The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - January-March (2010 vol.7)
pp: 80-90
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
ABSTRACT
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.
INDEX TERMS
Multidimensional profiling, evolving fronts, Voronoi tessellation, iterative scalar voting, E-cadherin, ionizing radiation.
CITATION
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
REFERENCES
[1] U. Cavallaro and G. Christofori, "Cell Adhesion and Signaling: Implications for Tumor Progression," Nature Rev. Cancer, vol. 11, no. 12, pp. 118-132, 2004.
[2] N. Prigozhina, L. Zhong, E. Hunter, I. Mikic, S. Callaway, D. Roop, M. Mancini, D. Zacharias, J. Price, and P. McDonough, "Plasma Membrane Assays and Three-Compartment Image Cytometry for High Content Screening," Assay and Drug Development Technologies, vol. 5, no. 1, pp. 29-48, 2007.
[3] L. Loo, L. Wu, and S. Altschuler, "Image-Based Multivariate Profiling of Drug Responses from Single Cells," Nature Methods, vol. 4, no. 5, pp. 445-453, 2007.
[4] M. Lamprecht, D. Sabatini, and A. Capenter, "Cellprofiler: Free, Versatile Software for Automated Biological Image Analysis," Biotechniques, vol. 42, pp. 71-75, 2007.
[5] R. Murphy, "Systematic Description of Subcellular Location for Integration with Proteomics Databases and Systems Biology Modeling," Proc. IEEE Int'l Symp. Biomedical Imaging, pp. 1052-1055, 2007.
[6] C. Bakal, J. Aach, G. Church, and N. Perrimon, "Quantitative Morphological Signatures Define Local Signaling Networks Regulating Cell Morphology," Science, vol. 22, no. 316, pp. 1753-1756, 2007.
[7] B. Parvin, Q. Yang, J. Han, H. Chang, B. Rydberg, and M. Barcellos-Hoff, "Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events," IEEE Trans. Image Processing, vol. 16, no. 3, pp. 615-623, Mar. 2007.
[8] S. Theodoridis and K. Koutroumbas, Pattern Recognition. Academic Press, 1999.
[9] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge Univ. Press, 2000.
[10] S. Raman, C. Maxwell, M. Barcellos-Hoff, and B. Parvin, "Geometric Approach to Segmentation and Protein Localization in Cell Culture Assays," J. Microscopy, vol. 225, no. 1, pp. 22-30, 2007.
[11] D. Mumford and J. Shah, "Optimal Approximation by Piecewise Smooth Functions and Associated Variational Problems," Comm. Pure Applied Math., vol. 42, pp. 577-685, 1989.
[12] C. Xu and J. Prince, "Gradient Vector Flow: A New External Force for Snakes," Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, pp. 66-71, 1997.
[13] R. Young, R. Lesperance, and W. Meyer, "The Gaussian Derivative Model for Spatial-Temporal Vision: I. Cortical Model," Spatial Vision, vol. 14, nos. 3/4, pp. 261-319, 2001.
[14] G. Goldberg, C. Allan, J. Burel, D. Creager, A. Falconi, H. Hochheiser, J. Johnston, J. Mellen, P. Sorger, and J. Swedlow, "The Open Microscopy Environment (OME) Data Model and Xml Files: Open Tools for Informatics and Quantitative Analysis in Biological Images," Genome and Biology, vol. 6, no. 5, p. R47, 2005.
[15] M. Barcellos-Hoff, "Radiation-Induced Changes in Transforming Growth Factor e1 and Subsequent Extracellular Matrix Reorganization in Irradiated Murine Mammary Gland," Cancer Research, vol. 53, pp. 3880-3886, 1993.
[16] E. Ehrhart, P. Segarini, A. Carroll, and M. Barcellos-Hoff, "Latent Transforming Growth Factor-b Activation In Situ: Quantitative and Functional Evidence Following Lowdose Irradiation," FASEB J., vol. 11, no. 12, pp. 991-1002, 1997.
[17] K. Ewan, R. Henshall-Powell, S. Ravani, M. Pajares, C. Arteaga, R. Warters, R. Akhurst, and M. Barcellos-Hoff, "Transforming Growth Factor-Beta1 Mediates Cellular Response to DNA Damage In Situ," Cancer Research, vol. 62, no. 20, pp. 5627-5631, 2002.
[18] M. Barcellos-Hoff and S. Ravani, "Irradiated Mammary Gland Stroma Promotes the Expression of Tumorigenic Potential by Unirradiated Epithelial Cells," Cancer Research, vol. 60, pp. 1254-1260, 2000.
[19] P. Dent, A. Yacoub, J. Contessa, R. Caron, G. Amorino, K. Valerie, M. Hagan, S. Grant, and R. Schmidt-Ullrich, "Stress and Radiation-Induced Activation of Multiple Intracellular Signalling Pathways," Radiation Research, vol. 159, no. 3, pp. 283-300, 2003.
[20] M. Barcellos-Hoff, C. Park, and E. Wright, "Radiation and the Microenvironment—Tumorigenesis and Therapy," Nature Rev. Cancer, vol. 5, no. 11, pp. 867-75, 2005.
[21] H. Bierie and B. Moses, "Tumor Microenvironment: Tgfbeta: The Molecular Jekyll and Hyde of Cancer," Nature Rev. Cancer, vol. 6, no. 7, pp. 506-520, 2006.
[22] C. Park, R. Henshall-Powell, A. Erickson, R. Talhou, B. Parvin, M. Bissell, and M. Barcellos-Hoff, "Ionizing Radiation Induces Heritable Disruption of Epithelial Cell Interactions," Proc. Nat'l Academy of Science USA, vol. 100, no. 19, pp. 10 728-10 733, 2003.
[23] H. Chang, K. Andarawewa, J. Han, M. Barcellos-Hoff, and B. Parvin, "Perceptual Grouping of Membrane Signals in Cell-Based Assays," Proc. IEEE Int. Symp. Biomedical Imaging: From Nano to Macro, pp. 532-535, 2007.
[24] J. Han, H. Chang, K. Andarawewa, P. Yaswen, M. Barcellos-Hoff, and P. Parvin, "Integrated Profiling of Cell Surface Protein and Nuclear Marker for Discriminant Analysis," Proc. IEEE Int'l Symp. Biomedical Imaging, pp. 1342-1346, 2008.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool