Bregenzer Michael E, Davis Ciara, Horst Eric N, Mehta Pooja, Novak Caymen M, Raghavan Shreya, Snyder Catherine S, Mehta Geeta
Department of Biomedical Engineering, University of Michigan.
Department of Materials Science and Engineering, University of Michigan.
J Vis Exp. 2019 Jul 5(149). doi: 10.3791/59696.
In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of anti-cancer therapeutics in a physiologically representative microenvironment. We outline the formation of patient derived cancer stem cell spheroids, as well as, the manipulation of these spheroids for thorough analysis following drug treatment. Specifically, we describe collection of spheroid morphology, proliferation, viability, drug toxicity, cell phenotype and cell localization data. This protocol focuses heavily on analysis techniques that are easily implemented using the 384-well hanging drop platform, making it ideal for high throughput drug screening. While we emphasize the importance of this model in ovarian cancer studies and cancer stem cell research, the 384-well platform is amenable to research of other cancer types and disease models, extending the utility of the platform to many fields. By improving the speed of personalized drug screening and the quality of screening results through easily implemented physiologically representative 3D cultures, this platform is predicted to aid in the development of new therapeutics and patient-specific treatment strategies, and thus have wide-reaching clinical impact.
在本方案中,我们概述了在384孔悬滴中生成肿瘤球体的程序,以便在生理代表性微环境中进行抗癌治疗药物的高通量筛选。我们概述了患者来源的癌症干细胞球体的形成,以及对这些球体进行药物处理后进行全面分析的操作。具体而言,我们描述了球体形态、增殖、活力、药物毒性、细胞表型和细胞定位数据的收集。本方案重点介绍了使用384孔悬滴平台易于实施的分析技术,使其成为高通量药物筛选的理想选择。虽然我们强调该模型在卵巢癌研究和癌症干细胞研究中的重要性,但384孔平台适用于其他癌症类型和疾病模型的研究,从而将该平台的效用扩展到许多领域。通过易于实施的具有生理代表性的3D培养提高个性化药物筛选的速度和筛选结果的质量,预计该平台将有助于开发新的治疗方法和针对患者的治疗策略,从而产生广泛的临床影响。