RAZA: A Rapid 3D z-crossings algorithm to segment electron tomograms and extract organelles and macromolecules

Resolving the 3D architecture of cells to atomic resolution is one of the most ambitious challenges of cellular and structural biology. Central to this process is the ability to automate tomogram segmentation to identify sub-cellular components, facilitate molecular docking and annotate detected objects with associated metadata. Here we demonstrate that RAZA (Rapid 3D z-crossings algorithm) provides a robust, accurate, intuitive, fast, and generally applicable segmentation algorithm capable of detecting organelles, membranes, macromolecular assemblies and extrinsic membrane protein domains. RAZA defines each continuous contour within a tomogram as a discrete object and extracts a set of 3D structural fingerprints (major, middle and minor axes, surface area and volume), enabling selective, semi-automated segmentation and object extraction. RAZA takes advantage of the fact that the underlying algorithm is a true 3D edge detector, allowing the axes of a detected object to be defined, independent of its random orientation within a cellular tomogram. The selectivity of object segmentation and extraction can be controlled by specifying a user-defined detection tolerance threshold for each fingerprint parameter, within which segmented objects must fall and/or by altering the number of search parameters, to define morphologically similar structures. We demonstrate the capability of RAZA to selectively extract subgroups of organelles (mitochondria) and macromolecular assemblies (ribosomes) from cellular tomograms. Furthermore, the ability of RAZA to define objects and their contours, provides a basis for molecular docking and rapid tomogram annotation

Application of 3D BLE to synthetic phantoms corrupted with simulated cytosolic noise.

<p>Performance of the 3D BLE, 3D recursive and 3D Canny filters was assessed using the same volume of 3D synthetic phantoms shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033697#pone-0033697-g001" target="_blank">Figure 1</a>, but contaminated with different levels of simulated experimental noise. (<b>A1–A5</b>) A graphical representation of the SNR present in the five representative cases shown. Coloring in A1–A5 is as follows: <b>green dotted line</b> shows the contrast and intensity of the original signal; <b>red line</b> shows the contrast and intensity of the noise; <b>green solid line</b> shows the scaling and shifting of signal profile towards noise profile. Overall, the graph shows the probability density function (G(I)) of the normal distribution (<b>B1–B5</b>) 2D sections taken from synthetic volumes contaminated with experimental noise. (<b>C1–C5</b>) 3D surface rendering of results obtained following application of the 3D BLE filter to the synthetic dataset. (<b>D1–D5</b>) Surface rendering of 3D recursive-filtered test dataset. (<b>E1–E5</b>) Surface rendering of 3D Canny-filtered dataset.</p

Detection of molecular volumes using 3D BLE.

<p>The ability of the3D BLE, 3D recursive and 3D Canny filters to resolve molecular contours was assessed using a test volume populated with 3D GroEL molecules. A representative region of the test volume showing 9 molecules is shown. (<b>A1–A5</b>) SNR illustrated as for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033697#pone-0033697-g002" target="_blank">Figure 2</a>. (<b>B1–B5</b>) 2D sections taken from synthetic volumes contaminated with experimental noise. (<b>C1–C5</b>) Surface rendering of results following application of the 3D BLE filter applied to the test volume contaminated with experimental noise. (<b>D1–D5</b>) Surface rendering of 3D recursive-filtered test volume. (<b>E1–E5</b>) Surface rendering of 3D Canny-filtered test volume.</p

Extraction of molecular contours from an electron tomogram subvolume.

<p>Application of the 3D BLE, 3D recursive and 3D Canny filters to a subvolume of an experimentally-recorded tomogram of a resin-embedded <i>C. reinhardtii</i> cell. (<b>A</b>) Unprocessed, central 2D cross-section of the subvolume extracted from the 3D tomogram showing a region of the chloroplast heavily populated with putative macromolecular assemblies (dark objects). The inset in (A) highlights a randomly chosen single particle, represented as an isosurface rendering and shown at a selected number of orientations around the y-axis. (<b>B</b>) 3D surface rendering of results obtained from application of the 3D BLE filter. (<b>C</b>) Surface rendering of the 3D recursive-filtered subvolume. (<b>D</b>) Surface rendering of the 3D Canny-filtered subvolume.</p

Statistical evaluation of filter performance using synthetic volumes contaminated with different levels of simulated experimental noise shown in Figure 2.^a

a<p>RMSE scores between the input volumes and the three filter outputs are shown in bold. Smaller scores represent higher levels of correlation with the input volume. Values in brackets are the percentage voxel variation between input volumes and the three filtered outputs.</p

Application of 3D BLE to synthetic phantoms corrupted with Gaussian and impulse noise.

<p>Performance of the 3D BLE, 3D recursive and 3D Canny filters was assessed using a volume of 3D synthetic phantoms contaminated with increasing levels of Gaussian and impulse noise. (<b>A1–A5</b>) 2D sections taken from synthetic volumes contaminated with increasing levels of Gaussian and impulse noise. (<b>B1–B5</b>) 3D surface rendering of results (B1–B5) obtained from the 3D BLE filter. (<b>C1–C5</b>) Surface rendering of the 3D recursive-filtered synthetic dataset. (<b>D1–D5</b>) Surface rendering of the 3D Canny-filtered synthetic dataset.</p

A 3D image filter for parameter-free segmentation of macromolecular structures from electron tomograms

3D image reconstruction of large cellular volumes by electron tomography (ET) at high (≤5 nm) resolution can now routinely resolve organellar and compartmental membrane structures, protein coats, cytoskeletal filaments, and macromolecules. However, current image analysis methods for identifying in situ macromolecular structures within the crowded 3D ultrastructural landscape of a cell remain labor-intensive, time-consuming, and prone to user-bias and/or error. This paper demonstrates the development and application of a parameter-free, 3D implementation of the bilateral edge-detection (BLE) algorithm for the rapid and accurate segmentation of cellular tomograms. The performance of the 3D BLE filter has been tested on a range of synthetic and real biological data sets and validated against current leading filters-the pseudo 3D recursive and Canny filters. The performance of the 3D BLE filter was found to be comparable to or better than that of both the 3D recursive and Canny filters while offering the significant advantage that it requires no parameter input or optimisation. Edge widths as little as 2 pixels are reproducibly detected with signal intensity and grey scale values as low as 0.72% above the mean of the background noise. The 3D BLE thus provides an efficient method for the automated segmentation of complex cellular structures across multiple scales for further downstream processing, such as cellular annotation and sub-tomogram averaging, and provides a valuable tool for the accurate and high-throughput identification and annotation of 3D structural complexity at the subcellular level, as well as for mapping the spatial and temporal rearrangement of macromolecular assemblies in situ within cellular tomograms