A complete data processing workflow for CryoET and subtomogram averaging

Abstract: Electron cryotomography (CryoET) is currently the only method capable of visualizing cells in 3D at nanometer resolutions. While modern instruments produce massive amounts of tomography data containing extremely rich structural information, the data processing is very labor intensive and results are often limited by the skills of the personnel rather than the data. We present an integrated workflow that covers the entire tomography data processing pipeline, from automated tilt series alignment to subnanometer resolution subtomogram averaging. This workflow greatly reduces human effort and increases throughput, and is capable of determining protein structures at state-of-the-art resolutions for both purified macromolecules and cells.

Overview of a Comprehensive Workflow for CryoET Data Processing and Subtomogram Averaging

The paper presents a robust and streamlined data processing workflow for Cryo-Electron Tomography (CryoET) and subtomogram averaging that aims to alleviate the extensive manual labor associated with the traditional methods. These processes are integrated within the EMAN2 environment, enhancing user experience and throughput in determining high-resolution protein structures in both purified macromolecule samples and cellular environments.

Introduction and Workflow Innovations

CryoET is an invaluable technique for visualizing cellular structures in 3D at nanometer resolutions, addressing the limitations faced by CryoEM, particularly in samples with significant conformational variability. Despite advancements in hardware, data processing remains the primary bottleneck in achieving high-throughput and precise structural determination. The authors introduce an innovative workflow that minimizes human intervention through several key algorithmic advancements:

1. Automated Tilt-Series Alignment: Utilizing an iterative landmark-based approach, the workflow eliminates human intervention for various tomograms, significantly enhancing efficiency. By leveraging direct Fourier methods with overlapping tiling, the workflow ensures rapid and accurate tomogram reconstruction, overcoming edge effects prevalent in real-space methods.
2. Per-Particle-Per-Tilt CTF Correction: The methodology innovates per-particle tilt-based CTF correction, crucial for achieving high-resolution subtomogram averages, especially in thick cellular samples. This technique ensures precise phase corrections for tilt images, enhancing data fidelity.
3. Stochastic Gradient Descent for Initial Model Generation: A novel SGD-based process facilitates reliable initial model generation from cell-derived particles, bypassing the need for exhaustive comparisons against potential candidate structures and reducing model bias.
4. Subtilt Refinement Technology: The workflow incorporates a per-particle-per-tilt refinement strategy, offering significant resolution improvements by addressing specimen-induced distortions through individual tilt refinements.

Numerical Results and Performance

The proposed workflow achieves subnanometer resolution in subtomogram averaging, even with limited sample sizes. For instance, using 1,000 - 2,000 particles, resolutions close to 10Å can be reached. A salient demonstration includes the reconstruction of the AcrAB-TolC pump in E. coli, achieving a 14Å resolution post-subtilt refinement. The capabilities illustrated through these results suggest a practical and efficient methodology suited for high-throughput applications in structural biology.

Practical and Theoretical Implications

The developments discussed in the paper suggest significant implications for both practical applications and theoretical advancements in structural biology. The workflow enables researchers to handle complex cellular tomograms more efficiently, potentially transforming the approach to cellular-structural biology. The reduction in manual processing time enhances throughput, enabling more comprehensive datasets to be analyzed in shorter periods.

Theoretically, the innovations in subtomogram refinement and CTF correction could lead to further advancements in the accuracy and resolution of tomographic reconstructions. The ability to map protein structures back to cellular tomograms with high precision could yield new insights into cellular architecture and protein interactions in situ.

Future Directions

Looking forward, the algorithms and methodologies developed in this research hold potential for further adaptation and improvements. Integration with deep learning approaches could further automate particle selection and annotation, enhancing the overall efficacy and resolution of structural determinations. The workflow's adaptability to handle phase-plate data, albeit with existing challenges, offers another avenue for expansion. Future work may extend these methodologies to expansive datasets, improving their applicability across various cellular contexts and extending such detailed structural analysis to even more complex biological systems.

In conclusion, the proposed integrated workflow for CryoET data processing presents a significant step forward in reducing the manual burden and enhancing the throughput of tomography studies. Its integration into the EMAN2 ecosystem aligns well with the growing need for effective and precise structural biology tools, setting the stage for further advancements and applications in the field.