Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.
Industrial Vision Lab, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium.
Cell Oncol (Dordr). 2023 Apr;46(2):299-314. doi: 10.1007/s13402-022-00750-0. Epub 2022 Dec 12.
Patient-derived organoids are invaluable for fundamental and translational cancer research and holds great promise for personalized medicine. However, the shortage of available analysis methods, which are often single-time point, severely impede the potential and routine use of organoids for basic research, clinical practise, and pharmaceutical and industrial applications.
Here, we developed a high-throughput compatible and automated live-cell image analysis software that allows for kinetic monitoring of organoids, named Organoid Brightfield Identification-based Therapy Screening (OrBITS), by combining computer vision with a convolutional network machine learning approach. The OrBITS deep learning analysis approach was validated against current standard assays for kinetic imaging and automated analysis of organoids. A drug screen of standard-of-care lung and pancreatic cancer treatments was also performed with the OrBITS platform and compared to the gold standard, CellTiter-Glo 3D assay. Finally, the optimal parameters and drug response metrics were identified to improve patient stratification.
OrBITS allowed for the detection and tracking of organoids in routine extracellular matrix domes, advanced Gri3D-96 well plates, and high-throughput 384-well microplates, solely based on brightfield imaging. The obtained organoid Count, Mean Area, and Total Area had a strong correlation with the nuclear staining, Hoechst, following pairwise comparison over a broad range of sizes. By incorporating a fluorescent cell death marker, intra-well normalization for organoid death could be achieved, which was tested with a 10-point titration of cisplatin and validated against the current gold standard ATP-assay, CellTiter-Glo 3D. Using this approach with OrBITS, screening of chemotherapeutics and targeted therapies revealed further insight into the mechanistic action of the drugs, a feature not achievable with the CellTiter-Glo 3D assay. Finally, we advise the use of the growth rate-based normalised drug response metric to improve accuracy and consistency of organoid drug response quantification.
Our findings validate that OrBITS, as a scalable, automated live-cell image analysis software, would facilitate the use of patient-derived organoids for drug development and therapy screening. The developed wet-lab workflow and software also has broad application potential, from providing a launching point for further brightfield-based assay development to be used for fundamental research, to guiding clinical decisions for personalized medicine.
患者来源的类器官对于基础和转化癌症研究非常宝贵，并且为个性化医疗带来了巨大的希望。然而，由于缺乏可用的分析方法（通常是单次检测），严重阻碍了类器官在基础研究、临床实践、药物和工业应用中的潜力和常规使用。
在这里，我们开发了一种高通量兼容的自动化活细胞图像分析软件，该软件通过结合计算机视觉和卷积网络机器学习方法，对类器官进行动力学监测，命名为基于类器官明场识别的治疗筛选（OrBITS）。OrBITS 深度学习分析方法针对当前的动力学成像标准检测和类器官自动化分析进行了验证。还使用 OrBITS 平台对标准的肺癌和胰腺癌治疗药物进行了筛选，并与金标准 CellTiter-Glo 3D 检测进行了比较。最后，确定了最佳参数和药物反应指标，以改善患者分层。
OrBITS 允许仅基于明场成像，在常规细胞外基质穹顶、高级 Gri3D-96 孔板和高通量 384 孔微板中检测和跟踪类器官。获得的类器官计数、平均面积和总面积与核染色 Hoechst 具有很强的相关性，在广泛的大小范围内进行两两比较。通过结合荧光细胞死亡标记物，可以实现类器官死亡的孔内归一化，这在顺铂 10 点滴定中进行了测试，并与当前的金标准 ATP 检测 CellTiter-Glo 3D 进行了验证。使用 OrBITS 进行这种方法的筛选，可以深入了解药物的作用机制，这是 CellTiter-Glo 3D 检测无法实现的。最后，我们建议使用基于增长率的归一化药物反应指标来提高类器官药物反应定量的准确性和一致性。
我们的研究结果验证了 OrBITS 作为一种可扩展的自动化活细胞图像分析软件，将促进患者来源的类器官在药物开发和治疗筛选中的应用。开发的湿实验室工作流程和软件也具有广泛的应用潜力，从为进一步基于明场的检测开发提供起点，用于基础研究，到指导个性化医疗的临床决策。
