Trossbach Martin, Åkerlund Emma, Langer Krzysztof, Seashore-Ludlow Brinton, Joensson Haakan N
KTH Royal Institute of Technology, and Science for Life Laboratory, Sweden.
Karolinska Institutet, and Science for Life Laboratory, Sweden.
SLAS Technol. 2023 Dec;28(6):423-432. doi: 10.1016/j.slast.2023.03.003. Epub 2023 Mar 28.
3D cell culture models are important tools in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. Microfluidics and culture model miniaturization technologies could overcome these challenges. Here, we present a high-throughput workflow to produce and characterize the formation of miniaturized spheroids using deep learning. We train a convolutional neural network (CNN) for cell ensemble morphology classification for droplet microfluidic minispheroid production, benchmark it against more conventional image analysis, and characterize minispheroid assembly determining optimal surfactant concentrations and incubation times for minispheroid production for three cell lines with different spheroid formation properties. Notably, this format is compatible with large-scale spheroid production and screening. The presented workflow and CNN offer a template for large scale minispheroid production and analysis and can be extended and re-trained to characterize morphological responses in spheroids to additives, culture conditions and large drug libraries.
3D细胞培养模型是转化研究中的重要工具，但由于其复杂性、需要大量细胞以及标准化不足，一直无法用于高通量筛选。微流控技术和培养模型小型化技术可以克服这些挑战。在此，我们展示了一种高通量工作流程，用于利用深度学习生成并表征小型化球体的形成。我们训练了一个卷积神经网络（CNN）用于对液滴微流控微型球体生产中的细胞聚集体形态进行分类，将其与更传统的图像分析方法进行基准测试，并表征微型球体组装过程，确定三种具有不同球体形成特性的细胞系在微型球体生产中的最佳表面活性剂浓度和孵育时间。值得注意的是，这种形式与大规模球体生产和筛选兼容。所展示的工作流程和CNN为大规模微型球体生产和分析提供了一个模板，并且可以扩展和重新训练，以表征球体对添加剂、培养条件和大型药物库的形态学反应。
