Automated tracing of filaments in 3D electron tomography reconstructions using Sculptor and Situs
Introduction
Situs is a widely used modeling package of command-line tools, originally developed for the interpretation of low-resolution electron microscopy density maps (Wriggers et al., 1999, Wriggers, 2010). Sculptor is a molecular graphics visualization program based in part on fast Situs algorithms that can be explored in real time (Birmanns and Wriggers, 2003, Birmanns et al., 2011). Both packages play important (and complementary) bridging roles between high-resolution atomic structures and lower-resolution structural data from other experimental sources. We recently began to focus on applications in cryo-electron tomography (cryo-ET; Baumeister, 2002), an important but particularly challenging application field in structural biology due to the currently limited resolution (4–5 nm) and high noise present in the 3D reconstructions (Medalia et al., 2002, Frangakis and Förster, 2004).
To allow for a meaningful and reproducible analysis of cryo-tomograms, it is necessary to separate features of interest from the background. This process is referred to as “segmentation” and is currently mostly performed manually by assigning a label to pixels sharing the visual characteristics of the feature of interest. For a tomographic stack of images, the corresponding contours of these labels can then be used to reconstruct the 3D shape of the feature of interest. This procedure often relies on non-objective user-dependent selection and tracing steps. Given the enormous amounts of information present in tomographic reconstructions, it is virtually impossible to perform a complete segmentation based on visual inspection of the tomogram. For a statistically sound analysis of this information, a robust automated segmentation approach is needed that is also capable of detecting small-scale features. Depending on the quality of the tomographic data, these features are usually hard to recognize and can be easily overlooked, especially if they have orientations perpendicular to the plane of a tomographic slice. This is particularly true for the filaments in cryo-ET tomograms of cellular actin networks studied in this paper.
In this report, we describe a software implementation for the extraction of actin filaments from 3D tomography maps comprising the following steps:
- 1.
Two denoising filters are described which reduce high-frequency noise artifacts and which facilitate the desired segmentation of features. Herein, we compared the performances of a linear Gaussian-weighted averaging filter and of a nonlinear Digital Paths Supervised Variance (DPSV) denoising filter.
- 2.
A Gaussian-weighted local normalization is applied to cryo-ET maps prior to any analysis. Such normalization is beneficial because it enhances the appearance of structural features and equalizes any uneven density distributions across experimental cryo-ET maps.
- 3.
Two map editing tools, polygon-clipping and a multi-point floodfill, are described which facilitate filament tracing by masking out non-filamentous density regions such as the cell membrane, extracellular space, ribosomes and storage vesicles.
- 4.
Cryo-ET reconstructions suffer from incomplete sampling of the Fourier space, resulting in anisotropic resolution and degradation of image quality in the reconstructed tomogram along the direction of the optical axis. We corrected for these effects in real space by a resolution anisotropy and an orientation-dependent attenuation of filament intensities.
- 5.
VolTrac (Volume Tracer) was originally developed for detecting alpha-helices (Rusu and Wriggers, 2012), but it was modified here to annotate filamentous actin networks in cryo-ET reconstructions. The VolTrac method combines a genetic algorithm (GA) for quasi-continuous sampling with a bidirectional expansion for following filament curvature and length.
- 6.
The resulting predictions are compared to subjective expert annotations of filaments.
In the following sections below we explain the six steps and apply them to the detection of actin networks in Dictyostelium discoideum cells. Actin polymerization powers the formation of different types of cellular actin networks (Pollard and Borisy, 2003), such as finger-like extensions of the cell membrane, referred to as filopodia. Filopodia are characterized by a central core of bundled actin filaments. Their backbone consists of shorter actin filaments, which are aligned in parallel or obliquely to the filopod’s axis (Medalia et al., 2007). A D. discoideum filopodium dataset (Medalia et al., 2007) is used for visualization purposes in Sections 3 Local normalization of map density, 4 Segmentation of non-filamentous densities, 5 Correction for missing Fourier information, 6 Segmentation of filamentous density, whereas a sub-volume containing cross-linked actin filaments (Rigort et al., 2012) is used for validation in Section 7. We conclude the paper with a discussion of implementation details, limitations, and future work in Section 8.
