Correction of missing-wedge artifacts in filamentous tomograms by template-based constrained deconvolution

Cryo-electron tomography maps often exhibit considerable noise and anisotropic resolution, due to the low-dose requirements and the missing wedge in Fourier space. These spurious features are visually unappealing and, more importantly, prevent an automated segmentation of geometric shapes, requiring a highly subjective, labor-intensive manual tracing. We developed a novel computational strategy for objectively denoising and correcting missing-wedge artifacts in the special but important case of repetitive basic shapes, such as filamentous structures. In this approach, we use the template and a non-negative "location map" to constrain the deconvolution scheme, allowing us to recover, to a considerable degree, the information lost in the missing wedge. We applied our method to data of actin-filament bundles of inner-ear stereocilia, which are critical in hearing transduction processes, and found a much better overlap with the experimental map than manual tracing was able to achieve, confirming that the latter is subject to visual interpretation errors. In addition, we demonstrate that our method can also be used for membrane detection.