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

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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.