Section snippets
Denoising 3D tomography reconstructions
Frozen-hydrated biological specimens are highly sensitive to ionizing radiation. Therefore, in cryo-ET, the cumulative electron dose must be minimized and distributed between images to avoid radiation damage-related structural changes within the vitrified sample. As a consequence of this electron dose fractionation, and due to the low contrast of ice-embedded specimens, the signal-to-noise ratio (SNR) in 3D reconstructions is very low. Therefore, careful noise reduction is essential prior to
Local normalization of map density
Cryo-ET volumes can suffer from uneven density levels across the map due to uneven specimen or ice thickness, or due to missing directions in the backprojection. Fig. 1 shows a reconstruction of a D. discoideum filopodium (Medalia et al., 2007). Upon inspection of the density isolevels of the filaments in the raw data (upper left in Fig. 1), a subtle increase in density from the lower left to the upper right was observed. Therefore, to normalize the features across the map, a Gaussian-weighted
Segmentation of non-filamentous densities
The filopodium dataset shown in Fig. 1 exhibits non-filamentous high-density features that were removed in a series of pre-processing steps.
First, the extracellular space (outside the membrane) was masked by polygon clipping using the established Situs tool voledit. This step involved an interactive tracing of the membrane in the specimen (X, Y) plane and the application of polygon-clipping to all Z sections. The masked density was removed (subtracted) from the map.
Second, the cell membrane
Correction for missing Fourier information
Cryo-ET reconstructions suffer from incomplete sampling of the Fourier space. The filopodium tomograms used in this work were recorded with single-axis tilt about the Y-axis, resulting in a ‘wedge’ of missing Fourier information in the Z-direction (Penczek and Frank, 2006). The missing wedge in Fourier space corresponds to anisotropic, object-dependent artifacts in the real space reconstruction.
For small spherical features there is an elongation in the Z-direction (optical axis) which can be
Segmentation of filamentous density
The work flow of VolTrac, as applied to filament detection in cryo-ET, is shown in Fig. 4. In Sculptor version 2.1 the filament detection can be applied to a map via the menus “Docking” → “Volume Tracer” (entering parameters described below in the pop-up dialog box). Here, we give an abridged overview of the procedure relevant for cryo-ET; for more details, see (Rusu and Wriggers, 2012).
VolTrac utilizes a genetic algorithm (GA; Goldberg, 1989) to optimize randomly placed cylindrical start
Comparison with expert annotations
A validation of the automated filament segmentation can be attempted using manual assignments. In a recent paper (Rigort et al., 2012) a sub-volume, from a tomogram showing a membrane protrusion region, was annotated by three different expert users. Fig. 3A shows the union of the manual segmentation sets performed by the experts and Fig. 3B shows the result of an automated segmentation. The union of the manual sets was chosen because the three expert sets formed smaller subsets of our
Conclusions
We have described a work flow for automatically segmenting filaments in cryo-ET maps including denoising, local normalization, feature detection, and tracing, and we adapted an exhaustive template-based search, VolTrac, to the problem of segmentation of filamentous densities. The results show that the application of these techniques, many of which were developed earlier for single particle cryo-electron microscopy, holds much promise for 3D reconstructions obtained by cryo-ET. The software
Acknowledgments
We thank Ohad Medalia, Cheri Hampton, and Joachim Frank for providing 3D cryo-ET data used in this work. We also thank Ananth Annapragada for support. This work was funded in part by the Polish Ministry of Science and Higher Education program “Support for International Mobility of Scientists Program Third Edition” (Journal of Laws No. 83, item 510) and also in part by the National Institutes of Health (R01GM62968).
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Present address: Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, USA.