A feature-guided, focused 3D signal permutation method for subtomogram averaging

John Jacob Peters; Jeremy Leitz; Qiang Guo; Florian Beck; Wolfgang Baumeister; Axel T Brunger

doi:10.1016/j.jsb.2022.107851

A feature-guided, focused 3D signal permutation method for subtomogram averaging

J Struct Biol. 2022 Jun;214(2):107851. doi: 10.1016/j.jsb.2022.107851. Epub 2022 Mar 26.

Authors

John Jacob Peters¹, Jeremy Leitz¹, Qiang Guo², Florian Beck³, Wolfgang Baumeister⁴, Axel T Brunger⁵

Affiliations

¹ Department of Molecular and Cellular Physiology, Stanford University, Stanford, United States; Department of Neurology and Neurological Sciences, Stanford University, Stanford, United States; Department of Structural Biology, Stanford University, Stanford, United States; Department of Photon Science, Stanford University, Stanford, United States; Howard Hughes Medical Institute, Stanford University, Stanford, United States.
² State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China; Department of Structural Biology, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany.
³ CryoEM Technology, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany.
⁴ Department of Structural Biology, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany.
⁵ Department of Molecular and Cellular Physiology, Stanford University, Stanford, United States; Department of Neurology and Neurological Sciences, Stanford University, Stanford, United States; Department of Structural Biology, Stanford University, Stanford, United States; Department of Photon Science, Stanford University, Stanford, United States; Howard Hughes Medical Institute, Stanford University, Stanford, United States. Electronic address: brunger@stanford.edu.

Abstract

Advances in electron microscope instrumentation, cryo-electron tomography data collection, and subtomogram averaging have allowed for the in-situ visualization of molecules and their complexes in their native environment. Current data processing pipelines commonly extract subtomograms as a cubic subvolume with the key assumption that the selected object of interest is discrete from its surroundings. However, in instances when the object is in its native environment, surrounding densities may negatively affect the subsequent alignment and refinement processes, leading to loss of information due to misalignment. For example, the strong densities from surrounding membranes may dominate the alignment process for membrane proteins. Here, we developed methods for feature-guided subtomogram alignment and 3D signal permutation for subtomogram averaging. Our 3D signal permutation method randomizes and filters voxels outside a mask of any shape and blurs the boundary of the mask that encapsulates the object of interest. The randomization preserves global statistical properties such as mean density and standard deviation of voxel density values, effectively producing a featureless background surrounding the object of interest. This signal permutation process can be repeatedly applied with intervening alignments of the 3D signal-permuted subvolumes, recentering of the mask, and optional adjustments of the shape of the mask. We have implemented these methods in a new processing pipeline which starts from tomograms, contains feature-guided subtomogram extraction and alignment, 3D signal-permutation, and subtomogram visualization tools. As an example, feature-guided alignment and 3D signal permutation leads to improved subtomogram average maps for a dataset of synaptic protein complexes in their native environment.

Keywords: 3D signal subtraction; Cryo-electron tomography; Feature-guided alignment; In situ cellular tomography; Subtomogram averaging.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Cryoelectron Microscopy / methods
Electron Microscope Tomography* / methods
Image Processing, Computer-Assisted* / methods

Grants and funding

R01 MH063105/MH/NIMH NIH HHS/United States