Biomedical subjects

Achilleas S Frangakis

Publications and source records attributed to Achilleas S Frangakis.

3 recordsLinked to original sources

PubMed2002

Macromolecular architecture in eukaryotic cells visualized by cryoelectron tomography.

Electron tomography of vitrified cells is a noninvasive three-dimensional imaging technique that opens up new vistas for exploring the supramolecular organization of the cytoplasm. We applied this technique to Dictyostelium cells, focusing on the actin cytoskeleton. In actin networks reconstructed without prior removal of membranes or extraction of soluble proteins, the cross-linking of individual microfilaments, their branching angles, and membrane attachment sites can be analyzed. At a resolution of 5 to 6 nanometers, single macromolecules with distinct shapes, such as the 26S proteasome, can be identified in an unperturbed cellular environment.

Actin Cytoskeleton↗

PubMed2002

Identification of macromolecular complexes in cryoelectron tomograms of phantom cells.

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 approximately 4 nm, macromolecules in the size range of 0.5-1 MDa can be identified with good fidelity.

Algorithms↗

PubMedDate not supplied

Segmentation of two- and three-dimensional data from electron microscopy using eigenvector analysis.

An automatic image segmentation method is used to improve processing and visualization of data obtained by electron microscopy. Exploiting affinity criteria between pixels, e.g., proximity and gray level similarity, in conjunction with an eigenvector analysis, the image is subdivided into areas which correspond to objects or meaningful regions. Extending a proposal by Shi and Malik (1997, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 731-737) the approach was adapted to the field of electron microscopy, especially to three-dimensional application as needed by electron tomography. Theory, implementation, parameter setting, and results obtained with a variety of data are presented and discussed. The method turns out to be a powerful tool for visualization with the potential for further improvement by developing and tuning new affinity.

Algorithms↗