(629g) Mapping the 3D Tumor Microenvironment at Single-Cell Resolution (Invited Speaker)

Obtaining high-content and high-resolution information from a complex tumor microenvironment in large volumetric landscapes represents a key challenge in the field of cancer biology. To address this challenge, we established a novel method to reconstruct three-dimensional (3D) centimeter-scale tissues containing billions of cells from serially sectioned histological samples named CODA. We utilize deep learning approaches to recognize distinct tissue subtypes from hematoxylin and eosin-stained sections at micrometer and single-cell resolution. Accumulating evidence indicates the tumor microenvironment is highly associated with tumorigenesis through regulation of cellular physiology, signaling systems, and gene expression profiles of cancer cells. Yet the mechanisms by which the microenvironment evolves from normal architecture to precursor lesions and invasive cancer is poorly understood. We use pancreatic tissues and tumors as a test bed. Using samples from a range of normal, precancerous, and invasive pancreatic cancer tissue, we map in 3D modes of cancer invasion in the tumor microenvironment, and emphasize the need for further 3D quantification of biological systems. Finally, we demonstrate an expansions of CODA that maps immune cell infiltration into pancreatic precursor lesions, producing large 3D immuno-maps.