Abstract A032: Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology

Abstract

Abstract Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular evolution, cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on manually annotated dataset of 3,862 cells. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivies. Cellos was able to accurately distinguish ratios of distinct fluorescently labeled cell populations in organoids, with < 3% deviation from seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent Hoechst stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantification by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclei morphology features associated with treatment and unique to each of the clones. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer clones beyond what arises from local cell division and organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging. Citation Format: Patience Mukashyaka, Pooja Kumar, David J. Mellert, Shadae Nicholas, Javad Noorbakhsh, Mattia Brugiolo, Olga Anczukow, Edison T. Liu, Jeffrey H. Chuang. Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A032.