M3-BIORES, KU Leuven, Leuven, Belgium.
Biomedical-Health Engineering, KU Leuven Campus Group T, Leuven, Belgium.
PLoS One. 2018 Jun 28;13(6):e0199092. doi: 10.1371/journal.pone.0199092. eCollection 2018.
Studies on monolayer cultures and whole-animal models for the prediction of the response of native human tissue are associated with limitations. Therefore, more and more laboratories are tending towards multicellular spheroids grown in vitro as a model of native tissues. In addition, they are increasingly used in a wide range of biofabrication methodologies. These 3D microspheroids are generated through a self-assembly process that is still poorly characterised, called cellular aggregation. Here, a system is proposed for the automated, non-invasive and high throughput monitoring of the morphological changes during cell aggregation. Microwell patterned inserts were used for spheroid formation while an automated microscope with 4x bright-field objective captured the morphological changes during this process. Subsequently, the acquired time-lapse images were automatically segmented and several morphological features such as minor axis length, major axis length, roundness, area, perimeter and circularity were extracted for each spheroid. The method was quantitatively validated with respect to manual segmentation on four sets of ± 60 spheroids. The average sensitivities and precisions of the proposed segmentation method ranged from 96.67-97.84% and 96.77-97.73%, respectively. In addition, the different morphological features were validated, obtaining average relative errors between 0.78-4.50%. On average, a spheroid was processed 73 times faster than a human operator. As opposed to existing algorithms, our methodology was not only able to automatically monitor compact spheroids but also the aggregation process of individual spheroids, and this in an accurate and high-throughput manner. In total, the aggregation behaviour of more than 700 individual spheroids was monitored over a duration of 16 hours with a time interval of 5 minutes, and this could be increased up to 48,000 for the described culture format. In conclusion, the proposed system has the potential to be used for unravelling the mechanisms involved in spheroid formation and monitoring their formation during large-scale manufacturing protocols.
用于预测天然人体组织反应的单层培养物和整体动物模型的研究存在局限性。因此，越来越多的实验室倾向于使用体外培养的多细胞球体作为天然组织模型。此外，它们越来越多地应用于广泛的生物制造方法中。这些 3D 微球体是通过一种自组装过程产生的，该过程仍然描述不足，称为细胞聚集。在这里，提出了一种用于自动、非侵入式和高通量监测细胞聚集过程中形态变化的系统。微井图案化插件用于球体形成，而具有 4x 明场物镜的自动显微镜捕获了该过程中的形态变化。随后，对获得的延时图像进行自动分割，并提取了每个球体的几个形态特征，如短轴长度、长轴长度、圆度、面积、周长和圆形度。该方法针对四组±60 个球体的手动分割进行了定量验证。所提出的分割方法的平均灵敏度和精度范围分别为 96.67-97.84%和 96.77-97.73%。此外，还验证了不同的形态特征，得到的平均相对误差在 0.78-4.50%之间。平均而言，一个球体的处理速度比人工操作员快 73 倍。与现有的算法不同，我们的方法不仅能够自动监测致密球体，还能够以准确和高通量的方式监测单个球体的聚集过程。总共，在 16 小时的时间内，以 5 分钟的时间间隔监测了超过 700 个单个球体的聚集行为，对于所描述的培养格式，这可以增加到 48000 个。总之，所提出的系统有可能用于揭示球体形成中涉及的机制，并监测它们在大规模制造方案中的形成。
