Optimizing data quality and completeness in visual proteomics experiments

Cryo-electron tomography (cryo-ET) is fast developing from a tool primarily used to investigate structures of individual macromolecular complexes in situ into a high-resolution probe for molecular processes within diverse functional contexts in intact cells. It is thus increasingly necessary that the data are analyzed and quantified as completely as possible. But annotating and structurally characterizing macromolecular complexes with a high degree of completeness is a significant challenge, especially for smaller molecular targets. In particular, it is difficult to avoid incomplete localizations of complexes, false identifications, or losses during computational classification. To address these issues, we assessed parameters in data processing, including the role of voxel size in template matching, the effects of Volta phase plate imaging on localization, classification, and map refinement, and the extent to which multi-particle-based refinement of tiltseries improves these data processing steps. Our analyses provide practical guidelines that help maximize completeness in cellular cryo-ET data; accurate description of the sample is crucial for visual proteomics experiments, and these optimizations help ensure that data annotation and analysis are comprehensive.