CAREER: The Morpho-Molecular Tissue Atlas: A Framework for the Generation and Comparative Profiling of Terabyte-Scale Tissue Phenotypes

职业：形态分子组织图谱：TB 级组织表型的生成和比较分析框架

• 批准号: 1943455
• 负责人: David Mayerich
• 金额: $ 50万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943455&HistoricalAwards=false
• 关键词: CAREER; Morpho; Molecular; Tissue; Atlas

This project will develop an imaging and computational framework to generate searchable digital atlases of whole organs at the cellular level. These atlases will enable tissue characterization and modeling at an unprecedented scale, opening the door to new methods for studying anatomy, quantifying disease progression, mapping potential treatments, and educating the next generation of biomedical professionals. Publicly available whole-organ atlases will fundamentally impact biomedical research and education, similar to how satellite imagery, global positioning, and search algorithms have changed navigation. Routine generation of tissue maps will also enable researchers to build detailed models of complex diseases, opening the door to new precision treatments and scalable drug discovery. This project will provide five specific contributions to the field of biomedical imaging: (1) data storage methods for encoding the complex data acquired using fast microscopes, (2) high-performance parallel algorithms leveraging recent research in machine learning and artificial intelligence, (3) software for efficient visualization, proofreading, and interpretation, (4) a comprehensive framework for building and browsing cellular-level whole-organ tissue atlases, and (5) an open repository containing data from next generation imaging methods allowing researchers to build on and expand this proposed framework. The imaging techniques and software proposed in this project will open the door to new methods for studying anatomy, quantifying disease progression, mapping potential treatments, and educating the next generation of biomedical professionals. A browsable atlas will be designed for integration into K-12 programs using virtual reality with game-based discovery through a Tissue Exploration and Discovery Workshop.Whole-organ mapping is a challenge because a cubic centimeter of tissue requires collecting multiple terabytes of data and encoding the complex mix of three-dimensional structures into geometric and volumetric representations suitable for analysis. This project will overcome these challenges by developing (1) instrumentation for fast tissue slicing and multispectral imaging and (2) synergistic parallel algorithms that convert 3D images into searchable atlases by exploiting their inherent spatial and spectral sparsity. This project will provide the transformational ability to construct three-dimensional searchable models from terabyte-scale multispectral images that integrate explicit structures and implicit molecular distributions, enabling tissue analytics at unprecedented scales. This study will provide five synergistic contributions for producing whole organ cellular level tissue atlases: (1) sparsity-exploiting data structures that integrate explicit three-dimensional structures and implicit molecular distributions for fast analysis, (2) massively parallel GPU-based algorithms integrating recent research in deep neural networks and perceptual grouping, (3) analytics-guided selective visualization methods that allow efficient visualization, proofreading, and interpretation, (4) a comprehensive framework for building browsable morpho-molecular cellular-level and whole-organ tissue atlases, and (5) an open repository containing data from next generation imaging methods including expansion microscopy (ExM), knife-edge scanning microscopy (KESM), and light sheet microscopy (LSM), to foster research, clinical, and educational software development.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.

该项目将开发一个成像和计算框架，以在细胞水平上生成整个器官的可搜索数字地图集。这些图谱将以前所未有的规模实现组织表征和建模，为研究解剖学、量化疾病进展、绘制潜在治疗方案和教育下一代生物医学专业人员的新方法打开大门。公共可用的全器官地图集将从根本上影响生物医学研究和教育，类似于卫星图像，全球定位和搜索算法如何改变导航。组织图谱的常规生成还将使研究人员能够建立复杂疾病的详细模型，为新的精确治疗和可扩展的药物发现打开大门。该项目将为生物医学成像领域提供五个具体贡献：（1）用于对使用快速显微镜获取的复杂数据进行编码的数据存储方法，（2）利用机器学习和人工智能的最新研究的高性能并行算法，（3）用于高效可视化、校对和解释的软件，（4）用于构建和浏览细胞水平的全器官组织图谱的综合框架，以及（5）包含来自下一代成像方法的数据的开放式存储库，允许研究人员建立和扩展此拟议框架。该项目中提出的成像技术和软件将为研究解剖学、量化疾病进展、绘制潜在治疗方案和教育下一代生物医学专业人员的新方法打开大门。通过组织探索和发现研讨会，一个可浏览的地图集将被设计成使用虚拟现实和基于游戏的发现的K-12项目。整个器官映射是一个挑战，因为一立方厘米的组织需要收集数TB的数据，并将复杂的三维结构编码成适合分析的几何和体积表示。该项目将通过开发（1）用于快速组织切片和多光谱成像的仪器和（2）协同并行算法来克服这些挑战，该算法通过利用其固有的空间和光谱稀疏性将3D图像转换为可搜索的地图集。该项目将提供从TB级多光谱图像构建三维可搜索模型的转换能力，这些图像集成了显式结构和隐式分子分布，从而能够在前所未有的规模上进行组织分析。本研究将为产生全器官细胞水平的组织图谱提供五个协同贡献：（1）稀疏性开发数据结构，其集成了显式三维结构和隐式分子分布以进行快速分析，（2）大规模并行的基于GPU的算法，其集成了深度神经网络和感知分组中的最新研究，（3）分析引导的选择性可视化方法，其允许有效的可视化，校对和解释，（4）用于构建可浏览的形态分子细胞水平和全器官组织图谱的综合框架，以及（5）包含来自下一代成像方法（包括扩展显微镜（ExM），刀口扫描显微镜（KESM）和光片显微镜（LSM））的数据的开放式存储库，以促进研究，临床，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

Adaptive Compressive Sampling for Mid-Infrared Spectroscopic Imaging

• DOI: 10.1109/icip46576.2022.9897796
• 发表时间: 2020-08
• 期刊: 2022 IEEE International Conference on Image Processing (ICIP)
• 作者: M. Lotfollahi;Nguyen H. Tran;Sebastian Berisha;C. Gajjela;Zhu Han;D. Mayerich;R. Reddy

Combined optical coherence tomography and light sheet fluorescence microscopy for embryonic imaging

结合光学相干断层扫描和光片荧光显微镜进行胚胎成像

• DOI: 10.1117/12.2610091
• 发表时间: 2022
• 期刊: Multimodal Biomedical Imaging XVII
• 作者: Karim, Md Mobarak;Sun, Ruijiao;Khajavi, Behzad;Singh, Manmohan;Chawla, Harshdeep S.;Ambekar, Yogeshwari S.;Schill, Alexander W.;Mayerich, David;Dickinson, Mary E.;Larin, Kirill V.
• 通讯作者: Larin, Kirill V.

Characterization and optimization of coupled-wave simulations for complex heterogeneous samples

复杂异质样品耦合波模拟的表征和优化

• DOI: 10.1117/12.2609179
• 发表时间: 2022
• 期刊: Physics and Simulation of Optoelectronic Devices XXX
• 作者: Sun, Ruijiao;Reddy, Rohith K.;Mayerich, David
• 通讯作者: Mayerich, David

Probe the Localized Electrochemical Environment Effects and Electrode Reaction Dynamics for Metal Batteries using In Situ 3D Microscopy

• DOI: 10.1002/aenm.202103484
• 发表时间: 2021-12-16
• 期刊: ADVANCED ENERGY MATERIALS
• 影响因子: 27.8
• 作者: Feng, Guangxia;Guo, Jiaming;Shan, Xiaonan
• 通讯作者: Shan, Xiaonan