Abstract 879: Multi-scale time-lapse intravital imaging of soft tissues to map single cell behavior

Abstract

Abstract After more than 10 years of research into the tumor micro-environment and the sources of tumor micro-heterogeneity, it is becoming increasingly clear that in addition to driver mutations, the tumor microenvironment determines tumor metastatic phenotype. This research has led to a new understanding of the impact of tumor microenvironments and their heterogeneity upon tumor cell proliferation, dissemination, dormancy, and survival. A full understanding of this heterogeneity, both temporally and spatially, how it supports tumor cell dissemination, dormancy and eventual further metastatic growth, and how it responds to therapeutic interventions, is crucial. Traditional single parameter studies have not been as productive as hoped in revealing the above relationships and have highlighted the need to understand tumors as integrated systems of genes, gene networks, and intracellular interactions, particularly with regard to the interplay between cells and their immediate microenvironment. To accomplish this, we have developed a new technology in the form of large volume intravital imaging using multiphoton intravital microscopy (MIVM) where images of large tumor volumes are stitched together to form a comprehensive record of the genes, gene networks, and intracellular interactions that occur throughout many tumor microenvironments at single cell resolution. Here, we report a protocol for obtaining large area high-resolution mosaic imaging in living animals. The protocol is composed of surgical techniques for the stabilization of soft tissues including mammary gland, lymph node, liver, and lung and the acquisition of multiple high-magnification tiles that are stitched together to form a large-area low-magnification image. We have developed specific protocols for the surgery as well as tools for tissue stabilization and the acquisition and stitching of the images to generate very large MVIM data sets. We have interfaced these very large MVIM image data sets with support vector machine (SVM) classification, a nonlinear, multiparametric classification algorithm suitable for analysis of systems with arbitrary distributions and/or non-linear parameter’s correlations. To define the combinations of microenvironment parameters where tumor cell phenotypes of interest are likely to occur, we used these microenvironment parameters as an “input” for the SVM classifier to identify associated tumor cell phenotypes as the classifier’s “output”. Using this approach at widely varying temporal and spatial scales (from minutes to weeks and from sub-cellular to tissue wide) and at different stages (early carcinoma on to metastasis), has resulted in a catalog of associations between microenvironment conditions and tumor cell phenotypes associated with progression. These associations resulted in new insights into the mechanisms of metastasis. Citation Format: Jessica M. Pastoriza, Maria Soledad Sosa, Kathryn Harper, Julio Aguirre-Ghiso, Maja H. Oktay, David Entenberg, Yarong Wang, John S. Condeelis, Aviv Bergman, Mihaela Skobe, Benedicte Lenoir. Multi-scale time-lapse intravital imaging of soft tissues to map single cell behavior [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 879. doi:10.1158/1538-7445.AM2017-879