III: Small: Improving automation and speed of macromolecule recognition and localization in cryo-electron tomography using unsupervised deep learning

III：小：使用无监督深度学习提高冷冻电子断层扫描中大分子识别和定位的自动化程度和速度

• 批准号: 2007595
• 负责人: Min Xu
• 金额: $ 25万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007595&HistoricalAwards=false
• 关键词: III; Small; Improving; automation; speed

Cells are basic structural and functional units of all known living organisms. Understanding the structures and spatial localizations of large individual macromolecules inside cells is fundamental to the biological research community. However, such information has been difficult to obtain due to the lack of data acquisition techniques. Recent advances in Cryo-electron tomography (cryo-ET) have enabled submolecular resolution 3D visualization of the near-native structures and spatial organizations of large macromolecules and their interactions with other subcellular components in single cells. However, the rapidly-increasing amount of diverse cryo-ET data brings along major challenges to high-throughput systematic analysis. Automation and computation efficiency have become bottlenecks. This project will focus on improving both automation and speed of macromolecule recognition and localization in tomograms using unsupervised deep learning. The proposed methods will have a wide range of applications in life science that involve cryo-ET. This project will train graduate and undergraduate students in computational biology, bioinformatics, and bioimage analysis, as well integrate research results into university curricula.Cryo-ET has emerged as the most powerful technique for the structural recovery, recognition, and localization of macromolecules in situ. To significantly improve the automation and speed of macromolecule recognition and localization in tomograms, this project will develop two key unsupervised deep learning techniques, including (1) a novel simultaneous simulator and denoiser to create realistically simulated subtomograms; and (2) a method for clustering macromolecule structures by disentangling structure information from orientations and displacements. To facilitate broad use of the methods developed from this project, the software implementation of the proposed methods will be integrated into the open-source software AITom, so that they are ready to be used by the structural biology community. The methods focus on efficiently constructing initial homogeneous subtomogram clusters and producing initial structures for further structure refinement, which well complements existing structural refinement methods and will significantly leverage the systematic de novo and in situ analysis of macromolecules in single cells. The results of this research will be provided on the Xu Lab website and GitHub site.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

细胞是所有已知生物体的基本结构和功能单位。了解细胞内单个大分子的结构和空间定位是生物研究界的基础。然而，由于缺乏数据采集技术，这种信息一直难以获得。冷冻电子断层扫描（cryo-ET）的最新进展使大分子的近天然结构和空间组织及其与单细胞中其他亚细胞成分的相互作用的亚分子分辨率3D可视化成为可能。然而，快速增加的各种冷冻ET数据量带来了沿着的主要挑战，高通量的系统分析。自动化和计算效率已经成为瓶颈。该项目将专注于使用无监督深度学习提高断层图像中大分子识别和定位的自动化和速度。所提出的方法将在涉及低温ET的生命科学中具有广泛的应用。该项目将培养计算生物学、生物信息学和生物图像分析方面的研究生和本科生，并将研究成果纳入大学课程。Cryo-ET已成为大分子结构恢复、识别和原位定位的最强大技术。为了显著提高断层图像中大分子识别和定位的自动化和速度，该项目将开发两种关键的无监督深度学习技术，包括（1）一种新型的同步模拟器和去噪器，以创建逼真的模拟子断层图像;以及（2）一种通过从方向和位移中分离结构信息来聚类大分子结构的方法。为了促进从这个项目开发的方法的广泛使用，所提出的方法的软件实现将被集成到开源软件AITom中，以便它们可以被结构生物学社区使用。该方法的重点是有效地构建初始均匀subtomography集群和产生初始结构，进一步的结构细化，这很好地补充了现有的结构细化方法，并将显着利用系统的从头和原位分析的大分子在单细胞。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography.

• DOI: 10.3389/fmolb.2020.613347
• 发表时间: 2020
• 期刊: Frontiers in molecular biosciences
• 影响因子: 5
• 作者: Zhou B;Yu H;Zeng X;Yang X;Zhang J;Xu M
• 通讯作者: Xu M

Deep Active Learning for Cryo-Electron Tomography Classification

• DOI: 10.1109/icip46576.2022.9898002
• 发表时间: 2022-10
• 期刊: 2022 IEEE International Conference on Image Processing (ICIP)
• 作者: Tianyang Wang;Bo Li;Jing Zhang;Xiangrui Zeng;Mostofa Rafid Uddin;Wei Wu;Min Xu

Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms.

• DOI: 10.1371/journal.pcbi.1008227
• 发表时间: 2020-11
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Li R;Yu L;Zhou B;Zeng X;Wang Z;Yang X;Zhang J;Gao X;Jiang R;Xu M
• 通讯作者: Xu M

Weakly Supervised 3D Semantic Segmentation Using Cross-Image Consensus and Inter-Voxel Affinity Relations.

• DOI: 10.1109/iccv48922.2021.00283
• 发表时间: 2021-10
• 期刊: Proceedings. IEEE International Conference on Computer Vision
• 作者: Zhu X;Chen J;Zeng X;Liang J;Li C;Liu S;Behpour S;Xu M
• 通讯作者: Xu M

Structure Detection in Three-Dimensional Cellular Cryoelectron Tomograms by Reconstructing Two-Dimensional Annotated Tilt Series

通过重建二维注释倾斜系列进行三维细胞冷冻电子断层扫描中的结构检测

• DOI: 10.1089/cmb.2021.0606
• 发表时间: 2022
• 期刊: Journal of Computational Biology
• 影响因子: 1.7
• 作者: Zeng, Xiangrui;Lin, Ziqian;Uddin, Mostofa Rafid;Zhou, Bo;Cheng, Chao;Zhang, Jing;Freyberg, Zachary;Xu, Min
• 通讯作者: Xu, Min