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

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学研究领域的基础。然而，由于缺乏数据采集技术，此类信息一直难以获得。低温电子断层成像(Cryo-Et)的最新进展使大分子的近自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三维可视化成为可能。然而，快速增长的多种多样的低温数据给高通量的系统分析带来了重大挑战。自动化和计算效率已成为瓶颈。该项目将致力于使用无监督的深度学习来提高断层图像中的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冷冻的生命科学中有广泛的应用。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，并将研究成果整合到大学课程中。冷冻-ET已成为原位大分子结构恢复、识别和定位的最强大的技术。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，该项目将开发两项关键的无监督深度学习技术，包括(1)一种新的同时模拟器和去噪器来创建逼真的模拟亚断层图像；(2)一种通过从取向和位移中分离结构信息来聚类大分子结构的方法。为了促进从该项目开发的方法的广泛使用，拟议方法的软件实施将被整合到开放源码软件AITom中，以便它们准备好供结构生物界使用。这些方法专注于有效地构建初始均一亚层析图像簇并产生用于进一步结构细化的初始结构，这很好地补充了现有的结构细化方法，并将显著地利用对单细胞中的大分子的系统从头和原位分析。这项研究的结果将在Xu Lab网站和GitHub网站上公布。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.