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

项目摘要 我们要求高通量X射线源，并获得新的团队专业知识，从microCT图像分割， 为了应用通过我们的母体R24开发的新的3D组织学形式来开始表征 COVID-19相关急性呼吸窘迫综合征（ARDS）的细胞和组织几何学 肺炎，我们流行病最常见的死亡原因。我们的新型成像工具，X射线组织断层扫描， 基于固定和金属染色组织的显微CT它在3D成像方法中是独一无二的， 非破坏性方式实现泛细胞成像（允许表征所有细胞类型和组织）， 可能是可行的。组织断层扫描独特地允许与当今的2D组织标准进行直接比较 诊断，组织学，能够以任何角度和任何角度生成3D渲染和未失真的2D切片 切片厚度与组织学不同的是，我们还将允许我们精确地表征细胞排列成组织 在用金属固定和染色样品之后。体积表征细胞类型及其 急性呼吸窘迫综合征（ARDS）的治疗尤为重要，因为它是导致死亡的原因。 在包括SARS（严重急性呼吸系统综合症）在内的冠状病毒大流行中， 2003年的冠状病毒），2012年的中东呼吸综合征冠状病毒（MERS），现在的COVID-19。的 拟议的工作将加强我们对未来大流行病的准备。ARDS肺是理想的人体组织 这是一个数学定义人类疾病的模型，因为所有类型的细胞都受到影响。拟议的工作与 COVID-19肺部将提高我们了解冠状病毒肺部不同阶段的准确性 感染，并作为表征所有器官系统疾病几何学的模型。 亲本R24的组织断层扫描目前仅限于动物模型，重点是斑马鱼。的 补充将使我们能够将我们的工作转化为人类健康，这最初是由PI设想的， 定义“疾病几何学”的一部分。我们在这项技术上的经验告诉我们， 表征每种基本炎性细胞类型的数量，包括淋巴细胞、中性粒细胞和 巨噬细胞（形态学上不同）在数量、体积、形状和密度方面的差异。 炎症组织，并且还表征肺上皮细胞（支气管纤毛上皮细胞和 肺细胞、细胞死亡和气道充满液体和粘液性渗出物以及血管炎症。 除了定量组织变化外，我们还将能够可视化组织中的病理变化 使用虚拟现实。组织断层扫描将作为验证COVID-19人源化小鼠模型的一种方法 通过与人类尸检样本中的数量变化进行比较，确定感染。我们将比较 标准组织切片和来自相邻组织的组织断层摄影图像。机器学习将 最终使我们能够自动识别细胞类型和病理变化。拟议的增加 我们的仪器和专业知识将促进跨器官系统的“疾病几何学”的定义。

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.