Department of Plastic and Hand Surgery, Faculty of Medicine, Medical Center, University of Freiburg, Freiburg, Germany.
Department of Computer Science, University of Innsbruck, Innsbruck, Austria.
Sci Rep. 2020 Jul 6;10(1):11071. doi: 10.1038/s41598-020-67960-0.
The last two decades saw the establishment of three-dimensional (3D) cell cultures as an acknowledged tool to investigate cell behaviour in a tissue-like environment. Cells growing in spheroids differentiate and develop different characteristics in comparison to their two-dimensionally grown counterparts and are hence seen to exhibit a more in vivo-like phenotype. However, generating, treating and analysing spheroids in high quantities remains labour intensive and therefore limits its applicability in drugs and compound research. Here we present a fully automated pipetting robot that is able to (a) seed hanging drops from single cell suspensions, (b) treat the spheroids formed in these hanging drops with drugs and (c) analyse the viability of the spheroids by an image-based deep learning based convolutional neuronal network (CNN). The model is trained to classify between 'unaffected', 'mildly affected' and 'affected' spheroids after drug exposure. All corresponding spheroids are initially analysed by viability flow cytometry analysis to build a labelled training set for the CNN to subsequently reduce the number of misclassifications. Hence, this approach allows to efficiently examine the efficacy of drug combinatorics or new compounds in 3D cell culture. Additionally, it may provide a valuable instrument to screen for new and individualized systemic therapeutic strategies in second and third line treatment of solid malignancies using patient derived primary cells.
过去二十年见证了三维(3D)细胞培养的建立,它被公认为研究组织样环境中细胞行为的工具。与二维培养的细胞相比,在球体中生长的细胞分化并表现出不同的特征,因此表现出更类似于体内的表型。然而,生成、处理和分析大量球体仍然是劳动密集型的,因此限制了其在药物和化合物研究中的应用。在这里,我们展示了一种完全自动化的移液机器人,它能够(a)从单细胞悬浮液中接种悬滴,(b)用药物处理这些悬滴中形成的球体,(c)通过基于图像的深度学习卷积神经网络(CNN)分析球体的活力。该模型经过训练,可以在药物暴露后对“未受影响”、“轻度受影响”和“受影响”的球体进行分类。所有相应的球体最初都通过活力流式细胞术分析进行分析,以构建 CNN 的标记训练集,随后减少误分类的数量。因此,这种方法可以有效地研究药物组合或新化合物在 3D 细胞培养中的功效。此外,它可能为使用患者来源的原代细胞在实体恶性肿瘤的二线和三线治疗中筛选新的和个体化的系统治疗策略提供有价值的工具。