Abstract
Methods to quantify cellular–level phenotypic differences between genetic groups are a key tool in genomics research. In disease processes such as cancer, phenotypic changes at the cellular level frequently manifest in the modification of cell population profiles. These changes are hard to detect due the ambiguity in identifying distinct cell phenotypes within a population. We present a methodology which enables the detection of such changes by generating a phenotypic signature of cell populations in a data–derived feature–space. Further, this signature is used to estimate a model for the redistribution of phenotypes that was induced by the genetic change. Results are presented on an experiment involving deletion of a tumor–suppressor gene dominant in breast cancer, where the methodology is used to detect changes in nuclear morphology between control and knockout groups.
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Andrey, P., et al.: Statistical Analysis of 3D Images Detects Regular Spatial Distributions of Centromeres and Chromocenters in Animal and Plant Nuclei. PLoS Computational Biology 6(7) (July 2010)
Boland, M.V., Murphy, R.F.: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17(12), 1213–1223 (2001)
Brechbühler, C.: Parametrization of Closed Surfaces for 3-D Shape Description. Computer Vision and Image Understanding 61(2), 154–170 (1995)
Gladilin, E., Goetze, S., Mateos-Langerak, J., Van Driel, R., Eils, R., Rohr, K.: Shape normalization of 3D cell nuclei using elastic spherical mapping. Journal of Microscopy 231(Pt. 1), 105–114 (2008)
Keren, K., Pincus, Z., Allen, G.M., Barnhart, E.L., Marriott, G., Mogilner, A., Theriot, J.A.: Mechanism of shape determination in motile cells. Nature 453(7194), 475–480 (2008)
Lin, G., Chawla, M.K., Olson, K., Barnes, C.A., Guzowski, J.F., Bjornsson, C., Shain, W., Roysam, B.: A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images. Cytometry Part A 71(9), 724–736 (2007)
Pincus, Z., Theriot, J.A.: Comparison of quantitative methods for cell-shape analysis. Journal of Microscopy 227(Pt. 2), 140–156 (2007)
Rittscher, J.: Characterization of Biological Processes through Automated Image Analysis. Annual Review of Biomedical Engineering 12, 315–344 (2010)
Rohde, G.K., Ribeiro, A.J.S., Dahl, K.N., Murphy, R.F.: Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa cells. Cytometry Part A 73(4), 341–350 (2008)
Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Movers Distance as a Metric for Image Retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)
Trimboli, A.J., Fukino, K., de Bruin, A., Wei, G., Shen, L., Tanner, S.M., Creasap, N., Rosol, T.J., Robinson, M.L., Eng, C., Ostrowski, M.C., Leone, G.: Direct evidence for epithelial-mesenchymal transitions in breast cancer. Cancer Research 68(3), 937–945 (2008)
Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31(3), 1116–1128 (2006)
Zink, D., Fischer, A.H., Nickerson, J.A.: Nuclear structure in cancer cells. Nature reviews. Cancer 4(9), 677–687 (2004)
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Singh, S. et al. (2011). Non-parametric Population Analysis of Cellular Phenotypes. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_42
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DOI: https://doi.org/10.1007/978-3-642-23629-7_42
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