Abstract
In human mesenchymal stem cells the envelope surrounding the nucleus, as visualized by the nuclear lamina, has a round and flat shape. The lamina structure is considerably deformed after activation of cell death (apoptosis). The spatial organization of the lamina is the initial structural change found after activation of the apoptotic pathway, therefore can be used as a marker to identify cells activated for apoptosis. Here we investigated whether the spatial changes in lamina spatial organization can be recognized by machine learning algorithms to classify normal and apoptotic cells. Classical machine learning algorithms were applied to classification of 3D image sections of nuclear lamina proteins, taken from normal and apoptotic cells. We found that the Evolutionary-optimized Support Vector Machine (SVM) algorithm succeeded in the classification of normal and apoptotic cells in a highly satisfying result.
This is the first time that cells are classified based on lamina spatial organization using the machine learning approach. We suggest that this approach can be used for diagnostic applications to classify normal and apoptotic cells.
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Gruenbaum, Y., Margalit, A., Goldman, R., Shumaker, D., Wilson, K.: The nuclear lamina comes to an age. Nat. Rev. Mol. Cell Biol. 6, 21–31 (2005)
Kosak, S., Groudine, M.: Gene order and dynamic domains. Science 306, 644–647 (2004)
Pickersgill, H., Kalverda, B., de Wit, E., Talhout, W., Fornerod, M., van Steensel, B.: Characterization of the drosophila melanogaster genome at the nuclear lamina. Nature Genetics 38, 1005–1014 (2006)
Raz, V., Carlotti, F., Vermolen, B., van der Poel, E., Sloos, W., Knaän-Shanzer, S., de Vries, A., Hoeben, R., Young, I., Tanke, H., Garini, Y., Dirks, R.: Changes in lamina structure are followed by spatial reorganization of heterochromatic regions in caspase-8-activated human mesenchymal stem cells. J. Cell Sci. 119, 4247–4256 (2006)
Broers, J., Machiels, B., van Eys, G., Kuijpers, H., Manders, E., van Driel, R., Ramaekers, F.: Dynamics of the nuclear lamina as monitored by gfp-tagged a-type lamins. J. Cell Sci. 112, 3463–3475 (1999)
Fricker, M., Hollinshead, M., White, N., Vaux, D.: Interphase nuclei of many mammalian cell types contain deep, dynamic, tubular membrane-bound invaginations of the nuclear envelope. J. Cell Biol. 136, 531–544 (1997)
Scaffidi, P., Misteli, T.: Lamin a-dependent nuclear defects in human aging. Science 312, 1059–1063 (2006)
Zhang, L., Samaras, D., Tomasi, D., Volkow, N., Goldstein, R.: Machine learning for clinical diagnosis from functional magnetic resonance imaging. In: IEEE International Conference Computer Vision and Pattern Recognition, vol. 1, pp. 1211–1217 (2005)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Santa Fe Institute Studies in the Sciences of Complexity. ASIN (1991)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Rosenblatt, F.: The perceptron: A perceiving and recognizing automaton. Report 85-460-1, Project PARA, Cornell Aeronautical Laboratory, Ithaca, New York (1957)
Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Fifth Annual Workshop on Computational Learing Theory, pp. 144–152 (1992)
Vermolen, B., Garini, Y., Mai, S., Mougey, V., Fest, T., Chuang, T., Chuang, A., Wark, L., Young, I.: Characterizing the three-dimensional organization of telomeres. Cytometry 67A, 144–150 (2005)
Hendriks, C.L.L., van Vliet, L.J., Rieger, B., van Ginkel, M., Ligteringen, R.: DIPimage: A Scientific Image Processing Toolbox for MATLAB (2005), http://www.ph.tn.tudelft.nl/DIPlib/
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Bäck, T.: Evolutionary algorithms in theory and practice. Oxford University Press, New York (1996)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)
Pittenger, M., Mackay, A., Beck, S., Jaiswal, R., Douglas, R., Mosca, J., Moorman, M., Simonetti, D., Craig, S., Marshak, D.: Multilineage potential of adult human mesenchymal stem cells. Science 284, 143–147 (1999)
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Shir, O.M., Raz, V., Dirks, R.W., Bäck, T. (2007). Classification of Cell Fates with Support Vector Machine Learning. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_25
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DOI: https://doi.org/10.1007/978-3-540-71783-6_25
Publisher Name: Springer, Berlin, Heidelberg
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