Groundwork for a Synchrotron MicroCT Imaging Resource for Biology (SMIRB)

Project: Research project

Project Details

Description

Project Summary Each major human disease is associated with a specific range of morphological changes to cells and tissues in the micron scale. Normal and abnormal structure was discovered and is still characterized using histology - a microscopic technique that depends on physical tissue slices. Presently, histology?s use in systems biology is limited by its largely descriptive and two-dimensional nature. Making histology quantitative and three-dimensional would be potentially transformational for research and diagnostics, but has been impractical. Accordingly, we have now created a 3D form of histology by customizing X-ray microtomography (micro-CT) of fixed and stained, millimeter-scale, whole organisms and tissue samples. We used fixed and metal-stained, whole zebrafish because they contain a full range of tissues within the size range currently studied histologically. The result is the first practical way to create virtual histology-like ?sections? in any plane. Three-dimensional, complete histological phenotyping has potential use in genetic and chemical screens, and in clinical and toxicological tissue diagnostics. Here, we propose the next steps needed to enable high-throughput, quantitative, 3D histological phenotyping of whole, millimeter-scale animals. The proposed work applies the principles of chemistry, physics, and computer science to improve image resolution, throughput, and analytics, organized into three specific aims. Specific Aim 1 will build on our developments in this project and further improve imaging volume and resolution by upgrading imaging array, optics, and sub-pixel shifting, and to throughput by changes in sample embedding, loading geometry and mechanics, helical CT scanning, scintillator material, and to data sharing by improvements to the ViewTool infrastructure and user interface. Specific Aim 2 will yield reference images to define the range of normal phenotypic variation and to obtain samples related to a range of potential applications. Specific Aim 3 will apply the power of machine learning to segmentation, annotation, and analytics. Together, this work will establish a practical foundation for large-scale genetic and chemical screens involving mm-scale, whole organisms based on 3-dimensional, quantitative, histological phenotyping. The instrumentation and analytics will be state-of-the-art in its combination of resolution, field-of-view, pancellularity, image quality, analytical potential, throughput, sample stability, and reproducibility and largely usable with both tube and synchrotron X-ray sources. The voxel resolution will be at least 0.5 ?m across fields-of-view of up to 1 cm. Representation of every cell type make the images suitable for cross-referencing across imaging modalities. Potential applications will be explored, ?wild-type? will begin to be defined, and training sets for automated segmentation generated. The potential impact will encompass the missions of most NIH Institutes and Centers. The whole-animal genetic and chemical screens enabled are expected to impact drug development, diagnostics, and our basic understanding of how genes and environment define phenotype.
StatusFinished
Effective start/end date8/1/157/31/20

Funding

  • NIH Office of the Director: $671,672.00
  • NIH Office of the Director: $654,197.00
  • NIH Office of the Director: $639,616.00

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