Niels Volkmann

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  Extracting structural information from electron tomograms
  Niels Volkmann

  Electron tomography is the most widely applicable method for obtaining 3D information by electron microscopy. It has been realized that electron tomography is capable of providing a complete, molecular resolution three-dimensional mapping of entire proteomes including their detailed interactions. However, to realize this goal, information needs to be extracted efficiently form these tomograms. Owing to low SNR, this task is currently mostly carried out manually. Apart from the subjectivity of the process, its time consuming nature precludes the prospects of high throughput processing. Standard template matching approaches rely on "matched filtering", which can be shown to be a Bayesian classifier as long as the template and the target are nearly identical and the noise is independent and identically distributed, Gaussian, and additive. These conditions are not very well met for electron tomographic reconstructions because the noise is spatially correlated by the reconstruction process and the point spread function. As a consequence, many false hits are generated by this method in areas of high density such as membranes or dense vesicles.

  In order to address this challenge, we are developing an alternative method for feature recognition in electron tomography, which is based on the use of reduced representation templates. Reduced representations approximate the target by a small number of anchor points. These anchor points are then used to calculate the scoring function within the search volume. This strategy makes the approach robust against noise and against local variations such as those expected from uneven staining. We recently completed a number of proof-of-concept application of this algorithm for detecting ribosomes in electron tomograms of high-pressure-frozen plastic embedded as well as cryo-sectioned mammalian cell sections. The percentage of false hits for this data drops dramatically from ~50% to under 20% if compared to the matched filter approach. Here, I will describe the principles underlying our approach and will present results obtained from its application.