BACKGROUND:Cryo-electron tomography is an important and powerful technique to explore the structure, abundance, and location of ultrastructure in a near-native state. It contains detailed information of all macromolecular complexes in a sample cell. However, due to the compact and crowded status, the missing edge effect, and low signal to noise ratio (SNR), it is extremely challenging to recover such information with existing image processing methods. Cryo-electron tomogram simulation is an effective solution to test and optimize the performance of the above image processing methods. The simulated images could be regarded as the labeled data which covers a wide range of macromolecular complexes and ultrastructure. To approximate the crowded cellular environment, it is very important to pack these heterogeneous structures as tightly as possible. Besides, simulating non-deformable and deformable components under a unified framework also need to be achieved. RESULT:In this paper, we proposed a unified framework for simulating crowded cryo-electron tomogram images including non-deformable macromolecular complexes and deformable ultrastructures. A macromolecule was approximated using multiple balls with fixed relative positions to reduce the vacuum volume. A ultrastructure, such as membrane and filament, was approximated using multiple balls with flexible relative positions so that this structure could deform under force field. In the experiment, 400 macromolecules of 20 representative types were packed into simulated cytoplasm by our framework, and numerical verification proved that our method has a smaller volume and higher compression ratio than the baseline single-ball model. We also packed filaments, membranes and macromolecules together, to obtain a simulated cryo-electron tomogram image with deformable structures. The simulated results are closer to the real Cryo-ET, making the analysis more difficult. The DOG particle picking method and the image segmentation method are tested on our simulation data, and the experimental results show that these methods still have much room for improvement. CONCLUSION:The proposed multi-ball model can achieve more crowded packaging results and contains richer elements with different properties to obtain more realistic cryo-electron tomogram simulation. This enables users to simulate cryo-electron tomogram images with non-deformable macromolecular complexes and deformable ultrastructures under a unified framework. To illustrate the advantages of our framework in improving the compression ratio, we calculated the volume of simulated macromolecular under our multi-ball method and traditional single-ball method. We also performed the packing experiment of filaments and membranes to demonstrate the simulation ability of deformable structures. Our method can be used to do a benchmark by generating large labeled cryo-ET dataset and evaluating existing image processing methods. Since the content of the simulated cryo-ET is more complex and crowded compared with previous ones, it will pose a greater challenge to existing image processing methods.
|Original language||English (US)|
|State||Published - Sep 10 2020|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): URF/1/2602-01, URF/1/3007-01
Acknowledgements: The authors acknowledge Deen Zhang, Jiawei Han and Hongchun li, Yifan Wu for their thoughtful discussion. The author acknowledge Wenqian Kang and Xinyu Liu from School of Precision Instruments and Opto-electronics Engineering in Tianjin University for their help in processing data.
FingerprintDive into the research topics of 'A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation.'. Together they form a unique fingerprint.
A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation
Liu, S. (Creator), Ban, X. (Creator), Zeng, X. (Creator), Zhao, F. (Creator), Gao, Y. (Creator), Wu, W. (Creator), Zhang, H. (Creator), Chen, F. (Creator), Hall, T. (Creator), Gao, X. (Creator), Xu, M. (Creator), Liu, S. (Creator), Ban, X. (Creator), Zeng, X. (Creator), Zhao, F. (Creator), Gao, Y. (Creator), Wu, W. (Creator), Zhang, H. (Creator), Chen, F. (Creator), Hall, T. (Creator), Gao, X. (Creator) & Xu, M. (Creator), figshare, 2020
DOI: 10.6084/m9.figshare.c.5116764, http://hdl.handle.net/10754/665210