Compressed sensing for electron cryotomography and high-resolution subtomogram averaging of biological specimens

Cryoelectron tomography (cryo-ET) and subtomogram averaging (STA) allow direct visualization and structural studies of biological macromolecules in their native cellular environment, in situ, Often, low signal-to-noise ratios in tomograms, low particle abundance within the cell, and low throughput in typical cryo-ET workflows severely limit the obtainable structural information. To help mitigate these limitations, here we apply a compressed sensing approach using 3D second-order total variation (CS-TV2) to tomographic reconstruction. We show that CS-TV2 increases the signal-to-noise ratio in tomograms, enhancing direct visualization of macromolecules, while preserving high-resolution information up to the secondary structure level. We show that, particularly with small datasets, CS-TV2 allows improvement of the resolution of STA maps. We further demonstrate that the CS-TV2 algorithm is applicable to cellular specimens, leading to increased visibility of molecular detail within tomograms. This work highlights the potential of compressed sensing-based reconstruction algorithms for cryo-ET and in situ structural biology.

Automated summary

Cryoelectron tomography and subtomogram averaging enable direct visualization and study of biological macromolecules in their native cellular environment. However, limitations such as low signal-to-noise ratios, low particle abundance, and low throughput have restricted the obtainable structural information. This study demonstrates the potential of a compressed sensing approach to enhance the visualization and resolution of tomograms, addressing these limitations.