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

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 thecellular 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, isbased on microCT of fixed and metal-stained tissue. It is unique among 3D imaging methods as the onlynondestructive way to achieve pan-cellular imaging (allowing characterization of all cell types and tissues) andis potentially practical. Histotomography uniquely allows direct comparison with today's 2D standard of tissuediagnosis, histology, capable of producing both 3D renderings and undistorted 2D slices at any angle and anyslice thickness. Unlike histology, we will also allow us to precisely characterize cellular arrangements into tissuesafter fixing and staining of samples with metal. The ability to volumetrically characterize cell types and theirarrangements in acute respiratory distress syndrome (ARDS) is particularly important because it is what killsmost patients in coronavirus-based pandemics, including SARS (severe acute respiratory syndromecoronavirus) in 2003, MERS (Middle East respiratory syndrome coronavirus) in 2012, COVID-19 now. Theproposed work will increase our preparedness for future pandemics. ARDS lungs are an ideal human tissuemodel for mathematically defining human disease because all cell types are affected. The proposed work withCOVID-19 lungs will increase the precision with which we understand the different stages of coronavirus lunginfection 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. Thesupplement will allow us to translate our work to human health, which was originally envisioned by the PI, aspart of defining the "Geometry of Disease". Our experience with this technology tells us that we will be able tocharacterize the numbers of each of the basic inflammatory cell types, including lymphocytes, neutrophils, andmacrophages (which are morphologically distinct) in terms of numbers, volumes, shapes, and density in theinflamed tissue, and to also characterize the changes in the lung epithelia (bronchial ciliated epithelial cells andpneumocytes, 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 tissuesusing virtual reality. Histotomography will serve as a way to validate a humanized mouse model of COVID-19infection by comparing the quantitative changes with those in human autopsy samples. We will be comparingboth standard histological sections and histotomographic images from adjacent tissue. Machine learning willultimately allow us to automate recognition of cell types and pathological change. The proposed augmentationof our instrumentation and expertise will facilitate definitions of the "Geometry of Disease" across organ systems.