Computational reconstruction of cell and tissue surfaces for modeling and data analysis.

We present a method for the computational reconstruction of the 3-D morphology of biological objects, such as cells, cell conjugates or 3-D arrangements of tissue structures, using data from high-resolution microscopy modalities. The method is based on the iterative optimization of Voronoi representations of the spatial structures. The reconstructions of biological surfaces automatically adapt to morphological features of varying complexity with flexible degrees of resolution. We show how 3-D confocal images of single cells can be used to generate numerical representations of cellular membranes that may serve as the basis for realistic, spatially resolved computational models of membrane processes or intracellular signaling. Another example shows how the protocol can be used to reconstruct tissue boundaries from segmented two-photon image data that facilitate the quantitative analysis of lymphocyte migration behavior in relation to microanatomical structures. Processing time is of the order of minutes depending on data features and reconstruction parameters.