A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation.
KAUST DepartmentKing Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.
Online Publication Date2020-09-09
Print Publication Date2020-12
Permanent link to this recordhttp://hdl.handle.net/10754/665088
MetadataShow full item record
AbstractBACKGROUND: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.
CitationLiu, S., Ban, X., Zeng, X., Zhao, F., Gao, Y., Wu, W., … Xu, M. (2020). A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation. BMC Bioinformatics, 21(1). doi:10.1186/s12859-020-03660-w
SponsorsThe 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.
This work was supported in part by National Natural Science Foundation of China grants 61873299 and 61572075 (to XB), Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing (to XB). The computing work is (partly) supported by MAGICOM Platform of Beijing Advanced Innovation Center for Materials Genome Engineering (to XB). The work was supported in part by U.S. National Institutes of Health grant P41GM103712 (to MX), U.S. National Science Foundation (NSF) grants DBI-1949629 (to MX), Mark Foundation for Cancer Research grant 19-044-ASP (to MX). SL was supported by China Scholar Council (CSC). XZ was supported by a predoctoral fellowship from Carnegie Mellon University’s Center for Machine Learning and Health. This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/2602-01 and URF/1/3007-01(to XG). The founder played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
PublisherSpringer Science and Business Media LLC
RelationsIs Supplemented By:
Sinuo Liu, Xiaojuan Ban, Xiangrui Zeng, Fengnian Zhao, Gao, Y., Wenjie Wu, Hongpan Zhang, Feiyang Chen, Hall, T., Gao, X., & Xu, M. (2020). A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation. figshare. https://doi.org/10.6084/M9.FIGSHARE.C.5116764. DOI: 10.6084/m9.figshare.c.5116764 Handle: 10754/665210
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking.
- Authors: Pei L, Xu M, Frazier Z, Alber F
- Issue date: 2016 Oct 5
- Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).
- Authors: Foffi G, Pastore A, Piazza F, Temussi PA
- Issue date: 2013 Aug
- Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization.
- Authors: Lü Y, Zeng X, Zhao X, Li S, Li H, Gao X, Xu M
- Issue date: 2019 Aug 28
- Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN.
- Authors: Li R, Zeng X, Sigmund SE, Lin R, Zhou B, Liu C, Wang K, Jiang R, Freyberg Z, Lv H, Xu M
- Issue date: 2019 Mar 29
- Hierarchical detection and analysis of macromolecular complexes in cryo-electron tomograms using Pyto software.
- Authors: Lučić V, Fernández-Busnadiego R, Laugks U, Baumeister W
- Issue date: 2016 Dec