PARTICLE ESTIMATION FOR ELECTRON TOMOGRAPHY

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Electron tomography (ET) of plastic sections is limited in quality by the low signal-to-noise ratio (SNR) of the data. This problem is even greater for ET of frozen-hydrated samples (cryo ET) because they have low contrast and are very sensitive to damage by the electron beam. SNR for all EM samples is typically a decreasing function of resolution one is trying to achieve, severely limiting the amount of structural detail that is accessible in a tomogram. For specimens containing multiple copies of a given structure, we have developed an algorithm to improve the SNR by estimating the true 3D structure that is present in the cell, based on the images of multiple copies of the same structure that are often visible in a tomogram. Our technique builds on the approach developed for single-particle electron microscopy, with the primary difference that alignment and averaging occur over the 3D tomographic volume. The advantage of this approach is that the structure of interest can be studied in situ instead of having to be isolated from its cellular context. Our algorithm for estimating the true 3D particle structure is as follows. Using manually selected particle locations within the tomogram, a sub volume containing each particle is excised and then aligned rotationally by explicitly comparing each sub volume with a reference volume over a range of discrete Euler rotations. Sub volume comparison is typically computed using a Fourier domain local correlation coefficient sequence function, which also provides the optimal translational shift for each rotation. The reference volume can be chosen from the collection of particles, or an unbiased reference can be generated by a pair-wise binary tree alignment of a subset of particles. This alignment procedure is typically iterated, reducing the rotational search space and granularity, and allowing an update of the reference volume at each iteration. Once we have rotation and translation estimates for each particle we estimate the particle volume by averaging the aligned sub volumes. We compensate for the wedge of missing data that is characteristic of single-axis tilting ET by accounting for the Fourier component contribution, or lack thereof, from each particle as it is transformed into alignment with the reference. Qualitatively, our particle estimation algorithm allows us to visualize structural details that are not visible in the original tomogram. Quantitatively, the spectral-signal-to-noise ratio measurements show SNR improvements close to that expected for the number of particles averaged.