Identification of macromolecular complexes in cryoelectron tomograms of phantom cells

Abstract

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.

Fig 1.

Schematic flow diagram showing the detection and identification strategy.

Fig 2.

Central x-y slices through the 3D reconstructions of ice-embedded phantom cells filled with 20S proteasomes (a), thermosomes (b), and a mixture of both particles (c). At low magnification, the macromolecules appear as small dots.

Fig 3.

Result of MSA analysis. Graph representing the Euclidian distance (y axis) of each sample from the particle average (absolute similarity). Samples are ordered along the x axis with descending correlation coefficients (relative similarity). Samples with higher correlation coefficients have a smaller distance from the average compared to samples with lower correlation coefficient; thereby, a clear step is visible. Note that among the samples with high correlation values, there are four outliers, i.e., miss-hits of the cross-correlation.

Fig 4.

(a) Histogram of the correlation coefficients of the particles found in the proteasome-containing phantom cell scanned with the “correct” proteasome and the “false” thermosome template. Of the 104 detected particles, 100 were identified correctly. The most probable correlation coefficient is 0.21 for the proteasome template and 0.12 for the thermosome template. (b) Histogram of the correlation coefficients of the particles found in the thermosome-containing phantom cell. Of the 88 detected particles, 77 were identified correctly. The most probable correlation value is 0.21 for the thermosome template and 0.16 for the proteasome template.

Fig 5.

(a) Gallery of x-y slices through the average of the particles found in the proteasome-containing phantom cell. The dimensions of the particle are in good agreement with those of a 20S proteasome. The sevenfold symmetry is barely discernible, but the central cavity is clearly revealed. The corresponding average generated with a thermosome template did not show a distinct structure. (b) Gallery of y-z slices through the same particle. (c) y-z slices of the 20S proteasome template used for the detection and identification procedure. (d) Gallery of x-y slices through the average of the particles found in the thermosome-containing phantom cell. Again the size of the particles is in good agreement with the structure of a thermosome. The eightfold symmetry can be recognized in the x-y slices; the symmetry of the particle was not imposed. (e) Gallery of y-z slices through the same particle. (f) y-z slices of the thermosome template.

Fig 6.

Fourier shell correlation of the detected particles. By using a threshold of 0.5 of the cross correlation coefficients, the resolution is estimated to be at ≈0.35 of the Nyquist frequency, indicating a resolution of ≈4 nm.

Fig 7.

Volume-rendered representation of a reconstructed ice-embedded phantom cell containing a mixture of thermosomes and 20S proteasomes. After applying the template-matching algorithm, the protein species were identified according to the maximal correlation coefficient. The molecules are represented by their averages; thermosomes are shown in blue, the 20S proteasomes in yellow.