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

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Summary/Abstract of the Proposed Project Phenotypes provide a classical biological approach to studying morphological changes. Under a microscope, adults and children alike can distinguish a normal cell arrangement like a single row of columnar-shaped gut cells, and differentiate this normal presentation from an abnormal clustered group of the same columnar-shaped cells. This distinction occurs by descriptively comparing the abnormal phenotype against normality. For a computer- based model to make the same distinction, a wild-type reference must be used for comparison. Studies on cellular phenotyping widely use histological examination to visualize cell morphological changes; however, it induces sectioning artifacts, thereby preventing the visualization of 3-dimensional (3D) tissue volumes. To circumvent these challenges, we plan to take advantage of an unbiased form of tissue imaging, termed X-ray computed microtomography (microCT). MicroCT will be used to develop a standard reference of normal cellular phenotypes needed to lay a foundation for automated segmentation and computational phenotyping. Unlike conventional histology, the imaging technology of microCT, which amounts to a non-destructive form of histology for small metal- stained biological samples, will enable 3D visualization of cell types in the micrometer range. The novel scale and resolution of microCT will thereby reveal the 3D morphology and spatial distribution of cells to quantify and characterize phenotypic variations across vertebrate and invertebrate models. Combining the resolution of microCT imaging with machine learning, we will apply supervised-manual segmentation to microCT datasets to create computational cell recognition mechanisms for morphological assessment. This integration will include open-source tools and devices that will enable community-driven research and data-sharing. As an added benefit of this AI integration, our computational approach will further assist in augmenting large amounts of data required to examine the statistical association of cellular phenotypes. To demonstrate the applicability of our methodology for phenotypic characterization in any given cell type or organism, our pilot for this project will focus on the single-variable approach of ‘gut epithelial cells’ across three organisms (i.e., zebrafish, daphnia, froglets). By prioritizing phenotypic investigation of these specialized cells to the analogous relationship of mammalian intestinal cells, this work will establish a practical foundation to increase our understanding of the 3D biological structure of wild-type cells, and advance morphological cellular phenotyping in whole-organisms for quantitative, histological, disease recognition.