Organoids are an emerging model system that more closely recapitulates the in vivo environment compared to traditional monolayer systems. Using patient-derived samples grown in a 3D matrix, we hope to advance personalized medicine by providing more patient-specific treatment plans. We have begun building a patient-derived organoid library from primary colon cancer tumors and metastases. Once cells are isolated and seeded into a Basement Membrane Extract (BME) matrix we analyze features on a high content imaging platform, the Operetta (Perkin Elmer). Using the Operetta we are able to obtain quantitative phenotypic data that includes the number, size, and morphology of organoids. In addition, we can capture the dynamics of organoid formation, growth, and death by tracking the same organoids over time using live-cell imaging.

More specifically, for each organoid, we capture z-stack images at various heights, and using the Harmony software, we perform quantitative analysis on the maximum projection of the complied images. Using a building block approach, we created a workflow that first relied on identifying regions of interest (ROI), which correspond to organoids. In each ROI we then obtained information such as geometric center, length and width measurements, and texture features. We used this information to determine inter-patient heterogeneity, as well as differences across samples isolated from the primary location (i.e. colon) versus metastatic sites (i.e. liver). After culturing patient-derived organoids for several weeks in laboratory conditions, we perturbed various aspects of the tumor microenvironment (e.g. drug and oxygen levels) and subsequently tracked changes in organoid number, size, and morphology over time. This workflow represents a unique method for image-based quantitative phenotypic analysis of organoids under varying environmental conditions.

Citation Format: Erin Spiller, Roy Lau, Sarah Choung, Shannon M. Mumenthaler. High-content 3D image analysis of patient-derived organoids. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr A18.