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Electron tomograms of intact frozen-hydrated cells are essentially three-dimensional images of the entire proteome of the cell, and they depict the whole network of macromolecular interactions. However, this information is not easily accessible because of the poor signal-to-noise ratio of the tomograms and the crowded nature of the cytoplasm. Here, we describe a template matching algorithm that is capable of detecting and identifying macromolecules in tomographic volumes in a fully automated manner. The algorithm is based on nonlinear cross correlation and incorporates elements of multivariate statistical analysis. Phantom cells, i.e., lipid vesicles filled with macromolecules, provide a realistic experimental scenario for an assessment of the fidelity of this approach. At the current resolution of Ϸ4 nm, macromolecules in the size range of 0.5-1 MDa can be identified with good fidelity.

Electron tomography is the only technique available that allows us to visualize the three-dimensional structure of unfixed and unstained cells currently with a resolution of 6 -8 nm, but with the prospect to reach 2-4 nm. This raises the possibility of detecting and identifying specific macromolecular complexes within their cellular context by virtue of their structural signature. Templates derived from the high-resolution structure of the molecule under scrutiny are used to search the reconstructed volume. Here we outline and test a computationally feasible two-step procedure: In a first step, mean-curvature motion is used for segmentation, yielding subvolumes that contain with a high probability macromolecules in the expected size range. Subsequently, the particles contained in the subvolumes are identified by cross-correlation, using a set of three-dimensional templates. With simulated and real tomographic data we demonstrate that such an approach is feasible and we explore the detection limits. Even structurally similar particles, such as the thermosome, GroEL, and the 20S proteasome can be identified with high fidelity. This opens up exciting prospects for mapping the territorial distribution of macromolecules and for analyzing molecular interactions in situ. It has been a dream of cell biologists to catch a glimpse of the molecular architecture inside cells or cellular organelles, ideally by using a noninvasive technique (1-3). Rapid freezing techniques have been developed, which allow the ''vitrification'' of biological materials and thus ensure their close-to-life preservation (4, 5). With the advent of automated electron tomography (6-9) it has become possible to obtain three-dimensional (3D) data sets of whole ice-embedded cells or organelles (10, 11) with subcritical doses. Currently, the resolution obtained in electron tomography of cellular structures ( Fig. 1) is in the range of 6 to 8 nm. It is reasonable to expect that high-end instrumentation will bring us into the realm of molecular resolution (2-4 nm). The goal of cellular electron tomography is not to obtain a high-resolution structure of a particular macromolecule; the goal is to identify a molecule by virtue of its structural signature and to locate it in the context of its cellular environment. Inevitably, the electron tomograms will suffer from a low signal-to-noise ratio (SNR), and so-called denoising techniques (12, 13) can provide only partial remedy. However, if we have high-or medium-resolution structures of the molecule under scrutiny, furnished by x-ray crystallography, electron microscopy, or any combination of structural biology techniques, these can be used as templates to search the reconstructed cellular volume. This type of scan will make it possible not only to map the distribution of molecules within the cell; it also will reveal the spatial relationships of molecules in functional modules.The purpose of this paper is to outline a computationally feasible strategy for the detection and identification of macromolecules in to...

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