Automated segmentation of electron tomograms for a quantitative description of actin filament networks
Introduction
Electron tomography provides, in a digital format, information about the three-dimensional density distribution within the object under study or, more precisely, its locally varying electron scattering power. For perception of these data by the human visual system, it is necessary to convert them into images either in the form of a series of two-dimensional (2D) images (‘slices’) or three-dimensional (3D) images generated by surface or volume rendering. For some purposes this form of visualization is sufficient. However, for a deeper analysis of structural features in the complex settings of cellular landscapes, it is necessary to characterize them in terms of parameters such as surface areas, volumes, distances or angles. A quantitative analysis of this kind makes it necessary to identify and localize the features of interest and to separate them from the background. In other words, the tomograms must be segmented.
Here we describe a novel approach for the automated segmentation of filamentous structures, in particular actin filaments. Currently, such segmentations are mostly performed manually under visual control by drawing contour lines, sometimes supported by drawing programs (Kremer et al., 1996, Stalling et al., 2005). Manual segmentation, however, is slow and tedious and not as reproducible as a thorough quantitative or statistical analysis might require. Although the expertise of the person executing this task may be a valuable asset, there is an element of subjectivity that might introduce some bias. Moreover, some features are easily overlooked since they must be segmented in 2D-slices; for example an actin filament running perpendicular to the plane of a tomographic slice is hard to recognize. Even though the results of an automated segmentation might not be perfect when compared with the ground truth – which is usually unknown – it is reproducible and allows a meaningful comparison of results from a multitude of experiments. Obviously, the results of an automated segmentation depend on user-defined parameter settings, but if the segmentation process is fast, many parameter settings can be explored and an optimal setting can be chosen, which is then applied throughout a set of experiments.
The poor signal-to-noise ratio (SNR) of electron tomograms recorded under low-dose conditions and the artifacts arising from incomplete data (‘missing wedge’) make segmentation a challenging task. Existing segmentation methods as used widely in biomedical imaging cannot cope with data of this kind (Al-Kofahi et al., 2002, Huang and Stockman, 1993, Kirbas and Quek, 2004). We have shown previously that template matching allows to identify known macromolecular structures in tomograms of frozen-hydrated phantom or real cells (Böhm et al., 2000, Frangakis et al., 2002, Ortiz et al., 2006). Subvolumes containing structures similar to the template can then be extracted from the tomograms, aligned and classified, and finally averaged. Template matching is usually preceded by a ‘denoising’ step. Here, we use denoising followed by template matching as a first step in the segmentation procedure. A generic cylinder, whose length and diameter has to be adapted to the imaging parameters, is used as a template. The cross-correlation involved in the template matching step provides not only a map of cross-correlation coefficients as a measure of the similarity between the template and the local density in the tomogram, but also a map representing the best local orientation of the cylindrical template. These two maps are then used to calculate a similarity function that allows to evaluate the likelihood that two neighboring voxels are connected by a filament. The second step of the segmentation process is a new tracing algorithm scanning all voxels for possible connection on the basis of the similarity function. This results in an array of filaments each represented by its central line.
The concept of using an orientation field for the segmentation of thin structures has been reported by Sandberg and Brega (2007). These authors applied a type of local Hough transform in order to derive the orientation field, in contrast to our approach, which is based on template matching. Mainly designed for the segmentation of surfaces, the former approach works only in 2D, making it necessary to process tomography data slice by slice. The resulting slices are still grey level images and need further processing for a geometric representation of surfaces.
We have used our automated segmentation approach to characterize actin networks in cellular tomograms. Actin polymerization and depolymerization underlie cytoplasmic organization and cell motility (see e.g. Pollard and Borisy, 2003, Ridley et al., 2003). Actin filament networks with different characteristics exist in distinct cellular territories as shown schematically in Fig. 1 and there is a need to describe them in quantitative terms. We have used the results of automated segmentations to describe properties of individual filaments (length, stiffness) and of actin networks (density, mutual orientations). We have also addressed the issue of actin branching, as mediated by the Arp2/3 complex (Pollard and Beltzner, 2002). The very existence of such actin branching has recently become a matter of some controversy (Higgs, 2011, Insall, 2011, Urban et al., 2010). Clearly, the detection of genuine branchings and their characterization cannot be based on visual inspection of tomograms or on manual segmentation. Large data sets must be analyzed and branching must be characterized by parameters based on statistics – a task calling for automation.
Section snippets
Preparation and vitrification of cells
Dictyostelium discoideum strain AX2-214 (wild type) cells were prepared and vitrified as described previously (Medalia et al., 2007). Cells were washed twice in 17 mM K/Na-phosphate buffer (PB), pH 6.0, before they were incubated in PB and allowed to settle on holey-carbon coated 200 mesh gold EM finder-grids (Quantifoil, Jena, Germany and Protochips, Raleigh, NC, USA). Grids were removed from culture dishes and immediately supplemented with 5 μl of protein-stabilized colloidal gold in PB. Excess
Template matching
Seen at a resolution of a few nanometers, an actin filament can be modeled as a rather stiff cable composed of monomeric cylinders that are concatenated end by end. It is reasonable to use such a model for template matching: The whole tomogram is traversed and a cylinder of appropriate length and diameter is locally fitted to the data, thereby determining for each voxel the orientation in which the cylinder is most similar to the image. An example of the cylindrical template is shown in the
Demonstration of the method
Fig. 3 shows the result of a segmentation applied to a tomogram of a peripheral region of a D. discoideum cell. A slice through the tomogram is shown in Fig. 3A and B; Fig. 3A is the original reconstruction and Fig. 3B the same after noise reduction using the non-local means filter. A dense network of filamentous structures and some interspersed macromolecular complexes are clearly visible. Fig. 3C and D show the results of the template matching step. Fig. 3C is a slice through the map of cross
Reliability of automated filament segmentation
An assessment of the automated segmentation is problematic, since the true arrangement of actin filaments is unknown in the case of real data. As usual, a comparison of manual and automated segmentations gives an impression of how reliable the automated method is. Fig. 6 shows four segmentations of a sub-frame of the membrane protrusion tomogram displayed in Fig. 4A. One (Fig. 6D) is the result of the automated approach and the others (Fig. 6A–C) were obtained by manual segmentation performed
Technical aspects
The automated segmentation approach described here has been implemented in the data analysis and visualization framework Amira (Stalling et al., 2005), commercially distributed by Visage Imaging, Berlin, Germany. Compiled binary versions (for Windows, Mac OS X and Linux) of the actin segmentation package will be made available for testing purposes to end-users via the Zuse Institute Berlin upon electronic registration under http://www.zib.de/en/visual/software/CryoEM.html.
Acknowledgments
The research leading to these results has received funding from an inter-institutional research initiative of the Max Planck Society. We thank Dr. Günther Gerisch and Mary Ecke for providing AX2 cells and we gratefully acknowledge Israel Patla for providing data on the stress fiber.
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These authors contributed equally to this work.