Invited Talk

Mapping the Three-Dimensional Tumor Microenvironment at Single-Cell Resolution Using CODA

Johns Hopkins University and Johns Hopkins School of Medicine, 3400 N. Charles St. Baltimore, MD 21218, USA

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A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multi centimeter cube) tissues at subcellular resolution using serially sectioned histological samples. Through deep learning, CODA labels distinct biological structures using hematoxylin and eosin staining alone. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labelling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. CODA empowers enumeration of the cellularity and structure of tumor microenvironments, allowing identification of distinct precancer phenotypes that vary in three-dimensional morphology. Finally, we show that cancer tends to spread along collagen fibers that are highly aligned to the existing ductal, lobular, vascular, and neural structures in the pancreas, allowing distant extension from the bulk tumor. CODA establishes a means to transform broadly the structural study of human diseases and provide fundamental quantitative metrics for improved design of physio-pathologically relevant 3D model biological systems.