Abstract /Summary: Lung function relies on a complex anatomical, histological and cellular organization. Understanding this organization requires knowledge of the cell types, states, differentiation events, and cellular interactions within an anatomic context. We will combine single cell and spatial genomics, microscopy and anatomic methods to assemble a working atlas of the lung that reflects normal variation across individuals. Our seed network spans pulmonary physicians, lung biologists, and computational biologists, including specialists in machine learning. We will leverage unique sources of normal human lung tissue, including biopsies from healthy volunteers and entire lungs from organ donors to ultimately analyze 100 healthy adult subjects that reflect geographic, gender, age, and ethnic diversity. We will systematically sample the entire lung along its proximodistal length in the context of a Common Coordinate Framework. We will employ computational methodology to integrate data across modalities, infer cell types, associate cell states to various environmental contexts, and assemble canonical histological neighborhoods within an anatomy-level lung atlas. Our healthy atlas of the developing, adult, and aging lung will serve as a roadmap for future studies focused on lung disease. Understanding how the normal crosstalk of spatially defined cell types is perturbed in pathologic states will ultimately provide tools for understanding the basis of the pathology associated with lung disease. Coupling a new understanding of cells, their spatial location, and assigning the loci of action of disease genes to particular cells and regions of the lung will form the requisite compendium of knowledge needed by the global research community.
|Effective start/end date||7/1/19 → 6/30/22|
- Chan Zuckerberg Initiative Foundation (CZF2019-002438)
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