Prof. Jürgen M. Plitzko
Phone:+49 89 8578 2645


Florian Beck


Dr. Stephan Nickell
Dr. Matthias Eibauer
Dr. Andreas Korinek
Dr. Michael Stölken

Software development


Automated data acquisition procedures have changed the perspectives of electron tomography in a profound manner. Elaborate data acquisition schemes with autotuning functions minimize exposure of the specimen to the electron beam and sophisticated image analysis routines retrieve a maximum of information from noisy data sets. Our in-house developed software package 'TOM' integrates established algorithms and new concepts tailored to the special needs of low dose ET. It provides a unified platform for all processing steps: acquisition, alignment, reconstruction and analysis (see TOM toolbox, Nickell et al. J Struct Biol, 2005, 149). The acquisition part of TOM serves as the basis for future high throughput applications (see below).

High-throughput data acquisition

Key to the attainment of higher resolution is the availability of large, high quality data sets; this requirement can be difficult to achieve, in particular with samples displaying structural heterogeneity. Automated data acquisition procedures can greatly facilitate the electron microscopic recording of large data sets of consistent quality. We have designed and implemented a high-throughput data acquisition mode. Basically it is a two step procedure, with an intermediate manual selection step. An initial scan (1, grid scan) of the central area of one EM grid is performed at very low magnification resulting in a map of the largest possible field of view. During the acquisition of the map, the stage is directed in a meander-like fashion (1, white arrows). The selection of suitable positions (typically one position corresponds to a single mesh) is the intermediate manual step, where the user defines the areas to be investigated subsequently at high magnification (2, selected mesh). The overall movement of the stage at high magnification is comparable to the grid scan procedure (3, acquisition scheme, trail indicated by the black arrows). Auto-focusing is supported by the generation of a pair of holes (4) prior to every acquisition at two different focus settings. The numbers on the right side indicate the magnitude in meters for the investigated field of view (Figure adapted from Nickell et al. FEBS Letters 581, 2007).

Advanced image analysis

The identification of a single 26S proteasome in the cytoplasm of a Dictyostelium cell suggested that a template matching approach could be used for mapping cellular proteomes. Given that one can see ‘everything’ in a tomogram it makes sense to probe the tomogram with a library of templates derived from known structures using intelligent pattern recognition algorithms. High-resolution structures of numerous macromolecules are available from X-ray crystallography or NMR. The intent is to locate these known structures and determine the molecular context in which complexes are organized in organelles or cells, with less emphasis on the discovery of novel molecular features and more on determining whether there is some correlation in the spatial arrangement of complexes, relative to one another. To achieve this, a ‘template-matching’ strategy is being pursued. However, it is computationally intensive, because not only must the positions matching a given template be determined, but also their spatial orientations have to be identified. The tomogram must be analysed with every template and the best match determined for each complex in the tomogram. Ideally, such a multi-template search would result in a three dimensional map with each low resolution complex identified replaced by the higher resolution template it matched, in the orientation which was determined. The information addressing the spatial relationship of different complexes fosters and complements other proteomic methods and will be indispensable for structural proteomic approaches (Robinson et al., Nature 2007, 450).

Classification and averaging of sub-tomograms - MLTOMO

Classification and averaging of sub-tomograms can improve the fidelity and resolution of structures obtained by electron tomography. The three-dimensional (3D) maximum likelihood algorithm--MLTOMO-- is characterized by integrating 3D alignment and classification into a single, unified processing step. The program calculates the probability of observing an individual sub-tomogram for a given reference structure. It assumes that the reference structure is affected by a 'compound wedge', resulting from the summation of many individual missing wedges in distinct orientations. The distance metric underlying our probability calculations effectively down-weights Fourier components that are observed less frequently. The software package include sample data and works on all Linux based systems. Application of the software to cryo-electron tomographic data of ice-embedded thermosomes revealed distinct conformations.

Download area



Program files



Correlative Microscopy Software 01/2009   Examples
MLTOMO 1.0 05/2010 mltomo.tar.gz    
TOM2 Aquisition 08/2010    


Stölken M., Beck F., Haller T., Hegerl R., Gutsche I., Carazo J.M., Baumeister W., Scheres S.H.W., Nickell S.: Maximum likelihood based classification of electron tomographic data. Journal of Structural Biology, 173(1):77-85, 2011

Korinek A., Beck F., Baumeister W., Nickell S. and Plitzko J.M.: Computer controlled cryo-electron microscopy - TOM(2) a software package for high-throughput applications. Journal of Structural Biology 175:394–405, 2011

Nickell S., Beck F., Korinek A., Mihalache O., Baumeister W. and Plitzko J.M.: Automated cryoelectron microscopy of "single particles" applied to the 26S proteasome. FEBS Letters 581:2751-2756, 2007

Nickell S., Forster F., Linaroudis A., Del Net W., Beck F., Hegerl R., Baumeister W. and Plitzko J.M.: TOM software toolbox: acquisition and analysis for electron tomography. Journal of Structural Biology 149:227-234, 2005

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