of cell membrane proteins, on a cell-by-cell basis, enable improved classification of treatment groups and identify important characteristics that can otherwise be hidden. We have developed a series of computational steps to 1) delineate cell membrane protein signals and associate them with a specific nucleus; 2) compute a coupled representation of the multiplexed DNA content with membrane proteins; 3) rank computed features associated with such a multidimensional representation; 4) visualize selected features for comparative evaluation through heatmaps; and 5) discriminate between treatment groups in an optimal fashion. The novelty of our method is in the segmentation of the membrane signal and the multidimensional representation of phenotypic signature on a cell-by-cell basis. To test the utility of this method, the proposed computational steps were applied to images of cells that have been irradiated with different radiation qualities in the presence and absence of other small molecules. These samples are labeled for their DNA content and E-cadherin membrane proteins. We demonstrate that multidimensional representations of cell-by-cell phenotypes improve predictive and visualization capabilities among different treatment groups, and identify hidden variables.
Multidimensional profiling, evolving fronts, Voronoi tessellation, iterative scalar voting, E-cadherin, ionizing radiation.
Ju Han, Hang Chang, Kumari Andarawewa, Paul Yaswen, Mary Helen Barcellos-Hoff, Bahram Parvin, "Multidimensional Profiling of Cell Surface Proteins and Nuclear Markers", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.7, no. 1, pp. 80-90, January-March 2010, doi:10.1109/TCBB.2008.134