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

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Project Summary We request a high-flux x-ray source and to acquire new team expertise in segmentation from microCT images, in order to apply a new 3D form of histology developed through our parent R24 to begin to characterize the cellular and tissue geometries of COVID-19-associated Acute Respiratory Distress Syndrome (ARDS) pneumonia, our pandemic’s most common cause of death. Our novel imaging tool, X-ray histotomography, is based on microCT of fixed and metal-stained tissue. It is unique among 3D imaging methods as the only nondestructive way to achieve pan-cellular imaging (allowing characterization of all cell types and tissues) and is potentially practical. Histotomography uniquely allows direct comparison with today’s 2D standard of tissue diagnosis, histology, capable of producing both 3D renderings and undistorted 2D slices at any angle and any slice thickness. Unlike histology, we will also allow us to precisely characterize cellular arrangements into tissues after fixing and staining of samples with metal. The ability to volumetrically characterize cell types and their arrangements in acute respiratory distress syndrome (ARDS) is particularly important because it is what kills most patients in coronavirus-based pandemics, including SARS (severe acute respiratory syndrome coronavirus) in 2003, MERS (Middle East respiratory syndrome coronavirus) in 2012, COVID-19 now. The proposed work will increase our preparedness for future pandemics. ARDS lungs are an ideal human tissue model for mathematically defining human disease because all cell types are affected. The proposed work with COVID-19 lungs will increase the precision with which we understand the different stages of coronavirus lung infection and serve as a model for characterizing the Geometry of Disease across all organ systems. Histotomography in the parent R24 is currently limited to animal models, focusing on the zebrafish. The supplement will allow us to translate our work to human health, which was originally envisioned by the PI, as part of defining the “Geometry of Disease”. Our experience with this technology tells us that we will be able to characterize the numbers of each of the basic inflammatory cell types, including lymphocytes, neutrophils, and macrophages (which are morphologically distinct) in terms of numbers, volumes, shapes, and density in the inflamed tissue, and to also characterize the changes in the lung epithelia (bronchial ciliated epithelial cells and pneumocytes, cell death, and the filling of airways with fluid and fibrinous exudate, and vascular inflammation. In addition to quantitation of tissue changes, we will also be able to visualize pathological change in the tissues using virtual reality. Histotomography will serve as a way to validate a humanized mouse model of COVID-19 infection by comparing the quantitative changes with those in human autopsy samples. We will be comparing both standard histological sections and histotomographic images from adjacent tissue. Machine learning will ultimately allow us to automate recognition of cell types and pathological change. The proposed augmentation of our instrumentation and expertise will facilitate definitions of the “Geometry of Disease” across organ systems.